diff --git a/.cursor/rules/backend-test-conventions.mdc b/.cursor/rules/backend-test-conventions.mdc new file mode 100644 index 00000000..fb7dc504 --- /dev/null +++ b/.cursor/rules/backend-test-conventions.mdc @@ -0,0 +1,63 @@ +--- +description: Backend Python test conventions (pytest) +globs: tests/backend/**/*.py +alwaysApply: false +--- + +# Backend Test Conventions + +## File Location & Naming + +- Place tests under `tests/backend/unit/`, `tests/backend/integration/`, or `tests/backend/contract/`. +- Name files `test_.py` with descriptive snake_case names. +- Shared test data goes in `tests/backend/fixtures/`. + +## File Structure + +```python +"""One-paragraph summary of what is being tested and why. + +Background +---------- +Brief context about the feature or bug fix these tests validate. +""" +from __future__ import annotations + +import pytest + +pytestmark = [pytest.mark.backend] +``` + +## Conventions + +- Always add `pytestmark = [pytest.mark.backend]` at module level. +- Group related tests in classes prefixed with `Test` (e.g. `TestStripImageBlocks`). +- Use `pytest.mark.parametrize` for data-driven tests instead of writing repetitive cases. +- Use `pytest.fixture()` for shared setup; keep fixtures close to where they are used. +- Unit tests must **not** depend on Flask, network, or external services. +- Mock external calls with `unittest.mock.patch` / `MagicMock`; never make real API calls. +- Each test function should verify **one** behavior and have a clear name: `test__`. + +## Running Tests + +- When running pytest, use `-q` (quiet mode) instead of `-v` (verbose mode) for cleaner output. +- **Preferred:** `python -m pytest tests/backend/ -q` +- **Avoid:** `python -m pytest tests/backend/ -v 2>&1` + +## Example + +```python +# ❌ BAD – vague name, no parametrize, no marker +def test_it_works(): + assert sanitize("hello world") == "hello_world" + +# ✅ GOOD +pytestmark = [pytest.mark.backend] + +@pytest.mark.parametrize("raw,expected", [ + ("hello world", "hello_world"), + ("订单明细", "订单明细"), +]) +def test_sanitize_preserves_unicode(raw: str, expected: str) -> None: + assert sanitize(raw) == expected +``` diff --git a/.cursor/rules/error-response-safety.mdc b/.cursor/rules/error-response-safety.mdc new file mode 100644 index 00000000..f2378fbb --- /dev/null +++ b/.cursor/rules/error-response-safety.mdc @@ -0,0 +1,61 @@ +--- +description: Prevent information exposure through exception messages in HTTP responses +globs: py-src/**/*.py +alwaysApply: false +--- + +# Error Response Safety + +Never return raw exception text (`str(e)`, `f"...{e}"`) directly in HTTP responses. +Python exceptions may contain stack traces, file paths, database connection strings, +API keys, or internal IP addresses — all of which are security risks (CWE-209). + +## Rules + +1. **5xx errors** — return a fixed generic message; never expose exception details. +2. **502 errors** — return `"Upstream service unavailable"`; never include upstream error body. +3. **4xx errors** — run `sanitize_error_message(str(e))` so business-validation messages + stay useful while secrets are stripped. +4. **Logging** — always log the full exception server-side (`logger.warning` / `logger.error` + with `exc_info=True` when needed). The client never needs the stack trace. + +## How To + +For Flask route `except` blocks, use `safe_error_response`: + +```python +from data_formulator.security.sanitize import safe_error_response + +except HTTPError as e: + return safe_error_response(e, 502, log_message="Upstream call failed") +except ValueError as e: + return safe_error_response(e, 400, log_message="Invalid input") +except Exception as e: + return safe_error_response(e, 500, log_message="Unexpected error") +``` + +For non-route contexts (generators, background tasks) where a Flask response +cannot be returned, use `sanitize_error_message` directly: + +```python +from data_formulator.security.sanitize import sanitize_error_message + +except Exception as exc: + logger.error("Task failed: %s", exc, exc_info=True) + payload = {"status": "error", "message": sanitize_error_message(str(exc))} +``` + +## Common Mistakes + +```python +# ❌ BAD — raw exception leaks internal details +return jsonify({"message": str(e)}), 500 +return jsonify({"message": f"Failed: {e}"}), 502 + +# ❌ BAD — manual traceback in response +import traceback +return jsonify({"message": traceback.format_exc()}), 500 + +# ✅ GOOD +return safe_error_response(e, 500, log_message="Operation failed") +``` diff --git a/.cursor/rules/frontend-test-conventions.mdc b/.cursor/rules/frontend-test-conventions.mdc new file mode 100644 index 00000000..a9cce5c8 --- /dev/null +++ b/.cursor/rules/frontend-test-conventions.mdc @@ -0,0 +1,64 @@ +--- +description: Frontend TypeScript test conventions (Vitest) +globs: tests/frontend/**/*.test.{ts,tsx} +alwaysApply: false +--- + +# Frontend Test Conventions + +## File Location & Naming + +- Place tests under `tests/frontend/unit/` mirroring the `src/` structure: + - `tests/frontend/unit/data/` → tests for `src/data/` + - `tests/frontend/unit/app/` → tests for `src/app/` + - `tests/frontend/unit/views/` → tests for `src/views/` +- Name files `.test.ts` (or `.test.tsx` for React rendering tests). + +## File Structure + +```typescript +import { describe, it, expect } from 'vitest'; +// For React rendering tests: +// import { render } from '@testing-library/react'; + +import { myFunction } from '../../../../src/'; + +describe('myFunction', () => { + it('should handle ', () => { + expect(myFunction(input)).toBe(expected); + }); +}); +``` + +## Conventions + +- Import `describe`, `it`, `expect` explicitly from `vitest` (globals are enabled but explicit imports improve readability). +- Use `@testing-library/react` and `@testing-library/jest-dom` for component rendering tests. +- Prefer testing **exported pure functions** over testing internal component state. +- When component logic is complex, extract it into an exported helper and test that directly. +- Group tests with `describe` blocks; use section comments (`// --- Null cases ---`) for clarity. +- One assertion per `it` block when possible; name tests as `should `. +- Do **not** import from `node_modules` internals; only use public API. +- Keep tests independent — no shared mutable state between `it` blocks. + +## Example + +```typescript +// ❌ BAD – no describe, vague test name +import { expect, test } from 'vitest'; +test('works', () => { expect(fn(1)).toBe(2); }); + +// ✅ GOOD +import { describe, it, expect } from 'vitest'; +import { checkIsLikelyTextOnlyModel } from '../../../../src/views/DataLoadingThread'; + +describe('checkIsLikelyTextOnlyModel', () => { + it('returns true for deepseek-chat', () => { + expect(checkIsLikelyTextOnlyModel('deepseek-chat')).toBe(true); + }); + + it('returns false for undefined', () => { + expect(checkIsLikelyTextOnlyModel(undefined)).toBe(false); + }); +}); +``` diff --git a/.cursor/rules/i18n-no-hardcoded-strings.mdc b/.cursor/rules/i18n-no-hardcoded-strings.mdc new file mode 100644 index 00000000..365b8c52 --- /dev/null +++ b/.cursor/rules/i18n-no-hardcoded-strings.mdc @@ -0,0 +1,40 @@ +--- +description: No hardcoded UI strings — use i18n translation keys +globs: src/**/*.{ts,tsx} +alwaysApply: false +--- + +# i18n: No Hardcoded UI Strings + +All user-visible text in the frontend MUST go through the i18n system. Never hardcode Chinese, English, or any other language string directly in components. + +## How to Use + +```tsx +import { useTranslation } from 'react-i18next'; + +const { t } = useTranslation(); + +// ✅ GOOD + + + +// ❌ BAD + + + +``` + +## Translation Files + +- English: `src/i18n/locales/en/.json` +- Chinese: `src/i18n/locales/zh/.json` +- Namespaces: `common`, `upload`, `chart`, `model`, `encoding`, `messages`, `navigation` + +When adding a new key, add it to **both** `en` and `zh` locale files. Pick the namespace that fits; create a new namespace only if none applies. + +## What Counts as User-Visible + +Must use `t()`: button labels, tooltips, placeholders, error messages, dialog titles, tab names, toast notifications, table headers, empty-state text. + +May stay hardcoded: log messages (`console.log`), error messages thrown but never displayed, internal constants, CSS class names, test IDs. diff --git a/.cursor/rules/language-injection-conventions.mdc b/.cursor/rules/language-injection-conventions.mdc new file mode 100644 index 00000000..c9473fbc --- /dev/null +++ b/.cursor/rules/language-injection-conventions.mdc @@ -0,0 +1,21 @@ +--- +description: Language injection conventions for LLM Agent prompts +globs: py-src/data_formulator/agents/**/*.py,py-src/data_formulator/agent_routes.py +alwaysApply: false +--- + +# Language Injection Conventions + +Language flows per-request: `Frontend i18n → Accept-Language header → get_language_instruction() → system prompt`. + +## Rules + +1. **User-facing LLM output** MUST inject language via `get_language_instruction(mode=...)` in the route handler. +2. **Mode selection:** `"full"` for text-heavy agents, `"compact"` for code-generation agents and short-text endpoints. +3. **Inject into system prompt only** — append or insert before a marker, never into user messages. +4. **Do NOT inject** for non-user-facing calls (health checks, internal tool calls). +5. **Do NOT duplicate** — if upstream messages already contain language instruction, skip. +6. **Do NOT** use env vars, global interceptors, or hardcoded language strings (e.g. `"回答请使用中文"`) — always use `build_language_instruction()`. +7. **New language?** Add to `LANGUAGE_DISPLAY_NAMES` in `agents/agent_language.py` and add locale files in `src/i18n/locales//`. + +For detailed architecture, code examples, and anti-pattern explanations, see the language-injection skill. diff --git a/.cursor/rules/package-manager-conventions.mdc b/.cursor/rules/package-manager-conventions.mdc new file mode 100644 index 00000000..5383e30a --- /dev/null +++ b/.cursor/rules/package-manager-conventions.mdc @@ -0,0 +1,12 @@ +--- +description: Use Yarn only, never npm/pnpm +globs: package.json, yarn.lock +alwaysApply: true +--- + +# Package Manager Rules + +- Use Yarn v1.22.22 only - never use npm or pnpm +- Never manually edit yarn.lock +- Keep yarn.lock changes minimal when adding deps +- Registry must be https://registry.yarnpkg.com diff --git a/.cursor/skills/language-injection/SKILL.md b/.cursor/skills/language-injection/SKILL.md new file mode 100644 index 00000000..a49cde7e --- /dev/null +++ b/.cursor/skills/language-injection/SKILL.md @@ -0,0 +1,72 @@ +# Language Injection for Agent Prompts + +Detailed guide for the language injection system. The short version lives in `.cursor/rules/language-injection-conventions.mdc`. + +## Architecture + +``` +Frontend i18n.language → Accept-Language header → get_language_instruction() + │ + build_language_instruction() + (agents/agent_language.py) + │ + ┌────────────┴────────────┐ + ▼ ▼ + mode="full" mode="compact" + (text-heavy agents) (code-gen agents) +``` + +### Core Modules + +| Module | Role | +|--------|------| +| `agents/agent_language.py` | `build_language_instruction(lang, mode)` — generates prompt fragments; supports 20 languages; returns `""` for English | +| `agent_routes.py` → `get_language_instruction()` | Reads `Accept-Language` header, delegates to `build_language_instruction` | +| `src/app/utils.tsx` → `fetchWithIdentity()` | Sets `Accept-Language` header on every API request from `i18n.language` | + +## Code Examples + +### Route handler — inject language + +```python +# In a Flask route handler: +lang_instruction = get_language_instruction(mode="compact") +lang_suffix = f"\n\n{lang_instruction}" if lang_instruction else "" + +messages = [ + {"role": "system", "content": "You are a helpful assistant." + lang_suffix}, + {"role": "user", "content": user_input}, +] +``` + +### Agent constructor — marker-based insertion + +```python +if language_instruction: + marker = "**About the execution environment:**" + idx = self.system_prompt.find(marker) + if idx > 0: + self.system_prompt = ( + self.system_prompt[:idx] + + language_instruction + "\n\n" + + self.system_prompt[idx:] + ) + else: + self.system_prompt += "\n\n" + language_instruction +``` + +## Anti-Patterns (with explanations) + +| Pattern | Why it's wrong | +|---------|---------------| +| `os.environ.get("DF_DEFAULT_LANGUAGE")` | Process-level — all users get same language; breaks multi-user | +| Global LLM client interceptor | Hidden behavior; can't distinguish full/compact mode; fragile string detection | +| New `MessageBuilder` class | Duplicates `agent_language.py`; creates parallel conflicting abstractions | +| Hardcoded `"回答请使用中文"` in prompts | Not configurable; skips the mode system; breaks for other languages | + +## Adding a New Language + +1. Add language code + display name to `LANGUAGE_DISPLAY_NAMES` in `agents/agent_language.py`. +2. Optionally add extra rules to `LANGUAGE_EXTRA_RULES` (e.g. simplified vs traditional Chinese). +3. Add frontend translations in `src/i18n/locales//` — copy an existing locale folder as template. +4. No Agent code changes needed — the existing flow picks up new languages automatically. diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 00000000..1838bc1b --- /dev/null +++ b/.dockerignore @@ -0,0 +1,12 @@ +.git +.gitignore +node_modules +__pycache__ +*.pyc +.env +.env.* +*.egg-info +dist +build +.pytest_cache +.mypy_cache diff --git a/.env.template b/.env.template index ec44c133..d4f77721 100644 --- a/.env.template +++ b/.env.template @@ -9,6 +9,30 @@ DISABLE_DISPLAY_KEYS=false # if true, API keys will not be shown in the frontend SANDBOX=local # code execution backend: 'local' (default) or 'docker' +# LOG_LEVEL=INFO # logging level for data_formulator modules (DEBUG, INFO, WARNING, ERROR) + +# Flask session secret key — used to sign cookies and encrypt session data. +# Required for SSO and plugin auth (Superset, etc.). Generate one with: +# python -c "import secrets; print(secrets.token_hex(32))" +# FLASK_SECRET_KEY= + +# Data directory — where workspaces and user data are stored on disk. +# Useful for server deployments with large datasets or dedicated storage volumes. +# Resolution order: --data-dir CLI flag > DATA_FORMULATOR_HOME env var > ~/.data_formulator +# Directory structure: +# DATA_FORMULATOR_HOME/ +# ├── users//workspaces/ (per-user workspace data: parquet, metadata) +# ├── workspaces/ (legacy default workspace root) +# └── cache/ (local cache, only for azure_blob backend) +# DATA_FORMULATOR_HOME= + +# Available UI languages (optional, comma-separated). +# Default: en,zh — if not set, both English and Chinese are available. +# Supported values: en, zh (add more after creating locale files) +# Examples: +# AVAILABLE_LANGUAGES=zh # only Chinese, language switcher hidden +# AVAILABLE_LANGUAGES=en,zh,ja # three languages +# AVAILABLE_LANGUAGES= # ------------------------------------------------------------------- # LLM provider API keys @@ -19,13 +43,13 @@ SANDBOX=local # code execution backend: 'local' (default) or 'docke # OpenAI OPENAI_ENABLED=true OPENAI_API_KEY=#your-openai-api-key -OPENAI_MODELS=gpt-5.2,gpt-5.1 # comma separated list of models +OPENAI_MODELS=gpt-5.4,gpt-4.1 # comma separated list of models # Azure OpenAI AZURE_ENABLED=true AZURE_API_KEY=#your-azure-openai-api-key AZURE_API_BASE=https://your-azure-openai-endpoint.openai.azure.com/ -AZURE_MODELS=gpt-5.1 +AZURE_MODELS=gpt-5.4 # Anthropic ANTHROPIC_ENABLED=true @@ -35,23 +59,136 @@ ANTHROPIC_MODELS=claude-sonnet-4-20250514 # Ollama OLLAMA_ENABLED=true OLLAMA_API_BASE=http://localhost:11434 -OLLAMA_MODELS=deepseek-v3.1:latest # models with good code generation capabilities recommended +OLLAMA_MODELS=qwen3:32b # models with good code generation capabilities recommended # Add other LiteLLM-supported providers with PROVIDER_API_KEY, PROVIDER_MODELS, etc. # ------------------------------------------------------------------- -# Azure Blob Storage Workspace (optional) +# API base URL allowlist (SSRF protection) +# ------------------------------------------------------------------- +# When users add custom models via the UI, they can provide an arbitrary +# api_base URL. To prevent the server from being used as an SSRF proxy, +# set a comma-separated list of allowed URL glob patterns. +# +# Open mode (default): leave unset to allow all URLs (convenient for local dev). +# Enforce mode: set to restrict which endpoints users can target. +# +# Glob patterns use fnmatch syntax (* matches anything, case-insensitive). +# Empty api_base (provider defaults like OpenAI/Anthropic) is always allowed. +# Global models (configured above via env vars) bypass this check. +# +# Examples: +# DF_ALLOWED_API_BASES=https://api.openai.com*,https://*.openai.azure.com/* +# DF_ALLOWED_API_BASES=https://api.openai.com*,https://*.openai.azure.com/*,http://localhost:11434/* +# DF_ALLOWED_API_BASES= + +# ------------------------------------------------------------------- +# Authentication (SSO / OAuth2) # ------------------------------------------------------------------- -# Set WORKSPACE_BACKEND=azure_blob to store workspace data in Azure Blob Storage -# instead of the local filesystem. +# Single-select: only one provider can be active at a time. +# Leave unset for anonymous mode (default, no login required). +# +# AUTH_PROVIDER values: +# oidc / oauth2 — Any OAuth2/OIDC identity provider with JWT + JWKS +# (Keycloak, Auth0, Okta, Authelia, custom SSO, etc.) +# github — GitHub OAuth2 +# azure_easyauth — Azure App Service built-in auth (platform headers) +# +# --- OIDC / OAuth2 --- +# AUTH_PROVIDER=oidc +# OIDC_ISSUER_URL=https://your-idp.example.com/realms/main +# OIDC_CLIENT_ID=your-client-id +# +# Mode A — Auto-discovery (recommended for standards-compliant OIDC IdPs): +# Only the two variables above are needed. The system fetches all endpoint +# URLs from {OIDC_ISSUER_URL}/.well-known/openid-configuration automatically. +# +# Mode B — Manual endpoints (for OAuth2 servers without discovery): +# When your IdP does NOT expose /.well-known/openid-configuration, set the +# endpoint URLs explicitly. Manual values always take precedence over discovery. +# +# OIDC_AUTHORIZE_URL=https://your-idp.example.com/oauth2/authorize +# OIDC_TOKEN_URL=https://your-idp.example.com/oauth2/token +# OIDC_USERINFO_URL=https://your-idp.example.com/oauth2/userinfo +# OIDC_JWKS_URL=https://your-idp.example.com/oauth2/jwks +# +# If your IdP has no JWKS endpoint, leave OIDC_JWKS_URL unset — the backend +# will validate tokens by calling the UserInfo endpoint instead. +# +# OAuth2 scopes to request (optional — auto-selected if unset): +# Mode A default: "openid profile email" +# Mode B default: "profile email" (omits 'openid' to avoid JWT id_token issues) +# OIDC_SCOPES=profile email +# +# Client secret (only for confidential clients — most browser apps should NOT +# need this; prefer PKCE for public clients): +# OIDC_CLIENT_SECRET= +# +# The frontend redirect URI to register in your IdP: http(s):///callback +# +# --- GitHub OAuth2 --- +# AUTH_PROVIDER=github +# GITHUB_CLIENT_ID=your-github-app-client-id +# GITHUB_CLIENT_SECRET=your-github-app-client-secret +# +# Create a GitHub OAuth App at https://github.com/settings/developers +# Set the callback URL to: http(s):///api/auth/github/callback # +# --- Azure App Service EasyAuth --- +# AUTH_PROVIDER=azure_easyauth +# (No extra variables needed — Azure injects identity via platform headers) +# +# --- Common options (all providers) --- +# ALLOW_ANONYMOUS=true # allow unauthenticated access as fallback (default: true) +# AUTH_DISPLAY_NAME=SSO Login # label shown on the login button + +# ------------------------------------------------------------------- +# Workspace storage backend +# ------------------------------------------------------------------- +# Controls where workspace data (tables, sessions, metadata) is persisted. +# Also configurable via CLI: --workspace-backend +# +# Choices: +# local — (default) stores data under DATA_FORMULATOR_HOME +# azure_blob — stores data in Azure Blob Storage (see below) +# ephemeral — temp dirs; data does NOT survive restart (if you want to prevent user data retention on server disk for privacy reasons) +# +# WORKSPACE_BACKEND=local + +# ------------------------------------------------------------------- +# Azure Blob Storage settings (only when WORKSPACE_BACKEND=azure_blob) +# ------------------------------------------------------------------- # Authentication — choose ONE of the following: # Option A: Connection string (shared key / SAS) # AZURE_BLOB_CONNECTION_STRING=DefaultEndpointsProtocol=https;AccountName=... # Option B: Entra ID (Managed Identity / az login / workload identity) # AZURE_BLOB_ACCOUNT_URL=https://.blob.core.windows.net # -# WORKSPACE_BACKEND=local +# Blob container name (default: data-formulator): +# AZURE_BLOB_CONTAINER=data-formulator +# +# CLI equivalents: +# --azure-blob-connection-string, --azure-blob-account-url, --azure-blob-container +# # AZURE_BLOB_CONNECTION_STRING= # AZURE_BLOB_ACCOUNT_URL= -# AZURE_BLOB_CONTAINER=data-formulator \ No newline at end of file +# AZURE_BLOB_CONTAINER=data-formulator + +# ------------------------------------------------------------------- +# Data source plugins +# ------------------------------------------------------------------- +# Plugins are activated by setting their required env vars. +# Each plugin uses a PLG__ prefix. +# +# --- Apache Superset --- +# PLG_SUPERSET_URL=http://superset.example.com:8088 +# +# SSO login URL (optional): +# By default DF opens {PLG_SUPERSET_URL}/df-sso-bridge/ as the SSO popup. +# Override this if your Superset requires going through the login page first: +# PLG_SUPERSET_SSO_LOGIN_URL=http://superset.example.com:8088/login/?next=/df-sso-bridge/ +# +# Superset-side setup: +# The Superset instance needs a small bridge endpoint at /df-sso-bridge/ +# that converts a Superset session into a JWT and posts it back to DF. +# See: superset-sso-bridge-setup.md \ No newline at end of file diff --git a/.gitignore b/.gitignore index f4cda9a6..f43b7bd7 100644 --- a/.gitignore +++ b/.gitignore @@ -8,6 +8,9 @@ build/ dist/ experiment_data/ +py-src/eval_rec_ts/* +py-src/evaluation_old/* + ## Ignore Visual Studio temporary files, build results, and ## files generated by popular Visual Studio add-ons. ## @@ -336,6 +339,7 @@ paket-files/ # Python Tools for Visual Studio (PTVS) __pycache__/ *.pyc +*.egg-info/ # Cake - Uncomment if you are using it # tools/** @@ -408,6 +412,11 @@ FodyWeavers.xsd *.sln.iml venv +# Temporary documentation directory +tmp-docs/ \.\NUL -NUL \ No newline at end of file +NUL + +# Package manager lock files (using yarn) +package-lock.json \ No newline at end of file diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 00000000..e1cb88e2 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,8 @@ +{ + "python.defaultInterpreterPath": "${workspaceFolder}/.venv/bin/python", + "chat.tools.terminal.autoApprove": { + "npx tsc": true, + "npx vite": true, + "npx tsx": true + } +} diff --git a/DEVELOPMENT.md b/DEVELOPMENT.md index cb9ed8e3..d5815480 100644 --- a/DEVELOPMENT.md +++ b/DEVELOPMENT.md @@ -121,6 +121,45 @@ uv run data_formulator --dev # Run backend only (for frontend development) Open [http://localhost:5567](http://localhost:5567) to view it in the browser. +## Docker + +Docker is the easiest way to run Data Formulator without installing Python or Node.js locally. + +### Quick start + +1. **Copy the environment template and add your API keys:** + + ```bash + cp .env.template .env + # Edit .env and set your OPENAI_API_KEY, ANTHROPIC_API_KEY, etc. + ``` + +2. **Build and start the container:** + + ```bash + docker compose up --build + ``` + +3. Open [http://localhost:5567](http://localhost:5567) in your browser. + +To stop the container: `docker compose down` + +Workspace data (uploaded files, sessions) is persisted in a Docker volume (`data_formulator_home`) so it survives container restarts. + +### Build the image manually + +```bash +docker build -t data-formulator . +docker run --rm -p 5567:5567 --env-file .env data-formulator +``` + +### Docker sandbox (`SANDBOX=docker`) is not supported inside a container + +The Docker sandbox backend works by calling `docker run -v :...` to bind-mount temporary workspace directories into child containers. When Data Formulator itself runs in a Docker container those paths refer to the *container* filesystem, not the host, so Docker daemon cannot mount them and the feature does not work. + +Use `SANDBOX=docker` only when running Data Formulator **directly on the host** (e.g. with `uv run data_formulator --sandbox docker` or `python -m data_formulator --sandbox docker`). When using the Docker image, keep the default `SANDBOX=local`. + + ## Sandbox AI-generated Python code runs inside a **sandbox** to isolate it from the main server process. Two backends are available: @@ -278,7 +317,7 @@ data-formulator/ ← container | Flag | Env var | Default | Description | |------|---------|---------|-------------| -| `--workspace-backend` | `WORKSPACE_BACKEND` | `local` | `local` or `azure_blob` | +| `--workspace-backend` | `WORKSPACE_BACKEND` | `local` | `local`, `azure_blob`, or `ephemeral` | | `--azure-blob-connection-string` | `AZURE_BLOB_CONNECTION_STRING` | — | Shared-key connection string | | `--azure-blob-account-url` | `AZURE_BLOB_ACCOUNT_URL` | — | Account URL for Entra ID auth | | `--azure-blob-container` | `AZURE_BLOB_CONTAINER` | `data-formulator` | Blob container name | @@ -290,23 +329,35 @@ data-formulator/ ← container When deploying Data Formulator to production, please be aware of the following security considerations: -### Database and Data Storage Security +### Data Storage + +Data Formulator supports three workspace backends: + +| Backend | Flag | Storage | Persistence | +|---------|------|---------|-------------| +| **local** (default) | `--workspace-backend local` | `~/.data_formulator/users//workspaces//` | Server filesystem | +| **azure_blob** | `--workspace-backend azure_blob` | Azure Blob container | Cloud | +| **ephemeral** | `--workspace-backend ephemeral` | Browser IndexedDB (frontend) + temp dirs (backend) | Browser session only | -1. **Workspace and table data**: Table data is stored in per-identity workspaces (e.g. parquet files). DuckDB is used only in-memory per request when needed (e.g. for SQL mode); no persistent DuckDB database files are created by the app. +Each workspace contains: +- `workspace.yaml` — table metadata +- `session_state.json` — auto-persisted frontend state +- `data/` — table data as parquet files -2. **Identity Management**: - - Each user's data is isolated by a namespaced identity key (e.g., `user:alice@example.com` or `browser:550e8400-...`) - - Anonymous users get a browser-based UUID stored in localStorage - - Authenticated users get their verified user ID from the auth provider +### Identity and Data Isolation -3. **Data persistence**: User data may be written to workspace storage (e.g. parquet) on the server. In multi-tenant deployments, ensure workspace directories are isolated and access-controlled. +- Each user's data is isolated by a namespaced identity key (e.g., `user:alice@example.com` or `browser:550e8400-...`) +- Anonymous users get a browser-based UUID stored in localStorage +- Authenticated users get their verified user ID from the auth provider +- In multi-tenant deployments, ensure workspace directories are isolated and access-controlled ### Recommended Security Measures For production deployment, consider: -1. **Use `--disable-database` flag** to disable table-connector routes when you do not need external or uploaded table support -2. **Implement proper authentication, authorization, and other security measures** as needed for your specific use case, for example: +1. **Use `--workspace-backend ephemeral`** for stateless public hosting (no server-side persistence; data lives only in the user's browser) +2. **Set `DF_ALLOWED_API_BASES`** to restrict which LLM endpoints users can target from the UI, preventing SSRF attacks (e.g. `DF_ALLOWED_API_BASES=https://api.openai.com*,https://*.openai.azure.com/*`). See `.env.template` for details. +3. **Implement proper authentication, authorization, and other security measures** as needed for your specific use case, for example: - User authentication (OAuth, JWT tokens, etc.) - Role-based access control - API rate limiting @@ -317,7 +368,7 @@ For production deployment, consider: ```bash # For stateless deployment (recommended for public hosting) -python -m data_formulator.app --disable-database +data_formulator --workspace-backend ephemeral ``` ## Authentication Architecture @@ -351,8 +402,8 @@ Data Formulator supports a **hybrid identity system** that supports both anonymo ┌─────────────────────────────────────────────────────────────────────┐ │ Storage Isolation │ ├─────────────────────────────────────────────────────────────────────┤ -│ "user:alice@example.com" → alice's DuckDB file (ONLY via auth) │ -│ "browser:550e8400-..." → anonymous user's DuckDB file │ +│ "user:alice@example.com" → alice's workspace dir (ONLY via auth) │ +│ "browser:550e8400-..." → anonymous user's workspace dir │ └─────────────────────────────────────────────────────────────────────┘ ``` @@ -374,7 +425,7 @@ The key security principle is **namespaced isolation with forced prefixing**: To add JWT-based authentication: -1. **Backend** (`tables_routes.py`): Uncomment and configure the JWT verification code in `get_identity_id()` +1. **Backend** (`security/auth.py`): Uncomment and configure the JWT verification code in `get_identity_id()` 2. **Frontend** (`utils.tsx`): Implement `getAuthToken()` to retrieve the JWT from your auth context 3. **Add JWT secret** to Flask config: `current_app.config['JWT_SECRET']` diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 00000000..d066f436 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,68 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +# --------------------------------------------------------------------------- +# Stage 1: Build the React/TypeScript frontend +# --------------------------------------------------------------------------- +FROM node:20-slim AS frontend-builder + +WORKDIR /app + +# Install dependencies +COPY package.json yarn.lock ./ +RUN yarn install --frozen-lockfile + +# Copy source and build +COPY index.html tsconfig.json vite.config.ts eslint.config.js ./ +COPY public ./public +COPY src ./src +RUN yarn build + +# --------------------------------------------------------------------------- +# Stage 2: Python runtime with the built frontend bundled in +# --------------------------------------------------------------------------- +FROM python:3.11-slim AS runtime + +# System dependencies needed by some Python packages +RUN apt-get update && apt-get install -y --no-install-recommends \ + gcc \ + g++ \ + libpq-dev \ + unixodbc-dev \ + curl \ + && rm -rf /var/lib/apt/lists/* + +# Create a non-root user to run the application +RUN useradd -m -s /bin/bash appuser + +# Ensure Unicode filenames work correctly (Chinese, Japanese, etc.) +ENV LANG=C.UTF-8 + +# Set the home directory for workspace data to a deterministic path +ENV DATA_FORMULATOR_HOME=/home/appuser/.data_formulator + +WORKDIR /app + +# Copy Python package sources +COPY pyproject.toml MANIFEST.in README.md ./ +COPY py-src ./py-src + +# Copy the compiled frontend into the package's expected location +COPY --from=frontend-builder /app/py-src/data_formulator/dist ./py-src/data_formulator/dist + +# Install the package and its dependencies +RUN pip install --no-cache-dir . + +# Switch to non-root user and ensure workspace and app directories are owned by it +RUN mkdir -p "${DATA_FORMULATOR_HOME}" && chown -R appuser:appuser /app "${DATA_FORMULATOR_HOME}" +USER appuser + +EXPOSE 5567 + +HEALTHCHECK --interval=30s --timeout=5s --retries=3 \ + CMD curl -f http://localhost:5567/ || exit 1 + +# Run the app on all interfaces so Docker port-forwarding works. +# We do not pass --dev so Flask runs in production mode (no debugger/reloader). +# webbrowser.open() fails silently in a headless container, which is harmless. +ENTRYPOINT ["python", "-m", "data_formulator", "--host", "0.0.0.0", "--port", "5567"] diff --git a/README.md b/README.md index b39d7722..19b9abfc 100644 --- a/README.md +++ b/README.md @@ -33,7 +33,7 @@ https://github.com/user-attachments/assets/8ca57b68-4d7a-42cb-bcce-43f8b1681ce2 ## News 🔥🔥🔥 -[03-02-2026] **Data Formulator 0.7 (alpha)** — More charts, new experience, enterprise-ready +[03-18-2026] **Data Formulator 0.7 (alpha)** — More charts, new experience, enterprise-ready - 📊 **30 chart types** with a new semantic chart engine (area, streamgraph, candlestick, pie, radar, maps, and more). - 💬 **Hybrid chat + data thread** — chat woven into the exploration timeline with lineage, previews, and reasoning. - 🤖 **Unified `DataAgent`** replacing four separate agents, plus new recommendation and insight agents. @@ -42,6 +42,11 @@ https://github.com/user-attachments/assets/8ca57b68-4d7a-42cb-bcce-43f8b1681ce2 - 📦 **UV-first build** — reproducible builds via `uv.lock`; `uv sync` + `uv run data_formulator`. - 📝 Detailed writeup on the new architecture coming soon — stay tuned! +> [!TIP] +> **Are you a developer?** Join us to shape the future of AI-powered data exploration! +> We're looking for help with new agents, data connectors, chart templates, and more. +> Check out the [Developers' Guide](DEVELOPMENT.md) and our [open issues](https://github.com/microsoft/data-formulator/issues). + ## Previous Updates Here are milestones that lead to the current design: @@ -60,179 +65,63 @@ Here are milestones that lead to the current design: - **Data Extraction**: Parse data from images and text ([demo](https://github.com/microsoft/data-formulator/pull/31#issuecomment-2403652717)) - **Initial Release**: [Blog](https://www.microsoft.com/en-us/research/blog/data-formulator-exploring-how-ai-can-help-analysts-create-rich-data-visualizations/) | [Video](https://youtu.be/3ndlwt0Wi3c) -
-View detailed update history - -- [07-10-2025] Data Formulator 0.2.2: Start with an analysis goal - - Some key frontend performance updates. - - You can start your exploration with a goal, or, tab and see if the agent can recommend some good exploration ideas for you. [Demo](https://github.com/microsoft/data-formulator/pull/176) - -- [05-13-2025] Data Formulator 0.2.1.3/4: External Data Loader - - We introduced external data loader class to make import data easier. [Readme](https://github.com/microsoft/data-formulator/tree/main/py-src/data_formulator/data_loader) and [Demo](https://github.com/microsoft/data-formulator/pull/155) - - Current data loaders: MySQL, Azure Data Explorer (Kusto), Azure Blob and Amazon S3 (json, parquet, csv). - - [07-01-2025] Updated with: Postgresql, mssql. - - Call for action [link](https://github.com/microsoft/data-formulator/issues/156): - - Users: let us know which data source you'd like to load data from. - - Developers: let's build more data loaders. - -- [04-23-2025] Data Formulator 0.2: working with *large* data 📦📦📦 - - Explore large data by: - 1. Upload large data file to the local database (powered by [DuckDB](https://github.com/duckdb/duckdb)). - 2. Use drag-and-drop to specify charts, and Data Formulator dynamically fetches data from the database to create visualizations (with ⚡️⚡️⚡️ speeds). - 3. Work with AI agents: they generate SQL queries to transform the data to create rich visualizations! - 4. Anchor the result / follow up / create a new branch / join tables; let's dive deeper. - - Checkout the demos at [[https://github.com/microsoft/data-formulator/releases/tag/0.2]](https://github.com/microsoft/data-formulator/releases/tag/0.2) - - Improved overall system performance, and enjoy the updated derive concept functionality. - -- [03-20-2025] Data Formulator 0.1.7: Anchoring ⚓︎ - - Anchor an intermediate dataset, so that followup data analysis are built on top of the anchored data, not the original one. - - Clean a data and work with only the cleaned data; create a subset from the original data or join multiple data, and then go from there. AI agents will be less likely to get confused and work faster. ⚡️⚡️ - - Check out the demos at [[https://github.com/microsoft/data-formulator/releases/tag/0.1.7]](https://github.com/microsoft/data-formulator/releases/tag/0.1.7) - - Don't forget to update Data Formulator to test it out! - -- [02-20-2025] Data Formulator 0.1.6 released! - - Now supports working with multiple datasets at once! Tell Data Formulator which data tables you would like to use in the encoding shelf, and it will figure out how to join the tables to create a visualization to answer your question. 🪄 - - Checkout the demo at [[https://github.com/microsoft/data-formulator/releases/tag/0.1.6]](https://github.com/microsoft/data-formulator/releases/tag/0.1.6). - - Update your Data Formulator to the latest version to play with the new features. - -- [02-12-2025] More models supported now! - - Now supports OpenAI, Azure, Ollama, and Anthropic models (and more powered by [LiteLLM](https://github.com/BerriAI/litellm)); - - Models with strong code generation and instruction following capabilities are recommended (gpt-4o, claude-3-5-sonnet etc.); - - You can store API keys in `.env` to avoid typing them every time (copy `.env.template` to `.env` and fill in your keys). - - Let us know which models you have good/bad experiences with, and what models you would like to see supported! [[comment here]](https://github.com/microsoft/data-formulator/issues/49) - -- [11-07-2024] Minor fun update: data visualization challenges! - - We added a few visualization challenges with the sample datasets. Can you complete them all? [[try them out!]](https://github.com/microsoft/data-formulator/issues/53#issue-2641841252) - - Comment in the issue when you did, or share your results/questions with others! [[comment here]](https://github.com/microsoft/data-formulator/issues/53) - -- [10-11-2024] Data Formulator python package released! - - You can now install Data Formulator using Python and run it locally, easily. [[check it out]](#get-started). - - Our Codespaces configuration is also updated for fast start up ⚡️. [[try it now!]](https://codespaces.new/microsoft/data-formulator?quickstart=1) - - New experimental feature: load an image or a messy text, and ask AI to parse and clean it for you(!). [[demo]](https://github.com/microsoft/data-formulator/pull/31#issuecomment-2403652717) - -- [10-01-2024] Initial release of Data Formulator, check out our [[blog]](https://www.microsoft.com/en-us/research/blog/data-formulator-exploring-how-ai-can-help-analysts-create-rich-data-visualizations/) and [[video]](https://youtu.be/3ndlwt0Wi3c)! - -
- ## Overview **Data Formulator** is a Microsoft Research prototype for data exploration with visualizations powered by AI agents. -Data Formulator enables analysts to iteratively explore and visualize data. Started with data in any format (screenshot, text, csv, or database), users can work with AI agents with a novel blended interface that combines *user interface interactions (UI)* and *natural language (NL) inputs* to communicate their intents, control branching exploration directions, and create reports to share their insights. +Data Formulator enables analysts to explore data with visualizations. Started with data in any format (screenshot, text, csv, or database), you can work with AI agents with a novel blended interface that combines *user interface interactions (UI)* and *natural language (NL) inputs* to communicate their intents, control branching exploration directions, and create reports to share their insights. ## Get Started -Play with Data Formulator with one of the following options: +Play with Data Formulator with one of the following options. - **Option 1: Install via uv (recommended)** [uv](https://docs.astral.sh/uv/) is an extremely fast Python package manager. If you have uv installed, you can run Data Formulator directly without any setup: ```bash - # Run data formulator directly (no install needed) uvx data_formulator ``` - Or install it in a project/virtual environment: - - ```bash - # Install data_formulator - uv pip install data_formulator - - # Run data formulator - python -m data_formulator - ``` - - Data Formulator will be automatically opened in the browser at [http://localhost:5567](http://localhost:5567). + Run `uvx data_formulator --help` to see all available options, such as custom port, sandboxing mode, and data storage location. - **Option 2: Install via pip** Use pip for installation (recommend: install it in a virtual environment). ```bash - # install data_formulator - pip install data_formulator - - # Run data formulator with this command - python -m data_formulator + pip install data_formulator # install + python -m data_formulator # run ``` Data Formulator will be automatically opened in the browser at [http://localhost:5567](http://localhost:5567). - *you can specify the port number (e.g., 8080) by `python -m data_formulator --port 8080` if the default port is occupied.* - -- **Option 3: Codespaces (5 minutes)** - - You can also run Data Formulator in Codespaces; we have everything pre-configured. For more details, see [CODESPACES.md](CODESPACES.md). - - [![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/microsoft/data-formulator?quickstart=1) +- **Option 3: Run with Docker** -- **Option 4: Working in the developer mode** - - You can build Data Formulator locally if you prefer full control over your development environment and develop your own version on top. For detailed instructions, refer to [DEVELOPMENT.md](DEVELOPMENT.md). + ```bash + docker compose up --build + ``` + Open [http://localhost:5567](http://localhost:5567) in your browser. To stop, press `Ctrl+C` or run `docker compose down`. -## Using Data Formulator +- **Option 4: Codespaces** -### Load Data + You can run Data Formulator in Codespaces; we have everything pre-configured. For more details, see [CODESPACES.md](CODESPACES.md). + + [![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/microsoft/data-formulator?quickstart=1) -Besides uploading csv, tsv or xlsx files that contain structured data, you can ask Data Formulator to extract data from screenshots, text blocks or websites, or load data from databases use connectors. Then you are ready to explore. -image +- **Option 5: Working as developer** + + You can build Data Formulator locally and develop your own version. Check out details in [DEVELOPMENT.md](DEVELOPMENT.md). -### Explore Data -There are four levels to explore data based depending on whether you want more vibe or more control: +## Using Data Formulator -- Level 1 (most control): Create charts with UI via drag-and-drop, if all fields to be visualized are already in the data. -- Level 2: Specify chart designs with natural language + NL. Describe how new fields should be visualized in your chart, AI will automatically transform data to realize the design. -- Level 3: Get recommendations: Ask AI agents to recommend charts directly from NL descriptions, or even directly ask for exploration ideas. -- Level 4 (most vibe): In agent mode, provide a high-level goal and let AI agents automatically plan and explore data in multiple turns. Exploration threads will be created automatically. +Besides uploading csv, tsv or xlsx files that contain structured data, you can ask Data Formulator to extract data from screenshots, text blocks or websites, or load data from databases use connectors. Then you are ready to explore. Ask visualizaiton questions, edit charts, or delegate some exploration tasks to agents. Then, create reports to share your insights. https://github.com/user-attachments/assets/164aff58-9f93-4792-b8ed-9944578fbb72 -- Level 5: In practice, leverage all of them to keep up with both vibe and control! - -### Create Reports - -Use the report builder to compose a report of the style you like, based on selected charts. Then share the reports to others! - - - -## Developers' Guide - -Follow the [developers' instructions](DEVELOPMENT.md) to build your new data analysis tools on top of Data Formulator. - -Help wanted: - -* Add more database connectors (https://github.com/microsoft/data-formulator/issues/156) -* Scaling up messy data extractor: more document types and larger files. -* Adding more chart templates (e.g., maps). -* other ideas? - ## Research Papers * [Data Formulator 2: Iteratively Creating Rich Visualizations with AI](https://arxiv.org/abs/2408.16119) diff --git a/design-docs/0-development-roadmap.md b/design-docs/0-development-roadmap.md new file mode 100644 index 00000000..87a0e540 --- /dev/null +++ b/design-docs/0-development-roadmap.md @@ -0,0 +1,725 @@ +# SSO + 数据源插件 开发路线图 + +> **定位**：本文档是开发实施计划，不重复设计细节。每个步骤链接到设计文档的对应章节。 +> +> **设计文档**： +> - `1-sso-plugin-architecture.md` — SSO 认证 + 统一架构（以下简称 **SSO 文档**） +> - `1-data-source-plugin-architecture.md` — 数据源插件详细设计（以下简称 **Plugin 文档**） +> - `2-external-dataloader-enhancements.md` — ExternalDataLoader 改进（独立推进，不在本路线图中） + +--- + +## 测试策略 + +### 工作流：测试先行 + +每个 Step 遵循 **测试 → 实现 → 通过** 的节奏： + +1. 先写测试 — 基于设计文档中的接口契约和预期行为 +2. 运行测试 — 确认全部失败（红） +3. 实现功能 — 写到测试通过为止（绿） +4. 重构 — 在测试保护下清理代码 + +### 测试分层与现有基础设施对齐 + +项目已有完善的测试体系（见 `tests/test_plan.md`），新增测试沿用现有分层和约定： + +| 层级 | 目录 | 运行方式 | 特征 | +|------|------|---------|------| +| 后端单元 | `tests/backend/unit/` | `pytest`（默认运行） | 纯函数、无网络、无 Docker | +| 后端安全 | `tests/backend/security/` | `pytest`（默认运行） | 认证、隔离、防伪造 | +| 后端集成 | `tests/backend/integration/` | `pytest`（默认运行） | Flask test_client、Workspace 交互 | +| 后端契约 | `tests/backend/contract/` | `pytest`（默认运行） | API 边界保证 | +| 前端单元 | `tests/frontend/unit/` | `vitest` | React 组件、工具函数 | + +新增标记（追加到 `pytest.ini`）： + +```ini +markers = + ...existing... + auth: authentication provider tests + plugin: data source plugin framework tests + vault: credential vault tests +``` + +### Mock 设计原则 + +**只 mock 外部边界，不 mock 自己的代码**： + +| 边界 | Mock 方式 | 说明 | +|------|----------|------| +| OIDC IdP（JWKS 端点） | 测试时生成 RSA 密钥对 → 用私钥签 JWT → 用公钥构造 JWKS 响应 | 验证真实的 JWT 验签逻辑，而不是跳过验签 | +| GitHub API | `unittest.mock.patch("requests.get")` | 返回录制的 GitHub `/user` 响应 | +| Superset REST API | `unittest.mock.patch` on `requests.Session` in SupersetClient | 返回录制的 Superset API 响应 fixture | +| Workspace 文件系统 | `tmp_path` fixture（pytest 内置） | 真实 Parquet 读写，但在临时目录 | +| SQLite（Vault） | `tmp_path` 下的临时 DB 文件 | 真实加密/解密，无需 mock | +| 前端 OIDC UserManager | vitest mock module | 模拟登录状态和 token | + +**不要 mock 的东西**： +- Workspace 内部逻辑（`write_parquet` / `list_tables`）— 用真实 temp workspace +- Fernet 加密 — 用真实密钥，验证端到端加密/解密 +- Flask 路由注册 — 用真实 `app.test_client()` + +### Superset API Mock Fixture 设计 + +Superset 插件的测试需要模拟 Superset REST API 的响应。在 `tests/backend/fixtures/superset/` 下存放录制的 JSON 响应： + +``` +tests/backend/fixtures/superset/ +├── auth_login_200.json # POST /api/v1/security/login 成功响应 +├── auth_login_401.json # 登录失败响应 +├── me_200.json # GET /api/v1/me/ 当前用户信息 +├── datasets_list_200.json # GET /api/v1/dataset/ 数据集列表 +├── dataset_detail_42.json # GET /api/v1/dataset/42 单个数据集详情 +├── dashboard_list_200.json # GET /api/v1/dashboard/ 仪表盘列表 +├── sqllab_execute_200.json # POST /api/v1/sqllab/execute/ 查询结果 +└── csrf_token_200.json # GET /api/v1/security/csrf_token/ +``` + +这些 fixture 从真实 Superset 实例录制（`curl` 输出保存），保证字段结构与实际 API 一致。测试中通过 `patch` 注入： + +```python +@pytest.fixture +def superset_responses(fixture_dir): + """加载 Superset API fixture 响应。""" + def _load(name): + return json.loads((fixture_dir / "superset" / name).read_text()) + return _load +``` + +### 什么不测 + +- **不测 IdP 本身**：Keycloak/Auth0 的行为不是我们的代码，集成测试只验证我们的对接逻辑 +- **不测前端 UI 样式**：不做截图对比或像素级验证 +- **不测第三方库内部**：不测 PyJWT 能不能解码、Fernet 加不加密——只测我们**调用**这些库的逻辑 +- **不重复已有测试**：`test_auth.py` 中已有的 `_validate_identity_value` 测试不重复，只扩展 Provider 链部分 + +--- + +## 开发顺序与依据 + +``` +Layer 1: AuthProvider (SSO) ← 地基，确定"你是谁" + │ + ├── Layer 3: CredentialVault ← 依赖身份，按用户存取凭证 + │ + └── Layer 2: DataSourcePlugin ← 依赖 Layer 1 获取 SSO token + 依赖 Layer 3 获取已存凭证 +``` + +**先做 SSO，后做插件**。理由： + +1. **单向依赖**：插件系统的 SSO 透传、凭证保险箱、Workspace 身份隔离，全部依赖 AuthProvider 提供的用户身份（[SSO 文档 § 2 架构全景](1-sso-plugin-architecture.md#2-架构全景)） +2. **插件不改 auth 代码**：先把认证层稳定下来，后续插件开发只在 `plugins/` 目录内工作，不触碰核心 +3. **渐进可验证**：每个 Phase 完成后都有独立可测试的交付物，不需要等到全部完成才能验证 + +> **注意**：Plugin 框架本身*可以*在无 SSO 时工作（匿名模式），但 SSO 透传是核心价值之一。先做 SSO 避免后期回头改 auth 代码。 + +--- + +## Phase 1：认证基础 — AuthProvider 链 + +> 对应：[SSO 文档 § 3 Layer 1](1-sso-plugin-architecture.md#3-layer-1可插拔认证体系-authprovider)、[SSO 文档 § 11 Phase 1](1-sso-plugin-architecture.md#11-实施路径) + +**目标**：将 `auth.py` 重构为可插拔 Provider，激活 OIDC + GitHub OAuth。 + +### Step 1.0 先写测试 + +在写任何实现代码之前，先创建以下测试文件。测试基于设计文档中的接口契约，此时运行应**全部失败**。 + +#### 后端测试 + +**`tests/backend/security/test_auth_provider_chain.py`** — Provider 链集成（扩展现有 `test_auth.py` 的思路） + +```python +# 要验证的行为（基于 SSO 文档 § 3.1 的优先级链）： +# - AUTH_PROVIDER=oidc 时，合法 JWT → user:sub_claim +# - AUTH_PROVIDER=oidc 时，无 JWT + ALLOW_ANONYMOUS=true → browser:xxx +# - AUTH_PROVIDER=oidc 时，无 JWT + ALLOW_ANONYMOUS=false → 401 +# - AUTH_PROVIDER 未设置 → 匿名模式（与现有行为一致） +# - init_auth() 加载指定 Provider，忽略其他 +# - get_sso_token() 在 OIDC 认证后返回 access_token +# - get_sso_token() 在匿名模式下返回 None + +# mock 策略：用 cryptography 生成 RSA 密钥对， +# 用私钥签发测试 JWT，patch JWKS 端点返回对应公钥。 +``` + +**`tests/backend/unit/test_oidc_provider.py`** — OIDC Provider 单元测试 + +```python +# 要验证的行为（基于 SSO 文档 § 3.4）： +# - 合法 JWT（正确 issuer + audience + 未过期）→ AuthResult(user_id=sub) +# - 过期 JWT → 抛 AuthenticationError +# - 错误 issuer → 抛 AuthenticationError +# - 错误 audience → 抛 AuthenticationError +# - 签名不匹配（用错误密钥签名）→ 抛 AuthenticationError +# - 请求无 Authorization 头 → 返回 None（此 Provider 不适用） +# - Authorization 头非 Bearer → 返回 None +# - get_auth_info() 返回 {action: "frontend", ...} 包含 OIDC 配置 +# - enabled 属性：OIDC_ISSUER_URL 缺失时返回 False + +# mock 策略：fixture 中生成 RSA 密钥对， +# 用 PyJWT 签发各种测试 JWT，monkeypatch JWKS HTTP 请求。 +``` + +**`tests/backend/unit/test_github_oauth_provider.py`** — GitHub OAuth Provider + +```python +# 要验证的行为（基于 SSO 文档 § 3.5）： +# - Flask session 中有 github_user → AuthResult(user_id=github_login) +# - Flask session 为空 → 返回 None +# - get_auth_info() 返回 {action: "redirect", url: "/api/auth/github/login"} +# - enabled 属性：GITHUB_CLIENT_ID 缺失时返回 False + +# mock 策略：Flask test_request_context + session mock，无需真实 GitHub。 +``` + +**`tests/backend/unit/test_azure_easyauth_provider.py`** — Azure EasyAuth Provider（迁移验证） + +```python +# 从现有 test_auth.py 中的 Azure 测试用例迁移验证： +# - X-MS-CLIENT-PRINCIPAL-ID 存在 → AuthResult(user_id=principal_id) +# - 头不存在 → 返回 None +# - 确保迁移后行为与原 get_identity_id() 中的 Azure 逻辑一致 +``` + +**`tests/backend/integration/test_auth_info_endpoint.py`** — `/api/auth/info` 端点 + +```python +# 要验证的行为（基于 SSO 文档 § 3.2 get_auth_info 自描述）： +# - OIDC Provider 激活时，返回 {action: "frontend", authority, client_id, ...} +# - GitHub Provider 激活时，返回 {action: "redirect", url: ...} +# - 匿名模式时，返回 {action: "none"} +# - 前端据此决定登录交互方式 + +# mock 策略：Flask test_client + 环境变量 patch。 +``` + +#### 前端测试 + +**`tests/frontend/unit/app/fetchWithIdentity.test.ts`** — Bearer token 附加 + 401 重试 + +```typescript +// 要验证的行为（基于 SSO 文档 § 3.8b）： +// - 有 OIDC token 时，请求携带 Authorization: Bearer +// - 无 token 时（匿名模式），只携带 X-Identity-Id（现有行为） +// - 收到 401 时，触发 token 刷新后重试一次 +// - 重试后仍 401 → 不再重试，返回错误 +// - 非 401 错误不触发重试 + +// mock 策略：vitest mock fetch，模拟各种响应状态码。 +``` + +### Step 1.1 后端 AuthProvider 框架 + +| 任务 | 产出文件 | 参考 | +|------|---------|------| +| 定义基类 `AuthProvider` + `AuthResult` | `auth_providers/base.py` | [SSO § 3.2](1-sso-plugin-architecture.md#32-authprovider-基类) | +| Provider 自动发现（`pkgutil` 扫描） | `auth_providers/__init__.py` | [SSO § 3.2b](1-sso-plugin-architecture.md#3-layer-1可插拔认证体系-authprovider) | +| 迁移 Azure EasyAuth 为 Provider | `auth_providers/azure_easyauth.py` | [SSO § 3.3](1-sso-plugin-architecture.md#3-layer-1可插拔认证体系-authprovider) | +| 实现 OIDC Provider（JWT 验签） | `auth_providers/oidc.py` | [SSO § 3.4](1-sso-plugin-architecture.md#3-layer-1可插拔认证体系-authprovider) | +| 实现 GitHub OAuth Provider | `auth_providers/github_oauth.py` | [SSO § 3.5](1-sso-plugin-architecture.md#3-layer-1可插拔认证体系-authprovider) | +| GitHub 授权码交换网关 | `auth_gateways/github_gateway.py` | [SSO § 3.5](1-sso-plugin-architecture.md#3-layer-1可插拔认证体系-authprovider) | +| 重构 `auth.py` — `init_auth()` + `get_sso_token()` | `auth.py` 修改 | [SSO § 3.2](1-sso-plugin-architecture.md#32-authprovider-基类) | + +**核心逻辑**：`AUTH_PROVIDER` 环境变量选择主 Provider → 匿名回退（`ALLOW_ANONYMOUS=true`）→ `get_identity_id()` 返回值格式不变（`user:xxx` / `browser:xxx`）。 + +### Step 1.2 前端 OIDC 集成 + +| 任务 | 产出文件 | 参考 | +|------|---------|------| +| OIDC 配置 + UserManager | `src/app/oidcConfig.ts` | [SSO § 3.6](1-sso-plugin-architecture.md#3-layer-1可插拔认证体系-authprovider) | +| OIDC 回调页面 | `src/app/OidcCallback.tsx` | [SSO § 3.7](1-sso-plugin-architecture.md#3-layer-1可插拔认证体系-authprovider) | +| 统一登录面板（`/api/auth/info` 驱动） | `src/app/LoginPanel.tsx` | [SSO § 3.8](1-sso-plugin-architecture.md#3-layer-1可插拔认证体系-authprovider) | +| `fetchWithIdentity` 携带 Bearer token + 401 重试 | `src/app/utils.tsx` 修改 | [SSO § 3.8b](1-sso-plugin-architecture.md#3-layer-1可插拔认证体系-authprovider) | +| `App.tsx` 统一 initAuth | `src/app/App.tsx` 修改 | [SSO § 3.8](1-sso-plugin-architecture.md#3-layer-1可插拔认证体系-authprovider) | + +**依赖安装**：`pip install PyJWT cryptography`，`npm install oidc-client-ts` + +### Step 1.3 验证 + +- [ ] 配置 Keycloak → OIDC 登录成功，`get_identity_id()` 返回 `user:sub_claim` +- [ ] 配置 GitHub OAuth → OAuth 登录成功 +- [ ] 不配置任何 Provider → 匿名模式，行为与 0.7 现版本一致 +- [ ] `get_sso_token()` 返回当前用户的 OIDC access_token + +--- + +## Phase 2：插件框架 + Superset 插件 + +> 对应：[Plugin 文档 § 5~10](1-data-source-plugin-architecture.md#5-插件架构总体设计)、[SSO 文档 § 4 Layer 2](1-sso-plugin-architecture.md#4-layer-2数据源插件系统-datasourceplugin)、[SSO 文档 § 11 Phase 2](1-sso-plugin-architecture.md#11-实施路径) + +**目标**：建立插件框架，将 0.6 Superset 集成迁移为第一个插件。 + +### Step 2.0 先写测试 + +#### 2.0.1 插件框架测试（实现 Step 2.1 之前写） + +**`tests/backend/unit/test_plugin_discovery.py`** — 插件自动发现 + +```python +# 要验证的行为（基于 SSO 文档 § 4.4）： +# - plugins/ 下有合法子包（含 plugin_class）→ 被发现并注册 +# - plugins/ 下有子包但缺 plugin_class → 跳过，记录警告 +# - plugin_class.manifest() 中 required_env 全满足 → 启用 +# - required_env 缺一个 → 跳过，记入 DISABLED_PLUGINS +# - PLUGIN_BLOCKLIST 中列出的 plugin_id → 强制跳过 +# - 导入异常（如缺依赖）→ 优雅降级，不影响其他插件 + +# mock 策略：在 tmp_path 下构造包含 __init__.py 的 dummy plugin 包， +# monkeypatch plugins 包的 __path__ 指向 tmp_path。 +``` + +**`tests/backend/unit/test_plugin_data_writer.py`** — PluginDataWriter 写入工具 + +```python +# 要验证的行为（基于 Plugin 文档 § 6.2）： +# - write_dataframe(df, name, overwrite=True) → 写入 Parquet，返回正确元数据 +# - write_dataframe(df, name, overwrite=True) 第二次 → 覆盖同名表 +# - write_dataframe(df, name, overwrite=False) 同名已存在 → 自动加后缀 _1 +# - write_arrow(arrow_table, name) → 跳过 pandas 转换，直接写入 +# - write_batches → append → finish → 合并为一个 Parquet 文件 +# - source_metadata 完整写入 loader_metadata +# - 表名 sanitize（特殊字符替换） + +# mock 策略：Workspace 使用 tmp_path 下的真实临时目录，验证实际 Parquet 文件。 +# 使用 Flask test_request_context 提供 identity（PluginDataWriter 内部调用 get_identity_id）。 +``` + +**`tests/backend/integration/test_plugin_app_config.py`** — `/api/app-config` 插件字段 + +```python +# 要验证的行为（基于 Plugin 文档 § 8.1）： +# - 有插件启用时，/api/app-config 响应包含 PLUGINS 字段 +# - PLUGINS 字段合并了 manifest() 和 get_frontend_config() 的内容 +# - 无插件启用时，PLUGINS 为空 dict 或不存在 +# - 插件的敏感配置（如 SUPERSET_URL 原始值）不暴露给前端 + +# mock 策略：注册一个 DummyPlugin 到 ENABLED_PLUGINS，用 Flask test_client 请求。 +``` + +#### 2.0.2 Superset 插件测试（实现 Step 2.2 之前写） + +先准备 Superset API 的 fixture 文件（从真实 Superset 录制或按 API 文档构造）： + +``` +tests/backend/fixtures/superset/ +├── auth_login_200.json # {"access_token": "eyJ...", "refresh_token": "..."} +├── auth_login_401.json # {"message": "Invalid credentials"} +├── me_200.json # {"result": {"username": "alice", ...}} +├── datasets_list_200.json # {"result": [{"id": 42, "table_name": "sales", ...}]} +├── dataset_detail_42.json # {"result": {"id": 42, "columns": [...], ...}} +├── dashboard_list_200.json # {"result": [{"id": 7, "dashboard_title": "Sales", ...}]} +├── sqllab_execute_200.json # {"data": [{"region": "Asia", "amount": 100}, ...]} +└── csrf_token_200.json # {"result": "abc123"} +``` + +**`tests/backend/integration/test_superset_plugin.py`** — Superset 插件路由集成测试 + +```python +# 要验证的行为（基于 Plugin 文档 § 9.1 端到端流程）： +# +# 认证路由： +# - POST /api/plugins/superset/auth/login {username, password} +# → mock SupersetClient 返回 auth_login_200 → 200 + session 中存入 token +# - POST /api/plugins/superset/auth/login 密码错误 +# → mock 返回 auth_login_401 → 401 +# - GET /api/plugins/superset/auth/status +# → session 无 token → {"authenticated": false} +# → session 有 token → {"authenticated": true, "user": "alice"} +# +# 目录路由： +# - GET /api/plugins/superset/catalog/datasets +# → mock 返回 datasets_list_200 → 200 + 数据集列表 +# - 未认证时访问目录 → 401 +# +# 数据加载路由： +# - POST /api/plugins/superset/data/load-dataset {dataset_id: 42} +# → mock SQL Lab 返回 sqllab_execute_200 +# → 验证 Workspace 中生成了 Parquet 文件 +# → 响应包含 {table_name, row_count, columns} +# - POST /api/plugins/superset/data/refresh +# → 用 stored load_params 重新加载 → 覆盖同名表 + +# mock 策略： +# - SupersetClient 的所有 HTTP 调用通过 patch("requests.Session.get/post") 拦截 +# - 返回 fixture 目录中对应的 JSON +# - Workspace 使用 tmp_path 真实临时目录 +# - Flask session 通过 test_client 的 session_transaction 注入 token +``` + +**`tests/backend/unit/test_superset_client.py`** — SupersetClient 单元测试 + +```python +# 要验证的行为（基于 0.6 superset_client.py 已有逻辑）： +# - get_datasets() → 正确解析 /api/v1/dataset/ 响应 +# - get_dataset(42) → 正确解析单个数据集详情 +# - execute_sql() → 正确调用 SQL Lab API，返回数据行 +# - get_dashboards() → 正确解析仪表盘列表 +# - HTTP 错误（500/超时）→ 抛出有意义的异常 +# - CSRF token 在需要时自动获取 + +# mock 策略：patch requests.Session，返回 fixture JSON。 +``` + +#### 2.0.3 前端插件框架测试（实现 Step 2.3 之前写） + +**`tests/frontend/unit/plugins/registry.test.ts`** — 插件动态加载 + +```typescript +// 要验证的行为（基于 Plugin 文档 § 7.2 + SSO 文档 § 4.4）： +// - 从 /api/app-config 获取的 plugins 列表 → 动态加载对应模块 +// - 插件模块导出 manifest + PanelComponent → 注册成功 +// - 插件模块导出不完整 → 跳过，console.warn +// - 空 plugins 列表 → 返回空数组，无报错 +``` + +**`tests/frontend/unit/plugins/PluginHost.test.tsx`** — 插件容器组件 + +```tsx +// 要验证的行为（基于 Plugin 文档 § 7.2）： +// - 有 2 个已注册插件 → 渲染 2 个 Tab +// - 点击 Tab → 切换到对应插件面板 +// - 0 个插件 → 不渲染插件区域 +// - 插件面板调用 onDataLoaded → 触发表列表刷新 +``` + +### Step 2.1 后端插件框架 + +| 任务 | 产出文件 | 参考 | +|------|---------|------| +| 插件基类 `DataSourcePlugin` | `plugins/base.py` | [Plugin § 6.1](1-data-source-plugin-architecture.md#61-插件基类)、[SSO § 4.2](1-sso-plugin-architecture.md#4-layer-2数据源插件系统-datasourceplugin) | +| 插件自动发现 `discover_and_register()` | `plugins/__init__.py` | [SSO § 4.4](1-sso-plugin-architecture.md#44-插件注册与发现) | +| 插件数据写入工具 `PluginDataWriter` | `plugins/data_writer.py` | [Plugin § 6.2](1-data-source-plugin-architecture.md#62-插件加载的数据怎么进入-workspace) | +| `app.py` 集成 — 调用 `discover_and_register()` | `app.py` 修改 | [SSO § 4.4](1-sso-plugin-architecture.md#44-插件注册与发现) | +| `/api/app-config` 返回 plugins 字段 | `app.py` 修改 | [Plugin § 8.1](1-data-source-plugin-architecture.md#81-apiapp-config-中的插件字段组装) | + +### Step 2.2 Superset 插件后端 + +| 任务 | 产出文件 | 参考 | +|------|---------|------| +| `SupersetPlugin` 实现（manifest + blueprint） | `plugins/superset/__init__.py` | [Plugin § 10.4](1-data-source-plugin-architecture.md#104-supersetplugin-实现) | +| 迁移 `superset_client.py` | `plugins/superset/superset_client.py` | [Plugin § 4.2](1-data-source-plugin-architecture.md#42-后端模块) | +| 迁移 `auth_bridge.py` | `plugins/superset/auth_bridge.py` | 同上 | +| 迁移 `catalog.py` | `plugins/superset/catalog.py` | 同上 | +| 迁移认证路由（+ SSO 透传） | `plugins/superset/routes/auth.py` | [SSO § 4.3](1-sso-plugin-architecture.md#43-插件与-sso-的集成模式)、[SSO § 6](1-sso-plugin-architecture.md#6-sso-token-透传机制) | +| 迁移目录路由 | `plugins/superset/routes/catalog.py` | [Plugin § 4.2](1-data-source-plugin-architecture.md#42-后端模块) | +| 迁移数据加载路由（DuckDB → Workspace Parquet） | `plugins/superset/routes/data.py` | [Plugin § 10.2](1-data-source-plugin-architecture.md#102-核心改动) | + +**关键改动**：`data_routes.py` 从 0.6 的 DuckDB 写入改为 0.7 的 Workspace Parquet 写入（[Plugin § 10.2](1-data-source-plugin-architecture.md#102-核心改动)）。 + +> **注**：0.6 Superset 集成代码在独立的定制分支中（`data-formulator-0.6`），0.7 上游代码库不含任何 Superset 残留。此处是将 0.6 代码**迁入**0.7 插件框架，无需清理。 + +### Step 2.3 前端插件框架 + +| 任务 | 产出文件 | 参考 | +|------|---------|------| +| 插件类型定义 | `src/plugins/types.ts` | [Plugin § 7.1](1-data-source-plugin-architecture.md#71-插件面板契约) | +| 插件动态加载（`import.meta.glob`） | `src/plugins/registry.ts` | [SSO § 4.4](1-sso-plugin-architecture.md#44-插件注册与发现) | +| 插件容器组件 `PluginHost` | `src/plugins/PluginHost.tsx` | [Plugin § 7.2](1-data-source-plugin-architecture.md#72-plugin-host前端插件容器) | +| `dfSlice.tsx` 增加 `plugins` 字段 | `src/app/dfSlice.tsx` 修改 | [SSO § 10.2](1-sso-plugin-architecture.md#102-前端新增文件) | +| `UnifiedDataUploadDialog.tsx` 渲染插件 Tab | 修改 | [Plugin § 7.2](1-data-source-plugin-architecture.md#72-plugin-host前端插件容器) | +| `onDataLoaded` 回调 → 刷新表列表 / loadTable | 修改 | [Plugin § 7.3](1-data-source-plugin-architecture.md#73-数据加载完成后的流程) | + +### Step 2.4 Superset 插件前端 + +| 任务 | 产出文件 | 参考 | +|------|---------|------| +| 插件入口 + manifest | `src/plugins/superset/index.ts` | [Plugin § 10.3](1-data-source-plugin-architecture.md#103-前端) | +| 迁移 SupersetPanel | `src/plugins/superset/SupersetPanel.tsx` | [Plugin § 4.3](1-data-source-plugin-architecture.md#43-前端组件) | +| 迁移 SupersetCatalog | `src/plugins/superset/SupersetCatalog.tsx` | 同上 | +| 迁移 SupersetDashboards | `src/plugins/superset/SupersetDashboards.tsx` | 同上 | +| 迁移 SupersetFilterDialog | `src/plugins/superset/SupersetFilterDialog.tsx` | 同上 | +| 迁移 SupersetLogin | `src/plugins/superset/SupersetLogin.tsx` | 同上 | +| API 封装 | `src/plugins/superset/api.ts` | 同上 | + +### Step 2.5 验证 + +- [ ] 设置 `SUPERSET_URL` → 前端自动出现 Superset Tab +- [ ] 手动登录 Superset → 浏览数据集 → 加载数据到 Workspace +- [ ] SSO 模式（Phase 1 已完成）→ 无需输入 Superset 密码即可访问 +- [ ] 不设置 `SUPERSET_URL` → 无任何影响，行为与现版本一致 +- [ ] 数据刷新：加载后点刷新按钮 → 重新拉取最新数据（[Plugin § 7.4](1-data-source-plugin-architecture.md#74-数据刷新协议)） + +--- + +## Phase 3：凭证保险箱 + +> 对应：[SSO 文档 § 5 Layer 3](1-sso-plugin-architecture.md#5-layer-3凭证保险箱-credentialvault)、[SSO 文档 § 11 Phase 3](1-sso-plugin-architecture.md#11-实施路径) + +**目标**：服务端加密凭证存储，替代 Session 级别的临时存储。 + +### Step 3.0 先写测试 + +**`tests/backend/unit/test_credential_vault.py`** — LocalCredentialVault 单元测试 + +```python +# 要验证的行为（基于 SSO 文档 § 5.2~5.3）： +# - store(user_a, "superset", {username, password}) → 成功存入 +# - retrieve(user_a, "superset") → 返回明文 {username, password} +# - retrieve(user_b, "superset") → 返回 None（用户隔离） +# - store 同一 (user, source) 两次 → 后者覆盖前者 +# - delete(user_a, "superset") → 删除后 retrieve 返回 None +# - list_sources(user_a) → ["superset"]，delete 后为 [] +# - 换一个 CREDENTIAL_VAULT_KEY 实例化 → 之前存的凭证解密失败，返回 None（非崩溃） +# - 空密钥 → 初始化时报错 + +# mock 策略：全部使用 tmp_path 下的真实 SQLite 文件 + 真实 Fernet 密钥。 +# 不需要 mock 任何东西——这个模块足够独立。 +``` + +**`tests/backend/unit/test_credential_vault_factory.py`** — Vault 工厂 + +```python +# 要验证的行为（基于 SSO 文档 § 5.4）： +# - CREDENTIAL_VAULT_KEY 已设置 → get_credential_vault() 返回 LocalCredentialVault 实例 +# - CREDENTIAL_VAULT_KEY 未设置 → 返回 None +# - CREDENTIAL_VAULT=local → 使用 LocalCredentialVault +# - CREDENTIAL_VAULT=unknown → 返回 None，记录警告 +# - 多次调用 get_credential_vault() → 返回同一个单例 + +# mock 策略：monkeypatch 环境变量 + tmp_path。 +``` + +**`tests/backend/integration/test_credential_routes.py`** — 凭证 API 端点 + +```python +# 要验证的行为（基于 SSO 文档 § 5.5）： +# - POST /api/credentials/store → 存储成功 +# - GET /api/credentials/list → 返回已存储的 source_key 列表（不含凭证内容） +# - POST /api/credentials/delete → 删除后 list 不再包含 +# - Vault 未配置时 → /store 和 /delete 返回 503 +# - 不同用户（不同 X-Identity-Id）之间凭证隔离 + +# mock 策略：Flask test_client + 真实 tmp_path Vault + X-Identity-Id 头切换身份。 +``` + +**`tests/backend/integration/test_plugin_auth_with_vault.py`** — 插件认证 + Vault 联动 + +```python +# 要验证的行为（基于 SSO 文档 § 4.3 三种认证模式）： +# - Vault 中有已存凭证 → 插件 auth/login 自动取出，无需用户输入 +# - Vault 中凭证已过期（外部系统密码已改）→ 返回 vault_stale 提示 +# - 用户手动输入 + remember=true → 凭证存入 Vault +# - SSO token 可用 + 插件 supports_sso_passthrough → 自动透传 + +# mock 策略：patch SupersetClient 的认证调用 + 真实 Vault。 +``` + +### Step 3.1 后端 + +| 任务 | 产出文件 | 参考 | +|------|---------|------| +| Vault 抽象接口 | `credential_vault/base.py` | [SSO § 5.2](1-sso-plugin-architecture.md#52-credentialvault-接口) | +| 本地加密实现（SQLite + Fernet） | `credential_vault/local_vault.py` | [SSO § 5.3](1-sso-plugin-architecture.md#53-本地加密实现) | +| Vault 工厂 | `credential_vault/__init__.py` | [SSO § 5.4](1-sso-plugin-architecture.md#54-vault-工厂) | +| 凭证管理 API | `credential_routes.py` | [SSO § 5.5](1-sso-plugin-architecture.md#55-凭证管理-api) | +| 插件认证路由增强 — 自动从 Vault 取凭证 | `plugins/superset/routes/auth.py` 修改 | [SSO § 4.3](1-sso-plugin-architecture.md#43-插件与-sso-的集成模式) | + +### Step 3.2 前端 + +| 任务 | 产出文件 | 参考 | +|------|---------|------| +| 凭证管理 UI | `src/plugins/CredentialManager.tsx` | [SSO § 10.2](1-sso-plugin-architecture.md#102-前端新增文件) | + +### Step 3.3 验证 + +- [ ] 设置 `CREDENTIAL_VAULT_KEY` → 用户输入 Superset 密码后加密存储 +- [ ] 换浏览器 → SSO 登录 → 已存凭证自动可用，无需重新输入 +- [ ] 不设置 `CREDENTIAL_VAULT_KEY` → 回退到 Session 存储（现有行为） + +--- + +## Phase 4：第二个插件验证 + +> 对应：[SSO 文档 § 11 Phase 4](1-sso-plugin-architecture.md#11-实施路径) + +**目标**：用 Metabase 插件验证框架通用性 — **核心代码零修改**。 + +### Step 4.0 先写测试 — 框架通用性验证 + +Phase 4 的测试本身就是核心交付物。它验证的不是 Metabase 的业务逻辑，而是**插件框架的扩展性承诺**。 + +**`tests/backend/contract/test_plugin_zero_core_change.py`** — 核心代码零修改契约 + +```python +# 这个测试在 Metabase 插件代码写完后运行： +# - 检查 plugins/__init__.py 的 git diff → 无修改 +# - 检查 app.py 的 git diff → 无修改 +# - 检查 src/plugins/registry.ts 的 git diff → 无修改 +# - Metabase plugin 仅存在于 plugins/metabase/ 和 src/plugins/metabase/ +# - discover_and_register() 能发现 Metabase 插件 +# - /api/app-config 返回的 PLUGINS 中包含 metabase +# +# 这是一个**契约测试**：如果未来框架改动导致新增插件需要改核心代码， +# 这个测试应当失败，提醒开发者修复框架的扩展性。 +``` + +**`tests/backend/integration/test_metabase_plugin.py`** — Metabase 插件路由 + +```python +# 与 Superset 插件测试同结构，mock Metabase REST API： +# - /api/plugins/metabase/auth/login → mock Metabase session API +# - /api/plugins/metabase/catalog/questions → mock /api/card/ 列表 +# - /api/plugins/metabase/data/load-question → mock 查询结果 + 写入 Workspace +``` + +| 任务 | 产出文件 | +|------|---------| +| Metabase 插件后端 | `plugins/metabase/` | +| Metabase 插件前端 | `src/plugins/metabase/` | + +**验证标准**：仅新增目录，无需修改 `plugins/__init__.py`、`registry.ts`、`app.py` 等任何现有文件。 + +--- + +## Phase 5：完善与增强 + +> 对应：[SSO 文档 § 11 Phase 5](1-sso-plugin-architecture.md#11-实施路径)、[Plugin 文档 § 7.7](1-data-source-plugin-architecture.md#77-外部系统元数据拉取) + +| 任务 | 优先级 | 参考 | +|------|--------|------| +| 外部元数据拉取（列描述、语义类型） | P0 | [Plugin § 7.7](1-data-source-plugin-architecture.md#77-外部系统元数据拉取) | +| 多协议 SSO 支持（SAML / LDAP / CAS） | P1 | [SSO § 3.9](1-sso-plugin-architecture.md#39-多协议支持从-oidc-扩展到-saml--ldap--cas--反向代理) | +| ExternalDataLoader 改进 | P1 | [2-external-dataloader-enhancements.md](2-external-dataloader-enhancements.md) | +| 插件错误边界和降级处理 | P1 | — | +| 管理员配置 UI | P2 | — | +| 审计日志 | P2 | — | + +> 注：单元测试和集成测试已嵌入 Phase 1~4 的每个 Step 中，不再单独列为待办。 + +--- + +## 全局依赖清单 + +| 包 | 用途 | 引入阶段 | 安装 | +|----|------|---------|------| +| `PyJWT` | OIDC JWT 验签 | Phase 1 | `pip install PyJWT` | +| `cryptography` | Fernet 加密 + JWT 验签 + 测试密钥生成 | Phase 1 | `pip install cryptography` | +| `oidc-client-ts` | 前端 OIDC PKCE | Phase 1 | `npm install oidc-client-ts` | +| `requests` | 插件 HTTP 调用 | Phase 2 | 已有 | + +`cryptography` 同时用于生产代码（Fernet 加密、JWT RS256 验签）和测试（生成 RSA 密钥对签发测试 JWT），不需要额外的测试专用依赖。 + +现有 `pytest`、`vitest`、`unittest.mock`、`flask.testing` 已满足所有测试需求，**不需要引入新的测试框架或 mock 库**。 + +> 参考：[SSO 文档 附录 B](1-sso-plugin-architecture.md#附录-b关键依赖) + +--- + +## 核心代码改动范围（一次性） + +以下文件在 Phase 1~2 中需要修改。Phase 3+ 不再触碰核心代码。 + +| 文件 | 改动阶段 | 改动量 | 说明 | +|------|---------|--------|------| +| `py-src/.../auth.py` | Phase 1 | ~60 行 | Provider 自动发现 + `get_sso_token()` | +| `py-src/.../app.py` | Phase 1+2 | ~25 行 | `init_auth()` + `discover_and_register()` + app-config | +| `src/app/App.tsx` | Phase 1 | ~35 行 | 统一 initAuth + 登录 UI | +| `src/app/utils.tsx` | Phase 1 | ~15 行 | Bearer token + 401 重试 | +| `src/app/dfSlice.tsx` | Phase 2 | ~5 行 | `ServerConfig.plugins` | +| `src/views/UnifiedDataUploadDialog.tsx` | Phase 2 | ~20 行 | PluginHost 渲染 | + +> 参考：[SSO 文档 § 10.3](1-sso-plugin-architecture.md#103-对现有文件的改动清单) + +--- + +## 文件结构总览 + +完成全部 Phase 后的新增文件结构（含测试）： + +``` +py-src/data_formulator/ +├── auth_providers/ ← Phase 1 +│ ├── base.py +│ ├── azure_easyauth.py +│ ├── oidc.py +│ └── github_oauth.py +├── auth_gateways/ ← Phase 1 +│ ├── github_gateway.py +│ └── logout.py +├── credential_vault/ ← Phase 3 +│ ├── base.py +│ └── local_vault.py +├── credential_routes.py ← Phase 3 +├── plugins/ ← Phase 2 +│ ├── base.py +│ ├── data_writer.py +│ └── superset/ +│ ├── __init__.py +│ ├── superset_client.py +│ ├── auth_bridge.py +│ ├── catalog.py +│ └── routes/ + +src/ +├── app/ +│ ├── oidcConfig.ts ← Phase 1 +│ └── OidcCallback.tsx ← Phase 1 +├── plugins/ ← Phase 2 +│ ├── types.ts +│ ├── registry.ts +│ ├── PluginHost.tsx +│ ├── CredentialManager.tsx ← Phase 3 +│ └── superset/ +│ ├── index.ts +│ ├── SupersetPanel.tsx +│ └── ... + +tests/ +├── backend/ +│ ├── unit/ +│ │ ├── test_oidc_provider.py ← Phase 1 +│ │ ├── test_github_oauth_provider.py ← Phase 1 +│ │ ├── test_azure_easyauth_provider.py ← Phase 1 +│ │ ├── test_plugin_discovery.py ← Phase 2 +│ │ ├── test_plugin_data_writer.py ← Phase 2 +│ │ ├── test_superset_client.py ← Phase 2 +│ │ ├── test_credential_vault.py ← Phase 3 +│ │ └── test_credential_vault_factory.py ← Phase 3 +│ ├── security/ +│ │ └── test_auth_provider_chain.py ← Phase 1 +│ ├── integration/ +│ │ ├── test_auth_info_endpoint.py ← Phase 1 +│ │ ├── test_plugin_app_config.py ← Phase 2 +│ │ ├── test_superset_plugin.py ← Phase 2 +│ │ ├── test_credential_routes.py ← Phase 3 +│ │ ├── test_plugin_auth_with_vault.py ← Phase 3 +│ │ └── test_metabase_plugin.py ← Phase 4 +│ ├── contract/ +│ │ └── test_plugin_zero_core_change.py ← Phase 4 +│ └── fixtures/ +│ └── superset/ ← Phase 2 +│ ├── auth_login_200.json +│ ├── datasets_list_200.json +│ ├── dataset_detail_42.json +│ ├── sqllab_execute_200.json +│ └── ... +├── frontend/ +│ └── unit/ +│ ├── app/ +│ │ └── fetchWithIdentity.test.ts ← Phase 1 +│ └── plugins/ +│ ├── registry.test.ts ← Phase 2 +│ └── PluginHost.test.tsx ← Phase 2 +``` + +> 参考：[SSO 文档 § 10](1-sso-plugin-architecture.md#10-目录结构) + +--- + +## 文档交付要求 + +每个 Phase 完成时，除代码和测试外，还需交付或更新以下文档： + +| Phase | 必须交付的文档 | 说明 | +|-------|-------------|------| +| Phase 1 | `auth_providers/README.md` | 如何新增一个 AuthProvider：基类契约、环境变量约定、`get_auth_info()` 返回格式、测试方法 | +| Phase 2 | `plugins/README.md` | **插件开发指南**：目录约定、`plugin_class` 暴露方式、manifest 字段说明、路由前缀规则、PluginDataWriter 用法、前端 `index.ts` 导出规范、fixture 录制方法 | +| Phase 2 | `.env.template` 更新 | 新增 `SUPERSET_URL` 等插件环境变量的说明 | +| Phase 3 | `credential_vault/README.md` | Vault 配置方式、密钥生成命令、插件如何调用 Vault API | +| Phase 4 | `plugins/README.md` 更新 | 用 Metabase 插件作为实际案例补充到指南中，验证文档的可操作性 | +| 每个 Phase | `CHANGELOG.md` 追加 | 简要记录本阶段新增的能力和配置变更 | + +**核心原则**：文档写给"下一个要开发新插件的人"看。如果按照 `plugins/README.md` 的步骤无法从零完成一个新插件，说明文档不合格。Phase 4（Metabase）就是对这份文档的实战验证。 diff --git a/design-docs/1-data-source-plugin-architecture.md b/design-docs/1-data-source-plugin-architecture.md new file mode 100644 index 00000000..f883cbd6 --- /dev/null +++ b/design-docs/1-data-source-plugin-architecture.md @@ -0,0 +1,1801 @@ +# Data Formulator 数据源插件架构设计方案 + +## 目录 + +1. [背景与动机](#1-背景与动机) +2. [部署模型分析：个人工具 vs 团队平台](#2-部署模型分析个人工具-vs-团队平台) +3. [现状分析](#3-现状分析) +4. [0.6 版本 Superset 集成回顾](#4-06-版本-superset-集成回顾) +5. [插件架构总体设计](#5-插件架构总体设计) +6. [后端插件接口](#6-后端插件接口) +7. [前端插件接口](#7-前端插件接口) +8. [插件注册与发现](#8-插件注册与发现) +9. [数据流设计](#9-数据流设计) +10. [Superset 插件迁移示例](#10-superset-插件迁移示例) +11. [与现有 ExternalDataLoader 的关系](#11-与现有-externaldataloader-的关系) +12. [插件 i18n 自包含方案](#12-插件-i18n-自包含方案) +13. [目录结构](#13-目录结构) +14. [实施路径](#14-实施路径) +15. [关键设计难点：外部系统配置与用户身份](#15-关键设计难点外部系统配置与用户身份) +16. [FAQ](#16-faq) +17. [附录 A：核心代码改动清单](#附录-a核心代码改动清单) +18. [附录 B：新增插件的完整步骤](#附录-b新增插件的完整步骤零核心改动) +19. [附录 C：关联文档](#附录-c关联文档) + +--- + +## 1. 背景与动机 + +### 1.1 核心需求 + +Data Formulator 需要对接外部 BI/报表系统（如 Apache Superset、Metabase、Power BI 等）作为数据源，让用户可以： + +- 用外部系统的**账号权限**登录 +- 浏览该用户**有权访问**的数据集、仪表盘、报表 +- 将数据拉取到 Data Formulator 中进行可视化分析 + +### 1.2 为什么需要插件机制 + +在 0.6 版本中，我们已经实现了 Superset 集成，但存在以下问题： + +| 问题 | 说明 | +|------|------| +| **对核心代码侵入较高** | 修改了 `app.py`、`dfSlice.tsx`、`App.tsx`、`utils.tsx`、`UnifiedDataUploadDialog.tsx` 等多个核心文件 | +| **不可复用** | 如果再集成一个 Metabase，需要重复修改同一批核心文件 | +| **耦合认证逻辑** | Superset 的 JWT 认证直接嵌入 Flask session，与应用认证逻辑耦合 | +| **升级困难** | 上游 Data Formulator 版本更新时，合并冲突概率高 | + +**插件机制的价值**：每个外部系统的集成代码自成一体（后端 + 前端），对核心代码的修改只需一次性地建立插件框架即可。后续新增任何 BI 系统，只需编写一个新插件，**不再需要修改核心代码**。 + +--- + +## 2. 部署模型分析：个人工具 vs 团队平台 + +### 2.1 数据存储模型 + +Data Formulator 的所有数据统一通过 **Workspace** 管理。Workspace 后端由 `WORKSPACE_BACKEND` 配置决定，支持多种部署形态： + +``` +┌────────────────────────────────────────────────────────┐ +│ Workspace 统一存储模型 │ +│ │ +│ 所有数据来源 (Upload/Paste/URL/DB/插件) │ +│ ↓ │ +│ loadTable → Workspace │ +│ ↓ │ +│ ┌──────────────────────────────────────────────────┐ │ +│ │ WORKSPACE_BACKEND = ? │ │ +│ │ │ │ +│ │ local → 本地磁盘 (~/.data_formulator/) │ │ +│ │ ephemeral → 仅内存（会话结束即消失） │ │ +│ │ cloud → 远程对象存储（未来） │ │ +│ └──────────────────────────────────────────────────┘ │ +│ │ +│ 前端始终只拿 sample rows + 元数据 │ +└────────────────────────────────────────────────────────┘ +``` + +> **历史说明**：早期版本中曾有 `storeOnServer` 用户开关和 `DISABLE_DATABASE` 环境变量， +> 分别用于让用户选择"浏览器临时存储 vs 磁盘持久化"和"禁用服务端存储"。 +> 现在这些概念已被 `WORKSPACE_BACKEND` 统一取代——`ephemeral` 模式等价于旧的纯浏览器模式。 + +### 2.2 接入 BI 系统后的模型变化 + +当需要集成 Superset 等 BI 系统时，部署模型发生了根本变化： + +``` +团队部署模式（插件场景的实际部署）: + + ┌───────────┐ ┌──────────────┐ ┌────────────┐ + │ 用户A浏览器│────→│ │────→│ │ + │ 用户B浏览器│────→│ DF 服务器 │────→│ Superset │ + │ 用户C浏览器│────→│ (IT部署管理) │────→│ (IT管理) │ + └───────────┘ └──────────────┘ └────────────┘ + + 在这个模型下： + - "服务器"不再是用户自己的电脑 + - 数据必然经过服务器（插件后端调 Superset API） + - 隐私关注点变成了"谁控制服务器"，而不是"数据在不在服务器上" + - BI 系统的连接地址是基础设施，由 IT 管理，不是用户自行添加 +``` + +### 2.3 团队部署下仍存在的差异 + +Workspace 统一存储解决了数据持久化的问题，但个人与团队部署之间仍有两个需要关注的差异： + +| 差异 | 个人模式 | 团队模式 | +|------|---------|---------| +| 数据库/BI 连接参数 | 前端填，自己用方便 | 应服务端集中管理（减少重复填写、防止 SSRF） | +| 模型 API Key | 前端填，自己的 Key | 服务端全局配置（0.7 已实现） | + +> 0.7 版本已将模型管理升级为"服务端全局配置"。插件系统沿着同样的方向继续——连接端点由服务端配置，用户只需认证。 + +### 2.4 对插件系统的设计决策 + +由于所有数据统一走 Workspace，插件系统只需关注两个插件特有的问题： + +**1. 插件配置（URL 等）：只在服务端配置，不在前端添加。** + +- BI 系统的 URL 是**基础设施端点**，不是用户数据，由 IT 部门管理 +- 用户只需**认证**（登录 Superset），而不是"添加一个 Superset 连接" +- 禁止前端输入任意 URL（防止 SSRF） + +**2. 插件认证：per-user 凭据，服务端管理。** + +- 用户对 BI 系统的登录凭据存储在服务端（CredentialVault），不暴露给前端 +- 尊重 BI 系统自身的权限模型（RBAC / RLS） + +``` +┌──────────────────────────────────────────────────────────────┐ +│ Data Formulator │ +│ │ +│ 数据来源 │ +│ ┌────────────────────────┐ ┌───────────────────────────┐ │ +│ │ 内置来源 │ │ 插件来源 │ │ +│ │ Upload / Paste / URL │ │ Superset / Metabase / ... │ │ +│ │ Database / Extract │ │ │ │ +│ └──────────┬─────────────┘ └─────────────┬─────────────┘ │ +│ │ │ │ +│ └──────────┬───────────────────┘ │ +│ ↓ │ +│ loadTable → Workspace │ +│ (统一数据入口 → 统一存储) │ +└──────────────────────────────────────────────────────────────┘ +``` + +### 2.5 各层的配置与数据归属总结 + +| 层级 | 谁配置 | 存在哪里 | 示例 | +|------|--------|---------|------| +| **插件端点** | IT 管理员 | 服务端 `.env` | `PLG_SUPERSET_URL=http://...` | +| **用户认证** | 用户自己 | Flask Session（服务端内存） | Superset JWT Token | +| **用户数据** | 插件自动加载 | Workspace（由 `WORKSPACE_BACKEND` 决定存储位置） | 从 Superset 拉取的数据集 | +| **前端状态** | 自动管理 | Redux Store（浏览器内存） | sample rows、表元数据 | + +--- + +## 3. 现状分析 + +### 3.1 当前数据加载架构（0.7 版本） + +``` +用户操作 + ├─ Upload (本地文件) ──→ 解析 → DictTable + ├─ Paste (粘贴数据) ──→ 解析 → DictTable + ├─ URL (远程文件) ──→ fetch → 解析 → DictTable + ├─ Explore (示例数据) ──→ fetch → 解析 → DictTable + ├─ Extract (AI 提取) ──→ Agent → 解析 → DictTable + └─ Database (外部数据库) ──→ ExternalDataLoader → Arrow → Parquet + │ + 所有路径最终 → loadTable thunk → Redux Store +``` + +### 3.2 后端现有扩展点 + +**ExternalDataLoader** — 面向数据库的抽象基类： + +```python +class ExternalDataLoader(ABC): + def __init__(self, params: dict) # 连接参数 + def list_tables(...) # 列出表 + def fetch_data_as_arrow(...) # 拉取数据（→ Arrow） + def ingest_to_workspace(...) # 写入 workspace（→ Parquet） + def list_params() # 声明所需参数 + def auth_instructions() # 认证说明 +``` + +注册方式（`data_loader/__init__.py`）： + +```python +_LOADER_SPECS = [ + ("mysql", "...mysql_data_loader", "MySQLDataLoader", "pymysql"), + ("postgresql", "...postgresql_data_loader", "PostgreSQLDataLoader", "psycopg2-binary"), + # ... 共 9 种 +] +``` + +**这套机制适合数据库连接器**，但 **不适合 BI 系统集成**，因为 BI 系统需要： + +| 能力 | ExternalDataLoader | BI 系统需要 | +|------|:--:|:--:| +| 连接参数 | ✅ 简单 key-value | 需要 URL + 认证流程 | +| 认证 | ✅ 用户名/密码/密钥 | JWT / OAuth / SSO | +| 列出数据 | ✅ `list_tables()` | 数据集 + 仪表盘 + 报表 + 筛选条件 | +| 拉取数据 | ✅ `fetch_data_as_arrow()` | 通过 BI 系统的 SQL Lab / API 拉取（尊重 RBAC/RLS） | +| 前端 UI | ❌ 无（通用表单） | 需要专用的目录浏览、筛选、登录等 UI | +| 自有 API 路由 | ❌ 无 | 需要注册独立的 Blueprint | + +### 3.3 前端现有扩展点 + +数据加载入口统一在 `UnifiedDataUploadDialog.tsx`，支持 6 种 Tab： + +```typescript +type UploadTabType = 'menu' | 'upload' | 'paste' | 'url' | 'database' | 'extract' | 'explore'; +``` + +数据库入口由 `DBManagerPane` 组件处理，支持上述 9 种 ExternalDataLoader。 + +所有数据加载最终通过 `loadTable` thunk 进入 Redux Store。 + +--- + +## 4. 0.6 版本 Superset 集成回顾 + +### 4.1 架构概览 + +``` +前端 后端 Superset +┌─────────────┐ HTTP ┌────────────────┐ REST ┌──────────┐ +│ LoginView │──────────→│ auth_routes │───────────→│ JWT 登录 │ +│ SupersetPanel│──────────→│ catalog_routes │───────────→│ 数据集API│ +│ SupersetCatalog│────────→│ data_routes │───────────→│ SQL Lab │ +│ SupersetDashboards│─────→│ auth_bridge │ └──────────┘ +└─────────────┘ │ superset_client│ + │ catalog │ + └────────────────┘ + │ + ↓ + 写入 DuckDB（0.6） + / Workspace Parquet（0.7 目标） +``` + +### 4.2 后端模块 + +| 文件 | 职责 | +|------|------| +| `superset_client.py` | Superset REST API 封装（数据集列表、详情、仪表盘、SQL Lab 执行） | +| `auth_bridge.py` | JWT 登录/刷新/验证 | +| `auth_routes.py` | `/api/superset/auth/*` 认证 API | +| `catalog_routes.py` | `/api/superset/catalog/*` 数据集/仪表盘目录 API | +| `data_routes.py` | `/api/superset/data/*` 数据加载 API（含筛选条件） | +| `catalog.py` | 带 TTL 缓存的数据目录（两级：摘要/详情） | + +### 4.3 前端组件 + +| 组件 | 职责 | +|------|------| +| `LoginView.tsx` | 登录页（用户名密码 / SSO 弹窗） | +| `SupersetPanel.tsx` | Tab 容器（仪表盘 + 数据集） | +| `SupersetCatalog.tsx` | 数据集目录浏览（搜索、预览、加载） | +| `SupersetDashboards.tsx` | 仪表盘列表（展开查看数据集） | +| `SupersetDashboardFilterDialog.tsx` | 仪表盘筛选条件对话框 | + +### 4.4 对核心代码的改动 + +``` +app.py +50 行 (配置、Blueprint 注册、app-config 扩展) +dfSlice.tsx +3 字段 (SUPERSET_ENABLED、SSO_LOGIN_URL、AUTH_USER) +App.tsx +20 行 (登录逻辑、LoginView) +utils.tsx +6 URL (Superset API 地址) +UnifiedDataUploadDialog.tsx +30 行 (SplitDatabasePane + SupersetPanel) +DBTableManager.tsx +4 行 (监听 superset-dataset-loaded 事件) +``` + +### 4.5 可以复用的部分 + +核心的业务逻辑（SupersetClient、AuthBridge、Catalog、FilterBuilder）可以直接迁移为 Superset 插件的实现。 + +--- + +## 5. 插件架构总体设计 + +### 5.1 设计原则 + +1. **最小侵入**：核心代码只需一次性改动来建立插件框架，后续新增插件不再修改核心 +2. **自包含**：每个插件独立提供后端路由 + 前端组件 + 配置声明 +3. **自动发现**：后端通过目录扫描、前端通过 `import.meta.glob` 自动发现插件，新增插件无需修改任何注册表 +4. **可选加载**：插件通过环境变量启用，未启用的插件不加载任何代码 +5. **统一出口**：插件加载的数据最终通过现有的 `loadTable` 进入系统 +6. **权限透传**：尊重外部系统自身的权限模型（RBAC / RLS） +7. **统一范式**：与 AuthProvider（认证）、CredentialVault（凭证）共享"抽象基类 + 动态注册 + 环境变量启用"的插件设计范式（详见 `sso-plugin-architecture.md`） + +### 5.2 环境变量命名约定 + +系统中有三类环境变量驱动的自动发现机制，各自使用不同的命名空间以避免冲突： + +| 类别 | 前缀 | 发现机制 | 示例 | +|------|------|---------|------| +| **系统配置** | 无（直接命名） | 固定读取 | `LOG_LEVEL`、`FLASK_SECRET_KEY`、`WORKSPACE_BACKEND` | +| **LLM 模型** | `{PROVIDER}_`（遗留命名） | 扫描 `*_ENABLED=true` | `DEEPSEEK_ENABLED`、`DEEPSEEK_API_KEY`、`QWEN_MODELS` | +| **数据源插件** | **`PLG_`** + `{PLUGIN}_` | manifest 中 `required_env` 全部存在 | `PLG_SUPERSET_URL`、`PLG_GRAFANA_TIMEOUT` | +| **认证 Provider** | `AUTH_PROVIDER` 单选 + Provider 自有前缀 | `AUTH_PROVIDER=xxx` 指定 | `AUTH_PROVIDER=oidc`、`OIDC_ISSUER_URL` | +| **凭证保险箱** | `CREDENTIAL_VAULT_` | `CREDENTIAL_VAULT_KEY` 存在 | `CREDENTIAL_VAULT=local`、`CREDENTIAL_VAULT_KEY=...` | + +**为什么插件需要 `PLG_` 前缀？** + +LLM 模型的自动发现靠扫描所有 `*_ENABLED=true` 的环境变量。如果插件也使用裸前缀（如 `SUPERSET_SSO_ENABLED=true`），会被模型注册器误识别为 model provider。加 `PLG_` 前缀后，命名空间彻底隔离： + +```env +# ============================================================= +# LLM 模型配置（遗留命名，{PROVIDER}_ 前缀） +# ============================================================= +DEEPSEEK_ENABLED=true +DEEPSEEK_API_KEY=sk-xxx +DEEPSEEK_API_BASE=https://api.deepseek.com +DEEPSEEK_MODELS=deepseek-chat + +# ============================================================= +# 数据源插件（PLG_{PLUGIN}_ 前缀） +# ============================================================= +PLG_SUPERSET_URL=http://superset:8088 +PLG_SUPERSET_SSO=true + +PLG_GRAFANA_URL=http://grafana.example.com:3000 +PLG_GRAFANA_TIMEOUT=10 + +# ============================================================= +# 认证（无 PLG_ 前缀，单选机制不会冲突） +# ============================================================= +AUTH_PROVIDER=oidc +OIDC_ISSUER_URL=https://login.microsoftonline.com/xxx/v2.0 +OIDC_CLIENT_ID=abc123 +``` + +> **注意**：LLM 模型的 `{PROVIDER}_` 命名是遗留约定，暂不添加 `MODEL_` 前缀以保持向后兼容。 +> 未来如需统一，可分步迁移。当前只为新增的插件系统建立 `PLG_` 前缀规范。 + +### 5.3 概念模型 + +``` +┌──────────────────────────────────────────────────────────────────────┐ +│ Data Formulator 核心 │ +│ │ +│ ┌──────────────────────────────────────────────────────────────────┐ │ +│ │ Layer 1: AuthProvider 链 (SSO / Azure EasyAuth / 浏览器 UUID) │ │ +│ │ → 确定"你是谁" │ │ +│ └──────────────────────────────────────────────────────────────────┘ │ +│ │ +│ ┌──────────────────┐ ┌───────────────┐ ┌──────────────┐ │ +│ │ 前端 Plugin Host │ │ loadTable │ │ Workspace │ │ +│ │ (渲染插件面板) │ │ (统一数据入口)│ │ (Parquet存储)│ │ +│ └────────┬─────────┘ └───────┬───────┘ └──────┬───────┘ │ +│ │ │ │ │ +│ ┌────────┴────────────────────┴──────────────────┴───────┐ │ +│ │ Layer 2: Plugin Registry (自动扫描 + 环境变量门控) │ │ +│ └────────┬──────────────┬──────────────┬─────────────────┘ │ +│ │ │ │ │ +│ ┌────────┴─────┐ ┌──────┴─────┐ ┌──────┴──────┐ │ +│ │ Superset │ │ Metabase │ │ Power BI │ ... │ +│ │ Plugin │ │ Plugin │ │ Plugin │ │ +│ │ │ │ │ │ │ │ +│ │ ┌──────────┐ │ │ ┌────────┐ │ │ ┌─────────┐ │ │ +│ │ │ 后端路由 │ │ │ │ 后端 │ │ │ │ 后端 │ │ │ +│ │ │ 前端面板 │ │ │ │ 前端 │ │ │ │ 前端 │ │ │ +│ │ │ 认证逻辑 │ │ │ │ 认证 │ │ │ │ 认证 │ │ │ +│ │ │ 目录缓存 │ │ │ │ 目录 │ │ │ │ 目录 │ │ │ +│ │ └──────────┘ │ │ └────────┘ │ │ └─────────┘ │ │ +│ └──────────────┘ └────────────┘ └─────────────┘ │ +│ │ +│ ┌──────────────────────────────────────────────────────────────────┐ │ +│ │ Layer 3: CredentialVault (加密凭证存储，per-user per-source) │ │ +│ │ → 插件认证时自动存/取凭证 │ │ +│ └──────────────────────────────────────────────────────────────────┘ │ +│ │ +│ → 三层统一范式: 抽象基类 + 动态注册 + 环境变量启用 │ +│ → 详见 sso-plugin-architecture.md │ +└──────────────────────────────────────────────────────────────────────┘ +``` + +### 5.3 核心概念 + +| 概念 | 说明 | +|------|------| +| **DataSourcePlugin** | 一个外部 BI 系统的完整集成，包含后端和前端 | +| **Plugin Manifest** | 插件的自我描述（ID、名称、图标、配置需求、启用条件） | +| **Plugin Backend** | Flask Blueprint + 认证 + 目录 + 数据拉取 | +| **Plugin Frontend** | React 组件（面板 UI），在 `UnifiedDataUploadDialog` 中以 Tab 形式呈现 | +| **Plugin Registry** | 后端的插件发现与注册中心 | +| **Plugin Host** | 前端的插件容器，负责渲染已启用插件的面板 | + +--- + +## 6. 后端插件接口 + +### 6.1 插件基类 + +```python +# py-src/data_formulator/plugins/base.py + +from abc import ABC, abstractmethod +from typing import Any +from flask import Blueprint + + +class DataSourcePlugin(ABC): + """外部数据源插件的基类。 + + 每个插件需要实现以下内容： + 1. manifest() — 描述插件自身的元数据 + 2. create_blueprint() — 提供 Flask Blueprint（后端 API 路由） + 3. get_frontend_config() — 声明前端需要的信息（组件标识、配置） + 4. on_enable() / on_disable() — 生命周期钩子 + """ + + @staticmethod + @abstractmethod + def manifest() -> dict[str, Any]: + """返回插件的自我描述。 + + manifest() 只包含后端框架需要的声明性信息。 + UI 相关配置（catalog_entry_types 等）由 get_frontend_config() 返回。 + + Returns: + { + "id": "superset", # 唯一标识符，用作路由前缀和前端标识 + "name": "Apache Superset", # 显示名称 + "icon": "superset", # 前端图标标识 + "description": "...", # 简短描述 + "version": "1.0.0", + "env_prefix": "PLG_SUPERSET", # 环境变量前缀（PLG_SUPERSET_URL, etc.） + "required_env": ["PLG_SUPERSET_URL"], # 必需的环境变量（缺失则不启用） + "optional_env": ["PLG_SUPERSET_TIMEOUT"], + "auth_modes": ["sso", "jwt", "password"], # 支持的认证方式（数组） + "capabilities": [ # 框架识别的标准能力标识 + "datasets", # 可以列出数据集 + "dashboards", # 可以列出仪表盘 + "filters", # 支持数据筛选 + "preview", # 支持预览（GET /data/preview） + "refresh", # 支持带参数刷新（POST /data/refresh） + "batch_load", # 支持分批流式加载（NDJSON） + "metadata", # 可提供列描述、表描述等外部元数据 + ], + } + """ + pass + + @abstractmethod + def create_blueprint(self) -> Blueprint: + """创建 Flask Blueprint。 + + Blueprint 的 url_prefix 应为 /api/plugins// + 插件内部路由自行组织，例如： + /api/plugins/superset/auth/login + /api/plugins/superset/catalog/datasets + /api/plugins/superset/data/load-dataset + + Returns: + 配置好路由的 Flask Blueprint + """ + pass + + @abstractmethod + def get_frontend_config(self) -> dict[str, Any]: + """返回传递给前端的配置信息。 + + 这些信息会通过 /api/app-config 的 plugins 字段下发到前端， + 前端据此决定显示哪些插件面板、如何配置。 + UI 相关的声明（如 catalog_entry_types）应放在这里而非 manifest()。 + + Returns: + { + "enabled": True, + "sso_login_url": "http://superset:8088/df-sso-bridge/", + "catalog_entry_types": [ + { + "type": "dataset", + "label": "Datasets", + "icon": "table_chart", + "supports_filters": True, + }, + { + "type": "dashboard_chart", + "label": "Dashboard Charts", + "icon": "dashboard", + "supports_filters": True, + }, + ], + # ... 其他前端需要的配置 + } + 注意：不要返回密钥等敏感信息。 + """ + pass + + def on_enable(self, app) -> None: + """插件被启用时调用（可选）。 + + 可以用来： + - 注册 Flask extensions + - 初始化连接池或缓存 + - 设置定时任务 + - 获取 CredentialVault 引用（用于 SSO token 或用户凭证的存取） + """ + pass + + def on_disable(self) -> None: + """插件被禁用时调用（可选）。""" + pass + + def get_auth_status(self, session: dict) -> dict[str, Any] | None: + """返回当前用户的认证状态（可选）。 + + 如果插件有自己的认证逻辑，实现此方法来返回当前用户信息。 + 返回 None 表示未认证。 + """ + return None + + def supports_sso_passthrough(self) -> bool: + """此插件是否支持 SSO token 透传。 + + 如果返回 True，插件可以从 auth.get_sso_token() 获取用户的 + OIDC access token，直接用于调用外部系统 API，无需用户单独登录。 + 默认 False。子类按需覆盖。 + """ + return False +``` + +### 6.2 插件加载的数据怎么进入 Workspace + +插件从外部系统拉取到数据后，需要将数据写入 DF 的 Workspace。这个过程参考了 0.6 版本 Superset 集成中的实际经验，需要解决三个问题：**表名管理**、**大数据量写入性能**、**写入方式选择**。 + +#### 5.2.1 表名管理：覆盖 vs 新建 + +0.6 版本的 Superset 集成支持两种表名策略，这个设计在实际使用中被证明非常实用： + +| 操作 | 行为 | 使用场景 | +|------|------|---------| +| **覆盖（默认）** | 用数据集原名写入，已存在则替换 | 刷新数据，获取最新版本 | +| **新建（加后缀）** | 用户指定后缀，生成 `name_suffix` 形式的新表名 | 保留历史快照，对比不同时间点的数据 | + +0.6 前端的实现方式是在每个数据集条目上提供两个按钮： +- 下载图标 → 直接覆盖加载（使用默认表名） +- 加号图标 → 弹出后缀对话框（用户输入后缀，如日期 `20250322`，生成 `sales_20250322`） + +后端通过 `table_name` 参数控制： +- 不传 `table_name`：使用数据集原名，覆盖同名表 +- 传 `table_name`：使用指定名称写入 + +在 0.7 的 Workspace 中，这映射到现有 API： + +```python +from data_formulator.workspace_factory import get_workspace +from data_formulator.datalake.parquet_utils import sanitize_table_name +from data_formulator.auth import get_identity_id + +workspace = get_workspace(get_identity_id()) +safe_name = sanitize_table_name(table_name_override or original_name) + +# write_parquet / write_parquet_from_arrow 已有覆盖逻辑： +# 如果 safe_name 已存在 → 删除旧 parquet → 写入新 parquet +workspace.write_parquet(df, safe_name, loader_metadata={ + "loader_type": "SupersetPlugin", + "loader_params": {"dataset_id": dataset_id, "filters": filters}, + "source_table": original_name, +}) +``` + +如果需要"不覆盖、自动加后缀"的行为（类似 `tables_routes.py` 中 `create_table` 的去重逻辑），可以这样处理： + +```python +# 自动去重表名（确保不覆盖已有表） +base_name = sanitize_table_name(table_name) +final_name = base_name +counter = 1 +existing_tables = workspace.list_tables() +while final_name in existing_tables: + final_name = f"{base_name}_{counter}" + counter += 1 +workspace.write_parquet(df, final_name) +``` + +#### 5.2.2 大数据量写入：插件专用写入工具函数 + +0.6 版本中，Superset 数据通过 SQL Lab 查询返回 JSON → 转 DataFrame → 写入存储。对于大数据量（10 万+ 行），有两个性能瓶颈： +1. 从外部系统拉取数据时的网络传输 +2. 写入 Workspace 时的序列化开销 + +为了让所有插件都能高效写入，我们提供一个**插件专用的写入工具函数**，封装常见的写入模式： + +```python +# py-src/data_formulator/plugins/data_writer.py + +import logging +import pandas as pd +import pyarrow as pa +from typing import Any, Optional + +from data_formulator.auth import get_identity_id +from data_formulator.workspace_factory import get_workspace +from data_formulator.datalake.parquet_utils import sanitize_table_name + +logger = logging.getLogger(__name__) + + +class PluginDataWriter: + """插件专用的数据写入工具。 + + 封装了表名管理、覆盖/新建策略、大数据量写入等常用逻辑， + 让插件开发者不需要直接操作 Workspace 底层 API。 + """ + + def __init__(self, plugin_id: str): + self.plugin_id = plugin_id + + def _get_workspace(self): + return get_workspace(get_identity_id()) + + def write_dataframe( + self, + df: pd.DataFrame, + table_name: str, + *, + overwrite: bool = True, + source_metadata: Optional[dict[str, Any]] = None, + ) -> dict[str, Any]: + """将 DataFrame 写入 Workspace。 + + Args: + df: 要写入的数据 + table_name: 目标表名（会自动 sanitize） + overwrite: True=覆盖同名表, False=自动加后缀避免冲突 + source_metadata: 来源元数据（用于刷新等场景） + + Returns: + {"table_name": str, "row_count": int, "columns": list, "is_renamed": bool} + """ + workspace = self._get_workspace() + base_name = sanitize_table_name(table_name) + final_name = base_name + is_renamed = False + + if not overwrite: + counter = 1 + existing = set(workspace.list_tables()) + while final_name in existing: + final_name = f"{base_name}_{counter}" + counter += 1 + is_renamed = True + + loader_metadata = { + "loader_type": f"plugin:{self.plugin_id}", + **(source_metadata or {}), + } + + meta = workspace.write_parquet( + df, final_name, loader_metadata=loader_metadata + ) + + logger.info( + "Plugin '%s' wrote table '%s': %d rows, %d cols", + self.plugin_id, final_name, len(df), len(df.columns), + ) + + return { + "table_name": meta.name, + "row_count": meta.row_count, + "columns": [c.name for c in (meta.columns or [])], + "is_renamed": is_renamed, + } + + def write_arrow( + self, + table: pa.Table, + table_name: str, + *, + overwrite: bool = True, + source_metadata: Optional[dict[str, Any]] = None, + ) -> dict[str, Any]: + """将 Arrow Table 写入 Workspace（更高效，跳过 pandas 转换）。""" + workspace = self._get_workspace() + base_name = sanitize_table_name(table_name) + final_name = base_name + is_renamed = False + + if not overwrite: + counter = 1 + existing = set(workspace.list_tables()) + while final_name in existing: + final_name = f"{base_name}_{counter}" + counter += 1 + is_renamed = True + + loader_metadata = { + "loader_type": f"plugin:{self.plugin_id}", + **(source_metadata or {}), + } + + meta = workspace.write_parquet_from_arrow( + table, final_name, loader_metadata=loader_metadata + ) + + return { + "table_name": meta.name, + "row_count": meta.row_count, + "columns": [c.name for c in (meta.columns or [])], + "is_renamed": is_renamed, + } + + def write_batches( + self, + first_batch: pd.DataFrame, + table_name: str, + *, + overwrite: bool = True, + source_metadata: Optional[dict[str, Any]] = None, + ) -> "BatchWriter": + """创建一个批量写入器，适用于数据需要分批拉取的场景。 + + 用法: + writer = data_writer.write_batches(first_df, "my_table") + writer.append(second_df) + writer.append(third_df) + result = writer.finish() + + 内部机制: 先将第一批数据写入临时文件，后续批次追加， + 最后合并为一个完整的 Parquet 文件。 + """ + return BatchWriter( + self, first_batch, table_name, + overwrite=overwrite, + source_metadata=source_metadata, + ) + + +class BatchWriter: + """支持分批追加写入的写入器。 + + 适用于外部系统的数据需要分页/分批拉取的场景（如 Superset SQL Lab + 有行数限制，或网络传输需要分批）。 + + 内部使用 PyArrow 的 RecordBatch 累积数据，最终一次性写入 Parquet， + 避免多次写入的 I/O 开销。 + """ + + def __init__( + self, + writer: PluginDataWriter, + first_batch: pd.DataFrame, + table_name: str, + *, + overwrite: bool, + source_metadata: Optional[dict[str, Any]], + ): + self._writer = writer + self._table_name = table_name + self._overwrite = overwrite + self._source_metadata = source_metadata + self._batches: list[pa.RecordBatch] = [] + self._total_rows = 0 + self.append(first_batch) + + def append(self, df: pd.DataFrame) -> int: + """追加一批数据。返回目前累积的总行数。""" + if len(df) == 0: + return self._total_rows + batch = pa.RecordBatch.from_pandas(df) + self._batches.append(batch) + self._total_rows += len(df) + return self._total_rows + + def finish(self) -> dict[str, Any]: + """将所有批次合并写入 Workspace，返回写入结果。""" + if not self._batches: + raise ValueError("No data batches to write") + + combined = pa.Table.from_batches(self._batches) + result = self._writer.write_arrow( + combined, + self._table_name, + overwrite=self._overwrite, + source_metadata=self._source_metadata, + ) + + logger.info( + "BatchWriter finished: %d batches, %d total rows → '%s'", + len(self._batches), self._total_rows, result["table_name"], + ) + self._batches.clear() + return result +``` + +#### 5.2.3 插件如何使用写入工具 + +以 Superset 插件的数据加载路由为例： + +```python +# plugins/superset/routes/data.py + +from data_formulator.plugins.data_writer import PluginDataWriter + +writer = PluginDataWriter("superset") + +@bp.route("/data/load-dataset", methods=["POST"]) +def load_dataset(): + # ... 认证、参数解析 ... + + # 从 Superset SQL Lab 拉取数据 + result = superset_client.execute_sql_with_session( + sql_session, db_id, full_sql, schema, row_limit + ) + all_rows = result.get("data", []) or [] + + if not all_rows: + return jsonify({"status": "error", "message": "No data returned"}), 404 + + df = pd.DataFrame(all_rows) + + # 使用写入工具：table_name_override 支持用户自定义表名 + # overwrite=True 表示覆盖同名表（0.6 中的默认下载行为） + # + # source_metadata 结构与前端 DataProvenance 对齐， + # 框架会将其完整存入 loader_metadata，用于后续刷新。 + write_result = writer.write_dataframe( + df, + table_name=table_name_override or original_table_name, + overwrite=True, + source_metadata={ + "source_type": "dataset", + "source_id": str(dataset_id), + "source_name": original_table_name, + "load_params": { + "entry_type": "dataset", + "dataset_id": dataset_id, + "database_id": database_id, + "schema": schema, + "filters": filters, + "row_limit": row_limit, + }, + "refreshable": True, + }, + ) + + return jsonify({ + "status": "ok", + **write_result, # table_name, row_count, columns, is_renamed + }) +``` + +对于需要分批拉取的大数据场景： + +```python +@bp.route("/data/load-dataset-batched", methods=["POST"]) +def load_dataset_batched(): + """分批拉取并写入，支持流式进度报告。""" + # ... 认证、参数解析 ... + + def _generate(): + batch_writer = None + offset = 0 + + while offset < row_limit: + batch_sql = f"{base_sql} LIMIT {batch_size} OFFSET {offset}" + result = superset_client.execute_sql_with_session( + sql_session, db_id, batch_sql, schema, batch_size + ) + rows = result.get("data", []) or [] + if not rows: + break + + df = pd.DataFrame(rows) + + if batch_writer is None: + batch_writer = writer.write_batches( + df, table_name, + overwrite=True, + source_metadata={...}, + ) + else: + batch_writer.append(df) + + offset += len(rows) + + # 流式报告进度 + yield json.dumps({ + "type": "progress", + "loaded_rows": offset, + "batch_size": len(rows), + }, ensure_ascii=False) + "\n" + + if len(rows) < batch_size: + break + + # 合并所有批次，写入 Parquet + if batch_writer: + result = batch_writer.finish() + yield json.dumps({ + "type": "done", + "status": "ok", + **result, + }, ensure_ascii=False) + "\n" + else: + yield json.dumps({ + "type": "done", + "status": "ok", + "row_count": 0, + }, ensure_ascii=False) + "\n" + + return Response( + stream_with_context(_generate()), + content_type="text/x-ndjson; charset=utf-8", + headers={"Cache-Control": "no-cache"}, + ) +``` + +#### 5.2.4 数据溯源描述：结构化条件 + 模板拼接 + +从外部系统加载数据时，用户通常会选择筛选条件（如地区、日期范围）。这些信息需要记录到数据上，以便后续明确"这份数据到底包含什么"。 + +**设计决策：不用 AI 生成描述，用模板拼接。** + +| | AI 生成描述 | 模板拼接 + 原始条件 | +|--|-----------|---------------------| +| 准确性 | 可能编造细节 | 100% 准确 | +| 成本 | 每次加载消耗 token | 零成本 | +| 可刷新 | 描述是文本，无法回放 | `loadParams` 原样回传即可刷新 | + +数据写入时**两层信息同时存储**，各司其职： + +``` +loader_metadata +├── loadParams ← 原始筛选条件（机器用：精确刷新） +│ {"filters": [{"col":"region","op":"==","val":"Asia"}], "row_limit": 50000} +│ +└── description ← 模板拼接的可读摘要（人/AI 用：理解数据内容） + "来源: superset · sales_data\n筛选: region = Asia, order_date >= 2025-01-01\n行数: 12,345" +``` + +`description` 会流向前端 `attachedMetadata`，AI Agent 分析数据时自动进入 prompt，帮助 AI 理解数据的子集范围。 + +**实现位置：`PluginDataWriter.write_dataframe()` 内部自动生成**，所有插件无需额外代码： + +```python +# plugins/data_writer.py — write_dataframe 内部 + +def _build_description(self, source_metadata: dict) -> str: + """从 source_metadata 模板拼接可读描述。""" + parts = [f"来源: {self.plugin_id} · {source_metadata.get('source_name', '')}"] + + load_params = source_metadata.get("load_params", {}) + + # 筛选条件（各插件格式不同，尝试通用提取） + filters = load_params.get("filters", []) + if filters: + filter_strs = [] + for f in filters: + if isinstance(f, dict) and "col" in f: + filter_strs.append(f"{f['col']} {f.get('op', '=')} {f['val']}") + if filter_strs: + parts.append(f"筛选: {', '.join(filter_strs)}") + + # 时间范围（常见于仪表盘数据） + time_range = load_params.get("time_range") + if time_range: + parts.append(f"时间范围: {time_range}") + + row_limit = load_params.get("row_limit") + if row_limit: + parts.append(f"行限制: {row_limit}") + + return "\n".join(parts) +``` + +调用时机：`write_dataframe()` / `write_arrow()` 在写入 Parquet 后，自动调用 `_build_description()` 将结果存入 `loader_metadata["description"]`。插件也可通过参数覆盖自动生成的描述。此描述与外部系统自带的表/列描述（[§ 7.7](#77-外部系统元数据拉取) 中的 `table_description`、`column_metadata`）互不覆盖——前者记录"加载时用了什么条件"，后者记录"这张表/列本身是什么含义"，两者并存于 metadata 中。 + +> **各平台的 `filters` 格式差异很大**（见 [§ 7.5 各 BI 平台查询参数兼容性分析](#75-各-bi-平台查询参数兼容性分析)）。 +> `_build_description()` 只做"尽力提取"：能解析的条件拼成可读文本，无法解析的保留在 `loadParams` 中。 +> 插件也可覆盖 `_build_description()` 来提供更精确的描述。 + +#### 5.2.5 两种写入路径对比 + +| | 直接写入 Workspace（推荐） | 返回 JSON 由前端处理 | +|---|---|---| +| **流向** | 插件后端 → Workspace Parquet | 插件后端 → JSON → 前端 → loadTable → Workspace | +| **适用数据量** | 任意大小 | < 5 万行（受 HTTP 响应大小和前端内存限制） | +| **性能** | 高（Parquet 压缩存储，无 JSON 序列化开销） | 低（JSON 序列化 + 网络传输 + 前端解析） | +| **进度反馈** | 通过 NDJSON 流式响应 | 无（等待完整响应） | +| **前端通知** | 返回 `table_name` → 前端刷新 `list-tables` | 前端收到数据后走 `loadTable` thunk | +| **刷新支持** | 有（`loader_metadata` 记录来源信息，可用于 `refresh-table`） | 无 | + +**结论**：插件应默认使用"直接写入 Workspace"路径。仅在特殊场景（如用户只想预览但不保存、或 `WORKSPACE_BACKEND=ephemeral`）时才返回 JSON。 + +--- + +## 7. 前端插件接口 + +### 7.1 插件面板契约 + +每个插件需要提供一个 React 组件，该组件遵循以下接口： + +```typescript +// src/plugins/types.ts + +export interface PluginManifest { + id: string; // 与后端 manifest.id 一致 + name: string; // 显示名称 + icon: string; // MUI 图标名或 SVG 路径 + description: string; + authType: 'jwt' | 'oauth' | 'api_key' | 'none'; + capabilities: string[]; +} + +// ───── 数据溯源：记录数据从哪来、用了什么参数 ───── + +/** + * 每次插件加载数据后，随 onDataLoaded 一起返回。 + * 核心框架会将其序列化到 loader_metadata，用于刷新/重放。 + * + * 各 BI 平台的 loadParams 差异极大（见下方"兼容性分析"）， + * 因此 loadParams 设计为 Record——框架不解析， + * 只原样存储，刷新时原样回传给插件后端。 + */ +export interface DataProvenance { + pluginId: string; // "superset" | "metabase" | ... + sourceType: string; // 插件自定义的来源类型，如 "dataset" | "dashboard_chart" | "question" + sourceId: string; // 外部系统中的唯一标识（dataset_id、card_id 等） + sourceName: string; // 人类可读的名称（用于 UI 显示"来自 xxx"） + loadParams: Record; // 加载时使用的完整参数（过滤器、行限制、时间范围等） + loadedAt: string; // ISO 8601 时间戳 + refreshable: boolean; // 是否支持用同样的参数刷新 +} + +export interface PluginPanelProps { + pluginId: string; + config: Record; // 从 /api/app-config 获取的插件配置 + onDataLoaded: (result: { // 数据加载完成后的回调 + tableName: string; + rowCount: number; + columns: string[]; + source: 'workspace' | 'json'; // 数据在 workspace 中还是在响应 JSON 中 + rows?: any[]; // source === 'json' 时提供 + provenance: DataProvenance; // 数据溯源信息（用于刷新和 UI 显示） + }) => void; + onPreviewLoaded?: (result: { // 预览数据回调（可选，框架支持但不强制） + columns: string[]; + sampleRows: any[]; // 预览行（通常 50-100 行） + totalRowEstimate?: number; // 总行数估计 + provenance: DataProvenance; + }) => void; +} + +export interface DataSourcePluginModule { + manifest: PluginManifest; + + // 主面板组件（显示在 UnifiedDataUploadDialog 的 Tab 中） + PanelComponent: React.ComponentType; + + // 登录组件（可选，如果插件有自己的认证流程） + LoginComponent?: React.ComponentType<{ + config: Record; + onLoginSuccess: () => void; + }>; +} +``` + +### 7.2 Plugin Host（前端插件容器） + +在 `UnifiedDataUploadDialog.tsx` 中增加一个通用的插件 Tab 渲染逻辑： + +```typescript +// 伪代码：插件面板的渲染 + +// 1. 从 /api/app-config 拿到已启用插件列表 +const enabledPlugins = serverConfig.plugins; // [{ id: "superset", ... }, ...] + +// 2. 动态导入对应的插件模块 +const pluginModules = usePluginModules(enabledPlugins); + +// 3. 在数据加载对话框中，为每个插件渲染一个 Tab +{pluginModules.map(plugin => ( + + p.id === plugin.manifest.id)} + onDataLoaded={handlePluginDataLoaded} + /> + +))} +``` + +### 7.3 数据加载完成后的流程 + +插件面板通过 `onDataLoaded` 回调通知宿主。宿主根据 `source` 字段决定下一步： + +``` +onDataLoaded({ tableName, source, provenance }) + │ + ├─ source === 'workspace' + │ → 调用 GET /api/tables/list-tables 刷新表列表 + │ → 新表自动出现在左侧面板 + │ → provenance 序列化到 loader_metadata（用于刷新） + │ + └─ source === 'json' + → 构建 DictTable + → dispatch(loadTable({ table })) + → 走常规 loadTable 流程（自动写入 Workspace） + → provenance 同样保存 +``` + +### 7.4 数据刷新协议 + +当一个表的 `loader_metadata` 中包含 `provenance`（即通过插件加载的数据），前端可以在表上显示"刷新"按钮。刷新时将 `provenance` 回传给插件后端： + +``` +用户点击表上的「刷新」按钮 + → 框架取出 loader_metadata.provenance + → POST /api/plugins/{provenance.pluginId}/data/refresh + Body: { + source_type: provenance.sourceType, + source_id: provenance.sourceId, + load_params: provenance.loadParams, ← 完整的原始查询参数 + table_name: 当前表名 ← 覆盖写入 + } + → 插件后端用同样的参数重新拉取数据 + → 写入同名表（覆盖） + → 前端刷新表列表 +``` + +后端实现：每个插件可选实现 `/data/refresh` 路由。由于 `load_params` 是插件自己定义和存储的，刷新时原样取出即可： + +```python +# plugins/superset/routes/data.py 中的 refresh 路由 + +@bp.route("/data/refresh", methods=["POST"]) +def refresh_dataset(): + data = request.get_json() + source_type = data["source_type"] # "dataset" + source_id = data["source_id"] # "42" + load_params = data["load_params"] # { "filters": [...], "row_limit": 10000, ... } + table_name = data["table_name"] # 覆盖同名表 + + # 用 load_params 中的参数重新执行查询——与首次加载逻辑复用 + df = _fetch_dataset(source_id, **load_params) + + return jsonify(writer.write_dataframe(df, table_name, overwrite=True, + source_metadata={"source_type": source_type, "source_id": source_id, + "loader_params": load_params})) +``` + +**关键设计决策**：`load_params` 是个**不透明的 JSON 对象**。框架只负责存储和回传，不解析其内部结构。这样每个 BI 平台都可以在里面放自己特有的参数，框架完全不需要知道各平台的参数细节。 + +### 7.5 各 BI 平台查询参数兼容性分析 + +这是选择"参数不透明"设计的核心原因——各平台的参数差异太大，无法统一： + +#### Superset 的 `load_params` 示例 + +```json +{ + "entry_type": "dataset", + "dataset_id": 42, + "database_id": 1, + "schema": "public", + "filters": [ + { "col": "region", "op": "==", "val": "Asia" }, + { "col": "order_date", "op": ">=", "val": "2025-01-01" } + ], + "row_limit": 50000, + "sql_override": null +} +``` + +从仪表盘图表加载时： + +```json +{ + "entry_type": "dashboard_chart", + "dashboard_id": 7, + "chart_id": 123, + "dataset_id": 42, + "native_filters": { + "NATIVE_FILTER-abc": { "col": "country", "op": "IN", "val": ["CN", "JP"] } + }, + "time_range": "Last 90 days", + "granularity": "P1D", + "row_limit": 10000 +} +``` + +#### Metabase 的 `load_params` 示例 + +```json +{ + "entry_type": "question", + "card_id": 156, + "parameters": [ + { "type": "date/range", "target": ["variable", ["template-tag", "date_range"]], "value": "2025-01-01~2025-03-31" }, + { "type": "category", "target": ["dimension", ["field", 23, null]], "value": ["Active"] } + ] +} +``` + +从 Dashboard 加载时： + +```json +{ + "entry_type": "dashboard", + "dashboard_id": 8, + "card_id": 156, + "dashboard_filters": { + "Status": "Active", + "Date Range": "past30days" + } +} +``` + +#### Power BI 的 `load_params` 示例 + +```json +{ + "entry_type": "report_visual", + "workspace_id": "aaa-bbb-ccc", + "report_id": "ddd-eee-fff", + "page_name": "ReportSection1", + "visual_name": "SalesChart", + "dax_query": "EVALUATE TOPN(10000, Sales, Sales[Date], DESC)", + "slicer_state": { + "Region": ["Asia", "Europe"], + "Year": [2024, 2025] + } +} +``` + +#### Grafana 的 `load_params` 示例 + +```json +{ + "entry_type": "panel", + "dashboard_uid": "abc123", + "panel_id": 4, + "datasource_uid": "prometheus-1", + "time_range": { "from": "now-24h", "to": "now" }, + "interval": "5m", + "variables": { + "host": "server-01", + "env": "production" + }, + "max_data_points": 1000 +} +``` + +#### Looker 的 `load_params` 示例 + +```json +{ + "entry_type": "explore", + "model_name": "ecommerce", + "explore_name": "orders", + "fields": ["orders.id", "orders.total", "users.name"], + "filters": { + "orders.created_date": "90 days", + "orders.status": "complete" + }, + "sorts": ["orders.total desc"], + "limit": 5000, + "pivots": ["orders.created_month"] +} +``` + +#### 为什么不尝试统一过滤器格式 + +可以看到，每个平台的过滤器语义完全不同： + +| 平台 | 过滤器模型 | 特点 | +|------|----------|------| +| Superset | `col + op + val` + SQL WHERE | 列级过滤 + 原生 SQL | +| Metabase | Template Tag + Dimension Target | 绑定到查询模板的参数化变量 | +| Power BI | Slicer State + DAX Expression | 交互式切片器 + 查询语言 | +| Grafana | Template Variable + Ad-hoc Filter | 变量替换 + 即席过滤 | +| Looker | LookML Filter Expression | 模型驱动的过滤表达式语言 | + +如果强行定义 `UnifiedFilter = { column, operator, value }` 这样的通用格式，会导致： +1. 丢失平台特有能力（如 Metabase 的 Template Tag、Grafana 的 Variable） +2. 每个插件需要做双向转换（通用格式 ↔ 平台原生格式），增加复杂度 +3. 通用格式不可避免地变成"最大公约数"，表达力反而最弱 + +**因此，`load_params` 保持为不透明 JSON 是正确的设计**：框架只负责"存储 → 回传 → 刷新"，不试图理解参数内容。 + +### 7.6 预览协议（可选能力） + +部分 BI 平台支持在全量加载前预览少量数据。插件可以通过 `onPreviewLoaded` 回调返回预览结果： + +``` +用户在 Superset 数据集上点击「预览」 + → 插件前端调用 GET /api/plugins/superset/data/preview?dataset_id=42&limit=50 + → 后端拉取 50 行样本数据 + → 返回 JSON（不写入 Workspace） + → 插件前端调用 onPreviewLoaded({ columns, sampleRows, totalRowEstimate }) + → 框架在对话框中渲染预览表格 + → 用户确认后点「加载」→ 走完整的 onDataLoaded 流程 +``` + +预览是可选能力。后端路由约定为 `/data/preview`，前端通过 `manifest.capabilities` 中是否包含 `"preview"` 来判断是否显示预览按钮。 + +### 7.7 外部系统元数据拉取 + +很多 BI 平台为数据集和字段维护了丰富的元数据（描述信息、语义标签、认证状态等）。将这些元数据随数据一起拉取到 DF 有极高价值——它们可以直接填入 DF 已有的 `semanticType` 和 `attachedMetadata` 字段，大幅提升 AI Agent 的分析质量。 + +#### 7.7.1 各 BI 平台提供的元数据对比 + +| 元数据类型 | Superset | Metabase | Power BI | Looker | +|-----------|----------|----------|----------|--------| +| **表级描述** | `dataset.description` | `table.description` | `table.description` | `explore.description` | +| **表级标签** | `dataset.owners`, `is_certified` | `table.visibility_type` | — | `explore.tags` | +| **列名** | `column.column_name` | `field.name` | `column.name` | `field.name` | +| **列描述** | `column.description` | `field.description` | `column.description` | `field.description` | +| **列数据类型** | `column.type` | `field.base_type` | `column.dataType` | `field.type` | +| **列语义类型** | `column.is_dttm`, `filterable`, `groupby` | `field.semantic_type` (如 `type/Email`, `type/FK`) | `column.formatString` | `field.tags` (如 `email`, `currency`) | +| **计算列/度量** | `metric.expression`, `metric.description` | `field.formula` | `measure.expression` | `measure.sql` | +| **值域统计** | — | `field.fingerprint` (分布统计) | — | `field.enumerations` | + +#### 7.7.2 设计：不透明 blob + 文本描述（简化方案） + +> **设计决策**：不扩展 `ColumnInfo` 和 `TableMetadata` 的结构化字段。 +> 理由与 `loadParams` 保持不透明（[§ 7.5](#75-各-bi-平台查询参数兼容性分析)）相同——各平台元数据格式差异极大，强行统一到 `semantic_type`、`is_metric` 等字段是有损抽象。 +> +> 外部元数据的主要消费者是 **AI prompt**（文本）和 **UI tooltip**（文本），不需要结构化查询。 +> 如果未来确实出现结构化需求（如"列出所有 certified 的表"），再从 blob 中提取，成本很低。 + +**后端改动最小化**：仅在 `TableMetadata` 新增 1 个可选字段： + +```python +@dataclass +class TableMetadata: + # ... 现有字段全部不动 ... + description: str | None = None # ← 已有，复用 + + # 新增：来自外部系统的原始元数据（插件写入，框架不解析） + external_metadata: dict | None = None +``` + +`ColumnInfo` **不改**。`list-tables` API **不改**。前端 `DictTable` 类型 **不改**。 + +#### 7.7.3 插件如何提供元数据 + +插件在调用 `PluginDataWriter.write_dataframe()` 时，将外部系统的原始元数据塞入 `external_metadata`： + +```python +# plugins/superset/routes/data.py + +result = writer.write_dataframe( + df, table_name, overwrite=True, + source_metadata={...}, # 用于刷新的 loadParams（不变） + external_metadata={ # 外部系统的原始元数据（新增，blob） + "source": "superset", + "dataset_description": dataset_info.get("description"), + "owners": [o["username"] for o in dataset_info.get("owners", [])], + "certified": dataset_info.get("is_certified", False), + "columns": { + col["column_name"]: { + "description": col.get("description"), + "is_dttm": col.get("is_dttm"), + "filterable": col.get("filterable"), + } + for col in dataset_info.get("columns", []) + }, + "metrics": { + m["metric_name"]: { + "description": m.get("description"), + "expression": m.get("expression"), + } + for m in dataset_info.get("metrics", []) + }, + }, +) +``` + +Metabase 插件塞的结构完全不同也没关系——框架不解析它。 + +#### 7.7.4 元数据如何流向 AI + +`PluginDataWriter` 自动将 `external_metadata` 拼成可读文本，写入 `TableMetadata.description`（已有字段）： + +``` +来源: superset · sales_data +描述: 公司季度销售数据，按区域和产品线划分 +所有者: alice@corp.com +筛选: region = Asia, order_date >= 2025-01-01 +列: region (销售区域), order_date (订单日期, 时间类型), amount (订单金额, SUM(amount)) +行数: 12,345 +``` + +这段文本通过现有的 `description` → `attachedMetadata` 链路自动进入 AI prompt，**无需修改任何前端代码**。 + +``` +外部 BI 系统 API 后端 前端 +───────────────── ───── ───── +dataset + columns + metrics → external_metadata (blob) + ↓ PluginDataWriter 拼接 + description (文本) → attachedMetadata → AI prompt + loader_params (结构化) → source_metadata → 刷新按钮 +``` + +#### 7.7.5 渐进增强 + +| 层次 | 场景 | 效果 | +|------|------|------| +| 0 | Upload/Paste/Database（现有行为） | `external_metadata=None`，AI 自行推断，完全不受影响 | +| 1 | 插件只传数据，不传元数据 | 与 Upload 行为一致 | +| 2 | 插件传了 `external_metadata` | `description` 自动丰富，AI prompt 质量提升 | + +插件开发者把能拿到的元数据原样塞进 `external_metadata` 即可。拼接逻辑在 `PluginDataWriter` 中统一处理，尽力提取可读信息；无法解析的字段静默忽略。 + +--- + +## 8. 插件注册与发现 + +> **权威定义**：插件自动发现与注册（`discover_and_register()`）的完整实现、 +> 插件约定、安全措施、发现流程图、`app.py` 集成方式、`/api/app-config` 暴露方式、 +> 以及前端 `import.meta.glob` 自动发现，均定义在 `1-sso-plugin-architecture.md` § 4.4。 +> +> 本文档不再重复这些内容。以下仅补充 Plugin 文档特有的上下文说明。 + +### 8.1 `/api/app-config` 中的插件字段组装 + +在 `get_app_config()` 中将 `manifest()` 和 `get_frontend_config()` 合并下发： + +```python +from data_formulator.plugins import ENABLED_PLUGINS + +plugins_config = {} +for plugin_id, plugin in ENABLED_PLUGINS.items(): + manifest = plugin.manifest() + frontend_config = plugin.get_frontend_config() + plugins_config[plugin_id] = { + "id": plugin_id, + "name": manifest["name"], + "icon": manifest.get("icon"), + "description": manifest.get("description"), + "auth_modes": manifest.get("auth_modes", ["none"]), + "capabilities": manifest.get("capabilities", []), + **frontend_config, # catalog_entry_types 等 UI 配置在此注入 + } +config["PLUGINS"] = plugins_config +``` + +注意 `auth_modes`（数组）取代了旧的 `auth_type`（单字符串），且 `catalog_entry_types` 由 `get_frontend_config()` 提供而非 `manifest()`。 + +--- + +## 9. 数据流设计 + +### 9.1 端到端流程 + +``` +┌─────────────────────────────────────────────────────────────────┐ +│ 启动阶段 │ +│ │ +│ 1. 环境变量设置 PLG_SUPERSET_URL=http://superset:8088 │ +│ 2. app.py → _register_blueprints() → discover_and_register() │ +│ 3. SupersetPlugin 被实例化，Blueprint 被注册 │ +│ 4. /api/app-config 返回 PLUGINS: { superset: { enabled, ... }} │ +└─────────────────────────────────────────────────────────────────┘ + │ + ↓ +┌─────────────────────────────────────────────────────────────────┐ +│ 前端初始化 │ +│ │ +│ 1. App.tsx 请求 /api/app-config │ +│ 2. 解析 PLUGINS 字段 → 存入 Redux (serverConfig.plugins) │ +│ 3. 动态加载对应的前端插件模块 │ +│ 4. 如果插件需要认证且未登录 → 显示登录入口 │ +└─────────────────────────────────────────────────────────────────┘ + │ + ↓ +┌─────────────────────────────────────────────────────────────────┐ +│ 认证阶段（以 Superset 为例） │ +│ │ +│ 用户点击「连接 Superset」 │ +│ → 显示 Superset 登录组件 │ +│ → POST /api/plugins/superset/auth/login │ +│ → 后端转发到 Superset JWT 登录 │ +│ → Token 存入 Flask Session │ +│ → 返回用户信息给前端 │ +└─────────────────────────────────────────────────────────────────┘ + │ + ↓ +┌─────────────────────────────────────────────────────────────────┐ +│ 浏览阶段 │ +│ │ +│ 用户打开「数据源」对话框 → 看到 Superset Tab │ +│ → 请求 /api/plugins/superset/catalog/datasets │ +│ → 后端用 JWT 调用 Superset API │ +│ → 返回用户有权限看到的数据集列表 │ +│ → 前端渲染数据集目录（搜索、预览等） │ +└─────────────────────────────────────────────────────────────────┘ + │ + ↓ +┌─────────────────────────────────────────────────────────────────┐ +│ 数据加载阶段 │ +│ │ +│ 用户选中一个数据集，点击「加载」 │ +│ → POST /api/plugins/superset/data/load-dataset │ +│ → 后端通过 Superset SQL Lab 执行查询（尊重 RBAC + RLS） │ +│ → pd.DataFrame → workspace.write_parquet() │ +│ → 返回 { table_name, row_count, columns } │ +│ │ +│ 前端收到响应： │ +│ → onDataLoaded({ tableName, source: 'workspace' }) │ +│ → 触发 list-tables 刷新 │ +│ → 新表出现在左侧面板 │ +│ → 用户可以开始数据分析 │ +└─────────────────────────────────────────────────────────────────┘ +``` + +### 9.2 认证会话管理 + +每个插件的认证信息独立存储在 Flask Session 中，以 `plugin_id` 为前缀隔离： + +```python +# 存储 +session[f"plugin_{plugin_id}_token"] = access_token +session[f"plugin_{plugin_id}_user"] = user_info + +# 读取 +token = session.get(f"plugin_{plugin_id}_token") +``` + +前端通过 `/api/plugins/{plugin_id}/auth/status` 查询当前认证状态。 + +#### 与 SSO 的集成（详见 `sso-plugin-architecture.md`） + +当系统配置了 OIDC SSO 后，插件的认证有三种模式自动协商： + +``` +场景 A: DF 有 SSO + 外部系统也接了同一 IdP + → 自动 SSO Token 透传（用户零交互） + +场景 B: DF 有 SSO + 外部系统没有接 SSO + → 用户首次输入凭证 → 存入 CredentialVault → 后续自动取出 + +场景 C: DF 无 SSO（本地匿名模式） + → 手动登录 → Token 存 Flask Session（现有行为） +``` + +--- + +## 10. Superset 插件迁移示例 + +将 0.6 版本的 Superset 集成迁移为插件： + +### 10.1 后端 + +``` +py-src/data_formulator/plugins/superset/ +├── __init__.py # SupersetPlugin 类（实现 DataSourcePlugin） +├── superset_client.py # ← 直接迁移自 0.6 +├── auth_bridge.py # ← 直接迁移自 0.6 +├── catalog.py # ← 直接迁移自 0.6 +├── routes/ +│ ├── __init__.py +│ ├── auth.py # ← 迁移自 0.6 auth_routes.py +│ ├── catalog.py # ← 迁移自 0.6 catalog_routes.py +│ └── data.py # ← 迁移自 0.6 data_routes.py（改用 workspace） +└── requirements.txt # requests（已内置，无额外依赖） +``` + +### 10.2 核心改动 + +**data_routes.py 的变化**：0.6 用 DuckDB，0.7 用 Workspace Parquet + +```python +# 0.6 版本：写入 DuckDB +with db_manager.connection(sid) as conn: + conn.execute(f'DROP TABLE IF EXISTS "{safe_name}"') + conn.execute(f'CREATE TABLE "{safe_name}" AS SELECT * FROM df') + +# 0.7 插件版本：写入 Workspace Parquet +from data_formulator.workspace_factory import get_workspace +from data_formulator.auth import get_identity_id + +workspace = get_workspace(get_identity_id()) +workspace.write_parquet(df, safe_name) +``` + +### 10.3 前端 + +``` +src/plugins/superset/ +├── index.ts # 导出 DataSourcePluginModule +├── SupersetPanel.tsx # ← 迁移自 0.6（Tab 容器） +├── SupersetCatalog.tsx # ← 迁移自 0.6（数据集目录） +├── SupersetDashboards.tsx # ← 迁移自 0.6（仪表盘列表） +├── SupersetFilterDialog.tsx # ← 迁移自 0.6（筛选条件对话框） +├── SupersetLogin.tsx # ← 迁移自 0.6 LoginView（Superset 部分） +└── api.ts # API 调用封装 +``` + +### 10.4 SupersetPlugin 实现 + +```python +class SupersetPlugin(DataSourcePlugin): + + @staticmethod + def manifest(): + return { + "id": "superset", + "name": "Apache Superset", + "icon": "superset", + "description": "Load data from Superset dashboards and datasets", + "version": "1.0.0", + "env_prefix": "PLG_SUPERSET", + "required_env": ["PLG_SUPERSET_URL"], + "auth_modes": ["sso", "jwt", "password"], + "capabilities": [ + "datasets", "dashboards", "filters", + "preview", "refresh", "batch_load", "metadata", + ], + } + + def create_blueprint(self): + from data_formulator.plugins.superset.routes import create_superset_blueprint + return create_superset_blueprint(self._client, self._catalog, self._bridge) + + def get_frontend_config(self): + url = os.environ.get("PLG_SUPERSET_URL", "") + return { + "enabled": True, + "sso_login_url": f"{url.rstrip('/')}/df-sso-bridge/" if url else None, + "catalog_entry_types": [ + { + "type": "dataset", + "label": "Datasets", + "icon": "table_chart", + "supports_filters": True, + }, + { + "type": "dashboard_chart", + "label": "Dashboard Charts", + "icon": "dashboard", + "supports_filters": True, + }, + ], + } + + def on_enable(self, app): + url = os.environ["PLG_SUPERSET_URL"] + self._client = SupersetClient(url) + self._bridge = SupersetAuthBridge(url) + self._catalog = SupersetCatalog(self._client) + app.extensions["superset_client"] = self._client + app.extensions["superset_bridge"] = self._bridge + app.extensions["superset_catalog"] = self._catalog +``` + +--- + +## 11. 与现有 ExternalDataLoader 的关系 + +### 11.1 两套机制并行 + +``` +数据源类型 │ 适用机制 │ 原因 +─────────────────┼─────────────────────┼────────────────────── +MySQL/PG/MSSQL │ ExternalDataLoader │ 标准数据库连接，无需额外认证/UI +MongoDB/BigQuery │ ExternalDataLoader │ 同上 +S3/Azure Blob │ ExternalDataLoader │ 文件存储，list+fetch 即可 +─────────────────┼─────────────────────┼────────────────────── +Superset │ DataSourcePlugin │ 需要认证流程、目录浏览、筛选、RBAC +Metabase │ DataSourcePlugin │ 同上 +Power BI │ DataSourcePlugin │ 同上 +Grafana │ DataSourcePlugin │ 同上 +``` + +### 11.2 判断依据 + +使用 **ExternalDataLoader** 的场景： +- 只需要连接参数 → 列出表 → 拉取数据 +- 前端用通用的 `DBManagerPane` 表单即可 + +使用 **DataSourcePlugin** 的场景： +- 有自己的认证体系（JWT / OAuth / SSO） +- 有自己的数据组织方式（仪表盘、报表、数据集等概念） +- 需要尊重外部系统的权限模型 +- 需要专用的 UI（目录浏览、筛选条件等） + +两套机制互不干扰，共存于系统中。 + +### 11.3 ExternalDataLoader 演进方向（已拆分） + +> ExternalDataLoader 的现有缺陷分析和三个改进方案（数据库元数据拉取 P0、SSO Token 透传 P1、凭证持久化 P2） +> 已拆分为独立文档：**`2-external-dataloader-enhancements.md`**。 +> +> 这些改进针对数据库连接器，与 DataSourcePlugin（BI 系统插件）互不干扰， +> 可以独立于插件框架按优先级逐步实施。 + +--- + +## 12. 插件 i18n 自包含方案 + +### 12.1 问题 + +宿主项目的翻译文件（如 `src/i18n/locales/en/common.json`）是核心项目的一部分。如果每个插件的翻译 key 都直接写入这些文件，会导致： + +1. **插件开发者被迫修改宿主项目文件** — 违反"自包含"原则 +2. **多个插件的翻译 key 混杂在同一个 JSON 中** — 职责不清 +3. **上游更新时容易冲突** — 宿主 `common.json` 频繁变动 + +### 12.2 方案：插件自带翻译 + 启动时自动合并 + +每个插件在自己的目录下维护翻译文件，通过 `DataSourcePluginModule.locales` 字段导出，框架在启动时自动合并到 i18next 的 `translation` namespace 中。 + +#### 目录结构 + +``` +src/plugins/superset/ + ├── locales/ + │ ├── en.json ← 插件自己的英文翻译 + │ └── zh.json ← 插件自己的中文翻译 + ├── api.ts + ├── SupersetPanel.tsx + └── index.tsx ← 通过 locales 字段导出翻译 +``` + +#### 翻译文件格式 + +JSON 结构保持与宿主项目相同的 key path 风格，以 `plugin..` 作为命名空间前缀： + +```json +{ + "plugin": { + "superset": { + "login": "Sign In", + "logout": "Sign Out", + "datasets": "Datasets" + } + } +} +``` + +#### 插件模块导出 + +```typescript +// src/plugins/superset/index.tsx +import en from './locales/en.json'; +import zh from './locales/zh.json'; + +const supersetPlugin: DataSourcePluginModule = { + id: 'superset', + Icon: SupersetIcon, + Panel: SupersetPanel, + locales: { en, zh }, +}; +``` + +#### 框架自动注册 + +`src/plugins/registry.ts` 提供 `registerPluginTranslations()` 函数，在应用启动时调用。该函数遍历所有已发现的插件模块，将它们的 `locales` deep-merge 到 i18next： + +```typescript +import i18n from '../i18n'; + +export function registerPluginTranslations(): void { + for (const [, mod] of _modules) { + if (!mod.locales) continue; + for (const [lang, bundle] of Object.entries(mod.locales)) { + i18n.addResourceBundle(lang, 'translation', bundle, true, true); + } + } +} +``` + +`addResourceBundle(lang, ns, bundle, deep=true, overwrite=true)` 是 i18next 内置 API，deep-merge 到已有 resources，无需重新初始化。 + +#### 调用时机 + +在 `src/index.tsx` 中，`import './i18n'`（初始化 i18next）之后、React 渲染之前调用： + +```typescript +import './i18n'; +import { registerPluginTranslations } from './plugins/registry'; +registerPluginTranslations(); +``` + +由于 `import.meta.glob` 使用 eager 模式，此时所有插件模块（包括其 locales JSON）已经加载完成。 + +### 12.3 运行机制说明 + +`locales: { en, zh }` 是**数据声明**而非语言选择——它声明"该插件提供 en 和 zh 两套翻译"。`registerPluginTranslations()` 会将**所有语言的 bundle 都注册**到 i18next 中。实际使用哪套翻译由 i18next 的语言检测器（`LanguageDetector`）或 `i18n.changeLanguage()` 决定，与宿主项目的语言切换行为完全一致。 + +### 12.4 不变的地方 + +- 所有插件组件继续使用 `useTranslation()` 不带参数 +- 所有 `t('plugin.superset.xxx')` 调用不变 +- 宿主项目自己的翻译文件和加载方式完全不变 +- 语言切换自动生效，插件翻译跟随系统设置 + +### 12.5 新增插件的 i18n 清单 + +1. 在插件目录下创建 `locales/en.json` 和 `locales/zh.json`（或其他语言） +2. JSON 顶层结构为 `{ "plugin": { "": { ... } } }` +3. 在插件的 `index.tsx` 中导入并通过 `locales` 字段导出 +4. 无需修改宿主项目的任何翻译文件 + diff --git a/design-docs/1-sso-plugin-architecture.md b/design-docs/1-sso-plugin-architecture.md new file mode 100644 index 00000000..3e0aba42 --- /dev/null +++ b/design-docs/1-sso-plugin-architecture.md @@ -0,0 +1,3695 @@ +# Data Formulator — SSO 认证 + 数据源插件 统一架构设计 + +## 目录 + +1. [概述与目标](#1-概述与目标) +2. [架构全景](#2-架构全景) +3. [Layer 1：可插拔认证体系 (AuthProvider)](#3-layer-1可插拔认证体系-authprovider) + - 3.1~3.2b — AuthProvider 基类（含 `get_auth_info()` 自描述）、Provider 自动发现 + - 3.3~3.5 — Azure EasyAuth、OIDC（仅 2 个环境变量）、GitHub OAuth、auth.py 重构（基于自动发现） + - 3.6~3.8 — 前端 OIDC 流程、回调页面、登录/登出 UI + - 3.8b — Token 生命周期管理（静默刷新、401 重试、CORS/CSP、Phase 1 不含 refresh_token 的设计决策与局限性） + - [3.9 多协议支持](#39-多协议支持从-oidc-扩展到-saml--ldap--cas--反向代理) + - 3.9.1~3.9.2 协议全景对比、双轨模型设计 + - 3.9.3~3.9.8 Phase 2+ 扩展摘要（反向代理 / SAML / LDAP / CAS） + - 3.9.9 前端适配（统一登录入口 + `/api/auth/info` 委托模式） + - 3.9.10~3.9.11 模型图、Token 透传差异 + - 3.9.12~3.9.14 协议选择指南、依赖、优先级 +4. [Layer 2：数据源插件系统 (DataSourcePlugin)](#4-layer-2数据源插件系统-datasourceplugin) +5. [Layer 3：凭证保险箱 (CredentialVault)](#5-layer-3凭证保险箱-credentialvault) +6. [SSO Token 透传机制](#6-sso-token-透传机制) +7. [现有 ExternalDataLoader 的演进路径](#7-现有-externaldataloader-的演进路径) +8. [身份管理：SSO 时代的简化](#8-身份管理sso-时代的简化) +9. [配置参考](#9-配置参考) +10. [目录结构](#10-目录结构) +11. [实施路径](#11-实施路径) +12. [安全模型](#12-安全模型) +13. [FAQ](#13-faq) + +--- + +## 1. 概述与目标 + +### 1.1 背景 + +Data Formulator 0.7 的身份体系基于两种机制： +- **Azure App Service EasyAuth** — 部署到 Azure 时由平台注入 `X-MS-CLIENT-PRINCIPAL-ID` +- **浏览器 UUID** — 本地使用时 `localStorage` 中的随机 UUID + +这套机制存在三个根本性限制： + +| 限制 | 影响 | +|------|------| +| 仅绑定 Azure 生态 | 使用 Keycloak、Okta、Auth0、Google 等 IdP 的团队无法接入 | +| 无真实用户身份 | 无法实现跨设备数据同步、审计追踪、细粒度权限 | +| 无法透传认证 | 当外部系统（Superset、Metabase）也接了同一个 IdP 时，用户仍需重复登录 | + +同时，数据源连接方面，现有的 `ExternalDataLoader` 体系面向数据库设计，无法覆盖 BI 报表系统的复杂认证、数据浏览和权限透传需求。 + +### 1.2 设计目标 + +构建一套 **SSO 认证 + 数据源插件 + 凭证管理** 的统一架构，实现： + +1. **通用 SSO 登录** — 用户通过 OIDC、SAML、LDAP、CAS 或反向代理等任意方式登录 Data Formulator +2. **插件化数据源** — BI 报表系统（Superset、Metabase、Power BI 等）以插件形式接入，新增系统不修改核心代码 +3. **SSO Token 透传** — 外部系统与 DF 共用同一 IdP 时，用户无需重复登录 +4. **凭证保险箱** — 未接 SSO 的外部系统，凭证在服务端加密存储，跨设备可用 +5. **向后兼容** — 本地个人使用场景下，匿名浏览器模式依然可用，零配置启动 + +### 1.3 设计原则 + +| 原则 | 说明 | +|------|------| +| **不自建用户管理** | 认证是 IdP 的事，DF 只做身份消费者，不管理密码和注册 | +| **插件自包含** | 每个外部系统的后端路由 + 前端 UI + 认证逻辑完全独立于核心代码 | +| **渐进式采纳** | 本地模式 → 加 SSO → 加插件 → 加凭证保险箱，每一步都可独立部署 | +| **安全纵深** | 认证链路上 OIDC JWT 验签、服务端凭证加密、Workspace 身份隔离 三层防护 | + +--- + +## 2. 架构全景 + +``` +┌─────────────────────────────────────────────────────────────────────────────┐ +│ 用户浏览器 │ +│ │ +│ ┌──────────────┐ ┌──────────────────┐ ┌──────────────────────────────┐ │ +│ │ OIDC Login │ │ Credential │ │ Data Source Dialog │ │ +│ │ (PKCE flow) │ │ Manager UI │ │ ┌──────┐ ┌────────┐ ┌────┐ │ │ +│ │ │ │ (per-source) │ │ │Upload│ │Database│ │插件│ │ │ +│ └──────┬───────┘ └────────┬─────────┘ │ │Paste │ │(现有) │ │Tab │ │ │ +│ │ │ │ │URL │ │ │ │ │ │ │ +│ ▼ ▼ │ └──────┘ └────────┘ └──┬─┘ │ │ +│ ┌─────────────────────────────────────────────────────────────────┘ │ +│ │ fetchWithIdentity (增强) │ +│ │ X-Identity-Id: user:alice@corp.com (SSO 登录后) │ +│ │ Authorization: Bearer │ +│ └──────────────────────────┬───────────────────────────────────────────┘ +│ │ │ +└──────────────────────────────┼──────────────────────────────────────────────┘ + │ HTTPS + ▼ +┌──────────────────────────────────────────────────────────────────────────────┐ +│ Flask 后端 │ +│ │ +│ ┌─────────────────────────────────────────────────────────────────────────┐ │ +│ │ auth.py — get_identity_id() │ │ +│ │ │ │ +│ │ AUTH_PROVIDER (单选) + 匿名回退: │ │ +│ │ ┌─────────────────────────────────┐ ┌───────────────────────────┐ │ │ +│ │ │ 主 Provider (由 AUTH_PROVIDER │ │ Browser UUID │ │ │ +│ │ │ 指定: oidc / github / azure / │→ │ (ALLOW_ANONYMOUS=true时) │ │ │ +│ │ │ proxy / saml / ldap / cas) │ │ │ │ │ +│ │ └─────────────────────────────────┘ └───────────────────────────┘ │ │ +│ └────────────────────────────────┬────────────────────────────────────────┘ │ +│ │ │ +│ ┌────────────────────────────────┼────────────────────────────────────────┐ │ +│ │ Plugin Registry + Credential Vault │ │ +│ │ │ │ +│ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ +│ │ │ Superset │ │ Metabase │ │ Power BI │ │ Grafana │ │ │ +│ │ │ Plugin │ │ Plugin │ │ Plugin │ │ Plugin │ ... │ │ +│ │ │ │ │ │ │ │ │ │ │ │ +│ │ │ 认证: SSO透传│ │ 认证: 用户名 │ │ 认证: OAuth │ │ 认证: API Key│ │ │ +│ │ │ 或 JWT登录 │ │ /密码+保险箱│ │ +SSO透传 │ │ +保险箱 │ │ │ +│ │ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ │ │ +│ │ │ │ │ │ │ │ +│ │ ▼ ▼ ▼ ▼ │ │ +│ │ ┌─────────────────────────────────────────────────────────────┐ │ │ +│ │ │ Credential Vault (加密凭证存储) │ │ │ +│ │ │ per-user, per-source 的服务端加密存储 │ │ │ +│ │ └─────────────────────────────────────────────────────────────┘ │ │ +│ └────────────────────────────────────────────────────────────────────────┘ │ +│ │ │ +│ ┌────────────────────────────────┼────────────────────────────────────────┐ │ +│ │ Data Layer (不变) │ │ +│ │ ExternalDataLoader (9种DB) + Workspace (Parquet) + Redux Store │ │ +│ └────────────────────────────────────────────────────────────────────────┘ │ +└──────────────────────────────────────────────────────────────────────────────┘ + │ + ▼ + ┌─────────────────────┐ + │ 外部系统 │ + │ Superset / Metabase │ + │ / Power BI / ... │ + └─────────────────────┘ +``` + +**三层分工**： + +| 层 | 职责 | 状态 | +|----|------|------| +| **Layer 1: AuthProvider** | 解决"谁在用 DF" — 单一认证源 + 匿名回退 | 扩展现有 `auth.py` | +| **Layer 2: DataSourcePlugin** | 解决"从哪拉数据" — 外部 BI 系统的插件化接入 | 新建插件框架 | +| **Layer 3: CredentialVault** | 解决"用什么身份访问外部系统" — 加密凭证存储 | 新建 | + +**三层分层依赖、向上服务**：Layer 1 是地基，确定用户身份（`user:xxx` 或 `browser:xxx`）；Layer 3 依赖 Layer 1 的身份信息按用户存取凭证；Layer 2 同时依赖 Layer 1（获取 SSO token）和 Layer 3（获取已存凭证）来访问外部系统。依赖方向单向向下，不存在循环依赖。 + +``` +Layer 2: DataSourcePlugin ──依赖──→ Layer 1: AuthProvider (身份 + SSO token) + │ ▲ + └──依赖──→ Layer 3: CredentialVault ────┘ (按用户身份存取) +``` + +### 统一的插件范式 + +三层虽然解决不同问题，但共享同一套 **"抽象基类 + 环境变量声明依赖 + 按需自动启用"** 的设计范式： + +| 维度 | AuthProvider | DataSourcePlugin | CredentialVault | +|------|-------------|-----------------|-----------------| +| 抽象基类 | `AuthProvider(ABC)` | `DataSourcePlugin(ABC)` | `CredentialVault(ABC)` | +| 动态加载 | `importlib.import_module` | 目录自动扫描 (`pkgutil`) | 工厂函数 `get_credential_vault()` | +| 启用判定 | `AUTH_PROVIDER` 环境变量指定 | `manifest()` 中 `required_env`（`PLG_` 前缀）全部存在 | `CREDENTIAL_VAULT_KEY` 存在 | +| 按需启用 | 未指定则匿名模式 | 缺 `required_env` 则跳过 | 缺密钥则返回 None | +| 新增方式 | **在 `auth_providers/` 下创建 `.py` 即可**（自动发现） | **在 `plugins/` 下创建目录即可**（零改动） | 写一个 `.py` + 工厂加一个分支 | +| 生命周期钩子 | `on_configure(app)` | `on_enable(app)` | — | + +**协作模式**： + +``` +AuthProvider — 单选 + 匿名回退 (Single Provider + Fallback) + ┌──────────────────┐ ┌──────────────┐ + │ 主 Provider │ ──→ │ Browser UUID │ + │ (由 AUTH_PROVIDER │ 未命中│ (匿名回退) │ + │ 环境变量指定) │ │ │ + └──────────────────┘ └──────────────┘ + ▸ Phase 1: AUTH_PROVIDER=oidc / github / azure_easyauth + ▸ Phase 2+: proxy_header / saml / ldap / cas + ▸ 同一时间只有一个主 Provider 生效 + ▸ ALLOW_ANONYMOUS=true 时允许匿名回退，否则未认证请求被拒绝 + +DataSourcePlugin — 并行 (Registry) + ┌──────────┐ ┌──────────┐ ┌──────────┐ + │ Superset │ │ Metabase │ │ Power BI │ ... + │ Plugin │ │ Plugin │ │ Plugin │ + └──────────┘ └──────────┘ └──────────┘ + ▸ 所有已启用插件同时存在（目录自动扫描，无需注册） + ▸ 每个插件注册独立的 Blueprint 路由 + ▸ 前端为每个插件渲染一个独立 Tab + ▸ 用户可以同时连多个系统 + +CredentialVault — 单例 (Strategy) + ┌──────────┐ 或 ┌──────────────┐ 或 ┌──────────────┐ + │ Local │ │ Azure │ │ HashiCorp │ + │ (SQLite) │ │ Key Vault │ │ Vault │ ... + └──────────┘ └──────────────┘ └──────────────┘ + ▸ 同一时间只有一个实现生效 + ▸ 由 CREDENTIAL_VAULT 环境变量选择 + ▸ 所有插件和 DataLoader 共享同一个 Vault 实例 +``` + +这套统一范式意味着：未来无论是新增认证方式、新增数据源（如 Grafana、Tableau）、还是新增凭证后端（如 HashiCorp Vault），步骤都是相同的 —— **写一个实现类，配置环境变量启用**。核心代码零修改。 + +认证体系进一步细分为两轨： +- **A 类（无状态）** — 直接实现 `AuthProvider.authenticate()`，无需额外路由（如 OIDC、Azure EasyAuth、反向代理头、GitHub OAuth） +- **B 类（有状态）** — 编写 Login Gateway Blueprint + 复用通用 `SessionProvider`（如 SAML、LDAP、CAS） + +详见 [3.9 多协议支持](#39-多协议支持从-oidc-扩展到-saml--ldap--cas--反向代理)。 + +--- + +## 3. Layer 1：可插拔认证体系 (AuthProvider) + +### 3.1 设计思路 + +将现有 `auth.py` 中硬编码的三级优先级，重构为 **单一 AuthProvider + 匿名回退** 模型。管理员通过 `AUTH_PROVIDER` 环境变量选择一种认证方式，框架自动加载对应的 Provider。支持无状态（OIDC/Header）和有状态（SAML/LDAP/CAS via Session）两种认证模型（详见 [3.9](#39-多协议支持从-oidc-扩展到-saml--ldap--cas--反向代理)）。 + +``` +请求进入 + │ + ├─ 主 Provider (由 AUTH_PROVIDER 环境变量指定，Phase 1): + │ ├─ oidc → 检查 Authorization: Bearer → 命中 → user:xxx + │ ├─ azure_easyauth → 检查 X-MS-CLIENT-PRINCIPAL-ID → 命中 → user:xxx + │ └─ github → 检查 Flask session (OAuth) → 命中 → user:xxx + │ + │ Phase 2+ 扩展: + │ ├─ proxy_header → 检查 X-Forwarded-User (可信IP) → 命中 → user:xxx + │ ├─ saml → 检查 Flask session (SAML ACS) → 命中 → user:xxx + │ ├─ ldap → 检查 Flask session (LDAP bind) → 命中 → user:xxx + │ └─ cas → 检查 Flask session (CAS ticket) → 命中 → user:xxx + │ + ├─ 匿名回退 (ALLOW_ANONYMOUS=true 时): + │ └─ BrowserIdentity → 检查 X-Identity-Id → 命中 → browser:xxx + │ + └─ 全部未命中 → 401 Unauthorized +``` + +### 3.2 AuthProvider 基类 + +```python +# py-src/data_formulator/auth_providers/base.py + +from abc import ABC, abstractmethod +from dataclasses import dataclass +from typing import Any, Optional +from flask import Request + + +@dataclass +class AuthResult: + """认证结果（Phase 1 精简版，仅保留核心字段）。 + + 设计决策 — 不包含 refresh_token： + Phase 1 采用纯前端（oidc-client-ts）管理 token 刷新，后端保持无状态。 + refresh_token 仅存在于前端 UserManager 内部，不经过后端，不在此结构中出现。 + 局限性及未来扩展方向详见 3.8b 节"设计决策与局限性"。 + """ + user_id: str # 唯一标识 (sub claim / principal ID / UUID) + display_name: Optional[str] = None # 显示名称 + email: Optional[str] = None # 邮箱 + raw_token: Optional[str] = None # 原始 access_token (用于 SSO 透传，非 refresh_token) + + +class AuthProvider(ABC): + """认证提供者基类。 + + 每个 Provider 从 HTTP 请求中尝试提取并验证用户身份。 + 返回 None 表示此 Provider 不适用，交给链中的下一个。 + 抛出异常表示认证信息存在但无效（如 token 过期）。 + """ + + @property + @abstractmethod + def name(self) -> str: + """Provider 名称，用于日志和调试。""" + ... + + @abstractmethod + def authenticate(self, request: Request) -> Optional[AuthResult]: + """尝试从请求中提取用户身份。 + + Returns: + AuthResult — 认证成功 + None — 此 Provider 不适用（请求中没有此 Provider 的认证信息） + + Raises: + AuthenticationError — 认证信息存在但无效（token 过期、签名错误等） + """ + ... + + @property + def enabled(self) -> bool: + """Provider 是否已正确配置（必需的环境变量等）。 + + 默认返回 True。子类可覆盖此属性，当必要配置缺失时返回 False， + init_auth() 会据此拒绝激活该 Provider 并输出日志。 + """ + return True + + def on_configure(self, app) -> None: + """Flask app 创建后调用，可用于初始化（如下载 JWKS）。""" + pass + + def get_auth_info(self) -> dict[str, Any]: + """返回前端所需的认证配置信息（供 /api/auth/info 端点使用）。 + + 每个 Provider 自描述其前端交互方式，消除 auth.py 中的 switch 语句。 + 新增 Provider 时只需实现此方法，无需修改 auth.py。 + + Returns: + { + "action": "frontend" | "redirect" | "form" | "transparent" | "none", + "label": "显示名称", + ... (Provider 特定的配置) + } + """ + return {"action": "none"} + + +class AuthenticationError(Exception): + """认证信息存在但验证失败。""" + def __init__(self, message: str, provider: str = ""): + self.provider = provider + super().__init__(message) +``` + +### 3.2b Provider 自动发现（auth_providers/__init__.py） + +`auth_providers` 包在导入时自动扫描同目录下的所有模块，发现并注册所有 `AuthProvider` 子类。 +新增 Provider 只需在 `auth_providers/` 下创建 `.py` 文件并实现 `AuthProvider` 子类，**无需修改任何注册表或配置文件**。 + +```python +# py-src/data_formulator/auth_providers/__init__.py + +import importlib +import inspect +import logging +import pkgutil +from typing import Optional + +from .base import AuthProvider + +_log = logging.getLogger(__name__) + +_PROVIDER_REGISTRY: dict[str, type[AuthProvider]] = {} + +def _discover_providers() -> None: + """扫描 auth_providers/ 目录，收集所有 AuthProvider 子类。""" + for finder, module_name, ispkg in pkgutil.iter_modules(__path__): + if module_name == "base": + continue + try: + mod = importlib.import_module(f".{module_name}", __package__) + for attr_name in dir(mod): + cls = getattr(mod, attr_name) + if (isinstance(cls, type) + and issubclass(cls, AuthProvider) + and cls is not AuthProvider): + instance = cls() + _PROVIDER_REGISTRY[instance.name] = cls + _log.debug("Discovered auth provider: '%s' from %s", + instance.name, module_name) + except ImportError as e: + _log.debug("Skipped '%s' (missing dep): %s", module_name, e) + +_discover_providers() + + +def get_provider_class(name: str) -> Optional[type[AuthProvider]]: + """根据 AUTH_PROVIDER 环境变量的值获取对应的 Provider 类。""" + return _PROVIDER_REGISTRY.get(name) + + +def list_available_providers() -> list[str]: + """返回所有已发现的 Provider 名称（用于日志和错误提示）。""" + return sorted(_PROVIDER_REGISTRY.keys()) +``` + +**工作原理**： + +1. `pkgutil.iter_modules(__path__)` 扫描 `auth_providers/` 目录下的所有 `.py` 文件 +2. 跳过 `base.py`（基类不是具体 Provider） +3. 对每个模块，用 `inspect` 逻辑找出所有 `AuthProvider` 子类 +4. 实例化以获取 `name` 属性（来自子类的 `@property`），作为注册表的 key +5. 依赖缺失的模块（如 Phase 2 的 SAML 需要 `python3-saml`）会被静默跳过 + +**安全性保障**：自动发现只决定"有哪些 Provider 可用"，实际激活哪个仍由 `AUTH_PROVIDER` 环境变量**单选**控制。 +未被选中的 Provider 不会执行 `on_configure()`，不会处理任何请求。 + +### 3.3 Azure EasyAuth Provider（迁移现有逻辑） + +```python +# py-src/data_formulator/auth_providers/azure_easyauth.py + +import logging +from flask import Request +from typing import Optional +from .base import AuthProvider, AuthResult + +logger = logging.getLogger(__name__) + + +class AzureEasyAuthProvider(AuthProvider): + """Azure App Service 内置认证 (EasyAuth)。 + + 当 DF 部署在 Azure App Service 并启用了身份验证时， + Azure 会在请求到达 Flask 之前验证用户身份，并注入以下头： + - X-MS-CLIENT-PRINCIPAL-ID: 用户的 Object ID + - X-MS-CLIENT-PRINCIPAL-NAME: 用户名 (可选) + + 这些头由 Azure 基础设施设置，客户端无法伪造。 + """ + + @property + def name(self) -> str: + return "azure_easyauth" + + def authenticate(self, request: Request) -> Optional[AuthResult]: + principal_id = request.headers.get("X-MS-CLIENT-PRINCIPAL-ID") + if not principal_id: + return None + + principal_name = request.headers.get("X-MS-CLIENT-PRINCIPAL-NAME", "") + logger.debug("Azure EasyAuth: principal_id=%s...", principal_id[:8]) + + return AuthResult( + user_id=principal_id.strip(), + display_name=principal_name.strip() or None, + ) +``` + +### 3.4 OIDC Provider（新增核心） + +```python +# py-src/data_formulator/auth_providers/oidc.py + +import logging +import os +import time +from typing import Optional + +import jwt +from jwt import PyJWKClient +from flask import Request + +from .base import AuthProvider, AuthResult, AuthenticationError + +logger = logging.getLogger(__name__) + + +class OIDCProvider(AuthProvider): + """通用 OIDC (OpenID Connect) 认证提供者。 + + 支持任何标准 OIDC 兼容的 Identity Provider： + - Keycloak + - Okta + - Auth0 + - Azure AD / Entra ID + - Google Identity Platform + - Authelia / Authentik + - Casdoor + + 工作流程： + 1. 前端通过 PKCE Authorization Code Flow 从 IdP 获取 access_token + 2. 前端将 access_token 放在 Authorization: Bearer 头中发送 + 3. 本 Provider 用 IdP 的 JWKS 公钥验证 token 签名和 claims + 4. 验证通过后提取 sub (用户唯一ID)、name、email 等信息 + + 配置（环境变量）— 仅需两项： + OIDC_ISSUER_URL — IdP 的 issuer URL + OIDC_CLIENT_ID — 注册的 client ID (同时用作 audience 校验) + + 其余信息（JWKS URI、签名算法）从 OIDC Discovery 自动获取， + claim 名称遵循 OIDC 标准 (sub / name / email)，无需配置。 + """ + + def __init__(self): + self._issuer = os.environ.get("OIDC_ISSUER_URL", "").strip().rstrip("/") + self._client_id = os.environ.get("OIDC_CLIENT_ID", "").strip() + + self._jwks_client: Optional[PyJWKClient] = None + self._jwks_uri: Optional[str] = None + self._algorithms: list[str] = ["RS256"] + + @property + def name(self) -> str: + return "oidc" + + @property + def enabled(self) -> bool: + return bool(self._issuer and self._client_id) + + def on_configure(self, app) -> None: + if not self.enabled: + logger.info("OIDC provider not configured (OIDC_ISSUER_URL / OIDC_CLIENT_ID missing)") + return + + # 从 OIDC Discovery 自动获取 JWKS URI 和签名算法 + try: + import urllib.request, json + discovery_url = f"{self._issuer}/.well-known/openid-configuration" + with urllib.request.urlopen(discovery_url, timeout=10) as resp: + discovery = json.loads(resp.read()) + + self._jwks_uri = discovery["jwks_uri"] + self._jwks_client = PyJWKClient(self._jwks_uri, cache_keys=True) + + if "id_token_signing_alg_values_supported" in discovery: + self._algorithms = discovery["id_token_signing_alg_values_supported"] + + logger.info( + "OIDC provider configured: issuer=%s, client_id=%s", + self._issuer, self._client_id, + ) + except Exception as e: + logger.error("Failed to initialize OIDC provider: %s", e) + + def get_auth_info(self) -> dict: + """返回 OIDC 前端配置，供 /api/auth/info 端点使用。""" + return { + "action": "frontend", + "label": os.environ.get("AUTH_DISPLAY_NAME", "SSO Login"), + "oidc": { + "authority": self._issuer, + "clientId": self._client_id, + "scopes": "openid profile email", + }, + } + + def authenticate(self, request: Request) -> Optional[AuthResult]: + if not self._jwks_client: + return None + + auth_header = request.headers.get("Authorization", "") + if not auth_header.startswith("Bearer "): + return None + + token = auth_header[7:].strip() + if not token: + return None + + try: + signing_key = self._jwks_client.get_signing_key_from_jwt(token) + payload = jwt.decode( + token, + signing_key.key, + algorithms=self._algorithms, + issuer=self._issuer, + audience=self._client_id, + options={ + "verify_exp": True, + "verify_iss": True, + "verify_aud": True, + }, + ) + except jwt.ExpiredSignatureError: + raise AuthenticationError("OIDC token expired", provider=self.name) + except jwt.InvalidTokenError as e: + raise AuthenticationError(f"Invalid OIDC token: {e}", provider=self.name) + + user_id = payload.get("sub") + if not user_id: + raise AuthenticationError( + "OIDC token missing 'sub' claim", + provider=self.name, + ) + + return AuthResult( + user_id=str(user_id), + display_name=payload.get("name"), + email=payload.get("email"), + raw_token=token, + ) +``` + +#### 对接 OIDC/OAuth2 Provider 的 IdP 要求 + +> **适用范围**：任何使用 `AUTH_PROVIDER=oidc`（或别名 `oauth2`）对接的身份提供者， +> 包括 Keycloak、Auth0、Okta、自建 SSO 等。 + +**为什么既写 `oidc` 又写 `oauth2`？** + +`AUTH_PROVIDER=oidc` 和 `AUTH_PROVIDER=oauth2` 是同一个 Provider 的两个名字（别名），实际行为完全相同：后端用 JWKS 验 JWT 签名，前端用 Authorization Code + PKCE 流程。之所以加别名，是因为该 Provider 不要求严格的 OpenID Connect 协议——任何支持 JWT + JWKS 的 OAuth2 身份提供者都可以使用。 + +**Data Formulator 支持两种配置模式**： + +##### 模式 A：自动发现（推荐，适用于标准 OIDC IdP） + +只需 3 个环境变量，其余端点自动从 Discovery 获取： + +``` +AUTH_PROVIDER=oidc # 或 oauth2，等价 +OIDC_ISSUER_URL=https://your-idp.example.com/path +OIDC_CLIENT_ID=your-client-id +``` + +**工作原理**：启动时后端请求 `{OIDC_ISSUER_URL}/.well-known/openid-configuration`，从返回的 JSON 中自动提取 `authorization_endpoint`、`token_endpoint`、`userinfo_endpoint`、`jwks_uri` 等全部端点地址。前端 `oidc-client-ts` 也会独立请求同一个 Discovery 端点获取授权和 Token 地址。 + +**适用场景**：Keycloak、Auth0、Okta、Azure AD / Entra ID、Google、Authelia、Authentik、Casdoor 等标准 OIDC IdP 均原生支持 Discovery，无需额外配置。 + +**IdP 要求**： +- `{OIDC_ISSUER_URL}/.well-known/openid-configuration` 必须可访问且返回合法 JSON +- 返回的 `issuer` 字段值必须与 `OIDC_ISSUER_URL` **完全一致**（含协议、端口、路径，不含尾部 `/`） +- Discovery JSON 中必须包含 `authorization_endpoint`、`token_endpoint`、`jwks_uri` +- JWKS 端点返回的公钥用于后端本地验证 JWT 签名（高性能，无额外网络开销） + +**判断你的 IdP 是否支持**：在浏览器中直接访问 `{你的 ISSUER URL}/.well-known/openid-configuration`，如果能看到 JSON 响应则支持模式 A；如果返回 404 或错误页面，请使用模式 B。 + +##### 模式 B：手动端点（适用于无 Discovery 的 OAuth2 服务器） + +许多企业自建的 OAuth2 系统没有实现 OIDC Discovery。此时可直接配置端点 URL： + +``` +AUTH_PROVIDER=oidc +OIDC_ISSUER_URL=https://sso.example.com +OIDC_CLIENT_ID=your-client-id +OIDC_AUTHORIZE_URL=https://sso.example.com/oauth2/authorize +OIDC_TOKEN_URL=https://sso.example.com/oauth2/token +OIDC_USERINFO_URL=https://sso.example.com/oauth2/userinfo +# OIDC_JWKS_URL=... # 可选，如无则通过 UserInfo 端点验证 token +# OIDC_CLIENT_SECRET=... # 可选，机密客户端才需要 +``` + +**优先级**：手动配置的值 > Discovery 自动发现的值。两者可以混用。 + +##### 端点功能说明 + +| 环境变量 | 用途 | 必须？ | +|----------|------|--------| +| `OIDC_ISSUER_URL` | IdP 标识 / Discovery 基础 URL | **必须** | +| `OIDC_CLIENT_ID` | 在 IdP 注册的应用 ID | **必须** | +| `OIDC_AUTHORIZE_URL` | 前端浏览器跳转进行用户授权 | 模式 B 必须 | +| `OIDC_TOKEN_URL` | 前端用 authorization code 换取 access_token | 模式 B 必须 | +| `OIDC_USERINFO_URL` | 后端验证 token（无 JWKS 时的回退方案） | 推荐 | +| `OIDC_JWKS_URL` | 后端本地验证 JWT 签名（更高效） | 可选 | +| `OIDC_CLIENT_SECRET` | 机密客户端的密钥 | 可选 | + +##### Token 验证策略 + +后端按以下优先级验证 access_token： + +1. **JWKS 本地验证**（首选）：用 `OIDC_JWKS_URL` 提供的公钥验证 JWT 签名，校验 `iss`、`aud`、`exp` claim。性能最优，无网络开销。 +2. **UserInfo 远程验证**（回退）：向 `OIDC_USERINFO_URL` 发送 `Authorization: Bearer ` 请求。如果 IdP 返回用户信息，则 token 有效。适用于无 JWKS 的 OAuth2 服务器或使用不透明 token 的场景。 + +##### Discovery 端点最小 JSON 格式（自建 SSO 如选择实现） + +```json +{ + "issuer": "https://your-sso.example.com/path", + "jwks_uri": "https://your-sso.example.com/path/jwks", + "authorization_endpoint": "https://your-sso.example.com/path/authorize", + "token_endpoint": "https://your-sso.example.com/path/token", + "userinfo_endpoint": "https://your-sso.example.com/path/userinfo" +} +``` + +> **自建 SSO 提示**：如果不想实现 Discovery，使用模式 B 手动配置端点即可，无需修改 SSO。 + +##### 回调地址 + +在 IdP 中将 `http(s):///callback` 注册为合法 redirect URI。 + +**JWT Access Token claim 要求**（使用 JWKS 验证时）： + +| Claim | 用途 | 必须？ | +|-------|------|--------| +| `sub` | 用户唯一 ID → `get_identity_id()` 返回 `user:` | **必须** | +| `iss` | 后端验证 token 来源 | **必须** | +| `aud` | 后端验证 token 受众 = `OIDC_CLIENT_ID` | **必须** | +| `exp` | 后端验证 token 未过期 | **必须** | +| `name` | 前端显示用户名 | 推荐 | +| `email` | 前端显示邮箱 | 推荐 | + +> 使用 UserInfo 验证时，后端从 UserInfo 响应中提取 `sub`/`id`/`user_id`、`name`/`username`、`email`，对 JWT 内部 claim 无要求。 + +### 3.4b GitHub OAuth Provider（新增 — 社交登录） + +GitHub OAuth 是纯 OAuth2（不是 OIDC，没有 `id_token`），需要后端完成授权码交换，因此属于 **B 类有状态 Provider**。 + +```python +# py-src/data_formulator/auth_providers/github_oauth.py + +import os +import logging +from typing import Optional +from flask import Request, session + +from .base import AuthProvider, AuthResult + +logger = logging.getLogger(__name__) + + +class GitHubOAuthProvider(AuthProvider): + """GitHub OAuth 2.0 认证。 + + 配置（环境变量）： + GITHUB_CLIENT_ID — GitHub OAuth App 的 Client ID (必需) + GITHUB_CLIENT_SECRET — GitHub OAuth App 的 Client Secret (必需) + + 工作流程（B 类有状态）： + 1. 前端重定向到 /api/auth/github/login → 302 到 GitHub 授权页 + 2. 用户授权后 GitHub 回调到 /api/auth/github/callback + 3. 后端用 code 换取 access_token，查询 /user API 获取用户信息 + 4. 写入 Flask session + 5. 后续请求由本 Provider 从 session 中读取身份 + """ + + def __init__(self): + self._client_id = os.environ.get("GITHUB_CLIENT_ID", "").strip() + self._client_secret = os.environ.get("GITHUB_CLIENT_SECRET", "").strip() + + @property + def name(self) -> str: + return "github" + + @property + def enabled(self) -> bool: + return bool(self._client_id and self._client_secret) + + def authenticate(self, request: Request) -> Optional[AuthResult]: + """从 Flask session 中读取 GitHub OAuth 认证结果。""" + user_data = session.get("df_user") + if not user_data or user_data.get("provider") != "github": + return None + + return AuthResult( + user_id=user_data["user_id"], + display_name=user_data.get("display_name"), + email=user_data.get("email"), + raw_token=user_data.get("raw_token"), + ) + + def get_auth_info(self) -> dict: + """返回 GitHub OAuth 前端配置。""" + return { + "action": "redirect", + "url": "/api/auth/github/login", + "label": os.environ.get("AUTH_DISPLAY_NAME", "GitHub Login"), + } +``` + +```python +# py-src/data_formulator/auth_gateways/github_gateway.py + +import os +import logging +import urllib.parse +from flask import Blueprint, request, redirect, session, jsonify + +from data_formulator.auth_providers.base import AuthResult + +logger = logging.getLogger(__name__) + +github_bp = Blueprint("github_auth", __name__, url_prefix="/api/auth/github") + + +@github_bp.route("/login") +def github_login(): + """重定向到 GitHub 授权页。""" + client_id = os.environ.get("GITHUB_CLIENT_ID", "") + redirect_uri = request.url_root.rstrip("/") + "/api/auth/github/callback" + scope = "read:user user:email" + params = urllib.parse.urlencode({ + "client_id": client_id, + "redirect_uri": redirect_uri, + "scope": scope, + }) + return redirect(f"https://github.com/login/oauth/authorize?{params}") + + +@github_bp.route("/callback") +def github_callback(): + """GitHub OAuth 回调 — 用授权码换取 access_token 并查询用户信息。""" + import requests as http_requests + + code = request.args.get("code") + if not code: + return jsonify({"error": "Missing authorization code"}), 400 + + client_id = os.environ.get("GITHUB_CLIENT_ID", "") + client_secret = os.environ.get("GITHUB_CLIENT_SECRET", "") + redirect_uri = request.url_root.rstrip("/") + "/api/auth/github/callback" + + # 用 code 换 access_token + token_resp = http_requests.post( + "https://github.com/login/oauth/access_token", + json={"client_id": client_id, "client_secret": client_secret, + "code": code, "redirect_uri": redirect_uri}, + headers={"Accept": "application/json"}, + timeout=10, + ) + if not token_resp.ok: + return jsonify({"error": "Failed to exchange code for token"}), 502 + + access_token = token_resp.json().get("access_token") + if not access_token: + return jsonify({"error": "No access_token in response"}), 502 + + # 查询 GitHub 用户信息 + user_resp = http_requests.get( + "https://api.github.com/user", + headers={"Authorization": f"Bearer {access_token}", "Accept": "application/json"}, + timeout=10, + ) + if not user_resp.ok: + return jsonify({"error": "Failed to fetch GitHub user info"}), 502 + + user_info = user_resp.json() + user_id = str(user_info.get("id", "")) + login = user_info.get("login", "") + + session["df_user"] = { + "user_id": f"github:{user_id}", + "display_name": user_info.get("name") or login, + "email": user_info.get("email"), + "raw_token": access_token, + "provider": "github", + } + logger.info("GitHub login successful: user=%s (%s)", login, user_id) + + return redirect("/") +``` + +### 3.5 重构后的 auth.py + +```python +# py-src/data_formulator/auth.py (重构) + +""" +Authentication and identity management for Data Formulator. + +Single AuthProvider + anonymous fallback: + AUTH_PROVIDER=oidc → OIDCProvider → user: + ALLOW_ANONYMOUS=true → Browser UUID → browser: +""" + +import logging +import re +import os +from typing import Optional +from flask import request, g, Flask + +from data_formulator.auth_providers.base import ( + AuthProvider, AuthResult, AuthenticationError, +) +from data_formulator.auth_providers import ( + get_provider_class, list_available_providers, +) + +logger = logging.getLogger(__name__) + +_MAX_IDENTITY_LENGTH = 256 +_IDENTITY_RE = re.compile(r'^[\w@.\-+/: ]+$', re.ASCII) + +# 主 Provider，由 init_auth() 初始化 +_provider: Optional[AuthProvider] = None +_allow_anonymous: bool = True + + +def _validate_identity_value(value: str, source: str) -> str: + value = value.strip() + if not value: + raise ValueError(f"Empty identity value from {source}") + if len(value) > _MAX_IDENTITY_LENGTH: + raise ValueError(f"Identity from {source} exceeds {_MAX_IDENTITY_LENGTH} chars") + if not _IDENTITY_RE.match(value): + raise ValueError(f"Identity from {source} contains disallowed characters") + return value + + +def init_auth(app: Flask) -> None: + """初始化认证。在 app 创建后调用一次。 + + 配置模型极简： + AUTH_PROVIDER=oidc ← 选一种（不设置 = 纯匿名模式） + ALLOW_ANONYMOUS=false ← 仅在需要强制登录时设置（默认 true，允许匿名回退） + """ + global _provider, _allow_anonymous + + _allow_anonymous = os.environ.get("ALLOW_ANONYMOUS", "true").lower() in ("true", "1", "yes") + provider_name = os.environ.get("AUTH_PROVIDER", "").strip().lower() + + if not provider_name or provider_name == "anonymous": + logger.info("Auth mode: anonymous only (no AUTH_PROVIDER configured)") + return + + provider_cls = get_provider_class(provider_name) + if not provider_cls: + logger.error("Unknown AUTH_PROVIDER: '%s'. Available: %s", + provider_name, ", ".join(list_available_providers())) + return + + try: + provider: AuthProvider = provider_cls() + + if not provider.enabled: + logger.error( + "AUTH_PROVIDER='%s' is set but required configuration is missing. " + "Provider will NOT be activated. Check environment variables.", + provider_name, + ) + return + + provider.on_configure(app) + _provider = provider + logger.info("Auth provider '%s' activated", provider_name) + except Exception as e: + logger.error("Auth provider '%s' failed to init: %s", provider_name, e) + + logger.info( + "Auth mode: %s%s", + provider_name or "anonymous", + " + anonymous fallback" if _allow_anonymous else " (login required)", + ) + + +def get_identity_id() -> str: + """获取当前请求的命名空间身份 ID。 + + 逻辑： + 1. 主 Provider 认证成功 → user: + 2. ALLOW_ANONYMOUS=true + X-Identity-Id 头 → browser: + 3. 以上均无 → 401 + """ + # 尝试主 Provider + if _provider: + try: + result = _provider.authenticate(request) + if result is not None: + validated = _validate_identity_value(result.user_id, _provider.name) + logger.debug("Authenticated via %s: user:%s...", _provider.name, validated[:8]) + g.df_auth_result = result + return f"user:{validated}" + except AuthenticationError as e: + logger.warning("Auth provider '%s' rejected request: %s", e.provider, e) + raise ValueError(f"Authentication failed: {e}") + + # 匿名回退 + if _allow_anonymous: + client_identity = request.headers.get("X-Identity-Id") + if client_identity: + if ":" in client_identity: + identity_value = client_identity.split(":", 1)[1] + else: + identity_value = client_identity + validated = _validate_identity_value(identity_value, "X-Identity-Id header") + return f"browser:{validated}" + + raise ValueError("Authentication required. Please log in.") + + +def get_auth_result() -> Optional[AuthResult]: + """获取当前请求的完整认证结果。 + + 仅在 get_identity_id() 通过主 Provider 认证成功后可用。 + browser 身份请求返回 None。 + + 用途： + - 获取 raw_token 用于 SSO 透传 + - 获取 display_name / email 用于 UI 显示 + """ + return getattr(g, "df_auth_result", None) + + +def get_sso_token() -> Optional[str]: + """获取当前用户的 SSO access token，用于透传给外部系统。 + + Returns: + access_token 字符串，或 None（匿名用户 / Provider 不提供 token） + """ + result = get_auth_result() + return result.raw_token if result else None +``` + +### 3.6 前端 OIDC 登录流程 + +前端使用 **PKCE (Proof Key for Code Exchange)** 流程 — 这是 SPA 的标准 OIDC 方式，不需要 client secret。 + +推荐使用 `oidc-client-ts` 库（轻量、标准兼容、维护活跃）。 + +```typescript +// src/app/oidcConfig.ts — 运行时从后端获取配置（简化版） + +import { UserManager, WebStorageStateStore, User } from "oidc-client-ts"; + +let _userManager: UserManager | null = null; +let _configPromise: Promise<{authority: string; clientId: string; redirectUri: string} | null> | null = null; + +// 从统一端点获取 OIDC 配置（无需前端编译时配置） +export async function getOidcConfig(): Promise<{authority: string; clientId: string; redirectUri: string} | null> { + if (_configPromise) return _configPromise; + + _configPromise = fetch('/api/auth/info') + .then(r => r.ok ? r.json() : null) + .then(info => { + if (info?.provider !== 'oidc' || !info?.oidc) return null; + return { + authority: info.oidc.authority, + clientId: info.oidc.clientId, + redirectUri: info.oidc.redirectUri || `${window.location.origin}/callback`, + }; + }) + .catch(() => null); + + return _configPromise; +} + +export async function getUserManager(): Promise { + if (_userManager) return _userManager; + const config = await getOidcConfig(); + if (!config) return null; + + _userManager = new UserManager({ + authority: config.authority, + client_id: config.clientId, + redirect_uri: config.redirectUri, + response_type: "code", + scope: "openid profile email", + automaticSilentRenew: true, + userStore: new WebStorageStateStore({ store: window.localStorage }), + }); + + return _userManager; +} + +export async function getAccessToken(): Promise { + const mgr = await getUserManager(); + if (!mgr) return null; + const user = await mgr.getUser(); + if (!user || user.expired) return null; + return user.access_token; +} +``` + +前端认证初始化统一通过 `/api/auth/info` 端点驱动（详见 [3.9.9 节](#399-前端适配统一登录入口)），一次请求确定认证模式，无需串行回退。 + +修改 `fetchWithIdentity` 以自动携带 OIDC token： + +```typescript +// src/app/utils.tsx — fetchWithIdentity 增强 + +export async function fetchWithIdentity( + url: string | URL, + options: RequestInit = {} +): Promise { + const urlString = typeof url === "string" ? url : url.toString(); + + if (urlString.startsWith("/api/")) { + const headers = new Headers(options.headers); + + // 身份标识 (所有请求) + const namespacedIdentity = await getCurrentNamespacedIdentity(); + headers.set("X-Identity-Id", namespacedIdentity); + headers.set("Accept-Language", getAgentLanguage()); + + // OIDC token (如果可用) + const accessToken = await getAccessToken(); // 从 oidcConfig.ts + if (accessToken) { + headers.set("Authorization", `Bearer ${accessToken}`); + } + + options = { ...options, headers }; + } + + return fetch(url, options); +} +``` + +### 3.7 OIDC 回调页面 + +> **国际化约定**：所有用户可见的文本使用 `react-i18next` 的 `useTranslation()` / `t()` 获取， +> 翻译 key 统一放在 `auth.*` 命名空间下（复用 0.6 已有的 i18n 基础设施）。 +> UI 样式对齐 0.6 `LoginView.tsx` 的 MUI Paper 居中卡片风格。 + +```typescript +// src/app/OidcCallback.tsx + +import { useEffect, useState } from "react"; +import { Box, CircularProgress, Typography, Alert, Paper, alpha, useTheme } from "@mui/material"; +import { useTranslation } from "react-i18next"; +import { getUserManager } from "./oidcConfig"; +import dfLogo from "../assets/df-logo.png"; + +export function OidcCallback() { + const { t } = useTranslation(); + const theme = useTheme(); + const [error, setError] = useState(null); + + useEffect(() => { + (async () => { + try { + const mgr = await getUserManager(); + if (mgr) { + await mgr.signinRedirectCallback(); + window.location.href = "/"; + } + } catch (err: any) { + setError(err?.message || "Unknown error"); + } + })(); + }, []); + + return ( + + + + {error ? ( + + {t("auth.callbackFailed", { message: error })} + + ) : ( + <> + + + {t("auth.completingLogin")} + + + )} + + + ); +} +``` + +在路由中注册回调路径（如使用 React Router）或在 `App.tsx` 中检测 URL path。 + +### 3.8 登录 / 登出 UI + +登录 UI 由 `/api/auth/info` 返回的 `action` 字段驱动（见 3.9.9）， +沿用 0.6 `LoginView.tsx` 的居中 Paper 卡片布局和 Fluent 配色。 + +```typescript +// src/app/AuthButton.tsx — AppBar 中的登录/登出按钮 + +import { useTranslation } from "react-i18next"; + +function AuthButton() { + const { t } = useTranslation(); + const identity = useSelector((state: DataFormulatorState) => state.identity); + const [mgr, setMgr] = useState(null); + + useEffect(() => { getUserManager().then(setMgr); }, []); + + if (identity?.type === "user") { + return ( + + + {t("auth.connectedAs", { name: identity.displayName || identity.id })} + + mgr?.signoutRedirect()} + title={t("auth.signOut")} + > + + + + ); + } + + if (mgr) { + return ( + + ); + } + + return null; +} +``` + +#### 3.8a 新增 i18n key + +在 `src/i18n/locales/` 的 `en/common.json` 和 `zh/common.json` 的 `auth` 节点下新增（复用 0.6 现有 key，仅补充 OIDC 新增的）： + +```json +// en/common.json — auth 节点新增 +{ + "auth": { + "completingLogin": "Completing login...", + "callbackFailed": "Login callback failed: {{message}}", + "oidcLogin": "SSO Login", + "oidcLoggingIn": "Logging in via SSO...", + "oidcDescription": "Login with your enterprise account via Single Sign-On", + "sessionExpired": "Session expired. Please sign in again.", + "silentRenewFailed": "Background token refresh failed. Redirecting to login..." + } +} +``` + +```json +// zh/common.json — auth 节点新增 +{ + "auth": { + "completingLogin": "正在完成登录...", + "callbackFailed": "登录回调失败：{{message}}", + "oidcLogin": "SSO 单点登录", + "oidcLoggingIn": "正在通过 SSO 登录...", + "oidcDescription": "使用企业账号通过单点登录系统认证", + "sessionExpired": "会话已过期，请重新登录。", + "silentRenewFailed": "后台令牌刷新失败，正在跳转到登录页..." + } +} +``` + +> **说明**：0.6 已有的 `auth.signIn`、`auth.signOut`、`auth.connectedAs`、`auth.continueAsGuest`、 +> `auth.guestDescription`、`auth.ssoLogin`、`auth.ssoPopupBlocked` 等 key 原样复用，不重复定义。 +> 新增 key 仅覆盖 OIDC PKCE 流程特有的场景（回调页、静默刷新失败等）。 + +### 3.8b Token 生命周期管理 + +OIDC access_token 有有限的有效期（通常 5~60 分钟）。前端必须妥善处理 token 过期和刷新，否则用户会在使用过程中突然收到 401 错误。 + +#### 静默刷新（Silent Renew） + +前端 `oidc-client-ts` 配置了 `automaticSilentRenew: true`，会在 token 过期前自动通过 iframe 向 IdP 发起无感刷新： + +``` +Token 有效期: 3600s (1h) + │ + ┌───────────────┼─────────────────┐ + │ │ │ + 0s 3300s (55min) 3600s + │ │ │ + 签发 自动触发 token + signinSilent() 过期 + │ + ├─ 成功 → 无缝更新 access_token + └─ 失败 → 触发重新登录 +``` + +#### 刷新失败的处理 + +静默刷新可能因以下原因失败： +- IdP session 已过期（用户在 IdP 端登出或 session 超时） +- iframe 被 CSP 策略阻止 +- 网络错误 + +```typescript +// src/app/oidcConfig.ts — 刷新失败处理 + +const mgr = await getUserManager(); +if (mgr) { + mgr.events.addSilentRenewError(() => { + console.warn("Silent renew failed, redirecting to login..."); + mgr.signinRedirect(); + }); +} +``` + +#### 后端 401 响应与前端重试 + +当后端 `OIDCProvider` 检测到过期 token 时，抛出 `AuthenticationError`，`get_identity_id()` 将其转为 `ValueError`，API 层返回 `401`。前端 `fetchWithIdentity` 应拦截 401 并触发 token 刷新： + +```typescript +// src/app/utils.tsx — fetchWithIdentity 增强：401 自动重试 + +export async function fetchWithIdentity( + url: string | URL, + options: RequestInit = {} +): Promise { + const resp = await _doFetch(url, options); + + if (resp.status === 401) { + const mgr = await getUserManager(); + if (mgr) { + try { + await mgr.signinSilent(); + return _doFetch(url, options); // 用新 token 重试一次 + } catch { + mgr.signinRedirect(); // 静默刷新失败，跳转登录 + return resp; + } + } + } + + return resp; +} +``` + +#### CORS 和 CSP 注意事项 + +OIDC PKCE 流程涉及跨域交互，生产部署需确保： + +| 配置项 | 说明 | +|-------|------| +| **CSP `frame-src`** | 允许 IdP 域名，`signinSilent()` 使用 iframe | +| **CORS** | 如果 DF 前端和后端不同源，后端需配置 `Access-Control-Allow-Origin` | +| **IdP redirect URI** | IdP 侧注册的 callback URL 必须与实际部署域名一致 | +| **HTTPS** | 生产环境必须全链路 HTTPS，否则 cookie / token 可能泄漏 | + +#### 设计决策与局限性 + +**Phase 1 决策：后端不持有 refresh_token，token 刷新完全由前端负责。** + +| 项目 | Phase 1 现状 | +|------|-------------| +| **access_token 存储** | 前端 `oidc-client-ts` UserManager 内存中 | +| **refresh_token 存储** | 前端 `oidc-client-ts` 内部管理，不传给后端 | +| **刷新方式** | 前端 `signinSilent()`（iframe 或 refresh_token grant） | +| **后端角色** | 纯无状态验证（每次请求校验 `Authorization: Bearer `） | +| **`AuthResult.raw_token`** | 仅存当次请求的 access_token，用于 SSO 透传到下游 API | + +**选择此方案的理由：** + +1. **架构简单** — 后端无需管理 token 存储、加密、过期清理等有状态逻辑，完全无状态可水平扩展。 +2. **安全边界清晰** — refresh_token 不经过 DF 后端，减少了服务端 token 泄漏的攻击面。SPA + PKCE 是 OIDC 推荐的公共客户端模式。 +3. **与 Data Formulator 使用场景匹配** — DF 是交互式数据分析工具，用户操作间隔较短（通常不超过 token 有效期），前端静默刷新足以覆盖绝大多数场景。 + +**已知局限性：** + +| 局限 | 影响 | 缓解措施 | +|------|------|---------| +| **长时间后台任务** | 如果 DF 未来支持长时间运行的后台任务（>token 有效期），后端持有的 access_token 会过期，下游 API 调用失败 | 目前不存在此场景；Phase 2 可引入后端 refresh_token 管理 | +| **IdP 不支持静默刷新** | 部分 IdP 禁用 iframe（X-Frame-Options）或不支持 `prompt=none`，导致前端 `signinSilent()` 失败 | 回退到 `signinRedirect()`（重新登录）；或在 IdP 侧配置允许 iframe | +| **短有效期 token + 高频操作** | 如果 IdP 签发的 access_token 有效期极短（<5 分钟），频繁的静默刷新可能产生明显延迟 | 建议 IdP 配置合理的 token 有效期（≥15 分钟） | +| **多标签页 token 同步** | 用户同时打开多个 DF 标签页时，各自独立持有 token，刷新时机不同步 | `oidc-client-ts` 支持 `monitorSession` 跨标签页同步；Phase 2 可评估 | + +**Phase 2 可选扩展（仅规划，不在 Phase 1 实现）：** + +如果未来出现后端需要长期持有 token 的场景（如后台定时任务、异步数据管道），可考虑： +- 后端 OIDC Confidential Client 模式（使用 `client_secret`），通过 Authorization Code Flow 获取 refresh_token +- 服务端加密存储 refresh_token（可复用 CredentialVault 基础设施） +- `AuthResult` 扩展 `refresh_token` 字段和 `token_expires_at` 时间戳 + +### 3.9 多协议支持：从 OIDC 扩展到 SAML / LDAP / CAS / 反向代理 + +#### 3.9.1 为什么不止 OIDC + +OIDC 覆盖了大部分现代 IdP（Keycloak、Okta、Auth0、Azure AD、Google），但企业环境中仍广泛存在其他认证协议： + +| 协议 | 验证模型 | 典型场景 | access_token 可透传 | +|------|---------|---------|:---:| +| **OAuth 2.0** | 无状态 (access_token per request) | 纯授权场景、旧系统 | **是** — access_token | +| **OIDC** | 无状态 (JWT per request，OAuth2 超集) | 现代 IdP、SaaS | **是** — access_token (同 OAuth2) | +| **Azure EasyAuth** | 无状态 (可信 Header) | Azure App Service | **是** — 通过 `/.auth/me` 获取 | +| **反向代理头** | 无状态 (可信 Header) | Authelia / Authentik / nginx / Traefik | 否 — 无 token | +| **SAML 2.0** | 有状态 (Assertion → Session) | ADFS、Shibboleth、PingFederate、OneLogin | 否 — 但可 Token Exchange 换取 | +| **LDAP / AD** | 有状态 (Bind → Session) | 无中心 IdP 的企业/高校 | 否 — 无 token | +| **CAS** | 有状态 (Ticket → Session) | 高校 (Apereo CAS) | 否 — ticket 一次性 | +| **Kerberos / SPNEGO** | 有状态 (Negotiate → Session) | Windows AD 域环境 | 否 — ticket 绑定特定服务 | + +**核心矛盾**：当前 `AuthProvider.authenticate(request)` 假设每个请求自带可验证的凭据（JWT/Header），这对 OIDC 和反向代理头完美适用。但 SAML/LDAP/CAS 需要先完成一个登录流程（浏览器重定向或表单提交），然后用服务端会话（session）识别后续请求。 + +#### 3.9.2 设计方案：双轨模型（Stateless + Session Gateway） + +解决思路是把认证协议分为两类，用不同的机制处理，但最终汇入同一条 AuthProvider 链： + +``` +┌─────────────────────────────────────────────────────────────────────┐ +│ AuthProvider 链 (per-request) │ +│ │ +│ ┌──────────────┐ ┌──────────┐ ┌──────────────┐ ┌────────────┐ │ +│ │ Azure │→ │ OIDC │→ │ Proxy Header │→ │ Session │ │ +│ │ EasyAuth │ │ (JWT) │ │ (可信头) │ │ (Cookie) │ │ +│ └──────────────┘ └──────────┘ └──────────────┘ └─────┬──────┘ │ +│ │ │ +│ A类: 无状态 A类: 无状态 B类 │ +│ (每次请求自带凭据) (每次请求自带凭据) (查session)│ +│ │ │ +│ ┌───────────────────────────────────────────────────────┘ │ +│ │ Session 中的身份从哪来？ → Login Gateway 在登录时写入 │ +│ │ │ +│ │ ┌──────────────────────────────────────────────────┐ │ +│ │ │ Login Gateway (Flask routes) │ │ +│ │ │ │ │ +│ │ │ /api/auth/saml/login ←→ SAML IdP │ │ +│ │ │ /api/auth/saml/acs ← SAML Assertion (POST) │ │ +│ │ │ /api/auth/ldap/login ← username + password │ │ +│ │ │ /api/auth/cas/login ←→ CAS Server │ │ +│ │ │ /api/auth/cas/callback← CAS ticket │ │ +│ │ │ │ │ +│ │ │ 验证通过 → session["df_user"] = AuthResult │ │ +│ │ └──────────────────────────────────────────────────┘ │ +│ └─────────────────────────────────────────────────────────────── │ +│ │ +│ 最终 Fallback: Browser UUID │ +└─────────────────────────────────────────────────────────────────────┘ +``` + +**A 类 — 无状态 Provider**（现有设计已覆盖）： +- 每个请求自带可独立验证的凭据（JWT Bearer / 可信 Header） +- 直接在 `authenticate(request)` 中完成验证 +- 代表：OIDC、Azure EasyAuth、反向代理头 + +**B 类 — 有状态 Provider**（新增 Login Gateway + SessionProvider）： +- 登录时走协议特定的流程（SAML redirect、LDAP bind、CAS redirect） +- 登录成功后在 Flask session 中存储 `AuthResult` +- 后续请求由通用的 `SessionProvider` 从 session 中读取身份 +- 代表：SAML、LDAP、CAS、Kerberos + +**核心优势：Login Gateway 是协议特定的，但 SessionProvider 是通用的。** 新增一种有状态协议只需要写一个 Login Gateway blueprint，不需要修改 AuthProvider 链。 + +#### 3.9.3 ~ 3.9.8 Phase 2+ 扩展（反向代理 / SAML / LDAP / CAS / Login Gateway） + +> **以下内容属于 Phase 2+ 规划，此处仅记录架构扩展点，不展开实现细节。** +> 具体实现代码将在需求明确时编写独立的协议扩展文档。 + +**架构预留的扩展点：** + +1. **`AuthResult` 扩展** — 在 Phase 2 引入 `groups`、`auth_protocol`、`token_expiry`、`to_session_dict()` / `from_session_dict()` 等字段和方法，支持会话序列化。 +2. **反向代理头 Provider** — A 类无状态，通过 `PROXY_TRUSTED_IPS` 校验可信 IP，从 `X-Forwarded-User` 等 header 提取身份。 +3. **SessionProvider** — B 类通用读取端，从 Flask session 中读取 Login Gateway 写入的身份。 +4. **Login Gateway Blueprint** — SAML ACS (`/api/auth/saml/acs`)、LDAP bind (`/api/auth/ldap/login`)、CAS ticket 验证 (`/api/auth/cas/callback`) 等协议特定的登录流程。 +5. **通用登出** — 清除 session + 返回协议特定的 SLO URL。 +6. **Gateway 注册** — `_register_login_gateways(app, provider_name)` 根据 `AUTH_PROVIDER` 值按需注册对应的 Blueprint。 + +**新增 B 类协议的步骤**： +1. 在 `auth_providers/` 下创建新的 `.py` 文件，实现 `AuthProvider` 子类（自动发现，无需修改注册表） +2. 实现 Login Gateway Blueprint（完成协议特定的登录流程，写入 `session["df_user"]`） +3. 在 Provider 类中实现 `get_auth_info()` 返回前端交互方式 + +核心代码零修改 — 自动发现机制会扫描到新 Provider，`AUTH_PROVIDER` 环境变量选择激活即可。 + +#### 3.9.9 前端适配：统一登录入口 + +前端通过单一的 `/api/auth/info` 端点获取当前认证模式和所需配置，一次请求搞定。 + +**后端统一认证信息 API — 委托 Provider 自描述（消除 switch 膨胀）：** + +```python +# auth.py — 新增 + +@app.route("/api/auth/info") +def auth_info(): + """返回当前认证模式 + 前端所需的配置信息。 + + 通过调用 Provider 的 get_auth_info() 方法获取 Provider 特定配置， + 而非在此处 switch 每种协议。新增 Provider 无需修改此端点。 + """ + provider_name = os.environ.get("AUTH_PROVIDER", "anonymous").strip().lower() + + info = { + "provider": provider_name, + "allow_anonymous": _allow_anonymous, + } + + if _provider: + info.update(_provider.get_auth_info()) + else: + info["action"] = "none" + + return jsonify(info) +``` + +每个 Provider 通过实现 `get_auth_info()` 声明前端交互方式（详见 3.2 节基类定义）。例如： +- `OIDCProvider.get_auth_info()` 返回 `{"action": "frontend", "oidc": {...}}` +- `GitHubOAuthProvider.get_auth_info()` 返回 `{"action": "redirect", "url": "/api/auth/github/login"}` +- `AzureEasyAuthProvider.get_auth_info()` 返回 `{"action": "transparent"}` + +新增 Provider 只需在自己的类中实现此方法，`auth.py` 无需任何修改。 + +**前端统一登录组件：** + +沿用 0.6 `LoginView.tsx` 的 Paper 居中卡片布局、Fluent 配色和 i18n 模式。 +所有用户可见文本通过 `t('auth.*')` 获取，不硬编码任何语言。 + +```typescript +// src/app/LoginPanel.tsx — 根据 /api/auth/info 渲染对应的登录 UI + +import React, { FC, useEffect, useState } from "react"; +import { + Box, Button, TextField, Typography, Divider, + CircularProgress, Alert, Paper, alpha, useTheme, +} from "@mui/material"; +import LoginIcon from "@mui/icons-material/Login"; +import OpenInNewIcon from "@mui/icons-material/OpenInNew"; +import PersonOutlineIcon from "@mui/icons-material/PersonOutline"; +import { useTranslation } from "react-i18next"; +import { getUserManager } from "./oidcConfig"; +import dfLogo from "../assets/df-logo.png"; +import { toolName } from "./App"; + +interface AuthInfo { + provider: string; + allow_anonymous: boolean; + action: "frontend" | "redirect" | "form" | "transparent" | "none"; + label?: string; + url?: string; + fields?: string[]; + oidc?: { authority: string; clientId: string; redirectUri: string; scopes: string }; +} + +interface LoginPanelProps { + onGuestContinue: () => void; +} + +export const LoginPanel: FC = ({ onGuestContinue }) => { + const theme = useTheme(); + const { t } = useTranslation(); + + const [authInfo, setAuthInfo] = useState(null); + const [formData, setFormData] = useState({ username: "", password: "" }); + const [loading, setLoading] = useState(false); + const [error, setError] = useState(null); + + useEffect(() => { + fetch("/api/auth/info").then(r => r.json()).then(setAuthInfo).catch(() => null); + }, []); + + if (!authInfo) return null; + if (authInfo.action === "none" || authInfo.action === "transparent") return null; + + const renderAuthAction = () => { + switch (authInfo.action) { + case "frontend": + return ( + <> + + + {t("auth.oidcDescription")} + + + ); + + case "redirect": + return ( + <> + + + {t("auth.ssoDescription")} + + + ); + + case "form": + return ( + { + e.preventDefault(); + if (!formData.username || !formData.password) return; + setLoading(true); + setError(null); + try { + const resp = await fetch(authInfo.url!, { + method: "POST", + credentials: "include", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify(formData), + }); + const data = await resp.json(); + if (resp.ok && data.status === "ok") { + window.location.reload(); + } else { + setError(data.message || t("auth.loginFailed", { message: "Unknown error" })); + } + } catch (err: any) { + setError(err.message || "Network error"); + } finally { + setLoading(false); + } + }} + sx={{ width: "100%", display: "flex", flexDirection: "column", gap: 2 }} + > + setFormData(f => ({ ...f, username: e.target.value }))} + autoComplete="username" + autoFocus + fullWidth + /> + setFormData(f => ({ ...f, password: e.target.value }))} + autoComplete="current-password" + fullWidth + /> + + + ); + + default: + return null; + } + }; + + return ( + + + + + + {toolName} + + + + + {t("auth.loginSubtitle")} + + + {error && ( + + {t("auth.loginFailed", { message: error })} + + )} + + {renderAuthAction()} + + {authInfo.allow_anonymous && ( + <> + + + {t("auth.or")} + + + + + + {t("auth.guestDescription")} + + + )} + + + ); +}; +``` + +> **说明**：组件中所有用户可见文本均通过 `t('auth.*')` 获取。 +> 复用的 0.6 已有 key：`auth.loginSubtitle`、`auth.username`、`auth.password`、`auth.signIn`、 +> `auth.signingIn`、`auth.loginFailed`、`auth.or`、`auth.continueAsGuest`、`auth.guestDescription`、 +> `auth.ssoLogin`、`auth.ssoDescription`。 +> 0.7 新增 key（已在 3.8a 中定义）：`auth.oidcLogin`、`auth.oidcLoggingIn`、`auth.oidcDescription`。 + +**前端 `initAuth` — 纯 action 驱动（不按 provider 名称分支）：** + +前端**只看 `action` 字段**，完全不看 `provider` 名称。这样新增 Provider 只要 +复用已有的 action 类型，前端就不用改。 + +```typescript +// src/app/App.tsx — initAuth：纯 action 驱动，零 provider 分支 + +useEffect(() => { + async function initAuth() { + const authInfo = await fetch("/api/auth/info").then(r => r.json()).catch(() => null); + if (!authInfo) { setAuthChecked(true); return; } + + switch (authInfo.action) { + case "frontend": { + // 前端管理的认证流程（OIDC PKCE 等） + // authInfo 中携带了所需配置（如 oidc.authority, oidc.clientId） + const mgr = await getUserManager(); + if (mgr) { + let user = await mgr.getUser(); + if (!user || user.expired) { + try { user = await mgr.signinSilent(); } catch { user = null; } + } + if (user) { + setUserInfo({ name: user.profile.name || "", userId: user.profile.sub }); + setAuthChecked(true); + return; + } + } + break; + } + + case "transparent": { + // 平台已完成认证（Azure EasyAuth、反向代理等），查询身份即可 + try { + const resp = await fetch("/api/auth/whoami"); + if (resp.ok) { + const data = await resp.json(); + if (data.user_id) { + setUserInfo({ name: data.display_name || "", userId: data.user_id }); + setAuthChecked(true); + return; + } + } + } catch { /* 未认证 */ } + break; + } + + case "redirect": + case "form": { + // 服务端管理的认证（GitHub OAuth、SAML、LDAP、CAS 等） + // 用户尚未登录时由 LoginPanel 渲染按钮/表单，已登录则从 session 读取 + try { + const resp = await fetch("/api/auth/whoami"); + if (resp.ok) { + const data = await resp.json(); + if (data.user_id) { + setUserInfo({ name: data.display_name || "", userId: data.user_id }); + setAuthChecked(true); + return; + } + } + } catch { /* 未登录 */ } + break; + } + + case "none": + default: + break; + } + + // 匿名模式（或 SSO 未登录 + 允许匿名） + setAuthChecked(true); + } + initAuth(); +}, []); +``` + +**关键设计约束**：`initAuth` 中没有任何 `authInfo.provider === "xxx"` 的判断。 +新增一个 Provider（如 SAML）时，只要其 `get_auth_info()` 返回已有的 action 类型 +（如 `"redirect"`），前端代码零修改。 + +**后端 `/api/auth/whoami` 端点：** + +```python +# auth.py — 新增 + +@app.route("/api/auth/whoami") +def whoami(): + """返回当前 session 中的用户信息（如有）。""" + user_data = session.get("df_user") + if user_data: + return jsonify({ + "user_id": user_data.get("user_id"), + "display_name": user_data.get("display_name"), + "email": user_data.get("email"), + }) + return jsonify({}), 401 +``` + +#### 3.9.10 AuthProvider 模型图 + +单一 Provider + 匿名回退的认证模型： + +``` +AUTH_PROVIDER=oidc (由管理员选一种) + + ┌──────────────────────┐ ┌──────────────┐ + │ 主 Provider │ │ Browser UUID │ + │ (OIDC / GitHub / │ ──→ │ (匿名回退) │ + │ Azure / SAML / LDAP │ 未命中│ │ + │ / CAS / proxy_header)│ │ 仅在 │ + │ │ │ ALLOW_ANONYMOUS│ + │ A类: 每次请求验证 │ │ =true 时生效 │ + │ B类: 从 session 读取 │ └──────────────┘ + └───────────┬───────────┘ + │ + B 类的 session 来自: + ┌────────────────┐ + │ Login Gateway │ + │ ├── GitHub OAuth│ + │ ├── SAML ACS │ + │ ├── LDAP login │ + │ └── CAS callback│ + └────────────────┘ +``` + +#### 3.9.11 SSO Token 透传的协议差异 + +不同协议对 "token 透传到下游数据源" 的支持差异很大。关键区分点是 **协议是否产出一个可作为 Bearer token 的 access_token**： + +> **OIDC 与 OAuth2 的关系**：OIDC 是 OAuth2 的超集（OIDC = OAuth2 + 身份层）。 +> OIDC 在 OAuth2 的 access_token 之上额外给出一个 id_token (JWT) 以标识"谁在用"。 +> **透传给下游 API 的始终是 OAuth2 的 access_token**，与 OIDC 的 id_token 无关。 +> 因此只要是基于 OAuth2 的流程（无论是否带 OIDC），access_token 都可以透传。 + +| 协议 | 产出物 | 能否透传 | 说明 | +|------|--------|:---:|------| +| **OAuth 2.0 / OIDC** | access_token（opaque 或 JWT） | **能** | `AuthResult.raw_token` 存储 access_token，直接作为下游 API 的 Bearer token | +| **Azure EasyAuth** | 平台托管的 token | **能** | 通过 `/.auth/me` 或 `X-MS-TOKEN-*` 头获取 access_token；本质仍是 OAuth2 | +| **反向代理头** | 无 token（仅 header） | **不能** | 代理已消费了原始 token，DF 只拿到用户名等纯文本 header | +| **SAML 2.0** | XML Assertion | **不能直接用** | Assertion 是 XML 格式且有 Audience 限制；但可通过 RFC 8693 Token Exchange 或 SAML Bearer Assertion Grant (RFC 7522) 向 IdP 换取 OAuth2 access_token | +| **LDAP / AD** | 无（仅验证密码） | **不能** | 没有任何 token 产出 | +| **CAS** | Service Ticket（一次性） | **不能** | Ticket 验证后即失效，不可复用 | +| **Kerberos** | Service Ticket | **不能直接用** | Kerberos ticket 绑定到特定服务，无法代用；但 Windows 域中 Kerberos → OAuth2 的桥接方案存在 | + +**设计应对**： + +- **OAuth2/OIDC 用户**：`AuthResult.raw_token` 持有 access_token，DataSourcePlugin 和 DataLoader 可直接用它调用共享同一 IdP 的下游 API（零额外登录）。 +- **SAML 用户（高级）**：如果 IdP 同时支持 OAuth2（如 ADFS、PingFederate 通常都支持），可在 Login Gateway 中用 SAML Assertion 通过 Token Exchange 换取 OAuth2 access_token，再存入 `AuthResult.raw_token`，从而获得透传能力。这种情况下建议直接走 OIDC 而非 SAML。 +- **LDAP / CAS / 反向代理头用户**：需要走 CredentialVault 路线 —— 用户手动配置下游数据源的凭证（或 API Key），由 CredentialVault 加密存储后供 Plugin 使用。 + +这也是为什么 **Layer 3 CredentialVault 是整个架构不可缺少的一层** —— 它为无法 token 透传的认证协议提供了凭证存储的兜底方案。同时这也说明 **OIDC 是首选协议**（P0 优先级），因为它是唯一能同时解决"身份识别"和"下游透传"两个问题的方案。 + +#### 3.9.12 协议选择指南 + +为方便运维人员选择，提供以下决策树： + +``` +你的组织使用什么身份系统？ + │ + ├─ Azure AD / Entra ID + │ ├─ 部署在 Azure App Service？ → 用 azure_easyauth (零配置) + │ └─ 其他部署 → 用 oidc (Azure AD 支持 OIDC) + │ + ├─ Keycloak / Okta / Auth0 / Google Workspace → 用 oidc + │ + ├─ ADFS / Shibboleth / PingFederate (仅 SAML) + │ ├─ 能配 OIDC 吗？ → 优先 oidc (ADFS/Ping 一般都支持) + │ └─ 只有 SAML → 用 saml (session 模式) + │ + ├─ IC 卡 / 智能卡 / PKI 证书 + │ ├─ IdP 能签发 OAuth2 token？ → 用 oidc/saml + Token Exchange (未来扩展，见 3.9.12) + │ └─ 否 → 暂不支持，建议升级 IdP 或使用 API Key + CredentialVault + │ + ├─ Authelia / Authentik / nginx / Traefik (反向代理已认证) + │ └─ 用 proxy_header + │ + ├─ 只有 LDAP / Active Directory (无 SSO 中心) + │ └─ 用 ldap (session 模式) + │ + ├─ CAS (高校) + │ └─ 用 cas (session 模式) + │ + └─ 无任何身份系统 + └─ 默认 Browser UUID (匿名模式) +``` + +#### 3.9.13 新增依赖说明 + +各协议的 Python 依赖作为 **可选依赖** 安装，基础安装不引入： + +```toml +# pyproject.toml — optional dependencies +[project.optional-dependencies] +oidc = ["PyJWT>=2.8", "cryptography>=41.0"] +saml = ["python3-saml>=1.16"] +ldap = ["ldap3>=2.9"] +cas = [] # 纯标准库实现，无额外依赖 + +# 快捷安装全部认证协议 +auth-all = ["PyJWT>=2.8", "cryptography>=41.0", "python3-saml>=1.16", "ldap3>=2.9"] +``` + +#### 3.9.14 优先级建议 + +| 优先级 | 协议 | 理由 | +|:---:|------|------| +| **P0** | OIDC | 覆盖面最广，现代 IdP 基本都支持，且是唯一支持 token 透传的协议 | +| **P0** | Browser UUID | 保持向后兼容的匿名模式 | +| **P1** | 反向代理头 | 自建部署最常见的方式，实现简单 | +| **P1** | LDAP | 覆盖没有 SSO 中心的传统企业/高校 | +| **P2** | SAML | 大型企业有时只提供 SAML，但 ADFS/PingFederate 通常也支持 OIDC | +| **P3** | CAS | 受众窄（主要是高校），需求出现时再实现 | + +--- + +## 4. Layer 2：数据源插件系统 (DataSourcePlugin) + +### 4.1 设计思路 + +BI 报表系统（Superset、Metabase、Power BI 等）的集成需求远超现有 `ExternalDataLoader` 的能力： + +| 能力 | ExternalDataLoader | BI 系统需要 | +|------|:--:|:--:| +| 连接参数 | 简单 key-value 表单 | URL + 认证流程 (JWT/OAuth/SSO) | +| 数据浏览 | `list_tables()` → 表名列表 | 数据集 + 仪表盘 + 报表 + 筛选条件 | +| 权限模型 | 无 (用数据库账号的权限) | 需尊重 BI 系统自身的 RBAC/RLS | +| 前端 UI | 通用字段表单 | 需要专用目录浏览、搜索、筛选等交互 | +| 独立 API 路由 | 无 | 需要注册 Blueprint | + +因此，BI 系统使用独立的 **DataSourcePlugin** 机制，与 `ExternalDataLoader` **并行存在**。 + +### 4.2 Plugin 基类 + +```python +# py-src/data_formulator/plugins/base.py + +from abc import ABC, abstractmethod +from typing import Any, Optional +from flask import Blueprint + + +class DataSourcePlugin(ABC): + """外部数据源插件基类。 + + 每个插件实现以下契约： + 1. manifest() — 自我描述（ID、名称、配置需求） + 2. create_blueprint() — Flask 路由（认证 + 目录 + 数据拉取） + 3. get_frontend_config() — 传给前端的非敏感配置 + 4. on_enable() / on_disable() — 生命周期钩子 + """ + + @staticmethod + @abstractmethod + def manifest() -> dict[str, Any]: + """插件元数据。 + + Returns: + { + "id": "superset", + "name": "Apache Superset", + "icon": "superset", + "description": "从 Superset 加载数据集和仪表盘数据", + "version": "1.0.0", + "env_prefix": "PLG_SUPERSET", + "required_env": ["PLG_SUPERSET_URL"], + "optional_env": ["PLG_SUPERSET_TIMEOUT"], + "auth_modes": ["sso", "jwt", "password"], + "capabilities": ["datasets", "dashboards", "filters"], + } + """ + ... + + @abstractmethod + def create_blueprint(self) -> Blueprint: + """创建 Flask Blueprint。 + + 路由前缀: /api/plugins// + 示例路由: + /api/plugins/superset/auth/login + /api/plugins/superset/auth/status + /api/plugins/superset/catalog/datasets + /api/plugins/superset/data/load-dataset + """ + ... + + @abstractmethod + def get_frontend_config(self) -> dict[str, Any]: + """返回传给前端的配置（不包含敏感信息）。 + + Returns: + { + "auth_modes": ["sso", "jwt", "password"], + "sso_login_url": "http://superset:8088/df-sso-bridge/", + "capabilities": ["datasets", "dashboards", "filters"], + } + """ + ... + + def on_enable(self, app) -> None: + """插件启用时调用。可初始化连接池、缓存等。""" + pass + + def on_disable(self) -> None: + """插件禁用时调用。""" + pass + + def get_auth_status(self, session: dict) -> Optional[dict[str, Any]]: + """返回当前用户在此插件中的认证状态。 + + Returns: + {"authenticated": True, "user": "john", ...} 或 None + """ + return None + + def supports_sso_passthrough(self) -> bool: + """此插件是否支持 SSO token 透传。 + + 如果返回 True，插件可以从 auth.get_sso_token() 获取用户的 + OIDC access token，直接用于调用外部系统 API。 + """ + return False +``` + +### 4.3 插件与 SSO 的集成模式 + +每个插件可以支持多种认证方式，根据部署环境自动选择： + +``` +┌──────────────────────────────────────────────────────────────────┐ +│ 插件认证模式选择 │ +│ │ +│ 场景 A: DF 有 SSO + 外部系统也接了同一 IdP │ +│ ───────────────────────────────────────── │ +│ → 自动使用 SSO Token 透传 │ +│ → 用户无需额外登录 │ +│ → 外部系统通过 token 识别用户，应用自身 RBAC │ +│ │ +│ 场景 B: DF 有 SSO + 外部系统没有接 SSO │ +│ ───────────────────────────────────── │ +│ → 首次使用时，用户在插件 UI 中输入外部系统的账号/密码/API Key │ +│ → 凭证存入 CredentialVault（按 SSO user_id 关联） │ +│ → 后续自动从 Vault 取出，无需重复输入 │ +│ → 换设备后只要 SSO 登录，凭证自动可用 │ +│ │ +│ 场景 C: DF 无 SSO（本地匿名模式） │ +│ ────────────────────────────── │ +│ → 用户在插件 UI 中输入外部系统的账号密码 │ +│ → Token 存在 Flask Session 中（仅当次会话有效） │ +│ → 行为与 0.6 版本一致 │ +└──────────────────────────────────────────────────────────────────┘ +``` + +插件内部的认证路由应检查这三种模式： + +```python +# 插件认证路由模板 + +@bp.route("/auth/login", methods=["POST"]) +def plugin_login(): + """处理插件认证。自动选择最佳模式。""" + + # 模式 1: SSO Token 透传 + sso_token = get_sso_token() + if sso_token and plugin.supports_sso_passthrough(): + # 用 SSO token 直接调用外部系统的 token exchange / introspection + external_token = exchange_sso_token(sso_token) + if external_token: + store_plugin_session(plugin_id, external_token) + return jsonify({"status": "ok", "auth_mode": "sso"}) + + # 模式 2: 从 Credential Vault 取已存储的凭证 + vault = get_credential_vault() + identity = get_identity_id() + stored = vault.retrieve(identity, plugin_id) if vault else None + if stored: + external_token = authenticate_with_stored_credentials(stored) + if external_token: + store_plugin_session(plugin_id, external_token) + return jsonify({"status": "ok", "auth_mode": "vault"}) + + # 模式 3: 用户手动输入 + data = request.get_json() + username = data.get("username") + password = data.get("password") + if username and password: + external_token = authenticate_with_credentials(username, password) + # 可选：存入 Vault 以便下次自动使用 + if vault and data.get("remember", True): + vault.store(identity, plugin_id, {"username": username, "password": password}) + store_plugin_session(plugin_id, external_token) + return jsonify({"status": "ok", "auth_mode": "credentials"}) + + return jsonify({"status": "needs_login", "available_modes": get_available_modes()}) +``` + +### 4.4 插件注册与发现 + +#### 4.4.1 注册机制方案选型 + +新增一个数据源插件时，注册表是否需要改代码？有三种方案： + +| 方案 | 新增插件要改代码吗 | 复杂度 | 安全性 | 适合场景 | +|------|:---:|:---:|:---:|------| +| **A. 硬编码列表** | 要，改注册表一行 | 最低 | 最高（只加载白名单） | 插件由同一团队开发，随主项目发布 | +| **B. 目录自动扫描** | 不要，放进目录就生效 | 低 | 中（需约定和校验） | 插件持续增加，希望"拖入即用" | +| **C. setuptools entry_points** | 不要，`pip install` 后自动注册 | 中 | 中 | 插件作为独立 pip 包发布 | + +**方案 A（硬编码列表）** 是现有 `ExternalDataLoader` 的做法（`_LOADER_SPECS` 列表），也是 0.7 系统的成熟模式。优点是简单透明，缺点是每加一个插件都要改 `__init__.py`。 + +**方案 C（entry_points）** 适合有第三方插件生态的平台（如 pytest、Flask 扩展），对当前项目来说过于重型，且前端部分无法通过 pip 安装（仍需编译到主 bundle）。 + +**选择方案 B（目录自动扫描）** — 理由： + +1. **插件会持续增长** — 未来对接的报表系统只会越来越多，每次加一个都改注册表是无意义的样板修改 +2. **插件都是内部开发** — 不需要跨包的 entry_points 机制 +3. **manifest 自描述** — 插件的 ID、必需环境变量等信息已经在 `manifest()` 中声明，不需要在注册表中重复 +4. **安全保底** — 通过 `PLUGIN_BLOCKLIST` 环境变量提供黑名单能力 + +> **统一范式**：AuthProvider 与 DataSourcePlugin 采用相同的目录自动扫描机制。 +> 区别仅在于激活策略 — 认证是**单选**的（`AUTH_PROVIDER` 环境变量指定唯一活跃 Provider）， +> DataSourcePlugin 是**并行**的（所有已发现的插件同时存在）。详见 4.4.8 对比表。 + +#### 4.4.2 插件约定 + +每个插件是 `plugins/` 目录下的一个 Python 子包，必须满足以下约定： + +``` +plugins/superset/ +├── __init__.py ← 必须暴露 plugin_class = SupersetPlugin +├── superset_client.py +├── auth_bridge.py +├── catalog.py +└── routes/ + ├── auth.py + ├── catalog.py + └── data.py +``` + +`__init__.py` 的最低要求： + +```python +# plugins/superset/__init__.py + +from .plugin import SupersetPlugin + +# 框架通过此变量发现插件类 +plugin_class = SupersetPlugin +``` + +框架通过 `plugin_class` 变量找到插件类，再调用 `plugin_class.manifest()` 获取自描述信息（ID、必需环境变量等）。不需要在任何注册表中手动登记。 + +#### 4.4.3 自动扫描实现 + +```python +# py-src/data_formulator/plugins/__init__.py + +""" +数据源插件自动发现与注册。 + +扫描 plugins/ 目录下所有子包，查找暴露 plugin_class 变量的模块。 +通过 manifest() 中的 required_env 判断是否启用。 +通过 PLUGIN_BLOCKLIST 环境变量支持显式禁用。 + +新增插件步骤： + 1. 在 plugins/ 下创建子目录 + 2. __init__.py 中暴露 plugin_class = YourPlugin + 3. .env 中设置必需环境变量 + 4. 重启服务 → 自动发现、自动注册 + 无需修改任何现有代码。 +""" + +import importlib +import logging +import os +import pkgutil +from typing import Any + +from data_formulator.plugins.base import DataSourcePlugin + +_log = logging.getLogger(__name__) + +ENABLED_PLUGINS: dict[str, DataSourcePlugin] = {} +DISABLED_PLUGINS: dict[str, str] = {} + +# 显式黑名单：PLUGIN_BLOCKLIST=powerbi,grafana +_BLOCKLIST = set( + p.strip() + for p in os.environ.get("PLUGIN_BLOCKLIST", "").split(",") + if p.strip() +) + + +def discover_and_register(app) -> None: + """扫描 plugins/ 子包，发现并注册所有已启用的插件。 + + 在 app.py 的 _register_blueprints() 中调用一次。 + """ + for finder, pkg_name, ispkg in pkgutil.iter_modules(__path__): + # 跳过非包（如 base.py, data_writer.py）和黑名单 + if not ispkg: + continue + if pkg_name in _BLOCKLIST: + DISABLED_PLUGINS[pkg_name] = "Blocked by PLUGIN_BLOCKLIST" + _log.info("Plugin '%s' blocked by PLUGIN_BLOCKLIST", pkg_name) + continue + + try: + mod = importlib.import_module(f"data_formulator.plugins.{pkg_name}") + except ImportError as exc: + DISABLED_PLUGINS[pkg_name] = f"Missing dependency: {exc.name}" + _log.info("Plugin '%s' disabled (import error): %s", pkg_name, exc) + continue + + # 检查是否暴露了 plugin_class + plugin_cls = getattr(mod, "plugin_class", None) + if plugin_cls is None: + continue # 不是插件目录（可能是工具模块），静默跳过 + if not (isinstance(plugin_cls, type) and issubclass(plugin_cls, DataSourcePlugin)): + _log.warning( + "Plugin '%s': plugin_class is not a DataSourcePlugin subclass, skipped", + pkg_name, + ) + continue + + # 从 manifest 获取元数据 + try: + manifest = plugin_cls.manifest() + except Exception as exc: + DISABLED_PLUGINS[pkg_name] = f"manifest() failed: {exc}" + _log.error("Plugin '%s' manifest() failed: %s", pkg_name, exc) + continue + + plugin_id = manifest["id"] + required_env = manifest.get("required_env", []) + + # 检查必需环境变量 + missing_env = [e for e in required_env if not os.environ.get(e)] + if missing_env: + DISABLED_PLUGINS[plugin_id] = f"Not configured: {', '.join(missing_env)}" + _log.info( + "Plugin '%s' disabled: missing env %s", + plugin_id, ", ".join(missing_env), + ) + continue + + # 实例化、注册 Blueprint、启用 + try: + plugin: DataSourcePlugin = plugin_cls() + bp = plugin.create_blueprint() + app.register_blueprint(bp) + plugin.on_enable(app) + + ENABLED_PLUGINS[plugin_id] = plugin + _log.info( + "Plugin '%s' enabled (auto-discovered from plugins/%s/)", + plugin_id, pkg_name, + ) + except Exception as exc: + DISABLED_PLUGINS[plugin_id] = str(exc) + _log.error( + "Plugin '%s' failed to initialize: %s", + plugin_id, exc, exc_info=True, + ) +``` + +#### 4.4.4 发现流程图 + +``` +plugins/ +├── __init__.py ← discover_and_register() 在这里 +├── base.py ← DataSourcePlugin 基类 (ispkg=False, 跳过) +├── data_writer.py ← 工具模块 (ispkg=False, 跳过) +├── superset/ ← ispkg=True +│ └── __init__.py → plugin_class = SupersetPlugin +│ → manifest(): required_env=["PLG_SUPERSET_URL"] +│ → os.environ["PLG_SUPERSET_URL"] 存在? +│ → 是 → 实例化 → 注册 Blueprint → ENABLED ✅ +│ → 否 → DISABLED (Not configured) +├── metabase/ ← ispkg=True +│ └── __init__.py → plugin_class = MetabasePlugin +│ → manifest(): required_env=["PLG_METABASE_URL"] +│ → os.environ["PLG_METABASE_URL"] 不存在 +│ → DISABLED (Not configured) +└── _helpers/ ← ispkg=True, 但无 plugin_class → 静默跳过 + └── __init__.py → (没有 plugin_class 变量) +``` + +#### 4.4.5 新增插件的完整步骤 + +以新增一个 Grafana 插件为例： + +**步骤 1**：创建插件目录和代码 + +``` +plugins/grafana/ +├── __init__.py # plugin_class = GrafanaPlugin +├── plugin.py # GrafanaPlugin(DataSourcePlugin) 实现 +├── grafana_client.py # Grafana REST API 封装 +└── routes/ + ├── auth.py # /api/plugins/grafana/auth/* + ├── catalog.py # /api/plugins/grafana/catalog/* + └── data.py # /api/plugins/grafana/data/* +``` + +**步骤 2**：在 `.env` 中设置环境变量 + +```bash +PLG_GRAFANA_URL=http://grafana.example.com:3000 +``` + +**步骤 3**：重启服务 + +``` + Loading data source plugins... + Plugin 'grafana' enabled (auto-discovered from plugins/grafana/) +``` + +**核心代码改动：0 行。** 不需要修改 `__init__.py`、`app.py` 或任何其他文件。 + +#### 4.4.6 安全措施 + +| 措施 | 说明 | +|------|------| +| **类型校验** | `plugin_class` 必须是 `DataSourcePlugin` 的子类，否则跳过 | +| **环境变量门控** | `required_env` 中的变量缺失则不启用，防止未配置的插件意外加载 | +| **显式黑名单** | `PLUGIN_BLOCKLIST=powerbi,grafana` 可以禁用特定插件 | +| **Blueprint 前缀隔离** | 插件路由强制在 `/api/plugins//` 下，无法覆盖核心路由 | +| **错误隔离** | 单个插件加载失败不影响其他插件和核心系统 | + +#### 4.4.7 前端的对应扫描机制 + +前端由于 Vite/Webpack 的编译时限制，无法做到运行时自动扫描。但可以用 **Vite 的 `import.meta.glob`** 实现编译时自动发现： + +```typescript +// src/plugins/registry.ts + +import { DataSourcePluginModule } from "./types"; + +// Vite 编译时自动扫描 src/plugins/*/index.ts +// 返回 { "./superset/index.ts": () => import(...), "./metabase/index.ts": () => import(...) } +const pluginModules = import.meta.glob<{ default: DataSourcePluginModule }>( + "./*/index.ts" +); + +// 提取插件 ID → 懒加载函数的映射 +const pluginLoaders: Record Promise> = {}; +for (const [path, loader] of Object.entries(pluginModules)) { + // "./superset/index.ts" → "superset" + const match = path.match(/^\.\/([^/]+)\/index\.ts$/); + if (match) { + const pluginId = match[1]; + pluginLoaders[pluginId] = () => loader().then((m) => m.default); + } +} + +export async function loadEnabledPlugins( + enabledPluginIds: string[] +): Promise { + const modules: DataSourcePluginModule[] = []; + for (const id of enabledPluginIds) { + const loader = pluginLoaders[id]; + if (loader) { + try { + modules.push(await loader()); + } catch (e) { + console.warn(`Failed to load plugin: ${id}`, e); + } + } + } + return modules; +} +``` + +这样前端也做到了"创建 `src/plugins/grafana/index.ts` 即自动纳入编译"，不需要手动维护 `pluginLoaders` 映射表。 + +> **后端自动扫描 + 前端 `import.meta.glob` = 全栈零注册新增插件。** + +#### 4.4.8 与 AuthProvider 注册机制的对比 + +| 维度 | DataSourcePlugin | AuthProvider | +|------|-----------------|-------------| +| 协作模式 | 并行（所有插件同时存在） | 单选（同一时间只有一个主 Provider） | +| 注册方式 | `plugins/` 目录自动扫描 | `auth_providers/` 目录自动扫描 | +| 激活方式 | 所有已发现的插件同时启用 | `AUTH_PROVIDER` 环境变量**单选**激活一个 | +| 新增方式 | 在 `plugins/` 下创建目录即可 | 在 `auth_providers/` 下创建 `.py` 即可 | +| 安全控制 | `PLUGIN_BLOCKLIST` 黑名单 | 仅被选中的 Provider 执行 `on_configure()` | + +**统一的设计哲学**：发现与激活分离 — 两者都通过目录扫描自动发现，但激活策略不同（插件全量启用，Provider 单选启用）。新增组件时核心代码零修改。 + +### 4.5 插件数据写入工具 + +插件从外部系统拉取到数据后，通过 `PluginDataWriter` 写入 Workspace： + +```python +# py-src/data_formulator/plugins/data_writer.py + +import logging +import pandas as pd +import pyarrow as pa +from typing import Any, Optional + +from data_formulator.auth import get_identity_id +from data_formulator.workspace_factory import get_workspace +from data_formulator.datalake.parquet_utils import sanitize_table_name + +logger = logging.getLogger(__name__) + + +class PluginDataWriter: + """插件专用的数据写入工具。""" + + def __init__(self, plugin_id: str): + self.plugin_id = plugin_id + + def _get_workspace(self): + return get_workspace(get_identity_id()) + + def write_dataframe( + self, + df: pd.DataFrame, + table_name: str, + *, + overwrite: bool = True, + source_metadata: Optional[dict[str, Any]] = None, + ) -> dict[str, Any]: + """将 DataFrame 写入当前用户的 Workspace。""" + workspace = self._get_workspace() + base_name = sanitize_table_name(table_name) + final_name = base_name + is_renamed = False + + if not overwrite: + counter = 1 + existing = set(workspace.list_tables()) + while final_name in existing: + final_name = f"{base_name}_{counter}" + counter += 1 + is_renamed = True + + loader_metadata = { + "loader_type": f"plugin:{self.plugin_id}", + **(source_metadata or {}), + } + + meta = workspace.write_parquet(df, final_name, loader_metadata=loader_metadata) + + logger.info( + "Plugin '%s' wrote '%s': %d rows, %d cols", + self.plugin_id, final_name, len(df), len(df.columns), + ) + + return { + "table_name": meta.name, + "row_count": meta.row_count, + "columns": [c.name for c in (meta.columns or [])], + "is_renamed": is_renamed, + } + + def write_arrow( + self, + table: pa.Table, + table_name: str, + *, + overwrite: bool = True, + source_metadata: Optional[dict[str, Any]] = None, + ) -> dict[str, Any]: + """将 Arrow Table 写入 Workspace（跳过 pandas 转换，更高效）。""" + workspace = self._get_workspace() + base_name = sanitize_table_name(table_name) + final_name = base_name + is_renamed = False + + if not overwrite: + counter = 1 + existing = set(workspace.list_tables()) + while final_name in existing: + final_name = f"{base_name}_{counter}" + counter += 1 + is_renamed = True + + loader_metadata = { + "loader_type": f"plugin:{self.plugin_id}", + **(source_metadata or {}), + } + + meta = workspace.write_parquet_from_arrow(table, final_name, loader_metadata=loader_metadata) + + return { + "table_name": meta.name, + "row_count": meta.row_count, + "columns": [c.name for c in (meta.columns or [])], + "is_renamed": is_renamed, + } +``` + +### 4.6 前端插件接口 + +```typescript +// src/plugins/types.ts + +export interface PluginManifest { + id: string; + name: string; + icon: string; + description: string; + authModes: Array<"sso" | "jwt" | "password" | "api_key" | "none">; + capabilities: string[]; +} + +// PluginPanelProps、DataProvenance、DataSourcePluginModule 的完整定义 +// 见 1-data-source-plugin-architecture.md § 7.1 +// +// 要点： +// - 前端组件不接收 ssoToken prop。SSO token 由插件后端通过 +// auth.get_sso_token() 从 Flask session 获取，前端无需感知。 +// - onDataLoaded 回调必须包含 DataProvenance（数据溯源）， +// 以支持"用同样的参数刷新"和 UI 显示数据来源。 +// - onPreviewLoaded（可选）支持"先预览再加载"的交互。 +// - LoginComponent 不接收 ssoToken，认证流程走插件自身后端。 +``` + +前端插件注册使用 `import.meta.glob` 自动扫描（详见 4.4.7 节），此处不再重复。新增插件只需在 `src/plugins/` 下创建子目录并导出 `index.ts`，无需手动维护注册表。 + +--- + +## 5. Layer 3：凭证保险箱 (CredentialVault) + +### 5.1 设计思路 + +用户连接未接 SSO 的外部系统时，需要输入该系统的账号密码。这些凭证应该： + +| 需求 | 现状 (0.7) | 目标 | +|------|-----------|------| +| 持久化 | 浏览器 IndexedDB (redux-persist)，换浏览器丢失 | 服务端加密存储，跟随用户身份 | +| 安全性 | 前端明文存储 | 服务端 Fernet 对称加密 | +| 跨设备 | 不支持 | SSO 登录后自动可用 | +| 按用户隔离 | 基于 browser UUID | 基于 SSO user_id 或 browser UUID | + +### 5.2 CredentialVault 接口 + +```python +# py-src/data_formulator/credential_vault/base.py + +from abc import ABC, abstractmethod +from typing import Optional + + +class CredentialVault(ABC): + """凭证保险箱抽象接口。 + + 按 (user_identity, source_key) 二元组存取加密凭证。 + - user_identity: 来自 auth.get_identity_id()，如 "user:alice@corp.com" + - source_key: 外部系统标识，如 "superset"、"metabase-prod" + """ + + @abstractmethod + def store(self, user_id: str, source_key: str, credentials: dict) -> None: + """存储凭证。已存在则覆盖。""" + ... + + @abstractmethod + def retrieve(self, user_id: str, source_key: str) -> Optional[dict]: + """取出凭证。不存在返回 None。""" + ... + + @abstractmethod + def delete(self, user_id: str, source_key: str) -> None: + """删除凭证。""" + ... + + @abstractmethod + def list_sources(self, user_id: str) -> list[str]: + """列出该用户所有已存储凭证的 source_key。""" + ... +``` + +### 5.3 本地加密实现 + +```python +# py-src/data_formulator/credential_vault/local_vault.py + +import json +import logging +import sqlite3 +from pathlib import Path +from typing import Optional + +from cryptography.fernet import Fernet + +from .base import CredentialVault + +logger = logging.getLogger(__name__) + + +class LocalCredentialVault(CredentialVault): + """基于 SQLite + Fernet 的本地加密凭证存储。 + + 存储位置: DATA_FORMULATOR_HOME/credentials.db + 加密密钥: CREDENTIAL_VAULT_KEY 环境变量 (Fernet key) + + 生成密钥: + python -c "from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())" + """ + + def __init__(self, db_path: str | Path, encryption_key: str): + self._db_path = str(db_path) + self._fernet = Fernet(encryption_key.encode() if isinstance(encryption_key, str) else encryption_key) + self._init_db() + + def _init_db(self): + with sqlite3.connect(self._db_path) as conn: + conn.execute(""" + CREATE TABLE IF NOT EXISTS credentials ( + user_id TEXT NOT NULL, + source_key TEXT NOT NULL, + encrypted_data BLOB NOT NULL, + updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + PRIMARY KEY (user_id, source_key) + ) + """) + + def store(self, user_id: str, source_key: str, credentials: dict) -> None: + encrypted = self._fernet.encrypt(json.dumps(credentials).encode("utf-8")) + with sqlite3.connect(self._db_path) as conn: + conn.execute( + "INSERT OR REPLACE INTO credentials (user_id, source_key, encrypted_data, updated_at) " + "VALUES (?, ?, ?, CURRENT_TIMESTAMP)", + (user_id, source_key, encrypted), + ) + logger.debug("Stored credentials for %s / %s", user_id[:16], source_key) + + def retrieve(self, user_id: str, source_key: str) -> Optional[dict]: + with sqlite3.connect(self._db_path) as conn: + row = conn.execute( + "SELECT encrypted_data FROM credentials WHERE user_id = ? AND source_key = ?", + (user_id, source_key), + ).fetchone() + if not row: + return None + try: + decrypted = self._fernet.decrypt(row[0]) + return json.loads(decrypted.decode("utf-8")) + except Exception as e: + logger.warning("Failed to decrypt credentials for %s / %s: %s", user_id[:16], source_key, e) + return None + + def delete(self, user_id: str, source_key: str) -> None: + with sqlite3.connect(self._db_path) as conn: + conn.execute( + "DELETE FROM credentials WHERE user_id = ? AND source_key = ?", + (user_id, source_key), + ) + + def list_sources(self, user_id: str) -> list[str]: + with sqlite3.connect(self._db_path) as conn: + rows = conn.execute( + "SELECT source_key FROM credentials WHERE user_id = ?", + (user_id,), + ).fetchall() + return [r[0] for r in rows] +``` + +### 5.4 Vault 工厂 + +```python +# py-src/data_formulator/credential_vault/__init__.py + +import os +import logging +from typing import Optional + +from .base import CredentialVault + +logger = logging.getLogger(__name__) + +_vault: Optional[CredentialVault] = None +_initialized = False + + +def get_credential_vault() -> Optional[CredentialVault]: + """获取全局 CredentialVault 实例。 + + 返回 None 表示 Vault 未配置（CREDENTIAL_VAULT_KEY 未设置）。 + 此时插件应回退到仅 Session 级别的凭证存储。 + """ + global _vault, _initialized + if _initialized: + return _vault + + _initialized = True + key = os.environ.get("CREDENTIAL_VAULT_KEY", "").strip() + if not key: + logger.info("Credential vault not configured (CREDENTIAL_VAULT_KEY not set)") + return None + + vault_type = os.environ.get("CREDENTIAL_VAULT", "local").strip().lower() + + if vault_type == "local": + from data_formulator.credential_vault.local_vault import LocalCredentialVault + from data_formulator.datalake.workspace import get_data_formulator_home + + db_path = get_data_formulator_home() / "credentials.db" + db_path.parent.mkdir(parents=True, exist_ok=True) + _vault = LocalCredentialVault(db_path, key) + logger.info("Credential vault initialized: local (%s)", db_path) + else: + logger.warning("Unknown credential vault type: %s", vault_type) + + return _vault +``` + +### 5.5 凭证管理 API + +```python +# py-src/data_formulator/credential_routes.py + +import flask +from flask import Blueprint, request, jsonify +from data_formulator.auth import get_identity_id +from data_formulator.credential_vault import get_credential_vault + +credential_bp = Blueprint("credentials", __name__, url_prefix="/api/credentials") + + +@credential_bp.route("/list", methods=["GET"]) +def list_credentials(): + """列出当前用户已存储凭证的外部系统。不返回凭证内容。""" + vault = get_credential_vault() + if not vault: + return jsonify({"sources": []}) + + identity = get_identity_id() + sources = vault.list_sources(identity) + return jsonify({"sources": sources}) + + +@credential_bp.route("/store", methods=["POST"]) +def store_credential(): + """存储或更新凭证。""" + vault = get_credential_vault() + if not vault: + return jsonify({"error": "Credential vault not configured"}), 503 + + data = request.get_json() + source_key = data.get("source_key") + credentials = data.get("credentials") + if not source_key or not credentials: + return jsonify({"error": "source_key and credentials required"}), 400 + + identity = get_identity_id() + vault.store(identity, source_key, credentials) + return jsonify({"status": "stored", "source_key": source_key}) + + +@credential_bp.route("/delete", methods=["POST"]) +def delete_credential(): + """删除凭证。""" + vault = get_credential_vault() + if not vault: + return jsonify({"error": "Credential vault not configured"}), 503 + + data = request.get_json() + source_key = data.get("source_key") + if not source_key: + return jsonify({"error": "source_key required"}), 400 + + identity = get_identity_id() + vault.delete(identity, source_key) + return jsonify({"status": "deleted", "source_key": source_key}) +``` + +--- + +## 6. SSO Token 透传机制 + +### 6.1 原理 + +当 Data Formulator 和外部 BI 系统（如 Superset）共用同一个 OIDC IdP 时，用户登录 DF 获得的 `access_token` 可以**直接用于调用外部系统的 API**，前提是外部系统信任同一个 Issuer。 + +``` + 同一个 IdP (Keycloak / Okta / ...) + │ + ┌──────────┼──────────┐ + │ │ + ▼ ▼ + Data Formulator Superset + (client_id: df) (client_id: superset) + │ │ + │ 用户的 access_token │ + │ (audience: df) │ + │ │ + └──────── ? ──────────┘ + +两种方式让 Superset 接受 DF 的 token: + +方式 A: Token Exchange (标准, 推荐) + DF 后端 → IdP token exchange endpoint + → 用 df 的 token 换取 superset audience 的 token + → 用新 token 调用 Superset API + +方式 B: 共享 Audience (简单, 适合内部系统) + IdP 中将 df 和 superset 配置为同一个 audience + → DF 的 token 直接被 Superset 接受 +``` + +### 6.2 插件中的 SSO 透传实现 + +```python +# 在 Superset 插件中 + +class SupersetPlugin(DataSourcePlugin): + + def supports_sso_passthrough(self) -> bool: + return bool(os.environ.get("PLG_SUPERSET_SSO", "").lower() == "true") + + def _get_superset_token_via_sso(self, sso_token: str) -> Optional[str]: + """用 DF 用户的 SSO token 获取 Superset 的 access token。""" + superset_url = os.environ["PLG_SUPERSET_URL"] + + # 方式 A: 如果 Superset 支持 OAuth token introspection / exchange + # 用 SSO token 调用 Superset 的 OAuth 端点换取 Superset session + try: + resp = requests.post( + f"{superset_url}/api/v1/security/login", + json={"token": sso_token, "provider": "oidc"}, + timeout=10, + ) + if resp.status_code == 200: + return resp.json().get("access_token") + except Exception as e: + logger.warning("SSO passthrough to Superset failed: %s", e) + + # 方式 B: 直接用 SSO token 作为 Bearer (如果 Superset 配置了同一 IdP) + try: + resp = requests.get( + f"{superset_url}/api/v1/me/", + headers={"Authorization": f"Bearer {sso_token}"}, + timeout=10, + ) + if resp.status_code == 200: + return sso_token # token 直接可用 + except Exception: + pass + + return None +``` + +### 6.3 认证模式自动协商 + +``` +用户打开插件面板 + │ + ▼ +前端: POST /api/plugins/superset/auth/status + │ + ▼ +后端检查: + ├─ Session 中已有有效 token? → {"authenticated": true} + │ + ├─ SSO token 可用 + 插件支持透传? + │ → 尝试透传 → 成功 → {"authenticated": true, "mode": "sso"} + │ → 失败 → 继续检查 + │ + ├─ Credential Vault 中有已存凭证? + │ → 尝试登录 → 成功 → {"authenticated": true, "mode": "vault"} + │ → 失败 (密码已改) → {"authenticated": false, "vault_stale": true} + │ + └─ 以上均无 → {"authenticated": false, "available_modes": ["password", "api_key"]} + +前端根据响应: + ├─ authenticated=true → 直接显示数据目录 + ├─ authenticated=false + SSO 可用 → "正在通过 SSO 登录..." (自动重试) + └─ authenticated=false + 需手动 → 显示登录表单 +``` + +--- + +## 7. 现有 ExternalDataLoader 的演进路径 + +### 7.1 短期：两套机制并行 + +``` +数据源类型 │ 使用机制 │ 原因 +───────────────────┼───────────────────────┼───────────────────────── +MySQL/PG/MSSQL │ ExternalDataLoader │ 标准数据库，通用表单即可 +MongoDB/BigQuery │ ExternalDataLoader │ 同上 +S3/Azure Blob │ ExternalDataLoader │ 文件存储 +───────────────────┼───────────────────────┼───────────────────────── +Superset │ DataSourcePlugin │ 有认证/目录/筛选/RBAC +Metabase │ DataSourcePlugin │ 同上 +Power BI │ DataSourcePlugin │ 同上 +``` + +**判断标准**：如果只需要 `连接参数 → list_tables → fetch_data`，用 DataLoader；如果需要自己的认证流程、数据浏览 UI、权限模型，用 Plugin。 + +### 7.2 中期：DataLoader 接入 CredentialVault + +现有 DataLoader 的连接参数（数据库密码等）可以选择性地存入 CredentialVault，而不是留在浏览器 IndexedDB 中： + +```python +# tables_routes.py 增强 + +@tables_bp.route("/data-loader/connect", methods=["POST"]) +def connect_data_loader(): + data = request.get_json() + data_loader_type = data["data_loader_type"] + data_loader_params = data["data_loader_params"] + remember = data.get("remember_credentials", False) + + # 正常连接逻辑... + loader = DATA_LOADERS[data_loader_type](data_loader_params) + tables = loader.list_tables() + + # 如果用户选择"记住凭证"，存入 Vault + if remember: + vault = get_credential_vault() + if vault: + identity = get_identity_id() + vault.store(identity, f"dataloader:{data_loader_type}", data_loader_params) + + return jsonify({"tables": tables}) +``` + +### 7.3 长期：统一为插件体系（可选） + +如果未来需要给数据库连接器也加上专用 UI（如 schema 浏览、SQL 编辑器），可以将其包装为 Plugin。但这不是必须的 — 现有的通用表单 UI 对数据库连接器已经够用。 + +``` +未来可能的架构: + +DataSourcePlugin (统一基类) +├── BI Plugin (Superset, Metabase, ...) +│ └── 自带完整 UI + 认证流程 +├── Database Plugin (PG, MySQL, ...) ← 可选迁移 +│ └── 复用 DBManagerPane 的通用表单 +└── Storage Plugin (S3, Azure Blob, ...) ← 可选迁移 + └── 复用 DBManagerPane 的通用表单 + +ExternalDataLoader 可以作为 Database/Storage Plugin 的内部实现被保留， +外面包一层 Plugin 壳即可。 +``` + +--- + +## 8. 身份管理：SSO 时代的简化 + +### 8.1 有 SSO vs 无 SSO 的身份模型对比 + +``` +┌─────────────────────────────────────────────────────────────┐ +│ 无 SSO (现有模式) │ +│ │ +│ 电脑A: browser:aaa-111 │ +│ 电脑B: browser:bbb-222 ← 完全不同的身份，数据不通 │ +│ │ +│ 需要 IdentityStore + 身份合并 才能跨设备 (复杂) │ +└─────────────────────────────────────────────────────────────┘ + +┌─────────────────────────────────────────────────────────────┐ +│ 有 SSO (新增) │ +│ │ +│ 电脑A: user:alice@corp.com (SSO 登录) │ +│ 电脑B: user:alice@corp.com (SSO 登录) ← 同一身份! │ +│ │ +│ 天然跨设备，无需身份合并。Workspace 按 user:xxx 隔离即可。 │ +│ CredentialVault 也按 user:xxx 存取，自动跨设备可用。 │ +└─────────────────────────────────────────────────────────────┘ +``` + +**SSO 从根本上解决了身份漫游问题**。原有文档中复杂的 `IdentityStore` + 身份链接 + 合并对话框，在 SSO 模式下完全不需要。 + +### 8.2 身份管理策略 + +| 部署模式 | 身份来源 | Workspace 键 | Credential Vault 键 | 跨设备 | +|---------|---------|-------------|--------------------|----| +| 本地匿名 | 浏览器 UUID | `browser:xxx` | `browser:xxx` (不可靠) | 不支持 | +| SSO 登录 | OIDC sub claim | `user:alice@corp.com` | `user:alice@corp.com` | 自动支持 | +| Azure EasyAuth | Azure Principal | `user:guid` | `user:guid` | 自动支持 | + +### 8.3 身份迁移（匿名 → 认证用户） + +当匿名用户（`browser:xxx`）首次通过 SSO 登录后，身份变为 `user:xxx`，两者分属不同 Workspace。系统自动检测此转换并提示用户选择： + +**检测机制**（前端 `App.tsx`）： +- 应用启动时，redux-persist 恢复上次 `identity`（`browser:uuid`） +- Auth useEffect 解析出新身份（`user:sub`） +- 如果 `旧.type === 'browser'` 且 `新.type === 'user'` → 触发迁移流程 + +**迁移流程**： +1. 前端调用 `GET /api/sessions/list?source_identity=browser:` 检查旧匿名身份是否有 workspace 数据 +2. 如果有 → 弹出 `IdentityMigrationDialog`，提供两个选择： + - **导入数据**：调用 `POST /api/sessions/migrate { source_identity: "browser:" }`，后端将旧身份的 workspace 文件夹复制到新身份下（不删除源数据，安全、幂等） + - **全新开始**：直接清空前端持久化状态 +3. 如果无 → 静默清空前端持久化状态，无弹窗 +4. 无论哪种选择，最后都执行 `persistor.purge()` 清除 localforage 中的旧 Redux 状态 + +**安全约束**： +- `source_identity` 参数仅接受 `browser:` 前缀，且调用者必须是 `user:` 身份 +- 迁移只做复制（`shutil.copytree`），不删除源数据 +- Ephemeral 模式下跳过（无服务端数据可迁移） + +--- + +## 9. 配置参考 + +### 9.1 完整 .env 配置示例 + +```bash +# ============================================================== +# Data Formulator — 完整配置示例 +# ============================================================== + +# -------------------------------------------------------------- +# 基础设置 +# -------------------------------------------------------------- +LOG_LEVEL=INFO +SANDBOX=local +DATA_FORMULATOR_HOME=/data/data-formulator + +# -------------------------------------------------------------- +# 认证设置（主认证 + 可选匿名回退） +# -------------------------------------------------------------- +# 主认证模式（选一种）： +# anonymous（默认）| oidc / oauth2 | github | azure_easyauth | proxy_header | saml | ldap | cas +# 注：oidc 和 oauth2 是同一个 Provider 的别名，适用于任何 OAuth2/OIDC + JWT + JWKS 的 IdP +AUTH_PROVIDER=oidc + +# 是否允许匿名访问（默认 true，无需配置） +# 仅在需要强制登录时设为 false： +# ALLOW_ANONYMOUS=false + +# ─── GitHub OAuth 配置 ─── +# GITHUB_CLIENT_ID=xxx +# GITHUB_CLIENT_SECRET=xxx + +# ─── OIDC / OAuth2 配置 ─── +# 模式 A（自动发现）：只需 OIDC_ISSUER_URL + OIDC_CLIENT_ID +# 模式 B（手动端点）：额外配置 OIDC_AUTHORIZE_URL / OIDC_TOKEN_URL 等 +# 详见 § 3.4「对接 OIDC/OAuth2 Provider 的 IdP 要求」 +OIDC_ISSUER_URL=https://keycloak.example.com/realms/my-org +OIDC_CLIENT_ID=data-formulator +# OIDC_AUTHORIZE_URL=https://sso.example.com/oauth2/authorize # 模式 B +# OIDC_TOKEN_URL=https://sso.example.com/oauth2/token # 模式 B +# OIDC_USERINFO_URL=https://sso.example.com/oauth2/userinfo # 推荐 +# OIDC_JWKS_URL=https://sso.example.com/oauth2/jwks # 可选 +# OIDC_CLIENT_SECRET=xxx # 机密客户端 + +# -------------------------------------------------------------- +# 凭证保险箱 +# -------------------------------------------------------------- +# 存储类型: local (默认) +CREDENTIAL_VAULT=local +# 加密密钥 (Fernet) +# 生成: python -c "from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())" +CREDENTIAL_VAULT_KEY=your-fernet-key-here + +# -------------------------------------------------------------- +# LLM 模型配置 +# -------------------------------------------------------------- +DEEPSEEK_ENABLED=true +DEEPSEEK_ENDPOINT=openai +DEEPSEEK_API_KEY=sk-xxx +DEEPSEEK_API_BASE=https://api.deepseek.com +DEEPSEEK_MODELS=deepseek-chat + +QWEN_ENABLED=true +QWEN_ENDPOINT=openai +QWEN_API_KEY=sk-xxx +QWEN_API_BASE=https://dashscope.aliyuncs.com/compatible-mode/v1 +QWEN_MODELS=qwen3-omni-flash + +# -------------------------------------------------------------- +# 数据源插件 +# -------------------------------------------------------------- +# Superset (配置了 PLG_SUPERSET_URL 即自动启用) +PLG_SUPERSET_URL=http://superset.example.com:8088 +PLG_SUPERSET_SSO=true # 启用 SSO token 透传到 Superset +# PLG_SUPERSET_TIMEOUT=30 # API 超时秒数 (可选) + +# Metabase (配置了 PLG_METABASE_URL 即自动启用) +# PLG_METABASE_URL=http://metabase.example.com:3000 + +# Power BI (配置了 PLG_POWERBI_TENANT_ID 即自动启用) +# PLG_POWERBI_TENANT_ID=your-tenant-id +# PLG_POWERBI_CLIENT_ID=your-client-id + +# -------------------------------------------------------------- +# Workspace 存储 +# -------------------------------------------------------------- +# WORKSPACE_BACKEND=local +# AZURE_BLOB_CONNECTION_STRING= +# AZURE_BLOB_ACCOUNT_URL= +``` + +### 9.2 免费 IdP 方案（生产可用） + +如果组织没有 Google Workspace、Microsoft 365 或 AWS 等企业订阅，以下免费方案可用于生产环境： + +| 方案 | 费用 | 适用场景 | 特点 | +|------|------|---------|------| +| **Keycloak** | 免费（自托管） | 中大型企业 | 功能最全，支持 OIDC/SAML/LDAP，需自行运维 | +| **Authelia** | 免费（自托管） | 个人/小团队 | 轻量级，与反向代理集成好，配置简单 | +| **Authentik** | 免费（自托管） | 中小团队 | 界面友好，功能丰富，支持 OIDC/SAML/LDAP | +| **Auth0 免费版** | 免费（7,500用户限制） | 小团队/初创公司 | 托管服务，无需运维，有用户数量限制 | + +#### Keycloak 配置示例 + +```bash +# 使用 Docker 运行 Keycloak +docker run -p 8080:8080 \ + -e KEYCLOAK_ADMIN=admin \ + -e KEYCLOAK_ADMIN_PASSWORD=admin \ + quay.io/keycloak/keycloak:22.0 start-dev + +# Data Formulator 配置（仅需后端配置，前端自动获取） +OIDC_ISSUER_URL=http://localhost:8080/realms/master +OIDC_CLIENT_ID=df-client +# Keycloak 中创建 client 时获取 +OIDC_CLIENT_SECRET=xxx +``` + +#### Authelia 配置示例 + +```bash +# docker-compose.yml 示例 +version: '3' +services: + authelia: + image: authelia/authelia:latest + ports: + - "9091:9091" + volumes: + - ./authelia:/config + +# Data Formulator 使用反向代理头认证 +AUTH_PROVIDER=proxy_header +PROXY_HEADER_USER=Remote-User +PROXY_HEADER_EMAIL=Remote-Email +PROXY_TRUSTED_IPS=127.0.0.1,172.16.0.0/12 +``` + +#### Auth0 免费版配置示例 + +```bash +# 在 https://auth0.com/ 注册免费账号，创建 Application +# 仅需后端配置，前端自动获取 +OIDC_ISSUER_URL=https://your-tenant.auth0.com/ +OIDC_CLIENT_ID=your-client-id +OIDC_CLIENT_SECRET=your-client-secret +``` + +**注意**：Google、Microsoft、Amazon 的 OIDC 服务都需要付费的企业订阅（Workspace、M365、AWS），个人账号无法作为 IdP 使用。 + +#### 社交登录集成（无需企业账号） + +如果不想部署自托管 IdP，可以使用社交登录平台。这些平台**不需要企业账号**，个人开发者账号即可免费使用： + +| 平台 | 需要企业账号？ | 费用 | 用户群体 | 推荐度 | +|------|--------------|------|---------|--------| +| **GitHub** | ❌ 不需要 | 免费 | 开发者 | ⭐⭐⭐⭐⭐ | +| **Google** | ❌ 不需要 | 免费 | 大众用户 | ⭐⭐⭐⭐ | +| **Microsoft** | ❌ 不需要 | 免费 | 企业/个人 | ⭐⭐⭐ | + +**GitHub OAuth 配置示例**（最简单）： + +```bash +# 1. 在 GitHub 注册 OAuth App +# 访问 https://github.com/settings/developers +# 点击 "New OAuth App" +# 填写： +# - Application name: Data Formulator +# - Homepage URL: https://your-domain.com +# - Authorization callback URL: https://your-domain.com/api/auth/callback + +# 2. Data Formulator 配置（仅需后端配置，前端自动获取） +AUTH_PROVIDER=github +GITHUB_CLIENT_ID=your-github-client-id +GITHUB_CLIENT_SECRET=your-github-client-secret +``` + +**简化配置模式**（主认证 + 匿名回退）： + +```bash +# 模式 1：仅匿名（本地个人使用） +AUTH_PROVIDER=anonymous + +# 模式 2：GitHub OAuth + 匿名回退 +AUTH_PROVIDER=github +GITHUB_CLIENT_ID=xxx +GITHUB_CLIENT_SECRET=xxx +# ALLOW_ANONYMOUS 默认 true，匿名用户可正常使用 + +# 模式 3：企业 SSO + 匿名回退 +AUTH_PROVIDER=oidc +OIDC_ISSUER_URL=https://keycloak.company.com/realms/main +OIDC_CLIENT_ID=data-formulator +# ALLOW_ANONYMOUS 默认 true，匿名用户可正常使用 +``` + +**说明**： +- 主认证方式（github/oidc）提供完整功能（数据同步、跨设备、SSO 透传） +- 匿名模式作为回退，方便临时使用或快速体验 +- 如需强制登录，设置 `ALLOW_ANONYMOUS=false` + +#### 一键 Docker 部署方案 + +为降低配置门槛，提供开箱即用的 Docker Compose 配置： + +**方案 A：Keycloak + Data Formulator（完整 SSO）** + +```yaml +# docker-compose.sso.yml +version: '3.8' + +services: + keycloak: + image: quay.io/keycloak/keycloak:22.0 + environment: + KEYCLOAK_ADMIN: admin + KEYCLOAK_ADMIN_PASSWORD: admin + KC_DB: postgres + KC_DB_URL: jdbc:postgresql://postgres:5432/keycloak + KC_DB_USERNAME: keycloak + KC_DB_PASSWORD: keycloak + KC_HOSTNAME: localhost + ports: + - "8080:8080" + command: start-dev + depends_on: + - postgres + + postgres: + image: postgres:15 + environment: + POSTGRES_DB: keycloak + POSTGRES_USER: keycloak + POSTGRES_PASSWORD: keycloak + volumes: + - postgres_data:/var/lib/postgresql/data + + data-formulator: + image: data-formulator:latest + environment: + # 仅需后端配置，前端运行时自动获取 + AUTH_PROVIDER: oidc + OIDC_ISSUER_URL: http://keycloak:8080/realms/master + OIDC_CLIENT_ID: df-client + OIDC_CLIENT_SECRET: ${OIDC_CLIENT_SECRET} + ALLOW_ANONYMOUS: "true" + CREDENTIAL_VAULT: local + CREDENTIAL_VAULT_KEY: ${VAULT_KEY} + ports: + - "5000:5000" + depends_on: + - keycloak + +volumes: + postgres_data: +``` + +启动命令： +```bash +# 1. 生成加密密钥 +export VAULT_KEY=$(python -c "from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())") + +# 2. 在 Keycloak 中创建 client，获取 secret +# 访问 http://localhost:8080 (admin/admin) +# 创建 realm → 创建 client → 获取 secret + +# 3. 启动服务 +export OIDC_CLIENT_SECRET=your-client-secret +docker-compose -f docker-compose.sso.yml up +``` + +**方案 B：Authelia + Data Formulator（轻量级）** + +```yaml +# docker-compose.authelia.yml +version: '3.8' + +services: + authelia: + image: authelia/authelia:latest + ports: + - "9091:9091" + volumes: + - ./authelia:/config + environment: + AUTHELIA_JWT_SECRET_FILE: /config/jwt_secret + AUTHELIA_SESSION_SECRET_FILE: /config/session_secret + + redis: + image: redis:alpine + + data-formulator: + image: data-formulator:latest + environment: + AUTH_PROVIDER: proxy_header + PROXY_HEADER_USER: Remote-User + PROXY_HEADER_EMAIL: Remote-Email + PROXY_TRUSTED_IPS: 172.16.0.0/12 + ports: + - "5000:5000" +``` + +**方案 C：仅 Data Formulator（匿名模式，零配置）** + +```yaml +# docker-compose.minimal.yml +version: '3.8' + +services: + data-formulator: + image: data-formulator:latest + environment: + # 无 SSO 配置，自动使用匿名模式 + DEEPSEEK_ENABLED: "true" + DEEPSEEK_API_KEY: ${DEEPSEEK_API_KEY} + ports: + - "5000:5000" + volumes: + - df_data:/data/data-formulator + +volumes: + df_data: +``` + +启动命令： +```bash +export DEEPSEEK_API_KEY=sk-xxx +docker-compose -f docker-compose.minimal.yml up +``` + +### 9.3 最小配置（本地匿名使用） + +```bash +# 最小配置 — 本地使用，无 SSO，无插件 +DEEPSEEK_ENABLED=true +DEEPSEEK_ENDPOINT=openai +DEEPSEEK_API_KEY=sk-xxx +DEEPSEEK_API_BASE=https://api.deepseek.com +DEEPSEEK_MODELS=deepseek-chat +``` + +### 9.4 团队部署配置（SSO + Superset） + +```bash +# 团队部署 — SSO + Superset（精简版） +AUTH_PROVIDER=oidc +OIDC_ISSUER_URL=https://keycloak.internal:8443/realms/team +OIDC_CLIENT_ID=data-formulator + +PLG_SUPERSET_URL=http://superset.internal:8088 +PLG_SUPERSET_SSO=true + +DEEPSEEK_ENABLED=true +DEEPSEEK_API_KEY=sk-xxx +``` + +--- + +## 10. 目录结构 + +### 10.1 后端新增文件 + +``` +py-src/data_formulator/ +├── auth.py # 重构：Provider 自动发现 + /api/auth/info 委托 +├── auth_providers/ # 新增：认证提供者（自动发现） +│ ├── __init__.py # Provider 自动扫描 + get_provider_class() API +│ ├── base.py # AuthProvider（含 get_auth_info()）/ AuthResult 基类 +│ ├── azure_easyauth.py # Azure EasyAuth (迁移现有逻辑) +│ ├── oidc.py # 通用 OIDC Provider ★（仅需 2 个环境变量） +│ └── github_oauth.py # GitHub OAuth Provider +├── auth_gateways/ # 新增：有状态协议的登录网关 +│ ├── github_gateway.py # GitHub OAuth 授权码交换 +│ └── logout.py # 通用登出 +├── credential_vault/ # 新增：凭证保险箱 +│ ├── __init__.py # get_credential_vault() 工厂 +│ ├── base.py # CredentialVault 抽象接口 +│ └── local_vault.py # SQLite + Fernet 加密实现 +├── credential_routes.py # 新增：凭证管理 API +├── plugins/ # 新增：插件系统 +│ ├── __init__.py # 插件注册中心 (discover_and_register) +│ ├── base.py # DataSourcePlugin 基类 +│ ├── data_writer.py # PluginDataWriter 写入工具 +│ ├── superset/ # Superset 插件 +│ │ ├── __init__.py # SupersetPlugin 实现 +│ │ ├── superset_client.py # Superset REST API 封装 +│ │ ├── auth_bridge.py # JWT/SSO 认证桥接 +│ │ ├── catalog.py # 带缓存的数据目录 +│ │ └── routes/ +│ │ ├── __init__.py +│ │ ├── auth.py # /api/plugins/superset/auth/* +│ │ ├── catalog.py # /api/plugins/superset/catalog/* +│ │ └── data.py # /api/plugins/superset/data/* +│ └── metabase/ # Metabase 插件 (未来) +│ └── ... +├── data_loader/ # 现有 ExternalDataLoader 体系 (不变) +│ └── ... +└── app.py # 修改：集成 init_auth() + 插件发现 +``` + +### 10.2 前端新增文件 + +``` +src/ +├── app/ +│ ├── oidcConfig.ts # 新增：OIDC 配置和 UserManager +│ ├── OidcCallback.tsx # 新增：OIDC 回调页面 +│ ├── identity.ts # 修改：增加 setBrowserId() +│ ├── utils.tsx # 修改：fetchWithIdentity 携带 Bearer token +│ ├── dfSlice.tsx # 修改：ServerConfig 增加 plugins 字段 +│ └── App.tsx # 修改：OIDC 初始化 + 登录UI +├── plugins/ # 新增：插件前端 +│ ├── types.ts # PluginManifest, PluginPanelProps 类型 +│ ├── registry.ts # 插件动态加载 +│ ├── PluginHost.tsx # 插件容器组件 +│ ├── CredentialManager.tsx # 凭证管理 UI +│ ├── superset/ # Superset 前端插件 +│ │ ├── index.ts +│ │ ├── SupersetPanel.tsx +│ │ ├── SupersetCatalog.tsx +│ │ ├── SupersetDashboards.tsx +│ │ ├── SupersetFilterDialog.tsx +│ │ ├── SupersetLogin.tsx +│ │ └── api.ts +│ └── metabase/ # Metabase 前端插件 (未来) +│ └── ... +└── views/ + └── UnifiedDataUploadDialog.tsx # 修改：增加 PluginHost 渲染 +``` + +### 10.3 对现有文件的改动清单 + +| 文件 | 改动类型 | 改动量 | 说明 | +|------|---------|--------|------| +| `py-src/.../auth.py` | 重构 | ~60 行 | 基于自动发现的 `init_auth()` + `/api/auth/info` 委托给 Provider 自描述 | +| `py-src/.../app.py` | 修改 | ~25 行 | 调用 `init_auth()`、`discover_and_register()`、注册 credential/identity blueprint、`app-config` 返回 plugins | +| `src/app/App.tsx` | 修改 | ~35 行 | 统一 initAuth（`/api/auth/info` 驱动，纯 action 分支）、登录/登出 UI | +| `src/app/utils.tsx` | 修改 | ~15 行 | `fetchWithIdentity` 携带 Bearer token + 401 自动重试 | +| `src/app/dfSlice.tsx` | 修改 | ~5 行 | `ServerConfig` 增加 `plugins` 字段 | +| `src/app/identity.ts` | 修改 | ~5 行 | 增加 `setBrowserId()` | +| `src/views/UnifiedDataUploadDialog.tsx` | 修改 | ~20 行 | 导入 PluginHost，渲染插件 Tab | + +--- + +## 11. 实施路径 + +### Phase 1：认证基础 (AuthProvider 链) + +**目标**：将现有 `auth.py` 重构为可插拔的 Provider 链，激活 OIDC Provider。 + +**交付物**： +- `auth_providers/__init__.py` — Provider 自动发现（`pkgutil` 扫描 + `get_provider_class()` API） +- `auth_providers/base.py` — 基类（含 `get_auth_info()` 自描述接口） +- `auth_providers/azure_easyauth.py` — 迁移现有 Azure 逻辑 +- `auth_providers/oidc.py` — 通用 OIDC 验签（仅需 `OIDC_ISSUER_URL` + `OIDC_CLIENT_ID`） +- `auth_providers/github_oauth.py` — GitHub OAuth Provider +- `auth_gateways/github_gateway.py` — GitHub 授权码交换 +- `auth.py` 重构 — 基于自动发现的 `init_auth()` + `/api/auth/info` 委托 +- `src/app/oidcConfig.ts` — 前端 OIDC 配置 +- `src/app/OidcCallback.tsx` — 回调页面 +- `src/app/LoginPanel.tsx` — 统一登录组件（由 `/api/auth/info` 驱动） +- `App.tsx` 修改 — 统一 initAuth（单端点驱动）+ 401 自动重试 +- `utils.tsx` 修改 — Bearer token + 401 重试逻辑 + +**验证标准**： +- 配置 Keycloak + OIDC 环境变量后，用户可以通过浏览器登录 +- 后端正确从 JWT 中提取 `sub` 作为 `user:xxx` 身份 +- 不配置 OIDC 时，行为与现有版本完全一致（浏览器 UUID） +- `get_sso_token()` 可以返回当前用户的 access token + +**依赖**：`pip install PyJWT cryptography`，`npm install oidc-client-ts` + +### Phase 2：插件框架 + Superset 插件 + +**目标**：建立插件框架，将 0.6 版本 Superset 集成迁移为第一个插件。 + +**交付物**： +- `plugins/__init__.py` — 插件注册中心 +- `plugins/base.py` — DataSourcePlugin 基类 +- `plugins/data_writer.py` — PluginDataWriter +- `plugins/superset/` — 完整的 Superset 插件 +- `src/plugins/` — 前端插件框架 +- `app.py` 修改 — 调用 `discover_and_register()` +- `UnifiedDataUploadDialog.tsx` 修改 — PluginHost + +**验证标准**： +- 配置 `PLG_SUPERSET_URL` 后，前端自动显示 Superset Tab +- 用户可以登录 Superset、浏览数据集、加载数据 +- SSO 模式下，用户无需再次输入 Superset 密码 +- 不配置 `PLG_SUPERSET_URL` 时，无任何影响 + +### Phase 3：凭证保险箱 + +**目标**：服务端加密凭证存储，替代浏览器 IndexedDB 的不安全存储。 + +**交付物**： +- `credential_vault/` — Vault 接口 + 本地加密实现 +- `credential_routes.py` — 凭证管理 API +- `src/plugins/CredentialManager.tsx` — 凭证管理 UI +- 插件认证路由增强 — 自动从 Vault 取凭证 + +**验证标准**： +- 凭证加密存储在服务端 SQLite +- SSO 用户换设备后，已存凭证自动可用 +- Vault 未配置时，回退到 Session 级别存储（现有行为） + +### Phase 4：第二个插件 (Metabase) + +**目标**：验证插件框架的通用性 —— 新增插件是否真的不需要修改核心代码。 + +**交付物**： +- `plugins/metabase/` — 完整的 Metabase 插件 + +**验证标准**： +- 仅新增 `plugins/metabase/` 目录和 `src/plugins/metabase/` 目录 +- **核心代码零修改**（目录自动扫描 + `import.meta.glob` 自动发现） + +### Phase 5：完善 + +- DataLoader 凭证接入 Vault（可选记住密码） +- 插件国际化 (i18n) +- 插件错误边界和降级处理 +- 管理员配置 UI +- 审计日志（谁在什么时候访问了哪些数据） +- 单元测试和集成测试 + +--- + +## 12. 安全模型 + +### 12.1 认证链路安全 + +``` +前端 OIDC PKCE 流程 (无 client secret 暴露) + │ + ▼ +IdP 签发 access_token (RS256 签名) + │ + ▼ +前端在 Authorization: Bearer 头中携带 + │ + ▼ +后端 OIDCProvider 用 JWKS 公钥验签 + ├─ 验证签名 (RS256) + ├─ 验证 issuer (防止跨 IdP 攻击) + ├─ 验证 audience (防止 token 被其他应用滥用) + ├─ 验证 exp (防止过期 token) + └─ 提取 sub → user:xxx +``` + +### 12.2 凭证存储安全 + +| 层次 | 措施 | +|------|------| +| 传输 | HTTPS (生产环境必须) | +| 存储加密 | Fernet 对称加密 (AES-128-CBC + HMAC-SHA256) | +| 密钥管理 | `CREDENTIAL_VAULT_KEY` 环境变量，不存在代码中 | +| 访问隔离 | 凭证按 `(user_identity, source_key)` 隔离，用户只能访问自己的 | +| 前端不触碰 | 凭证仅在服务端存取，前端只知道"有没有已存凭证"，不知道内容 | + +### 12.3 插件隔离安全 + +| 风险 | 缓解 | +|------|------| +| 插件 A 访问插件 B 的 Session | Session key 按 `plugin_{id}_` 前缀隔离 | +| 插件窃取 SSO token | `get_sso_token()` 是只读的，插件不能修改；且 token 本来就是要透传的 | +| 恶意插件注册危险路由 | 插件 Blueprint 强制 prefix `/api/plugins//`，无法覆盖核心路由 | +| SSRF (前端输入任意 URL) | 插件端点 URL 在 `.env` 中由管理员配置，不接受前端输入 | + +### 12.4 匿名模式的安全限制 + +当无 SSO 时（`browser:` 身份），系统不对安全性做强保证： + +- 同一浏览器的所有 Tab 共享同一 `browser:xxx` 身份 +- 清除 localStorage 即可获得新身份 +- Credential Vault 中按 `browser:xxx` 存储的凭证仅在同一浏览器可用 +- 这与现有行为一致，且与"个人本地工具"的定位匹配 + +--- + +## 13. FAQ + +### Q1: 为什么不自建用户注册/登录系统？ + +密码存储、哈希、重置、邮件验证、安全审计 —— 这些都是沉重的安全负担。Data Formulator 的核心价值是数据可视化，不是身份管理。OIDC 把这些责任交给专业的 IdP (Keycloak 一个 Docker 容器就能跑)，更安全、维护成本更低。 + +### Q2: 小团队不想搭建 IdP 怎么办？ + +有几个极轻量的选择： +- **Keycloak**: `docker run -p 8080:8080 quay.io/keycloak/keycloak start-dev` +- **Authelia**: 支持 OIDC 的轻量级认证网关 +- **Authentik**: 现代化的开源 IdP +- 或者直接使用 SaaS: Auth0 免费版支持 7000 月活用户 + +不搭 IdP 也没关系 —— 不配置 `OIDC_ISSUER_URL`，系统自动回退到匿名浏览器模式，与现在完全一致。 + +### Q3: 如果外部 BI 系统既没接 SSO，也不想让用户输密码？ + +插件支持多种认证模式，包括 **API Key**。例如 Superset 和 Metabase 都支持生成 API Token： +- 管理员在 BI 系统中为每个用户生成 long-lived API token +- 用户在 DF 中输入一次 API token，存入 Credential Vault +- 后续自动使用 + +### Q4: 如果某个外部系统的 SDK 没有 Python 包怎么办？ + +所有 BI 系统都有 REST API，插件通过 `requests` 库调用即可。不需要专用 SDK。如果某个系统需要特殊的 Python 包，在 `__init__.py` 中 `import` 即可——导入失败时插件自动扫描机制会将其标记为 `DISABLED_PLUGINS`（与 `ExternalDataLoader` 的降级机制一致）。 + +### Q5: 如何开发一个新的数据源插件？ + +**最小步骤**： + +1. 创建 `py-src/data_formulator/plugins/your_system/` 目录 +2. 实现 `DataSourcePlugin` 子类 (manifest + blueprint + frontend_config) +3. 在 blueprint 中实现 `auth/login`, `catalog/list`, `data/load` 三组路由 +4. 创建 `src/plugins/your_system/` 目录，导出 `index.ts` +5. 实现 `PanelComponent` (列表浏览 + 加载按钮) +6. 在 `.env` 中设置环境变量启用 +7. 重启服务 → 后端自动扫描发现，前端 `import.meta.glob` 自动编译 + +**核心代码改动：0 行。** 无需修改任何注册表或配置文件。 + +### Q6: 现有的 ExternalDataLoader (数据库连接器) 会被废弃吗？ + +不会。数据库连接器的需求（连接参数 → 列表 → 拉取）与插件不同，`ExternalDataLoader` 的通用表单 UI 完全够用。两套机制长期并行。如果未来需要给某个数据库加专用 UI，可以考虑包装为 Plugin，但不是必须的。 + +### Q7: 多个 IdP 可以同时配置吗？ + +当前设计每次只有一个 OIDC issuer。如果需要支持多个 IdP（如同时支持 Google 和 Okta），有两种路径： +- **推荐**：用一个 IdP (如 Keycloak) 作为联合身份代理，配置多个上游 IdP +- **扩展**：修改 `OIDCProvider` 支持多 issuer（需在 `_providers` 中注册多个实例） + +### Q8: 这套架构对上游 Data Formulator 的兼容性如何？ + +`auth.py` 的重构是最大的改动，但保持了 `get_identity_id()` 的签名和返回值格式（`user:xxx` / `browser:xxx`）不变。所有调用 `get_identity_id()` 的代码无需修改。插件系统和凭证保险箱是纯新增代码，不修改任何现有模块。 + +--- + +## 附录 A：开发新插件的检查清单 + +``` +□ 后端 + □ plugins/your_system/__init__.py — 暴露 plugin_class = YourPlugin + □ plugins/your_system/plugin.py — 实现 DataSourcePlugin (manifest + blueprint) + □ plugins/your_system/routes/ — auth, catalog, data 三组路由 + □ plugins/your_system/ — API client, auth bridge 等 + □ 无需修改 plugins/__init__.py（目录自动扫描） + □ 测试：启用/禁用切换正常 + +□ 前端 + □ src/plugins/your_system/index.ts — 导出 DataSourcePluginModule + □ src/plugins/your_system/*Panel.tsx — 主面板组件 + □ src/plugins/your_system/*Login.tsx — 登录组件 (如需要) + □ 无需修改 registry.ts（import.meta.glob 自动发现） + □ 测试：Tab 显示/隐藏正常 + +□ 配置 + □ .env.template 增加环境变量说明 + □ 文档更新 + +□ 认证模式 + □ SSO 透传测试 (如果外部系统支持) + □ Credential Vault 存取测试 + □ 手动登录测试 + □ Session 过期 / Token 刷新测试 +``` + +## 附录 B：关键依赖 + +| 包 | 用途 | 安装 | +|-----|------|------| +| `PyJWT` | OIDC JWT 验签 | `pip install PyJWT` | +| `cryptography` | Fernet 加密 (Vault + JWT) | `pip install cryptography` | +| `oidc-client-ts` | 前端 OIDC PKCE 流程 | `npm install oidc-client-ts` | +| `requests` | 插件 HTTP 调用 (已有) | — | + +## 附录 C：与原有插件架构文档的关系 + +本文档是 `data-source-plugin-architecture.md` 的**上层补充**。原文档详细描述了： +- 插件基类和前端接口的完整设计 +- Superset 0.6→0.7 迁移的具体方案 +- PluginDataWriter 和 BatchWriter 的完整实现 +- 身份链接表 (IdentityStore) 的详细设计 + +本文档新增的内容： +- **AuthProvider 可插拔认证层** — 原文档假设浏览器 UUID 为主要身份，本文档用 OIDC 替代 +- **CredentialVault 凭证保险箱** — 原文档中插件凭证存在 Flask Session，本文档增加持久化加密存储 +- **SSO Token 透传** — 原文档中每个插件独立认证，本文档增加 SSO 自动透传 +- **身份模型简化** — 有了 SSO，原文档中复杂的 IdentityStore + 身份合并被简化为一次性 browser→user 迁移 + +两份文档互补使用：本文档定义整体架构和集成方式，原文档提供插件内部的详细实现指导。 diff --git a/design-docs/2-external-dataloader-enhancements.md b/design-docs/2-external-dataloader-enhancements.md new file mode 100644 index 00000000..9ace229a --- /dev/null +++ b/design-docs/2-external-dataloader-enhancements.md @@ -0,0 +1,787 @@ +# ExternalDataLoader 演进方案 + +> **来源**：从 `1-data-source-plugin-architecture.md` Section 11.3~11.6 拆分。 +> 这些改进针对现有的 ExternalDataLoader（数据库连接器），与 DataSourcePlugin（BI 系统插件）互不干扰。 + +--- + +## 目录 + +1. [现有缺陷与演进方向](#1-现有缺陷与演进方向) +2. [改进方案一：数据库元数据拉取 (P0)](#2-改进方案一数据库元数据拉取-p0) +3. [改进方案二：SSO Token 透传到数据库 (P1)](#3-改进方案二sso-token-透传到数据库-p1) +4. [改进方案三：凭证持久化 (P2)](#4-改进方案三凭证持久化-p2) + +--- + +## 1. 现有缺陷与演进方向 + +审查了全部 9 个 DataLoader 后，发现两大类可改进的问题： + +### 缺陷一：数据库元数据（注释/描述）未拉取 + +所有 DataLoader 的 `list_tables()` 只查了 `information_schema` 的列名和数据类型，**完全忽略了数据库自带的注释系统**。这些注释是 DBA 维护的宝贵业务知识： + +| DataLoader | 只查了 | 数据库有但没查的 | 查询方法 | +|---|---|---|---| +| **PostgreSQL** | `column_name`, `data_type` | 表/列注释 (`COMMENT ON`) | `SELECT obj_description(oid) FROM pg_class` + `SELECT col_description(attrelid, attnum) FROM pg_attribute` | +| **MSSQL** | `COLUMN_NAME`, `DATA_TYPE`, `IS_NULLABLE` | 扩展属性 (`MS_Description`) | `SELECT value FROM sys.extended_properties WHERE name='MS_Description'` | +| **BigQuery** | `field.name`, `field.field_type` | `table.description`, `field.description` | `table_ref.description`, `field.description`（已有 API，一行代码） | +| **MySQL** | (预估同样缺失) | `COLUMN_COMMENT` | `SELECT COLUMN_COMMENT FROM information_schema.COLUMNS` | +| **Kusto** | `Name`, `Type` | 表和列的 DocString | `.show table T schema` 返回的 `DocString` 字段 | + +**改进方案**：扩展 `list_tables()` 返回的 `columns` 结构，增加 `description` 字段。以 PostgreSQL 为例： + +```python +# postgresql_data_loader.py — list_tables() 增加注释查询 + +# 现有查询只拿了列名和类型： +columns_query = """ + SELECT column_name, data_type + FROM information_schema.columns ... +""" + +# 改进后同时查询列注释： +columns_query = """ + SELECT + c.column_name, + c.data_type, + pgd.description AS column_comment + FROM information_schema.columns c + LEFT JOIN pg_catalog.pg_statio_all_tables st + ON st.schemaname = c.table_schema AND st.relname = c.table_name + LEFT JOIN pg_catalog.pg_description pgd + ON pgd.objoid = st.relid AND pgd.objsubid = c.ordinal_position + WHERE c.table_schema = '{schema}' AND c.table_name = '{table_name}' + ORDER BY c.ordinal_position +""" + +# 表注释也一并查询： +table_comment_query = """ + SELECT obj_description(c.oid) AS table_comment + FROM pg_catalog.pg_class c + JOIN pg_catalog.pg_namespace n ON n.oid = c.relnamespace + WHERE n.nspname = '{schema}' AND c.relname = '{table_name}' +""" +``` + +BigQuery 改进更简单（已有现成属性，只是没用）： + +```python +# bigquery_data_loader.py — list_tables() 中已经有 table_ref，只是没取 description + +table_ref = self.client.get_table(table.reference) + +# 现有代码： +columns = [{"name": field.name, "type": field.field_type} for field in table_ref.schema[:10]] + +# 改进后： +columns = [{ + "name": field.name, + "type": field.field_type, + "description": field.description, # ← 一行代码，BigQuery SDK 直接支持 +} for field in table_ref.schema[:10]] + +# 表描述也直接有： +table_description = table_ref.description # ← 同样一行 +``` + +**改进成本**：每个 DataLoader 改 5-20 行代码即可。注释不存在的列/表返回 `None`，与 `ColumnInfo` 扩展字段完美对齐——对前端和 AI prompt 的提升效果与插件拉取的元数据一致。 + +### 缺陷二：认证方式单一，缺少 SSO/集成认证 + +现有 DataLoader 的认证能力参差不齐，很多数据库明明支持 SSO 或集成认证，但 DataLoader 只实现了用户名/密码模式： + +| DataLoader | 现有认证 | 数据库支持但 DataLoader 未实现的 | +|---|---|---| +| **PostgreSQL** | user + password | Kerberos/GSSAPI; Azure AD token (`password=`); AWS IAM token | +| **MSSQL** | user/password 或 Windows Auth | Azure AD token (`Authentication=ActiveDirectoryAccessToken`) | +| **BigQuery** | Service Account JSON 或 ADC | OIDC 联邦身份 (`google.auth.identity_pool`); Workforce Identity | +| **Kusto** | App Key 或 `az login` | Azure AD user token (`with_aad_user_token_authentication`) | +| **Snowflake** | (未实现) | OAuth 2.0 token (`authenticator='oauth'`, `token=`) | +| **Databricks** | (未实现) | Azure AD token / PAT | + +Kusto 和 BigQuery 已经有了 CLI 认证（`az login` / `gcloud auth`），但这要求用户**在服务器终端上手动执行命令**——在团队部署模式下不现实。 + +**改进方案**：在 `ExternalDataLoader` 基类中增加 SSO token 注入能力。 + +```python +class ExternalDataLoader(ABC): + + @staticmethod + def supported_auth_methods() -> list[str]: + """返回支持的认证方式列表。 + + 可选值: + - "credentials" — 用户名/密码（默认，所有 loader 都支持） + - "sso_token" — OIDC/OAuth access_token + - "azure_ad_token" — Azure AD access_token + - "iam_token" — AWS IAM 认证 token + - "service_account" — 服务账号 JSON key + - "cli" — 本地 CLI 认证（az login / gcloud auth） + """ + return ["credentials"] + + def set_auth_token(self, token: str, token_type: str = "bearer") -> None: + """注入来自 DF SSO 层的认证 token（可选实现）。""" + raise NotImplementedError( + f"{self.__class__.__name__} does not support token injection" + ) +``` + +### 缺陷三：凭证不持久化 + +当前 DataLoader 的连接参数（包括密码）存在**前端 Redux Store** 中，刷新页面即丢失。用户每次打开都要重新输入。接入 CredentialVault（`sso-plugin-architecture.md` 中设计）后可以提供"记住密码"能力。 + +### 综合改进路线图 + +| 改进项 | 复杂度 | 价值 | 优先级 | 前置依赖 | +|--------|:---:|:---:|:---:|------| +| **数据库注释拉取** | 低（5-20 行/loader） | 高（直接提升 AI 分析质量） | P0 | 无（现在可做） | +| **SSO Token 透传** | 中 | 高（团队部署必需） | P1 | SSO AuthProvider 上线 | +| **凭证持久化** | 中 | 中（用户体验提升） | P2 | CredentialVault 上线 | +| **升级为 Plugin** | 高 | 低 | P3 | 仅 Snowflake 等需要 | + +--- + +## 2. 改进方案一：数据库元数据拉取 (P0) + +**目标**：让 DataLoader 在 `list_tables()` 和 `ingest_to_workspace()` 时一并拉取数据库的表/列注释，写入 `ColumnInfo` 和 `TableMetadata`，最终流入前端 AI prompt。 + +**不修改基类接口**，仅在各 DataLoader 内部实现中增强查询逻辑。对前端无感，通过现有 `list-tables` API 自然下发。 + +### 2.1 基类增加可选方法 + +```python +# external_data_loader.py — 新增可选方法 + +class ExternalDataLoader(ABC): + # ... 现有方法不变 ... + + def fetch_table_description(self, source_table: str) -> str | None: + """获取表的描述/注释（可选实现）。""" + return None + + def fetch_column_descriptions(self, source_table: str) -> dict[str, str]: + """获取各列的描述/注释（可选实现）。 + + Returns: + {"column_name": "列描述", ...}，缺失注释的列不含在结果中。 + """ + return {} +``` + +这两个方法**不是 abstract 的**——默认返回空，对现有 loader 零影响。哪个 loader 想支持元数据就 override 即可。 + +### 2.2 各 DataLoader 的具体实现 + +**PostgreSQL**： + +```python +# postgresql_data_loader.py — 新增方法 + +def fetch_table_description(self, source_table: str) -> str | None: + schema, table = self._parse_table_name(source_table) + query = f""" + SELECT obj_description(c.oid) AS comment + FROM pg_catalog.pg_class c + JOIN pg_catalog.pg_namespace n ON n.oid = c.relnamespace + WHERE n.nspname = '{schema}' AND c.relname = '{table}' + """ + result = self._read_sql(query).to_pandas() + if len(result) > 0 and result.iloc[0]['comment']: + return str(result.iloc[0]['comment']) + return None + +def fetch_column_descriptions(self, source_table: str) -> dict[str, str]: + schema, table = self._parse_table_name(source_table) + query = f""" + SELECT + a.attname AS column_name, + d.description AS comment + FROM pg_catalog.pg_attribute a + JOIN pg_catalog.pg_class c ON c.oid = a.attrelid + JOIN pg_catalog.pg_namespace n ON n.oid = c.relnamespace + LEFT JOIN pg_catalog.pg_description d + ON d.objoid = a.attrelid AND d.objsubid = a.attnum + WHERE n.nspname = '{schema}' + AND c.relname = '{table}' + AND a.attnum > 0 + AND NOT a.attisdropped + AND d.description IS NOT NULL + """ + result = self._read_sql(query).to_pandas() + return {row['column_name']: row['comment'] for _, row in result.iterrows()} +``` + +**MSSQL**： + +```python +# mssql_data_loader.py — 新增方法 + +def fetch_table_description(self, source_table: str) -> str | None: + schema, table = self._parse_table_name(source_table) + query = f""" + SELECT CAST(ep.value AS NVARCHAR(MAX)) AS comment + FROM sys.extended_properties ep + JOIN sys.tables t ON ep.major_id = t.object_id + JOIN sys.schemas s ON t.schema_id = s.schema_id + WHERE s.name = '{schema}' AND t.name = '{table}' + AND ep.minor_id = 0 AND ep.name = 'MS_Description' + """ + result = self._execute_query(query).to_pandas() + if len(result) > 0 and result.iloc[0]['comment']: + return str(result.iloc[0]['comment']) + return None + +def fetch_column_descriptions(self, source_table: str) -> dict[str, str]: + schema, table = self._parse_table_name(source_table) + query = f""" + SELECT c.name AS column_name, + CAST(ep.value AS NVARCHAR(MAX)) AS comment + FROM sys.columns c + JOIN sys.tables t ON c.object_id = t.object_id + JOIN sys.schemas s ON t.schema_id = s.schema_id + LEFT JOIN sys.extended_properties ep + ON ep.major_id = c.object_id + AND ep.minor_id = c.column_id + AND ep.name = 'MS_Description' + WHERE s.name = '{schema}' AND t.name = '{table}' + AND ep.value IS NOT NULL + """ + result = self._execute_query(query).to_pandas() + return {row['column_name']: row['comment'] for _, row in result.iterrows()} +``` + +**BigQuery**（最简单——SDK 已有属性，只需取出）： + +```python +# bigquery_data_loader.py — 新增方法 + +def fetch_table_description(self, source_table: str) -> str | None: + table_ref = self.client.get_table(source_table) + return table_ref.description or None + +def fetch_column_descriptions(self, source_table: str) -> dict[str, str]: + table_ref = self.client.get_table(source_table) + return { + field.name: field.description + for field in table_ref.schema + if field.description + } +``` + +**MySQL**： + +```python +# mysql_data_loader.py — 新增方法 + +def fetch_table_description(self, source_table: str) -> str | None: + schema, table = self._parse_table_name(source_table) + query = f""" + SELECT TABLE_COMMENT + FROM information_schema.TABLES + WHERE TABLE_SCHEMA = '{schema}' AND TABLE_NAME = '{table}' + """ + result = self._read_sql(query).to_pandas() + comment = result.iloc[0]['TABLE_COMMENT'] if len(result) > 0 else None + return comment if comment and comment.strip() else None + +def fetch_column_descriptions(self, source_table: str) -> dict[str, str]: + schema, table = self._parse_table_name(source_table) + query = f""" + SELECT COLUMN_NAME, COLUMN_COMMENT + FROM information_schema.COLUMNS + WHERE TABLE_SCHEMA = '{schema}' AND TABLE_NAME = '{table}' + AND COLUMN_COMMENT IS NOT NULL AND COLUMN_COMMENT != '' + """ + result = self._read_sql(query).to_pandas() + return {row['COLUMN_NAME']: row['COLUMN_COMMENT'] for _, row in result.iterrows()} +``` + +### 2.3 元数据如何写入 Workspace + +在 `ingest_to_workspace()` 中调用这两个方法，将注释写入 `ColumnInfo` 和 `TableMetadata`： + +```python +# external_data_loader.py — ingest_to_workspace 增强 + +def ingest_to_workspace(self, workspace, table_name, source_table, size=1000000, + sort_columns=None, sort_order='asc'): + arrow_table = self.fetch_data_as_arrow(source_table, size, sort_columns, sort_order) + + loader_metadata = { + "loader_type": self.__class__.__name__, + "loader_params": self.get_safe_params(), + "source_table": source_table, + } + + table_metadata = workspace.write_parquet_from_arrow( + table=arrow_table, table_name=table_name, loader_metadata=loader_metadata, + ) + + # ---- 新增：拉取并写入元数据 ---- + try: + table_desc = self.fetch_table_description(source_table) + col_descs = self.fetch_column_descriptions(source_table) + + if table_desc or col_descs: + from data_formulator.datalake.metadata import ColumnInfo, update_metadata + + def _enrich(meta): + tbl = meta.get_table(table_name) + if tbl is None: + return + if table_desc: + tbl.description = table_desc + if col_descs and tbl.columns: + for col in tbl.columns: + desc = col_descs.get(col.name) + if desc: + col.description = desc + + update_metadata(workspace._workspace_path, _enrich) + logger.info("Enriched metadata for '%s': table_desc=%s, col_descs=%d", + table_name, bool(table_desc), len(col_descs)) + except Exception as e: + logger.warning("Failed to fetch metadata for '%s': %s (data is still saved)", + source_table, e) + # ---- 元数据增强结束，失败不影响数据写入 ---- + + return table_metadata +``` + +**关键设计决策**：元数据拉取在数据写入**之后**，用 try/except 包裹。即使元数据查询失败（权限不足、数据库不支持等），数据本身已经安全写入 workspace。 + +### 2.4 `list_tables()` 返回值增强 + +同时在 `list_tables()` 中也返回注释信息，让前端在浏览数据库表时就能看到描述： + +```python +# postgresql_data_loader.py — list_tables 增强 + +def _list_tables(self, table_filter=None): + # ... 现有的 tables 查询 ... + + for _, row in tables_df.iterrows(): + schema = row['schemaname'] + table_name = row['tablename'] + full_table_name = f"{schema}.{table_name}" + + # 列信息（现有）+ 列注释（新增） + columns_query = f""" + SELECT + c.column_name, + c.data_type, + pgd.description AS column_comment + FROM information_schema.columns c + LEFT JOIN pg_catalog.pg_statio_all_tables st + ON st.schemaname = c.table_schema AND st.relname = c.table_name + LEFT JOIN pg_catalog.pg_description pgd + ON pgd.objoid = st.relid AND pgd.objsubid = c.ordinal_position + WHERE c.table_schema = '{schema}' AND c.table_name = '{table_name}' + ORDER BY c.ordinal_position + """ + columns_df = self._read_sql(columns_query).to_pandas() + columns = [{ + 'name': r['column_name'], + 'type': r['data_type'], + 'description': r['column_comment'] or None, # ← 新增 + } for _, r in columns_df.iterrows()] + + # 表注释（新增） + table_comment = self.fetch_table_description(full_table_name) + + table_metadata = { + "row_count": int(row_count), + "columns": columns, + "sample_rows": sample_rows, + "description": table_comment, # ← 新增 + } + results.append({"name": full_table_name, "metadata": table_metadata}) +``` + +### 2.5 前端展示（自然兼容） + +现有前端 `DBManagerPane` 已经渲染了 `columns` 列表。只需小幅调整，当 `column.description` 存在时显示 tooltip： + +``` +┌─────────────────────────────────────────────────┐ +│ public.orders — 订单主表，记录所有用户订单 │ ← table description +│ │ +│ 列名 类型 描述 │ +│ ────── ──── ──── │ +│ id integer 订单唯一标识 │ +│ customer_id integer 关联客户表 (FK) │ +│ created_at timestamp 订单创建时间（UTC） │ +│ total_amount numeric 订单总金额（含税） │ ← column descriptions +│ status varchar pending/paid/shipped │ +│ │ +│ 行数: 1,234,567 [ 加载 ▾ ] │ +└─────────────────────────────────────────────────┘ +``` + +### 2.6 改动文件清单 + +| 文件 | 改动 | 行数估计 | +|------|------|:---:| +| `external_data_loader.py` | 新增 `fetch_table_description()`、`fetch_column_descriptions()` 默认实现；`ingest_to_workspace()` 增加元数据写入 | ~30 行 | +| `postgresql_data_loader.py` | 实现两个描述方法 + `list_tables()` 查询增强 | ~25 行 | +| `mssql_data_loader.py` | 实现两个描述方法 + `list_tables()` 查询增强 | ~30 行 | +| `bigquery_data_loader.py` | 实现两个描述方法（各 3 行）+ `list_tables()` 取 description | ~10 行 | +| `mysql_data_loader.py` | 实现两个描述方法 + `list_tables()` 取 `COLUMN_COMMENT` | ~20 行 | +| `kusto_data_loader.py` | 实现两个描述方法（从 schema JSON 取 DocString） | ~15 行 | +| `metadata.py` | `ColumnInfo` 增加 `description` 字段；`TableMetadata` 增加 `description` 字段 | ~15 行 | +| **前端 `DBManagerPane`** | columns 列表显示 description tooltip | ~10 行 | +| **总计** | | **~155 行** | + +--- + +## 3. 改进方案二：SSO Token 透传到数据库 (P1) + +**目标**：当 DF 用户通过 OIDC SSO 登录后，如果目标数据库也信任同一 IdP，DataLoader 自动使用 SSO token 连接数据库，用户无需输入数据库密码。 + +**前置条件**：SSO AuthProvider 链已上线（`sso-plugin-architecture.md` Phase 1）。 + +### 3.1 基类增加认证能力声明 + +```python +# external_data_loader.py — 新增 + +class ExternalDataLoader(ABC): + # ... 现有方法 ... + + @staticmethod + def supported_auth_methods() -> list[dict]: + """声明该 loader 支持哪些认证方式。 + + 框架据此在前端渲染不同的认证 UI（密码表单 vs SSO 按钮等）。 + """ + return [ + {"method": "credentials", "label": "Username & Password", "default": True}, + ] +``` + +各 DataLoader 按实际能力 override： + +```python +# mssql_data_loader.py +@staticmethod +def supported_auth_methods(): + return [ + {"method": "credentials", "label": "SQL Server Authentication"}, + {"method": "windows_auth", "label": "Windows Integrated Authentication"}, + {"method": "azure_ad_token", "label": "Azure AD (SSO)", + "requires_sso": True, "token_audience": "https://database.windows.net/"}, + ] + +# postgresql_data_loader.py +@staticmethod +def supported_auth_methods(): + return [ + {"method": "credentials", "label": "Username & Password", "default": True}, + {"method": "azure_ad_token", "label": "Azure AD (SSO)", + "requires_sso": True, "token_audience": "https://ossrdbms-aad.database.windows.net"}, + {"method": "aws_iam_token", "label": "AWS IAM", + "requires_env": ["AWS_REGION"]}, + ] + +# bigquery_data_loader.py +@staticmethod +def supported_auth_methods(): + return [ + {"method": "service_account", "label": "Service Account JSON", "default": True}, + {"method": "cli", "label": "gcloud CLI (local dev)"}, + {"method": "oidc_federation", "label": "OIDC Federation (SSO)", + "requires_sso": True}, + ] +``` + +### 3.2 Token 注入流程 + +``` +用户通过 OIDC SSO 登录 DF + → 获得 access_token（存在 AuthResult 中） + → 用户打开数据库连接面板 + +前端渲染: + GET /api/data-loader/postgresql/auth-methods + → 返回 supported_auth_methods() + → 发现有 "azure_ad_token"，且 requires_sso=true + → DF 当前有 SSO 登录 → 显示「使用 SSO 连接」按钮 + +用户点击「使用 SSO 连接」: + → 前端带上 auth_method: "azure_ad_token" + → 后端从 auth.get_sso_token() 拿到 access_token + → 如果 token_audience 不同，用 token exchange 获取目标 audience 的 token + → 将 token 注入 DataLoader 的 params + → DataLoader 用 token 连接数据库 +``` + +### 3.3 各数据库的 Token 认证实现 + +**Azure SQL / MSSQL**： + +```python +def __init__(self, params): + auth_method = params.get("auth_method", "credentials") + + if auth_method == "azure_ad_token": + token = params["access_token"] + conn_str = f"DRIVER={{{self.driver}}};SERVER={self.server},{self.port};DATABASE={self.database};" + + SQL_COPT_SS_ACCESS_TOKEN = 1256 + token_struct = struct.pack( + f' m.requires_sso); +const isSsoLoggedIn = !!serverConfig.auth_user; + +{authMethods.length > 1 && ( + + {authMethods.map(m => ( + } + /> + ))} + +)} + +{selectedAuthMethod === "credentials" && ( + <> + + + +)} +{selectedAuthMethod === "azure_ad_token" && ( + + {t('db.ssoConnectInfo', { user: serverConfig.auth_user })} + +)} +``` + +### 3.5 改动文件清单 + +| 文件 | 改动 | 行数估计 | +|------|------|:---:| +| `external_data_loader.py` | 新增 `supported_auth_methods()` 默认实现 | ~10 行 | +| `mssql_data_loader.py` | override `supported_auth_methods()`；`__init__` 增加 `azure_ad_token` 分支 | ~25 行 | +| `postgresql_data_loader.py` | 同上 | ~20 行 | +| `bigquery_data_loader.py` | 同上（OIDC federation） | ~20 行 | +| `kusto_data_loader.py` | 同上（`with_aad_user_token_authentication`） | ~15 行 | +| `tables_routes.py` | 新增 `/api/data-loader/{type}/auth-methods` 路由 | ~15 行 | +| **前端** `DBManagerPane.tsx` | 动态渲染认证方式选择 UI | ~40 行 | +| **总计** | | **~145 行** | + +--- + +## 4. 改进方案三：凭证持久化 (P2) + +**目标**：用户连接数据库后，可以选择"记住连接"，凭证加密存入 CredentialVault，下次打开 DF 无需重新输入。 + +**前置条件**：CredentialVault 已上线（`sso-plugin-architecture.md` Layer 3）。 + +### 4.1 用户体验流程 + +``` +首次连接: + 用户填写 host/port/user/password → 连接成功 + → 弹出「保存此连接？」提示 + → 用户确认 → 凭证加密存入 CredentialVault + 键: credential:dataloader:postgresql:{user_id}:{host}:{database} + 值: AES 加密的 {"user": "...", "password": "...", "host": "...", ...} + +再次打开 DF: + → 前端请求 GET /api/data-loader/saved-connections + → 返回已保存的连接列表（不含明文密码，只有名称和类型） + → 用户点击已保存的连接 → 一键连接 + → 后端从 CredentialVault 解密取出凭证 → 创建 DataLoader + +管理: + → 用户可以查看、删除已保存的连接 + → 删除操作同时清除 CredentialVault 中的加密凭证 +``` + +### 4.2 后端 API + +```python +# tables_routes.py — 新增路由 + +@tables_bp.route('/data-loader/saved-connections', methods=['GET']) +def list_saved_connections(): + """列出当前用户保存的数据库连接（不含明文密码）。""" + user_id = get_identity_id() + vault = get_credential_vault() + connections = vault.list_credentials(user_id, prefix="dataloader:") + return jsonify([{ + "id": conn.credential_id, + "loader_type": conn.metadata.get("loader_type"), + "display_name": conn.metadata.get("display_name"), + "host": conn.metadata.get("host"), + "database": conn.metadata.get("database"), + "saved_at": conn.metadata.get("saved_at"), + } for conn in connections]) + +@tables_bp.route('/data-loader/saved-connections', methods=['POST']) +def save_connection(): + """保存一个数据库连接（凭证加密存储）。""" + data = request.get_json() + user_id = get_identity_id() + vault = get_credential_vault() + + loader_type = data["loader_type"] + params = data["params"] + display_name = data.get("display_name", f"{loader_type}:{params.get('host','')}/{params.get('database','')}") + + credential_id = f"dataloader:{loader_type}:{params.get('host','')}:{params.get('database','')}" + + vault.store_credential( + user_id=user_id, + credential_id=credential_id, + secret_data=params, + metadata={ + "loader_type": loader_type, + "display_name": display_name, + "host": params.get("host"), + "database": params.get("database"), + "saved_at": datetime.now(timezone.utc).isoformat(), + }, + ) + return jsonify({"status": "ok", "credential_id": credential_id}) + +@tables_bp.route('/data-loader/saved-connections//connect', methods=['POST']) +def connect_saved(): + """使用已保存的凭证连接数据库。""" + user_id = get_identity_id() + vault = get_credential_vault() + + cred = vault.get_credential(user_id, credential_id) + if cred is None: + return jsonify({"status": "error", "message": "Connection not found"}), 404 + + loader_type = cred.metadata["loader_type"] + params = cred.secret_data # 自动解密 + + loader_cls = DATA_LOADERS.get(loader_type) + loader = loader_cls(params) + # ... 后续逻辑与手动连接相同 ... +``` + +### 4.3 前端 UI + +``` +┌─────────────────────────────────────────────────────┐ +│ 数据库连接 │ +│ │ +│ ┌─── 已保存的连接 ─────────────────────────────────┐ │ +│ │ 🔗 生产 PostgreSQL (pg.company.com/analytics) │ │ +│ │ 上次连接: 2025-03-20 [ 连接 ] [ 🗑 删除 ] │ │ +│ │ │ │ +│ │ 🔗 测试 MSSQL (sql-test/reporting) │ │ +│ │ 上次连接: 2025-03-18 [ 连接 ] [ 🗑 删除 ] │ │ +│ └───────────────────────────────────────────────────┘ │ +│ │ +│ ┌─── 新建连接 ─────────────────────────────────────┐ │ +│ │ 类型: [PostgreSQL ▾] │ │ +│ │ 认证: ● 用户名密码 ○ SSO (Azure AD) │ │ +│ │ Host: [________________] │ │ +│ │ ... │ │ +│ │ ☑ 记住此连接 │ │ +│ │ │ │ +│ │ [ 连接 ] │ │ +│ └───────────────────────────────────────────────────┘ │ +└─────────────────────────────────────────────────────┘ +``` + +### 4.4 安全性 + +| 措施 | 说明 | +|------|------| +| **加密存储** | 密码等敏感字段通过 CredentialVault 使用 Fernet (AES-128-CBC) 加密 | +| **per-user 隔离** | 每个用户只能访问自己保存的连接（通过 `user_id` 隔离） | +| **不回显密码** | `list_saved_connections` 只返回元数据，不返回明文密码 | +| **手动删除** | 用户随时可以删除已保存的连接和对应的加密凭证 | +| **SSO 优先** | 如果数据库支持 SSO 且 DF 已 SSO 登录，优先推荐 SSO（无需存密码） | + +--- + +## 关联文档 + +| 文档 | 关系 | +|------|------| +| `1-data-source-plugin-architecture.md` § 11.1~11.2 | ExternalDataLoader vs DataSourcePlugin 的分工定义 | +| `1-sso-plugin-architecture.md` | SSO AuthProvider（P1 前置）、CredentialVault（P2 前置） | diff --git a/design-docs/3-language-injection-analysis.md b/design-docs/3-language-injection-analysis.md new file mode 100644 index 00000000..dbac5628 --- /dev/null +++ b/design-docs/3-language-injection-analysis.md @@ -0,0 +1,239 @@ +# Data Formulator 多语言提示词注入分析 + +## 1. 问题概述 + +项目已接入多语言（i18n）支持，核心 Agent 提示词通过 `language_instruction` 参数注入语言指令。但仍有个别 LLM 调用点遗漏了语言注入，导致部分场景下输出语言与用户界面语言不一致。 + +--- + +## 2. 现有语言注入架构 + +### 2.1 整体流程 + +``` +前端 i18n.language ──► Accept-Language header ──► get_language_instruction() + │ + ▼ + build_language_instruction() + (agent_language.py) + │ + ┌────────────┴────────────┐ + ▼ ▼ + mode="full" mode="compact" + (文本型 Agent) (代码生成 Agent) +``` + +**关键模块**： + +| 模块 | 职责 | +|------|------| +| `src/app/utils.tsx` → `getAgentLanguage()` | 从 `i18n.language` 提取语言代码 | +| `src/app/utils.tsx` → `fetchWithIdentity()` | 在每个 API 请求的 `Accept-Language` header 中注入语言代码 | +| `py-src/.../agents/agent_language.py` | `build_language_instruction(lang, mode)` — 根据语言代码和模式生成提示词片段 | +| `py-src/.../agent_routes.py` → `get_language_instruction()` | 从 `Accept-Language` header 解析语言，调用 `build_language_instruction` | + +### 2.2 已正确注入语言的 Agent + +#### `agent_data_rec.py` 和 `agent_data_transform.py` + +通过构造函数接收 `language_instruction`，注入到 system prompt 中： + +```python +if language_instruction: + marker = "**About the execution environment:**" + idx = self.system_prompt.find(marker) + if idx > 0: + self.system_prompt = ( + self.system_prompt[:idx] + + language_instruction + "\n\n" + + self.system_prompt[idx:] + ) + else: + self.system_prompt = self.system_prompt + "\n\n" + language_instruction +``` + +**注入位置策略**：在 `"**About the execution environment:**"` 标记之前插入，确保语言要求在技术细节之前被声明。如果找不到标记，则追加到末尾。 + +#### `data_agent.py` + +通过 `self.language_instruction` 实例属性，在 `_build_system_prompt()` 中注入： + +```python +def _build_system_prompt(self) -> str: + # ... 构建 prompt ... + if self.language_instruction: + prompt = prompt + "\n\n" + self.language_instruction + return prompt +``` + +#### 其他已注入的路由 + +`agent_routes.py` 中的大部分路由处理函数都已正确调用 `get_language_instruction(mode=...)` 并传入对应 Agent 构造函数。 + +### 2.3 `agent_language.py` 的两种模式 + +| 模式 | 适用场景 | 特点 | +|------|---------|------| +| `"full"` | 文本型 Agent（ChartInsight、InteractiveExplore、ReportGen 等） | 详细的逐字段规则，区分用户可见字段和内部字段 | +| `"compact"` | 代码生成 Agent（DataRec、DataTransformation、DataLoad） | 简短 3 句话指令，避免干扰模型生成代码 | + +支持 20 种语言（en、zh、ja、ko、fr、de 等），当语言为 `"en"` 时返回空字符串（无需注入）。 + +--- + +## 3. 遗漏分析 + +### 3.1 `agent_routes.py` — 工作区命名（需修复） + +```python +# L1073-1086 +messages = [ + { + "role": "system", + "content": ( + "You are a helpful assistant. Generate a very short name (3-5 words) " + "for a data analysis workspace based on the context below. " + "Return ONLY the name, no quotes, no explanation." + ), + }, + {"role": "user", "content": context_str}, +] +``` + +**问题**：工作区名称直接展示在 UI 中，应跟随用户界面语言。当前未注入语言指令，中文用户会看到英文工作区名称。 + +**影响级别**：中 — 用户可见，体验不一致。 + +### 3.2 `agent_routes.py` — 健康检查（无需修复） + +```python +# L227-230 +messages=[ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Respond 'I can hear you.' if you can hear me. Do not say anything other than 'I can hear you.'"}, +] +``` + +**分析**：这是 `/test-model` 的连通性测试，期望固定返回 `"I can hear you."`。不应注入语言指令，原因： + +- 返回内容不面向最终用户展示 +- 注入语言指令会增加无意义的 token 消耗 +- 如果 LLM 遵从语言指令返回中文，可能导致连通性判断逻辑异常 + +**影响级别**：无 — 无需修改。 + +### 3.3 `agent_utils.py` — 补充代码生成（无需修复） + +```python +# L243-247 +supp_resp = client.get_completion(messages=[ + *messages, + {"role": "assistant", "content": assistant_content}, + {"role": "user", "content": prompt}, +]) +``` + +**分析**：`messages` 列表从上游 Agent 传入，上游 Agent 在构造 system prompt 时已注入了 `language_instruction`。因此补充生成继承了上游的语言指令，无需重复注入。 + +**影响级别**：无 — 无需修改。 + +--- + +## 4. 修复方案 + +### 4.1 修复工作区命名（唯一需要修改的地方） + +复用现有的 `get_language_instruction()` 函数，使用 `mode="compact"` 模式（因为工作区名称是短文本）： + +```python +# agent_routes.py — workspace_summary() + +lang_instruction = get_language_instruction(mode="compact") +lang_suffix = f"\n\n{lang_instruction}" if lang_instruction else "" + +messages = [ + { + "role": "system", + "content": ( + "You are a helpful assistant. Generate a very short name (3-5 words) " + "for a data analysis workspace based on the context below. " + "Return ONLY the name, no quotes, no explanation." + + lang_suffix + ), + }, + {"role": "user", "content": context_str}, +] +``` + +**注意**：`get_language_instruction()` 在 `lang == "en"` 时返回空字符串，所以英文用户不会受影响。 + +### 4.2 不需要新建 `MessageBuilder` 或全局拦截 + +现有架构已经提供了完整的语言注入体系： + +- `agent_language.py` 管理语言模板和生成逻辑 +- `get_language_instruction()` 从请求 header 解析语言 +- 各 Agent 构造函数接收 `language_instruction` 参数 + +**不建议**引入 `MessageBuilder` 工具类或 LLMClient 全局拦截，原因： + +| 方案 | 问题 | +|------|------| +| `MessageBuilder` + 环境变量 | 语言来源从 per-request（`Accept-Language` header）退化为 per-process（环境变量），破坏多用户场景 | +| LLMClient 全局拦截 | 隐式修改 system prompt 导致调试困难；字符串检测 `"**Language Requirement:**"` 脆弱；无法区分 full/compact 模式 | + +--- + +## 5. 防止未来遗漏的建议 + +### 5.1 开发规范 + +每次新增 LLM 调用点时，开发者应检查： + +1. 该调用的输出是否面向用户展示？ +2. 如果是，是否调用了 `get_language_instruction()` 并注入到 system prompt 中？ +3. 选择正确的 mode：文本型用 `"full"`，短文本/代码型用 `"compact"` + +### 5.2 测试验证 + +实施完成后验证以下场景： + +- [ ] 新建工作区时生成的名称跟随 UI 语言 +- [ ] 数据推荐 Agent 的代码注释和说明跟随 UI 语言 +- [ ] 数据转换 Agent 的解释说明跟随 UI 语言 +- [ ] 数据探索 Agent 的交互内容跟随 UI 语言 +- [ ] 英文用户不受影响（`build_language_instruction("en")` 返回 `""`） + +--- + +## 6. 相关文件 + +| 文件路径 | 说明 | +|---------|------| +| `py-src/data_formulator/agents/agent_language.py` | 语言指令构建核心模块（模板、模式、多语言支持） | +| `py-src/data_formulator/agent_routes.py` | 路由层：`get_language_instruction()` + 各端点调用 | +| `py-src/data_formulator/agents/data_agent.py` | 数据探索 Agent（已有语言注入） | +| `py-src/data_formulator/agents/agent_data_rec.py` | 数据推荐 Agent（已有语言注入） | +| `py-src/data_formulator/agents/agent_data_transform.py` | 数据转换 Agent（已有语言注入） | +| `py-src/data_formulator/agents/agent_utils.py` | 补充代码生成工具（继承上游语言指令） | +| `src/app/utils.tsx` | 前端：`getAgentLanguage()` + `fetchWithIdentity()` | +| `src/i18n/index.ts` | 前端 i18n 配置（i18next + LanguageDetector） | + +--- + +## 7. 附录：`agent_language.py` 架构说明 + +### 语言注册表 + +`LANGUAGE_DISPLAY_NAMES` 定义了 20 种语言的显示名称，用于生成提示词中的语言标识。 + +### 特定语言的额外规则 + +`LANGUAGE_EXTRA_RULES` 为特定语言提供补充说明（如中文要求使用简体中文、日文要求使用敬体）。 + +### 输出逻辑 + +- 当 `language == "en"` 时，返回空字符串（不注入任何语言指令） +- 当 `language != "en"` 时，根据 `mode` 参数返回 full 或 compact 格式的语言指令 +- full 模式详细列出哪些字段用目标语言、哪些字段保持英文 +- compact 模式仅用 3 句话说明基本规则，适合代码生成场景 diff --git a/design-docs/6-path-safety-confined-dir.md b/design-docs/6-path-safety-confined-dir.md new file mode 100644 index 00000000..fd36a23c --- /dev/null +++ b/design-docs/6-path-safety-confined-dir.md @@ -0,0 +1,607 @@ +# 服务端路径安全加固 — ConfinedDir 统一防护 + +> **来源**：CodeQL `py/path-injection` 审计 + 全量代码人工审查。 +> **目标**：将"根目录 + 用户/外部输入"拼接这一高频模式收敛到单一原语，消除散落式校验遗漏。 + +--- + +## 目录 + +1. [问题分析](#1-问题分析) +2. [现有防护盘点](#2-现有防护盘点) +3. [方案设计：ConfinedDir](#3-方案设计confineddir) +4. [改造清单](#4-改造清单) +5. [HTTP 响应头注入修复](#5-http-响应头注入修复) +6. [测试计划](#6-测试计划) +7. [实施步骤](#7-实施步骤) +8. [后续防线](#8-后续防线) + +--- + +## 1. 问题分析 + +### 1.1 攻击面 + +服务端存在"根目录 + 不可信子路径"拼接的场景： + +| 场景 | 根目录 | 子路径来源 | 攻击方式 | +|------|--------|-----------|---------| +| 用户上传文件 | workspace `data/` | HTTP `filename` | `../../etc/passwd` | +| Azure Blob 物化到本地 | `tempfile.mkdtemp()` | Blob 名称 | blob key 含 `../` 段 | +| Session zip 导入 | 临时目录 | zip 内文件路径 | Zip-Slip | +| Cache 层本地镜像 | `~/.data_formulator/cache/` | blob 相对路径 | blob key 含 `../` 段 | +| Workspace ID → 目录名 | `workspaces//` | 用户/浏览器 ID | `../../other_user` | + +### 1.2 现有代码的核心问题 + +防护**分散在调用点**，每个开发者写新代码时必须主动记住调用清洗函数。遗漏 = 漏洞。 + +**已发现的 2 处遗漏**： + +1. `azure_blob_workspace.py` — `local_dir()` (第 576–598 行)、`save_workspace_snapshot()` (第 616–626 行) + - blob 相对路径直接拼接到临时目录，无 `..` 段检查或 `resolve()` 校验 + - 如果 blob 名称包含 `..` 段，可写出临时目录之外（任意文件写入） + +2. `tables_routes.py` — `export-table-csv` (第 667–672 行) + - `table_name` 直接拼入 `Content-Disposition` 响应头 + - 可注入 `"`、`\r\n` 等字符，造成 HTTP 响应头注入 + +### 1.3 为什么"每次手动校验"不可持续 + +- 0.7 已有 **87 个 Python 文件**，路径操作分布在 datalake、routes、sandbox、plugins 多个模块 +- 未来 Plugin 系统会引入第三方开发者写的路径代码，控制力更弱 +- CodeQL 对"先拼接后校验"模式报误报，开发者可能习惯性忽略告警 + +--- + +## 2. 现有防护盘点 + +### 2.1 已有的防护措施（保留并继续使用） + +| 措施 | 位置 | 作用 | +|------|------|------| +| `safe_data_filename()` | `parquet_utils.py:42-66` | 提取 basename，拒绝 `.` / `..`，保留 Unicode | +| `Workspace.get_file_path()` | `workspace.py:226-252` | `safe_data_filename` + `resolve().relative_to()` 双重校验 | +| `CachedAzureBlobWorkspace._cache_path()` | `cached_azure_blob_workspace.py:230-242` | `resolve()` + `is_relative_to()` | +| `Workspace._sanitize_identity_id()` | `workspace.py:205-218` | `secure_filename` 清洗用户 ID | +| `WorkspaceManager._safe_id()` | `workspace_manager.py:72-78` | `secure_filename` 清洗 workspace ID | +| `import_session_zip()` | `workspace.py:770-784` | 逐段 `secure_filename` + 跳过空段 | + +### 2.2 这些措施的共同模式 + +每处防护本质上在做同一件事： + +``` +给定 root_dir + untrusted_relative_path: + 1. 清洗 untrusted_relative_path（basename / 拒绝 .. / secure_filename） + 2. 拼接：candidate = root_dir / cleaned_path + 3. 校验：candidate.resolve().is_relative_to(root_dir.resolve()) + 4. 否则 raise ValueError +``` + +**ConfinedDir 就是把这四步封装成一个对象。** + +--- + +## 3. 方案设计：ConfinedDir + +### 3.1 核心类 + +新增文件 `py-src/data_formulator/security/path_safety.py`： + +```python +"""Path confinement primitive — prevents path traversal at the API level. + +Usage: + jail = ConfinedDir("/tmp/workspace") + safe = jail / "data/sales.parquet" # OK + jail / "../etc/passwd" # raises ValueError + jail.write("data/out.parquet", raw_bytes) # resolve + mkdir + write +""" + +from __future__ import annotations + +import logging +import re +from pathlib import Path + +logger = logging.getLogger(__name__) + + +class ConfinedDir: + """A directory jail that prevents any path operation from escaping its root. + + All path resolution goes through this single chokepoint. If the + resolved path escapes the root, ``ValueError`` is raised immediately. + + Thread-safe: instances are immutable after construction; Path.resolve() + and is_relative_to() are OS-level and inherently safe for concurrent use. + """ + + __slots__ = ("_root",) + + def __init__(self, root: Path | str, *, mkdir: bool = True): + self._root = Path(root).resolve() + if mkdir: + self._root.mkdir(parents=True, exist_ok=True) + + # -- properties -------------------------------------------------------- + + @property + def root(self) -> Path: + """The resolved, canonical root directory.""" + return self._root + + # -- core API ---------------------------------------------------------- + + def resolve(self, relative: str, *, mkdir_parents: bool = False) -> Path: + """Resolve *relative* within this jail. + + Raises ``ValueError`` if the result would escape the root. + + Defence is layered: + 1. Reject absolute paths outright. + 2. Reject path segments equal to ``..``. + 3. Join onto root, canonicalise with ``resolve()``, and confirm + the result is still under root (catches symlink escapes). + """ + if not relative: + raise ValueError("Empty relative path") + if Path(relative).is_absolute(): + raise ValueError(f"Absolute path not allowed: {relative!r}") + + parts = Path(relative).parts + if ".." in parts: + raise ValueError(f"Path traversal segment '..' in: {relative!r}") + + candidate = (self._root / relative).resolve() + if not candidate.is_relative_to(self._root): + raise ValueError( + f"Path escapes confined directory: {relative!r} " + f"resolves to {candidate}" + ) + + if mkdir_parents: + candidate.parent.mkdir(parents=True, exist_ok=True) + + return candidate + + def write(self, relative: str, data: bytes) -> Path: + """Resolve, create parent dirs, and write *data* atomically.""" + target = self.resolve(relative, mkdir_parents=True) + target.write_bytes(data) + return target + + def __truediv__(self, relative: str) -> Path: + """Operator overload: ``jail / "sub/path"`` → ``jail.resolve("sub/path")``.""" + return self.resolve(relative) + + def __repr__(self) -> str: + return f"ConfinedDir({self._root})" +``` + +### 3.2 设计要点 + +| 要点 | 说明 | +|------|------| +| **不可变** | 构造后 `_root` 不可修改，线程安全 | +| **三层防御** | 拒绝绝对路径 → 拒绝 `..` 段 → `resolve()` + `is_relative_to()` | +| **Symlink 安全** | `resolve()` 在 OS 层面展开符号链接后再检查包含关系 | +| **操作符重载** | `jail / "sub/path"` 语法糖，让调用点代码简洁 | +| **mkdir 内置** | `write()` 方法自动创建父目录，减少调用点样板代码 | + +### 3.3 与现有 API 的兼容策略 + +`ConfinedDir` 作为**底层原语**引入，不替换现有的 `safe_data_filename()` 或 `secure_filename()`。层次关系： + +``` +调用者传入的 filename / relative_path + │ + ▼ +safe_data_filename() / secure_filename() ← 第一层：输入清洗 + │ + ▼ +ConfinedDir.resolve() ← 第二层：路径约束（新增） + │ + ▼ +最终的 Path 对象 ← 安全的文件路径 +``` + +### 3.4 在 security 包中注册 + +更新 `py-src/data_formulator/security/__init__.py`： + +```python +from data_formulator.security.path_safety import ConfinedDir + +__all__ = [ + ..., + "ConfinedDir", +] +``` + +--- + +## 4. 改造清单 + +### 4.1 [漏洞] `AzureBlobWorkspace.local_dir()` — 中等风险 + +**文件**：`py-src/data_formulator/datalake/azure_blob_workspace.py`，第 576–598 行 + +**Before**： + +```python +@contextmanager +def local_dir(self): + tmp = tempfile.mkdtemp(prefix="df_blob_ws_") + tmp_path = Path(tmp) + try: + for blob in self._container.list_blobs(name_starts_with=self._prefix): + rel = blob.name[len(self._prefix):] + if not rel or rel == METADATA_FILENAME: + continue + local_file = tmp_path / rel # ← 无校验 + local_file.parent.mkdir(parents=True, exist_ok=True) + data = self._container.download_blob(blob.name).readall() + local_file.write_bytes(data) + yield tmp_path + finally: + shutil.rmtree(tmp, ignore_errors=True) +``` + +**After**： + +```python +from data_formulator.security.path_safety import ConfinedDir + +@contextmanager +def local_dir(self): + tmp = tempfile.mkdtemp(prefix="df_blob_ws_") + tmp_path = Path(tmp) + jail = ConfinedDir(tmp_path, mkdir=False) + try: + for blob in self._container.list_blobs(name_starts_with=self._prefix): + rel = blob.name[len(self._prefix):] + if not rel or rel == METADATA_FILENAME: + continue + try: + data = self._container.download_blob(blob.name).readall() + jail.write(rel, data) + except ValueError: + logger.warning( + "Skipping blob with unsafe path: %s", blob.name, + ) + yield tmp_path + finally: + shutil.rmtree(tmp, ignore_errors=True) +``` + +### 4.2 [漏洞] `AzureBlobWorkspace.save_workspace_snapshot()` — 中等风险 + +**文件**：同上，第 616–626 行 + +**Before**： + +```python +def save_workspace_snapshot(self, dst: Path) -> None: + for blob in self._container.list_blobs(name_starts_with=self._prefix): + rel = blob.name[len(self._prefix):] + if not rel: + continue + dst.mkdir(parents=True, exist_ok=True) + local_file = dst / rel # ← 无校验 + local_file.parent.mkdir(parents=True, exist_ok=True) + data = self._container.download_blob(blob.name).readall() + local_file.write_bytes(data) +``` + +**After**： + +```python +def save_workspace_snapshot(self, dst: Path) -> None: + jail = ConfinedDir(dst) + for blob in self._container.list_blobs(name_starts_with=self._prefix): + rel = blob.name[len(self._prefix):] + if not rel: + continue + try: + data = self._container.download_blob(blob.name).readall() + jail.write(rel, data) + except ValueError: + logger.warning( + "Skipping blob with unsafe path in snapshot: %s", blob.name, + ) +``` + +### 4.3 [加固] `CachedAzureBlobWorkspace._cache_path()` — 替换手写逻辑 + +**文件**：`py-src/data_formulator/datalake/cached_azure_blob_workspace.py`，第 230–242 行 + +**Before**： + +```python +def _cache_path(self, filename: str) -> Path: + resolved = (self._cache_dir / filename).resolve() + if not resolved.is_relative_to(self._cache_dir.resolve()): + raise ValueError( + f"Path traversal detected: {filename!r} resolves outside " + f"the cache directory" + ) + return resolved +``` + +**After**： + +```python +def __init__(self, ...): + ... + self._cache_jail = ConfinedDir(self._cache_dir, mkdir=True) + +def _cache_path(self, filename: str) -> Path: + return self._cache_jail.resolve(filename) +``` + +收益：消除 CodeQL `py/path-injection` 在此处的告警（验证逻辑在 `ConfinedDir` 内部完成，不再是"先拼接后验证"）。 + +### 4.4 [加固] `Workspace.get_file_path()` — 可选改造 + +**文件**：`py-src/data_formulator/datalake/workspace.py`，第 226–252 行 + +当前已有 `safe_data_filename` + `resolve().relative_to()` 双重防护，功能正确。可选择用 `ConfinedDir` 替换以统一风格： + +```python +def __init__(self, ...): + ... + self._data_jail = ConfinedDir(self._path / "data") + +def get_file_path(self, filename: str) -> Path: + basename = safe_data_filename(filename) + return self._data_jail.resolve(basename) +``` + +此项为**可选优化**，现有逻辑已安全。 + +--- + +## 5. HTTP 响应头注入修复 + +### 5.1 工具函数 + +新增 `py-src/data_formulator/security/http_headers.py`： + +```python +"""HTTP response header safety helpers.""" + +from werkzeug.utils import secure_filename + + +def safe_download_name(name: str, fallback: str = "export") -> str: + """Sanitize a user-provided name for Content-Disposition filename. + + Strips directory components, special characters (quotes, newlines), + and falls back to *fallback* if the result is empty. + """ + safe = secure_filename(name) if name else "" + return safe or fallback +``` + +### 5.2 改造点 + +**文件**：`py-src/data_formulator/tables_routes.py`，第 667–672 行 + +**Before**： + +```python +headers={ + 'Content-Disposition': f'attachment; filename="{table_name}.{ext}"', +} +``` + +**After**： + +```python +from data_formulator.security.http_headers import safe_download_name + +safe_name = safe_download_name(table_name) +headers={ + 'Content-Disposition': f'attachment; filename="{safe_name}.{ext}"', +} +``` + +--- + +## 6. 测试计划 + +### 6.1 `ConfinedDir` 单元测试 + +**文件**：`tests/backend/security/test_path_safety.py` + +```python +# 要验证的行为： +# +# --- 正常路径 --- +# - jail.resolve("file.txt") → root/file.txt +# - jail.resolve("sub/dir/file.txt") → root/sub/dir/file.txt +# - jail / "file.txt" → 等价于 resolve +# - jail.write("out.bin", b"data") → 文件创建成功，内容正确 +# - resolve(mkdir_parents=True) → 父目录自动创建 +# +# --- 路径穿越拒绝 --- +# - jail.resolve("../etc/passwd") → raises ValueError +# - jail.resolve("sub/../../etc/passwd") → raises ValueError +# - jail.resolve("..") → raises ValueError +# - jail.resolve("/etc/passwd") → raises ValueError（绝对路径） +# - jail.resolve("") → raises ValueError（空路径） +# - jail.resolve("sub/\x00hidden") → 行为取决于 OS，至少不能逃逸 +# +# --- Symlink 逃逸 --- +# - 在 jail 内创建指向 jail 外的 symlink → resolve 后检测逃逸 → raises ValueError +# +# --- Unicode 安全 --- +# - jail.resolve("数据/报表.parquet") → 正常工作（CJK 字符保留） +# - jail.resolve("données/résumé.csv") → 正常工作（Latin 扩展字符保留） +# +# --- 边界情况 --- +# - 多层嵌套 "../../../.." → raises ValueError +# - Windows 风格分隔符 "sub\\..\\..\\etc" → raises ValueError +# - 以 "~" 开头的路径 "~root/.ssh/key" → 不逃逸即允许 + +# 测试策略：使用 pytest tmp_path fixture，真实文件系统操作 +``` + +### 6.2 Azure Blob 路径安全回归测试 + +**文件**：`tests/backend/security/test_blob_path_traversal.py` + +```python +# 要验证的行为： +# +# - local_dir() 遇到 blob key 含 "../" 时 → 跳过该 blob，不抛异常，日志 warning +# - local_dir() 正常 blob → 文件正确物化到 tmp 目录内 +# - save_workspace_snapshot() 同上 +# - 构造含 "../" 的 mock blob → 验证不会写出目标目录 +# +# Mock 策略： +# - Mock ContainerClient.list_blobs() 返回包含恶意 blob name 的列表 +# - Mock download_blob().readall() 返回测试数据 +# - 用 tmp_path 作为 local_dir / snapshot 目标 +# - 验证 tmp_path 之外没有被写入文件 +``` + +### 6.3 HTTP 响应头注入测试 + +**文件**：`tests/backend/security/test_http_headers.py` + +```python +# 要验证的行为： +# +# - safe_download_name("normal_name") → "normal_name" +# - safe_download_name('name"with"quotes') → 引号被移除 +# - safe_download_name("name\r\ninjection") → 换行被移除 +# - safe_download_name("") → "export"（fallback） +# - safe_download_name("数据报表") → "export"（secure_filename 清除非 ASCII） +# 注：下载文件名不需要保留 Unicode，不同于文件存储 +# - safe_download_name(None) → "export" +``` + +### 6.4 现有测试不受影响 + +`ConfinedDir` 是在现有防护之下的**新增底层防线**，不改变上层 API 的行为契约。以下现有测试应继续通过： + +- `test_safe_data_filename.py` — `safe_data_filename()` 功能不变 +- `test_workspace_manager.py` — Workspace ID 清洗不变 +- `test_workspace_source_file_ops.py` — `get_file_path()` 行为不变 +- `test_same_basename_upload.py` — 上传管道不变 + +--- + +## 7. 实施步骤 + +### Step 1：新增 `ConfinedDir` + 测试 (安全基础) + +| 任务 | 文件 | +|------|------| +| 实现 `ConfinedDir` | `py-src/data_formulator/security/path_safety.py` | +| 注册到 `security/__init__.py` | `py-src/data_formulator/security/__init__.py` | +| 单元测试 | `tests/backend/security/test_path_safety.py` | + +**验收标准**：`pytest tests/backend/security/test_path_safety.py` 全部通过。 + +### Step 2：修复 Azure Blob 路径穿越漏洞 + +| 任务 | 文件 | +|------|------| +| `local_dir()` 使用 `ConfinedDir` | `azure_blob_workspace.py` | +| `save_workspace_snapshot()` 使用 `ConfinedDir` | `azure_blob_workspace.py` | +| 回归测试 | `tests/backend/security/test_blob_path_traversal.py` | + +**验收标准**：恶意 blob 名称被安全跳过；正常 blob 正确物化；现有 workspace 测试全部通过。 + +### Step 3：加固 Cache 层 + Workspace + +| 任务 | 文件 | +|------|------| +| `_cache_path()` 改用 `ConfinedDir` | `cached_azure_blob_workspace.py` | +| （可选）`get_file_path()` 改用 `ConfinedDir` | `workspace.py` | + +**验收标准**：现有测试全部通过；CodeQL `py/path-injection` 告警消失或减少。 + +### Step 4：修复 HTTP 响应头注入 + +| 任务 | 文件 | +|------|------| +| 实现 `safe_download_name()` | `py-src/data_formulator/security/http_headers.py` | +| 改造 `export-table-csv` | `tables_routes.py` | +| 测试 | `tests/backend/security/test_http_headers.py` | + +**验收标准**：含特殊字符的 `table_name` 不再注入响应头。 + +### Step 5：验证与清理 + +| 任务 | 说明 | +|------|------| +| 全量测试 | `pytest tests/backend/` 全部通过 | +| CodeQL 扫描 | `py/path-injection` 告警清零或仅剩已标注的可接受误报 | +| 代码审查 | 搜索 `Path(...) / variable` 模式，确认无遗漏 | + +--- + +## 8. 后续防线 + +### 8.1 Code Review Checklist + +PR 模板中新增检查项： + +```markdown +### 安全检查 +- [ ] 文件路径操作使用了 `ConfinedDir` 或 `safe_data_filename()` +- [ ] 未直接使用 `Path(root) / user_input` 模式 +- [ ] HTTP 响应头中的用户输入已清洗 +``` + +### 8.2 Lint 规则（可选进阶） + +通过自定义 Ruff 或 Semgrep 规则，检测裸路径拼接模式： + +```yaml +# .semgrep/path-safety.yaml +rules: + - id: no-bare-path-join-with-variable + pattern: $ROOT / $USER_INPUT + message: "Use ConfinedDir instead of bare path joining with variables" + severity: WARNING + languages: [python] +``` + +### 8.3 Plugin 开发者指南 + +在 `5-plugin-development-guide.md` 中补充路径安全章节： + +- 插件代码中**禁止**直接操作 `Path`，必须通过 `ConfinedDir` 或 Workspace API +- 写入文件必须使用 `PluginDataWriter`（内部已经过 Workspace 的路径校验） +- 示例代码展示正确和错误的路径操作对比 + +### 8.4 CodeQL Annotation + +对 `ConfinedDir.resolve()` 方法添加 CodeQL 建模，告知静态分析器该方法是路径校验点： + +```python +class ConfinedDir: + def resolve(self, relative: str, ...) -> Path: + # CodeQL: this method is a path sanitizer + # See: https://codeql.github.com/docs/codeql-for-python/ + ... +``` + +具体方式是在 `.github/codeql/` 下添加自定义 query 或 `qlpack.yml` 中的 sanitizer 建模。 + +--- + +## 附录：风险矩阵 + +| 编号 | 问题 | 严重度 | 可利用性 | 修复步骤 | +|------|------|--------|---------|---------| +| V-01 | `AzureBlobWorkspace.local_dir()` 路径穿越 | 中 | 需要控制 blob 存储内容 | Step 2 | +| V-02 | `AzureBlobWorkspace.save_workspace_snapshot()` 路径穿越 | 中 | 同上 | Step 2 | +| V-03 | `export-table-csv` Content-Disposition 头注入 | 低-中 | 需要能创建含特殊字符的表名 | Step 4 | +| H-01 | `cached_azure_blob_workspace._cache_path()` CodeQL 误报 | 信息 | 已有防护，仅静态分析噪音 | Step 3 | diff --git a/design-docs/7-language-standardization-plan.md b/design-docs/7-language-standardization-plan.md new file mode 100644 index 00000000..be50d4be --- /dev/null +++ b/design-docs/7-language-standardization-plan.md @@ -0,0 +1,506 @@ +# 多语言（i18n）规范化开发计划 + +> 编号：design-doc-7 | 创建：2026-04-12 | 状态：草案 + +--- + +## 0. 背景与动机 + +项目已建立了 `agent_language.py` 作为 LLM 提示词多语言注入的核心模块，并在 `agent_routes.py` 中通过 `get_language_instruction()` 从 `Accept-Language` header 读取用户语言。然而： + +1. **调用覆盖不完整** — 部分 Agent 路由遗漏了语言注入（如 `workspace-summary`、`sort-data`） +2. **注入方式不统一** — 各 Agent 以不同方式拼接 language_instruction（有的用 marker 定位插入、有的直接追加到末尾），没有统一的接口约束 +3. **前端只支持 en/zh** — `agent_language.py` 注册了 20 种语言，但前端 i18n 翻译文件只有 `en` 和 `zh` 两组 +4. **缺少自动化保障** — 没有 lint 规则或单元测试来防止新的 LLM 调用点遗漏语言注入 +5. **已有规范文档分散** — Cursor rule、SKILL.md、design-doc-3 分别有一些约定，但开发者容易遗漏 + +本文档定义一个系统性的规范化方案，确保多语言处理有统一的模式、完整的覆盖和可持续的质量保障。 + +--- + +## 1. 现状审计 + +### 1.1 后端 Agent 语言注入覆盖表 + +| Agent 类 | 文件 | 接收 `language_instruction` | 路由注入 | mode | 状态 | +|----------|------|:---:|:---:|------|------| +| `DataRecAgent` | `agent_data_rec.py` | ✅ | ✅ `derive-data` | compact | **正常** | +| `DataTransformationAgent` | `agent_data_transform.py` | ✅ | ✅ `derive-data` / `refine-data` | compact | **正常** | +| `DataAgent` | `data_agent.py` | ✅ | ✅ `data-agent-streaming` | full + compact(rec) | **正常** | +| `DataLoadAgent` | `agent_data_load.py` | ✅ | ✅ `process-data-on-load` | compact | **正常** | +| `DataCleanAgentStream` | `agent_data_clean_stream.py` | ✅ | ✅ `clean-data-stream` | full | **正常** | +| `CodeExplanationAgent` | `agent_code_explanation.py` | ✅ | ✅ `code-expl` | full | **正常** | +| `ChartInsightAgent` | `agent_chart_insight.py` | ✅ | ✅ `chart-insight` | full | **正常** | +| `InteractiveExploreAgent` | `agent_interactive_explore.py` | ✅ | ✅ `get-recommendation-questions` | full | **正常** | +| `ReportGenAgent` | `agent_report_gen.py` | ✅ | ✅ `generate-report-stream` | full | **正常** | +| `SortDataAgent` | `agent_sort_data.py` | ❌ | ❌ `sort-data` | — | **⚠️ 遗漏** | +| *(inline)* workspace-summary | `agent_routes.py` L992-1046 | — | ❌ | — | **⚠️ 遗漏** | +| *(inline)* test-model | `agent_routes.py` L188-227 | — | ❌ | — | **无需注入**（非用户可见） | + +### 1.2 遗漏详情 + +#### SortDataAgent（`sort-data` 路由） + +`SortDataAgent.__init__()` 不接收 `language_instruction` 参数，路由处理也未调用 `get_language_instruction()`。虽然排序结果本身是数据值的重排列（不涉及翻译），但返回的 `reason` 字段是面向用户的自然语言文本，应该跟随 UI 语言。 + +**影响级别**：低 — `reason` 字段在 UI 中显示但不是核心功能文本。 + +#### workspace-summary 路由 + +工作区名称直接展示在侧边栏，但 system prompt 中未注入语言指令。中文用户会看到英文的工作区名称。 + +**影响级别**：中 — 用户每次打开应用都会看到。 + +### 1.3 注入方式一致性审计 + +| Agent | 注入方式 | 说明 | +|-------|---------|------| +| `DataRecAgent` | marker 定位插入 (`"You are a data scientist"` 之后) | 策略性插入到 role 声明之后 | +| `DataTransformationAgent` | marker 定位插入 (`"**About the execution environment:**"` 之前) | 策略性插入到技术细节之前 | +| `DataAgent` | `_build_system_prompt()` 末尾追加 | 动态构建 prompt，末尾追加 | +| `DataLoadAgent` | system prompt 末尾追加 | 简单追加 | +| `DataCleanAgentStream` | system prompt 末尾追加 | 简单追加 | +| `CodeExplanationAgent` | system prompt 末尾追加 | 简单追加 | +| `ChartInsightAgent` | system prompt 末尾追加 | 简单追加 | +| `InteractiveExploreAgent` | system prompt 末尾追加 | 简单追加 | +| `ReportGenAgent` | system prompt 末尾追加 | 简单追加 | + +**结论**：有两种注入策略（marker 定位 vs 末尾追加），两种都是可接受的。marker 策略适用于需要精确控制指令位置的复杂 prompt，末尾追加适用于简单场景。**当前不需要强制统一**，但需要在规范中明确这两种模式的适用条件。 + +### 1.4 前端 i18n 覆盖 + +| 层面 | 状态 | +|------|------| +| UI 翻译文件 (locales) | 仅 `en` 和 `zh` | +| `agent_language.py` 语言注册表 | 20 种语言 | +| 前端语言切换器 | 从 Redux `availableLanguages` 动态读取 | +| `fetchWithIdentity()` header | ✅ 正确注入 `Accept-Language` | +| Plugin 翻译 (Superset) | 仅 `en` 和 `zh` | + +--- + +## 2. 规范化目标 + +### P0（必须完成） +1. 补齐遗漏的语言注入点（SortDataAgent、workspace-summary） +2. 建立 Agent 基类或 Mixin，统一 `language_instruction` 的接收和注入接口 +3. 添加单元测试，确保所有 Agent 构造函数支持 `language_instruction` +4. 更新开发者文档，合并分散的约定到一个权威参考文档 + +### P1（应该完成） +5. 添加 lint 或静态检查规则，检测新增的 LLM 调用点是否注入了语言指令 +6. 创建语言注入集成测试，模拟不同语言请求验证完整链路 +7. 规范 `mode` 选择决策树，让新 Agent 开发者能快速判断 + +### P2（锦上添花） +8. 扩展前端翻译覆盖（优先添加 ja、ko、fr、de 等高需求语言） +9. 将 `agent_language.py` 中的模板抽象为配置文件，支持运行时热加载 +10. 建立翻译贡献流程（community translation） + +--- + +## 3. 详细方案 + +### 3.1 Phase 1：补齐遗漏（P0，预计 0.5 天） + +#### 3.1.1 SortDataAgent 添加语言支持 + +```python +# agent_sort_data.py +class SortDataAgent(object): + + def __init__(self, client, language_instruction=""): + self.client = client + self.language_instruction = language_instruction + + def run(self, name, values, n=1): + system_prompt = SYSTEM_PROMPT + if self.language_instruction: + system_prompt = system_prompt + "\n\n" + self.language_instruction + + # ... 其余不变 ... +``` + +```python +# agent_routes.py — sort_data_request() +language_instruction = get_language_instruction(mode="compact") +agent = SortDataAgent(client=client, language_instruction=language_instruction) +``` + +**mode 选择**：`"compact"` — SortDataAgent 的输出是结构化 JSON，仅 `reason` 字段面向用户，用 compact 模式足够且不干扰排序逻辑。 + +#### 3.1.2 workspace-summary 添加语言注入 + +```python +# agent_routes.py — workspace_summary() +lang_instruction = get_language_instruction(mode="compact") +lang_suffix = f"\n\n{lang_instruction}" if lang_instruction else "" + +messages = [ + { + "role": "system", + "content": ( + "You are a helpful assistant. Generate a very short name (3-5 words) " + "for a data analysis workspace based on the context below. " + "Return ONLY the name, no quotes, no explanation." + + lang_suffix + ), + }, + {"role": "user", "content": context_str}, +] +``` + +### 3.2 Phase 2：统一 Agent 接口约束（P0，预计 1 天） + +#### 3.2.1 定义 Agent 协议（Protocol/ABC） + +不强制所有 Agent 继承同一基类（避免大范围重构），而是采用 Python Protocol 约束： + +```python +# agents/agent_protocol.py +from typing import Protocol, runtime_checkable + +@runtime_checkable +class LanguageAwareAgent(Protocol): + """Any Agent that receives LLM language instructions must expose this attribute.""" + language_instruction: str +``` + +#### 3.2.2 统一注入辅助函数 + +抽取重复的 "追加到 system prompt" 逻辑为公共函数： + +```python +# agents/agent_language.py（在 build_language_instruction 之后添加） + +def inject_language_instruction( + system_prompt: str, + language_instruction: str, + *, + marker: str | None = None, +) -> str: + """Inject language instruction into a system prompt. + + Parameters + ---------- + system_prompt : str + The base system prompt. + language_instruction : str + The language instruction block (empty string = no-op). + marker : str | None + If provided, insert before this marker. Otherwise append. + """ + if not language_instruction: + return system_prompt + + if marker: + idx = system_prompt.find(marker) + if idx > 0: + return ( + system_prompt[:idx] + + language_instruction + "\n\n" + + system_prompt[idx:] + ) + + return system_prompt + "\n\n" + language_instruction +``` + +然后各 Agent 统一调用： + +```python +from data_formulator.agents.agent_language import inject_language_instruction + +# DataTransformationAgent.__init__() +self.system_prompt = inject_language_instruction( + self.system_prompt, language_instruction, + marker="**About the execution environment:**" +) + +# ChartInsightAgent.__init__() (简单场景) +system_prompt = inject_language_instruction(system_prompt, self.language_instruction) +``` + +### 3.3 Phase 3：测试保障（P1，预计 1 天） + +#### 3.3.1 单元测试：所有 Agent 支持 language_instruction + +```python +# tests/test_language_injection.py +import pytest +from data_formulator.agents.agent_language import ( + build_language_instruction, + inject_language_instruction, + LANGUAGE_DISPLAY_NAMES, +) + +ALL_AGENTS = [ + ("DataRecAgent", "data_formulator.agents.agent_data_rec", "DataRecAgent"), + ("DataTransformationAgent", "data_formulator.agents.agent_data_transform", "DataTransformationAgent"), + ("DataAgent", "data_formulator.agents.data_agent", "DataAgent"), + ("DataLoadAgent", "data_formulator.agents.agent_data_load", "DataLoadAgent"), + ("DataCleanAgentStream", "data_formulator.agents.agent_data_clean_stream", "DataCleanAgentStream"), + ("CodeExplanationAgent", "data_formulator.agents.agent_code_explanation", "CodeExplanationAgent"), + ("ChartInsightAgent", "data_formulator.agents.agent_chart_insight", "ChartInsightAgent"), + ("InteractiveExploreAgent", "data_formulator.agents.agent_interactive_explore", "InteractiveExploreAgent"), + ("ReportGenAgent", "data_formulator.agents.agent_report_gen", "ReportGenAgent"), + ("SortDataAgent", "data_formulator.agents.agent_sort_data", "SortDataAgent"), +] + + +class TestBuildLanguageInstruction: + def test_english_returns_empty(self): + assert build_language_instruction("en") == "" + + def test_non_english_returns_instruction(self): + result = build_language_instruction("zh") + assert "[LANGUAGE INSTRUCTION]" in result + assert "Simplified Chinese" in result + + def test_compact_mode(self): + full = build_language_instruction("zh", mode="full") + compact = build_language_instruction("zh", mode="compact") + assert len(compact) < len(full) + + @pytest.mark.parametrize("lang", [k for k in LANGUAGE_DISPLAY_NAMES if k != "en"]) + def test_all_registered_languages(self, lang): + result = build_language_instruction(lang) + assert result != "" + assert "[LANGUAGE INSTRUCTION]" in result + + +class TestInjectLanguageInstruction: + def test_empty_instruction_noop(self): + prompt = "You are a data scientist." + assert inject_language_instruction(prompt, "") == prompt + + def test_append_without_marker(self): + prompt = "You are a data scientist." + result = inject_language_instruction(prompt, "[LANG]") + assert result.endswith("[LANG]") + + def test_insert_before_marker(self): + prompt = "Role description.\n\n**About the execution environment:**\nDetails." + result = inject_language_instruction( + prompt, "[LANG]", + marker="**About the execution environment:**" + ) + assert result.index("[LANG]") < result.index("**About the execution environment:**") + + +class TestAgentLanguageParam: + """Verify each Agent constructor accepts language_instruction.""" + + @pytest.mark.parametrize("label,module_path,class_name", ALL_AGENTS) + def test_constructor_has_language_instruction(self, label, module_path, class_name): + import importlib, inspect + mod = importlib.import_module(module_path) + cls = getattr(mod, class_name) + sig = inspect.signature(cls.__init__) + params = list(sig.parameters.keys()) + assert "language_instruction" in params, ( + f"{label}.__init__() missing language_instruction parameter" + ) +``` + +#### 3.3.2 路由层集成测试 + +```python +# tests/test_route_language_injection.py +"""Verify that all user-facing agent routes call get_language_instruction().""" + +ROUTES_NEEDING_LANGUAGE = [ + "process-data-on-load", + "clean-data-stream", + "derive-data", + "refine-data", + "data-agent-streaming", + "code-expl", + "chart-insight", + "get-recommendation-questions", + "generate-report-stream", + "sort-data", + "workspace-summary", +] + +ROUTES_EXEMPT = [ + "test-model", + "list-global-models", + "check-available-models", + "refresh-derived-data", +] +``` + +### 3.4 Phase 4：开发者规范文档整合（P0，预计 0.5 天） + +将 `design-docs/3-language-injection-analysis.md`、`.cursor/rules/language-injection-conventions.mdc`、`.cursor/skills/language-injection/SKILL.md` 的核心约定整合为一个权威参考，避免信息分散。 + +#### 3.4.1 核心决策树：新增 LLM 调用点 + +``` +新增 LLM 调用 → 输出是否面向用户展示？ + │ + ├── 否（健康检查、内部工具调用、日志） + │ └── ✅ 不需要注入语言指令 + │ + └── 是 + ├── 是否为独立 Agent 类？ + │ ├── 是 → 构造函数添加 language_instruction="" 参数 + │ │ 使用 inject_language_instruction() 注入到 system prompt + │ │ 在路由中调用 get_language_instruction(mode=?) 传入 + │ │ + │ └── 否（内联 LLM 调用） + │ └── 直接在路由中拼接到 system prompt + │ + └── mode 选择： + ├── 输出主要是自然语言文本 → mode="full" + │ （ChartInsight、Report、Explore、CodeExplanation、DataClean） + │ + └── 输出主要是代码/结构化 JSON → mode="compact" + （DataRec、DataTransform、DataLoad、Sort、workspace-summary） +``` + +#### 3.4.2 开发者检查清单 + +新增或修改 Agent/LLM 调用时，PR reviewer 应检查： + +- [ ] Agent 构造函数是否接收 `language_instruction` 参数？ +- [ ] 路由是否调用 `get_language_instruction(mode=...)` 并传入 Agent？ +- [ ] mode 选择是否正确（full vs compact）？ +- [ ] 是否使用了 `inject_language_instruction()` 辅助函数？ +- [ ] 是否有硬编码的语言字符串（如 `"回答请使用中文"`）？ +- [ ] `agent_diagnostics.py` 是否记录了 language_instruction？ +- [ ] 是否有对应的单元测试验证 language_instruction 参数存在？ + +### 3.5 Phase 5：静态检查与 CI 保障（P1，预计 0.5 天） + +#### 3.5.1 自定义 lint 脚本 + +创建一个简单的 Python 脚本检测 `agent_routes.py` 中所有调用 `client.get_completion()` 或实例化 Agent 类的地方，验证上下文中是否有 `get_language_instruction` 调用： + +```python +# scripts/check_language_injection.py +"""CI check: verify all user-facing LLM calls in agent_routes.py inject language.""" + +import ast, sys + +EXEMPT_FUNCTIONS = {"test_model", "check_available_models", "list_global_models"} + +# 解析 AST，对每个路由函数检查是否包含 get_language_instruction 调用 +# ... +``` + +#### 3.5.2 Pre-commit hook + +```yaml +# .pre-commit-config.yaml (追加) +- repo: local + hooks: + - id: check-language-injection + name: Check language injection in agent routes + entry: python scripts/check_language_injection.py + language: python + files: agent_routes\.py$ +``` + +### 3.6 Phase 6：前端翻译扩展（P2，按需） + +#### 3.6.1 优先扩展的语言 + +根据 `agent_language.py` 注册表和用户需求，推荐优先级： + +| 优先级 | 语言 | 理由 | +|--------|------|------| +| 1 | ja (日语) | 东亚高活跃用户群 | +| 2 | ko (韩语) | 东亚高活跃用户群 | +| 3 | fr (法语) | 欧洲及非洲广泛使用 | +| 4 | de (德语) | 欧洲技术社区活跃 | +| 5 | es (西班牙语) | 全球第二大母语人口 | + +#### 3.6.2 翻译文件结构 + +每种新语言需要： + +``` +src/i18n/locales// +├── common.json +├── upload.json +├── chart.json +├── model.json +├── encoding.json +├── messages.json +├── navigation.json +└── index.ts +``` + +加上 `i18n/index.ts` 和 `i18n/locales/index.ts` 的注册。 + +--- + +## 4. 实施计划 + +| Phase | 内容 | 优先级 | 预估工期 | 前置依赖 | +|-------|------|--------|---------|---------| +| **Phase 1** | 补齐 SortDataAgent、workspace-summary 的语言注入 | P0 | 0.5 天 | 无 | +| **Phase 2** | 定义 `inject_language_instruction()` 辅助函数；重构各 Agent 统一调用 | P0 | 1 天 | Phase 1 | +| **Phase 3** | 单元测试 + 路由集成测试 | P1 | 1 天 | Phase 2 | +| **Phase 4** | 整合开发者规范文档，更新 Cursor rule 和 SKILL | P0 | 0.5 天 | Phase 2 | +| **Phase 5** | 静态检查脚本 + pre-commit hook | P1 | 0.5 天 | Phase 4 | +| **Phase 6** | 前端翻译扩展（ja、ko、fr 等） | P2 | 按需 | Phase 1 | + +**总计 Phase 1-5**：约 3.5 个开发日 + +--- + +## 5. 反模式清单（明确禁止） + +| 反模式 | 为什么不行 | 正确做法 | +|--------|-----------|---------| +| 使用环境变量 `os.environ.get("DF_DEFAULT_LANGUAGE")` | 退化为 per-process 语言，破坏多用户场景 | 始终从 `Accept-Language` header 读取 | +| 在 LLM client 层做全局拦截注入 | 隐式行为、无法区分 full/compact mode、调试困难 | 在路由层显式注入 | +| 硬编码语言字符串 `"回答请使用中文"` | 不可配置、不支持其他语言 | 使用 `build_language_instruction()` | +| 新建 `MessageBuilder` 工具类 | 与 `agent_language.py` 形成并行抽象，增加维护成本 | 复用现有 `inject_language_instruction()` | +| 在 user message 中注入语言指令 | 与 OpenAI 最佳实践相悖（系统指令应在 system prompt） | 只在 system prompt 中注入 | +| 跳过 `get_language_instruction()` 直接调用 `build_language_instruction()` | 绕过了从 request header 读取语言的标准链路 | 在路由中使用 `get_language_instruction(mode=...)` | + +--- + +## 6. 风险与注意事项 + +| 风险 | 缓解措施 | +|------|---------| +| Phase 2 重构可能引入 system prompt 格式变化 | 通过对比测试确保重构前后生成的 prompt 内容一致 | +| 新增语言翻译质量难以保证 | 建立 community review 流程，先覆盖高需求语言 | +| compact mode 下语言指令过简导致 LLM 不遵从 | 对各语言进行 A/B 测试，必要时调整 compact 模板 | +| SortDataAgent 注入语言后 LLM 排序行为变化 | 排序测试用例覆盖中文、日文等非拉丁文字数据 | + +--- + +## 7. 相关文件索引 + +| 文件 | 角色 | +|------|------| +| `py-src/data_formulator/agents/agent_language.py` | 语言指令构建核心模块 | +| `py-src/data_formulator/agent_routes.py` | 路由层：`get_language_instruction()` + 各端点调用 | +| `py-src/data_formulator/agents/agent_sort_data.py` | **待修复**：缺少 language_instruction | +| `src/app/utils.tsx` | 前端：`getAgentLanguage()` + `fetchWithIdentity()` | +| `src/i18n/index.ts` | 前端 i18n 配置 | +| `src/i18n/locales/` | 前端翻译文件（当前仅 en/zh） | +| `.cursor/rules/language-injection-conventions.mdc` | Cursor 开发规范 | +| `.cursor/skills/language-injection/SKILL.md` | 详细架构说明 | +| `design-docs/3-language-injection-analysis.md` | 早期分析文档（本文档是其后续） | + +--- + +## 8. 验收标准 + +Phase 1-4 完成后，以下测试全部通过： + +- [ ] `build_language_instruction()` 对所有 20 种注册语言返回非空指令 +- [ ] 所有 Agent 构造函数均接受 `language_instruction` 参数 +- [ ] 所有面向用户的路由端点均调用 `get_language_instruction()` +- [ ] SortDataAgent 返回的 `reason` 字段跟随 UI 语言 +- [ ] workspace-summary 返回的名称跟随 UI 语言 +- [ ] 英文用户不受影响（`build_language_instruction("en")` 返回 `""`） +- [ ] `inject_language_instruction()` 辅助函数被所有 Agent 使用 +- [ ] PR review 检查清单已纳入团队流程 +- [ ] 静态检查脚本能检测到新增的未注入语言的 LLM 调用点 diff --git a/docker-compose.test.yml b/docker-compose.test.yml new file mode 100644 index 00000000..4df9d4fa --- /dev/null +++ b/docker-compose.test.yml @@ -0,0 +1,69 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +# Unified test-database stack for data-loader integration tests. +# +# Usage: +# docker compose -f docker-compose.test.yml up -d # start all +# docker compose -f docker-compose.test.yml up -d mysql # start one +# pytest tests/plugin/test_mysql/ -v # run tests +# docker compose -f docker-compose.test.yml down # tear down +# +# See tests/plugin/README.md for details. + +services: + mysql: + build: tests/plugin/test_mysql + container_name: df-test-mysql + ports: + - "${MYSQL_PORT:-3307}:3306" + environment: + MYSQL_ROOT_PASSWORD: mysql + MYSQL_DATABASE: testdb + healthcheck: + test: ["CMD", "mysqladmin", "ping", "-h", "localhost", "-uroot", "-pmysql"] + interval: 5s + timeout: 3s + retries: 15 + + postgres: + build: tests/plugin/test_postgres + container_name: df-test-postgres + ports: + - "${PG_PORT:-5433}:5432" + environment: + POSTGRES_USER: postgres + POSTGRES_PASSWORD: postgres + POSTGRES_DB: testdb + healthcheck: + test: ["CMD-SHELL", "pg_isready -U postgres"] + interval: 5s + timeout: 3s + retries: 10 + + mongodb: + build: tests/plugin/test_mongodb + container_name: df-test-mongodb + ports: + - "${MONGO_PORT:-27018}:27017" + environment: + MONGO_INITDB_ROOT_USERNAME: admin + MONGO_INITDB_ROOT_PASSWORD: admin + MONGO_INITDB_DATABASE: testdb + healthcheck: + test: ["CMD", "mongosh", "--quiet", "--eval", "db.adminCommand('ping')"] + interval: 5s + timeout: 3s + retries: 10 + + bigquery: + build: tests/plugin/test_bigquery + container_name: df-test-bigquery + ports: + - "${BQ_PORT:-9050}:9050" + - "${BQ_GRPC_PORT:-9060}:9060" + healthcheck: + test: ["CMD-SHELL", "wget -q --spider http://localhost:9050 || exit 0"] + interval: 5s + timeout: 3s + retries: 10 diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 00000000..b0eb02f4 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,27 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +# Docker Compose configuration for Data Formulator. +# +# Quick start: +# 1. Copy .env.template to .env and fill in your API keys. +# 2. docker compose up --build +# 3. Open http://localhost:5567 in your browser. + +services: + data-formulator: + build: + context: . + dockerfile: Dockerfile + image: data-formulator:latest + ports: + - "5567:5567" + env_file: + - .env + volumes: + # Persist workspace data (uploaded files, sessions, etc.) across container restarts. + - data_formulator_home:/home/appuser/.data_formulator + restart: unless-stopped + +volumes: + data_formulator_home: diff --git a/docs-cn/5-datasource_plugin-development-guide.md b/docs-cn/5-datasource_plugin-development-guide.md new file mode 100644 index 00000000..f243dc60 --- /dev/null +++ b/docs-cn/5-datasource_plugin-development-guide.md @@ -0,0 +1,595 @@ +# 数据源插件开发指南 + +> **目标读者**：要为 Data Formulator 开发新数据源插件的开发者。 +> +> **前置阅读**：[1-data-source-plugin-architecture.md](1-data-source-plugin-architecture.md)（设计原理）、[1-sso-plugin-architecture.md](1-sso-plugin-architecture.md)（SSO + 凭证架构）。 +> +> **参考实现**：`plugins/superset/`（后端）+ `src/plugins/superset/`（前端）。 + +--- + +## 目录 + +1. [快速上手：新增一个插件](#1-快速上手新增一个插件) +2. [后端：目录结构与约定](#2-后端目录结构与约定) +3. [后端：基类契约](#3-后端基类契约) +4. [后端：路由设计](#4-后端路由设计) +5. [后端：PluginDataWriter](#5-后端plugindatawriter) +6. [后端：认证路由规范（三模式协商）](#6-后端认证路由规范三模式协商) +7. [后端：CredentialVault 集成](#7-后端credentialvault-集成) +8. [前端：目录结构与约定](#8-前端目录结构与约定) +9. [前端：模块契约](#9-前端模块契约) +10. [前端：国际化](#10-前端国际化) +11. [环境变量命名规范](#11-环境变量命名规范) +12. [测试规范](#12-测试规范) +13. [核心代码零修改原则](#13-核心代码零修改原则) +14. [Checklist：插件上线前检查](#14-checklist插件上线前检查) + +--- + +## 1. 快速上手：新增一个插件 + +假设要接入 Metabase，只需两步： + +**后端**：在 `py-src/data_formulator/plugins/` 下创建 `metabase/` 目录。 + +``` +plugins/ +├── base.py # 框架，不要修改 +├── data_writer.py # 框架，不要修改 +├── __init__.py # 框架，不要修改 +└── metabase/ # ← 新增 + ├── __init__.py # plugin_class = MetabasePlugin + ├── metabase_client.py + └── routes/ + ├── __init__.py + ├── auth.py + ├── catalog.py + └── data.py +``` + +**前端**：在 `src/plugins/` 下创建 `metabase/` 目录。 + +``` +src/plugins/ +├── types.ts # 框架，不要修改 +├── registry.ts # 框架，不要修改 +├── PluginHost.tsx # 框架，不要修改 +├── index.ts # 框架，不要修改 +└── metabase/ # ← 新增 + ├── index.ts # default export: DataSourcePluginModule + ├── MetabasePanel.tsx + └── locales/ + ├── en.json + └── zh.json +``` + +**配置**：在 `.env` 中设置必须环境变量： + +```bash +PLG_METABASE_URL=https://metabase.example.com +``` + +**启动**：重启 Data Formulator → 框架自动发现并启用 → 前端数据上传对话框中出现 Metabase Tab。 + +**核心原则：不修改任何框架文件。** 如果需要改 `plugins/__init__.py`、`registry.ts`、`app.py` 才能让新插件工作，说明框架有 bug，应该修框架。 + +--- + +## 2. 后端：目录结构与约定 + +``` +plugins// +├── __init__.py # 必须：暴露 plugin_class 属性 +├── _client.py # 建议：封装外部系统 HTTP API +├── auth_bridge.py # 可选：SSO 透传桥接 +├── catalog.py # 可选：目录浏览逻辑 +├── session_helpers.py # 建议：Plugin-namespaced session 操作 +└── routes/ + ├── __init__.py + ├── auth.py # 必须：认证路由 + ├── catalog.py # 建议：目录路由 + └── data.py # 必须：数据加载路由 +``` + +### 关键约定 + +- `__init__.py` 必须有一个模块级属性 `plugin_class`，指向 `DataSourcePlugin` 的具体子类。 +- 如果插件有重量级依赖（如某个 SDK），应在 `__init__.py` 的顶层 import 中引入。框架会 `try/except ImportError`，缺依赖时优雅跳过并记录原因。 +- Session key 必须用 `plugin__` 前缀隔离（如 `plugin_superset_token`、`plugin_metabase_token`），防止多插件间状态冲突。 + +--- + +## 3. 后端：基类契约 + +```python +from data_formulator.plugins.base import DataSourcePlugin + +class MetabasePlugin(DataSourcePlugin): + + @staticmethod + def manifest() -> dict: + return { + # ── 必须 ── + "id": "metabase", # 全局唯一 slug + "name": "Metabase", # 显示名 + "env_prefix": "PLG_METABASE", # 环境变量前缀 + "required_env": ["PLG_METABASE_URL"], # 全部存在才启用 + + # ── 可选 ── + "icon": "metabase", + "description": "Connect to Metabase to browse and load questions.", + "auth_modes": ["password", "sso"], # 支持的认证方式 + "capabilities": ["questions", "dashboards"], + } + + def create_blueprint(self) -> Blueprint: + """组装路由。url_prefix 必须是 /api/plugins//""" + ... + + def get_frontend_config(self) -> dict: + """返回给前端的非敏感配置。绝对不能包含密钥。""" + ... + + def on_enable(self, app) -> None: + """初始化共享服务（client、catalog 等），存到 app.extensions。""" + ... + + def supports_sso_passthrough(self) -> bool: + """外部系统与 DF 共享 IdP 时返回 True。""" + return False +``` + +### manifest 字段说明 + +| 字段 | 类型 | 必须 | 说明 | +|------|------|------|------| +| `id` | `str` | 是 | 全局唯一 slug，用作路由前缀、session key 前缀、前端匹配 key | +| `name` | `str` | 是 | 人类可读名称，显示在 UI | +| `env_prefix` | `str` | 是 | 环境变量命名前缀（如 `PLG_METABASE`） | +| `required_env` | `list[str]` | 是 | 必须环境变量列表，缺任一则插件不启用 | +| `icon` | `str` | 否 | 图标标识，前端 Icon 组件使用 | +| `description` | `str` | 否 | 简短描述 | +| `auth_modes` | `list[str]` | 否 | 支持的认证方式：`"password"` / `"sso"` / `"api_key"` | +| `capabilities` | `list[str]` | 否 | 能力声明，前端可据此决定 UI | +| `version` | `str` | 否 | 插件版本号 | +| `optional_env` | `list[str]` | 否 | 可选环境变量（缺失不影响启用） | + +--- + +## 4. 后端：路由设计 + +### URL 前缀 + +所有路由都在 `/api/plugins//` 下： + +``` +/api/plugins//auth/login POST 登录 +/api/plugins//auth/status GET 认证状态 +/api/plugins//auth/logout POST 登出 +/api/plugins//catalog/... GET 数据目录 +/api/plugins//data/load-* POST 数据加载 +``` + +### 响应格式 + +统一 JSON 格式： + +```json +// 成功 +{"status": "ok", ...payload} + +// 错误 +{"status": "error", "message": "Human-readable error description"} +``` + +HTTP 状态码语义： +- `200` — 成功 +- `400` — 请求参数错误（插件负责校验） +- `401` — 未认证或认证过期 +- `502` — 外部系统不可达或返回错误 +- `503` — 插件依赖的服务不可用（如 Vault 未配置） + +--- + +## 5. 后端：PluginDataWriter + +插件加载的数据通过 `PluginDataWriter` 写入用户 Workspace： + +```python +from data_formulator.plugins.data_writer import PluginDataWriter + +writer = PluginDataWriter("metabase") # plugin_id + +result = writer.write_dataframe( + df, # pandas DataFrame + "sales_data", # 表名 + overwrite=True, # True=覆盖同名, False=自动加后缀 + source_metadata={ # 记录来源，供刷新使用 + "plugin": "metabase", + "question_id": 42, + }, +) +# result = {"table_name": "sales_data", "row_count": 1234, "columns": [...], "is_renamed": False} +``` + +**不要直接调用 `workspace.write_parquet()`**。`PluginDataWriter` 封装了身份解析、表名清洗、元数据打标等逻辑。 + +--- + +## 6. 后端：认证路由规范（三模式协商） + +**这是插件开发中最重要的规范。** + +> **代码层面强制约束**：所有插件必须继承 `PluginAuthHandler` 基类（`plugins/auth_base.py`）。 +> 基类自动生成 `/login`、`/logout`、`/status`、`/me` 标准路由，内置 Vault 生命周期管理。 +> **插件作者无需手动处理 Vault 存储、清除、自动登录逻辑**——基类全部代劳。 + +### 三模式协商流程 + +``` +用户打开插件面板 → GET /api/plugins//auth/status + │ + ▼ +后端按优先级依次尝试: + │ + ├─ ① Session 中已有有效 token? + │ → {"authenticated": true, "mode": "session"} + │ + ├─ ② SSO token 可用 + 插件 supports_sso_passthrough()? + │ → 尝试 SSO 透传登录 + │ → 成功 → {"authenticated": true, "mode": "sso"} + │ → 失败 → 继续 + │ + ├─ ③ CredentialVault 中有已存凭证? + │ → 尝试用已存凭证登录外部系统 + │ → 成功 → {"authenticated": true, "mode": "vault"} + │ → 失败 → {"authenticated": false, "vault_stale": true} + │ + └─ 全部未命中 + → {"authenticated": false, "available_modes": ["password", "api_key"]} + +前端根据响应: + ├─ authenticated=true → 直接显示数据目录 + ├─ vault_stale=true → 显示登录表单 + 提示"已保存的凭证已失效" + └─ authenticated=false → 显示登录表单 +``` + +### 使用 PluginAuthHandler（必须） + +```python +# routes/auth.py + +from data_formulator.plugins.auth_base import PluginAuthHandler + +class MetabaseAuthHandler(PluginAuthHandler): + """插件作者只需实现以下 4 个方法。""" + + def do_login(self, username: str, password: str) -> dict: + """与外部系统认证，成功后写入 Flask session。 + 返回 {"user": {"id": ..., "username": ..., ...}} + 失败时抛异常。""" + result = _bridge().login(username, password) + save_session(result["access_token"], ...) + return {"user": {...}} + + def do_clear_session(self) -> None: + """清除插件在 Flask session 中的所有 key。""" + clear_session() + + def get_session_auth(self) -> dict | None: + """检查当前 session 是否已认证。 + 已认证返回 {"authenticated": True, "mode": "session", "user": {...}} + 未认证返回 None。""" + token, user = require_auth() + if token and user: + return {"authenticated": True, "mode": "session", "user": format_user(user)} + return None + + def get_current_user(self) -> dict | None: + """从 session 中取当前用户，或 None。""" + return get_user() + + +# 创建 handler 实例 → 生成标准路由 Blueprint +_handler = MetabaseAuthHandler("metabase") +auth_bp = _handler.create_auth_blueprint("/api/plugins/metabase/auth") + +# 如有插件专属路由，可继续追加到同一个 Blueprint +@auth_bp.route("/sso/callback", methods=["POST"]) +def sso_callback(): + ... +``` + +### 基类自动处理的路由 + +| 路由 | 方法 | 基类自动行为 | +|------|------|-------------| +| `/login` | POST | 调用 `do_login()` + remember=true 存 Vault / remember=false 清 Vault | +| `/logout` | POST | 调用 `do_clear_session()` + **强制清除 Vault**（不可遗漏） | +| `/status` | GET | Session → Vault 自动登录 → 未认证（三模式协商） | +| `/me` | GET | 返回 `get_current_user()` 或 401 | + +### 核心规则总结 + +| 规则 | 说明 | 由基类强制 | +|------|------|-----------| +| **先 Session → 再 SSO → 再 Vault → 最后手动** | 严格按优先级链，不跳步 | ✅ status 路由 | +| **Vault 凭证必须实测验证** | 从 Vault 取出后必须**实际登录**外部系统 | ✅ try_vault_login | +| **失效凭证返回 vault_stale** | 外部系统密码已改时返回 `vault_stale: true` | ✅ try_vault_login | +| **remember=true 才存 Vault** | 用户主动勾选"记住凭证"后才写入 | ✅ login 路由 | +| **remember=false 要清理 Vault** | 用户不勾选记住 → 删除旧凭证 | ✅ login 路由 | +| **退出必须同时清 Session + Vault** | 防止"退出后 Vault 自动登录回来"的死循环 | ✅ logout 路由 | +| **凭证只在服务端流转** | 前端只知道 authenticated + mode，不接触明文 | ✅ 架构设计 | +| **Vault 操作 best-effort** | 存/删/取失败时静默跳过，不阻断主流程 | ✅ vault_* 方法 | + +--- + +## 7. 后端：CredentialVault 集成 + +### 概述 + +CredentialVault 是一个可选组件（本地部署时自动零配置启用）。插件**不应假设** Vault 一定存在——Vault 不可用时，基类自动跳过，回退到纯 Session 模式。 + +> **重要**：插件作者**不需要手写** Vault 辅助函数。`PluginAuthHandler` 基类已内置 +> `vault_store()`、`vault_delete()`、`vault_retrieve()`、`try_vault_login()` 方法， +> 并在 login / logout / status 路由中自动调用。 + +### 如果需要在自定义路由中访问 Vault + +在极少数情况下（如自定义的凭证管理路由），可通过 handler 实例直接调用： + +```python +# 前提：_handler 是你的 PluginAuthHandler 子类实例 +_handler.vault_store({"username": "alice", "password": "pw"}) +_handler.vault_delete() +creds = _handler.vault_retrieve() # → dict | None +``` + +这些方法内部已处理 Vault 不可用、identity 解析失败等异常，不会抛出。 + +### Vault source_key 命名 + +`source_key` 统一使用 `manifest()["id"]`（即插件 ID），如 `"superset"`、`"metabase"`。一个插件只有一个 source_key，不需要更细粒度的区分。 + +### 匿名用户与 Vault + +Vault 按 `get_identity_id()` 的返回值隔离凭证。匿名用户的 identity 是 `browser:xxx`（来自浏览器 localStorage UUID），因此： + +- 匿名用户**可以**使用 Vault 保存凭证 +- 但凭证绑定的是浏览器 UUID，**不跨设备、不跨浏览器** +- 清除 localStorage 即失去关联 +- 这是可接受的行为——需要可靠凭证存储的用户应配置 SSO + +### Vault 不可用时的行为 + +| Vault 状态 | 插件行为 | +|------------|---------| +| 未配置（`CREDENTIAL_VAULT_KEY` 未设置） | `get_credential_vault()` 返回 None，插件跳过 Vault 步骤，直接展示登录表单 | +| 已配置但无凭证 | `vault.retrieve()` 返回 None，跳过 Vault 步骤 | +| 已配置且有凭证 | 取出并验证，成功则自动登录，失败则返回 vault_stale | +| 密钥已更换（旧凭证无法解密） | `vault.retrieve()` 返回 None（解密失败静默返回 None，不崩溃） | + +--- + +## 8. 前端：目录结构与约定 + +``` +src/plugins// +├── index.ts # 必须：default export DataSourcePluginModule +├── Panel.tsx # 必须：主面板组件 +├── Login.tsx # 建议：登录组件 +├── Catalog.tsx # 建议：数据目录组件 +├── api.ts # 建议：封装后端 API 调用 +└── locales/ + ├── en.json # 建议：英文翻译 + └── zh.json # 建议：中文翻译 +``` + +### 发现机制 + +前端使用 Vite 的 `import.meta.glob` 在**构建时**扫描 `src/plugins/*/index.{ts,tsx}`。你只需要在正确的位置创建文件，无需手动注册。 + +--- + +## 9. 前端：模块契约 + +```typescript +// src/plugins/metabase/index.ts + +import type { DataSourcePluginModule } from '../types'; +import { MetabasePanel } from './MetabasePanel'; +import en from './locales/en.json'; +import zh from './locales/zh.json'; + +const MetabaseIcon: React.FC<{ sx?: object }> = (props) => (/* SVG icon */); + +const metabasePlugin: DataSourcePluginModule = { + id: 'metabase', // 必须与后端 manifest.id 一致 + Icon: MetabaseIcon, // 数据源菜单中的图标 + Panel: MetabasePanel, // 主面板组件 + locales: { en, zh }, // 可选：国际化 +}; + +export default metabasePlugin; +``` + +### Panel 组件接口 + +```typescript +interface PluginPanelProps { + config: PluginConfig; // 后端 get_frontend_config() 的内容 + callbacks: PluginHostCallbacks; // 框架提供的回调 +} + +interface PluginHostCallbacks { + onDataLoaded: (info: DataLoadedInfo) => void; // 数据加载完成后调用 + onClose: () => void; // 关闭对话框 +} +``` + +**数据加载完成后必须调用 `callbacks.onDataLoaded()`**，框架会据此刷新 Workspace 表列表。 + +### 登录面板中的"记住凭证" + +如果插件支持密码登录，登录表单中应提供"记住凭证"（Remember credentials）复选框： + +```typescript +const [remember, setRemember] = useState(false); + +// 登录请求中传递 remember 标志 +const loginPayload = { username, password, remember }; +``` + +**复选框必须附带注释说明**，避免用户与浏览器内置的"记住密码"功能混淆： + +```typescript +} + label={t('plugin.xxx.rememberCredentials')} +/> + + {t('plugin.xxx.rememberCredentialsHint')} + +``` + +注释文案应说明：**凭证存储在服务器端（非浏览器），便于自动化 Agent 代用户拉取数据**。 + +当 `auth/status` 返回 `vault_stale: true` 时，应显示明确的提示： + +```typescript +if (authStatus.vault_stale) { + showWarning("已保存的凭证已失效，请重新输入"); +} +``` + +--- + +## 10. 前端：国际化 + +每个插件自带翻译文件，通过 `locales` 字段导出。框架在启动时自动合并到全局 i18n。 + +```json +// locales/en.json +{ + "plugin.metabase.name": "Metabase", + "plugin.metabase.login.title": "Connect to Metabase", + "plugin.metabase.login.remember": "Remember credentials" +} +``` + +**命名规范**：`plugin...`，避免与其他插件或核心翻译冲突。 + +--- + +## 11. 环境变量命名规范 + +| 前缀 | 用途 | 示例 | +|------|------|------| +| `PLG__` | 插件专属 | `PLG_SUPERSET_URL`、`PLG_METABASE_URL` | +| `PLG__SSO_*` | SSO 透传相关 | `PLG_SUPERSET_SSO_LOGIN_URL` | + +- `required_env` 中列出的变量全部存在时，插件才启用 +- 管理员通过 `.env` 文件或 Docker environment 配置 +- `get_frontend_config()` 可以暴露 URL 等非敏感值，但**绝不暴露密钥** + +--- + +## 12. 测试规范 + +### 测试文件位置 + +``` +tests/backend/unit/test__*.py # 单元测试 +tests/backend/integration/test__*.py # 集成测试 +tests/backend/fixtures// # API 响应 fixture +tests/frontend/unit/plugins// # 前端测试 +``` + +### 外部 API Mock 策略 + +不要直接调用真实外部系统。使用 fixture JSON（从真实系统录制）+ `unittest.mock.patch`： + +```python +@pytest.fixture +def mock_responses(fixture_dir): + def _load(name): + return json.loads((fixture_dir / "metabase" / name).read_text()) + return _load +``` + +### Vault 测试 + +测试 Vault 集成时使用 `tmp_path` 下的**真实 SQLite 文件 + 真实 Fernet 密钥**，不 mock 加密逻辑： + +```python +@pytest.fixture +def vault(tmp_path): + from cryptography.fernet import Fernet + key = Fernet.generate_key().decode() + return LocalCredentialVault(tmp_path / "test.db", key) +``` + +--- + +## 13. 核心代码零修改原则 + +添加新插件时，以下文件**不得修改**： + +| 文件 | 职责 | +|------|------| +| `plugins/__init__.py` | 自动发现逻辑 | +| `plugins/base.py` | 基类定义 | +| `plugins/auth_base.py` | 认证基类（Vault 生命周期） | +| `plugins/data_writer.py` | 数据写入工具 | +| `src/plugins/types.ts` | 前端类型 | +| `src/plugins/registry.ts` | 前端注册表 | +| `src/plugins/PluginHost.tsx` | 前端容器 | +| `src/plugins/index.ts` | 前端导出 | +| `app.py` | 应用入口 | +| `credential_vault/*` | 凭证保险箱框架 | +| `credential_routes.py` | 凭证管理 API | + +如果你发现必须修改以上文件才能完成新插件，请先提 issue 讨论框架层面的修复方案。 + +--- + +## 14. Checklist：插件上线前检查 + +### 后端 + +- [ ] `plugin_class` 是 `DataSourcePlugin` 子类 +- [ ] `manifest()` 包含 `id`、`name`、`env_prefix`、`required_env` +- [ ] `create_blueprint()` 的 `url_prefix` 是 `/api/plugins//` +- [ ] `get_frontend_config()` 不包含任何密钥 +- [ ] Session key 使用 `plugin__` 前缀 +- [ ] 数据写入使用 `PluginDataWriter`，不直接调用 workspace +- [ ] **认证路由继承 `PluginAuthHandler` 基类**（代码层面约束 Vault 生命周期） +- [ ] 只实现 `do_login`、`do_clear_session`、`get_session_auth`、`get_current_user` 四个方法 +- [ ] 退出时 Session + Vault 同时清除（由基类自动保证） +- [ ] Vault 不可用时优雅降级（由基类自动保证） +- [ ] HTTP 错误有意义的错误信息和正确的状态码 +- [ ] 缺少必须环境变量时不启用，不报错 + +### 前端 + +- [ ] `index.ts` default export 包含 `id`、`Icon`、`Panel` +- [ ] `id` 与后端 `manifest.id` 一致 +- [ ] 数据加载成功后调用 `callbacks.onDataLoaded()` +- [ ] 登录表单包含"记住凭证"复选框 + **注释说明**（服务器端存储，供自动化 Agent 使用） +- [ ] `vault_stale` 时显示"凭证已失效"提示 +- [ ] 翻译 key 使用 `plugin..*` 前缀 +- [ ] 不引用其他插件的代码 + +### 配置 + +- [ ] `.env.template` 中有本插件的环境变量说明 +- [ ] `required_env` 中的变量缺失时，插件自动跳过 + +### 测试 + +- [ ] 认证路由测试（含 Vault 自动取用、vault_stale、remember） +- [ ] 目录路由测试 +- [ ] 数据加载路由测试 +- [ ] fixture 文件从真实系统录制 +- [ ] 核心文件 git diff 为空（零修改验证） diff --git a/docs-cn/5.1-superset-sso-bridge-setup.md b/docs-cn/5.1-superset-sso-bridge-setup.md new file mode 100644 index 00000000..4cb646e7 --- /dev/null +++ b/docs-cn/5.1-superset-sso-bridge-setup.md @@ -0,0 +1,307 @@ +# Superset 端 SSO 桥接配置指南 + +本文档面向 **Superset 管理员**，说明如何在 Superset 端配置 SSO 桥接端点，使 Data Formulator（以下简称 DF）能通过 Superset 的 SSO 登录获取 JWT，从而调用 Superset REST API。 + +--- + +## 1. 背景 + +DF 需要调用 Superset REST API 来浏览数据集、仪表盘等。API 调用需要 Superset JWT。 + +由于 DF 和 Superset 部署在不同地址上（跨域），DF 无法直接读取 Superset 的 Session/Cookie。因此需要在 Superset 中添加一个小型桥接端点：用户通过 SSO 登录 Superset 后，该端点将 Session 转换为 JWT，通过浏览器 `postMessage` 传回 DF。 + +--- + +## 2. 工作原理 + +``` +DF 前端 Superset + │ │ + │ ① window.open(弹窗) │ + │ ──────────────────────────────────>│ /df-sso-bridge/?df_origin=http://DF地址 + │ │ + │ │ ② 如果用户未登录 → Superset 重定向到 SSO 登录 + │ │ 如果用户已登录 → 直接到步骤 ④ + │ │ + │ │ ③ 用户在 SSO 完成登录（账密/企微扫码等） + │ │ Superset 创建 Session，重定向回 /df-sso-bridge/ + │ │ + │ │ ④ bridge 端点： + │ │ - 检查 Session（用户已登录） + │ │ - 颁发 JWT access_token + refresh_token + │ │ - 返回 HTML，执行 postMessage 将 token 发给 DF + │ │ - 自动关闭弹窗 + │ ⑤ 收到 postMessage │ + │<─────────────────────────────────── │ + │ │ + │ ⑥ DF 拿到 Superset JWT，后续正常调用 API +``` + +--- + +## 3. 只需修改一个文件 + +在 Superset 服务器上编辑 `superset_config.py`，在末尾追加以下代码，然后**重启 Superset**。 + +不需要修改 Superset 源码、不需要安装额外包、不需要配置 CORS。 + +--- + +## 4. 完整代码 + +将以下代码追加到 `superset_config.py` 文件末尾： + +```python +# ============================================================================= +# Data Formulator SSO 桥接端点 +# 用途：DF 通过弹窗打开此端点，SSO 登录成功后将 Superset JWT +# 通过 postMessage 传回 DF 前端。 +# ============================================================================= + +from superset.security import SupersetSecurityManager +from flask_appbuilder import expose +from flask import request, Response +from flask_login import current_user + + +class CustomSecurityManager(SupersetSecurityManager): + + @expose("/df-sso-bridge/", methods=["GET"]) + def df_sso_bridge(self): + """ + Data Formulator SSO 桥接端点。 + + 当用户通过 SSO 登录 Superset 后，此端点： + 1. 为当前用户颁发 JWT access_token 和 refresh_token + 2. 通过 postMessage 将 token 发送给 DF 父窗口 + 3. 自动关闭弹窗 + + URL 参数: + df_origin: DF 前端的 origin（如 http://10.0.1.1:5567）， + 由 DF 前端自动传入，用于 postMessage 的 targetOrigin 安全校验。 + """ + df_origin = request.args.get("df_origin", "*") + + if not current_user.is_authenticated: + return Response( + "

未登录，请关闭此窗口重试。

", + status=401, + mimetype="text/html", + ) + + from flask_jwt_extended import create_access_token, create_refresh_token + + access_token = create_access_token(identity=current_user.id, fresh=True) + refresh_token = create_refresh_token(identity=current_user.id) + + user_data = { + "id": current_user.id, + "username": current_user.username, + "first_name": getattr(current_user, "first_name", "") or "", + "last_name": getattr(current_user, "last_name", "") or "", + } + + import json + + html = f""" +SSO Bridge + +

正在完成登录...

+ +""" + return Response(html, mimetype="text/html") + + +CUSTOM_SECURITY_MANAGER_CLASS = CustomSecurityManager +``` + +--- + +## 5. 注意事项 + +### 5.1 如果已有 CustomSecurityManager + +如果 `superset_config.py` 中**已经定义**了 `CustomSecurityManager`（或其他自定义 SecurityManager 类），不要重复创建新类，只需将 `df_sso_bridge` 方法添加到现有类中即可： + +```python +# 假设已有: +class CustomSecurityManager(SupersetSecurityManager): + # ... 现有的自定义方法 ... + + # 追加这个方法: + @expose("/df-sso-bridge/", methods=["GET"]) + def df_sso_bridge(self): + # ... 上面第 4 节中的完整方法体 ... +``` + +### 5.2 如果已有 CUSTOM_SECURITY_MANAGER_CLASS + +如果已经设置了 `CUSTOM_SECURITY_MANAGER_CLASS`，确认它指向的是包含 `df_sso_bridge` 方法的那个类。不需要重复设置。 + +### 5.3 无需额外配置 + +- **不需要**在 Superset 中配置任何 DF 的地址 +- **不需要**新增环境变量 +- **不需要**修改 CORS 配置 +- DF 的地址通过 URL 参数 `df_origin` 动态传入，无需硬编码 + +--- + +## 6. DF 端配置 + +在 DF 的 `.env` 中确保 Superset 插件已启用： + +```env +# 设置 Superset URL 即可自动启用插件 +PLG_SUPERSET_URL=http://你的SUPERSET地址:8088/ +``` + +DF 会自动： + +- 生成 SSO 登录 URL：`{PLG_SUPERSET_URL}/df-sso-bridge/` +- 在 Superset 插件界面显示"SSO 登录"按钮 +- 弹窗打开时自动拼接 `?df_origin=` 参数 + +如果需要自定义 SSO 入口 URL（例如先跳转到 Superset 登录页再重定向），可设置： + +```env +# 可选：自定义 SSO 入口 URL（默认直接打开 /df-sso-bridge/） +# 如果 Superset 的桥接端点需要先经过登录页，可以设置为： +# PLG_SUPERSET_SSO_LOGIN_URL=http://你的SUPERSET地址:8088/login/?next=/df-sso-bridge/ +``` + +--- + +## 7. 验证步骤 + +部署后按以下步骤验证： + +### 7.1 未登录状态测试 + +浏览器直接访问（不要先登录 Superset）： + +``` +http://SUPERSET地址:端口/df-sso-bridge/ +``` + +**预期**：返回 401 页面，显示"未登录，请关闭此窗口重试。" + +### 7.2 已登录状态测试 + +1. 先通过 Superset 正常登录（SSO 或账密都行） +2. 在同一浏览器访问： + +``` +http://SUPERSET地址:端口/df-sso-bridge/?df_origin=http://test +``` + +**预期**：页面显示"正在完成登录..."，因为没有 `window.opener`（不是从弹窗打开），最终显示"登录成功，请关闭此窗口并返回 Data Formulator。" + +### 7.3 验证 JWT 有效性 + +在步骤 7.2 的页面上，打开浏览器开发者工具 → 查看页面源码中的 `access_token` 值，然后用它调用： + +```bash +curl -H "Authorization: Bearer " http://SUPERSET地址:端口/api/v1/me/ +``` + +**预期**：返回当前登录用户的信息。 + +### 7.4 完整端到端测试 + +1. 启动 DF（确保 `PLG_SUPERSET_URL` 已配置） +2. 在 DF 界面打开数据上传 → 选择 Superset 标签 +3. 点击"SSO 登录"按钮 +4. 弹窗打开 → 如果已有 SSO 会话则自动完成，否则先完成 SSO 登录 +5. 弹窗自动关闭，DF 显示 Superset 数据目录 + +--- + +## 8. 安全说明 + +| 关注点 | 说明 | +|--------|------| +| **谁能访问 bridge 端点？** | 只有已通过 Superset 认证（有有效 Session）的用户，未登录返回 401 | +| **JWT 发给谁？** | 通过 `postMessage` 只发给 `window.opener`（即打开弹窗的 DF 页面） | +| **targetOrigin 安全性** | 使用 DF 传入的 `df_origin` 作为 `targetOrigin`，浏览器会校验接收窗口的实际 origin 是否匹配，不匹配则消息被丢弃 | +| **df_origin 被伪造？** | `postMessage` 始终发给 `window.opener`，`targetOrigin` 只是过滤条件。伪造只会导致消息被丢弃，不会泄露到第三方 | +| **JWT 生命周期** | 获取的 JWT 与正常登录的完全一致，DF 后端自动处理过期刷新 | + +--- + +## 9. 故障排查 + +| 现象 | 可能原因 | 解决方法 | +|------|----------|----------| +| 弹窗显示"未登录" | 用户没有有效的 Superset Session | 先在 Superset 正常登录一次，再从 DF 发起 SSO | +| 弹窗打开后空白 | `superset_config.py` 代码未生效 | 确认已重启 Superset，检查日志是否有导入错误 | +| 弹窗显示"登录成功"但 DF 无反应 | postMessage type 不匹配 | 确认桥接代码中 `type: 'df-sso-auth'` 拼写正确 | +| curl 测试 JWT 返回 401 | `create_access_token` 异常 | 检查 Superset 日志，确认 `flask_jwt_extended` 已正确配置 | +| DF 显示"SSO 登录失败" | save-tokens 验证失败 | 检查 DF 后端日志，确认 DF 能访问 Superset `/api/v1/me/` | +| 弹窗被浏览器拦截 | 浏览器 popup 拦截 | 提示用户允许弹出窗口 | + +--- + +## 10. 技术细节：DF 端 SSO 流程（供开发者参考） + +### 10.1 前端流程（`SupersetLogin.tsx`） + +``` +用户点击"SSO 登录" + → 构造 URL: {sso_login_url}?df_origin={window.location.origin} + → window.open(url) 打开弹窗 + → window.addEventListener('message', handler) + → 同时 setInterval 检测弹窗是否关闭 + +收到 postMessage (type === 'df-sso-auth') + → 移除 listener，关闭弹窗 + → POST /api/plugins/superset/auth/sso/save-tokens + { access_token, refresh_token, user } + → 后端验证 token → 存入 Flask plugin session + → 前端标记已认证 → 展示数据目录 +``` + +### 10.2 后端 token 保存（`routes/auth.py`） + +`POST /api/plugins/superset/auth/sso/save-tokens` 接收前端传来的 Superset JWT： + +1. 用 `access_token` 调用 Superset `/api/v1/me/` 验证有效性 +2. 获取用户信息（id, username 等） +3. 存入 Flask plugin session（`plugin_superset_token`, `plugin_superset_user`） +4. 后续所有 Superset API 调用使用此 JWT + +### 10.3 postMessage 协议 + +桥接页发送的消息格式： + +```json +{ + "type": "df-sso-auth", + "access_token": "eyJhbGci...", + "refresh_token": "eyJhbGci...", + "user": { + "id": 1, + "username": "zhangsan", + "first_name": "三", + "last_name": "张" + } +} +``` + +DF 前端只接受 `type === 'df-sso-auth'` 的消息，忽略其他所有 postMessage。 diff --git a/package.json b/package.json index a421de92..96101464 100644 --- a/package.json +++ b/package.json @@ -11,6 +11,10 @@ "@mui/lab": "^7.0.1-beta.18", "@mui/material": "^7.1.1", "@reduxjs/toolkit": "^1.8.6", + "@tiptap/extension-image": "^3.22.2", + "@tiptap/pm": "^3.22.2", + "@tiptap/react": "^3.22.2", + "@tiptap/starter-kit": "^3.22.2", "@types/dompurify": "^3.0.5", "@types/validator": "^13.12.2", "allotment": "^1.20.4", @@ -22,12 +26,14 @@ "exceljs": "^4.4.0", "gofish-graphics": "^0.0.22", "html2canvas": "^1.4.1", + "i18next": "^26.0.1", + "i18next-browser-languagedetector": "^8.2.1", "js-yaml": "^4.1.1", "katex": "^0.16.22", "localforage": "^1.10.0", "lodash": "^4.17.23", "markdown-to-jsx": "^7.4.0", - "mui-markdown": "^2.0.3", + "oidc-client-ts": "3.5.0", "prettier": "^2.8.3", "prism-react-renderer": "^1.3.5", "prismjs": "^1.30.0", @@ -38,6 +44,7 @@ "react-dnd": "^16.0.1", "react-dnd-html5-backend": "^16.0.1", "react-dom": "^18.2.0", + "react-i18next": "^16.5.4", "react-katex": "^3.1.0", "react-redux": "^8.0.4", "react-router-dom": "^6.22.0", @@ -47,6 +54,7 @@ "react-virtuoso": "^4.3.10", "redux": "^4.2.0", "redux-persist": "^6.0.0", + "tiptap-markdown": "^0.9.0", "typescript": "^4.9.5", "validator": "^13.15.20", "vega": "^6.2.0", @@ -57,7 +65,9 @@ "scripts": { "lint": "eslint -c eslint.config.js src/**/*.{ts,tsx} --fix", "start": "vite", - "build": "vite build" + "build": "vite build", + "test": "vitest run", + "test:watch": "vitest" }, "browserslist": { "production": [ @@ -73,6 +83,9 @@ }, "devDependencies": { "@eslint/js": "^9.15.0", + "@testing-library/dom": "^10.4.1", + "@testing-library/jest-dom": "^6.9.1", + "@testing-library/react": "^16.3.2", "@types/d3": "^7.4.3", "@types/lodash": "^4.17.7", "@types/node": "^20.14.10", @@ -87,8 +100,10 @@ "eslint-plugin-jsx-a11y": "^6.10.2", "eslint-plugin-react": "^7.37.2", "globals": "^15.12.0", + "jsdom": "^29.0.1", "sass": "^1.77.6", "typescript-eslint": "^8.16.0", - "vite": "^5.4.21" + "vite": "^5.4.21", + "vitest": "^4.1.0" } } diff --git a/public/df_gas_prices.json b/public/df_gas_prices.json index a06c48c7..1cd183c8 100644 --- a/public/df_gas_prices.json +++ b/public/df_gas_prices.json @@ -1 +1 @@ -{"tables":[{"id":"weekly_gas_prices","displayId":"gas-prices","names":["date","fuel","grade","formulation","price"],"metadata":{"date":{"type":"date","semanticType":"Date"},"fuel":{"type":"string","semanticType":"String"},"grade":{"type":"string","semanticType":"String","levels":["all","regular","midgrade","premium","low_sulfur","ultra_low_sulfur"]},"formulation":{"type":"string","semanticType":"String","levels":["all","conventional","reformulated","NA"]},"price":{"type":"number","semanticType":"Number"}},"rows":[{"date":"1990-08-20","fuel":"gasoline","grade":"regular","formulation":"all","price":1.191},{"date":"1990-08-20","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.191},{"date":"1990-08-27","fuel":"gasoline","grade":"regular","formulation":"all","price":1.245},{"date":"1990-08-27","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.245},{"date":"1990-09-03","fuel":"gasoline","grade":"regular","formulation":"all","price":1.242},{"date":"1990-09-03","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.242},{"date":"1990-09-10","fuel":"gasoline","grade":"regular","formulation":"all","price":1.252},{"date":"1990-09-10","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.252},{"date":"1990-09-17","fuel":"gasoline","grade":"regular","formulation":"all","price":1.266},{"date":"1990-09-17","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.266},{"date":"1990-09-24","fuel":"gasoline","grade":"regular","formulation":"all","price":1.272},{"date":"1990-09-24","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.272},{"date":"1990-10-01","fuel":"gasoline","grade":"regular","formulation":"all","price":1.321},{"date":"1990-10-01","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.321},{"date":"1990-10-08","fuel":"gasoline","grade":"regular","formulation":"all","price":1.333},{"date":"1990-10-08","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.333},{"date":"1990-10-15","fuel":"gasoline","grade":"regular","formulation":"all","price":1.339},{"date":"1990-10-15","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.339},{"date":"1990-10-22","fuel":"gasoline","grade":"regular","formulation":"all","price":1.345},{"date":"1990-10-22","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.345},{"date":"1990-10-29","fuel":"gasoline","grade":"regular","formulation":"all","price":1.339},{"date":"1990-10-29","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.339},{"date":"1990-11-05","fuel":"gasoline","grade":"regular","formulation":"all","price":1.334},{"date":"1990-11-05","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.334},{"date":"1990-11-12","fuel":"gasoline","grade":"regular","formulation":"all","price":1.328},{"date":"1990-11-12","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.328},{"date":"1990-11-19","fuel":"gasoline","grade":"regular","formulation":"all","price":1.323},{"date":"1990-11-19","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.323},{"date":"1990-11-26","fuel":"gasoline","grade":"regular","formulation":"all","price":1.311},{"date":"1990-11-26","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.311},{"date":"1990-12-03","fuel":"gasoline","grade":"regular","formulation":"all","price":1.341},{"date":"1990-12-03","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.341},{"date":"1991-01-21","fuel":"gasoline","grade":"regular","formulation":"all","price":1.192},{"date":"1991-01-21","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.192},{"date":"1991-01-28","fuel":"gasoline","grade":"regular","formulation":"all","price":1.168},{"date":"1991-01-28","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.168},{"date":"1991-02-04","fuel":"gasoline","grade":"regular","formulation":"all","price":1.139},{"date":"1991-02-04","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.139},{"date":"1991-02-11","fuel":"gasoline","grade":"regular","formulation":"all","price":1.106},{"date":"1991-02-11","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.106},{"date":"1991-02-18","fuel":"gasoline","grade":"regular","formulation":"all","price":1.078},{"date":"1991-02-18","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.078},{"date":"1991-02-25","fuel":"gasoline","grade":"regular","formulation":"all","price":1.054},{"date":"1991-02-25","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.054},{"date":"1991-03-04","fuel":"gasoline","grade":"regular","formulation":"all","price":1.025},{"date":"1991-03-04","fuel":"gasoline","grade":"regular","formulation":"conventional","price":1.025},{"date":"1991-03-11","fuel":"gasoline","g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pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n transformed_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Rename the price column to avg_price for clarity\n transformed_df = transformed_df.rename(columns={'price': 'avg_price'})\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values('date')\n \n return transformed_df\n","source":["weekly_gas_prices"],"dialog":[{"role":"system","content":"You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"},{"role":"user","content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[GOAL]\n\nWhat are the major **price trends** across all fuel types from 1990 to 2025?"},{"role":"assistant","content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to visualize the major price trends across all fuel types from 1990 to 2025 to understand how fuel prices have evolved over time.\",\n \"display_instruction\": \"Show **price** trends over time for different **fuel** types\",\n \"recommendation\": \"To visualize price trends over time across fuel types, we should aggregate the data by date and fuel type. Since there are multiple grades and formulations for each fuel type, we'll calculate the average price for each fuel type on each date. A line chart with date on the x-axis, average price on the y-axis, and different colored lines for each fuel type will clearly show the trends and allow easy comparison.\",\n \"output_fields\": [\"date\", \"fuel\", \"avg_price\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n transformed_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Rename the price column to avg_price for clarity\n transformed_df = transformed_df.rename(columns={'price': 'avg_price'})\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values('date')\n \n return transformed_df\n```"}],"trigger":{"tableId":"weekly_gas_prices","sourceTableIds":["weekly_gas_prices"],"instruction":"What are the major **price trends** across all fuel types from 1990 to 2025?","displayInstruction":"Show **price** trends over time for different **fuel** types","chart":{"id":"chart-1760744901532","chartType":"Auto","encodingMap":{},"tableRef":"weekly_gas_prices","saved":false,"source":"trigger","unread":true},"resultTableId":"table-904593"},"explanation":{"agent":"CodeExplanationAgent","code":"The code transforms weekly gas price data by performing the following steps:\n\n- **Groups** the data by `date` and `fuel` type (gasoline or diesel)\n- **Calculates** the mean `price` across all `grade` categories (regular, premium, midgrade, etc.) and `formulation` types (conventional, reformulated, etc.) for each date-fuel combination\n- **Renames** the aggregated price column to `avg_price` for clarity\n- **Sorts** the resulting data chronologically by `date` to ensure proper time-series visualization","concepts":[],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n transformed_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Rename the price column to avg_price for clarity\n transformed_df = transformed_df.rename(columns={'price': 'avg_price'})\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values('date')\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThe code transforms weekly gas price data by performing the following steps:\n\n- **Groups** the data by `date` and `fuel` type (gasoline or diesel)\n- **Calculates** the mean `price` across all `grade` categories (regular, premium, midgrade, etc.) and `formulation` types (conventional, reformulated, etc.) for each date-fuel combination\n- **Renames** the aggregated price column to `avg_price` for clarity\n- **Sorts** the resulting data chronologically by `date` to ensure proper time-series visualization\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-906894","displayId":"gas-price-prem","names":["date","fuel","grade","price_premium"],"rows":[{"date":"1993-04-05","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-04-12","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-04-19","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-04-26","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-05-03","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-05-10","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-05-17","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-05-24","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-05-31","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-06-07","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-06-14","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-06-21","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-06-28","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-07-05","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-07-12","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-07-19","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-07-26","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-08-02","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-08-09","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-08-16","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-08-23","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-08-30","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-09-06","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-09-13","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-09-20","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-09-27","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-10-04","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-10-11","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-10-18","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-10-25","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-11-01","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-11-08","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-11-15","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-11-22","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-11-29","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-12-06","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-12-13","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-12-20","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1993-12-27","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-01-03","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-01-10","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-01-17","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-01-24","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-01-31","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-02-07","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-02-14","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-02-21","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-02-28","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-03-07","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-03-14","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-03-21","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-03-28","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-04-04","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-04-11","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-04-18","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-04-25","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-05-02","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-05-09","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-05-16","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-05-23","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-05-30","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-06-06","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-06-13","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-06-20","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-06-27","fuel":"gasoline","grade":"all","price_premium":0},{"date":"1994-07-04","fuel":"gasoline","grade":"all","price_premium":0},{"date":"199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4","fuel":"gasoline","grade":"midgrade","price_premium":0.581},{"date":"2025-02-24","fuel":"gasoline","grade":"premium","price_premium":0.95},{"date":"2025-03-03","fuel":"gasoline","grade":"all","price_premium":0.128},{"date":"2025-03-03","fuel":"gasoline","grade":"midgrade","price_premium":0.576},{"date":"2025-03-03","fuel":"gasoline","grade":"premium","price_premium":0.944},{"date":"2025-03-10","fuel":"gasoline","grade":"all","price_premium":0.128},{"date":"2025-03-10","fuel":"gasoline","grade":"midgrade","price_premium":0.571},{"date":"2025-03-10","fuel":"gasoline","grade":"premium","price_premium":0.936},{"date":"2025-03-17","fuel":"gasoline","grade":"all","price_premium":0.126},{"date":"2025-03-17","fuel":"gasoline","grade":"midgrade","price_premium":0.569},{"date":"2025-03-17","fuel":"gasoline","grade":"premium","price_premium":0.931},{"date":"2025-03-24","fuel":"gasoline","grade":"all","price_premium":0.125},{"date":"2025-03-24","fuel":"gasoline","grade":"midgrade","price_premium":0.559},{"date":"2025-03-24","fuel":"gasoline","grade":"premium","price_premium":0.917},{"date":"2025-03-31","fuel":"gasoline","grade":"all","price_premium":0.126},{"date":"2025-03-31","fuel":"gasoline","grade":"midgrade","price_premium":0.568},{"date":"2025-03-31","fuel":"gasoline","grade":"premium","price_premium":0.931},{"date":"2025-04-07","fuel":"gasoline","grade":"all","price_premium":0.127},{"date":"2025-04-07","fuel":"gasoline","grade":"midgrade","price_premium":0.572},{"date":"2025-04-07","fuel":"gasoline","grade":"premium","price_premium":0.931},{"date":"2025-04-14","fuel":"gasoline","grade":"all","price_premium":0.127},{"date":"2025-04-14","fuel":"gasoline","grade":"midgrade","price_premium":0.575},{"date":"2025-04-14","fuel":"gasoline","grade":"premium","price_premium":0.935},{"date":"2025-04-21","fuel":"gasoline","grade":"all","price_premium":0.127},{"date":"2025-04-21","fuel":"gasoline","grade":"midgrade","price_premium":0.577},{"date":"2025-04-21","fuel":"gasoline","grade":"premium","price_premium":0.933},{"date":"2025-04-28","fuel":"gasoline","grade":"all","price_premium":0.128},{"date":"2025-04-28","fuel":"gasoline","grade":"midgrade","price_premium":0.58},{"date":"2025-04-28","fuel":"gasoline","grade":"premium","price_premium":0.938},{"date":"2025-05-05","fuel":"gasoline","grade":"all","price_premium":0.126},{"date":"2025-05-05","fuel":"gasoline","grade":"midgrade","price_premium":0.568},{"date":"2025-05-05","fuel":"gasoline","grade":"premium","price_premium":0.925},{"date":"2025-05-12","fuel":"gasoline","grade":"all","price_premium":0.129},{"date":"2025-05-12","fuel":"gasoline","grade":"midgrade","price_premium":0.583},{"date":"2025-05-12","fuel":"gasoline","grade":"premium","price_premium":0.948},{"date":"2025-05-19","fuel":"gasoline","grade":"all","price_premium":0.129},{"date":"2025-05-19","fuel":"gasoline","grade":"midgrade","price_premium":0.583},{"date":"2025-05-19","fuel":"gasoline","grade":"premium","price_premium":0.943},{"date":"2025-05-26","fuel":"gasoline","grade":"all","price_premium":0.128},{"date":"2025-05-26","fuel":"gasoline","grade":"midgrade","price_premium":0.58},{"date":"2025-05-26","fuel":"gasoline","grade":"premium","price_premium":0.942},{"date":"2025-06-02","fuel":"gasoline","grade":"all","price_premium":0.129},{"date":"2025-06-02","fuel":"gasoline","grade":"midgrade","price_premium":0.585},{"date":"2025-06-02","fuel":"gasoline","grade":"premium","price_premium":0.942},{"date":"2025-06-09","fuel":"gasoline","grade":"all","price_premium":0.127},{"date":"2025-06-09","fuel":"gasoline","grade":"midgrade","price_premium":0.579},{"date":"2025-06-09","fuel":"gasoline","grade":"premium","price_premium":0.929},{"date":"2025-06-16","fuel":"gasoline","grade":"all","price_premium":0.126},{"date":"2025-06-16","fuel":"gasoline","grade":"midgrade","price_premium":0.572},{"date":"2025-06-16","fuel":"gasoline","grade":"premium","price_premium":0.923},{"date":"2025-06-23","fuel":"gasoline","grade":"all","price_premium":0.125},{"date":"2025-06-23","fuel":"gasoline","grade":"midgrade","price_premium":0.562},{"date":"2025-06-23","fuel":"gasoline","grade":"premium","price_premium":0.915}],"metadata":{"date":{"type":"date","semanticType":"Date"},"fuel":{"type":"string","semanticType":"String"},"grade":{"type":"string","semanticType":"String","levels":["all","midgrade","premium"]},"price_premium":{"type":"number","semanticType":"Number"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas):\n # Filter for 'all' formulation to avoid duplicates and focus on overall prices\n df_filtered = df_gas[df_gas['formulation'] == 'all'].copy()\n \n # Get regular grade prices as baseline\n df_regular = df_filtered[df_filtered['grade'] == 'regular'][['date', 'fuel', 'price']].rename(columns={'price': 'regular_price'})\n \n # Get all non-regular grades\n df_premium_grades = df_filtered[df_filtered['grade'] != 'regular'][['date', 'fuel', 'grade', 'price']].copy()\n \n # Merge to calculate premium\n df_merged = df_premium_grades.merge(df_regular, on=['date', 'fuel'], how='left')\n \n # Calculate price premium\n df_merged['price_premium'] = df_merged['price'] - df_merged['regular_price']\n \n # Select output fields\n transformed_df = df_merged[['date', 'fuel', 'grade', 'price_premium']].copy()\n \n # Remove rows with missing premium values\n transformed_df = transformed_df.dropna(subset=['price_premium'])\n \n return transformed_df\n","source":["weekly_gas_prices"],"dialog":[{"content":"You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[GOAL]\n\nWhich **fuel grade** commands the highest **price premium** and how has this premium evolved?","role":"user"},{"content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"Analyze which fuel grade commands the highest price premium compared to regular grade, and visualize how this premium has evolved over time.\",\n \"display_instruction\": \"Compare **price premium** across **fuel grades** over time\",\n \"recommendation\": \"Calculate the price premium for each fuel grade by comparing it to the regular grade baseline for the same date and fuel type. This will show both the absolute premium values and their trends over time.\",\n \"output_fields\": [\"date\", \"fuel\", \"grade\", \"price_premium\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"price_premium\",\n \"color\": \"grade\",\n \"facet\": \"fuel\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas):\n # Filter for 'all' formulation to avoid duplicates and focus on overall prices\n df_filtered = df_gas[df_gas['formulation'] == 'all'].copy()\n \n # Get regular grade prices as baseline\n df_regular = df_filtered[df_filtered['grade'] == 'regular'][['date', 'fuel', 'price']].rename(columns={'price': 'regular_price'})\n \n # Get all non-regular grades\n df_premium_grades = df_filtered[df_filtered['grade'] != 'regular'][['date', 'fuel', 'grade', 'price']].copy()\n \n # Merge to calculate premium\n df_merged = df_premium_grades.merge(df_regular, on=['date', 'fuel'], how='left')\n \n # Calculate price premium\n df_merged['price_premium'] = df_merged['price'] - df_merged['regular_price']\n \n # Select output fields\n transformed_df = df_merged[['date', 'fuel', 'grade', 'price_premium']].copy()\n \n # Remove rows with missing premium values\n transformed_df = transformed_df.dropna(subset=['price_premium'])\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"weekly_gas_prices","sourceTableIds":["weekly_gas_prices"],"instruction":"Which **fuel grade** commands the highest **price premium** and how has this premium evolved?","displayInstruction":"Compare **price premium** across **fuel grades** over time","chart":{"id":"chart-1760744911829","chartType":"Auto","encodingMap":{},"tableRef":"weekly_gas_prices","saved":false,"source":"trigger","unread":true},"resultTableId":"table-906894"},"explanation":{"agent":"CodeExplanationAgent","code":"- Filter the dataset to only include entries where **formulation** is `'all'` to avoid duplicate records and focus on overall price data\n- Extract **regular grade** prices as a baseline, creating a reference table with **date**, **fuel**, and **regular_price**\n- Extract all **non-regular grades** (such as **premium**, **midgrade**, etc.) with their corresponding **date**, **fuel**, **grade**, and **price**\n- Merge the non-regular grades with the regular grade baseline by matching on **date** and **fuel** type\n- Calculate the **price_premium** for each non-regular grade by subtracting the **regular_price** from the grade's **price**\n- Select the final output fields: **date**, **fuel**, **grade**, and **price_premium**\n- Remove any rows with missing **price_premium** values to ensure data completeness","concepts":[{"explanation":"The additional cost of a specific fuel grade compared to regular grade fuel. Calculated as: \\( \\text{price\\_premium} = \\text{price}_{\\text{grade}} - \\text{price}_{\\text{regular}} \\). This metric indicates how much more consumers pay for higher-quality fuel grades (premium, midgrade, etc.) relative to the baseline regular grade on the same date and for the same fuel type.","field":"price_premium"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas):\n # Filter for 'all' formulation to avoid duplicates and focus on overall prices\n df_filtered = df_gas[df_gas['formulation'] == 'all'].copy()\n \n # Get regular grade prices as baseline\n df_regular = df_filtered[df_filtered['grade'] == 'regular'][['date', 'fuel', 'price']].rename(columns={'price': 'regular_price'})\n \n # Get all non-regular grades\n df_premium_grades = df_filtered[df_filtered['grade'] != 'regular'][['date', 'fuel', 'grade', 'price']].copy()\n \n # Merge to calculate premium\n df_merged = df_premium_grades.merge(df_regular, on=['date', 'fuel'], how='left')\n \n # Calculate price premium\n df_merged['price_premium'] = df_merged['price'] - df_merged['regular_price']\n \n # Select output fields\n transformed_df = df_merged[['date', 'fuel', 'grade', 'price_premium']].copy()\n \n # Remove rows with missing premium values\n transformed_df = transformed_df.dropna(subset=['price_premium'])\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\n- Filter the dataset to only include entries where **formulation** is `'all'` to avoid duplicate records and focus on overall price data\n- Extract **regular grade** prices as a baseline, creating a reference table with **date**, **fuel**, and **regular_price**\n- Extract all **non-regular grades** (such as **premium**, **midgrade**, etc.) with their corresponding **date**, **fuel**, **grade**, and **price**\n- Merge the non-regular grades with the regular grade baseline by matching on **date** and **fuel** type\n- Calculate the **price_premium** for each non-regular grade by subtracting the **regular_price** from the grade's **price**\n- Select the final output fields: **date**, **fuel**, **grade**, and **price_premium**\n- Remove any rows with missing **price_premium** values to ensure data completeness\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"price_premium\",\n \"explanation\": \"The additional cost of a specific fuel grade compared to regular grade fuel. Calculated as: \\\\( \\\\text{price\\\\_premium} = \\\\text{price}_{\\\\text{grade}} - \\\\text{price}_{\\\\text{regular}} \\\\). This metric indicates how much more consumers pay for higher-quality fuel grades (premium, midgrade, etc.) relative to the baseline regular grade on the same date and for the same fuel type.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-922023","displayId":"fuel-price-yoy","names":["date","fuel","current_price","yoy_price","absolute_change","percentage_change"],"rows":[{"date":"1995-03-20","fuel":"diesel","current_price":1.085,"yoy_price":1.106,"absolute_change":-0.021,"percentage_change":-1.8987341772},{"date":"1995-03-27","fuel":"diesel","current_price":1.088,"yoy_price":1.107,"absolute_change":-0.019,"percentage_change":-1.7163504968},{"date":"1995-04-03","fuel":"diesel","current_price":1.094,"yoy_price":1.109,"absolute_change":-0.015,"percentage_change":-1.3525698828},{"date":"1995-04-10","fuel":"diesel","current_price":1.101,"yoy_price":1.108,"absolute_change":-0.007,"percentage_change":-0.6317689531},{"date":"1995-04-17","fuel":"diesel","current_price":1.106,"yoy_price":1.105,"absolute_change":0.001,"percentage_change":0.0904977376},{"date":"1995-04-24","fuel":"diesel","current_price":1.115,"yoy_price":1.106,"absolute_change":0.009,"percentage_change":0.8137432188},{"date":"1995-05-01","fuel":"diesel","current_price":1.119,"yoy_price":1.104,"absolute_change":0.015,"percentage_change":1.3586956522},{"date":"1995-05-08","fuel":"diesel","current_price":1.126,"yoy_price":1.101,"absolute_change":0.025,"percentage_change":2.2706630336},{"date":"1995-05-15","fuel":"diesel","current_price":1.126,"yoy_price":1.099,"absolute_change":0.027,"percentage_change":2.4567788899},{"date":"1995-05-22","fuel":"diesel","current_price":1.124,"yoy_price":1.099,"absolute_change":0.025,"percentage_change":2.2747952684},{"date":"1995-05-29","fuel":"diesel","current_price":1.13,"yoy_price":1.098,"absolute_change":0.032,"percentage_change":2.9143897996},{"date":"1995-06-05","fuel":"diesel","current_price":1.124,"yoy_price":1.101,"absolute_change":0.023,"percentage_change":2.0890099909},{"date":"1995-06-12","fuel":"diesel","current_price":1.122,"yoy_price":1.098,"absolute_change":0.024,"percentage_change":2.1857923497},{"date":"1995-06-19","fuel":"diesel","current_price":1.117,"yoy_price":1.103,"absolute_change":0.014,"percentage_change":1.2692656392},{"date":"1995-06-26","fuel":"diesel","current_price":1.112,"yoy_price":1.108,"absolute_change":0.004,"percentage_change":0.3610108303},{"date":"1995-07-03","fuel":"diesel","current_price":1.106,"yoy_price":1.109,"absolute_change":-0.003,"percentage_change":-0.2705139766},{"date":"1995-07-10","fuel":"diesel","current_price":1.103,"yoy_price":1.11,"absolute_change":-0.007,"percentage_change":-0.6306306306},{"date":"1995-07-17","fuel":"diesel","current_price":1.099,"yoy_price":1.111,"absolute_change":-0.012,"percentage_change":-1.0801080108},{"date":"1995-07-24","fuel":"diesel","current_price":1.098,"yoy_price":1.111,"absolute_change":-0.013,"percentage_change":-1.1701170117},{"date":"1995-07-31","fuel":"diesel","current_price":1.093,"yoy_price":1.116,"absolute_change":-0.023,"percentage_change":-2.0609318996},{"date":"1995-08-07","fuel":"diesel","current_price":1.099,"yoy_price":1.127,"absolute_change":-0.028,"percentage_change":-2.4844720497},{"date":"1995-08-14","fuel":"diesel","current_price":1.106,"yoy_price":1.127,"absolute_change":-0.021,"percentage_change":-1.8633540373},{"date":"1995-08-21","fuel":"diesel","current_price":1.106,"yoy_price":1.124,"absolute_change":-0.018,"percentage_change":-1.6014234875},{"date":"1995-08-28","fuel":"diesel","current_price":1.109,"yoy_price":1.122,"absolute_change":-0.013,"percentage_change":-1.1586452763},{"date":"1995-09-04","fuel":"diesel","current_price":1.115,"yoy_price":1.126,"absolute_change":-0.011,"percentage_change":-0.9769094139},{"date":"1995-09-11","fuel":"diesel","current_price":1.119,"yoy_price":1.128,"absolute_change":-0.009,"percentage_change":-0.7978723404},{"date":"1995-09-18","fuel":"diesel","current_price":1.122,"yoy_price":1.126,"absolute_change":-0.004,"percentage_change":-0.3552397869},{"date":"1995-09-25","fuel":"diesel","current_price":1.121,"yoy_price":1.12,"absolute_change":0.001,"percentage_change":0.0892857143},{"date":"1995-10-02","fuel":"diesel","current_price":1.117,"yoy_price":1.118,"absolute_change":-0.001,"percentage_change":-0.0894454383},{"date":"1995-10-09","fuel":"diesel","current_price":1.117,"yoy_price":1.117,"absolute_change":0,"percentage_change":0},{"date":"1995-10-16","fuel":"diesel","current_price":1.117,"yoy_price":1.119,"absolute_change":-0.002,"percentage_change":-0.1787310098},{"date":"1995-10-23","fuel":"diesel","current_price":1.114,"yoy_price":1.122,"absolute_change":-0.008,"percentage_change":-0.7130124777},{"date":"1995-10-30","fuel":"diesel","current_price":1.11,"yoy_price":1.133,"absolute_change":-0.023,"percentage_change":-2.0300088261},{"date":"1995-11-06","fuel":"diesel","current_price":1.118,"yoy_price":1.133,"absolute_change":-0.015,"percentage_change":-1.3239187996},{"date":"1995-11-13","fuel":"diesel","current_price":1.118,"yoy_price":1.135,"absolute_change":-0.017,"percentage_change":-1.4977973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pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n df_agg = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Convert date to datetime for proper sorting and manipulation\n df_agg['date'] = pd.to_datetime(df_agg['date'])\n \n # Sort by fuel and date\n df_agg = df_agg.sort_values(['fuel', 'date'])\n \n # Calculate year-over-year changes (52 weeks ago for weekly data)\n df_agg['yoy_price'] = df_agg.groupby('fuel')['price'].shift(52)\n df_agg['absolute_change'] = df_agg['price'] - df_agg['yoy_price']\n df_agg['percentage_change'] = ((df_agg['price'] - df_agg['yoy_price']) / df_agg['yoy_price']) * 100\n \n # Rename columns for clarity\n transformed_df = df_agg.rename(columns={'price': 'current_price'})\n \n # Convert date back to string format for visualization\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n # Keep only rows where we have year-over-year data (remove first 52 weeks)\n transformed_df = transformed_df.dropna(subset=['yoy_price'])\n \n # Select and order output columns\n transformed_df = transformed_df[['date', 'fuel', 'current_price', 'yoy_price', 'absolute_change', 'percentage_change']]\n \n return transformed_df\n","source":["weekly_gas_prices"],"dialog":[{"role":"system","content":"You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"},{"role":"user","content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[GOAL]\n\nWhat are the major **price trends** across all fuel types from 1990 to 2025?"},{"role":"assistant","content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to visualize the major price trends across all fuel types from 1990 to 2025 to understand how fuel prices have evolved over time.\",\n \"display_instruction\": \"Show **price** trends over time for different **fuel** types\",\n \"recommendation\": \"To visualize price trends over time across fuel types, we should aggregate the data by date and fuel type. Since there are multiple grades and formulations for each fuel type, we'll calculate the average price for each fuel type on each date. A line chart with date on the x-axis, average price on the y-axis, and different colored lines for each fuel type will clearly show the trends and allow easy comparison.\",\n \"output_fields\": [\"date\", \"fuel\", \"avg_price\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n transformed_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Rename the price column to avg_price for clarity\n transformed_df = transformed_df.rename(columns={'price': 'avg_price'})\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values('date')\n \n return transformed_df\n```"},{"role":"user","content":"This is the result from the latest python code:\n\n date fuel avg_price\n0 1990-08-20 gasoline 1.191\n1 1990-08-27 gasoline 1.245\n2 1990-09-03 gasoline 1.242\n3 1990-09-10 gasoline 1.252\n4 1990-09-17 gasoline 1.266\n5 1990-09-24 gasoline 1.272\n6 1990-10-01 gasoline 1.321\n7 1990-10-08 gasoline 1.333\n8 1990-10-15 gasoline 1.339\n9 1990-10-22 gasoline 1.345\n......\n\nUpdate the code above based on the following instruction:\n\nCalculate year-over-year price changes to identify the most dramatic price spikes for both diesel and gasoline, showing which periods had the largest absolute and percentage increases with their peak prices."},{"role":"assistant","content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to identify the most dramatic price spikes by calculating year-over-year price changes for both diesel and gasoline, showing periods with the largest absolute and percentage increases along with their peak prices.\",\n \"display_instruction\": \"Identify periods with largest **year-over-year price changes** (absolute and percentage) and **peak prices**\",\n \"recommendation\": \"To analyze year-over-year price changes, we need to calculate the price difference and percentage change compared to the same period one year prior. We'll compute both absolute change and percentage change, then identify the periods with the most dramatic spikes. The output should include the date, fuel type, current price, previous year's price, absolute change, and percentage change to provide a comprehensive view of price volatility.\",\n \"output_fields\": [\"date\", \"fuel\", \"current_price\", \"yoy_price\", \"absolute_change\", \"percentage_change\"],\n \"chart_type\": \"point\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"absolute_change\",\n \"color\": \"fuel\",\n \"size\": \"percentage_change\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n df_agg = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Convert date to datetime for proper sorting and manipulation\n df_agg['date'] = pd.to_datetime(df_agg['date'])\n \n # Sort by fuel and date\n df_agg = df_agg.sort_values(['fuel', 'date'])\n \n # Calculate year-over-year changes (52 weeks ago for weekly data)\n df_agg['yoy_price'] = df_agg.groupby('fuel')['price'].shift(52)\n df_agg['absolute_change'] = df_agg['price'] - df_agg['yoy_price']\n df_agg['percentage_change'] = ((df_agg['price'] - df_agg['yoy_price']) / df_agg['yoy_price']) * 100\n \n # Rename columns for clarity\n transformed_df = df_agg.rename(columns={'price': 'current_price'})\n \n # Convert date back to string format for visualization\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n # Keep only rows where we have year-over-year data (remove first 52 weeks)\n transformed_df = transformed_df.dropna(subset=['yoy_price'])\n \n # Select and order output columns\n transformed_df = transformed_df[['date', 'fuel', 'current_price', 'yoy_price', 'absolute_change', 'percentage_change']]\n \n return transformed_df\n```"}],"trigger":{"tableId":"table-904593","sourceTableIds":["weekly_gas_prices"],"instruction":"Calculate year-over-year price changes to identify the most dramatic price spikes for both diesel and gasoline, showing which periods had the largest absolute and percentage increases with their peak prices.","displayInstruction":"Identify periods with largest **year-over-year price changes** (absolute and percentage) and **peak prices**","chart":{"id":"chart-1760744928220","chartType":"Auto","encodingMap":{},"tableRef":"weekly_gas_prices","saved":false,"source":"trigger","unread":true},"resultTableId":"table-922023"},"explanation":{"agent":"CodeExplanationAgent","code":"The code analyzes **year-over-year price trends** for different fuel types by performing the following steps:\n\n- Aggregates the data by **date** and **fuel** type, calculating the **average price** across all grades and formulations for each fuel type on each date\n- Converts **date** field to datetime format and sorts the data chronologically by **fuel** and **date**\n- Calculates **year-over-year comparisons** by comparing each week's price to the price from **52 weeks ago** (one year prior)\n- Computes both **absolute change** (dollar difference) and **percentage change** between current prices and prices from one year ago\n- Removes the first **52 weeks** of data where year-over-year comparisons are not available\n- Returns a dataset containing **current prices**, **year-ago prices**, and their **changes** for each fuel type over time","concepts":[{"explanation":"The average fuel price from exactly 52 weeks (one year) prior to the current date, used as a baseline for year-over-year comparison. This represents the historical price point from the same week in the previous year.","field":"yoy_price"},{"explanation":"The dollar amount difference between the current week's price and the price from one year ago, calculated as: \\( \\text{absolute\\_change} = \\text{current\\_price} - \\text{yoy\\_price} \\). A positive value indicates price increase, while a negative value indicates price decrease.","field":"absolute_change"},{"explanation":"The year-over-year price change expressed as a percentage, calculated as: \\( \\text{percentage\\_change} = \\frac{\\text{current\\_price} - \\text{yoy\\_price}}{\\text{yoy\\_price}} \\times 100 \\). This metric normalizes price changes to allow comparison across different price levels and time periods.","field":"percentage_change"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n df_agg = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Convert date to datetime for proper sorting and manipulation\n df_agg['date'] = pd.to_datetime(df_agg['date'])\n \n # Sort by fuel and date\n df_agg = df_agg.sort_values(['fuel', 'date'])\n \n # Calculate year-over-year changes (52 weeks ago for weekly data)\n df_agg['yoy_price'] = df_agg.groupby('fuel')['price'].shift(52)\n df_agg['absolute_change'] = df_agg['price'] - df_agg['yoy_price']\n df_agg['percentage_change'] = ((df_agg['price'] - df_agg['yoy_price']) / df_agg['yoy_price']) * 100\n \n # Rename columns for clarity\n transformed_df = df_agg.rename(columns={'price': 'current_price'})\n \n # Convert date back to string format for visualization\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n # Keep only rows where we have year-over-year data (remove first 52 weeks)\n transformed_df = transformed_df.dropna(subset=['yoy_price'])\n \n # Select and order output columns\n transformed_df = transformed_df[['date', 'fuel', 'current_price', 'yoy_price', 'absolute_change', 'percentage_change']]\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThe code analyzes **year-over-year price trends** for different fuel types by performing the following steps:\n\n- Aggregates the data by **date** and **fuel** type, calculating the **average price** across all grades and formulations for each fuel type on each date\n- Converts **date** field to datetime format and sorts the data chronologically by **fuel** and **date**\n- Calculates **year-over-year comparisons** by comparing each week's price to the price from **52 weeks ago** (one year prior)\n- Computes both **absolute change** (dollar difference) and **percentage change** between current prices and prices from one year ago\n- Removes the first **52 weeks** of data where year-over-year comparisons are not available\n- Returns a dataset containing **current prices**, **year-ago prices**, and their **changes** for each fuel type over time\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"yoy_price\",\n \"explanation\": \"The average fuel price from exactly 52 weeks (one year) prior to the current date, used as a baseline for year-over-year comparison. This represents the historical price point from the same week in the previous year.\"\n },\n {\n \"field\": \"absolute_change\",\n \"explanation\": \"The dollar amount difference between the current week's price and the price from one year ago, calculated as: \\\\( \\\\text{absolute\\\\_change} = \\\\text{current\\\\_price} - \\\\text{yoy\\\\_price} \\\\). A positive value indicates price increase, while a negative value indicates price decrease.\"\n },\n {\n \"field\": \"percentage_change\",\n \"explanation\": \"The year-over-year price change expressed as a percentage, calculated as: \\\\( \\\\text{percentage\\\\_change} = \\\\frac{\\\\text{current\\\\_price} - \\\\text{yoy\\\\_price}}{\\\\text{yoy\\\\_price}} \\\\times 100 \\\\). This metric normalizes price changes to allow comparison across different price levels and time periods.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-937785","displayId":"fuel-price-stats","names":["period","fuel","avg_price","std_dev","coefficient_of_variation"],"rows":[{"period":"Post-2000 (2000-2025)","fuel":"diesel","avg_price":2.933,"std_dev":0.982,"coefficient_of_variation":33.48},{"period":"Post-2000 (2000-2025)","fuel":"gasoline","avg_price":2.847,"std_dev":0.838,"coefficient_of_variation":29.43},{"period":"Pre-2000 (1990-1999)","fuel":"diesel","avg_price":1.138,"std_dev":0.087,"coefficient_of_variation":7.65},{"period":"Pre-2000 (1990-1999)","fuel":"gasoline","avg_price":1.171,"std_dev":0.106,"coefficient_of_variation":9.09}],"metadata":{"period":{"type":"string","semanticType":"TimeRange","levels":["Pre-2000 (1990-1999)","Post-2000 (2000-2025)"]},"fuel":{"type":"string","semanticType":"String"},"avg_price":{"type":"number","semanticType":"Number"},"std_dev":{"type":"number","semanticType":"Number"},"coefficient_of_variation":{"type":"number","semanticType":"Number"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n df_agg = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Convert date to datetime for period classification\n df_agg['date'] = pd.to_datetime(df_agg['date'])\n \n # Extract year for period classification\n df_agg['year'] = df_agg['date'].dt.year\n \n # Classify into pre-2000 and post-2000 periods\n df_agg['period'] = df_agg['year'].apply(lambda x: 'Pre-2000 (1990-1999)' if x < 2000 else 'Post-2000 (2000-2025)')\n \n # Calculate volatility metrics by period and fuel\n volatility_stats = df_agg.groupby(['period', 'fuel']).agg({\n 'price': ['mean', 'std']\n }).reset_index()\n \n # Flatten column names\n volatility_stats.columns = ['period', 'fuel', 'avg_price', 'std_dev']\n \n # Calculate coefficient of variation (CV = std_dev / mean * 100)\n volatility_stats['coefficient_of_variation'] = (volatility_stats['std_dev'] / volatility_stats['avg_price']) * 100\n \n # Round values for readability\n volatility_stats['avg_price'] = volatility_stats['avg_price'].round(3)\n volatility_stats['std_dev'] = volatility_stats['std_dev'].round(3)\n volatility_stats['coefficient_of_variation'] = volatility_stats['coefficient_of_variation'].round(2)\n \n transformed_df = volatility_stats[['period', 'fuel', 'avg_price', 'std_dev', 'coefficient_of_variation']]\n \n return transformed_df\n","source":["weekly_gas_prices"],"dialog":[{"role":"system","content":"You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"},{"role":"user","content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[GOAL]\n\nWhat are the major **price trends** across all fuel types from 1990 to 2025?"},{"role":"assistant","content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to visualize the major price trends across all fuel types from 1990 to 2025 to understand how fuel prices have evolved over time.\",\n \"display_instruction\": \"Show **price** trends over time for different **fuel** types\",\n \"recommendation\": \"To visualize price trends over time across fuel types, we should aggregate the data by date and fuel type. Since there are multiple grades and formulations for each fuel type, we'll calculate the average price for each fuel type on each date. A line chart with date on the x-axis, average price on the y-axis, and different colored lines for each fuel type will clearly show the trends and allow easy comparison.\",\n \"output_fields\": [\"date\", \"fuel\", \"avg_price\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n transformed_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Rename the price column to avg_price for clarity\n transformed_df = transformed_df.rename(columns={'price': 'avg_price'})\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values('date')\n \n return transformed_df\n```"},{"role":"user","content":"This is the result from the latest python code:\n\n date fuel avg_price\n0 1990-08-20 gasoline 1.191\n1 1990-08-27 gasoline 1.245\n2 1990-09-03 gasoline 1.242\n3 1990-09-10 gasoline 1.252\n4 1990-09-17 gasoline 1.266\n5 1990-09-24 gasoline 1.272\n6 1990-10-01 gasoline 1.321\n7 1990-10-08 gasoline 1.333\n8 1990-10-15 gasoline 1.339\n9 1990-10-22 gasoline 1.345\n......\n\nUpdate the code above based on the following instruction:\n\nCalculate year-over-year price changes to identify the most dramatic price spikes for both diesel and gasoline, showing which periods had the largest absolute and percentage increases with their peak prices."},{"role":"assistant","content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to identify the most dramatic price spikes by calculating year-over-year price changes for both diesel and gasoline, showing periods with the largest absolute and percentage increases along with their peak prices.\",\n \"display_instruction\": \"Identify periods with largest **year-over-year price changes** (absolute and percentage) and **peak prices**\",\n \"recommendation\": \"To analyze year-over-year price changes, we need to calculate the price difference and percentage change compared to the same period one year prior. We'll compute both absolute change and percentage change, then identify the periods with the most dramatic spikes. The output should include the date, fuel type, current price, previous year's price, absolute change, and percentage change to provide a comprehensive view of price volatility.\",\n \"output_fields\": [\"date\", \"fuel\", \"current_price\", \"yoy_price\", \"absolute_change\", \"percentage_change\"],\n \"chart_type\": \"point\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"absolute_change\",\n \"color\": \"fuel\",\n \"size\": \"percentage_change\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n df_agg = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Convert date to datetime for proper sorting and manipulation\n df_agg['date'] = pd.to_datetime(df_agg['date'])\n \n # Sort by fuel and date\n df_agg = df_agg.sort_values(['fuel', 'date'])\n \n # Calculate year-over-year changes (52 weeks ago for weekly data)\n df_agg['yoy_price'] = df_agg.groupby('fuel')['price'].shift(52)\n df_agg['absolute_change'] = df_agg['price'] - df_agg['yoy_price']\n df_agg['percentage_change'] = ((df_agg['price'] - df_agg['yoy_price']) / df_agg['yoy_price']) * 100\n \n # Rename columns for clarity\n transformed_df = df_agg.rename(columns={'price': 'current_price'})\n \n # Convert date back to string format for visualization\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n # Keep only rows where we have year-over-year data (remove first 52 weeks)\n transformed_df = transformed_df.dropna(subset=['yoy_price'])\n \n # Select and order output columns\n transformed_df = transformed_df[['date', 'fuel', 'current_price', 'yoy_price', 'absolute_change', 'percentage_change']]\n \n return transformed_df\n```"},{"role":"user","content":"This is the result from the latest python code:\n\n date fuel current_price yoy_price absolute_change percentage_change\n0 1995-03-20 diesel 1.085 1.106 -0.021 -1.898734\n1 1995-03-27 diesel 1.088 1.107 -0.019 -1.716350\n2 1995-04-03 diesel 1.094 1.109 -0.015 -1.352570\n3 1995-04-10 diesel 1.101 1.108 -0.007 -0.631769\n4 1995-04-17 diesel 1.106 1.105 0.001 0.090498\n5 1995-04-24 diesel 1.115 1.106 0.009 0.813743\n6 1995-05-01 diesel 1.119 1.104 0.015 1.358696\n7 1995-05-08 diesel 1.126 1.101 0.025 2.270663\n8 1995-05-15 diesel 1.126 1.099 0.027 2.456779\n9 1995-05-22 diesel 1.124 1.099 0.025 2.274795\n......\n\nUpdate the code above based on the following instruction:\n\nCompare price volatility (standard deviation and coefficient of variation) between pre-2000 (1990-1999) and post-2000 (2000-2025) periods for both fuel types to show how price stability has changed over time."},{"role":"assistant","content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to compare price volatility between pre-2000 (1990-1999) and post-2000 (2000-2025) periods for both fuel types by calculating standard deviation and coefficient of variation to understand how price stability has changed over time.\",\n \"display_instruction\": \"Compare **price volatility** (standard deviation and coefficient of variation) between **pre-2000** and **post-2000** periods\",\n \"recommendation\": \"To analyze price volatility across different time periods, we need to segment the data into pre-2000 (1990-1999) and post-2000 (2000-2025) periods, then calculate volatility metrics including standard deviation and coefficient of variation for each fuel type in each period. A grouped bar chart comparing these metrics across periods and fuel types will clearly show how price stability has evolved.\",\n \"output_fields\": [\"period\", \"fuel\", \"avg_price\", \"std_dev\", \"coefficient_of_variation\"],\n \"chart_type\": \"group_bar\",\n \"chart_encodings\": {\n \"x\": \"period\",\n \"y\": \"coefficient_of_variation\",\n \"color\": \"fuel\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n df_agg = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Convert date to datetime for period classification\n df_agg['date'] = pd.to_datetime(df_agg['date'])\n \n # Extract year for period classification\n df_agg['year'] = df_agg['date'].dt.year\n \n # Classify into pre-2000 and post-2000 periods\n df_agg['period'] = df_agg['year'].apply(lambda x: 'Pre-2000 (1990-1999)' if x < 2000 else 'Post-2000 (2000-2025)')\n \n # Calculate volatility metrics by period and fuel\n volatility_stats = df_agg.groupby(['period', 'fuel']).agg({\n 'price': ['mean', 'std']\n }).reset_index()\n \n # Flatten column names\n volatility_stats.columns = ['period', 'fuel', 'avg_price', 'std_dev']\n \n # Calculate coefficient of variation (CV = std_dev / mean * 100)\n volatility_stats['coefficient_of_variation'] = (volatility_stats['std_dev'] / volatility_stats['avg_price']) * 100\n \n # Round values for readability\n volatility_stats['avg_price'] = volatility_stats['avg_price'].round(3)\n volatility_stats['std_dev'] = volatility_stats['std_dev'].round(3)\n volatility_stats['coefficient_of_variation'] = volatility_stats['coefficient_of_variation'].round(2)\n \n transformed_df = volatility_stats[['period', 'fuel', 'avg_price', 'std_dev', 'coefficient_of_variation']]\n \n return transformed_df\n```"}],"trigger":{"tableId":"table-922023","sourceTableIds":["weekly_gas_prices"],"instruction":"Compare price volatility (standard deviation and coefficient of variation) between pre-2000 (1990-1999) and post-2000 (2000-2025) periods for both fuel types to show how price stability has changed over time.","displayInstruction":"Compare **price volatility** (standard deviation and coefficient of variation) between **pre-2000** and **post-2000** periods","chart":{"id":"chart-1760744940045","chartType":"Auto","encodingMap":{},"tableRef":"weekly_gas_prices","saved":false,"source":"trigger","unread":true},"resultTableId":"table-937785"},"explanation":{"agent":"CodeExplanationAgent","code":"This code performs a statistical analysis of **gas price volatility** across two time periods:\n\n1. **Aggregate prices** by computing the mean **price** for each **date** and **fuel** type, averaging across all **grades** and **formulations**\n2. **Convert dates** to datetime format and extract the **year** from each date\n3. **Classify data** into two time periods: **Pre-2000 (1990-1999)** and **Post-2000 (2000-2025)** based on the year\n4. **Calculate volatility metrics** for each **period** and **fuel** type combination:\n - Compute the **mean price** (average price level)\n - Compute the **standard deviation** (absolute price variability)\n - Calculate the **coefficient of variation** to measure relative volatility\n5. **Round numerical values** for improved readability (**avg_price** and **std_dev** to 3 decimals, **coefficient_of_variation** to 2 decimals)\n6. **Return summary statistics** showing volatility characteristics for **diesel** and **gasoline** in each time period","concepts":[{"explanation":"A normalized measure of price volatility that indicates the relative variability of prices independent of their absolute level. Calculated as \\( \\frac{\\text{std\\_dev}}{\\text{avg\\_price}} \\times 100 \\), this metric allows for fair comparison of price volatility between different time periods or fuel types that may have substantially different average price levels. A higher coefficient indicates more relative price instability.","field":"coefficient_of_variation"},{"explanation":"This analysis employs a **comparative volatility assessment** framework to examine fuel price stability across two distinct time periods. The approach uses **temporal aggregation** (grouping by date and fuel) followed by **period-based stratification** (pre-2000 vs. post-2000) to compute **descriptive volatility statistics** (mean, standard deviation, and coefficient of variation). This methodology allows for direct comparison of both absolute and relative price fluctuations across time periods and fuel types. **Alternative approaches** could include: (1) time series decomposition to separate trend, seasonal, and irregular components; (2) GARCH models to capture time-varying volatility; (3) moving window analysis to track volatility changes over shorter intervals; or (4) regime-switching models to identify distinct volatility states.","field":"Statistical Analysis"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n df_agg = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Convert date to datetime for period classification\n df_agg['date'] = pd.to_datetime(df_agg['date'])\n \n # Extract year for period classification\n df_agg['year'] = df_agg['date'].dt.year\n \n # Classify into pre-2000 and post-2000 periods\n df_agg['period'] = df_agg['year'].apply(lambda x: 'Pre-2000 (1990-1999)' if x < 2000 else 'Post-2000 (2000-2025)')\n \n # Calculate volatility metrics by period and fuel\n volatility_stats = df_agg.groupby(['period', 'fuel']).agg({\n 'price': ['mean', 'std']\n }).reset_index()\n \n # Flatten column names\n volatility_stats.columns = ['period', 'fuel', 'avg_price', 'std_dev']\n \n # Calculate coefficient of variation (CV = std_dev / mean * 100)\n volatility_stats['coefficient_of_variation'] = (volatility_stats['std_dev'] / volatility_stats['avg_price']) * 100\n \n # Round values for readability\n volatility_stats['avg_price'] = volatility_stats['avg_price'].round(3)\n volatility_stats['std_dev'] = volatility_stats['std_dev'].round(3)\n volatility_stats['coefficient_of_variation'] = volatility_stats['coefficient_of_variation'].round(2)\n \n transformed_df = volatility_stats[['period', 'fuel', 'avg_price', 'std_dev', 'coefficient_of_variation']]\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThis code performs a statistical analysis of **gas price volatility** across two time periods:\n\n1. **Aggregate prices** by computing the mean **price** for each **date** and **fuel** type, averaging across all **grades** and **formulations**\n2. **Convert dates** to datetime format and extract the **year** from each date\n3. **Classify data** into two time periods: **Pre-2000 (1990-1999)** and **Post-2000 (2000-2025)** based on the year\n4. **Calculate volatility metrics** for each **period** and **fuel** type combination:\n - Compute the **mean price** (average price level)\n - Compute the **standard deviation** (absolute price variability)\n - Calculate the **coefficient of variation** to measure relative volatility\n5. **Round numerical values** for improved readability (**avg_price** and **std_dev** to 3 decimals, **coefficient_of_variation** to 2 decimals)\n6. **Return summary statistics** showing volatility characteristics for **diesel** and **gasoline** in each time period\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"coefficient_of_variation\",\n \"explanation\": \"A normalized measure of price volatility that indicates the relative variability of prices independent of their absolute level. Calculated as \\\\( \\\\frac{\\\\text{std\\\\_dev}}{\\\\text{avg\\\\_price}} \\\\times 100 \\\\), this metric allows for fair comparison of price volatility between different time periods or fuel types that may have substantially different average price levels. A higher coefficient indicates more relative price instability.\"\n },\n {\n \"field\": \"Statistical Analysis\",\n \"explanation\": \"This analysis employs a **comparative volatility assessment** framework to examine fuel price stability across two distinct time periods. The approach uses **temporal aggregation** (grouping by date and fuel) followed by **period-based stratification** (pre-2000 vs. post-2000) to compute **descriptive volatility statistics** (mean, standard deviation, and coefficient of variation). This methodology allows for direct comparison of both absolute and relative price fluctuations across time periods and fuel types. **Alternative approaches** could include: (1) time series decomposition to separate trend, seasonal, and irregular components; (2) GARCH models to capture time-varying volatility; (3) moving window analysis to track volatility changes over shorter intervals; or (4) regime-switching models to identify distinct volatility states.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-34","displayId":"fuel-price-forecast","names":["avg_price","date","fuel","is_predicted"],"rows":[{"avg_price":1.106,"date":"1994-03-21","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0292206988,"date":"1994-03-21","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.107,"date":"1994-03-28","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0311478914,"date":"1994-03-28","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.109,"date":"1994-04-04","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0330750841,"date":"1994-04-04","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.108,"date":"1994-04-11","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0350022767,"date":"1994-04-11","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.105,"date":"1994-04-18","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0369294694,"date":"1994-04-18","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.106,"date":"1994-04-25","fuel":"diesel","is_predicted":"original"},{"avg_price":1.038856662,"date":"1994-04-25","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.104,"date":"1994-05-02","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0407838547,"date":"1994-05-02","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.101,"date":"1994-05-09","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0427110473,"date":"1994-05-09","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.099,"date":"1994-05-16","fuel":"diesel","is_predicted":"original"},{"avg_price":1.04463824,"date":"1994-05-16","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.099,"date":"1994-05-23","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0465654326,"date":"1994-05-23","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.098,"date":"1994-05-30","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0484926253,"date":"1994-05-30","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.101,"date":"1994-06-06","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0504198179,"date":"1994-06-06","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.098,"date":"1994-06-13","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0523470106,"date":"1994-06-13","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.103,"date":"1994-06-20","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0542742032,"date":"1994-06-20","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.108,"date":"1994-06-27","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0562013959,"date":"1994-06-27","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.109,"date":"1994-07-04","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0581285885,"date":"1994-07-04","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.11,"date":"1994-07-11","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0600557812,"date":"1994-07-11","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.111,"date":"1994-07-18","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0619829738,"date":"1994-07-18","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.111,"date":"1994-07-25","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0639101665,"date":"1994-07-25","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.116,"date":"1994-08-01","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0658373591,"date":"1994-08-01","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.127,"date":"1994-08-08","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0677645518,"date":"1994-08-08","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.127,"date":"1994-08-15","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0696917444,"date":"1994-08-15","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.124,"date":"1994-08-22","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0716189371,"date":"1994-08-22","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.122,"date":"1994-08-29","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0735461297,"date":"1994-08-29","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.126,"date":"1994-09-05","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0754733223,"date":"1994-09-05","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.128,"date":"1994-09-12","fuel":"diesel","is_predicted":"original"},{"avg_price":1.077400515,"date":"1994-09-12","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.126,"date":"1994-09-19","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0793277076,"date":"1994-09-19","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.12,"date":"1994-09-26","fuel":"diesel","is_predicted":"original"},{"avg_price":1.0812549003,"date":"19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erive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n historical_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n historical_df = historical_df.rename(columns={'price': 'avg_price'})\n historical_df = historical_df.sort_values('date')\n \n # Convert date to datetime for easier manipulation\n historical_df['date'] = pd.to_datetime(historical_df['date'])\n \n # Prepare the result dataframe\n all_data = []\n \n # Process each fuel type separately\n for fuel_type in historical_df['fuel'].unique():\n fuel_data = historical_df[historical_df['fuel'] == fuel_type].copy()\n \n # Create numeric representation of dates for regression (days since first date)\n min_date = fuel_data['date'].min()\n fuel_data['days_since_start'] = (fuel_data['date'] - min_date).dt.days\n \n # Prepare training data\n X = fuel_data[['days_since_start']].values\n y = fuel_data['avg_price'].values\n \n # Train linear regression model\n model = LinearRegression()\n model.fit(X, y)\n \n # Add original data with 'original' label\n fuel_data_original = fuel_data[['date', 'avg_price']].copy()\n fuel_data_original['fuel'] = fuel_type\n fuel_data_original['is_predicted'] = 'original'\n all_data.append(fuel_data_original[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate regression values for historical dates\n regression_prices = model.predict(X)\n fuel_data_regression = fuel_data[['date']].copy()\n fuel_data_regression['fuel'] = fuel_type\n fuel_data_regression['avg_price'] = regression_prices\n fuel_data_regression['is_predicted'] = 'regression'\n all_data.append(fuel_data_regression[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate forecast for next 5 years\n max_date = fuel_data['date'].max()\n future_dates = pd.date_range(start=max_date + pd.Timedelta(days=7), periods=260, freq='W')\n future_days = (future_dates - min_date).days.values.reshape(-1, 1)\n \n future_prices = model.predict(future_days)\n \n forecast_df = pd.DataFrame({\n 'date': future_dates,\n 'fuel': fuel_type,\n 'avg_price': future_prices,\n 'is_predicted': 'forecasting'\n })\n all_data.append(forecast_df)\n \n # Combine all data\n transformed_df = pd.concat(all_data, ignore_index=True)\n transformed_df = transformed_df.sort_values(['fuel', 'date'])\n \n # Convert date back to string format for output\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n return transformed_df\n","source":["weekly_gas_prices"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[GOAL]\n\nWhat are the major **price trends** across all fuel types from 1990 to 2025?","role":"user"},{"content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to visualize the major price trends across all fuel types from 1990 to 2025 to understand how fuel prices have evolved over time.\",\n \"display_instruction\": \"Show **price** trends over time for different **fuel** types\",\n \"recommendation\": \"To visualize price trends over time across fuel types, we should aggregate the data by date and fuel type. Since there are multiple grades and formulations for each fuel type, we'll calculate the average price for each fuel type on each date. A line chart with date on the x-axis, average price on the y-axis, and different colored lines for each fuel type will clearly show the trends and allow easy comparison.\",\n \"output_fields\": [\"date\", \"fuel\", \"avg_price\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n transformed_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Rename the price column to avg_price for clarity\n transformed_df = transformed_df.rename(columns={'price': 'avg_price'})\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values('date')\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n date fuel avg_price\n0 1990-08-20 gasoline 1.191\n1 1990-08-27 gasoline 1.245\n2 1990-09-03 gasoline 1.242\n3 1990-09-10 gasoline 1.252\n4 1990-09-17 gasoline 1.266\n5 1990-09-24 gasoline 1.272\n6 1990-10-01 gasoline 1.321\n7 1990-10-08 gasoline 1.333\n8 1990-10-15 gasoline 1.339\n9 1990-10-22 gasoline 1.345\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"forecast the price trend for the next 5 years with some propert model!\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"column\": \"fuel\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Build upon the previous price trends visualization by adding forecasted prices for the next 5 years (2025-2030) using a time series forecasting model. The output should include both historical actual prices and forecasted prices, with an indicator to distinguish between them.\",\n \"display_instruction\": \"Forecast **price** trends for the next 5 years for each **fuel** type\",\n \"output_fields\": [\"date\", \"fuel\", \"avg_price\", \"is_predicted\"],\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"facet\": \"is_predicted\"\n },\n \"reason\": \"The user wants to extend the price trend analysis with forecasting for the next 5 years. Following the guidelines for forecasting, the output should include a 'is_predicted' column to distinguish between actual historical data, regression on historical data, and forecasted future values. The chart should be a line chart showing both historical and predicted values for each fuel type.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n historical_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n historical_df = historical_df.rename(columns={'price': 'avg_price'})\n historical_df = historical_df.sort_values('date')\n \n # Convert date to datetime for easier manipulation\n historical_df['date'] = pd.to_datetime(historical_df['date'])\n \n # Prepare the result dataframe\n all_data = []\n \n # Process each fuel type separately\n for fuel_type in historical_df['fuel'].unique():\n fuel_data = historical_df[historical_df['fuel'] == fuel_type].copy()\n \n # Create numeric representation of dates for regression (days since first date)\n min_date = fuel_data['date'].min()\n fuel_data['days_since_start'] = (fuel_data['date'] - min_date).dt.days\n \n # Prepare training data\n X = fuel_data[['days_since_start']].values\n y = fuel_data['avg_price'].values\n \n # Train linear regression model\n model = LinearRegression()\n model.fit(X, y)\n \n # Add original data with 'original' label\n fuel_data_original = fuel_data[['date', 'avg_price']].copy()\n fuel_data_original['fuel'] = fuel_type\n fuel_data_original['is_predicted'] = 'original'\n all_data.append(fuel_data_original[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate regression values for historical dates\n regression_prices = model.predict(X)\n fuel_data_regression = fuel_data[['date']].copy()\n fuel_data_regression['fuel'] = fuel_type\n fuel_data_regression['avg_price'] = regression_prices\n fuel_data_regression['is_predicted'] = 'regression'\n all_data.append(fuel_data_regression[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate forecast for next 5 years\n max_date = fuel_data['date'].max()\n future_dates = pd.date_range(start=max_date + pd.Timedelta(days=7), periods=260, freq='W')\n future_days = (future_dates - min_date).days.values.reshape(-1, 1)\n \n future_prices = model.predict(future_days)\n \n forecast_df = pd.DataFrame({\n 'date': future_dates,\n 'fuel': fuel_type,\n 'avg_price': future_prices,\n 'is_predicted': 'forecasting'\n })\n all_data.append(forecast_df)\n \n # Combine all data\n transformed_df = pd.concat(all_data, ignore_index=True)\n transformed_df = transformed_df.sort_values(['fuel', 'date'])\n \n # Convert date back to string format for output\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"table-904593","sourceTableIds":["weekly_gas_prices"],"instruction":"forecast the price trend for the next 5 years with some propert model!","displayInstruction":"Forecast **price** trends for the next 5 years for each **fuel** type","chart":{"id":"chart-1760745146503","chartType":"Line Chart","encodingMap":{"x":{"fieldID":"original--weekly_gas_prices--date"},"y":{"fieldID":"concept-avg_price-1760744859365"},"color":{"fieldID":"original--weekly_gas_prices--fuel"},"opacity":{"channel":"opacity","bin":false},"column":{"fieldID":"original--weekly_gas_prices--fuel"},"row":{"channel":"row","bin":false}},"tableRef":"table-904593","saved":false,"source":"trigger","unread":false},"resultTableId":"table-34"},"explanation":{"agent":"CodeExplanationAgent","code":"This code performs **time series analysis and forecasting** on historical gas prices:\n\n1. **Aggregate historical prices** by calculating the mean `price` across all `grade` and `formulation` values for each `date` and `fuel` type combination, creating an `avg_price` metric.\n\n2. **Prepare temporal features** by converting `date` to datetime format and calculating `days_since_start` (the number of days elapsed since the earliest date) for each `fuel` type separately.\n\n3. **Train linear regression models** for each `fuel` type (diesel, gasoline) using `days_since_start` as the predictor variable and `avg_price` as the target variable.\n\n4. **Generate three data series** for each fuel type:\n - **Original**: Historical `avg_price` values with label `is_predicted = 'original'`\n - **Regression**: Fitted values from the linear model for historical dates with label `is_predicted = 'regression'`\n - **Forecasting**: Predicted prices for the next **5 years** (260 weekly periods) beyond the latest historical date with label `is_predicted = 'forecasting'`\n\n5. **Combine and format** all series into a single dataset sorted by `fuel` and `date`, with dates converted back to string format.","concepts":[{"explanation":"This analysis uses **linear regression modeling** to capture long-term price trends and forecast future gas prices. The model treats time as a continuous predictor variable (days_since_start) to predict average price. Each fuel type is modeled independently to account for different pricing dynamics. The approach assumes a linear relationship between time and price, which captures overall trend but may not account for cyclical patterns, seasonality, or market shocks. **Alternative modeling approaches** could include: (1) ARIMA/SARIMA models to capture autocorrelation and seasonality, (2) exponential smoothing methods (Holt-Winters) for trend and seasonal components, (3) polynomial regression for non-linear trends, or (4) more advanced machine learning methods like LSTM neural networks for complex temporal patterns.","field":"Statistical Analysis"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n historical_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n historical_df = historical_df.rename(columns={'price': 'avg_price'})\n historical_df = historical_df.sort_values('date')\n \n # Convert date to datetime for easier manipulation\n historical_df['date'] = pd.to_datetime(historical_df['date'])\n \n # Prepare the result dataframe\n all_data = []\n \n # Process each fuel type separately\n for fuel_type in historical_df['fuel'].unique():\n fuel_data = historical_df[historical_df['fuel'] == fuel_type].copy()\n \n # Create numeric representation of dates for regression (days since first date)\n min_date = fuel_data['date'].min()\n fuel_data['days_since_start'] = (fuel_data['date'] - min_date).dt.days\n \n # Prepare training data\n X = fuel_data[['days_since_start']].values\n y = fuel_data['avg_price'].values\n \n # Train linear regression model\n model = LinearRegression()\n model.fit(X, y)\n \n # Add original data with 'original' label\n fuel_data_original = fuel_data[['date', 'avg_price']].copy()\n fuel_data_original['fuel'] = fuel_type\n fuel_data_original['is_predicted'] = 'original'\n all_data.append(fuel_data_original[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate regression values for historical dates\n regression_prices = model.predict(X)\n fuel_data_regression = fuel_data[['date']].copy()\n fuel_data_regression['fuel'] = fuel_type\n fuel_data_regression['avg_price'] = regression_prices\n fuel_data_regression['is_predicted'] = 'regression'\n all_data.append(fuel_data_regression[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate forecast for next 5 years\n max_date = fuel_data['date'].max()\n future_dates = pd.date_range(start=max_date + pd.Timedelta(days=7), periods=260, freq='W')\n future_days = (future_dates - min_date).days.values.reshape(-1, 1)\n \n future_prices = model.predict(future_days)\n \n forecast_df = pd.DataFrame({\n 'date': future_dates,\n 'fuel': fuel_type,\n 'avg_price': future_prices,\n 'is_predicted': 'forecasting'\n })\n all_data.append(forecast_df)\n \n # Combine all data\n transformed_df = pd.concat(all_data, ignore_index=True)\n transformed_df = transformed_df.sort_values(['fuel', 'date'])\n \n # Convert date back to string format for output\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThis code performs **time series analysis and forecasting** on historical gas prices:\n\n1. **Aggregate historical prices** by calculating the mean `price` across all `grade` and `formulation` values for each `date` and `fuel` type combination, creating an `avg_price` metric.\n\n2. **Prepare temporal features** by converting `date` to datetime format and calculating `days_since_start` (the number of days elapsed since the earliest date) for each `fuel` type separately.\n\n3. **Train linear regression models** for each `fuel` type (diesel, gasoline) using `days_since_start` as the predictor variable and `avg_price` as the target variable.\n\n4. **Generate three data series** for each fuel type:\n - **Original**: Historical `avg_price` values with label `is_predicted = 'original'`\n - **Regression**: Fitted values from the linear model for historical dates with label `is_predicted = 'regression'`\n - **Forecasting**: Predicted prices for the next **5 years** (260 weekly periods) beyond the latest historical date with label `is_predicted = 'forecasting'`\n\n5. **Combine and format** all series into a single dataset sorted by `fuel` and `date`, with dates converted back to string format.\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"Statistical Analysis\",\n \"explanation\": \"This analysis uses **linear regression modeling** to capture long-term price trends and forecast future gas prices. The model treats time as a continuous predictor variable (days_since_start) to predict average price. Each fuel type is modeled independently to account for different pricing dynamics. The approach assumes a linear relationship between time and price, which captures overall trend but may not account for cyclical patterns, seasonality, or market shocks. **Alternative modeling approaches** could include: (1) ARIMA/SARIMA models to capture autocorrelation and seasonality, (2) exponential smoothing methods (Holt-Winters) for trend and seasonal components, (3) polynomial regression for non-linear trends, or (4) more advanced machine learning methods like LSTM neural networks for complex temporal patterns.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-22","displayId":"fuel-price-forecast1","names":["avg_price","date","fuel","is_predicted"],"rows":[{"avg_price":1.106,"date":"1994-03-21","fuel":"diesel","is_predicted":"original"},{"avg_price":0.5951388141,"date":"1994-03-21","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.107,"date":"1994-03-28","fuel":"diesel","is_predicted":"original"},{"avg_price":0.5986628745,"date":"1994-03-28","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.109,"date":"1994-04-04","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6021849755,"date":"1994-04-04","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.108,"date":"1994-04-11","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6057051172,"date":"1994-04-11","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.105,"date":"1994-04-18","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6092232996,"date":"1994-04-18","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.106,"date":"1994-04-25","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6127395226,"date":"1994-04-25","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.104,"date":"1994-05-02","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6162537863,"date":"1994-05-02","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.101,"date":"1994-05-09","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6197660906,"date":"1994-05-09","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.099,"date":"1994-05-16","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6232764356,"date":"1994-05-16","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.099,"date":"1994-05-23","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6267848212,"date":"1994-05-23","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.098,"date":"1994-05-30","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6302912475,"date":"1994-05-30","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.101,"date":"1994-06-06","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6337957144,"date":"1994-06-06","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.098,"date":"1994-06-13","fuel":"diesel","is_predicted":"original"},{"avg_price":0.637298222,"date":"1994-06-13","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.103,"date":"1994-06-20","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6407987703,"date":"1994-06-20","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.108,"date":"1994-06-27","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6442973591,"date":"1994-06-27","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.109,"date":"1994-07-04","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6477939887,"date":"1994-07-04","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.11,"date":"1994-07-11","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6512886589,"date":"1994-07-11","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.111,"date":"1994-07-18","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6547813697,"date":"1994-07-18","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.111,"date":"1994-07-25","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6582721212,"date":"1994-07-25","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.116,"date":"1994-08-01","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6617609134,"date":"1994-08-01","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.127,"date":"1994-08-08","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6652477462,"date":"1994-08-08","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.127,"date":"1994-08-15","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6687326197,"date":"1994-08-15","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.124,"date":"1994-08-22","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6722155338,"date":"1994-08-22","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.122,"date":"1994-08-29","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6756964886,"date":"1994-08-29","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.126,"date":"1994-09-05","fuel":"diesel","is_predicted":"original"},{"avg_price":0.679175484,"date":"1994-09-05","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.128,"date":"1994-09-12","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6826525201,"date":"1994-09-12","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.126,"date":"1994-09-19","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6861275968,"date":"1994-09-19","fuel":"diesel","is_predicted":"regression"},{"avg_price":1.12,"date":"1994-09-26","fuel":"diesel","is_predicted":"original"},{"avg_price":0.6896007142,"date":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ive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.pipeline import make_pipeline\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n historical_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n historical_df = historical_df.rename(columns={'price': 'avg_price'})\n historical_df = historical_df.sort_values('date')\n \n # Convert date to datetime for easier manipulation\n historical_df['date'] = pd.to_datetime(historical_df['date'])\n \n # Prepare the result dataframe\n all_data = []\n \n # Process each fuel type separately\n for fuel_type in historical_df['fuel'].unique():\n fuel_data = historical_df[historical_df['fuel'] == fuel_type].copy()\n \n # Create numeric representation of dates for regression (days since first date)\n min_date = fuel_data['date'].min()\n fuel_data['days_since_start'] = (fuel_data['date'] - min_date).dt.days\n \n # Prepare training data\n X = fuel_data[['days_since_start']].values\n y = fuel_data['avg_price'].values\n \n # Train polynomial regression model (degree 2 for non-linear trends)\n model = make_pipeline(PolynomialFeatures(degree=2), LinearRegression())\n model.fit(X, y)\n \n # Add original data with 'original' label\n fuel_data_original = fuel_data[['date', 'avg_price']].copy()\n fuel_data_original['fuel'] = fuel_type\n fuel_data_original['is_predicted'] = 'original'\n all_data.append(fuel_data_original[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate regression values for historical dates\n regression_prices = model.predict(X)\n fuel_data_regression = fuel_data[['date']].copy()\n fuel_data_regression['fuel'] = fuel_type\n fuel_data_regression['avg_price'] = regression_prices\n fuel_data_regression['is_predicted'] = 'regression'\n all_data.append(fuel_data_regression[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate forecast for next 5 years\n max_date = fuel_data['date'].max()\n future_dates = pd.date_range(start=max_date + pd.Timedelta(days=7), periods=260, freq='W')\n future_days = (future_dates - min_date).days.values.reshape(-1, 1)\n \n future_prices = model.predict(future_days)\n \n forecast_df = pd.DataFrame({\n 'date': future_dates,\n 'fuel': fuel_type,\n 'avg_price': future_prices,\n 'is_predicted': 'forecasting'\n })\n all_data.append(forecast_df)\n \n # Combine all data\n transformed_df = pd.concat(all_data, ignore_index=True)\n transformed_df = transformed_df.sort_values(['fuel', 'date'])\n \n # Convert date back to string format for output\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n return transformed_df\n","source":["weekly_gas_prices"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[GOAL]\n\nWhat are the major **price trends** across all fuel types from 1990 to 2025?","role":"user"},{"content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to visualize the major price trends across all fuel types from 1990 to 2025 to understand how fuel prices have evolved over time.\",\n \"display_instruction\": \"Show **price** trends over time for different **fuel** types\",\n \"recommendation\": \"To visualize price trends over time across fuel types, we should aggregate the data by date and fuel type. Since there are multiple grades and formulations for each fuel type, we'll calculate the average price for each fuel type on each date. A line chart with date on the x-axis, average price on the y-axis, and different colored lines for each fuel type will clearly show the trends and allow easy comparison.\",\n \"output_fields\": [\"date\", \"fuel\", \"avg_price\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n transformed_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Rename the price column to avg_price for clarity\n transformed_df = transformed_df.rename(columns={'price': 'avg_price'})\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values('date')\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n date fuel avg_price\n0 1990-08-20 gasoline 1.191\n1 1990-08-27 gasoline 1.245\n2 1990-09-03 gasoline 1.242\n3 1990-09-10 gasoline 1.252\n4 1990-09-17 gasoline 1.266\n5 1990-09-24 gasoline 1.272\n6 1990-10-01 gasoline 1.321\n7 1990-10-08 gasoline 1.333\n8 1990-10-15 gasoline 1.339\n9 1990-10-22 gasoline 1.345\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"forecast the price trend for the next 5 years with some propert model!\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"column\": \"fuel\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Build upon the previous price trends visualization by adding forecasted prices for the next 5 years (2025-2030) using a time series forecasting model. The output should include both historical actual prices and forecasted prices, with an indicator to distinguish between them.\",\n \"display_instruction\": \"Forecast **price** trends for the next 5 years for each **fuel** type\",\n \"output_fields\": [\"date\", \"fuel\", \"avg_price\", \"is_predicted\"],\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"facet\": \"is_predicted\"\n },\n \"reason\": \"The user wants to extend the price trend analysis with forecasting for the next 5 years. Following the guidelines for forecasting, the output should include a 'is_predicted' column to distinguish between actual historical data, regression on historical data, and forecasted future values. The chart should be a line chart showing both historical and predicted values for each fuel type.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n historical_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n historical_df = historical_df.rename(columns={'price': 'avg_price'})\n historical_df = historical_df.sort_values('date')\n \n # Convert date to datetime for easier manipulation\n historical_df['date'] = pd.to_datetime(historical_df['date'])\n \n # Prepare the result dataframe\n all_data = []\n \n # Process each fuel type separately\n for fuel_type in historical_df['fuel'].unique():\n fuel_data = historical_df[historical_df['fuel'] == fuel_type].copy()\n \n # Create numeric representation of dates for regression (days since first date)\n min_date = fuel_data['date'].min()\n fuel_data['days_since_start'] = (fuel_data['date'] - min_date).dt.days\n \n # Prepare training data\n X = fuel_data[['days_since_start']].values\n y = fuel_data['avg_price'].values\n \n # Train linear regression model\n model = LinearRegression()\n model.fit(X, y)\n \n # Add original data with 'original' label\n fuel_data_original = fuel_data[['date', 'avg_price']].copy()\n fuel_data_original['fuel'] = fuel_type\n fuel_data_original['is_predicted'] = 'original'\n all_data.append(fuel_data_original[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate regression values for historical dates\n regression_prices = model.predict(X)\n fuel_data_regression = fuel_data[['date']].copy()\n fuel_data_regression['fuel'] = fuel_type\n fuel_data_regression['avg_price'] = regression_prices\n fuel_data_regression['is_predicted'] = 'regression'\n all_data.append(fuel_data_regression[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate forecast for next 5 years\n max_date = fuel_data['date'].max()\n future_dates = pd.date_range(start=max_date + pd.Timedelta(days=7), periods=260, freq='W')\n future_days = (future_dates - min_date).days.values.reshape(-1, 1)\n \n future_prices = model.predict(future_days)\n \n forecast_df = pd.DataFrame({\n 'date': future_dates,\n 'fuel': fuel_type,\n 'avg_price': future_prices,\n 'is_predicted': 'forecasting'\n })\n all_data.append(forecast_df)\n \n # Combine all data\n transformed_df = pd.concat(all_data, ignore_index=True)\n transformed_df = transformed_df.sort_values(['fuel', 'date'])\n \n # Convert date back to string format for output\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n avg_price date fuel is_predicted\n0 1.106000 1994-03-21 diesel original\n1 1.029221 1994-03-21 diesel regression\n2 1.107000 1994-03-28 diesel original\n3 1.031148 1994-03-28 diesel regression\n4 1.109000 1994-04-04 diesel original\n5 1.033075 1994-04-04 diesel regression\n6 1.108000 1994-04-11 diesel original\n7 1.035002 1994-04-11 diesel regression\n8 1.105000 1994-04-18 diesel original\n9 1.036929 1994-04-18 diesel regression\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"use some more complex model\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"is_predicted\",\n \"column\": \"fuel\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Improve the forecasting model by using a more sophisticated time series model (polynomial regression with degree 2) instead of simple linear regression to better capture non-linear trends in fuel price data. The output should still include historical actual prices, regression fitted values, and forecasted prices for the next 5 years.\",\n \"display_instruction\": \"Forecast **price** trends using a more complex model for each **fuel** type\",\n \"output_fields\": [\"date\", \"fuel\", \"avg_price\", \"is_predicted\"],\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"is_predicted\",\n \"facet\": \"fuel\"\n },\n \"reason\": \"The user wants to use a more complex forecasting model to potentially capture non-linear trends better. We'll use polynomial regression (degree 2) which can model curved trends while still being interpretable. The chart encodings are updated to color by prediction type and facet by fuel type as specified by the user.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.pipeline import make_pipeline\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n historical_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n historical_df = historical_df.rename(columns={'price': 'avg_price'})\n historical_df = historical_df.sort_values('date')\n \n # Convert date to datetime for easier manipulation\n historical_df['date'] = pd.to_datetime(historical_df['date'])\n \n # Prepare the result dataframe\n all_data = []\n \n # Process each fuel type separately\n for fuel_type in historical_df['fuel'].unique():\n fuel_data = historical_df[historical_df['fuel'] == fuel_type].copy()\n \n # Create numeric representation of dates for regression (days since first date)\n min_date = fuel_data['date'].min()\n fuel_data['days_since_start'] = (fuel_data['date'] - min_date).dt.days\n \n # Prepare training data\n X = fuel_data[['days_since_start']].values\n y = fuel_data['avg_price'].values\n \n # Train polynomial regression model (degree 2 for non-linear trends)\n model = make_pipeline(PolynomialFeatures(degree=2), LinearRegression())\n model.fit(X, y)\n \n # Add original data with 'original' label\n fuel_data_original = fuel_data[['date', 'avg_price']].copy()\n fuel_data_original['fuel'] = fuel_type\n fuel_data_original['is_predicted'] = 'original'\n all_data.append(fuel_data_original[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate regression values for historical dates\n regression_prices = model.predict(X)\n fuel_data_regression = fuel_data[['date']].copy()\n fuel_data_regression['fuel'] = fuel_type\n fuel_data_regression['avg_price'] = regression_prices\n fuel_data_regression['is_predicted'] = 'regression'\n all_data.append(fuel_data_regression[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate forecast for next 5 years\n max_date = fuel_data['date'].max()\n future_dates = pd.date_range(start=max_date + pd.Timedelta(days=7), periods=260, freq='W')\n future_days = (future_dates - min_date).days.values.reshape(-1, 1)\n \n future_prices = model.predict(future_days)\n \n forecast_df = pd.DataFrame({\n 'date': future_dates,\n 'fuel': fuel_type,\n 'avg_price': future_prices,\n 'is_predicted': 'forecasting'\n })\n all_data.append(forecast_df)\n \n # Combine all data\n transformed_df = pd.concat(all_data, ignore_index=True)\n transformed_df = transformed_df.sort_values(['fuel', 'date'])\n \n # Convert date back to string format for output\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"table-34","sourceTableIds":["weekly_gas_prices"],"instruction":"use some more complex model","displayInstruction":"Forecast **price** trends using a more complex model for each **fuel** type","chart":{"id":"chart-1760745227478","chartType":"Line Chart","encodingMap":{"x":{"fieldID":"original--weekly_gas_prices--date"},"y":{"fieldID":"concept-avg_price-1760744859365"},"color":{"fieldID":"concept-is_predicted-1760745153175","sortBy":"[\"original\",\"regression\",\"forecasting\"]"},"opacity":{"channel":"opacity","bin":false},"column":{"fieldID":"original--weekly_gas_prices--fuel"},"row":{"channel":"row","bin":false}},"tableRef":"table-34","saved":false,"source":"trigger","unread":false},"resultTableId":"table-22"},"explanation":{"agent":"CodeExplanationAgent","code":"This code performs **price trend analysis and forecasting** for different fuel types:\n\n1. **Aggregates** historical gas prices by calculating the **mean price** across all **grades** and **formulations** for each **date** and **fuel** type\n2. **Prepares** time series data by converting **dates** to datetime format and creating a **days_since_start** numeric variable (days elapsed since the first recorded date)\n3. **Trains** a **polynomial regression model** (degree 2) separately for each **fuel type** to capture non-linear price trends over time\n4. **Generates** three types of price records for each fuel:\n - **Original**: actual historical average prices with label `'original'`\n - **Regression**: model-fitted prices for historical dates with label `'regression'`\n - **Forecasting**: predicted prices for the **next 5 years** (260 weekly periods) with label `'forecasting'`\n5. **Combines** all records and sorts by **fuel** type and **date**","concepts":[{"explanation":"A numeric time variable representing the number of days elapsed since the first recorded date in the dataset. This transforms calendar dates into a continuous numeric scale suitable for regression modeling: \\( \\text{days\\_since\\_start} = (\\text{current\\_date} - \\text{min\\_date}) \\)","field":"days_since_start"},{"explanation":"A categorical label indicating the source and nature of the price data: 'original' for actual historical observations, 'regression' for model-fitted values on historical dates (representing the polynomial trend), and 'forecasting' for future price predictions beyond the observed data range.","field":"is_predicted"},{"explanation":"This analysis models fuel price trends using **polynomial regression (degree 2)** to capture non-linear patterns in price evolution over time. The model treats time (days since start) as the independent variable and average price as the dependent variable, fitted separately for each fuel type. The quadratic polynomial allows the model to capture acceleration or deceleration in price trends (e.g., prices rising faster over time or stabilizing). Alternative approaches could include: **ARIMA models** for capturing autocorrelation and seasonality, **exponential smoothing** for weighted recent observations, **Prophet** for handling multiple seasonality patterns and holidays, or **LSTM neural networks** for complex non-linear temporal dependencies.","field":"Statistical Analysis"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.pipeline import make_pipeline\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n historical_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n historical_df = historical_df.rename(columns={'price': 'avg_price'})\n historical_df = historical_df.sort_values('date')\n \n # Convert date to datetime for easier manipulation\n historical_df['date'] = pd.to_datetime(historical_df['date'])\n \n # Prepare the result dataframe\n all_data = []\n \n # Process each fuel type separately\n for fuel_type in historical_df['fuel'].unique():\n fuel_data = historical_df[historical_df['fuel'] == fuel_type].copy()\n \n # Create numeric representation of dates for regression (days since first date)\n min_date = fuel_data['date'].min()\n fuel_data['days_since_start'] = (fuel_data['date'] - min_date).dt.days\n \n # Prepare training data\n X = fuel_data[['days_since_start']].values\n y = fuel_data['avg_price'].values\n \n # Train polynomial regression model (degree 2 for non-linear trends)\n model = make_pipeline(PolynomialFeatures(degree=2), LinearRegression())\n model.fit(X, y)\n \n # Add original data with 'original' label\n fuel_data_original = fuel_data[['date', 'avg_price']].copy()\n fuel_data_original['fuel'] = fuel_type\n fuel_data_original['is_predicted'] = 'original'\n all_data.append(fuel_data_original[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate regression values for historical dates\n regression_prices = model.predict(X)\n fuel_data_regression = fuel_data[['date']].copy()\n fuel_data_regression['fuel'] = fuel_type\n fuel_data_regression['avg_price'] = regression_prices\n fuel_data_regression['is_predicted'] = 'regression'\n all_data.append(fuel_data_regression[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate forecast for next 5 years\n max_date = fuel_data['date'].max()\n future_dates = pd.date_range(start=max_date + pd.Timedelta(days=7), periods=260, freq='W')\n future_days = (future_dates - min_date).days.values.reshape(-1, 1)\n \n future_prices = model.predict(future_days)\n \n forecast_df = pd.DataFrame({\n 'date': future_dates,\n 'fuel': fuel_type,\n 'avg_price': future_prices,\n 'is_predicted': 'forecasting'\n })\n all_data.append(forecast_df)\n \n # Combine all data\n transformed_df = pd.concat(all_data, ignore_index=True)\n transformed_df = transformed_df.sort_values(['fuel', 'date'])\n \n # Convert date back to string format for output\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThis code performs **price trend analysis and forecasting** for different fuel types:\n\n1. **Aggregates** historical gas prices by calculating the **mean price** across all **grades** and **formulations** for each **date** and **fuel** type\n2. **Prepares** time series data by converting **dates** to datetime format and creating a **days_since_start** numeric variable (days elapsed since the first recorded date)\n3. **Trains** a **polynomial regression model** (degree 2) separately for each **fuel type** to capture non-linear price trends over time\n4. **Generates** three types of price records for each fuel:\n - **Original**: actual historical average prices with label `'original'`\n - **Regression**: model-fitted prices for historical dates with label `'regression'`\n - **Forecasting**: predicted prices for the **next 5 years** (260 weekly periods) with label `'forecasting'`\n5. **Combines** all records and sorts by **fuel** type and **date**\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"days_since_start\",\n \"explanation\": \"A numeric time variable representing the number of days elapsed since the first recorded date in the dataset. This transforms calendar dates into a continuous numeric scale suitable for regression modeling: \\\\( \\\\text{days\\\\_since\\\\_start} = (\\\\text{current\\\\_date} - \\\\text{min\\\\_date}) \\\\)\"\n },\n {\n \"field\": \"is_predicted\",\n \"explanation\": \"A categorical label indicating the source and nature of the price data: 'original' for actual historical observations, 'regression' for model-fitted values on historical dates (representing the polynomial trend), and 'forecasting' for future price predictions beyond the observed data range.\"\n },\n {\n \"field\": \"Statistical Analysis\",\n \"explanation\": \"This analysis models fuel price trends using **polynomial regression (degree 2)** to capture non-linear patterns in price evolution over time. The model treats time (days since start) as the independent variable and average price as the dependent variable, fitted separately for each fuel type. The quadratic polynomial allows the model to capture acceleration or deceleration in price trends (e.g., prices rising faster over time or stabilizing). Alternative approaches could include: **ARIMA models** for capturing autocorrelation and seasonality, **exponential smoothing** for weighted recent observations, **Prophet** for handling multiple seasonality patterns and holidays, or **LSTM neural networks** for complex non-linear temporal dependencies.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""}],"charts":[{"id":"chart-1760745227133","chartType":"Line Chart","encodingMap":{"x":{"fieldID":"original--weekly_gas_prices--date"},"y":{"fieldID":"concept-avg_price-1760744859365"},"color":{"fieldID":"concept-is_predicted-1760745153175","sortBy":"[\"original\",\"regression\",\"forecasting\"]"},"opacity":{"channel":"opacity","bin":false},"column":{"fieldID":"original--weekly_gas_prices--fuel"},"row":{"channel":"row","bin":false}},"tableRef":"table-22","saved":false,"source":"user","unread":false},{"id":"chart-1760745149814","chartType":"Line 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Diesel consistently priced higher than gasoline throughout most of the period.\n\n• **Year-over-year volatility**: The scatter plot reveals several dramatic price spikes, with the largest absolute changes (~2.5) and percentage changes (70-75%) occurring in recent years (2021-2025), indicating major price disruptions in the post-pandemic period.\n\n• **Price stability comparison**: Price volatility increased dramatically post-2000, with coefficient of variation rising from ~7-9% (pre-2000) to ~29-33% (post-2000) for both fuel types, indicating prices became 3-4x more volatile in recent decades. Diesel shows slightly higher volatility than gasoline in both periods.","status":"completed","hidden":false,"lastUpdate":1760744954367}],"dataCleanBlocks":[],"cleanInProgress":false,"generatedReports":[{"id":"report-1760831021256-7310","content":"# Fuel Price Volatility and Premium Trends: A Three-Decade Analysis\n\nThe U.S. fuel market has undergone dramatic shifts in pricing dynamics over the past 35 years, with post-2000 prices showing significantly higher volatility and expanding premium differentials across gasoline grades.\n\n[IMAGE(chart-1760744944490)]\n\n**Price volatility has intensified dramatically in the modern era.** The coefficient of variation—a measure of price instability relative to average costs—reveals that both diesel and gasoline experienced roughly **four-fold increases** in volatility post-2000. Diesel prices showed particularly heightened instability with a coefficient of variation of 33.48% (2000-2025) compared to just 7.65% in the 1990s. This shift reflects increased sensitivity to global crude oil market fluctuations, geopolitical tensions, and refining capacity constraints. Average prices also more than doubled across both fuel types, with diesel climbing from $1.14 to $2.93 per gallon.\n\n[IMAGE(chart-1760744910486)]\n\n**Premium gasoline pricing has steadily diverged from regular grades over time.** The price premium commanded by higher-octane gasoline grades has expanded substantially, with premium gasoline reaching spreads of **$0.90-$0.95 above regular** by 2025—nearly five times the $0.18-$0.20 premium observed in the mid-1990s. Midgrade gasoline follows a similar trajectory, reaching $0.55-$0.60 premiums. This widening gap suggests evolving refining economics, increased consumer segmentation, and potentially higher margins on premium products.\n\n**In summary**, the fuel market has transitioned from relative price stability in the 1990s to an era characterized by substantial volatility and expanding price differentiation. Consumers and businesses face both higher absolute costs and greater uncertainty in budgeting for fuel expenses. Key questions for further investigation include: What specific market mechanisms drive the premium expansion? How do regional variations affect these national trends? What role do alternative fuels play in moderating future volatility?","style":"executive summary","selectedChartIds":["chart-1760744944490","chart-1760744910486"],"createdAt":1760831035718},{"id":"report-1760830985775-9885","content":"# Fuel Prices Set to Continue Rising Through 2030\n\nHistorical data reveals a clear upward trend in both diesel and gasoline prices since the 1990s. Our polynomial regression model shows prices have increased nearly sixfold over three decades, with notable volatility around 2008 and 2022 reflecting global economic shocks.\n\n[IMAGE(chart-1760745227133)]\n\nThe forecast suggests this upward trajectory will persist, with both fuel types projected to reach approximately $3.80-$3.90 per gallon by 2030. While short-term fluctuations are inevitable, the long-term trend remains consistently upward.\n\n**In summary**, fuel prices have demonstrated sustained growth over 35 years and are expected to continue rising. Consumers and businesses should anticipate higher energy costs in coming years and consider efficiency improvements or alternative energy sources.","style":"short note","selectedChartIds":["chart-1760745227133"],"createdAt":1760830993075}],"currentReport":{"id":"report-1760750575650-2619","content":"# Hollywood's Billion-Dollar Hitmakers\n\n*Avatar* stands alone—earning over $2.5B in profit, dwarfing all competition. Action and Adventure films dominate the most profitable titles, with franchises like *Jurassic Park*, *The Dark Knight*, and *Lord of the Rings* proving blockbuster formulas work.\n\n\"Chart\"\n\nSteven Spielberg leads all directors with $7.2B in total profit across his career, showcasing remarkable consistency with hits spanning decades—from *Jurassic Park* to *E.T.* His nearest competitors trail by billions, underlining his unmatched commercial impact.\n\n\"Chart\"\n\n**In summary**, mega-budget Action and Adventure films generate extraordinary returns when they succeed, and a handful of elite directors—led by Spielberg—have mastered the formula for sustained box office dominance.","style":"short note","selectedChartIds":["chart-1760743347871","chart-1760743768741"],"chartImages":{},"createdAt":1760750584189,"title":"Report - 10/17/2025"},"activeChallenges":[],"agentWorkInProgress":[],"_persist":{"version":-1,"rehydrated":true}} \ No newline at end of file +{"tables": [{"kind": "table", "id": "weekly_gas_prices", "displayId": "gas-prices", "names": ["date", "fuel", "grade", "formulation", "price"], "metadata": {"date": {"type": "date", "semanticType": "Date"}, "fuel": {"type": "string", "semanticType": "String"}, "grade": {"type": "string", "semanticType": "String", "levels": ["all", "regular", "midgrade", "premium", "low_sulfur", "ultra_low_sulfur"]}, "formulation": {"type": "string", "semanticType": "String", "levels": ["all", "conventional", "reformulated", "NA"]}, "price": {"type": "number", "semanticType": "Number"}}, "rows": [{"date": "1990-08-20", "fuel": "gasoline", "grade": "regular", "formulation": "all", "price": 1.191}, {"date": "1990-08-20", "fuel": "gasoline", "grade": "regular", "formulation": "conventional", "price": 1.191}, {"date": "1990-08-27", "fuel": "gasoline", "grade": "regular", "formulation": "all", "price": 1.245}, {"date": "1990-08-27", "fuel": "gasoline", "grade": "regular", "formulation": "conventional", "price": 1.245}, {"date": "1990-09-03", "fuel": "gasoline", "grade": "regular", "formulation": "all", "price": 1.242}, {"date": "1990-09-03", "fuel": "gasoline", "grade": "regular", "formulation": "conventional", "price": 1.242}, {"date": "1990-09-10", "fuel": "gasoline", "grade": "regular", "formulation": "all", "price": 1.252}, {"date": "1990-09-10", "fuel": "gasoline", "grade": "regular", "formulation": "conventional", "price": 1.252}, {"date": "1990-09-17", "fuel": "gasoline", "grade": "regular", "formulation": "all", "price": 1.266}, {"date": "1990-09-17", "fuel": "gasoline", "grade": "regular", "formulation": "conventional", "price": 1.266}, {"date": "1990-09-24", "fuel": "gasoline", "grade": "regular", "formulation": "all", "price": 1.272}, {"date": "1990-09-24", "fuel": "gasoline", "grade": "regular", "formulation": "conventional", "price": 1.272}, {"date": "1990-10-01", "fuel": "gasoline", "grade": "regular", "formulation": "all", "price": 1.321}, {"date": "1990-10-01", "fuel": "gasoline", "grade": 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"price": 1.334}, {"date": "1990-11-05", "fuel": "gasoline", "grade": "regular", "formulation": "conventional", "price": 1.334}, {"date": "1990-11-12", "fuel": "gasoline", "grade": "regular", "formulation": "all", "price": 1.328}, {"date": "1990-11-12", "fuel": "gasoline", "grade": "regular", "formulation": "conventional", "price": 1.328}, {"date": "1990-11-19", "fuel": "gasoline", "grade": "regular", "formulation": "all", "price": 1.323}, {"date": "1990-11-19", "fuel": "gasoline", "grade": "regular", "formulation": "conventional", "price": 1.323}, {"date": "1990-11-26", "fuel": "gasoline", "grade": "regular", "formulation": "all", "price": 1.311}, {"date": "1990-11-26", "fuel": "gasoline", "grade": "regular", "formulation": "conventional", "price": 1.311}, {"date": "1990-12-03", "fuel": "gasoline", "grade": "regular", "formulation": "all", "price": 1.341}, {"date": "1990-12-03", "fuel": "gasoline", "grade": "regular", "formulation": "conventional", "price": 1.341}, {"date": 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"diesel", "avg_price": 3.458}, {"date": "2024-12-16", "fuel": "diesel", "avg_price": 3.494}, {"date": "2024-12-16", "fuel": "gasoline", "avg_price": 3.431}, {"date": "2024-12-23", "fuel": "diesel", "avg_price": 3.476}, {"date": "2024-12-23", "fuel": "gasoline", "avg_price": 3.4395}, {"date": "2024-12-30", "fuel": "diesel", "avg_price": 3.503}, {"date": "2024-12-30", "fuel": "gasoline", "avg_price": 3.4266666667}, {"date": "2025-01-06", "fuel": "diesel", "avg_price": 3.561}, {"date": "2025-01-06", "fuel": "gasoline", "avg_price": 3.4620833333}, {"date": "2025-01-13", "fuel": "diesel", "avg_price": 3.602}, {"date": "2025-01-13", "fuel": "gasoline", "avg_price": 3.4610833333}, {"date": "2025-01-20", "fuel": "gasoline", "avg_price": 3.5243333333}, {"date": "2025-01-20", "fuel": "diesel", "avg_price": 3.715}, {"date": "2025-01-27", "fuel": "diesel", "avg_price": 3.659}, {"date": "2025-01-27", "fuel": "gasoline", "avg_price": 3.522}, {"date": "2025-02-03", "fuel": "diesel", "avg_price": 3.66}, {"date": "2025-02-03", "fuel": "gasoline", "avg_price": 3.5101666667}, {"date": "2025-02-10", "fuel": "diesel", "avg_price": 3.665}, {"date": "2025-02-10", "fuel": "gasoline", "avg_price": 3.5611666667}, {"date": "2025-02-17", "fuel": "diesel", "avg_price": 3.677}, {"date": "2025-02-17", "fuel": "gasoline", "avg_price": 3.5964166667}, {"date": "2025-02-24", "fuel": "diesel", "avg_price": 3.697}, {"date": "2025-02-24", "fuel": "gasoline", "avg_price": 3.57775}, {"date": "2025-03-03", "fuel": "gasoline", "avg_price": 3.5271666667}, {"date": "2025-03-03", "fuel": "diesel", "avg_price": 3.635}, {"date": "2025-03-10", "fuel": "gasoline", "avg_price": 3.51375}, {"date": "2025-03-10", "fuel": "diesel", "avg_price": 3.582}, {"date": "2025-03-17", "fuel": "diesel", "avg_price": 3.549}, {"date": "2025-03-17", "fuel": "gasoline", "avg_price": 3.4978333333}, {"date": "2025-03-24", "fuel": "diesel", "avg_price": 3.567}, {"date": "2025-03-24", "fuel": "gasoline", "avg_price": 3.5485833333}, {"date": "2025-03-31", "fuel": "diesel", "avg_price": 3.592}, {"date": "2025-03-31", "fuel": "gasoline", "avg_price": 3.6035}, {"date": "2025-04-07", "fuel": "diesel", "avg_price": 3.639}, {"date": "2025-04-07", "fuel": "gasoline", "avg_price": 3.6863333333}, {"date": "2025-04-14", "fuel": "diesel", "avg_price": 3.579}, {"date": "2025-04-14", "fuel": "gasoline", "avg_price": 3.613}, {"date": "2025-04-21", "fuel": "gasoline", "avg_price": 3.58525}, {"date": "2025-04-21", "fuel": "diesel", "avg_price": 3.534}, {"date": "2025-04-28", "fuel": "diesel", "avg_price": 3.514}, {"date": "2025-04-28", "fuel": "gasoline", "avg_price": 3.57875}, {"date": "2025-05-05", "fuel": "diesel", "avg_price": 3.497}, {"date": "2025-05-05", "fuel": "gasoline", "avg_price": 3.5851666667}, {"date": "2025-05-12", "fuel": "diesel", "avg_price": 3.476}, {"date": "2025-05-12", "fuel": "gasoline", "avg_price": 3.5728333333}, {"date": "2025-05-19", "fuel": "diesel", "avg_price": 3.536}, {"date": "2025-05-19", "fuel": "gasoline", "avg_price": 3.6244166667}, {"date": "2025-05-26", "fuel": "diesel", "avg_price": 3.487}, {"date": "2025-05-26", "fuel": "gasoline", "avg_price": 3.6089166667}, {"date": "2025-06-02", "fuel": "diesel", "avg_price": 3.451}, {"date": "2025-06-02", "fuel": "gasoline", "avg_price": 3.5746666667}, {"date": "2025-06-09", "fuel": "diesel", "avg_price": 3.471}, {"date": "2025-06-09", "fuel": "gasoline", "avg_price": 3.5501666667}, {"date": "2025-06-16", "fuel": "diesel", "avg_price": 3.571}, {"date": "2025-06-16", "fuel": "gasoline", "avg_price": 3.5765}, {"date": "2025-06-23", "fuel": "diesel", "avg_price": 3.775}, {"date": "2025-06-23", "fuel": "gasoline", "avg_price": 3.6445833333}], "metadata": {"date": {"type": "date", "semanticType": "Date"}, "fuel": {"type": "string", "semanticType": "String"}, "avg_price": {"type": "number", "semanticType": "Number"}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n transformed_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Rename the price column to avg_price for clarity\n transformed_df = transformed_df.rename(columns={'price': 'avg_price'})\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values('date')\n \n return transformed_df\n", "source": ["weekly_gas_prices"], "dialog": [{"role": "system", "content": "You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"}, {"role": "user", "content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[GOAL]\n\nWhat are the major **price trends** across all fuel types from 1990 to 2025?"}, {"role": "assistant", "content": "```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to visualize the major price trends across all fuel types from 1990 to 2025 to understand how fuel prices have evolved over time.\",\n \"display_instruction\": \"Show **price** trends over time for different **fuel** types\",\n \"recommendation\": \"To visualize price trends over time across fuel types, we should aggregate the data by date and fuel type. Since there are multiple grades and formulations for each fuel type, we'll calculate the average price for each fuel type on each date. A line chart with date on the x-axis, average price on the y-axis, and different colored lines for each fuel type will clearly show the trends and allow easy comparison.\",\n \"output_fields\": [\"date\", \"fuel\", \"avg_price\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n transformed_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Rename the price column to avg_price for clarity\n transformed_df = transformed_df.rename(columns={'price': 'avg_price'})\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values('date')\n \n return transformed_df\n```"}], "trigger": {"tableId": "weekly_gas_prices", "resultTableId": "table-904593", "chart": {"id": "chart-1760744901532", "chartType": "Auto", "encodingMap": {}, "tableRef": "weekly_gas_prices", "saved": false, "source": "trigger", "unread": true}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "What are the major **price trends** across all fuel types from 1990 to 2025?", "displayContent": "Show **price** trends over time for different **fuel** types"}]}, "explanation": {"agent": "CodeExplanationAgent", "code": "The code transforms weekly gas price data by performing the following steps:\n\n- **Groups** the data by `date` and `fuel` type (gasoline or diesel)\n- **Calculates** the mean `price` across all `grade` categories (regular, premium, midgrade, etc.) and `formulation` types (conventional, reformulated, etc.) for each date-fuel combination\n- **Renames** the aggregated price column to `avg_price` for clarity\n- **Sorts** the resulting data chronologically by `date` to ensure proper time-series visualization", "concepts": [], "dialog": [{"content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n transformed_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Rename the price column to avg_price for clarity\n transformed_df = transformed_df.rename(columns={'price': 'avg_price'})\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values('date')\n \n return transformed_df\n\n\n[EXPLANATION]\n", "role": "user"}, {"content": "[CODE EXPLANATION]\n\nThe code transforms weekly gas price data by performing the following steps:\n\n- **Groups** the data by `date` and `fuel` type (gasoline or diesel)\n- **Calculates** the mean `price` across all `grade` categories (regular, premium, midgrade, etc.) and `formulation` types (conventional, reformulated, etc.) for each date-fuel combination\n- **Renames** the aggregated price column to `avg_price` for clarity\n- **Sorts** the resulting data chronologically by `date` to ensure proper time-series visualization\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```", "role": "assistant"}], "status": "ok"}, "status": "completed"}, "anchored": false, "attachedMetadata": ""}, {"kind": "table", "id": "table-906894", "displayId": "gas-price-prem", "names": ["date", "fuel", "grade", "price_premium"], "rows": [{"date": "1993-04-05", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-04-12", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-04-19", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-04-26", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-05-03", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-05-10", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-05-17", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-05-24", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-05-31", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-06-07", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-06-14", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-06-21", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-06-28", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-07-05", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-07-12", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-07-19", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-07-26", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-08-02", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-08-09", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-08-16", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-08-23", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-08-30", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-09-06", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-09-13", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-09-20", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-09-27", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-10-04", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-10-11", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-10-18", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-10-25", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-11-01", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-11-08", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-11-15", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-11-22", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-11-29", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-12-06", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-12-13", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-12-20", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1993-12-27", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-01-03", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-01-10", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-01-17", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-01-24", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-01-31", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-02-07", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-02-14", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-02-21", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-02-28", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-03-07", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-03-14", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-03-21", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-03-28", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-04-04", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-04-11", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-04-18", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-04-25", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-05-02", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-05-09", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-05-16", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-05-23", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-05-30", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-06-06", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-06-13", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-06-20", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-06-27", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-07-04", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-07-11", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-07-18", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-07-25", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-08-01", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-08-08", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-08-15", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-08-22", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-08-29", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-09-05", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-09-12", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-09-19", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-09-26", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-10-03", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-10-10", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-10-17", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-10-24", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-10-31", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-11-07", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-11-14", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-11-21", "fuel": "gasoline", "grade": "all", "price_premium": 0}, {"date": "1994-11-28", "fuel": "gasoline", "grade": "all", "price_premium": 0.012}, {"date": "1994-11-28", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.092}, {"date": "1994-11-28", "fuel": "gasoline", "grade": "premium", "price_premium": 0.198}, {"date": "1994-12-05", "fuel": "gasoline", "grade": "all", "price_premium": 0.024}, {"date": "1994-12-05", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.094}, {"date": "1994-12-05", "fuel": "gasoline", "grade": "premium", "price_premium": 0.198}, {"date": "1994-12-12", "fuel": "gasoline", "grade": "all", "price_premium": 0.036}, {"date": "1994-12-12", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.093}, {"date": "1994-12-12", "fuel": "gasoline", "grade": "premium", "price_premium": 0.193}, {"date": "1994-12-19", "fuel": "gasoline", "grade": "all", "price_premium": 0.047}, {"date": "1994-12-19", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.092}, {"date": "1994-12-19", "fuel": "gasoline", "grade": "premium", "price_premium": 0.192}, {"date": "1994-12-26", "fuel": "gasoline", "grade": "all", "price_premium": 0.048}, {"date": "1994-12-26", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.094}, {"date": "1994-12-26", "fuel": "gasoline", "grade": "premium", "price_premium": 0.193}, {"date": "1995-01-02", "fuel": "gasoline", "grade": "all", "price_premium": 0.048}, {"date": "1995-01-02", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.091}, {"date": "1995-01-02", "fuel": "gasoline", "grade": "premium", "price_premium": 0.193}, {"date": "1995-01-09", "fuel": "gasoline", "grade": "all", "price_premium": 0.048}, {"date": "1995-01-09", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.091}, {"date": "1995-01-09", "fuel": "gasoline", "grade": "premium", "price_premium": 0.193}, {"date": "1995-01-16", "fuel": "gasoline", "grade": "all", "price_premium": 0.048}, {"date": "1995-01-16", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.09}, {"date": "1995-01-16", "fuel": "gasoline", "grade": "premium", "price_premium": 0.193}, {"date": "1995-01-23", "fuel": "gasoline", "grade": "all", "price_premium": 0.049}, {"date": "1995-01-23", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.094}, {"date": "1995-01-23", "fuel": "gasoline", "grade": "premium", "price_premium": 0.194}, {"date": "1995-01-30", "fuel": "gasoline", "grade": "all", "price_premium": 0.048}, {"date": "1995-01-30", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.093}, {"date": "1995-01-30", "fuel": "gasoline", "grade": "premium", "price_premium": 0.192}, {"date": "1995-02-06", "fuel": "gasoline", "grade": "all", "price_premium": 0.048}, {"date": "1995-02-06", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.093}, {"date": "1995-02-06", "fuel": "gasoline", "grade": "premium", "price_premium": 0.194}, {"date": "1995-02-13", "fuel": "gasoline", "grade": "all", "price_premium": 0.047}, {"date": "1995-02-13", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.092}, {"date": "1995-02-13", "fuel": "gasoline", "grade": "premium", "price_premium": 0.191}, {"date": "1995-02-20", "fuel": "gasoline", "grade": "all", "price_premium": 0.048}, {"date": "1995-02-20", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.093}, {"date": "1995-02-20", "fuel": "gasoline", "grade": "premium", "price_premium": 0.192}, {"date": "1995-02-27", "fuel": "gasoline", "grade": "all", "price_premium": 0.048}, {"date": "1995-02-27", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.091}, {"date": "1995-02-27", "fuel": "gasoline", "grade": "premium", "price_premium": 0.192}, {"date": "1995-03-06", "fuel": "gasoline", "grade": "all", "price_premium": 0.047}, {"date": "1995-03-06", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.091}, {"date": "1995-03-06", "fuel": "gasoline", "grade": "premium", "price_premium": 0.187}, {"date": "1995-03-13", "fuel": "gasoline", "grade": "all", "price_premium": 0.047}, {"date": "1995-03-13", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.089}, {"date": "1995-03-13", "fuel": "gasoline", "grade": "premium", "price_premium": 0.187}, {"date": "1995-03-20", "fuel": "gasoline", "grade": "all", "price_premium": 0.046}, {"date": "1995-03-20", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.09}, {"date": "1995-03-20", "fuel": "gasoline", "grade": "premium", "price_premium": 0.186}, {"date": "1995-03-27", "fuel": "gasoline", "grade": "all", "price_premium": 0.046}, {"date": "1995-03-27", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.087}, {"date": "1995-03-27", "fuel": "gasoline", "grade": "premium", "price_premium": 0.184}, {"date": "1995-04-03", "fuel": "gasoline", "grade": "all", "price_premium": 0.046}, {"date": "1995-04-03", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.087}, {"date": "1995-04-03", "fuel": "gasoline", "grade": "premium", "price_premium": 0.183}, {"date": "1995-04-10", "fuel": "gasoline", "grade": "all", "price_premium": 0.046}, {"date": "1995-04-10", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.087}, {"date": "1995-04-10", "fuel": "gasoline", "grade": "premium", "price_premium": 0.183}, {"date": "1995-04-17", "fuel": "gasoline", "grade": "all", "price_premium": 0.046}, {"date": "1995-04-17", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.088}, {"date": "1995-04-17", "fuel": "gasoline", "grade": "premium", "price_premium": 0.183}, {"date": "1995-04-24", "fuel": "gasoline", "grade": "all", "price_premium": 0.046}, {"date": "1995-04-24", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.088}, {"date": "1995-04-24", "fuel": "gasoline", "grade": "premium", "price_premium": 0.185}, {"date": "1995-05-01", "fuel": "gasoline", "grade": "all", "price_premium": 0.046}, {"date": "1995-05-01", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.088}, {"date": "1995-05-01", "fuel": "gasoline", "grade": "premium", "price_premium": 0.184}, {"date": "1995-05-08", "fuel": "gasoline", "grade": "all", "price_premium": 0.047}, {"date": "1995-05-08", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.09}, {"date": "1995-05-08", "fuel": "gasoline", "grade": "premium", "price_premium": 0.187}, {"date": "1995-05-15", "fuel": "gasoline", "grade": "all", "price_premium": 0.047}, {"date": "1995-05-15", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.09}, {"date": "1995-05-15", "fuel": "gasoline", "grade": "premium", "price_premium": 0.185}, {"date": "1995-05-22", "fuel": "gasoline", "grade": "all", "price_premium": 0.047}, {"date": "1995-05-22", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.091}, {"date": "1995-05-22", "fuel": "gasoline", "grade": "premium", "price_premium": 0.186}, {"date": "1995-05-29", "fuel": "gasoline", "grade": "all", "price_premium": 0.047}, {"date": "1995-05-29", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.091}, {"date": "1995-05-29", "fuel": "gasoline", "grade": "premium", "price_premium": 0.187}, {"date": "1995-06-05", "fuel": "gasoline", "grade": "all", "price_premium": 0.047}, {"date": "1995-06-05", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.091}, {"date": "1995-06-05", "fuel": "gasoline", "grade": "premium", "price_premium": 0.187}, {"date": "1995-06-12", "fuel": "gasoline", "grade": "all", "price_premium": 0.047}, {"date": "1995-06-12", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.092}, {"date": "1995-06-12", "fuel": "gasoline", "grade": "premium", "price_premium": 0.187}, {"date": "1995-06-19", "fuel": "gasoline", "grade": "all", "price_premium": 0.047}, {"date": "1995-06-19", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.091}, {"date": "1995-06-19", "fuel": "gasoline", "grade": "premium", "price_premium": 0.187}, {"date": "1995-06-26", "fuel": "gasoline", "grade": "all", "price_premium": 0.047}, {"date": "1995-06-26", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.091}, {"date": "1995-06-26", "fuel": "gasoline", "grade": "premium", "price_premium": 0.188}, {"date": "1995-07-03", "fuel": "gasoline", "grade": "all", "price_premium": 0.047}, {"date": "1995-07-03", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.091}, {"date": "1995-07-03", "fuel": "gasoline", "grade": "premium", "price_premium": 0.187}, {"date": "1995-07-10", "fuel": "gasoline", "grade": "all", "price_premium": 0.047}, {"date": "1995-07-10", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.09}, {"date": "1995-07-10", "fuel": "gasoline", "grade": "premium", "price_premium": 0.187}, {"date": "1995-07-17", "fuel": "gasoline", "grade": "all", "price_premium": 0.047}, {"date": "1995-07-17", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.091}, {"date": "1995-07-17", "fuel": "gasoline", "grade": "premium", "price_premium": 0.189}, {"date": "1995-07-24", "fuel": "gasoline", "grade": "all", "price_premium": 0.047}, {"date": "1995-07-24", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.092}, {"date": "1995-07-24", "fuel": "gasoline", "grade": "premium", "price_premium": 0.189}, {"date": "1995-07-31", "fuel": "gasoline", "grade": "all", "price_premium": 0.047}, {"date": "1995-07-31", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.09}, {"date": "1995-07-31", "fuel": "gasoline", "grade": "premium", "price_premium": 0.187}, {"date": "1995-08-07", "fuel": "gasoline", "grade": "all", "price_premium": 0.047}, {"date": "1995-08-07", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.09}, {"date": "1995-08-07", "fuel": "gasoline", "grade": "premium", "price_premium": 0.189}, {"date": "1995-08-14", "fuel": "gasoline", "grade": "all", "price_premium": 0.047}, {"date": "1995-08-14", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.09}, {"date": "1995-08-14", "fuel": "gasoline", "grade": "premium", "price_premium": 0.187}, {"date": "1995-08-21", "fuel": "gasoline", "grade": "all", "price_premium": 0.047}, {"date": "1995-08-21", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.09}, {"date": "1995-08-21", "fuel": "gasoline", "grade": "premium", "price_premium": 0.187}, {"date": "1995-08-28", "fuel": "gasoline", "grade": "all", "price_premium": 0.046}, {"date": "1995-08-28", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.088}, {"date": "1995-08-28", "fuel": "gasoline", "grade": "premium", "price_premium": 0.188}, {"date": "1995-09-04", "fuel": "gasoline", "grade": "all", "price_premium": 0.047}, 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"2024-09-09", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.555}, {"date": "2024-09-09", "fuel": "gasoline", "grade": "premium", "price_premium": 0.917}, {"date": "2024-09-16", "fuel": "gasoline", "grade": "all", "price_premium": 0.127}, {"date": "2024-09-16", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.575}, {"date": "2024-09-16", "fuel": "gasoline", "grade": "premium", "price_premium": 0.939}, {"date": "2024-09-23", "fuel": "gasoline", "grade": "all", "price_premium": 0.126}, {"date": "2024-09-23", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.566}, {"date": "2024-09-23", "fuel": "gasoline", "grade": "premium", "price_premium": 0.923}, {"date": "2024-09-30", "fuel": "gasoline", "grade": "all", "price_premium": 0.124}, {"date": "2024-09-30", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.559}, {"date": "2024-09-30", "fuel": "gasoline", "grade": "premium", "price_premium": 0.915}, {"date": "2024-10-07", "fuel": "gasoline", "grade": "all", "price_premium": 0.124}, {"date": "2024-10-07", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.556}, {"date": "2024-10-07", "fuel": "gasoline", "grade": "premium", "price_premium": 0.913}, {"date": "2024-10-14", "fuel": "gasoline", "grade": "all", "price_premium": 0.123}, {"date": "2024-10-14", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.548}, {"date": "2024-10-14", "fuel": "gasoline", "grade": "premium", "price_premium": 0.906}, {"date": "2024-10-21", "fuel": "gasoline", "grade": "all", "price_premium": 0.124}, {"date": "2024-10-21", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.556}, {"date": "2024-10-21", "fuel": "gasoline", "grade": "premium", "price_premium": 0.914}, {"date": "2024-10-28", "fuel": "gasoline", "grade": "all", "price_premium": 0.123}, {"date": "2024-10-28", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.557}, {"date": "2024-10-28", "fuel": "gasoline", "grade": "premium", "price_premium": 0.915}, {"date": "2024-11-04", "fuel": "gasoline", "grade": "all", "price_premium": 0.122}, {"date": "2024-11-04", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.547}, {"date": "2024-11-04", "fuel": "gasoline", "grade": "premium", "price_premium": 0.908}, {"date": "2024-11-11", "fuel": "gasoline", "grade": "all", "price_premium": 0.124}, {"date": "2024-11-11", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.561}, {"date": "2024-11-11", "fuel": "gasoline", "grade": "premium", "price_premium": 0.912}, {"date": "2024-11-18", "fuel": "gasoline", "grade": "all", "price_premium": 0.122}, {"date": "2024-11-18", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.547}, {"date": "2024-11-18", "fuel": "gasoline", "grade": "premium", "price_premium": 0.9}, {"date": "2024-11-25", "fuel": "gasoline", "grade": "all", "price_premium": 0.122}, {"date": "2024-11-25", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.542}, {"date": "2024-11-25", "fuel": "gasoline", "grade": "premium", "price_premium": 0.906}, {"date": "2024-12-02", "fuel": "gasoline", "grade": "all", "price_premium": 0.122}, {"date": "2024-12-02", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.54}, {"date": "2024-12-02", "fuel": "gasoline", "grade": "premium", "price_premium": 0.903}, {"date": "2024-12-09", "fuel": "gasoline", "grade": "all", "price_premium": 0.123}, {"date": "2024-12-09", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.55}, {"date": "2024-12-09", "fuel": "gasoline", "grade": "premium", "price_premium": 0.906}, {"date": "2024-12-16", "fuel": "gasoline", "grade": "all", "price_premium": 0.121}, {"date": "2024-12-16", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.54}, {"date": "2024-12-16", "fuel": "gasoline", "grade": "premium", "price_premium": 0.895}, {"date": "2024-12-23", "fuel": "gasoline", "grade": "all", "price_premium": 0.121}, {"date": "2024-12-23", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.54}, {"date": "2024-12-23", "fuel": "gasoline", "grade": "premium", "price_premium": 0.891}, {"date": "2024-12-30", "fuel": "gasoline", "grade": "all", "price_premium": 0.122}, {"date": "2024-12-30", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.548}, {"date": "2024-12-30", "fuel": "gasoline", "grade": "premium", "price_premium": 0.901}, {"date": "2025-01-06", "fuel": "gasoline", "grade": "all", "price_premium": 0.121}, {"date": "2025-01-06", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.54}, {"date": "2025-01-06", "fuel": "gasoline", "grade": "premium", "price_premium": 0.888}, {"date": "2025-01-13", "fuel": "gasoline", "grade": "all", "price_premium": 0.121}, {"date": "2025-01-13", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.542}, {"date": "2025-01-13", "fuel": "gasoline", "grade": "premium", "price_premium": 0.896}, {"date": "2025-01-20", "fuel": "gasoline", "grade": "all", "price_premium": 0.12}, {"date": "2025-01-20", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.533}, {"date": "2025-01-20", "fuel": "gasoline", "grade": "premium", "price_premium": 0.889}, {"date": "2025-01-27", "fuel": "gasoline", "grade": "all", "price_premium": 0.121}, {"date": "2025-01-27", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.541}, {"date": "2025-01-27", "fuel": "gasoline", "grade": "premium", "price_premium": 0.896}, {"date": "2025-02-03", "fuel": "gasoline", "grade": "all", "price_premium": 0.123}, {"date": "2025-02-03", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.551}, {"date": "2025-02-03", "fuel": "gasoline", "grade": "premium", "price_premium": 0.908}, {"date": "2025-02-10", "fuel": "gasoline", "grade": "all", "price_premium": 0.125}, {"date": "2025-02-10", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.557}, {"date": "2025-02-10", "fuel": "gasoline", "grade": "premium", "price_premium": 0.922}, {"date": "2025-02-17", "fuel": "gasoline", "grade": "all", "price_premium": 0.128}, {"date": "2025-02-17", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.575}, {"date": "2025-02-17", "fuel": "gasoline", "grade": "premium", "price_premium": 0.94}, {"date": "2025-02-24", "fuel": "gasoline", "grade": "all", "price_premium": 0.13}, {"date": "2025-02-24", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.581}, {"date": "2025-02-24", "fuel": "gasoline", "grade": "premium", "price_premium": 0.95}, {"date": "2025-03-03", "fuel": "gasoline", "grade": "all", "price_premium": 0.128}, {"date": "2025-03-03", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.576}, {"date": "2025-03-03", "fuel": "gasoline", "grade": "premium", "price_premium": 0.944}, {"date": "2025-03-10", "fuel": "gasoline", "grade": "all", "price_premium": 0.128}, {"date": "2025-03-10", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.571}, {"date": "2025-03-10", "fuel": "gasoline", "grade": "premium", "price_premium": 0.936}, {"date": "2025-03-17", "fuel": "gasoline", "grade": "all", "price_premium": 0.126}, {"date": "2025-03-17", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.569}, {"date": "2025-03-17", "fuel": "gasoline", "grade": "premium", "price_premium": 0.931}, {"date": "2025-03-24", "fuel": "gasoline", "grade": "all", "price_premium": 0.125}, {"date": "2025-03-24", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.559}, {"date": "2025-03-24", "fuel": "gasoline", "grade": "premium", "price_premium": 0.917}, {"date": "2025-03-31", "fuel": "gasoline", "grade": "all", "price_premium": 0.126}, {"date": "2025-03-31", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.568}, {"date": "2025-03-31", "fuel": "gasoline", "grade": "premium", "price_premium": 0.931}, {"date": "2025-04-07", "fuel": "gasoline", "grade": "all", "price_premium": 0.127}, {"date": "2025-04-07", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.572}, {"date": "2025-04-07", "fuel": "gasoline", "grade": "premium", "price_premium": 0.931}, {"date": "2025-04-14", "fuel": "gasoline", "grade": "all", "price_premium": 0.127}, {"date": "2025-04-14", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.575}, {"date": "2025-04-14", "fuel": "gasoline", "grade": "premium", "price_premium": 0.935}, {"date": "2025-04-21", "fuel": "gasoline", "grade": "all", "price_premium": 0.127}, {"date": "2025-04-21", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.577}, {"date": "2025-04-21", "fuel": "gasoline", "grade": "premium", "price_premium": 0.933}, {"date": "2025-04-28", "fuel": "gasoline", "grade": "all", "price_premium": 0.128}, {"date": "2025-04-28", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.58}, {"date": "2025-04-28", "fuel": "gasoline", "grade": "premium", "price_premium": 0.938}, {"date": "2025-05-05", "fuel": "gasoline", "grade": "all", "price_premium": 0.126}, {"date": "2025-05-05", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.568}, {"date": "2025-05-05", "fuel": "gasoline", "grade": "premium", "price_premium": 0.925}, {"date": "2025-05-12", "fuel": "gasoline", "grade": "all", "price_premium": 0.129}, {"date": "2025-05-12", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.583}, {"date": "2025-05-12", "fuel": "gasoline", "grade": "premium", "price_premium": 0.948}, {"date": "2025-05-19", "fuel": "gasoline", "grade": "all", "price_premium": 0.129}, {"date": "2025-05-19", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.583}, {"date": "2025-05-19", "fuel": "gasoline", "grade": "premium", "price_premium": 0.943}, {"date": "2025-05-26", "fuel": "gasoline", "grade": "all", "price_premium": 0.128}, {"date": "2025-05-26", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.58}, {"date": "2025-05-26", "fuel": "gasoline", "grade": "premium", "price_premium": 0.942}, {"date": "2025-06-02", "fuel": "gasoline", "grade": "all", "price_premium": 0.129}, {"date": "2025-06-02", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.585}, {"date": "2025-06-02", "fuel": "gasoline", "grade": "premium", "price_premium": 0.942}, {"date": "2025-06-09", "fuel": "gasoline", "grade": "all", "price_premium": 0.127}, {"date": "2025-06-09", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.579}, {"date": "2025-06-09", "fuel": "gasoline", "grade": "premium", "price_premium": 0.929}, {"date": "2025-06-16", "fuel": "gasoline", "grade": "all", "price_premium": 0.126}, {"date": "2025-06-16", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.572}, {"date": "2025-06-16", "fuel": "gasoline", "grade": "premium", "price_premium": 0.923}, {"date": "2025-06-23", "fuel": "gasoline", "grade": "all", "price_premium": 0.125}, {"date": "2025-06-23", "fuel": "gasoline", "grade": "midgrade", "price_premium": 0.562}, {"date": "2025-06-23", "fuel": "gasoline", "grade": "premium", "price_premium": 0.915}], "metadata": {"date": {"type": "date", "semanticType": "Date"}, "fuel": {"type": "string", "semanticType": "String"}, "grade": {"type": "string", "semanticType": "String", "levels": ["all", "midgrade", "premium"]}, "price_premium": {"type": "number", "semanticType": "Number"}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas):\n # Filter for 'all' formulation to avoid duplicates and focus on overall prices\n df_filtered = df_gas[df_gas['formulation'] == 'all'].copy()\n \n # Get regular grade prices as baseline\n df_regular = df_filtered[df_filtered['grade'] == 'regular'][['date', 'fuel', 'price']].rename(columns={'price': 'regular_price'})\n \n # Get all non-regular grades\n df_premium_grades = df_filtered[df_filtered['grade'] != 'regular'][['date', 'fuel', 'grade', 'price']].copy()\n \n # Merge to calculate premium\n df_merged = df_premium_grades.merge(df_regular, on=['date', 'fuel'], how='left')\n \n # Calculate price premium\n df_merged['price_premium'] = df_merged['price'] - df_merged['regular_price']\n \n # Select output fields\n transformed_df = df_merged[['date', 'fuel', 'grade', 'price_premium']].copy()\n \n # Remove rows with missing premium values\n transformed_df = transformed_df.dropna(subset=['price_premium'])\n \n return transformed_df\n", "source": ["weekly_gas_prices"], "dialog": [{"content": "You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[GOAL]\n\nWhich **fuel grade** commands the highest **price premium** and how has this premium evolved?", "role": "user"}, {"content": "```json\n{\n \"mode\": \"infer\",\n \"recap\": \"Analyze which fuel grade commands the highest price premium compared to regular grade, and visualize how this premium has evolved over time.\",\n \"display_instruction\": \"Compare **price premium** across **fuel grades** over time\",\n \"recommendation\": \"Calculate the price premium for each fuel grade by comparing it to the regular grade baseline for the same date and fuel type. This will show both the absolute premium values and their trends over time.\",\n \"output_fields\": [\"date\", \"fuel\", \"grade\", \"price_premium\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"price_premium\",\n \"color\": \"grade\",\n \"facet\": \"fuel\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas):\n # Filter for 'all' formulation to avoid duplicates and focus on overall prices\n df_filtered = df_gas[df_gas['formulation'] == 'all'].copy()\n \n # Get regular grade prices as baseline\n df_regular = df_filtered[df_filtered['grade'] == 'regular'][['date', 'fuel', 'price']].rename(columns={'price': 'regular_price'})\n \n # Get all non-regular grades\n df_premium_grades = df_filtered[df_filtered['grade'] != 'regular'][['date', 'fuel', 'grade', 'price']].copy()\n \n # Merge to calculate premium\n df_merged = df_premium_grades.merge(df_regular, on=['date', 'fuel'], how='left')\n \n # Calculate price premium\n df_merged['price_premium'] = df_merged['price'] - df_merged['regular_price']\n \n # Select output fields\n transformed_df = df_merged[['date', 'fuel', 'grade', 'price_premium']].copy()\n \n # Remove rows with missing premium values\n transformed_df = transformed_df.dropna(subset=['price_premium'])\n \n return transformed_df\n```", "role": "assistant"}], "trigger": {"tableId": "weekly_gas_prices", "resultTableId": "table-906894", "chart": {"id": "chart-1760744911829", "chartType": "Auto", "encodingMap": {}, "tableRef": "weekly_gas_prices", "saved": false, "source": "trigger", "unread": true}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "Which **fuel grade** commands the highest **price premium** and how has this premium evolved?", "displayContent": "Compare **price premium** across **fuel grades** over time"}]}, "explanation": {"agent": "CodeExplanationAgent", "code": "- Filter the dataset to only include entries where **formulation** is `'all'` to avoid duplicate records and focus on overall price data\n- Extract **regular grade** prices as a baseline, creating a reference table with **date**, **fuel**, and **regular_price**\n- Extract all **non-regular grades** (such as **premium**, **midgrade**, etc.) with their corresponding **date**, **fuel**, **grade**, and **price**\n- Merge the non-regular grades with the regular grade baseline by matching on **date** and **fuel** type\n- Calculate the **price_premium** for each non-regular grade by subtracting the **regular_price** from the grade's **price**\n- Select the final output fields: **date**, **fuel**, **grade**, and **price_premium**\n- Remove any rows with missing **price_premium** values to ensure data completeness", "concepts": [{"explanation": "The additional cost of a specific fuel grade compared to regular grade fuel. Calculated as: \\( \\text{price\\_premium} = \\text{price}_{\\text{grade}} - \\text{price}_{\\text{regular}} \\). This metric indicates how much more consumers pay for higher-quality fuel grades (premium, midgrade, etc.) relative to the baseline regular grade on the same date and for the same fuel type.", "field": "price_premium"}], "dialog": [{"content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas):\n # Filter for 'all' formulation to avoid duplicates and focus on overall prices\n df_filtered = df_gas[df_gas['formulation'] == 'all'].copy()\n \n # Get regular grade prices as baseline\n df_regular = df_filtered[df_filtered['grade'] == 'regular'][['date', 'fuel', 'price']].rename(columns={'price': 'regular_price'})\n \n # Get all non-regular grades\n df_premium_grades = df_filtered[df_filtered['grade'] != 'regular'][['date', 'fuel', 'grade', 'price']].copy()\n \n # Merge to calculate premium\n df_merged = df_premium_grades.merge(df_regular, on=['date', 'fuel'], how='left')\n \n # Calculate price premium\n df_merged['price_premium'] = df_merged['price'] - df_merged['regular_price']\n \n # Select output fields\n transformed_df = df_merged[['date', 'fuel', 'grade', 'price_premium']].copy()\n \n # Remove rows with missing premium values\n transformed_df = transformed_df.dropna(subset=['price_premium'])\n \n return transformed_df\n\n\n[EXPLANATION]\n", "role": "user"}, {"content": "[CODE EXPLANATION]\n\n- Filter the dataset to only include entries where **formulation** is `'all'` to avoid duplicate records and focus on overall price data\n- Extract **regular grade** prices as a baseline, creating a reference table with **date**, **fuel**, and **regular_price**\n- Extract all **non-regular grades** (such as **premium**, **midgrade**, etc.) with their corresponding **date**, **fuel**, **grade**, and **price**\n- Merge the non-regular grades with the regular grade baseline by matching on **date** and **fuel** type\n- Calculate the **price_premium** for each non-regular grade by subtracting the **regular_price** from the grade's **price**\n- Select the final output fields: **date**, **fuel**, **grade**, and **price_premium**\n- Remove any rows with missing **price_premium** values to ensure data completeness\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"price_premium\",\n \"explanation\": \"The additional cost of a specific fuel grade compared to regular grade fuel. Calculated as: \\\\( \\\\text{price\\\\_premium} = \\\\text{price}_{\\\\text{grade}} - \\\\text{price}_{\\\\text{regular}} \\\\). This metric indicates how much more consumers pay for higher-quality fuel grades (premium, midgrade, etc.) relative to the baseline regular grade on the same date and for the same fuel type.\"\n }\n]\n```", "role": "assistant"}], "status": "ok"}, "status": "completed"}, "anchored": false, "attachedMetadata": ""}, {"kind": "table", "id": "table-922023", "displayId": "fuel-price-yoy", "names": ["date", "fuel", "current_price", "yoy_price", "absolute_change", "percentage_change"], "rows": [{"date": "1995-03-20", "fuel": "diesel", "current_price": 1.085, "yoy_price": 1.106, "absolute_change": -0.021, "percentage_change": -1.8987341772}, {"date": "1995-03-27", "fuel": "diesel", "current_price": 1.088, "yoy_price": 1.107, "absolute_change": -0.019, "percentage_change": -1.7163504968}, {"date": "1995-04-03", "fuel": "diesel", "current_price": 1.094, "yoy_price": 1.109, "absolute_change": -0.015, "percentage_change": -1.3525698828}, {"date": "1995-04-10", "fuel": "diesel", "current_price": 1.101, "yoy_price": 1.108, "absolute_change": -0.007, "percentage_change": -0.6317689531}, {"date": "1995-04-17", "fuel": "diesel", "current_price": 1.106, "yoy_price": 1.105, "absolute_change": 0.001, "percentage_change": 0.0904977376}, {"date": "1995-04-24", "fuel": "diesel", "current_price": 1.115, "yoy_price": 1.106, "absolute_change": 0.009, "percentage_change": 0.8137432188}, {"date": "1995-05-01", "fuel": "diesel", "current_price": 1.119, "yoy_price": 1.104, "absolute_change": 0.015, "percentage_change": 1.3586956522}, {"date": "1995-05-08", "fuel": "diesel", "current_price": 1.126, "yoy_price": 1.101, "absolute_change": 0.025, "percentage_change": 2.2706630336}, {"date": "1995-05-15", "fuel": "diesel", "current_price": 1.126, "yoy_price": 1.099, "absolute_change": 0.027, "percentage_change": 2.4567788899}, {"date": "1995-05-22", "fuel": "diesel", "current_price": 1.124, "yoy_price": 1.099, "absolute_change": 0.025, "percentage_change": 2.2747952684}, {"date": "1995-05-29", "fuel": "diesel", "current_price": 1.13, "yoy_price": 1.098, "absolute_change": 0.032, "percentage_change": 2.9143897996}, {"date": "1995-06-05", "fuel": "diesel", "current_price": 1.124, "yoy_price": 1.101, "absolute_change": 0.023, "percentage_change": 2.0890099909}, {"date": "1995-06-12", "fuel": "diesel", "current_price": 1.122, "yoy_price": 1.098, "absolute_change": 0.024, "percentage_change": 2.1857923497}, {"date": "1995-06-19", "fuel": "diesel", "current_price": 1.117, "yoy_price": 1.103, "absolute_change": 0.014, "percentage_change": 1.2692656392}, {"date": "1995-06-26", "fuel": "diesel", "current_price": 1.112, "yoy_price": 1.108, "absolute_change": 0.004, "percentage_change": 0.3610108303}, {"date": "1995-07-03", "fuel": "diesel", "current_price": 1.106, "yoy_price": 1.109, "absolute_change": -0.003, "percentage_change": -0.2705139766}, {"date": "1995-07-10", "fuel": "diesel", "current_price": 1.103, "yoy_price": 1.11, "absolute_change": -0.007, "percentage_change": -0.6306306306}, {"date": "1995-07-17", "fuel": "diesel", "current_price": 1.099, "yoy_price": 1.111, "absolute_change": -0.012, "percentage_change": -1.0801080108}, {"date": "1995-07-24", "fuel": "diesel", "current_price": 1.098, "yoy_price": 1.111, "absolute_change": -0.013, "percentage_change": -1.1701170117}, {"date": "1995-07-31", "fuel": "diesel", "current_price": 1.093, "yoy_price": 1.116, "absolute_change": -0.023, "percentage_change": -2.0609318996}, {"date": "1995-08-07", "fuel": "diesel", "current_price": 1.099, "yoy_price": 1.127, "absolute_change": -0.028, "percentage_change": -2.4844720497}, {"date": "1995-08-14", "fuel": "diesel", "current_price": 1.106, "yoy_price": 1.127, "absolute_change": -0.021, "percentage_change": -1.8633540373}, {"date": "1995-08-21", "fuel": "diesel", "current_price": 1.106, "yoy_price": 1.124, "absolute_change": -0.018, "percentage_change": -1.6014234875}, {"date": "1995-08-28", "fuel": "diesel", "current_price": 1.109, "yoy_price": 1.122, "absolute_change": -0.013, "percentage_change": -1.1586452763}, {"date": "1995-09-04", "fuel": "diesel", "current_price": 1.115, "yoy_price": 1.126, "absolute_change": -0.011, "percentage_change": -0.9769094139}, {"date": "1995-09-11", "fuel": "diesel", "current_price": 1.119, "yoy_price": 1.128, "absolute_change": -0.009, "percentage_change": -0.7978723404}, {"date": "1995-09-18", "fuel": "diesel", "current_price": 1.122, "yoy_price": 1.126, "absolute_change": -0.004, "percentage_change": -0.3552397869}, {"date": "1995-09-25", "fuel": "diesel", "current_price": 1.121, "yoy_price": 1.12, "absolute_change": 0.001, "percentage_change": 0.0892857143}, {"date": "1995-10-02", "fuel": "diesel", "current_price": 1.117, "yoy_price": 1.118, "absolute_change": -0.001, "percentage_change": -0.0894454383}, {"date": "1995-10-09", "fuel": "diesel", "current_price": 1.117, "yoy_price": 1.117, "absolute_change": 0, "percentage_change": 0}, {"date": "1995-10-16", "fuel": "diesel", "current_price": 1.117, "yoy_price": 1.119, "absolute_change": -0.002, "percentage_change": -0.1787310098}, {"date": "1995-10-23", "fuel": "diesel", "current_price": 1.114, "yoy_price": 1.122, "absolute_change": -0.008, "percentage_change": -0.7130124777}, {"date": "1995-10-30", "fuel": "diesel", "current_price": 1.11, "yoy_price": 1.133, "absolute_change": -0.023, "percentage_change": -2.0300088261}, {"date": "1995-11-06", "fuel": "diesel", "current_price": 1.118, "yoy_price": 1.133, "absolute_change": -0.015, "percentage_change": -1.3239187996}, {"date": "1995-11-13", "fuel": "diesel", "current_price": 1.118, "yoy_price": 1.135, "absolute_change": -0.017, "percentage_change": -1.4977973568}, {"date": "1995-11-20", "fuel": "diesel", "current_price": 1.119, "yoy_price": 1.13, "absolute_change": -0.011, "percentage_change": -0.9734513274}, {"date": "1995-11-27", "fuel": "diesel", "current_price": 1.124, "yoy_price": 1.126, "absolute_change": -0.002, "percentage_change": -0.1776198934}, {"date": 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"fuel": "gasoline", "current_price": 3.8125, "yoy_price": 4.2440833333, "absolute_change": -0.4315833333, "percentage_change": -10.1690588859}, {"date": "2024-08-19", "fuel": "gasoline", "current_price": 3.7886666667, "yoy_price": 4.2770833333, "absolute_change": -0.4884166667, "percentage_change": -11.419386264}, {"date": "2024-08-26", "fuel": "gasoline", "current_price": 3.7275, "yoy_price": 4.23275, "absolute_change": -0.50525, "percentage_change": -11.9366841888}, {"date": "2024-09-02", "fuel": "gasoline", "current_price": 3.7145, "yoy_price": 4.22625, "absolute_change": -0.51175, "percentage_change": -12.1088435374}, {"date": "2024-09-09", "fuel": "gasoline", "current_price": 3.6693333333, "yoy_price": 4.2460833333, "absolute_change": -0.57675, "percentage_change": -13.5831059996}, {"date": "2024-09-16", "fuel": "gasoline", "current_price": 3.62675, "yoy_price": 4.323, "absolute_change": -0.69625, "percentage_change": -16.1057136248}, {"date": "2024-09-23", "fuel": "gasoline", "current_price": 3.6224166667, "yoy_price": 4.2991666667, "absolute_change": -0.67675, "percentage_change": -15.7414227563}, {"date": "2024-09-30", "fuel": "gasoline", "current_price": 3.6073333333, "yoy_price": 4.28625, "absolute_change": -0.6789166667, "percentage_change": -15.8394089628}, {"date": "2024-10-07", "fuel": "gasoline", "current_price": 3.5685833333, "yoy_price": 4.1599166667, "absolute_change": -0.5913333333, "percentage_change": -14.2150283459}, {"date": "2024-10-14", "fuel": "gasoline", "current_price": 3.5970833333, "yoy_price": 4.0485, "absolute_change": -0.4514166667, "percentage_change": -11.1502202462}, {"date": "2024-10-21", "fuel": "gasoline", "current_price": 3.5735, "yoy_price": 3.99475, "absolute_change": -0.42125, "percentage_change": -10.5450904312}, {"date": "2024-10-28", "fuel": "gasoline", "current_price": 3.5249166667, "yoy_price": 3.9290833333, "absolute_change": -0.4041666667, "percentage_change": -10.2865384208}, {"date": "2024-11-04", "fuel": 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"gasoline", "current_price": 3.431, "yoy_price": 3.4815, "absolute_change": -0.0505, "percentage_change": -1.4505241993}, {"date": "2024-12-23", "fuel": "gasoline", "current_price": 3.4395, "yoy_price": 3.5409166667, "absolute_change": -0.1014166667, "percentage_change": -2.8641359347}, {"date": "2024-12-30", "fuel": "gasoline", "current_price": 3.4266666667, "yoy_price": 3.5228333333, "absolute_change": -0.0961666667, "percentage_change": -2.7298102853}, {"date": "2025-01-06", "fuel": "gasoline", "current_price": 3.4620833333, "yoy_price": 3.5079166667, "absolute_change": -0.0458333333, "percentage_change": -1.3065684761}, {"date": "2025-01-13", "fuel": "gasoline", "current_price": 3.4610833333, "yoy_price": 3.4788333333, "absolute_change": -0.01775, "percentage_change": -0.5102285249}, {"date": "2025-01-20", "fuel": "gasoline", "current_price": 3.5243333333, "yoy_price": 3.4768333333, "absolute_change": 0.0475, "percentage_change": 1.3661857054}, {"date": "2025-01-27", "fuel": "gasoline", "current_price": 3.522, "yoy_price": 3.5109166667, "absolute_change": 0.0110833333, "percentage_change": 0.3156820394}, {"date": "2025-02-03", "fuel": "gasoline", "current_price": 3.5101666667, "yoy_price": 3.54975, "absolute_change": -0.0395833333, "percentage_change": -1.1151020025}, {"date": "2025-02-10", "fuel": "gasoline", "current_price": 3.5611666667, "yoy_price": 3.5989166667, "absolute_change": -0.03775, "percentage_change": -1.0489267604}, {"date": "2025-02-17", "fuel": "gasoline", "current_price": 3.5964166667, "yoy_price": 3.6731666667, "absolute_change": -0.07675, "percentage_change": -2.089477744}, {"date": "2025-02-24", "fuel": "gasoline", "current_price": 3.57775, "yoy_price": 3.6526666667, "absolute_change": -0.0749166667, "percentage_change": -2.0510129586}, {"date": "2025-03-03", "fuel": "gasoline", "current_price": 3.5271666667, "yoy_price": 3.7561666667, "absolute_change": -0.229, "percentage_change": -6.0966410791}, {"date": "2025-03-10", "fuel": "gasoline", "current_price": 3.51375, "yoy_price": 3.781, "absolute_change": -0.26725, "percentage_change": -7.0682359164}, {"date": "2025-03-17", "fuel": "gasoline", "current_price": 3.4978333333, "yoy_price": 3.8548333333, "absolute_change": -0.357, "percentage_change": -9.2611007826}, {"date": "2025-03-24", "fuel": "gasoline", "current_price": 3.5485833333, "yoy_price": 3.9271666667, "absolute_change": -0.3785833333, "percentage_change": -9.6401137376}, {"date": "2025-03-31", "fuel": "gasoline", "current_price": 3.6035, "yoy_price": 3.9306666667, "absolute_change": -0.3271666667, "percentage_change": -8.3234396201}, {"date": "2025-04-07", "fuel": "gasoline", "current_price": 3.6863333333, "yoy_price": 4.0188333333, "absolute_change": -0.3325, "percentage_change": -8.2735453905}, {"date": "2025-04-14", "fuel": "gasoline", "current_price": 3.613, "yoy_price": 4.06375, "absolute_change": -0.45075, "percentage_change": -11.091971701}, {"date": "2025-04-21", "fuel": "gasoline", "current_price": 3.58525, "yoy_price": 4.10625, "absolute_change": -0.521, "percentage_change": -12.6879756469}, {"date": "2025-04-28", "fuel": "gasoline", "current_price": 3.57875, "yoy_price": 4.09125, "absolute_change": -0.5125, "percentage_change": -12.5267338833}, {"date": "2025-05-05", "fuel": "gasoline", "current_price": 3.5851666667, "yoy_price": 4.0814166667, "absolute_change": -0.49625, "percentage_change": -12.1587684015}, {"date": "2025-05-12", "fuel": "gasoline", "current_price": 3.5728333333, "yoy_price": 4.0420833333, "absolute_change": -0.46925, "percentage_change": -11.6091124626}, {"date": "2025-05-19", "fuel": "gasoline", "current_price": 3.6244166667, "yoy_price": 4.0134166667, "absolute_change": -0.389, "percentage_change": -9.6924897739}, {"date": "2025-05-26", "fuel": "gasoline", "current_price": 3.6089166667, "yoy_price": 4.00925, "absolute_change": -0.4003333333, "percentage_change": -9.9852424601}, {"date": "2025-06-02", "fuel": "gasoline", "current_price": 3.5746666667, "yoy_price": 3.9465, "absolute_change": -0.3718333333, "percentage_change": -9.4218505849}, {"date": "2025-06-09", "fuel": "gasoline", "current_price": 3.5501666667, "yoy_price": 3.8579166667, "absolute_change": -0.30775, "percentage_change": -7.9771033589}, {"date": "2025-06-16", "fuel": "gasoline", "current_price": 3.5765, "yoy_price": 3.8586666667, "absolute_change": -0.2821666667, "percentage_change": -7.3125431928}, {"date": "2025-06-23", "fuel": "gasoline", "current_price": 3.6445833333, "yoy_price": 3.8534166667, "absolute_change": -0.2088333333, "percentage_change": -5.4194329707}], "metadata": {"date": {"type": "date", "semanticType": "Date"}, "fuel": {"type": "string", "semanticType": "String"}, "current_price": {"type": "number", "semanticType": "Number"}, "yoy_price": {"type": "number", "semanticType": "Number"}, "absolute_change": {"type": "number", "semanticType": "Number"}, "percentage_change": {"type": "number", "semanticType": "Percentage"}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n df_agg = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Convert date to datetime for proper sorting and manipulation\n df_agg['date'] = pd.to_datetime(df_agg['date'])\n \n # Sort by fuel and date\n df_agg = df_agg.sort_values(['fuel', 'date'])\n \n # Calculate year-over-year changes (52 weeks ago for weekly data)\n df_agg['yoy_price'] = df_agg.groupby('fuel')['price'].shift(52)\n df_agg['absolute_change'] = df_agg['price'] - df_agg['yoy_price']\n df_agg['percentage_change'] = ((df_agg['price'] - df_agg['yoy_price']) / df_agg['yoy_price']) * 100\n \n # Rename columns for clarity\n transformed_df = df_agg.rename(columns={'price': 'current_price'})\n \n # Convert date back to string format for visualization\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n # Keep only rows where we have year-over-year data (remove first 52 weeks)\n transformed_df = transformed_df.dropna(subset=['yoy_price'])\n \n # Select and order output columns\n transformed_df = transformed_df[['date', 'fuel', 'current_price', 'yoy_price', 'absolute_change', 'percentage_change']]\n \n return transformed_df\n", "source": ["weekly_gas_prices"], "dialog": [{"role": "system", "content": "You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"}, {"role": "user", "content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[GOAL]\n\nWhat are the major **price trends** across all fuel types from 1990 to 2025?"}, {"role": "assistant", "content": "```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to visualize the major price trends across all fuel types from 1990 to 2025 to understand how fuel prices have evolved over time.\",\n \"display_instruction\": \"Show **price** trends over time for different **fuel** types\",\n \"recommendation\": \"To visualize price trends over time across fuel types, we should aggregate the data by date and fuel type. Since there are multiple grades and formulations for each fuel type, we'll calculate the average price for each fuel type on each date. A line chart with date on the x-axis, average price on the y-axis, and different colored lines for each fuel type will clearly show the trends and allow easy comparison.\",\n \"output_fields\": [\"date\", \"fuel\", \"avg_price\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n transformed_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Rename the price column to avg_price for clarity\n transformed_df = transformed_df.rename(columns={'price': 'avg_price'})\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values('date')\n \n return transformed_df\n```"}, {"role": "user", "content": "This is the result from the latest python code:\n\n date fuel avg_price\n0 1990-08-20 gasoline 1.191\n1 1990-08-27 gasoline 1.245\n2 1990-09-03 gasoline 1.242\n3 1990-09-10 gasoline 1.252\n4 1990-09-17 gasoline 1.266\n5 1990-09-24 gasoline 1.272\n6 1990-10-01 gasoline 1.321\n7 1990-10-08 gasoline 1.333\n8 1990-10-15 gasoline 1.339\n9 1990-10-22 gasoline 1.345\n......\n\nUpdate the code above based on the following instruction:\n\nCalculate year-over-year price changes to identify the most dramatic price spikes for both diesel and gasoline, showing which periods had the largest absolute and percentage increases with their peak prices."}, {"role": "assistant", "content": "```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to identify the most dramatic price spikes by calculating year-over-year price changes for both diesel and gasoline, showing periods with the largest absolute and percentage increases along with their peak prices.\",\n \"display_instruction\": \"Identify periods with largest **year-over-year price changes** (absolute and percentage) and **peak prices**\",\n \"recommendation\": \"To analyze year-over-year price changes, we need to calculate the price difference and percentage change compared to the same period one year prior. We'll compute both absolute change and percentage change, then identify the periods with the most dramatic spikes. The output should include the date, fuel type, current price, previous year's price, absolute change, and percentage change to provide a comprehensive view of price volatility.\",\n \"output_fields\": [\"date\", \"fuel\", \"current_price\", \"yoy_price\", \"absolute_change\", \"percentage_change\"],\n \"chart_type\": \"point\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"absolute_change\",\n \"color\": \"fuel\",\n \"size\": \"percentage_change\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n df_agg = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Convert date to datetime for proper sorting and manipulation\n df_agg['date'] = pd.to_datetime(df_agg['date'])\n \n # Sort by fuel and date\n df_agg = df_agg.sort_values(['fuel', 'date'])\n \n # Calculate year-over-year changes (52 weeks ago for weekly data)\n df_agg['yoy_price'] = df_agg.groupby('fuel')['price'].shift(52)\n df_agg['absolute_change'] = df_agg['price'] - df_agg['yoy_price']\n df_agg['percentage_change'] = ((df_agg['price'] - df_agg['yoy_price']) / df_agg['yoy_price']) * 100\n \n # Rename columns for clarity\n transformed_df = df_agg.rename(columns={'price': 'current_price'})\n \n # Convert date back to string format for visualization\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n # Keep only rows where we have year-over-year data (remove first 52 weeks)\n transformed_df = transformed_df.dropna(subset=['yoy_price'])\n \n # Select and order output columns\n transformed_df = transformed_df[['date', 'fuel', 'current_price', 'yoy_price', 'absolute_change', 'percentage_change']]\n \n return transformed_df\n```"}], "trigger": {"tableId": "table-904593", "resultTableId": "table-922023", "chart": {"id": "chart-1760744928220", "chartType": "Auto", "encodingMap": {}, "tableRef": "weekly_gas_prices", "saved": false, "source": "trigger", "unread": true}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "Calculate year-over-year price changes to identify the most dramatic price spikes for both diesel and gasoline, showing which periods had the largest absolute and percentage increases with their peak prices.", "displayContent": "Identify periods with largest **year-over-year price changes** (absolute and percentage) and **peak prices**"}]}, "explanation": {"agent": "CodeExplanationAgent", "code": "The code analyzes **year-over-year price trends** for different fuel types by performing the following steps:\n\n- Aggregates the data by **date** and **fuel** type, calculating the **average price** across all grades and formulations for each fuel type on each date\n- Converts **date** field to datetime format and sorts the data chronologically by **fuel** and **date**\n- Calculates **year-over-year comparisons** by comparing each week's price to the price from **52 weeks ago** (one year prior)\n- Computes both **absolute change** (dollar difference) and **percentage change** between current prices and prices from one year ago\n- Removes the first **52 weeks** of data where year-over-year comparisons are not available\n- Returns a dataset containing **current prices**, **year-ago prices**, and their **changes** for each fuel type over time", "concepts": [{"explanation": "The average fuel price from exactly 52 weeks (one year) prior to the current date, used as a baseline for year-over-year comparison. This represents the historical price point from the same week in the previous year.", "field": "yoy_price"}, {"explanation": "The dollar amount difference between the current week's price and the price from one year ago, calculated as: \\( \\text{absolute\\_change} = \\text{current\\_price} - \\text{yoy\\_price} \\). A positive value indicates price increase, while a negative value indicates price decrease.", "field": "absolute_change"}, {"explanation": "The year-over-year price change expressed as a percentage, calculated as: \\( \\text{percentage\\_change} = \\frac{\\text{current\\_price} - \\text{yoy\\_price}}{\\text{yoy\\_price}} \\times 100 \\). This metric normalizes price changes to allow comparison across different price levels and time periods.", "field": "percentage_change"}], "dialog": [{"content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n df_agg = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Convert date to datetime for proper sorting and manipulation\n df_agg['date'] = pd.to_datetime(df_agg['date'])\n \n # Sort by fuel and date\n df_agg = df_agg.sort_values(['fuel', 'date'])\n \n # Calculate year-over-year changes (52 weeks ago for weekly data)\n df_agg['yoy_price'] = df_agg.groupby('fuel')['price'].shift(52)\n df_agg['absolute_change'] = df_agg['price'] - df_agg['yoy_price']\n df_agg['percentage_change'] = ((df_agg['price'] - df_agg['yoy_price']) / df_agg['yoy_price']) * 100\n \n # Rename columns for clarity\n transformed_df = df_agg.rename(columns={'price': 'current_price'})\n \n # Convert date back to string format for visualization\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n # Keep only rows where we have year-over-year data (remove first 52 weeks)\n transformed_df = transformed_df.dropna(subset=['yoy_price'])\n \n # Select and order output columns\n transformed_df = transformed_df[['date', 'fuel', 'current_price', 'yoy_price', 'absolute_change', 'percentage_change']]\n \n return transformed_df\n\n\n[EXPLANATION]\n", "role": "user"}, {"content": "[CODE EXPLANATION]\n\nThe code analyzes **year-over-year price trends** for different fuel types by performing the following steps:\n\n- Aggregates the data by **date** and **fuel** type, calculating the **average price** across all grades and formulations for each fuel type on each date\n- Converts **date** field to datetime format and sorts the data chronologically by **fuel** and **date**\n- Calculates **year-over-year comparisons** by comparing each week's price to the price from **52 weeks ago** (one year prior)\n- Computes both **absolute change** (dollar difference) and **percentage change** between current prices and prices from one year ago\n- Removes the first **52 weeks** of data where year-over-year comparisons are not available\n- Returns a dataset containing **current prices**, **year-ago prices**, and their **changes** for each fuel type over time\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"yoy_price\",\n \"explanation\": \"The average fuel price from exactly 52 weeks (one year) prior to the current date, used as a baseline for year-over-year comparison. This represents the historical price point from the same week in the previous year.\"\n },\n {\n \"field\": \"absolute_change\",\n \"explanation\": \"The dollar amount difference between the current week's price and the price from one year ago, calculated as: \\\\( \\\\text{absolute\\\\_change} = \\\\text{current\\\\_price} - \\\\text{yoy\\\\_price} \\\\). A positive value indicates price increase, while a negative value indicates price decrease.\"\n },\n {\n \"field\": \"percentage_change\",\n \"explanation\": \"The year-over-year price change expressed as a percentage, calculated as: \\\\( \\\\text{percentage\\\\_change} = \\\\frac{\\\\text{current\\\\_price} - \\\\text{yoy\\\\_price}}{\\\\text{yoy\\\\_price}} \\\\times 100 \\\\). This metric normalizes price changes to allow comparison across different price levels and time periods.\"\n }\n]\n```", "role": "assistant"}], "status": "ok"}, "status": "completed"}, "anchored": false, "attachedMetadata": ""}, {"kind": "table", "id": "table-937785", "displayId": "fuel-price-stats", "names": ["period", "fuel", "avg_price", "std_dev", "coefficient_of_variation"], "rows": [{"period": "Post-2000 (2000-2025)", "fuel": "diesel", "avg_price": 2.933, "std_dev": 0.982, "coefficient_of_variation": 33.48}, {"period": "Post-2000 (2000-2025)", "fuel": "gasoline", "avg_price": 2.847, "std_dev": 0.838, "coefficient_of_variation": 29.43}, {"period": "Pre-2000 (1990-1999)", "fuel": "diesel", "avg_price": 1.138, "std_dev": 0.087, "coefficient_of_variation": 7.65}, {"period": "Pre-2000 (1990-1999)", "fuel": "gasoline", "avg_price": 1.171, "std_dev": 0.106, "coefficient_of_variation": 9.09}], "metadata": {"period": {"type": "string", "semanticType": "TimeRange", "levels": ["Pre-2000 (1990-1999)", "Post-2000 (2000-2025)"]}, "fuel": {"type": "string", "semanticType": "String"}, "avg_price": {"type": "number", "semanticType": "Number"}, "std_dev": {"type": "number", "semanticType": "Number"}, "coefficient_of_variation": {"type": "number", "semanticType": "Number"}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n df_agg = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Convert date to datetime for period classification\n df_agg['date'] = pd.to_datetime(df_agg['date'])\n \n # Extract year for period classification\n df_agg['year'] = df_agg['date'].dt.year\n \n # Classify into pre-2000 and post-2000 periods\n df_agg['period'] = df_agg['year'].apply(lambda x: 'Pre-2000 (1990-1999)' if x < 2000 else 'Post-2000 (2000-2025)')\n \n # Calculate volatility metrics by period and fuel\n volatility_stats = df_agg.groupby(['period', 'fuel']).agg({\n 'price': ['mean', 'std']\n }).reset_index()\n \n # Flatten column names\n volatility_stats.columns = ['period', 'fuel', 'avg_price', 'std_dev']\n \n # Calculate coefficient of variation (CV = std_dev / mean * 100)\n volatility_stats['coefficient_of_variation'] = (volatility_stats['std_dev'] / volatility_stats['avg_price']) * 100\n \n # Round values for readability\n volatility_stats['avg_price'] = volatility_stats['avg_price'].round(3)\n volatility_stats['std_dev'] = volatility_stats['std_dev'].round(3)\n volatility_stats['coefficient_of_variation'] = volatility_stats['coefficient_of_variation'].round(2)\n \n transformed_df = volatility_stats[['period', 'fuel', 'avg_price', 'std_dev', 'coefficient_of_variation']]\n \n return transformed_df\n", "source": ["weekly_gas_prices"], "dialog": [{"role": "system", "content": "You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"}, {"role": "user", "content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[GOAL]\n\nWhat are the major **price trends** across all fuel types from 1990 to 2025?"}, {"role": "assistant", "content": "```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to visualize the major price trends across all fuel types from 1990 to 2025 to understand how fuel prices have evolved over time.\",\n \"display_instruction\": \"Show **price** trends over time for different **fuel** types\",\n \"recommendation\": \"To visualize price trends over time across fuel types, we should aggregate the data by date and fuel type. Since there are multiple grades and formulations for each fuel type, we'll calculate the average price for each fuel type on each date. A line chart with date on the x-axis, average price on the y-axis, and different colored lines for each fuel type will clearly show the trends and allow easy comparison.\",\n \"output_fields\": [\"date\", \"fuel\", \"avg_price\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n transformed_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Rename the price column to avg_price for clarity\n transformed_df = transformed_df.rename(columns={'price': 'avg_price'})\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values('date')\n \n return transformed_df\n```"}, {"role": "user", "content": "This is the result from the latest python code:\n\n date fuel avg_price\n0 1990-08-20 gasoline 1.191\n1 1990-08-27 gasoline 1.245\n2 1990-09-03 gasoline 1.242\n3 1990-09-10 gasoline 1.252\n4 1990-09-17 gasoline 1.266\n5 1990-09-24 gasoline 1.272\n6 1990-10-01 gasoline 1.321\n7 1990-10-08 gasoline 1.333\n8 1990-10-15 gasoline 1.339\n9 1990-10-22 gasoline 1.345\n......\n\nUpdate the code above based on the following instruction:\n\nCalculate year-over-year price changes to identify the most dramatic price spikes for both diesel and gasoline, showing which periods had the largest absolute and percentage increases with their peak prices."}, {"role": "assistant", "content": "```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to identify the most dramatic price spikes by calculating year-over-year price changes for both diesel and gasoline, showing periods with the largest absolute and percentage increases along with their peak prices.\",\n \"display_instruction\": \"Identify periods with largest **year-over-year price changes** (absolute and percentage) and **peak prices**\",\n \"recommendation\": \"To analyze year-over-year price changes, we need to calculate the price difference and percentage change compared to the same period one year prior. We'll compute both absolute change and percentage change, then identify the periods with the most dramatic spikes. The output should include the date, fuel type, current price, previous year's price, absolute change, and percentage change to provide a comprehensive view of price volatility.\",\n \"output_fields\": [\"date\", \"fuel\", \"current_price\", \"yoy_price\", \"absolute_change\", \"percentage_change\"],\n \"chart_type\": \"point\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"absolute_change\",\n \"color\": \"fuel\",\n \"size\": \"percentage_change\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n df_agg = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Convert date to datetime for proper sorting and manipulation\n df_agg['date'] = pd.to_datetime(df_agg['date'])\n \n # Sort by fuel and date\n df_agg = df_agg.sort_values(['fuel', 'date'])\n \n # Calculate year-over-year changes (52 weeks ago for weekly data)\n df_agg['yoy_price'] = df_agg.groupby('fuel')['price'].shift(52)\n df_agg['absolute_change'] = df_agg['price'] - df_agg['yoy_price']\n df_agg['percentage_change'] = ((df_agg['price'] - df_agg['yoy_price']) / df_agg['yoy_price']) * 100\n \n # Rename columns for clarity\n transformed_df = df_agg.rename(columns={'price': 'current_price'})\n \n # Convert date back to string format for visualization\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n # Keep only rows where we have year-over-year data (remove first 52 weeks)\n transformed_df = transformed_df.dropna(subset=['yoy_price'])\n \n # Select and order output columns\n transformed_df = transformed_df[['date', 'fuel', 'current_price', 'yoy_price', 'absolute_change', 'percentage_change']]\n \n return transformed_df\n```"}, {"role": "user", "content": "This is the result from the latest python code:\n\n date fuel current_price yoy_price absolute_change percentage_change\n0 1995-03-20 diesel 1.085 1.106 -0.021 -1.898734\n1 1995-03-27 diesel 1.088 1.107 -0.019 -1.716350\n2 1995-04-03 diesel 1.094 1.109 -0.015 -1.352570\n3 1995-04-10 diesel 1.101 1.108 -0.007 -0.631769\n4 1995-04-17 diesel 1.106 1.105 0.001 0.090498\n5 1995-04-24 diesel 1.115 1.106 0.009 0.813743\n6 1995-05-01 diesel 1.119 1.104 0.015 1.358696\n7 1995-05-08 diesel 1.126 1.101 0.025 2.270663\n8 1995-05-15 diesel 1.126 1.099 0.027 2.456779\n9 1995-05-22 diesel 1.124 1.099 0.025 2.274795\n......\n\nUpdate the code above based on the following instruction:\n\nCompare price volatility (standard deviation and coefficient of variation) between pre-2000 (1990-1999) and post-2000 (2000-2025) periods for both fuel types to show how price stability has changed over time."}, {"role": "assistant", "content": "```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to compare price volatility between pre-2000 (1990-1999) and post-2000 (2000-2025) periods for both fuel types by calculating standard deviation and coefficient of variation to understand how price stability has changed over time.\",\n \"display_instruction\": \"Compare **price volatility** (standard deviation and coefficient of variation) between **pre-2000** and **post-2000** periods\",\n \"recommendation\": \"To analyze price volatility across different time periods, we need to segment the data into pre-2000 (1990-1999) and post-2000 (2000-2025) periods, then calculate volatility metrics including standard deviation and coefficient of variation for each fuel type in each period. A grouped bar chart comparing these metrics across periods and fuel types will clearly show how price stability has evolved.\",\n \"output_fields\": [\"period\", \"fuel\", \"avg_price\", \"std_dev\", \"coefficient_of_variation\"],\n \"chart_type\": \"group_bar\",\n \"chart_encodings\": {\n \"x\": \"period\",\n \"y\": \"coefficient_of_variation\",\n \"color\": \"fuel\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n df_agg = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Convert date to datetime for period classification\n df_agg['date'] = pd.to_datetime(df_agg['date'])\n \n # Extract year for period classification\n df_agg['year'] = df_agg['date'].dt.year\n \n # Classify into pre-2000 and post-2000 periods\n df_agg['period'] = df_agg['year'].apply(lambda x: 'Pre-2000 (1990-1999)' if x < 2000 else 'Post-2000 (2000-2025)')\n \n # Calculate volatility metrics by period and fuel\n volatility_stats = df_agg.groupby(['period', 'fuel']).agg({\n 'price': ['mean', 'std']\n }).reset_index()\n \n # Flatten column names\n volatility_stats.columns = ['period', 'fuel', 'avg_price', 'std_dev']\n \n # Calculate coefficient of variation (CV = std_dev / mean * 100)\n volatility_stats['coefficient_of_variation'] = (volatility_stats['std_dev'] / volatility_stats['avg_price']) * 100\n \n # Round values for readability\n volatility_stats['avg_price'] = volatility_stats['avg_price'].round(3)\n volatility_stats['std_dev'] = volatility_stats['std_dev'].round(3)\n volatility_stats['coefficient_of_variation'] = volatility_stats['coefficient_of_variation'].round(2)\n \n transformed_df = volatility_stats[['period', 'fuel', 'avg_price', 'std_dev', 'coefficient_of_variation']]\n \n return transformed_df\n```"}], "trigger": {"tableId": "table-922023", "resultTableId": "table-937785", "chart": {"id": "chart-1760744940045", "chartType": "Auto", "encodingMap": {}, "tableRef": "weekly_gas_prices", "saved": false, "source": "trigger", "unread": true}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "Compare price volatility (standard deviation and coefficient of variation) between pre-2000 (1990-1999) and post-2000 (2000-2025) periods for both fuel types to show how price stability has changed over time.", "displayContent": "Compare **price volatility** (standard deviation and coefficient of variation) between **pre-2000** and **post-2000** periods"}]}, "explanation": {"agent": "CodeExplanationAgent", "code": "This code performs a statistical analysis of **gas price volatility** across two time periods:\n\n1. **Aggregate prices** by computing the mean **price** for each **date** and **fuel** type, averaging across all **grades** and **formulations**\n2. **Convert dates** to datetime format and extract the **year** from each date\n3. **Classify data** into two time periods: **Pre-2000 (1990-1999)** and **Post-2000 (2000-2025)** based on the year\n4. **Calculate volatility metrics** for each **period** and **fuel** type combination:\n - Compute the **mean price** (average price level)\n - Compute the **standard deviation** (absolute price variability)\n - Calculate the **coefficient of variation** to measure relative volatility\n5. **Round numerical values** for improved readability (**avg_price** and **std_dev** to 3 decimals, **coefficient_of_variation** to 2 decimals)\n6. **Return summary statistics** showing volatility characteristics for **diesel** and **gasoline** in each time period", "concepts": [{"explanation": "A normalized measure of price volatility that indicates the relative variability of prices independent of their absolute level. Calculated as \\( \\frac{\\text{std\\_dev}}{\\text{avg\\_price}} \\times 100 \\), this metric allows for fair comparison of price volatility between different time periods or fuel types that may have substantially different average price levels. A higher coefficient indicates more relative price instability.", "field": "coefficient_of_variation"}, {"explanation": "This analysis employs a **comparative volatility assessment** framework to examine fuel price stability across two distinct time periods. The approach uses **temporal aggregation** (grouping by date and fuel) followed by **period-based stratification** (pre-2000 vs. post-2000) to compute **descriptive volatility statistics** (mean, standard deviation, and coefficient of variation). This methodology allows for direct comparison of both absolute and relative price fluctuations across time periods and fuel types. **Alternative approaches** could include: (1) time series decomposition to separate trend, seasonal, and irregular components; (2) GARCH models to capture time-varying volatility; (3) moving window analysis to track volatility changes over shorter intervals; or (4) regime-switching models to identify distinct volatility states.", "field": "Statistical Analysis"}], "dialog": [{"content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n df_agg = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Convert date to datetime for period classification\n df_agg['date'] = pd.to_datetime(df_agg['date'])\n \n # Extract year for period classification\n df_agg['year'] = df_agg['date'].dt.year\n \n # Classify into pre-2000 and post-2000 periods\n df_agg['period'] = df_agg['year'].apply(lambda x: 'Pre-2000 (1990-1999)' if x < 2000 else 'Post-2000 (2000-2025)')\n \n # Calculate volatility metrics by period and fuel\n volatility_stats = df_agg.groupby(['period', 'fuel']).agg({\n 'price': ['mean', 'std']\n }).reset_index()\n \n # Flatten column names\n volatility_stats.columns = ['period', 'fuel', 'avg_price', 'std_dev']\n \n # Calculate coefficient of variation (CV = std_dev / mean * 100)\n volatility_stats['coefficient_of_variation'] = (volatility_stats['std_dev'] / volatility_stats['avg_price']) * 100\n \n # Round values for readability\n volatility_stats['avg_price'] = volatility_stats['avg_price'].round(3)\n volatility_stats['std_dev'] = volatility_stats['std_dev'].round(3)\n volatility_stats['coefficient_of_variation'] = volatility_stats['coefficient_of_variation'].round(2)\n \n transformed_df = volatility_stats[['period', 'fuel', 'avg_price', 'std_dev', 'coefficient_of_variation']]\n \n return transformed_df\n\n\n[EXPLANATION]\n", "role": "user"}, {"content": "[CODE EXPLANATION]\n\nThis code performs a statistical analysis of **gas price volatility** across two time periods:\n\n1. **Aggregate prices** by computing the mean **price** for each **date** and **fuel** type, averaging across all **grades** and **formulations**\n2. **Convert dates** to datetime format and extract the **year** from each date\n3. **Classify data** into two time periods: **Pre-2000 (1990-1999)** and **Post-2000 (2000-2025)** based on the year\n4. **Calculate volatility metrics** for each **period** and **fuel** type combination:\n - Compute the **mean price** (average price level)\n - Compute the **standard deviation** (absolute price variability)\n - Calculate the **coefficient of variation** to measure relative volatility\n5. **Round numerical values** for improved readability (**avg_price** and **std_dev** to 3 decimals, **coefficient_of_variation** to 2 decimals)\n6. **Return summary statistics** showing volatility characteristics for **diesel** and **gasoline** in each time period\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"coefficient_of_variation\",\n \"explanation\": \"A normalized measure of price volatility that indicates the relative variability of prices independent of their absolute level. Calculated as \\\\( \\\\frac{\\\\text{std\\\\_dev}}{\\\\text{avg\\\\_price}} \\\\times 100 \\\\), this metric allows for fair comparison of price volatility between different time periods or fuel types that may have substantially different average price levels. A higher coefficient indicates more relative price instability.\"\n },\n {\n \"field\": \"Statistical Analysis\",\n \"explanation\": \"This analysis employs a **comparative volatility assessment** framework to examine fuel price stability across two distinct time periods. The approach uses **temporal aggregation** (grouping by date and fuel) followed by **period-based stratification** (pre-2000 vs. post-2000) to compute **descriptive volatility statistics** (mean, standard deviation, and coefficient of variation). This methodology allows for direct comparison of both absolute and relative price fluctuations across time periods and fuel types. **Alternative approaches** could include: (1) time series decomposition to separate trend, seasonal, and irregular components; (2) GARCH models to capture time-varying volatility; (3) moving window analysis to track volatility changes over shorter intervals; or (4) regime-switching models to identify distinct volatility states.\"\n }\n]\n```", "role": "assistant"}], "status": "ok"}, "status": "completed"}, "anchored": false, "attachedMetadata": ""}, {"kind": "table", "id": "table-34", "displayId": "fuel-price-forecast", "names": ["avg_price", "date", "fuel", "is_predicted"], "rows": [{"avg_price": 1.106, "date": "1994-03-21", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0292206988, "date": "1994-03-21", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.107, "date": "1994-03-28", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0311478914, "date": "1994-03-28", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.109, "date": "1994-04-04", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0330750841, "date": "1994-04-04", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.108, "date": "1994-04-11", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0350022767, "date": "1994-04-11", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.105, "date": "1994-04-18", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0369294694, "date": "1994-04-18", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.106, "date": "1994-04-25", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.038856662, "date": "1994-04-25", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.104, "date": "1994-05-02", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0407838547, "date": "1994-05-02", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.101, "date": "1994-05-09", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0427110473, "date": "1994-05-09", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.099, "date": "1994-05-16", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.04463824, "date": "1994-05-16", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.099, "date": "1994-05-23", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0465654326, "date": "1994-05-23", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.098, "date": "1994-05-30", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0484926253, "date": "1994-05-30", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.101, "date": "1994-06-06", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0504198179, "date": "1994-06-06", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.098, "date": "1994-06-13", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0523470106, "date": "1994-06-13", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.103, "date": "1994-06-20", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0542742032, "date": "1994-06-20", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.108, "date": "1994-06-27", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0562013959, "date": "1994-06-27", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.109, "date": "1994-07-04", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0581285885, "date": "1994-07-04", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.11, "date": "1994-07-11", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0600557812, "date": "1994-07-11", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.111, "date": "1994-07-18", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0619829738, "date": "1994-07-18", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.111, "date": "1994-07-25", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0639101665, "date": "1994-07-25", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.116, "date": "1994-08-01", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0658373591, "date": "1994-08-01", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.127, "date": "1994-08-08", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0677645518, "date": "1994-08-08", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.127, "date": "1994-08-15", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0696917444, "date": "1994-08-15", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.124, "date": "1994-08-22", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0716189371, "date": "1994-08-22", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.122, "date": "1994-08-29", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0735461297, "date": "1994-08-29", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.126, "date": "1994-09-05", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0754733223, "date": "1994-09-05", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.128, "date": "1994-09-12", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.077400515, "date": "1994-09-12", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.126, "date": "1994-09-19", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0793277076, "date": "1994-09-19", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.12, "date": "1994-09-26", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0812549003, "date": "1994-09-26", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.118, "date": "1994-10-03", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0831820929, "date": "1994-10-03", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.117, "date": "1994-10-10", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0851092856, "date": "1994-10-10", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.119, "date": "1994-10-17", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0870364782, "date": "1994-10-17", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.122, "date": "1994-10-24", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0889636709, "date": "1994-10-24", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.133, "date": "1994-10-31", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 1.0908908635, "date": "1994-10-31", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.133, 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"is_predicted": "forecasting"}, {"avg_price": 4.1583710097, "date": "2028-02-27", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.1600518633, "date": "2028-03-05", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.1617327169, "date": "2028-03-12", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.1634135705, "date": "2028-03-19", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.1650944241, "date": "2028-03-26", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.1667752777, "date": "2028-04-02", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.1684561312, "date": "2028-04-09", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.1701369848, "date": "2028-04-16", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.1718178384, "date": "2028-04-23", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.173498692, "date": "2028-04-30", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.1751795456, "date": "2028-05-07", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.1768603991, "date": "2028-05-14", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.1785412527, "date": "2028-05-21", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.1802221063, "date": "2028-05-28", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.1819029599, "date": "2028-06-04", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.1835838135, "date": "2028-06-11", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.185264667, "date": "2028-06-18", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.1869455206, "date": "2028-06-25", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.1886263742, "date": "2028-07-02", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.1903072278, "date": "2028-07-09", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.1919880814, "date": "2028-07-16", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.193668935, "date": "2028-07-23", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.1953497885, "date": "2028-07-30", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.1970306421, "date": "2028-08-06", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.1987114957, "date": "2028-08-13", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2003923493, "date": "2028-08-20", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2020732029, "date": "2028-08-27", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2037540564, "date": "2028-09-03", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.20543491, "date": "2028-09-10", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2071157636, "date": "2028-09-17", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2087966172, "date": "2028-09-24", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2104774708, "date": "2028-10-01", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2121583243, "date": "2028-10-08", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2138391779, "date": "2028-10-15", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2155200315, "date": "2028-10-22", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2172008851, "date": "2028-10-29", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2188817387, "date": "2028-11-05", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2205625923, "date": "2028-11-12", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2222434458, "date": "2028-11-19", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2239242994, "date": "2028-11-26", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.225605153, "date": "2028-12-03", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2272860066, "date": "2028-12-10", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2289668602, "date": "2028-12-17", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2306477137, "date": "2028-12-24", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2323285673, "date": "2028-12-31", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2340094209, "date": "2029-01-07", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2356902745, "date": "2029-01-14", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2373711281, "date": "2029-01-21", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2390519816, "date": "2029-01-28", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2407328352, "date": "2029-02-04", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2424136888, "date": "2029-02-11", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2440945424, "date": "2029-02-18", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.245775396, "date": "2029-02-25", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2474562496, "date": "2029-03-04", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2491371031, "date": "2029-03-11", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2508179567, "date": "2029-03-18", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2524988103, "date": "2029-03-25", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2541796639, "date": "2029-04-01", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2558605175, "date": "2029-04-08", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.257541371, "date": "2029-04-15", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2592222246, "date": "2029-04-22", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2609030782, "date": "2029-04-29", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2625839318, "date": "2029-05-06", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2642647854, "date": "2029-05-13", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2659456389, "date": "2029-05-20", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2676264925, "date": "2029-05-27", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2693073461, "date": "2029-06-03", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2709881997, "date": "2029-06-10", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2726690533, "date": "2029-06-17", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2743499069, "date": "2029-06-24", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2760307604, "date": "2029-07-01", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.277711614, "date": "2029-07-08", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2793924676, "date": "2029-07-15", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2810733212, "date": "2029-07-22", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2827541748, "date": "2029-07-29", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2844350283, "date": "2029-08-05", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2861158819, "date": "2029-08-12", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2877967355, "date": "2029-08-19", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2894775891, "date": "2029-08-26", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2911584427, "date": "2029-09-02", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2928392962, "date": "2029-09-09", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2945201498, "date": "2029-09-16", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2962010034, "date": "2029-09-23", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.297881857, "date": "2029-09-30", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.2995627106, "date": "2029-10-07", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3012435642, "date": "2029-10-14", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3029244177, "date": "2029-10-21", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3046052713, "date": "2029-10-28", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3062861249, "date": "2029-11-04", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3079669785, "date": "2029-11-11", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3096478321, "date": "2029-11-18", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3113286856, "date": "2029-11-25", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3130095392, "date": "2029-12-02", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3146903928, "date": "2029-12-09", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3163712464, "date": "2029-12-16", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3180521, "date": "2029-12-23", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3197329535, "date": "2029-12-30", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3214138071, "date": "2030-01-06", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3230946607, "date": "2030-01-13", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3247755143, "date": "2030-01-20", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3264563679, "date": "2030-01-27", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3281372215, "date": "2030-02-03", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.329818075, "date": "2030-02-10", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3314989286, "date": "2030-02-17", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3331797822, "date": "2030-02-24", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3348606358, "date": "2030-03-03", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3365414894, "date": "2030-03-10", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3382223429, "date": "2030-03-17", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3399031965, "date": "2030-03-24", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3415840501, "date": "2030-03-31", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3432649037, "date": "2030-04-07", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3449457573, "date": "2030-04-14", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3466266108, "date": "2030-04-21", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3483074644, "date": "2030-04-28", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.349988318, "date": "2030-05-05", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3516691716, "date": "2030-05-12", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3533500252, "date": "2030-05-19", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3550308788, "date": "2030-05-26", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3567117323, "date": "2030-06-02", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3583925859, "date": "2030-06-09", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3600734395, "date": "2030-06-16", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 4.3617542931, "date": "2030-06-23", "fuel": "gasoline", "is_predicted": "forecasting"}], "metadata": {"avg_price": {"type": "number", "semanticType": "Number"}, "date": {"type": "date", "semanticType": "Date"}, "fuel": {"type": "string", "semanticType": "String"}, "is_predicted": {"type": "string", "semanticType": "String", "levels": ["original", "regression", "forecasting"]}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n historical_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n historical_df = historical_df.rename(columns={'price': 'avg_price'})\n historical_df = historical_df.sort_values('date')\n \n # Convert date to datetime for easier manipulation\n historical_df['date'] = pd.to_datetime(historical_df['date'])\n \n # Prepare the result dataframe\n all_data = []\n \n # Process each fuel type separately\n for fuel_type in historical_df['fuel'].unique():\n fuel_data = historical_df[historical_df['fuel'] == fuel_type].copy()\n \n # Create numeric representation of dates for regression (days since first date)\n min_date = fuel_data['date'].min()\n fuel_data['days_since_start'] = (fuel_data['date'] - min_date).dt.days\n \n # Prepare training data\n X = fuel_data[['days_since_start']].values\n y = fuel_data['avg_price'].values\n \n # Train linear regression model\n model = LinearRegression()\n model.fit(X, y)\n \n # Add original data with 'original' label\n fuel_data_original = fuel_data[['date', 'avg_price']].copy()\n fuel_data_original['fuel'] = fuel_type\n fuel_data_original['is_predicted'] = 'original'\n all_data.append(fuel_data_original[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate regression values for historical dates\n regression_prices = model.predict(X)\n fuel_data_regression = fuel_data[['date']].copy()\n fuel_data_regression['fuel'] = fuel_type\n fuel_data_regression['avg_price'] = regression_prices\n fuel_data_regression['is_predicted'] = 'regression'\n all_data.append(fuel_data_regression[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate forecast for next 5 years\n max_date = fuel_data['date'].max()\n future_dates = pd.date_range(start=max_date + pd.Timedelta(days=7), periods=260, freq='W')\n future_days = (future_dates - min_date).days.values.reshape(-1, 1)\n \n future_prices = model.predict(future_days)\n \n forecast_df = pd.DataFrame({\n 'date': future_dates,\n 'fuel': fuel_type,\n 'avg_price': future_prices,\n 'is_predicted': 'forecasting'\n })\n all_data.append(forecast_df)\n \n # Combine all data\n transformed_df = pd.concat(all_data, ignore_index=True)\n transformed_df = transformed_df.sort_values(['fuel', 'date'])\n \n # Convert date back to string format for output\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n return transformed_df\n", "source": ["weekly_gas_prices"], "dialog": [{"content": "You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[GOAL]\n\nWhat are the major **price trends** across all fuel types from 1990 to 2025?", "role": "user"}, {"content": "```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to visualize the major price trends across all fuel types from 1990 to 2025 to understand how fuel prices have evolved over time.\",\n \"display_instruction\": \"Show **price** trends over time for different **fuel** types\",\n \"recommendation\": \"To visualize price trends over time across fuel types, we should aggregate the data by date and fuel type. Since there are multiple grades and formulations for each fuel type, we'll calculate the average price for each fuel type on each date. A line chart with date on the x-axis, average price on the y-axis, and different colored lines for each fuel type will clearly show the trends and allow easy comparison.\",\n \"output_fields\": [\"date\", \"fuel\", \"avg_price\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n transformed_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Rename the price column to avg_price for clarity\n transformed_df = transformed_df.rename(columns={'price': 'avg_price'})\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values('date')\n \n return transformed_df\n```", "role": "assistant"}, {"content": "This is the result from the latest python code:\n\n date fuel avg_price\n0 1990-08-20 gasoline 1.191\n1 1990-08-27 gasoline 1.245\n2 1990-09-03 gasoline 1.242\n3 1990-09-10 gasoline 1.252\n4 1990-09-17 gasoline 1.266\n5 1990-09-24 gasoline 1.272\n6 1990-10-01 gasoline 1.321\n7 1990-10-08 gasoline 1.333\n8 1990-10-15 gasoline 1.339\n9 1990-10-22 gasoline 1.345\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"forecast the price trend for the next 5 years with some propert model!\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"column\": \"fuel\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Build upon the previous price trends visualization by adding forecasted prices for the next 5 years (2025-2030) using a time series forecasting model. The output should include both historical actual prices and forecasted prices, with an indicator to distinguish between them.\",\n \"display_instruction\": \"Forecast **price** trends for the next 5 years for each **fuel** type\",\n \"output_fields\": [\"date\", \"fuel\", \"avg_price\", \"is_predicted\"],\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"facet\": \"is_predicted\"\n },\n \"reason\": \"The user wants to extend the price trend analysis with forecasting for the next 5 years. Following the guidelines for forecasting, the output should include a 'is_predicted' column to distinguish between actual historical data, regression on historical data, and forecasted future values. The chart should be a line chart showing both historical and predicted values for each fuel type.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n historical_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n historical_df = historical_df.rename(columns={'price': 'avg_price'})\n historical_df = historical_df.sort_values('date')\n \n # Convert date to datetime for easier manipulation\n historical_df['date'] = pd.to_datetime(historical_df['date'])\n \n # Prepare the result dataframe\n all_data = []\n \n # Process each fuel type separately\n for fuel_type in historical_df['fuel'].unique():\n fuel_data = historical_df[historical_df['fuel'] == fuel_type].copy()\n \n # Create numeric representation of dates for regression (days since first date)\n min_date = fuel_data['date'].min()\n fuel_data['days_since_start'] = (fuel_data['date'] - min_date).dt.days\n \n # Prepare training data\n X = fuel_data[['days_since_start']].values\n y = fuel_data['avg_price'].values\n \n # Train linear regression model\n model = LinearRegression()\n model.fit(X, y)\n \n # Add original data with 'original' label\n fuel_data_original = fuel_data[['date', 'avg_price']].copy()\n fuel_data_original['fuel'] = fuel_type\n fuel_data_original['is_predicted'] = 'original'\n all_data.append(fuel_data_original[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate regression values for historical dates\n regression_prices = model.predict(X)\n fuel_data_regression = fuel_data[['date']].copy()\n fuel_data_regression['fuel'] = fuel_type\n fuel_data_regression['avg_price'] = regression_prices\n fuel_data_regression['is_predicted'] = 'regression'\n all_data.append(fuel_data_regression[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate forecast for next 5 years\n max_date = fuel_data['date'].max()\n future_dates = pd.date_range(start=max_date + pd.Timedelta(days=7), periods=260, freq='W')\n future_days = (future_dates - min_date).days.values.reshape(-1, 1)\n \n future_prices = model.predict(future_days)\n \n forecast_df = pd.DataFrame({\n 'date': future_dates,\n 'fuel': fuel_type,\n 'avg_price': future_prices,\n 'is_predicted': 'forecasting'\n })\n all_data.append(forecast_df)\n \n # Combine all data\n transformed_df = pd.concat(all_data, ignore_index=True)\n transformed_df = transformed_df.sort_values(['fuel', 'date'])\n \n # Convert date back to string format for output\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n return transformed_df\n```", "role": "assistant"}], "trigger": {"tableId": "table-904593", "resultTableId": "table-34", "chart": {"id": "chart-1760745146503", "chartType": "Line Chart", "encodingMap": {"x": {"fieldID": "original--weekly_gas_prices--date"}, "y": {"fieldID": "concept-avg_price-1760744859365"}, "color": {"fieldID": "original--weekly_gas_prices--fuel"}, "opacity": {"channel": "opacity", "bin": false}, "column": {"fieldID": "original--weekly_gas_prices--fuel"}, "row": {"channel": "row", "bin": false}}, "tableRef": "table-904593", "saved": false, "source": "trigger", "unread": false}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "forecast the price trend for the next 5 years with some propert model!", "displayContent": "Forecast **price** trends for the next 5 years for each **fuel** type"}]}, "explanation": {"agent": "CodeExplanationAgent", "code": "This code performs **time series analysis and forecasting** on historical gas prices:\n\n1. **Aggregate historical prices** by calculating the mean `price` across all `grade` and `formulation` values for each `date` and `fuel` type combination, creating an `avg_price` metric.\n\n2. **Prepare temporal features** by converting `date` to datetime format and calculating `days_since_start` (the number of days elapsed since the earliest date) for each `fuel` type separately.\n\n3. **Train linear regression models** for each `fuel` type (diesel, gasoline) using `days_since_start` as the predictor variable and `avg_price` as the target variable.\n\n4. **Generate three data series** for each fuel type:\n - **Original**: Historical `avg_price` values with label `is_predicted = 'original'`\n - **Regression**: Fitted values from the linear model for historical dates with label `is_predicted = 'regression'`\n - **Forecasting**: Predicted prices for the next **5 years** (260 weekly periods) beyond the latest historical date with label `is_predicted = 'forecasting'`\n\n5. **Combine and format** all series into a single dataset sorted by `fuel` and `date`, with dates converted back to string format.", "concepts": [{"explanation": "This analysis uses **linear regression modeling** to capture long-term price trends and forecast future gas prices. The model treats time as a continuous predictor variable (days_since_start) to predict average price. Each fuel type is modeled independently to account for different pricing dynamics. The approach assumes a linear relationship between time and price, which captures overall trend but may not account for cyclical patterns, seasonality, or market shocks. **Alternative modeling approaches** could include: (1) ARIMA/SARIMA models to capture autocorrelation and seasonality, (2) exponential smoothing methods (Holt-Winters) for trend and seasonal components, (3) polynomial regression for non-linear trends, or (4) more advanced machine learning methods like LSTM neural networks for complex temporal patterns.", "field": "Statistical Analysis"}], "dialog": [{"content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n historical_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n historical_df = historical_df.rename(columns={'price': 'avg_price'})\n historical_df = historical_df.sort_values('date')\n \n # Convert date to datetime for easier manipulation\n historical_df['date'] = pd.to_datetime(historical_df['date'])\n \n # Prepare the result dataframe\n all_data = []\n \n # Process each fuel type separately\n for fuel_type in historical_df['fuel'].unique():\n fuel_data = historical_df[historical_df['fuel'] == fuel_type].copy()\n \n # Create numeric representation of dates for regression (days since first date)\n min_date = fuel_data['date'].min()\n fuel_data['days_since_start'] = (fuel_data['date'] - min_date).dt.days\n \n # Prepare training data\n X = fuel_data[['days_since_start']].values\n y = fuel_data['avg_price'].values\n \n # Train linear regression model\n model = LinearRegression()\n model.fit(X, y)\n \n # Add original data with 'original' label\n fuel_data_original = fuel_data[['date', 'avg_price']].copy()\n fuel_data_original['fuel'] = fuel_type\n fuel_data_original['is_predicted'] = 'original'\n all_data.append(fuel_data_original[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate regression values for historical dates\n regression_prices = model.predict(X)\n fuel_data_regression = fuel_data[['date']].copy()\n fuel_data_regression['fuel'] = fuel_type\n fuel_data_regression['avg_price'] = regression_prices\n fuel_data_regression['is_predicted'] = 'regression'\n all_data.append(fuel_data_regression[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate forecast for next 5 years\n max_date = fuel_data['date'].max()\n future_dates = pd.date_range(start=max_date + pd.Timedelta(days=7), periods=260, freq='W')\n future_days = (future_dates - min_date).days.values.reshape(-1, 1)\n \n future_prices = model.predict(future_days)\n \n forecast_df = pd.DataFrame({\n 'date': future_dates,\n 'fuel': fuel_type,\n 'avg_price': future_prices,\n 'is_predicted': 'forecasting'\n })\n all_data.append(forecast_df)\n \n # Combine all data\n transformed_df = pd.concat(all_data, ignore_index=True)\n transformed_df = transformed_df.sort_values(['fuel', 'date'])\n \n # Convert date back to string format for output\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n return transformed_df\n\n\n[EXPLANATION]\n", "role": "user"}, {"content": "[CODE EXPLANATION]\n\nThis code performs **time series analysis and forecasting** on historical gas prices:\n\n1. **Aggregate historical prices** by calculating the mean `price` across all `grade` and `formulation` values for each `date` and `fuel` type combination, creating an `avg_price` metric.\n\n2. **Prepare temporal features** by converting `date` to datetime format and calculating `days_since_start` (the number of days elapsed since the earliest date) for each `fuel` type separately.\n\n3. **Train linear regression models** for each `fuel` type (diesel, gasoline) using `days_since_start` as the predictor variable and `avg_price` as the target variable.\n\n4. **Generate three data series** for each fuel type:\n - **Original**: Historical `avg_price` values with label `is_predicted = 'original'`\n - **Regression**: Fitted values from the linear model for historical dates with label `is_predicted = 'regression'`\n - **Forecasting**: Predicted prices for the next **5 years** (260 weekly periods) beyond the latest historical date with label `is_predicted = 'forecasting'`\n\n5. **Combine and format** all series into a single dataset sorted by `fuel` and `date`, with dates converted back to string format.\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"Statistical Analysis\",\n \"explanation\": \"This analysis uses **linear regression modeling** to capture long-term price trends and forecast future gas prices. The model treats time as a continuous predictor variable (days_since_start) to predict average price. Each fuel type is modeled independently to account for different pricing dynamics. The approach assumes a linear relationship between time and price, which captures overall trend but may not account for cyclical patterns, seasonality, or market shocks. **Alternative modeling approaches** could include: (1) ARIMA/SARIMA models to capture autocorrelation and seasonality, (2) exponential smoothing methods (Holt-Winters) for trend and seasonal components, (3) polynomial regression for non-linear trends, or (4) more advanced machine learning methods like LSTM neural networks for complex temporal patterns.\"\n }\n]\n```", "role": "assistant"}], "status": "ok"}, "status": "completed"}, "anchored": false, "attachedMetadata": ""}, {"kind": "table", "id": "table-22", "displayId": "fuel-price-forecast1", "names": ["avg_price", "date", "fuel", "is_predicted"], "rows": [{"avg_price": 1.106, "date": "1994-03-21", "fuel": "diesel", "is_predicted": "original"}, {"avg_price": 0.5951388141, "date": "1994-03-21", "fuel": "diesel", "is_predicted": "regression"}, {"avg_price": 1.107, 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"2029-12-30", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 3.8688514574, "date": "2030-01-06", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 3.8695299101, "date": "2030-01-13", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 3.8702074863, "date": "2030-01-20", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 3.8708841859, "date": "2030-01-27", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 3.871560009, "date": "2030-02-03", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 3.8722349555, "date": "2030-02-10", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 3.8729090254, "date": "2030-02-17", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 3.8735822188, "date": "2030-02-24", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 3.8742545356, "date": "2030-03-03", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 3.8749259759, "date": "2030-03-10", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 3.8755965396, "date": "2030-03-17", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 3.8762662268, "date": "2030-03-24", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 3.8769350374, "date": "2030-03-31", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 3.8776029714, "date": "2030-04-07", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 3.8782700289, "date": "2030-04-14", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 3.8789362098, "date": "2030-04-21", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 3.8796015142, "date": "2030-04-28", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 3.880265942, "date": "2030-05-05", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 3.8809294933, "date": "2030-05-12", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 3.881592168, "date": "2030-05-19", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 3.8822539662, "date": "2030-05-26", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 3.8829148877, "date": "2030-06-02", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 3.8835749328, "date": "2030-06-09", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 3.8842341013, "date": "2030-06-16", "fuel": "gasoline", "is_predicted": "forecasting"}, {"avg_price": 3.8848923932, "date": "2030-06-23", "fuel": "gasoline", "is_predicted": "forecasting"}], "metadata": {"avg_price": {"type": "number", "semanticType": "Number"}, "date": {"type": "date", "semanticType": "Date"}, "fuel": {"type": "string", "semanticType": "String"}, "is_predicted": {"type": "string", "semanticType": "String", "levels": ["original", "regression", "forecasting"]}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.pipeline import make_pipeline\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n historical_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n historical_df = historical_df.rename(columns={'price': 'avg_price'})\n historical_df = historical_df.sort_values('date')\n \n # Convert date to datetime for easier manipulation\n historical_df['date'] = pd.to_datetime(historical_df['date'])\n \n # Prepare the result dataframe\n all_data = []\n \n # Process each fuel type separately\n for fuel_type in historical_df['fuel'].unique():\n fuel_data = historical_df[historical_df['fuel'] == fuel_type].copy()\n \n # Create numeric representation of dates for regression (days since first date)\n min_date = fuel_data['date'].min()\n fuel_data['days_since_start'] = (fuel_data['date'] - min_date).dt.days\n \n # Prepare training data\n X = fuel_data[['days_since_start']].values\n y = fuel_data['avg_price'].values\n \n # Train polynomial regression model (degree 2 for non-linear trends)\n model = make_pipeline(PolynomialFeatures(degree=2), LinearRegression())\n model.fit(X, y)\n \n # Add original data with 'original' label\n fuel_data_original = fuel_data[['date', 'avg_price']].copy()\n fuel_data_original['fuel'] = fuel_type\n fuel_data_original['is_predicted'] = 'original'\n all_data.append(fuel_data_original[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate regression values for historical dates\n regression_prices = model.predict(X)\n fuel_data_regression = fuel_data[['date']].copy()\n fuel_data_regression['fuel'] = fuel_type\n fuel_data_regression['avg_price'] = regression_prices\n fuel_data_regression['is_predicted'] = 'regression'\n all_data.append(fuel_data_regression[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate forecast for next 5 years\n max_date = fuel_data['date'].max()\n future_dates = pd.date_range(start=max_date + pd.Timedelta(days=7), periods=260, freq='W')\n future_days = (future_dates - min_date).days.values.reshape(-1, 1)\n \n future_prices = model.predict(future_days)\n \n forecast_df = pd.DataFrame({\n 'date': future_dates,\n 'fuel': fuel_type,\n 'avg_price': future_prices,\n 'is_predicted': 'forecasting'\n })\n all_data.append(forecast_df)\n \n # Combine all data\n transformed_df = pd.concat(all_data, ignore_index=True)\n transformed_df = transformed_df.sort_values(['fuel', 'date'])\n \n # Convert date back to string format for output\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n return transformed_df\n", "source": ["weekly_gas_prices"], "dialog": [{"content": "You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[GOAL]\n\nWhat are the major **price trends** across all fuel types from 1990 to 2025?", "role": "user"}, {"content": "```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to visualize the major price trends across all fuel types from 1990 to 2025 to understand how fuel prices have evolved over time.\",\n \"display_instruction\": \"Show **price** trends over time for different **fuel** types\",\n \"recommendation\": \"To visualize price trends over time across fuel types, we should aggregate the data by date and fuel type. Since there are multiple grades and formulations for each fuel type, we'll calculate the average price for each fuel type on each date. A line chart with date on the x-axis, average price on the y-axis, and different colored lines for each fuel type will clearly show the trends and allow easy comparison.\",\n \"output_fields\": [\"date\", \"fuel\", \"avg_price\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n transformed_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n \n # Rename the price column to avg_price for clarity\n transformed_df = transformed_df.rename(columns={'price': 'avg_price'})\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values('date')\n \n return transformed_df\n```", "role": "assistant"}, {"content": "This is the result from the latest python code:\n\n date fuel avg_price\n0 1990-08-20 gasoline 1.191\n1 1990-08-27 gasoline 1.245\n2 1990-09-03 gasoline 1.242\n3 1990-09-10 gasoline 1.252\n4 1990-09-17 gasoline 1.266\n5 1990-09-24 gasoline 1.272\n6 1990-10-01 gasoline 1.321\n7 1990-10-08 gasoline 1.333\n8 1990-10-15 gasoline 1.339\n9 1990-10-22 gasoline 1.345\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"forecast the price trend for the next 5 years with some propert model!\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"column\": \"fuel\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Build upon the previous price trends visualization by adding forecasted prices for the next 5 years (2025-2030) using a time series forecasting model. The output should include both historical actual prices and forecasted prices, with an indicator to distinguish between them.\",\n \"display_instruction\": \"Forecast **price** trends for the next 5 years for each **fuel** type\",\n \"output_fields\": [\"date\", \"fuel\", \"avg_price\", \"is_predicted\"],\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"fuel\",\n \"facet\": \"is_predicted\"\n },\n \"reason\": \"The user wants to extend the price trend analysis with forecasting for the next 5 years. Following the guidelines for forecasting, the output should include a 'is_predicted' column to distinguish between actual historical data, regression on historical data, and forecasted future values. The chart should be a line chart showing both historical and predicted values for each fuel type.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n historical_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n historical_df = historical_df.rename(columns={'price': 'avg_price'})\n historical_df = historical_df.sort_values('date')\n \n # Convert date to datetime for easier manipulation\n historical_df['date'] = pd.to_datetime(historical_df['date'])\n \n # Prepare the result dataframe\n all_data = []\n \n # Process each fuel type separately\n for fuel_type in historical_df['fuel'].unique():\n fuel_data = historical_df[historical_df['fuel'] == fuel_type].copy()\n \n # Create numeric representation of dates for regression (days since first date)\n min_date = fuel_data['date'].min()\n fuel_data['days_since_start'] = (fuel_data['date'] - min_date).dt.days\n \n # Prepare training data\n X = fuel_data[['days_since_start']].values\n y = fuel_data['avg_price'].values\n \n # Train linear regression model\n model = LinearRegression()\n model.fit(X, y)\n \n # Add original data with 'original' label\n fuel_data_original = fuel_data[['date', 'avg_price']].copy()\n fuel_data_original['fuel'] = fuel_type\n fuel_data_original['is_predicted'] = 'original'\n all_data.append(fuel_data_original[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate regression values for historical dates\n regression_prices = model.predict(X)\n fuel_data_regression = fuel_data[['date']].copy()\n fuel_data_regression['fuel'] = fuel_type\n fuel_data_regression['avg_price'] = regression_prices\n fuel_data_regression['is_predicted'] = 'regression'\n all_data.append(fuel_data_regression[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate forecast for next 5 years\n max_date = fuel_data['date'].max()\n future_dates = pd.date_range(start=max_date + pd.Timedelta(days=7), periods=260, freq='W')\n future_days = (future_dates - min_date).days.values.reshape(-1, 1)\n \n future_prices = model.predict(future_days)\n \n forecast_df = pd.DataFrame({\n 'date': future_dates,\n 'fuel': fuel_type,\n 'avg_price': future_prices,\n 'is_predicted': 'forecasting'\n })\n all_data.append(forecast_df)\n \n # Combine all data\n transformed_df = pd.concat(all_data, ignore_index=True)\n transformed_df = transformed_df.sort_values(['fuel', 'date'])\n \n # Convert date back to string format for output\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n return transformed_df\n```", "role": "assistant"}, {"content": "This is the result from the latest python code:\n\n avg_price date fuel is_predicted\n0 1.106000 1994-03-21 diesel original\n1 1.029221 1994-03-21 diesel regression\n2 1.107000 1994-03-28 diesel original\n3 1.031148 1994-03-28 diesel regression\n4 1.109000 1994-04-04 diesel original\n5 1.033075 1994-04-04 diesel regression\n6 1.108000 1994-04-11 diesel original\n7 1.035002 1994-04-11 diesel regression\n8 1.105000 1994-04-18 diesel original\n9 1.036929 1994-04-18 diesel regression\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"use some more complex model\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"is_predicted\",\n \"column\": \"fuel\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Improve the forecasting model by using a more sophisticated time series model (polynomial regression with degree 2) instead of simple linear regression to better capture non-linear trends in fuel price data. The output should still include historical actual prices, regression fitted values, and forecasted prices for the next 5 years.\",\n \"display_instruction\": \"Forecast **price** trends using a more complex model for each **fuel** type\",\n \"output_fields\": [\"date\", \"fuel\", \"avg_price\", \"is_predicted\"],\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"avg_price\",\n \"color\": \"is_predicted\",\n \"facet\": \"fuel\"\n },\n \"reason\": \"The user wants to use a more complex forecasting model to potentially capture non-linear trends better. We'll use polynomial regression (degree 2) which can model curved trends while still being interpretable. The chart encodings are updated to color by prediction type and facet by fuel type as specified by the user.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.pipeline import make_pipeline\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n historical_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n historical_df = historical_df.rename(columns={'price': 'avg_price'})\n historical_df = historical_df.sort_values('date')\n \n # Convert date to datetime for easier manipulation\n historical_df['date'] = pd.to_datetime(historical_df['date'])\n \n # Prepare the result dataframe\n all_data = []\n \n # Process each fuel type separately\n for fuel_type in historical_df['fuel'].unique():\n fuel_data = historical_df[historical_df['fuel'] == fuel_type].copy()\n \n # Create numeric representation of dates for regression (days since first date)\n min_date = fuel_data['date'].min()\n fuel_data['days_since_start'] = (fuel_data['date'] - min_date).dt.days\n \n # Prepare training data\n X = fuel_data[['days_since_start']].values\n y = fuel_data['avg_price'].values\n \n # Train polynomial regression model (degree 2 for non-linear trends)\n model = make_pipeline(PolynomialFeatures(degree=2), LinearRegression())\n model.fit(X, y)\n \n # Add original data with 'original' label\n fuel_data_original = fuel_data[['date', 'avg_price']].copy()\n fuel_data_original['fuel'] = fuel_type\n fuel_data_original['is_predicted'] = 'original'\n all_data.append(fuel_data_original[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate regression values for historical dates\n regression_prices = model.predict(X)\n fuel_data_regression = fuel_data[['date']].copy()\n fuel_data_regression['fuel'] = fuel_type\n fuel_data_regression['avg_price'] = regression_prices\n fuel_data_regression['is_predicted'] = 'regression'\n all_data.append(fuel_data_regression[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate forecast for next 5 years\n max_date = fuel_data['date'].max()\n future_dates = pd.date_range(start=max_date + pd.Timedelta(days=7), periods=260, freq='W')\n future_days = (future_dates - min_date).days.values.reshape(-1, 1)\n \n future_prices = model.predict(future_days)\n \n forecast_df = pd.DataFrame({\n 'date': future_dates,\n 'fuel': fuel_type,\n 'avg_price': future_prices,\n 'is_predicted': 'forecasting'\n })\n all_data.append(forecast_df)\n \n # Combine all data\n transformed_df = pd.concat(all_data, ignore_index=True)\n transformed_df = transformed_df.sort_values(['fuel', 'date'])\n \n # Convert date back to string format for output\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n return transformed_df\n```", "role": "assistant"}], "trigger": {"tableId": "table-34", "resultTableId": "table-22", "chart": {"id": "chart-1760745227478", "chartType": "Line Chart", "encodingMap": {"x": {"fieldID": "original--weekly_gas_prices--date"}, "y": {"fieldID": "concept-avg_price-1760744859365"}, "color": {"fieldID": "concept-is_predicted-1760745153175", "sortBy": "[\"original\",\"regression\",\"forecasting\"]"}, "opacity": {"channel": "opacity", "bin": false}, "column": {"fieldID": "original--weekly_gas_prices--fuel"}, "row": {"channel": "row", "bin": false}}, "tableRef": "table-34", "saved": false, "source": "trigger", "unread": false}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "use some more complex model", "displayContent": "Forecast **price** trends using a more complex model for each **fuel** type"}]}, "explanation": {"agent": "CodeExplanationAgent", "code": "This code performs **price trend analysis and forecasting** for different fuel types:\n\n1. **Aggregates** historical gas prices by calculating the **mean price** across all **grades** and **formulations** for each **date** and **fuel** type\n2. **Prepares** time series data by converting **dates** to datetime format and creating a **days_since_start** numeric variable (days elapsed since the first recorded date)\n3. **Trains** a **polynomial regression model** (degree 2) separately for each **fuel type** to capture non-linear price trends over time\n4. **Generates** three types of price records for each fuel:\n - **Original**: actual historical average prices with label `'original'`\n - **Regression**: model-fitted prices for historical dates with label `'regression'`\n - **Forecasting**: predicted prices for the **next 5 years** (260 weekly periods) with label `'forecasting'`\n5. **Combines** all records and sorts by **fuel** type and **date**", "concepts": [{"explanation": "A numeric time variable representing the number of days elapsed since the first recorded date in the dataset. This transforms calendar dates into a continuous numeric scale suitable for regression modeling: \\( \\text{days\\_since\\_start} = (\\text{current\\_date} - \\text{min\\_date}) \\)", "field": "days_since_start"}, {"explanation": "A categorical label indicating the source and nature of the price data: 'original' for actual historical observations, 'regression' for model-fitted values on historical dates (representing the polynomial trend), and 'forecasting' for future price predictions beyond the observed data range.", "field": "is_predicted"}, {"explanation": "This analysis models fuel price trends using **polynomial regression (degree 2)** to capture non-linear patterns in price evolution over time. The model treats time (days since start) as the independent variable and average price as the dependent variable, fitted separately for each fuel type. The quadratic polynomial allows the model to capture acceleration or deceleration in price trends (e.g., prices rising faster over time or stabilizing). Alternative approaches could include: **ARIMA models** for capturing autocorrelation and seasonality, **exponential smoothing** for weighted recent observations, **Prophet** for handling multiple seasonality patterns and holidays, or **LSTM neural networks** for complex non-linear temporal dependencies.", "field": "Statistical Analysis"}], "dialog": [{"content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (weekly_gas_prices)\n\n## fields\n\t*date -- type: object, values: 1990-08-20, 1990-08-27, 1990-09-03, ..., 2025-06-02, 2025-06-09, 2025-06-16, 2025-06-23\n\t*fuel -- type: object, values: diesel, gasoline\n\t*grade -- type: object, values: all, low_sulfur, midgrade, premium, regular, ultra_low_sulfur\n\t*formulation -- type: object, values: NA, all, conventional, reformulated\n\t*price -- type: float64, values: 0.885, 0.891, 0.899, ..., 5.955, 5.964, 6.033, 6.064\n\n## sample\n date fuel grade formulation price\n0 1990-08-20 gasoline regular all 1.191\n1 1990-08-20 gasoline regular conventional 1.191\n2 1990-08-27 gasoline regular all 1.245\n3 1990-08-27 gasoline regular conventional 1.245\n4 1990-09-03 gasoline regular all 1.242\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.pipeline import make_pipeline\n\ndef transform_data(df_gas_prices):\n # Group by date and fuel to get average price across all grades and formulations\n historical_df = df_gas_prices.groupby(['date', 'fuel'], as_index=False).agg({\n 'price': 'mean'\n })\n historical_df = historical_df.rename(columns={'price': 'avg_price'})\n historical_df = historical_df.sort_values('date')\n \n # Convert date to datetime for easier manipulation\n historical_df['date'] = pd.to_datetime(historical_df['date'])\n \n # Prepare the result dataframe\n all_data = []\n \n # Process each fuel type separately\n for fuel_type in historical_df['fuel'].unique():\n fuel_data = historical_df[historical_df['fuel'] == fuel_type].copy()\n \n # Create numeric representation of dates for regression (days since first date)\n min_date = fuel_data['date'].min()\n fuel_data['days_since_start'] = (fuel_data['date'] - min_date).dt.days\n \n # Prepare training data\n X = fuel_data[['days_since_start']].values\n y = fuel_data['avg_price'].values\n \n # Train polynomial regression model (degree 2 for non-linear trends)\n model = make_pipeline(PolynomialFeatures(degree=2), LinearRegression())\n model.fit(X, y)\n \n # Add original data with 'original' label\n fuel_data_original = fuel_data[['date', 'avg_price']].copy()\n fuel_data_original['fuel'] = fuel_type\n fuel_data_original['is_predicted'] = 'original'\n all_data.append(fuel_data_original[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate regression values for historical dates\n regression_prices = model.predict(X)\n fuel_data_regression = fuel_data[['date']].copy()\n fuel_data_regression['fuel'] = fuel_type\n fuel_data_regression['avg_price'] = regression_prices\n fuel_data_regression['is_predicted'] = 'regression'\n all_data.append(fuel_data_regression[['date', 'fuel', 'avg_price', 'is_predicted']])\n \n # Generate forecast for next 5 years\n max_date = fuel_data['date'].max()\n future_dates = pd.date_range(start=max_date + pd.Timedelta(days=7), periods=260, freq='W')\n future_days = (future_dates - min_date).days.values.reshape(-1, 1)\n \n future_prices = model.predict(future_days)\n \n forecast_df = pd.DataFrame({\n 'date': future_dates,\n 'fuel': fuel_type,\n 'avg_price': future_prices,\n 'is_predicted': 'forecasting'\n })\n all_data.append(forecast_df)\n \n # Combine all data\n transformed_df = pd.concat(all_data, ignore_index=True)\n transformed_df = transformed_df.sort_values(['fuel', 'date'])\n \n # Convert date back to string format for output\n transformed_df['date'] = transformed_df['date'].dt.strftime('%Y-%m-%d')\n \n return transformed_df\n\n\n[EXPLANATION]\n", "role": "user"}, {"content": "[CODE EXPLANATION]\n\nThis code performs **price trend analysis and forecasting** for different fuel types:\n\n1. **Aggregates** historical gas prices by calculating the **mean price** across all **grades** and **formulations** for each **date** and **fuel** type\n2. **Prepares** time series data by converting **dates** to datetime format and creating a **days_since_start** numeric variable (days elapsed since the first recorded date)\n3. **Trains** a **polynomial regression model** (degree 2) separately for each **fuel type** to capture non-linear price trends over time\n4. **Generates** three types of price records for each fuel:\n - **Original**: actual historical average prices with label `'original'`\n - **Regression**: model-fitted prices for historical dates with label `'regression'`\n - **Forecasting**: predicted prices for the **next 5 years** (260 weekly periods) with label `'forecasting'`\n5. **Combines** all records and sorts by **fuel** type and **date**\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"days_since_start\",\n \"explanation\": \"A numeric time variable representing the number of days elapsed since the first recorded date in the dataset. This transforms calendar dates into a continuous numeric scale suitable for regression modeling: \\\\( \\\\text{days\\\\_since\\\\_start} = (\\\\text{current\\\\_date} - \\\\text{min\\\\_date}) \\\\)\"\n },\n {\n \"field\": \"is_predicted\",\n \"explanation\": \"A categorical label indicating the source and nature of the price data: 'original' for actual historical observations, 'regression' for model-fitted values on historical dates (representing the polynomial trend), and 'forecasting' for future price predictions beyond the observed data range.\"\n },\n {\n \"field\": \"Statistical Analysis\",\n \"explanation\": \"This analysis models fuel price trends using **polynomial regression (degree 2)** to capture non-linear patterns in price evolution over time. The model treats time (days since start) as the independent variable and average price as the dependent variable, fitted separately for each fuel type. The quadratic polynomial allows the model to capture acceleration or deceleration in price trends (e.g., prices rising faster over time or stabilizing). Alternative approaches could include: **ARIMA models** for capturing autocorrelation and seasonality, **exponential smoothing** for weighted recent observations, **Prophet** for handling multiple seasonality patterns and holidays, or **LSTM neural networks** for complex non-linear temporal dependencies.\"\n }\n]\n```", "role": "assistant"}], "status": "ok"}, "status": "completed"}, "anchored": false, "attachedMetadata": ""}], "charts": [{"id": "chart-1760745227133", "chartType": "Line Chart", "encodingMap": {"x": {"fieldID": "original--weekly_gas_prices--date"}, "y": {"fieldID": "concept-avg_price-1760744859365"}, "color": {"fieldID": "concept-is_predicted-1760745153175", "sortBy": "[\"original\",\"regression\",\"forecasting\"]"}, "opacity": {"channel": "opacity", "bin": false}, "column": {"fieldID": "original--weekly_gas_prices--fuel"}, "row": {"channel": "row", "bin": false}}, "tableRef": "table-22", "saved": false, "source": "user", "unread": false}, {"id": "chart-1760745149814", "chartType": "Line Chart", "encodingMap": {"x": {"fieldID": "original--weekly_gas_prices--date"}, "y": {"fieldID": "concept-avg_price-1760744859365"}, "color": {"fieldID": "concept-is_predicted-1760745153175", "sortBy": "[\"original\",\"regression\",\"forecasting\"]"}, "opacity": {"channel": "opacity", "bin": false}, "column": {"fieldID": "original--weekly_gas_prices--fuel"}, "row": {"channel": "row", "bin": false}}, "tableRef": "table-34", "saved": false, "source": "user", "unread": false}, {"id": "chart-1760744944490", "chartType": "Grouped Bar Chart", "encodingMap": {"x": {"fieldID": "original--weekly_gas_prices--fuel"}, "y": {"fieldID": "concept-coefficient_of_variation-1760744946001-0.1428610684738313"}, "column": {"channel": "column", "bin": false}, "row": {"channel": "row", "bin": false}, "group": {"fieldID": "concept-period-1760744946001-0.2526720867029991", "sortBy": "[\"Pre-2000 (1990-1999)\",\"Post-2000 (2000-2025)\"]"}}, "tableRef": "table-937785", "saved": false, "source": 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Diesel prices showed particularly heightened instability with a coefficient of variation of 33.48% (2000-2025) compared to just 7.65% in the 1990s. This shift reflects increased sensitivity to global crude oil market fluctuations, geopolitical tensions, and refining capacity constraints. Average prices also more than doubled across both fuel types, with diesel climbing from $1.14 to $2.93 per gallon.\n\n[IMAGE(chart-1760744910486)]\n\n**Premium gasoline pricing has steadily diverged from regular grades over time.** The price premium commanded by higher-octane gasoline grades has expanded substantially, with premium gasoline reaching spreads of **$0.90-$0.95 above regular** by 2025—nearly five times the $0.18-$0.20 premium observed in the mid-1990s. Midgrade gasoline follows a similar trajectory, reaching $0.55-$0.60 premiums. This widening gap suggests evolving refining economics, increased consumer segmentation, and potentially higher margins on premium products.\n\n**In summary**, the fuel market has transitioned from relative price stability in the 1990s to an era characterized by substantial volatility and expanding price differentiation. Consumers and businesses face both higher absolute costs and greater uncertainty in budgeting for fuel expenses. Key questions for further investigation include: What specific market mechanisms drive the premium expansion? How do regional variations affect these national trends? What role do alternative fuels play in moderating future volatility?", "style": "executive summary", "selectedChartIds": ["chart-1760744944490", "chart-1760744910486"], "createdAt": 1760831035718, "status": "completed", "title": "Fuel Price Volatility and Premium Trends: A Three-Decade Analysis", "anchorChartId": "chart-1760744944490"}, {"id": "report-1760830985775-9885", "content": "# Fuel Prices Set to Continue Rising Through 2030\n\nHistorical data reveals a clear upward trend in both diesel and gasoline prices since the 1990s. Our polynomial regression model shows prices have increased nearly sixfold over three decades, with notable volatility around 2008 and 2022 reflecting global economic shocks.\n\n[IMAGE(chart-1760745227133)]\n\nThe forecast suggests this upward trajectory will persist, with both fuel types projected to reach approximately $3.80-$3.90 per gallon by 2030. While short-term fluctuations are inevitable, the long-term trend remains consistently upward.\n\n**In summary**, fuel prices have demonstrated sustained growth over 35 years and are expected to continue rising. Consumers and businesses should anticipate higher energy costs in coming years and consider efficiency improvements or alternative energy sources.", "style": "short note", "selectedChartIds": ["chart-1760745227133"], "createdAt": 1760830993075, "status": "completed", "title": "Fuel Prices Set to Continue Rising Through 2030", "anchorChartId": "chart-1760745227133"}], "currentReport": {"id": "report-1760750575650-2619", "content": "# Hollywood's Billion-Dollar Hitmakers\n\n*Avatar* stands alone—earning over $2.5B in profit, dwarfing all competition. Action and Adventure films dominate the most profitable titles, with franchises like *Jurassic Park*, *The Dark Knight*, and *Lord of the Rings* proving blockbuster formulas work.\n\n\"Chart\"\n\nSteven Spielberg leads all directors with $7.2B in total profit across his career, showcasing remarkable consistency with hits spanning decades—from *Jurassic Park* to *E.T.* His nearest competitors trail by billions, underlining his unmatched commercial impact.\n\n\"Chart\"\n\n**In summary**, mega-budget Action and Adventure films generate extraordinary returns when they succeed, and a handful of elite directors—led by Spielberg—have mastered the formula for sustained box office dominance.", "style": "short note", "selectedChartIds": ["chart-1760743347871", "chart-1760743768741"], "chartImages": {}, "createdAt": 1760750584189, "title": "Report - 10/17/2025"}, "activeChallenges": [], "_persist": {"version": -1, "rehydrated": true}, "draftNodes": [], "focusedId": {"type": "report", "reportId": 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Africa","Year":2020,"energy":12.83,"source":"renewables"},{"Entity":"Spain","Year":2000,"energy":34.49,"source":"renewables"},{"Entity":"Spain","Year":2001,"energy":49.3,"source":"renewables"},{"Entity":"Spain","Year":2002,"energy":33.17,"source":"renewables"},{"Entity":"Spain","Year":2003,"energy":55.75,"source":"renewables"},{"Entity":"Spain","Year":2004,"energy":50.13,"source":"renewables"},{"Entity":"Spain","Year":2005,"energy":42.27,"source":"renewables"},{"Entity":"Spain","Year":2006,"energy":52.15,"source":"renewables"},{"Entity":"Spain","Year":2007,"energy":58.3,"source":"renewables"},{"Entity":"Spain","Year":2008,"energy":62.15,"source":"renewables"},{"Entity":"Spain","Year":2009,"energy":74.08,"source":"renewables"},{"Entity":"Spain","Year":2010,"energy":97.77,"source":"renewables"},{"Entity":"Spain","Year":2011,"energy":87.53,"source":"renewables"},{"Entity":"Spain","Year":2012,"energy":86.97,"source":"renewables"},{"Entity":"Spain","Year":2013,"energy":111.42,"source":"renewables"},{"Entity":"Spain","Year":2014,"energy":110.26,"source":"renewables"},{"Entity":"Spain","Year":2015,"energy":97.09,"source":"renewables"},{"Entity":"Spain","Year":2016,"energy":104.63,"source":"renewables"},{"Entity":"Spain","Year":2017,"energy":87.93,"source":"renewables"},{"Entity":"Spain","Year":2018,"energy":103.88,"source":"renewables"},{"Entity":"Spain","Year":2019,"energy":100.99,"source":"renewables"},{"Entity":"Spain","Year":2020,"energy":113.79,"source":"renewables"},{"Entity":"Thailand","Year":2000,"energy":6.38,"source":"renewables"},{"Entity":"Thailand","Year":2001,"energy":6.76,"source":"renewables"},{"Entity":"Thailand","Year":2002,"energy":8.07,"source":"renewables"},{"Entity":"Thailand","Year":2003,"energy":8.36,"source":"renewables"},{"Entity":"Thailand","Year":2004,"energy":7.63,"source":"renewables"},{"Entity":"Thailand","Year":2005,"energy":7.42,"source":"renewables"},{"Entity":"Thailand","Year":2006,"energy":9.82,"source":"renewables"},{"Entity":"Thailand","Year":2007,"energy":10.2,"source":"renewables"},{"Entity":"Thailand","Year":2008,"energy":8.95,"source":"renewables"},{"Entity":"Thailand","Year":2009,"energy":9.09,"source":"renewables"},{"Entity":"Thailand","Year":2010,"energy":8.58,"source":"renewables"},{"Entity":"Thailand","Year":2011,"energy":11.83,"source":"renewables"},{"Entity":"Thailand","Year":2012,"energy":13.42,"source":"renewables"},{"Entity":"Thailand","Year":2013,"energy":12.33,"source":"renewables"},{"Entity":"Thailand","Year":2014,"energy":13.68,"source":"renewables"},{"Entity":"Thailand","Year":2015,"energy":13.33,"source":"renewables"},{"Entity":"Thailand","Year":2016,"energy":15.97,"source":"renewables"},{"Entity":"Thailand","Year":2017,"energy":19.92,"source":"renewables"},{"Entity":"Thailand","Year":2018,"energy":25.84,"source":"renewables"},{"Entity":"Thailand","Year":2019,"energy":28.02,"source":"renewables"},{"Entity":"Thailand","Year":2020,"energy":24.73,"source":"renewables"},{"Entity":"Ukraine","Year":2000,"energy":11.28,"source":"renewables"},{"Entity":"Ukraine","Year":2001,"energy":12.05,"source":"renewables"},{"Entity":"Ukraine","Year":2002,"energy":9.65,"source":"renewables"},{"Entity":"Ukraine","Year":2003,"energy":9.27,"source":"renewables"},{"Entity":"Ukraine","Year":2004,"energy":11.78,"source":"renewables"},{"Entity":"Ukraine","Year":2005,"energy":12.4,"source":"renewables"},{"Entity":"Ukraine","Year":2006,"energy":12.92,"source":"renewables"},{"Entity":"Ukraine","Year":2007,"energy":10.47,"source":"renewables"},{"Entity":"Ukraine","Year":2008,"energy":11.82,"source":"renewables"},{"Entity":"Ukraine","Year":2009,"energy":12.12,"source":"renewables"},{"Entity":"Ukraine","Year":2010,"energy":13.39,"source":"renewables"},{"Entity":"Ukraine","Year":2011,"energy":11.2,"source":"renewables"},{"Entity":"Ukraine","Year":2012,"energy":11.23,"source":"renewables"},{"Entity":"Ukraine","Year":2013,"energy":15.11,"source":"renewables"},{"Entity":"Ukraine","Year":2014,"energy":10.17,"source":"renewables"},{"Entity":"Ukraine","Year":2015,"energy":7.1,"source":"renewables"},{"Entity":"Ukraine","Year":2016,"energy":9.25,"source":"renewables"},{"Entity":"Ukraine","Year":2017,"energy":10.88,"source":"renewables"},{"Entity":"Ukraine","Year":2018,"energy":13.02,"source":"renewables"},{"Entity":"Ukraine","Year":2019,"energy":11.87,"source":"renewables"},{"Entity":"Ukraine","Year":2020,"energy":17.56,"source":"renewables"},{"Entity":"United Kingdom","Year":2000,"energy":9.98,"source":"renewables"},{"Entity":"United Kingdom","Year":2001,"energy":9.56,"source":"renewables"},{"Entity":"United Kingdom","Year":2002,"energy":11.13,"source":"renewables"},{"Entity":"United Kingdom","Year":2003,"energy":10.62,"source":"renewables"},{"Entity":"United Kingdom","Year":2004,"energy":14.14,"source":"renewables"},{"Entity":"United Kingdom","Year":2005,"energy":16.93,"source":"renewables"},{"Entity":"United Kingdom","Year":2006,"energy":18.11,"source":"renewables"},{"Entity":"United Kingdom","Year":2007,"energy":19.69,"source":"renewables"},{"Entity":"United Kingdom","Year":2008,"energy":21.85,"source":"renewables"},{"Entity":"United Kingdom","Year":2009,"energy":25.25,"source":"renewables"},{"Entity":"United Kingdom","Year":2010,"energy":26.18,"source":"renewables"},{"Entity":"United Kingdom","Year":2011,"energy":35.2,"source":"renewables"},{"Entity":"United Kingdom","Year":2012,"energy":41.24,"source":"renewables"},{"Entity":"United Kingdom","Year":2013,"energy":53.21,"source":"renewables"},{"Entity":"United Kingdom","Year":2014,"energy":64.52,"source":"renewables"},{"Entity":"United Kingdom","Year":2015,"energy":82.57,"source":"renewables"},{"Entity":"United Kingdom","Year":2016,"energy":82.99,"source":"renewables"},{"Entity":"United Kingdom","Year":2017,"energy":98.85,"source":"renewables"},{"Entity":"United Kingdom","Year":2018,"energy":110.03,"source":"renewables"},{"Entity":"United Kingdom","Year":2019,"energy":120.48,"source":"renewables"},{"Entity":"United Kingdom","Year":2020,"energy":131.74,"source":"renewables"},{"Entity":"United States","Year":2000,"energy":350.93,"source":"renewables"},{"Entity":"United States","Year":2001,"energy":280.06,"source":"renewables"},{"Entity":"United States","Year":2002,"energy":336.34,"source":"renewables"},{"Entity":"United States","Year":2003,"energy":349.18,"source":"renewables"},{"Entity":"United States","Year":2004,"energy":345.14,"source":"renewables"},{"Entity":"United States","Year":2005,"energy":353.04,"source":"renewables"},{"Entity":"United States","Year":2006,"energy":381.16,"source":"renewables"},{"Entity":"United States","Year":2007,"energy":347.91,"source":"renewables"},{"Entity":"United States","Year":2008,"energy":377.11,"source":"renewables"},{"Entity":"United States","Year":2009,"energy":415.56,"source":"renewables"},{"Entity":"United States","Year":2010,"energy":424.48,"source":"renewables"},{"Entity":"United States","Year":2011,"energy":509.74,"source":"renewables"},{"Entity":"United States","Year":2012,"energy":492.32,"source":"renewables"},{"Entity":"United States","Year":2013,"energy":520.38,"source":"renewables"},{"Entity":"United States","Year":2014,"energy":546.83,"source":"renewables"},{"Entity":"United States","Year":2015,"energy":556.49,"source":"renewables"},{"Entity":"United States","Year":2016,"energy":624.91,"source":"renewables"},{"Entity":"United States","Year":2017,"energy":707.19,"source":"renewables"},{"Entity":"United States","Year":2018,"energy":733.17,"source":"renewables"},{"Entity":"United States","Year":2019,"energy":760.76,"source":"renewables"},{"Entity":"United States","Year":2020,"energy":821.4,"source":"renewables"}],"metadata":{"Entity":{"type":"string","semanticType":"Location"},"Year":{"type":"number","semanticType":"Year"},"energy":{"type":"number","semanticType":"Number"},"source":{"type":"string","semanticType":"String","levels":["fossil fuels","nuclear","renewables"]}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Unpivot the energy source columns into long format\n transformed_df = pd.melt(\n df_energy,\n id_vars=['Year', 'Entity'],\n value_vars=[\n 'Electricity from fossil fuels (TWh)',\n 'Electricity from nuclear (TWh)',\n 'Electricity from renewables (TWh)'\n ],\n var_name='source',\n value_name='energy'\n )\n \n # Clean up the source names to be more readable\n transformed_df['source'] = transformed_df['source'].str.replace('Electricity from ', '').str.replace(' (TWh)', '')\n \n return transformed_df\n","source":["global-energy-20-small.csv"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 158029.9988, 190729.9957, 209229.9957, ..., 6993180.176, 9861099.609, 9874660.156, 10096009.77\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 90.14, 91.79, 92.54, 804.95\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"energy\",\n \"color\": \"Entity\",\n \"column\": \"source\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Transform the data into a long format where different energy sources (fossil fuels, nuclear, renewables) are unpivoted into separate rows. Each row should contain the year, entity (country), energy source type, and the corresponding energy value in TWh. This will enable visualization of energy generation trends over time by source and country.\",\n \"display_instruction\": \"Show **energy generation** trends by **source** and **Entity** over **Year**\",\n \"output_fields\": [\"Year\", \"Entity\", \"source\", \"energy\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"energy\",\n \"color\": \"Entity\",\n \"column\": \"source\"\n },\n \"reason\": \"The user wants to visualize energy data across different sources with a line chart faceted by source. The original data has energy sources in separate columns (fossil fuels, nuclear, renewables), so we need to unpivot these columns into a long format with 'source' and 'energy' fields. The chart_encodings provided by the user are appropriate for this visualization goal, using faceting (column) to separate different energy sources.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Unpivot the energy source columns into long format\n transformed_df = pd.melt(\n df_energy,\n id_vars=['Year', 'Entity'],\n value_vars=[\n 'Electricity from fossil fuels (TWh)',\n 'Electricity from nuclear (TWh)',\n 'Electricity from renewables (TWh)'\n ],\n var_name='source',\n value_name='energy'\n )\n \n # Clean up the source names to be more readable\n transformed_df['source'] = transformed_df['source'].str.replace('Electricity from ', '').str.replace(' (TWh)', '')\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"global-energy-20-small.csv","sourceTableIds":["global-energy-20-small.csv"],"instruction":"","displayInstruction":"Show **energy generation** trends by **source** and **Entity** over **Year**","chart":{"id":"chart-1760738394155","chartType":"Line Chart","encodingMap":{"x":{"fieldID":"original--global-energy-20-small.csv--Year"},"y":{"channel":"y","bin":false,"fieldID":"concept-1760738385163"},"color":{"fieldID":"original--global-energy-20-small.csv--Entity"},"opacity":{"channel":"opacity","bin":false},"column":{"channel":"column","bin":false,"fieldID":"concept-1760738389404"},"row":{"channel":"row","bin":false}},"tableRef":"global-energy-20-small.csv","saved":false,"source":"trigger","unread":false},"resultTableId":"table-82"},"explanation":{"agent":"CodeExplanationAgent","code":"The code performs a data transformation to restructure the energy data from a wide format to a long format:\n\n1. **Unpivot energy columns**: Uses `pd.melt()` to transform the three energy source columns (**Electricity from fossil fuels**, **Electricity from nuclear**, and **Electricity from renewables**) from separate columns into rows, while keeping **Year** and **Entity** as identifier columns.\n\n2. **Create source classification**: Generates a new **source** column that captures which energy type each row represents, and stores the corresponding energy values in an **energy** column.\n\n3. **Clean source labels**: Removes the prefix \"Electricity from \" and suffix \" (TWh)\" from the source names, resulting in cleaner labels: **fossil fuels**, **nuclear**, and **renewables**.\n\n4. **Return restructured data**: The final dataset has each country-year-energy source combination as a separate row, making it easier to analyze and visualize energy mix across countries and time periods.","concepts":[],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small_csv)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 190729.9957, 227580.0018, 233600.0061, ..., 5736319.824, 5738290.039, nan, 9861099.609\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 90.14, 91.79, 92.54, nan\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Unpivot the energy source columns into long format\n transformed_df = pd.melt(\n df_energy,\n id_vars=['Year', 'Entity'],\n value_vars=[\n 'Electricity from fossil fuels (TWh)',\n 'Electricity from nuclear (TWh)',\n 'Electricity from renewables (TWh)'\n ],\n var_name='source',\n value_name='energy'\n )\n \n # Clean up the source names to be more readable\n transformed_df['source'] = transformed_df['source'].str.replace('Electricity from ', '').str.replace(' (TWh)', '')\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThe code performs a data transformation to restructure the energy data from a wide format to a long format:\n\n1. **Unpivot energy columns**: Uses `pd.melt()` to transform the three energy source columns (**Electricity from fossil fuels**, **Electricity from nuclear**, and **Electricity from renewables**) from separate columns into rows, while keeping **Year** and **Entity** as identifier columns.\n\n2. **Create source classification**: Generates a new **source** column that captures which energy type each row represents, and stores the corresponding energy values in an **energy** column.\n\n3. **Clean source labels**: Removes the prefix \"Electricity from \" and suffix \" (TWh)\" from the source names, resulting in cleaner labels: **fossil fuels**, **nuclear**, and **renewables**.\n\n4. **Return restructured data**: The final dataset has each country-year-energy source combination as a separate row, making it easier to analyze and visualize energy mix across countries and time periods.\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-45","displayId":"renewable-energy","names":["Entity","Year","renewable_percentage"],"rows":[{"Entity":"Australia","Year":2000,"renewable_percentage":8.6344368187},{"Entity":"Australia","Year":2001,"renewable_percentage":8.2180135078},{"Entity":"Australia","Year":2002,"renewable_percentage":8.0833022736},{"Entity":"Australia","Year":2003,"renewable_percentage":8.6598324205},{"Entity":"Australia","Year":2004,"renewable_percentage":8.7013045232},{"Entity":"Australia","Year":2005,"renewable_percentage":9.1562355123},{"Entity":"Australia","Year":2006,"renewable_percentage":9.6357600837},{"Entity":"Australia","Year":2007,"renewable_percentage":9.1190310213},{"Entity":"Australia","Year":2008,"renewable_percentage":8.0548900022},{"Entity":"Australia","Year":2009,"renewable_percentage":7.8043793133},{"Entity":"Australia","Year":2010,"renewable_percentage":9.0442152121},{"Entity":"Australia","Year":2011,"renewable_percentage":11.3454273735},{"Entity":"Australia","Year":2012,"renewable_percentage":11.4105750279},{"Entity":"Australia","Year":2013,"renewable_percentage":14.8708583355},{"Entity":"Australia","Year":2014,"renewable_percentage":14.9621290509},{"Entity":"Australia","Year":2015,"renewable_percentage":14.3476000693},{"Entity":"Australia","Year":2016,"renewable_percentage":15.6093794449},{"Entity":"Australia","Year":2017,"renewable_percentage":16.3138729943},{"Entity":"Australia","Year":2018,"renewable_percentage":17.145938174},{"Entity":"Australia","Year":2019,"renewable_percentage":21.3759705435},{"Entity":"Australia","Year":2020,"renewable_percentage":25.5031684668},{"Entity":"Brazil","Year":2000,"renewable_percentage":90.1307723743},{"Entity":"Brazil","Year":2001,"renewable_percentage":84.6953615744},{"Entity":"Brazil","Year":2002,"renewable_percentage":86.0883364189},{"Entity":"Brazil","Year":2003,"renewable_percentage":87.4561159097},{"Entity":"Brazil","Year":2004,"renewable_percentage":86.4260041451},{"Entity":"Brazil","Year":2005,"renewable_percentage":87.6781562721},{"Entity":"Brazil","Year":2006,"renewable_percentage":87.2842473236},{"Entity":"Brazil","Year":2007,"renewable_percentage":88.7252098726},{"Entity":"Brazil","Year":2008,"renewable_percentage":84.8072313004},{"Entity":"Brazil","Year":2009,"renewable_percentage":89.4172280725},{"Entity":"Brazil","Year":2010,"renewable_percentage":85.3576882415},{"Entity":"Brazil","Year":2011,"renewable_percentage":87.6618820986},{"Entity":"Brazil","Year":2012,"renewable_percentage":83.1164558813},{"Entity":"Brazil","Year":2013,"renewable_percentage":77.5240022006},{"Entity":"Brazil","Year":2014,"renewable_percentage":74.0418657409},{"Entity":"Brazil","Year":2015,"renewable_percentage":75.0231817625},{"Entity":"Brazil","Year":2016,"renewable_percentage":81.0938046902},{"Entity":"Brazil","Year":2017,"renewable_percentage":79.9091472228},{"Entity":"Brazil","Year":2018,"renewable_percentage":82.9198505403},{"Entity":"Brazil","Year":2019,"renewable_percentage":82.8548799017},{"Entity":"Brazil","Year":2020,"renewable_percentage":84.6411771408},{"Entity":"Canada","Year":2000,"renewable_percentage":61.8095917882},{"Entity":"Canada","Year":2001,"renewable_percentage":59.3287558747},{"Entity":"Canada","Year":2002,"renewable_percentage":61.1477403113},{"Entity":"Canada","Year":2003,"renewable_percentage":60.0789685174},{"Entity":"Canada","Year":2004,"renewable_percentage":59.6967771845},{"Entity":"Canada","Year":2005,"renewable_percentage":60.8208155391},{"Entity":"Canada","Year":2006,"renewable_percentage":60.8271602855},{"Entity":"Canada","Year":2007,"renewable_percentage":61.2460642446},{"Entity":"Canada","Year":2008,"renewable_percentage":62.6520720838},{"Entity":"Canada","Year":2009,"renewable_percentage":63.8919227732},{"Entity":"Canada","Year":2010,"renewable_percentage":62.9421470558},{"Entity":"Canada","Year":2011,"renewable_percentage":64.0922915917},{"Entity":"Canada","Year":2012,"renewable_percentage":65.098730952},{"Entity":"Canada","Year":2013,"renewable_percentage":65.4320794066},{"Entity":"Canada","Year":2014,"renewable_percentage":64.791145907},{"Entity":"Canada","Year":2015,"renewable_percentage":65.2946239925},{"Entity":"Canada","Year":2016,"renewable_percentage":66.1890584295},{"Entity":"Canada","Year":2017,"renewable_percentage":67.5399410579},{"Entity":"Canada","Year":2018,"renewable_percentage":67.3685700357},{"Entity":"Canada","Year":2019,"renewable_percentage":67.1741623137},{"Entity":"Canada","Year":2020,"renewable_percentage":68.7796436354},{"Entity":"China","Year":2000,"renewable_percentage":16.639126586},{"Entity":"China","Year":2001,"renewable_percentage":18.9581237042},{"Entity":"China","Year":2002,"renewable_percentage":17.6185006046},{"Entity":"China","Year":2003,"renewable_percentage":15.0362717081},{"Entity":"China","Year":2004,"renewable_percentage":16.2224108273},{"Entity":"China","Year":2005,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Africa","Year":2008,"renewable_percentage":0.6895692269},{"Entity":"South Africa","Year":2009,"renewable_percentage":0.8031088083},{"Entity":"South Africa","Year":2010,"renewable_percentage":1.0330068318},{"Entity":"South Africa","Year":2011,"renewable_percentage":1.0184465622},{"Entity":"South Africa","Year":2012,"renewable_percentage":0.6890826069},{"Entity":"South Africa","Year":2013,"renewable_percentage":0.6792168043},{"Entity":"South Africa","Year":2014,"renewable_percentage":1.4288129861},{"Entity":"South Africa","Year":2015,"renewable_percentage":2.6256790549},{"Entity":"South Africa","Year":2016,"renewable_percentage":3.2586126531},{"Entity":"South Africa","Year":2017,"renewable_percentage":4.2202606137},{"Entity":"South Africa","Year":2018,"renewable_percentage":5.1554655529},{"Entity":"South Africa","Year":2019,"renewable_percentage":5.3589699864},{"Entity":"South Africa","Year":2020,"renewable_percentage":5.780581212},{"Entity":"Spain","Year":2000,"renewable_percentage":15.6119862394},{"Entity":"Spain","Year":2001,"renewable_percentage":21.1524434719},{"Entity":"Spain","Year":2002,"renewable_percentage":13.8260180901},{"Entity":"Spain","Year":2003,"renewable_percentage":21.667314419},{"Entity":"Spain","Year":2004,"renewable_percentage":18.3190206468},{"Entity":"Spain","Year":2005,"renewable_percentage":14.8597342333},{"Entity":"Spain","Year":2006,"renewable_percentage":17.6623992413},{"Entity":"Spain","Year":2007,"renewable_percentage":19.3347262296},{"Entity":"Spain","Year":2008,"renewable_percentage":20.0051501593},{"Entity":"Spain","Year":2009,"renewable_percentage":25.4107639008},{"Entity":"Spain","Year":2010,"renewable_percentage":32.7922186819},{"Entity":"Spain","Year":2011,"renewable_percentage":30.0408415417},{"Entity":"Spain","Year":2012,"renewable_percentage":29.6047928652},{"Entity":"Spain","Year":2013,"renewable_percentage":39.5850357054},{"Entity":"Spain","Year":2014,"renewable_percentage":40.1032952644},{"Entity":"Spain","Year":2015,"renewable_percentage":34.9899091826},{"Entity":"Spain","Year":2016,"renewable_percentage":38.5818061138},{"Entity":"Spain","Year":2017,"renewable_percentage":32.220593624},{"Entity":"Spain","Year":2018,"renewable_percentage":38.2080329557},{"Entity":"Spain","Year":2019,"renewable_percentage":37.280815091},{"Entity":"Spain","Year":2020,"renewable_percentage":43.8108805298},{"Entity":"Thailand","Year":2000,"renewable_percentage":7.1261029822},{"Entity":"Thailand","Year":2001,"renewable_percentage":7.061527212},{"Entity":"Thailand","Year":2002,"renewable_percentage":7.9444772593},{"Entity":"Thailand","Year":2003,"renewable_percentage":7.6718362852},{"Entity":"Thailand","Year":2004,"renewable_percentage":6.5163549406},{"Entity":"Thailand","Year":2005,"renewable_percentage":6.0325203252},{"Entity":"Thailand","Year":2006,"renewable_percentage":7.5988547551},{"Entity":"Thailand","Year":2007,"renewable_percentage":7.7085852479},{"Entity":"Thailand","Year":2008,"renewable_percentage":6.5625458278},{"Entity":"Thailand","Year":2009,"renewable_percentage":6.6263303689},{"Entity":"Thailand","Year":2010,"renewable_percentage":5.7085828343},{"Entity":"Thailand","Year":2011,"renewable_percentage":8.039961941},{"Entity":"Thailand","Year":2012,"renewable_percentage":8.5396118358},{"Entity":"Thailand","Year":2013,"renewable_percentage":7.6765035487},{"Entity":"Thailand","Year":2014,"renewable_percentage":8.395728489},{"Entity":"Thailand","Year":2015,"renewable_percentage":7.9949619145},{"Entity":"Thailand","Year":2016,"renewable_percentage":8.9840234023},{"Entity":"Thailand","Year":2017,"renewable_percentage":10.9570957096},{"Entity":"Thailand","Year":2018,"renewable_percentage":14.1900054915},{"Entity":"Thailand","Year":2019,"renewable_percentage":14.7001731284},{"Entity":"Thailand","Year":2020,"renewable_percentage":13.7963737796},{"Entity":"Ukraine","Year":2000,"renewable_percentage":6.5860921352},{"Entity":"Ukraine","Year":2001,"renewable_percentage":6.9729761009},{"Entity":"Ukraine","Year":2002,"renewable_percentage":5.5597165409},{"Entity":"Ukraine","Year":2003,"renewable_percentage":5.1442841287},{"Entity":"Ukraine","Year":2004,"renewable_percentage":6.4718162839},{"Entity":"Ukraine","Year":2005,"renewable_percentage":6.6698940347},{"Entity":"Ukraine","Year":2006,"renewable_percentage":6.68633235},{"Entity":"Ukraine","Year":2007,"renewable_percentage":5.3380238605},{"Entity":"Ukraine","Year":2008,"renewable_percentage":6.1377090041},{"Entity":"Ukraine","Year":2009,"renewable_percentage":6.980762585},{"Entity":"Ukraine","Year":2010,"renewable_percentage":7.0914098083},{"Entity":"Ukraine","Year":2011,"renewable_percentage":5.7450628366},{"Entity":"Ukraine","Year":2012,"renewable_percentage":5.6614236741},{"Entity":"Ukraine","Year":2013,"renewable_percentage":7.8003200661},{"Entity":"Ukraine","Year":2014,"renewable_percentage":5.5885262117},{"Entity":"Ukraine","Year":2015,"renewable_percentage":4.3924771096},{"Entity":"Ukraine","Year":2016,"renewable_percentage":5.6797249171},{"Entity":"Ukraine","Year":2017,"renewable_percentage":7.0457194664},{"Entity":"Ukraine","Year":2018,"renewable_percentage":8.228528092},{"Entity":"Ukraine","Year":2019,"renewable_percentage":7.7754487096},{"Entity":"Ukraine","Year":2020,"renewable_percentage":11.8440577364},{"Entity":"United Kingdom","Year":2000,"renewable_percentage":2.6657406913},{"Entity":"United Kingdom","Year":2001,"renewable_percentage":2.5001961451},{"Entity":"United Kingdom","Year":2002,"renewable_percentage":2.8939157566},{"Entity":"United Kingdom","Year":2003,"renewable_percentage":2.6854802003},{"Entity":"United Kingdom","Year":2004,"renewable_percentage":3.6136880575},{"Entity":"United Kingdom","Year":2005,"renewable_percentage":4.2815234434},{"Entity":"United Kingdom","Year":2006,"renewable_percentage":4.6029890199},{"Entity":"United Kingdom","Year":2007,"renewable_percentage":5.0104331009},{"Entity":"United Kingdom","Year":2008,"renewable_percentage":5.6776842324},{"Entity":"United Kingdom","Year":2009,"renewable_percentage":6.7679854187},{"Entity":"United Kingdom","Year":2010,"renewable_percentage":6.9092924441},{"Entity":"United Kingdom","Year":2011,"renewable_percentage":9.6422505889},{"Entity":"United Kingdom","Year":2012,"renewable_percentage":11.4273047189},{"Entity":"United Kingdom","Year":2013,"renewable_percentage":14.9727052732},{"Entity":"United Kingdom","Year":2014,"renewable_percentage":19.2476358104},{"Entity":"United Kingdom","Year":2015,"renewable_percentage":24.6227709191},{"Entity":"United Kingdom","Year":2016,"renewable_percentage":24.6788390627},{"Entity":"United Kingdom","Year":2017,"renewable_percentage":29.4986571173},{"Entity":"United Kingdom","Year":2018,"renewable_percentage":33.2919818457},{"Entity":"United Kingdom","Year":2019,"renewable_percentage":37.4568630499},{"Entity":"United Kingdom","Year":2020,"renewable_percentage":42.8603962651},{"Entity":"United States","Year":2000,"renewable_percentage":9.2298992662},{"Entity":"United States","Year":2001,"renewable_percentage":7.5132056541},{"Entity":"United States","Year":2002,"renewable_percentage":8.749216358},{"Entity":"United States","Year":2003,"renewable_percentage":9.0252110397},{"Entity":"United States","Year":2004,"renewable_percentage":8.7334100887},{"Entity":"United States","Year":2005,"renewable_percentage":8.7494640631},{"Entity":"United States","Year":2006,"renewable_percentage":9.4184742052},{"Entity":"United States","Year":2007,"renewable_percentage":8.3984096829},{"Entity":"United States","Year":2008,"renewable_percentage":9.180943292},{"Entity":"United States","Year":2009,"renewable_percentage":10.547689996},{"Entity":"United States","Year":2010,"renewable_percentage":10.3180892283},{"Entity":"United States","Year":2011,"renewable_percentage":12.4665249812},{"Entity":"United States","Year":2012,"renewable_percentage":12.1841179804},{"Entity":"United States","Year":2013,"renewable_percentage":12.8311154179},{"Entity":"United States","Year":2014,"renewable_percentage":13.3503092033},{"Entity":"United States","Year":2015,"renewable_percentage":13.6261352256},{"Entity":"United States","Year":2016,"renewable_percentage":15.2880936304},{"Entity":"United States","Year":2017,"renewable_percentage":17.4515276472},{"Entity":"United States","Year":2018,"renewable_percentage":17.4499886946},{"Entity":"United States","Year":2019,"renewable_percentage":18.2946243489},{"Entity":"United States","Year":2020,"renewable_percentage":20.3156921037}],"metadata":{"Entity":{"type":"string","semanticType":"Location"},"Year":{"type":"number","semanticType":"Year"},"renewable_percentage":{"type":"number","semanticType":"Percentage"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total energy for each row\n df_energy['total_energy'] = (\n df_energy['Electricity from fossil fuels (TWh)'] + \n df_energy['Electricity from nuclear (TWh)'] + \n df_energy['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_energy['renewable_percentage'] = (\n df_energy['Electricity from renewables (TWh)'] / df_energy['total_energy'] * 100\n )\n \n # Handle division by zero cases\n df_energy['renewable_percentage'] = df_energy['renewable_percentage'].replace([np.inf, -np.inf], np.nan)\n \n # Select only the required columns\n transformed_df = df_energy[['Year', 'Entity', 'renewable_percentage']].copy()\n \n return transformed_df\n","source":["global-energy-20-small.csv"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 158029.9988, 190729.9957, 209229.9957, ..., 6993180.176, 9861099.609, 9874660.156, 10096009.77\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 90.14, 91.79, 92.54, 804.95\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"energy\",\n \"color\": \"Entity\",\n \"column\": \"source\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Transform the data into a long format where different energy sources (fossil fuels, nuclear, renewables) are unpivoted into separate rows. Each row should contain the year, entity (country), energy source type, and the corresponding energy value in TWh. This will enable visualization of energy generation trends over time by source and country.\",\n \"display_instruction\": \"Show **energy generation** trends by **source** and **Entity** over **Year**\",\n \"output_fields\": [\"Year\", \"Entity\", \"source\", \"energy\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"energy\",\n \"color\": \"Entity\",\n \"column\": \"source\"\n },\n \"reason\": \"The user wants to visualize energy data across different sources with a line chart faceted by source. The original data has energy sources in separate columns (fossil fuels, nuclear, renewables), so we need to unpivot these columns into a long format with 'source' and 'energy' fields. The chart_encodings provided by the user are appropriate for this visualization goal, using faceting (column) to separate different energy sources.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Unpivot the energy source columns into long format\n transformed_df = pd.melt(\n df_energy,\n id_vars=['Year', 'Entity'],\n value_vars=[\n 'Electricity from fossil fuels (TWh)',\n 'Electricity from nuclear (TWh)',\n 'Electricity from renewables (TWh)'\n ],\n var_name='source',\n value_name='energy'\n )\n \n # Clean up the source names to be more readable\n transformed_df['source'] = transformed_df['source'].str.replace('Electricity from ', '').str.replace(' (TWh)', '')\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n Entity Year energy source\n0 Australia 2000 181.05 fossil fuels\n1 Australia 2001 194.33 fossil fuels\n2 Australia 2002 197.29 fossil fuels\n3 Australia 2003 195.13 fossil fuels\n4 Australia 2004 203.66 fossil fuels\n5 Australia 2005 195.95 fossil fuels\n6 Australia 2006 198.72 fossil fuels\n7 Australia 2007 208.59 fossil fuels\n8 Australia 2008 211.06 fossil fuels\n9 Australia 2009 216.42 fossil fuels\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show renewable percentage for each country\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"energy\",\n \"color\": \"Entity\",\n \"column\": \"source\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Calculate the renewable energy percentage for each country and year. The renewable percentage is computed as the ratio of renewable energy to total energy (sum of fossil fuels, nuclear, and renewables) multiplied by 100. The output should contain year, entity (country), and the renewable percentage value.\",\n \"display_instruction\": \"Show **renewable percentage** for each **Entity** over **Year**\",\n \"output_fields\": [\"Year\", \"Entity\", \"renewable_percentage\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"renewable_percentage\",\n \"color\": \"Entity\"\n },\n \"reason\": \"The user wants to see renewable percentage trends, which requires calculating the proportion of renewable energy relative to total energy generation. This is a single metric per country per year, so we don't need the 'column' faceting by source anymore. The chart_encodings are updated to reflect that we're now showing a percentage metric rather than absolute energy values by source.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total energy for each row\n df_energy['total_energy'] = (\n df_energy['Electricity from fossil fuels (TWh)'] + \n df_energy['Electricity from nuclear (TWh)'] + \n df_energy['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_energy['renewable_percentage'] = (\n df_energy['Electricity from renewables (TWh)'] / df_energy['total_energy'] * 100\n )\n \n # Handle division by zero cases\n df_energy['renewable_percentage'] = df_energy['renewable_percentage'].replace([np.inf, -np.inf], np.nan)\n \n # Select only the required columns\n transformed_df = df_energy[['Year', 'Entity', 'renewable_percentage']].copy()\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"table-82","sourceTableIds":["global-energy-20-small.csv"],"instruction":"show renewable percentage for each country","displayInstruction":"Show **renewable percentage** for each **Entity** over **Year**","chart":{"id":"chart-1760738416626","chartType":"Line Chart","encodingMap":{"x":{"fieldID":"original--global-energy-20-small.csv--Year"},"y":{"fieldID":"concept-1760738385163"},"color":{"fieldID":"original--global-energy-20-small.csv--Entity"},"opacity":{"channel":"opacity","bin":false},"column":{"fieldID":"concept-1760738389404","sortBy":"[\"fossil fuels\",\"nuclear\",\"renewables\"]"},"row":{"channel":"row","bin":false}},"tableRef":"table-82","saved":false,"source":"trigger","unread":false},"resultTableId":"table-45"},"explanation":{"agent":"CodeExplanationAgent","code":"The code transforms global energy data to analyze renewable energy adoption:\n\n- Calculates **total_energy** by summing electricity generation from three sources: **fossil fuels**, **nuclear**, and **renewables** (all measured in TWh)\n- Computes **renewable_percentage** by dividing **Electricity from renewables (TWh)** by **total_energy** and multiplying by **100**\n- Handles edge cases by replacing infinite values (from division by zero) with **NaN**\n- Filters the dataset to retain only **Year**, **Entity** (country), and **renewable_percentage** columns","concepts":[{"explanation":"The proportion of a country's total electricity generation that comes from renewable sources, expressed as a percentage. Calculated as: \\( \\frac{\\text{Electricity from renewables}}{\\text{Total electricity generation}} \\times 100 \\). This metric indicates the degree of renewable energy adoption in each country's energy mix.","field":"renewable_percentage"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small_csv)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 117440.0, 146139.9994, 158029.9988, ..., 5738290.039, 9861099.609, 10502929.69, 10707219.73\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., nan, nan, nan, nan\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total energy for each row\n df_energy['total_energy'] = (\n df_energy['Electricity from fossil fuels (TWh)'] + \n df_energy['Electricity from nuclear (TWh)'] + \n df_energy['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_energy['renewable_percentage'] = (\n df_energy['Electricity from renewables (TWh)'] / df_energy['total_energy'] * 100\n )\n \n # Handle division by zero cases\n df_energy['renewable_percentage'] = df_energy['renewable_percentage'].replace([np.inf, -np.inf], np.nan)\n \n # Select only the required columns\n transformed_df = df_energy[['Year', 'Entity', 'renewable_percentage']].copy()\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThe code transforms global energy data to analyze renewable energy adoption:\n\n- Calculates **total_energy** by summing electricity generation from three sources: **fossil fuels**, **nuclear**, and **renewables** (all measured in TWh)\n- Computes **renewable_percentage** by dividing **Electricity from renewables (TWh)** by **total_energy** and multiplying by **100**\n- Handles edge cases by replacing infinite values (from division by zero) with **NaN**\n- Filters the dataset to retain only **Year**, **Entity** (country), and **renewable_percentage** columns\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"renewable_percentage\",\n \"explanation\": \"The proportion of a country's total electricity generation that comes from renewable sources, expressed as a percentage. Calculated as: \\\\( \\\\frac{\\\\text{Electricity from renewables}}{\\\\text{Total electricity generation}} \\\\times 100 \\\\). This metric indicates the degree of renewable energy adoption in each country's energy mix.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-78","displayId":"renewable-energy-rank","names":["Entity","Year","rank","renewable_percentage"],"rows":[{"Entity":"Australia","Year":2000,"rank":11,"renewable_percentage":8.6344368187},{"Entity":"Australia","Year":2001,"rank":10,"renewable_percentage":8.2180135078},{"Entity":"Australia","Year":2002,"rank":11,"renewable_percentage":8.0833022736},{"Entity":"Australia","Year":2003,"rank":11,"renewable_percentage":8.6598324205},{"Entity":"Australia","Year":2004,"rank":12,"renewable_percentage":8.7013045232},{"Entity":"Australia","Year":2005,"rank":11,"renewable_percentage":9.1562355123},{"Entity":"Australia","Year":2006,"rank":11,"renewable_percentage":9.6357600837},{"Entity":"Australia","Year":2007,"rank":11,"renewable_percentage":9.1190310213},{"Entity":"Australia","Year":2008,"rank":12,"renewable_percentage":8.0548900022},{"Entity":"Australia","Year":2009,"rank":12,"renewable_percentage":7.8043793133},{"Entity":"Australia","Year":2010,"rank":12,"renewable_percentage":9.0442152121},{"Entity":"Australia","Year":2011,"rank":11,"renewable_percentage":11.3454273735},{"Entity":"Australia","Year":2012,"rank":12,"renewable_percentage":11.4105750279},{"Entity":"Australia","Year":2013,"rank":11,"renewable_percentage":14.8708583355},{"Entity":"Australia","Year":2014,"rank":11,"renewable_percentage":14.9621290509},{"Entity":"Australia","Year":2015,"rank":12,"renewable_percentage":14.3476000693},{"Entity":"Australia","Year":2016,"rank":11,"renewable_percentage":15.6093794449},{"Entity":"Australia","Year":2017,"rank":12,"renewable_percentage":16.3138729943},{"Entity":"Australia","Year":2018,"rank":12,"renewable_percentage":17.145938174},{"Entity":"Australia","Year":2019,"rank":8,"renewable_percentage":21.3759705435},{"Entity":"Australia","Year":2020,"rank":8,"renewable_percentage":25.5031684668},{"Entity":"Brazil","Year":2000,"rank":1,"renewable_percentage":90.1307723743},{"Entity":"Brazil","Year":2001,"rank":1,"renewable_percentage":84.6953615744},{"Entity":"Brazil","Year":2002,"rank":1,"renewable_percentage":86.0883364189},{"Entity":"Brazil","Year":2003,"rank":1,"renewable_percentage":87.4561159097},{"Entity":"Brazil","Year":2004,"rank":1,"renewable_percentage":86.4260041451},{"Entity":"Brazil","Year":2005,"rank":1,"renewable_percentage":87.6781562721},{"Entity":"Brazil","Year":2006,"rank":1,"renewable_percentage":87.2842473236},{"Entity":"Brazil","Year":2007,"rank":1,"renewable_percentage":88.7252098726},{"Entity":"Brazil","Year":2008,"rank":1,"renewable_percentage":84.8072313004},{"Entity":"Brazil","Year":2009,"rank":1,"renewable_percentage":89.4172280725},{"Entity":"Brazil","Year":2010,"rank":1,"renewable_percentage":85.3576882415},{"Entity":"Brazil","Year":2011,"rank":1,"renewable_percentage":87.6618820986},{"Entity":"Brazil","Year":2012,"rank":1,"renewable_percentage":83.1164558813},{"Entity":"Brazil","Year":2013,"rank":1,"renewable_percentage":77.5240022006},{"Entity":"Brazil","Year":2014,"rank":1,"renewable_percentage":74.0418657409},{"Entity":"Brazil","Year":2015,"rank":1,"renewable_percentage":75.0231817625},{"Entity":"Brazil","Year":2016,"rank":1,"renewable_percentage":81.0938046902},{"Entity":"Brazil","Year":2017,"rank":1,"renewable_percentage":79.9091472228},{"Entity":"Brazil","Year":2018,"rank":1,"renewable_percentage":82.9198505403},{"Entity":"Brazil","Year":2019,"rank":1,"renewable_percentage":82.8548799017},{"Entity":"Brazil","Year":2020,"rank":1,"renewable_percentage":84.6411771408},{"Entity":"Canada","Year":2000,"rank":2,"renewable_percentage":61.8095917882},{"Entity":"Canada","Year":2001,"rank":2,"renewable_percentage":59.3287558747},{"Entity":"Canada","Year":2002,"rank":2,"renewable_percentage":61.1477403113},{"Entity":"Canada","Year":2003,"rank":2,"renewable_percentage":60.0789685174},{"Entity":"Canada","Year":2004,"rank":2,"renewable_percentage":59.6967771845},{"Entity":"Canada","Year":2005,"rank":2,"renewable_percentage":60.8208155391},{"Entity":"Canada","Year":2006,"rank":2,"renewable_percentage":60.8271602855},{"Entity":"Canada","Year":2007,"rank":2,"renewable_percentage":61.2460642446},{"Entity":"Canada","Year":2008,"rank":2,"renewable_percentage":62.6520720838},{"Entity":"Canada","Year":2009,"rank":2,"renewable_percentage":63.8919227732},{"Entity":"Canada","Year":2010,"rank":2,"renewable_percentage":62.9421470558},{"Entity":"Canada","Year":2011,"rank":2,"renewable_percentage":64.0922915917},{"Entity":"Canada","Year":2012,"rank":2,"renewable_percentage":65.098730952},{"Entity":"Canada","Year":2013,"rank":2,"renewable_percentage":65.4320794066},{"Entity":"Canada","Year":2014,"rank":2,"renewable_percentage":64.791145907},{"Entity":"Canada","Year":2015,"rank":2,"renewable_percentage":65.2946239925},{"Entity":"Canada","Year":2016,"rank":2,"renewable_percentage":66.1890584295},{"Entity":"Canada","Year":2017,"rank":2,"renewable_percentage":67.5399410579},{"Entity":"Canada","Year":20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Africa","Year":2020,"rank":17,"renewable_percentage":5.780581212},{"Entity":"Spain","Year":2000,"rank":6,"renewable_percentage":15.6119862394},{"Entity":"Spain","Year":2001,"rank":3,"renewable_percentage":21.1524434719},{"Entity":"Spain","Year":2002,"rank":6,"renewable_percentage":13.8260180901},{"Entity":"Spain","Year":2003,"rank":3,"renewable_percentage":21.667314419},{"Entity":"Spain","Year":2004,"rank":3,"renewable_percentage":18.3190206468},{"Entity":"Spain","Year":2005,"rank":7,"renewable_percentage":14.8597342333},{"Entity":"Spain","Year":2006,"rank":4,"renewable_percentage":17.6623992413},{"Entity":"Spain","Year":2007,"rank":3,"renewable_percentage":19.3347262296},{"Entity":"Spain","Year":2008,"rank":4,"renewable_percentage":20.0051501593},{"Entity":"Spain","Year":2009,"rank":3,"renewable_percentage":25.4107639008},{"Entity":"Spain","Year":2010,"rank":3,"renewable_percentage":32.7922186819},{"Entity":"Spain","Year":2011,"rank":3,"renewable_percentage":30.0408415417},{"Entity":"Spain","Year":2012,"rank":4,"renewable_percentage":29.6047928652},{"Entity":"Spain","Year":2013,"rank":3,"renewable_percentage":39.5850357054},{"Entity":"Spain","Year":2014,"rank":4,"renewable_percentage":40.1032952644},{"Entity":"Spain","Year":2015,"rank":4,"renewable_percentage":34.9899091826},{"Entity":"Spain","Year":2016,"rank":3,"renewable_percentage":38.5818061138},{"Entity":"Spain","Year":2017,"rank":5,"renewable_percentage":32.220593624},{"Entity":"Spain","Year":2018,"rank":4,"renewable_percentage":38.2080329557},{"Entity":"Spain","Year":2019,"rank":6,"renewable_percentage":37.280815091},{"Entity":"Spain","Year":2020,"rank":4,"renewable_percentage":43.8108805298},{"Entity":"Thailand","Year":2000,"rank":12,"renewable_percentage":7.1261029822},{"Entity":"Thailand","Year":2001,"rank":12,"renewable_percentage":7.061527212},{"Entity":"Thailand","Year":2002,"rank":12,"renewable_percentage":7.9444772593},{"Entity":"Thailand","Year":2003,"rank":13,"renewable_percentage":7.6718362852},{"Entity":"Thailand","Year":2004,"rank":13,"renewable_percentage":6.5163549406},{"Entity":"Thailand","Year":2005,"rank":14,"renewable_percentage":6.0325203252},{"Entity":"Thailand","Year":2006,"rank":13,"renewable_percentage":7.5988547551},{"Entity":"Thailand","Year":2007,"rank":13,"renewable_percentage":7.7085852479},{"Entity":"Thailand","Year":2008,"rank":13,"renewable_percentage":6.5625458278},{"Entity":"Thailand","Year":2009,"rank":15,"renewable_percentage":6.6263303689},{"Entity":"Thailand","Year":2010,"rank":16,"renewable_percentage":5.7085828343},{"Entity":"Thailand","Year":2011,"rank":15,"renewable_percentage":8.039961941},{"Entity":"Thailand","Year":2012,"rank":15,"renewable_percentage":8.5396118358},{"Entity":"Thailand","Year":2013,"rank":16,"renewable_percentage":7.6765035487},{"Entity":"Thailand","Year":2014,"rank":15,"renewable_percentage":8.395728489},{"Entity":"Thailand","Year":2015,"rank":15,"renewable_percentage":7.9949619145},{"Entity":"Thailand","Year":2016,"rank":15,"renewable_percentage":8.9840234023},{"Entity":"Thailand","Year":2017,"rank":15,"renewable_percentage":10.9570957096},{"Entity":"Thailand","Year":2018,"rank":14,"renewable_percentage":14.1900054915},{"Entity":"Thailand","Year":2019,"rank":15,"renewable_percentage":14.7001731284},{"Entity":"Thailand","Year":2020,"rank":15,"renewable_percentage":13.7963737796},{"Entity":"Ukraine","Year":2000,"rank":13,"renewable_percentage":6.5860921352},{"Entity":"Ukraine","Year":2001,"rank":13,"renewable_percentage":6.9729761009},{"Entity":"Ukraine","Year":2002,"rank":14,"renewable_percentage":5.5597165409},{"Entity":"Ukraine","Year":2003,"rank":14,"renewable_percentage":5.1442841287},{"Entity":"Ukraine","Year":2004,"rank":14,"renewable_percentage":6.4718162839},{"Entity":"Ukraine","Year":2005,"rank":13,"renewable_percentage":6.6698940347},{"Entity":"Ukraine","Year":2006,"rank":14,"renewable_percentage":6.68633235},{"Entity":"Ukraine","Year":2007,"rank":14,"renewable_percentage":5.3380238605},{"Entity":"Ukraine","Year":2008,"rank":14,"renewable_percentage":6.1377090041},{"Entity":"Ukraine","Year":2009,"rank":13,"renewable_percentage":6.980762585},{"Entity":"Ukraine","Year":2010,"rank":13,"renewable_percentage":7.0914098083},{"Entity":"Ukraine","Year":2011,"rank":16,"renewable_percentage":5.7450628366},{"Entity":"Ukraine","Year":2012,"rank":16,"renewable_percentage":5.6614236741},{"Entity":"Ukraine","Year":2013,"rank":15,"renewable_percentage":7.8003200661},{"Entity":"Ukraine","Year":2014,"rank":16,"renewable_percentage":5.5885262117},{"Entity":"Ukraine","Year":2015,"rank":16,"renewable_percentage":4.3924771096},{"Entity":"Ukraine","Year":2016,"rank":16,"renewable_percentage":5.6797249171},{"Entity":"Ukraine","Year":2017,"rank":16,"renewable_percentage":7.0457194664},{"Entity":"Ukraine","Year":2018,"rank":16,"renewable_percentage":8.228528092},{"Entity":"Ukraine","Year":2019,"rank":16,"renewable_percentage":7.7754487096},{"Entity":"Ukraine","Year":2020,"rank":16,"renewable_percentage":11.8440577364},{"Entity":"United Kingdom","Year":2000,"rank":15,"renewable_percentage":2.6657406913},{"Entity":"United Kingdom","Year":2001,"rank":15,"renewable_percentage":2.5001961451},{"Entity":"United Kingdom","Year":2002,"rank":15,"renewable_percentage":2.8939157566},{"Entity":"United Kingdom","Year":2003,"rank":15,"renewable_percentage":2.6854802003},{"Entity":"United Kingdom","Year":2004,"rank":15,"renewable_percentage":3.6136880575},{"Entity":"United Kingdom","Year":2005,"rank":15,"renewable_percentage":4.2815234434},{"Entity":"United Kingdom","Year":2006,"rank":15,"renewable_percentage":4.6029890199},{"Entity":"United Kingdom","Year":2007,"rank":15,"renewable_percentage":5.0104331009},{"Entity":"United Kingdom","Year":2008,"rank":15,"renewable_percentage":5.6776842324},{"Entity":"United Kingdom","Year":2009,"rank":14,"renewable_percentage":6.7679854187},{"Entity":"United Kingdom","Year":2010,"rank":15,"renewable_percentage":6.9092924441},{"Entity":"United Kingdom","Year":2011,"rank":13,"renewable_percentage":9.6422505889},{"Entity":"United Kingdom","Year":2012,"rank":11,"renewable_percentage":11.4273047189},{"Entity":"United Kingdom","Year":2013,"rank":10,"renewable_percentage":14.9727052732},{"Entity":"United Kingdom","Year":2014,"rank":8,"renewable_percentage":19.2476358104},{"Entity":"United Kingdom","Year":2015,"rank":6,"renewable_percentage":24.6227709191},{"Entity":"United Kingdom","Year":2016,"rank":7,"renewable_percentage":24.6788390627},{"Entity":"United Kingdom","Year":2017,"rank":6,"renewable_percentage":29.4986571173},{"Entity":"United Kingdom","Year":2018,"rank":6,"renewable_percentage":33.2919818457},{"Entity":"United Kingdom","Year":2019,"rank":5,"renewable_percentage":37.4568630499},{"Entity":"United Kingdom","Year":2020,"rank":5,"renewable_percentage":42.8603962651},{"Entity":"United States","Year":2000,"rank":10,"renewable_percentage":9.2298992662},{"Entity":"United States","Year":2001,"rank":11,"renewable_percentage":7.5132056541},{"Entity":"United States","Year":2002,"rank":10,"renewable_percentage":8.749216358},{"Entity":"United States","Year":2003,"rank":10,"renewable_percentage":9.0252110397},{"Entity":"United States","Year":2004,"rank":11,"renewable_percentage":8.7334100887},{"Entity":"United States","Year":2005,"rank":12,"renewable_percentage":8.7494640631},{"Entity":"United States","Year":2006,"rank":12,"renewable_percentage":9.4184742052},{"Entity":"United States","Year":2007,"rank":12,"renewable_percentage":8.3984096829},{"Entity":"United States","Year":2008,"rank":11,"renewable_percentage":9.180943292},{"Entity":"United States","Year":2009,"rank":10,"renewable_percentage":10.547689996},{"Entity":"United States","Year":2010,"rank":11,"renewable_percentage":10.3180892283},{"Entity":"United States","Year":2011,"rank":9,"renewable_percentage":12.4665249812},{"Entity":"United States","Year":2012,"rank":10,"renewable_percentage":12.1841179804},{"Entity":"United States","Year":2013,"rank":12,"renewable_percentage":12.8311154179},{"Entity":"United States","Year":2014,"rank":12,"renewable_percentage":13.3503092033},{"Entity":"United States","Year":2015,"rank":14,"renewable_percentage":13.6261352256},{"Entity":"United States","Year":2016,"rank":12,"renewable_percentage":15.2880936304},{"Entity":"United States","Year":2017,"rank":9,"renewable_percentage":17.4515276472},{"Entity":"United States","Year":2018,"rank":11,"renewable_percentage":17.4499886946},{"Entity":"United States","Year":2019,"rank":13,"renewable_percentage":18.2946243489},{"Entity":"United States","Year":2020,"rank":12,"renewable_percentage":20.3156921037}],"metadata":{"Entity":{"type":"string","semanticType":"Location"},"Year":{"type":"number","semanticType":"Year"},"rank":{"type":"number","semanticType":"Number"},"renewable_percentage":{"type":"number","semanticType":"Percentage"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total energy for each row\n df_energy['total_energy'] = (\n df_energy['Electricity from fossil fuels (TWh)'] + \n df_energy['Electricity from nuclear (TWh)'] + \n df_energy['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_energy['renewable_percentage'] = (\n df_energy['Electricity from renewables (TWh)'] / df_energy['total_energy'] * 100\n )\n \n # Handle division by zero cases\n df_energy['renewable_percentage'] = df_energy['renewable_percentage'].replace([np.inf, -np.inf], np.nan)\n \n # Rank countries by renewable percentage within each year (highest percentage = rank 1)\n df_energy['rank'] = df_energy.groupby('Year')['renewable_percentage'].rank(\n method='dense', \n ascending=False\n )\n \n # Select only the required columns\n transformed_df = df_energy[['Year', 'Entity', 'renewable_percentage', 'rank']].copy()\n \n return transformed_df\n","source":["global-energy-20-small.csv"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 158029.9988, 190729.9957, 209229.9957, ..., 6993180.176, 9861099.609, 9874660.156, 10096009.77\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 90.14, 91.79, 92.54, 804.95\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"energy\",\n \"color\": \"Entity\",\n \"column\": \"source\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Transform the data into a long format where different energy sources (fossil fuels, nuclear, renewables) are unpivoted into separate rows. Each row should contain the year, entity (country), energy source type, and the corresponding energy value in TWh. This will enable visualization of energy generation trends over time by source and country.\",\n \"display_instruction\": \"Show **energy generation** trends by **source** and **Entity** over **Year**\",\n \"output_fields\": [\"Year\", \"Entity\", \"source\", \"energy\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"energy\",\n \"color\": \"Entity\",\n \"column\": \"source\"\n },\n \"reason\": \"The user wants to visualize energy data across different sources with a line chart faceted by source. The original data has energy sources in separate columns (fossil fuels, nuclear, renewables), so we need to unpivot these columns into a long format with 'source' and 'energy' fields. The chart_encodings provided by the user are appropriate for this visualization goal, using faceting (column) to separate different energy sources.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Unpivot the energy source columns into long format\n transformed_df = pd.melt(\n df_energy,\n id_vars=['Year', 'Entity'],\n value_vars=[\n 'Electricity from fossil fuels (TWh)',\n 'Electricity from nuclear (TWh)',\n 'Electricity from renewables (TWh)'\n ],\n var_name='source',\n value_name='energy'\n )\n \n # Clean up the source names to be more readable\n transformed_df['source'] = transformed_df['source'].str.replace('Electricity from ', '').str.replace(' (TWh)', '')\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n Entity Year energy source\n0 Australia 2000 181.05 fossil fuels\n1 Australia 2001 194.33 fossil fuels\n2 Australia 2002 197.29 fossil fuels\n3 Australia 2003 195.13 fossil fuels\n4 Australia 2004 203.66 fossil fuels\n5 Australia 2005 195.95 fossil fuels\n6 Australia 2006 198.72 fossil fuels\n7 Australia 2007 208.59 fossil fuels\n8 Australia 2008 211.06 fossil fuels\n9 Australia 2009 216.42 fossil fuels\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show renewable percentage for each country\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"energy\",\n \"color\": \"Entity\",\n \"column\": \"source\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Calculate the renewable energy percentage for each country and year. The renewable percentage is computed as the ratio of renewable energy to total energy (sum of fossil fuels, nuclear, and renewables) multiplied by 100. The output should contain year, entity (country), and the renewable percentage value.\",\n \"display_instruction\": \"Show **renewable percentage** for each **Entity** over **Year**\",\n \"output_fields\": [\"Year\", \"Entity\", \"renewable_percentage\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"renewable_percentage\",\n \"color\": \"Entity\"\n },\n \"reason\": \"The user wants to see renewable percentage trends, which requires calculating the proportion of renewable energy relative to total energy generation. This is a single metric per country per year, so we don't need the 'column' faceting by source anymore. The chart_encodings are updated to reflect that we're now showing a percentage metric rather than absolute energy values by source.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total energy for each row\n df_energy['total_energy'] = (\n df_energy['Electricity from fossil fuels (TWh)'] + \n df_energy['Electricity from nuclear (TWh)'] + \n df_energy['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_energy['renewable_percentage'] = (\n df_energy['Electricity from renewables (TWh)'] / df_energy['total_energy'] * 100\n )\n \n # Handle division by zero cases\n df_energy['renewable_percentage'] = df_energy['renewable_percentage'].replace([np.inf, -np.inf], np.nan)\n \n # Select only the required columns\n transformed_df = df_energy[['Year', 'Entity', 'renewable_percentage']].copy()\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n Entity Year renewable_percentage\n0 Australia 2000 8.634437\n1 Australia 2001 8.218014\n2 Australia 2002 8.083302\n3 Australia 2003 8.659832\n4 Australia 2004 8.701305\n5 Australia 2005 9.156236\n6 Australia 2006 9.635760\n7 Australia 2007 9.119031\n8 Australia 2008 8.054890\n9 Australia 2009 7.804379\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"rank countries by their renewable percentage\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"renewable_percentage\",\n \"color\": \"Entity\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Calculate the renewable energy percentage for each country and year, then rank countries based on their renewable percentage. The ranking should be computed for each year, showing which countries have the highest to lowest renewable percentage. The output should contain year, entity (country), renewable percentage, and the rank of each country within that year.\",\n \"display_instruction\": \"Show **countries ranked** by **renewable percentage** over **Year**\",\n \"output_fields\": [\"Year\", \"Entity\", \"renewable_percentage\", \"rank\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"renewable_percentage\",\n \"color\": \"Entity\"\n },\n \"reason\": \"The user wants to rank countries by their renewable percentage, which requires adding a rank field that orders countries within each year based on their renewable percentage (highest percentage = rank 1). This allows comparison of countries' relative positions in renewable energy adoption over time. The chart_encodings remain the same as the previous step since we're still visualizing renewable percentage trends, but now with ranking information available.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total energy for each row\n df_energy['total_energy'] = (\n df_energy['Electricity from fossil fuels (TWh)'] + \n df_energy['Electricity from nuclear (TWh)'] + \n df_energy['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_energy['renewable_percentage'] = (\n df_energy['Electricity from renewables (TWh)'] / df_energy['total_energy'] * 100\n )\n \n # Handle division by zero cases\n df_energy['renewable_percentage'] = df_energy['renewable_percentage'].replace([np.inf, -np.inf], np.nan)\n \n # Rank countries by renewable percentage within each year (highest percentage = rank 1)\n df_energy['rank'] = df_energy.groupby('Year')['renewable_percentage'].rank(\n method='dense', \n ascending=False\n )\n \n # Select only the required columns\n transformed_df = df_energy[['Year', 'Entity', 'renewable_percentage', 'rank']].copy()\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"table-45","sourceTableIds":["global-energy-20-small.csv"],"instruction":"rank countries by their renewable percentage","displayInstruction":"Show **countries ranked** by **renewable percentage** over **Year**","chart":{"id":"chart-1760738441793","chartType":"Line Chart","encodingMap":{"x":{"fieldID":"original--global-energy-20-small.csv--Year"},"y":{"fieldID":"concept-renewable_percentage-1760738424337"},"color":{"fieldID":"original--global-energy-20-small.csv--Entity"},"opacity":{"channel":"opacity","bin":false},"column":{},"row":{"channel":"row","bin":false}},"tableRef":"table-45","saved":false,"source":"trigger","unread":false},"resultTableId":"table-78"},"explanation":{"agent":"CodeExplanationAgent","code":"- Calculate **total_energy** for each country-year by summing electricity generated from **fossil fuels**, **nuclear**, and **renewables** (all measured in TWh)\n- Compute **renewable_percentage** by dividing **Electricity from renewables (TWh)** by **total_energy** and multiplying by **100**\n- Replace any infinite values in **renewable_percentage** with NaN to handle cases where total energy is zero\n- Assign a **rank** to each country within each **Year** based on **renewable_percentage**, where rank **1** represents the highest renewable percentage (using dense ranking method)\n- Return a dataset containing **Year**, **Entity** (country name), **renewable_percentage**, and **rank**","concepts":[{"explanation":"The proportion of a country's total electricity generation that comes from renewable sources, expressed as a percentage. Calculated as: \\[ \\text{renewable\\_percentage} = \\frac{\\text{Electricity from renewables (TWh)}}{\\text{total\\_energy}} \\times 100 \\]","field":"renewable_percentage"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small_csv)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 190729.9957, 227580.0018, 233600.0061, ..., 9282549.805, 9861099.609, 9874660.156, 10096009.77\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 88.76, 90.14, 91.79, 92.54\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total energy for each row\n df_energy['total_energy'] = (\n df_energy['Electricity from fossil fuels (TWh)'] + \n df_energy['Electricity from nuclear (TWh)'] + \n df_energy['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_energy['renewable_percentage'] = (\n df_energy['Electricity from renewables (TWh)'] / df_energy['total_energy'] * 100\n )\n \n # Handle division by zero cases\n df_energy['renewable_percentage'] = df_energy['renewable_percentage'].replace([np.inf, -np.inf], np.nan)\n \n # Rank countries by renewable percentage within each year (highest percentage = rank 1)\n df_energy['rank'] = df_energy.groupby('Year')['renewable_percentage'].rank(\n method='dense', \n ascending=False\n )\n \n # Select only the required columns\n transformed_df = df_energy[['Year', 'Entity', 'renewable_percentage', 'rank']].copy()\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\n- Calculate **total_energy** for each country-year by summing electricity generated from **fossil fuels**, **nuclear**, and **renewables** (all measured in TWh)\n- Compute **renewable_percentage** by dividing **Electricity from renewables (TWh)** by **total_energy** and multiplying by **100**\n- Replace any infinite values in **renewable_percentage** with NaN to handle cases where total energy is zero\n- Assign a **rank** to each country within each **Year** based on **renewable_percentage**, where rank **1** represents the highest renewable percentage (using dense ranking method)\n- Return a dataset containing **Year**, **Entity** (country name), **renewable_percentage**, and **rank**\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"renewable_percentage\",\n \"explanation\": \"The proportion of a country's total electricity generation that comes from renewable sources, expressed as a percentage. Calculated as: \\\\[ \\\\text{renewable\\\\_percentage} = \\\\frac{\\\\text{Electricity from renewables (TWh)}}{\\\\text{total\\\\_energy}} \\\\times 100 \\\\]\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-97","displayId":"renewable-elec","names":["Electricity from renewables (TWh)","Entity","Year"],"rows":[{"Electricity from renewables (TWh)":17.11,"Entity":"Australia","Year":"2000"},{"Electricity from renewables (TWh)":63.99,"Entity":"Australia","Year":"2020"},{"Electricity from renewables (TWh)":308.77,"Entity":"Brazil","Year":"2000"},{"Electricity from renewables (TWh)":520.01,"Entity":"Brazil","Year":"2020"},{"Electricity from renewables (TWh)":363.7,"Entity":"Canada","Year":"2000"},{"Electricity from renewables (TWh)":429.24,"Entity":"Canada","Year":"2020"},{"Electricity from renewables (TWh)":225.56,"Entity":"China","Year":"2000"},{"Electricity from renewables (TWh)":2184.94,"Entity":"China","Year":"2020"},{"Electricity from renewables (TWh)":67.83,"Entity":"France","Year":"2000"},{"Electricity from renewables (TWh)":125.28,"Entity":"France","Year":"2020"},{"Electricity from renewables (TWh)":35.47,"Entity":"Germany","Year":"2000"},{"Electricity from renewables (TWh)":251.48,"Entity":"Germany","Year":"2020"},{"Electricity from renewables (TWh)":80.27,"Entity":"India","Year":"2000"},{"Electricity from renewables (TWh)":315.76,"Entity":"India","Year":"2020"},{"Electricity from renewables (TWh)":19.6,"Entity":"Indonesia","Year":"2000"},{"Electricity from renewables (TWh)":52.91,"Entity":"Indonesia","Year":"2020"},{"Electricity from renewables (TWh)":50.87,"Entity":"Italy","Year":"2000"},{"Electricity from renewables (TWh)":116.9,"Entity":"Italy","Year":"2020"},{"Electricity from renewables (TWh)":104.16,"Entity":"Japan","Year":"2000"},{"Electricity from renewables (TWh)":205.6,"Entity":"Japan","Year":"2020"},{"Electricity from renewables (TWh)":7.53,"Entity":"Kazakhstan","Year":"2000"},{"Electricity from renewables (TWh)":11.94,"Entity":"Kazakhstan","Year":"2020"},{"Electricity from renewables (TWh)":44.51,"Entity":"Mexico","Year":"2000"},{"Electricity from renewables (TWh)":69.19,"Entity":"Mexico","Year":"2020"},{"Electricity from renewables (TWh)":2.33,"Entity":"Poland","Year":"2000"},{"Electricity from renewables (TWh)":28.23,"Entity":"Poland","Year":"2020"},{"Electricity from renewables (TWh)":0,"Entity":"Saudi Arabia","Year":"2000"},{"Electricity from renewables (TWh)":0.21,"Entity":"Saudi Arabia","Year":"2020"},{"Electricity from renewables (TWh)":1.79,"Entity":"South Africa","Year":"2000"},{"Electricity from renewables (TWh)":12.83,"Entity":"South Africa","Year":"2020"},{"Electricity from renewables (TWh)":34.49,"Entity":"Spain","Year":"2000"},{"Electricity from renewables (TWh)":113.79,"Entity":"Spain","Year":"2020"},{"Electricity from renewables (TWh)":6.38,"Entity":"Thailand","Year":"2000"},{"Electricity from renewables (TWh)":24.73,"Entity":"Thailand","Year":"2020"},{"Electricity from renewables (TWh)":11.28,"Entity":"Ukraine","Year":"2000"},{"Electricity from renewables (TWh)":17.56,"Entity":"Ukraine","Year":"2020"},{"Electricity from renewables (TWh)":9.98,"Entity":"United Kingdom","Year":"2000"},{"Electricity from renewables (TWh)":131.74,"Entity":"United Kingdom","Year":"2020"},{"Electricity from renewables (TWh)":350.93,"Entity":"United States","Year":"2000"},{"Electricity from renewables (TWh)":821.4,"Entity":"United States","Year":"2020"}],"metadata":{"Electricity from renewables (TWh)":{"type":"number","semanticType":"Number"},"Entity":{"type":"string","semanticType":"Location"},"Year":{"type":"number","semanticType":"Year"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n transformed_df = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Select only the required columns\n transformed_df = transformed_df[['Entity', 'Year', 'Electricity from renewables (TWh)']]\n \n # Convert Year to string for better visualization as a categorical variable\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year for better readability\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n","source":["global-energy-20-small.csv"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 117440.0, 146139.9994, 158029.9988, ..., 5736319.824, 5738290.039, nan, 9861099.609\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 61.99, 62.21, 63.02, 63.71\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"compare each countries renewable between 2000 and 2020\",\n \"chart_type\": \"Grouped Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Renewable Energy\",\n \"color\": \"Year\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Compare renewable energy (Electricity from renewables) for each country between the years 2000 and 2020. Filter the data to include only years 2000 and 2020, and prepare the output with country names, year, and renewable energy values for visualization as a grouped bar chart.\",\n \"display_instruction\": \"Compare **renewable energy** by **country** between **2000** and **2020**\",\n \"output_fields\": [\"Entity\", \"Year\", \"Electricity from renewables (TWh)\"],\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Electricity from renewables (TWh)\",\n \"color\": \"Year\"\n },\n \"reason\": \"The user wants to compare renewable energy across countries between 2000 and 2020. The output field 'Renewable Energy' mentioned in the original chart_encodings maps to 'Electricity from renewables (TWh)' in the dataset. To create a grouped bar chart comparing these two years, we need to filter the data to only include years 2000 and 2020, and output the Entity (country), Year, and the renewable energy values. The chart_encodings are updated to use the actual column name from the dataset.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n transformed_df = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Select only the required columns\n transformed_df = transformed_df[['Entity', 'Year', 'Electricity from renewables (TWh)']]\n \n # Convert Year to string for better visualization as a categorical variable\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year for better readability\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"global-energy-20-small.csv","sourceTableIds":["global-energy-20-small.csv"],"instruction":"compare each countries renewable between 2000 and 2020","displayInstruction":"Compare **renewable energy** by **country** between **2000** and **2020**","chart":{"id":"chart-1760738769634","chartType":"Grouped Bar Chart","encodingMap":{"x":{"fieldID":"original--global-energy-20-small.csv--Entity"},"y":{"fieldID":"concept-1760738743125"},"color":{"channel":"color","bin":false,"fieldID":"original--global-energy-20-small.csv--Year"},"column":{"channel":"column","bin":false},"row":{"channel":"row","bin":false}},"tableRef":"global-energy-20-small.csv","saved":false,"source":"trigger","unread":false},"resultTableId":"table-97"},"explanation":{"agent":"CodeExplanationAgent","code":"The code performs a straightforward comparison analysis of renewable energy generation across countries:\n\n- **Filters** the dataset to include only data from years **2000** and **2020**, creating a 20-year comparison window\n- **Selects** three key columns: `Entity` (country name), `Year`, and `Electricity from renewables (TWh)` (renewable energy generation)\n- **Converts** the `Year` field to string format to treat it as a categorical variable for visualization purposes\n- **Sorts** the results by `Entity` and `Year` to organize countries alphabetically with their respective year data grouped together\n- **Returns** a cleaned dataset showing how renewable electricity generation has changed for each country between 2000 and 2020","concepts":[],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small_csv)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 117440.0, 146139.9994, 158029.9988, ..., 10006669.92, 10502929.69, 10707219.73, nan\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 56.18, 61.99, 63.02, 63.71\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n transformed_df = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Select only the required columns\n transformed_df = transformed_df[['Entity', 'Year', 'Electricity from renewables (TWh)']]\n \n # Convert Year to string for better visualization as a categorical variable\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year for better readability\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThe code performs a straightforward comparison analysis of renewable energy generation across countries:\n\n- **Filters** the dataset to include only data from years **2000** and **2020**, creating a 20-year comparison window\n- **Selects** three key columns: `Entity` (country name), `Year`, and `Electricity from renewables (TWh)` (renewable energy generation)\n- **Converts** the `Year` field to string format to treat it as a categorical variable for visualization purposes\n- **Sorts** the results by `Entity` and `Year` to organize countries alphabetically with their respective year data grouped together\n- **Returns** a cleaned dataset showing how renewable electricity generation has changed for each country between 2000 and 2020\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-27","displayId":"renewable-energy1","names":["Entity","Renewable Percentage","Year"],"rows":[{"Entity":"Australia","Renewable Percentage":8.6344368187,"Year":"2000"},{"Entity":"Australia","Renewable Percentage":25.5031684668,"Year":"2020"},{"Entity":"Brazil","Renewable Percentage":90.1307723743,"Year":"2000"},{"Entity":"Brazil","Renewable Percentage":84.6411771408,"Year":"2020"},{"Entity":"Canada","Renewable Percentage":61.8095917882,"Year":"2000"},{"Entity":"Canada","Renewable Percentage":68.7796436354,"Year":"2020"},{"Entity":"China","Renewable Percentage":16.639126586,"Year":"2000"},{"Entity":"China","Renewable Percentage":28.2464606924,"Year":"2020"},{"Entity":"France","Renewable Percentage":12.7117691154,"Year":"2000"},{"Entity":"France","Renewable Percentage":23.7610241821,"Year":"2020"},{"Entity":"Germany","Renewable Percentage":6.1977983575,"Year":"2000"},{"Entity":"Germany","Renewable Percentage":44.3324048937,"Year":"2020"},{"Entity":"Global Average","Renewable Percentage":16.4213212559,"Year":"2000"},{"Entity":"Global Average","Renewable Percentage":29.2955247263,"Year":"2020"},{"Entity":"India","Renewable Percentage":14.0481982534,"Year":"2000"},{"Entity":"India","Renewable Percentage":20.2059243238,"Year":"2020"},{"Entity":"Indonesia","Renewable Percentage":null,"Year":"2000"},{"Entity":"Indonesia","Renewable Percentage":null,"Year":"2020"},{"Entity":"Italy","Renewable Percentage":18.900241501,"Year":"2000"},{"Entity":"Italy","Renewable Percentage":42.0397741576,"Year":"2020"},{"Entity":"Japan","Renewable Percentage":10.5382436261,"Year":"2000"},{"Entity":"Japan","Renewable Percentage":21.324925062,"Year":"2020"},{"Entity":"Kazakhstan","Renewable Percentage":null,"Year":"2000"},{"Entity":"Kazakhstan","Renewable Percentage":null,"Year":"2020"},{"Entity":"Mexico","Renewable Percentage":22.9291160107,"Year":"2000"},{"Entity":"Mexico","Renewable Percentage":21.2552224134,"Year":"2020"},{"Entity":"Poland","Renewable Percentage":1.6273222517,"Year":"2000"},{"Entity":"Poland","Renewable Percentage":17.9648720886,"Year":"2020"},{"Entity":"Saudi Arabia","Renewable Percentage":null,"Year":"2000"},{"Entity":"Saudi Arabia","Renewable Percentage":null,"Year":"2020"},{"Entity":"South Africa","Renewable Percentage":0.9110805721,"Year":"2000"},{"Entity":"South Africa","Renewable Percentage":5.780581212,"Year":"2020"},{"Entity":"Spain","Renewable Percentage":15.6119862394,"Year":"2000"},{"Entity":"Spain","Renewable Percentage":43.8108805298,"Year":"2020"},{"Entity":"Thailand","Renewable Percentage":7.1261029822,"Year":"2000"},{"Entity":"Thailand","Renewable Percentage":13.7963737796,"Year":"2020"},{"Entity":"Ukraine","Renewable Percentage":6.5860921352,"Year":"2000"},{"Entity":"Ukraine","Renewable Percentage":11.8440577364,"Year":"2020"},{"Entity":"United Kingdom","Renewable Percentage":2.6657406913,"Year":"2000"},{"Entity":"United Kingdom","Renewable Percentage":42.8603962651,"Year":"2020"},{"Entity":"United States","Renewable Percentage":9.2298992662,"Year":"2000"},{"Entity":"United States","Renewable Percentage":20.3156921037,"Year":"2020"}],"metadata":{"Entity":{"type":"string","semanticType":"Name"},"Renewable Percentage":{"type":"number","semanticType":"Percentage"},"Year":{"type":"number","semanticType":"Year"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n df_filtered = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Calculate total electricity generation\n df_filtered['Total Electricity'] = (\n df_filtered['Electricity from fossil fuels (TWh)'] + \n df_filtered['Electricity from nuclear (TWh)'] + \n df_filtered['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_filtered['Renewable Percentage'] = (\n df_filtered['Electricity from renewables (TWh)'] / df_filtered['Total Electricity'] * 100\n )\n \n # Select required columns for individual countries\n country_data = df_filtered[['Entity', 'Year', 'Renewable Percentage']].copy()\n \n # Calculate global average for each year\n global_avg = df_filtered.groupby('Year').apply(\n lambda x: pd.Series({\n 'Entity': 'Global Average',\n 'Year': x['Year'].iloc[0],\n 'Renewable Percentage': (\n x['Electricity from renewables (TWh)'].sum() / x['Total Electricity'].sum() * 100\n )\n })\n ).reset_index(drop=True)\n \n # Combine country data with global average\n transformed_df = pd.concat([country_data, global_avg], ignore_index=True)\n \n # Convert Year to string for better visualization\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n","source":["global-energy-20-small.csv"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 117440.0, 146139.9994, 158029.9988, ..., 5736319.824, 5738290.039, nan, 9861099.609\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 61.99, 62.21, 63.02, 63.71\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"compare each countries renewable between 2000 and 2020\",\n \"chart_type\": \"Grouped Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Renewable Energy\",\n \"color\": \"Year\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Compare renewable energy (Electricity from renewables) for each country between the years 2000 and 2020. Filter the data to include only years 2000 and 2020, and prepare the output with country names, year, and renewable energy values for visualization as a grouped bar chart.\",\n \"display_instruction\": \"Compare **renewable energy** by **country** between **2000** and **2020**\",\n \"output_fields\": [\"Entity\", \"Year\", \"Electricity from renewables (TWh)\"],\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Electricity from renewables (TWh)\",\n \"color\": \"Year\"\n },\n \"reason\": \"The user wants to compare renewable energy across countries between 2000 and 2020. The output field 'Renewable Energy' mentioned in the original chart_encodings maps to 'Electricity from renewables (TWh)' in the dataset. To create a grouped bar chart comparing these two years, we need to filter the data to only include years 2000 and 2020, and output the Entity (country), Year, and the renewable energy values. The chart_encodings are updated to use the actual column name from the dataset.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n transformed_df = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Select only the required columns\n transformed_df = transformed_df[['Entity', 'Year', 'Electricity from renewables (TWh)']]\n \n # Convert Year to string for better visualization as a categorical variable\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year for better readability\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n Electricity from renewables (TWh) Entity Year\n0 17.11 Australia 2000\n1 63.99 Australia 2020\n2 308.77 Brazil 2000\n3 520.01 Brazil 2020\n4 363.70 Canada 2000\n5 429.24 Canada 2020\n6 225.56 China 2000\n7 2184.94 China 2020\n8 67.83 France 2000\n9 125.28 France 2020\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show their renewable percentage instead, and add an entity to show global average\",\n \"chart_type\": \"Grouped Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Electricity from renewables (TWh)\",\n \"color\": \"Year\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Calculate the renewable energy percentage (proportion of renewables to total electricity generation from all sources) for each country between 2000 and 2020. Add a 'Global Average' entity that represents the worldwide average renewable percentage for each year. The output should include Entity (with 'Global Average' added), Year, and the renewable percentage.\",\n \"display_instruction\": \"Show **renewable energy percentage** by **country** with **global average** for **2000** and **2020**\",\n \"output_fields\": [\"Entity\", \"Year\", \"Renewable Percentage\"],\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Renewable Percentage\",\n \"color\": \"Year\"\n },\n \"reason\": \"The user wants to see renewable percentage instead of absolute values, and wants to add a global average entity for comparison. Renewable percentage is calculated as the ratio of renewable energy to total electricity generation (fossil fuels + nuclear + renewables). A 'Global Average' entity needs to be computed by aggregating all countries' data for each year. The y-axis encoding is updated to reflect the new metric 'Renewable Percentage'.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n df_filtered = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Calculate total electricity generation\n df_filtered['Total Electricity'] = (\n df_filtered['Electricity from fossil fuels (TWh)'] + \n df_filtered['Electricity from nuclear (TWh)'] + \n df_filtered['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_filtered['Renewable Percentage'] = (\n df_filtered['Electricity from renewables (TWh)'] / df_filtered['Total Electricity'] * 100\n )\n \n # Select required columns for individual countries\n country_data = df_filtered[['Entity', 'Year', 'Renewable Percentage']].copy()\n \n # Calculate global average for each year\n global_avg = df_filtered.groupby('Year').apply(\n lambda x: pd.Series({\n 'Entity': 'Global Average',\n 'Year': x['Year'].iloc[0],\n 'Renewable Percentage': (\n x['Electricity from renewables (TWh)'].sum() / x['Total Electricity'].sum() * 100\n )\n })\n ).reset_index(drop=True)\n \n # Combine country data with global average\n transformed_df = pd.concat([country_data, global_avg], ignore_index=True)\n \n # Convert Year to string for better visualization\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"table-97","sourceTableIds":["global-energy-20-small.csv"],"instruction":"show their renewable percentage instead, and add an entity to show global average","displayInstruction":"Show **renewable energy percentage** by **country** with **global average** for **2000** and **2020**","chart":{"id":"chart-1760738813383","chartType":"Grouped Bar Chart","encodingMap":{"x":{"fieldID":"original--global-energy-20-small.csv--Entity"},"y":{"fieldID":"original--global-energy-20-small.csv--Electricity from renewables (TWh)"},"color":{"fieldID":"original--global-energy-20-small.csv--Year"},"column":{"channel":"column","bin":false},"row":{"channel":"row","bin":false}},"tableRef":"table-97","saved":false,"source":"trigger","unread":false},"resultTableId":"table-27"},"explanation":{"agent":"CodeExplanationAgent","code":"The code performs the following transformation steps:\n\n1. **Filter data** to include only records from **2000** and **2020**\n2. Calculate **Total Electricity** generation by summing three sources: **fossil fuels**, **nuclear**, and **renewables** (all measured in TWh)\n3. Compute **Renewable Percentage** for each country and year by dividing **Electricity from renewables** by **Total Electricity** and multiplying by 100\n4. Extract relevant columns: **Entity** (country name), **Year**, and **Renewable Percentage**\n5. Calculate **Global Average** renewable percentage for each year by:\n - Summing all countries' **renewable electricity** generation\n - Dividing by the sum of all countries' **total electricity** generation\n - Multiplying by 100 to get percentage\n6. Combine individual country data with the **Global Average** statistics\n7. Convert **Year** values to string format\n8. Sort results by **Entity** name and **Year**","concepts":[{"explanation":"The sum of electricity generated from all three sources (fossil fuels, nuclear, and renewables) measured in terawatt-hours (TWh). This represents the total electricity production capacity for each country.","field":"Total Electricity"},{"explanation":"The proportion of electricity generated from renewable sources relative to total electricity production, expressed as a percentage: \\( \\text{Renewable Percentage} = \\frac{\\text{Electricity from renewables}}{\\text{Total Electricity}} \\times 100 \\). This metric indicates the extent of renewable energy adoption in a country's electricity mix.","field":"Renewable Percentage"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small_csv)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 117440.0, 146139.9994, 158029.9988, ..., 10096009.77, 10502929.69, 10707219.73, nan\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., nan, nan, nan, nan\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n df_filtered = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Calculate total electricity generation\n df_filtered['Total Electricity'] = (\n df_filtered['Electricity from fossil fuels (TWh)'] + \n df_filtered['Electricity from nuclear (TWh)'] + \n df_filtered['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_filtered['Renewable Percentage'] = (\n df_filtered['Electricity from renewables (TWh)'] / df_filtered['Total Electricity'] * 100\n )\n \n # Select required columns for individual countries\n country_data = df_filtered[['Entity', 'Year', 'Renewable Percentage']].copy()\n \n # Calculate global average for each year\n global_avg = df_filtered.groupby('Year').apply(\n lambda x: pd.Series({\n 'Entity': 'Global Average',\n 'Year': x['Year'].iloc[0],\n 'Renewable Percentage': (\n x['Electricity from renewables (TWh)'].sum() / x['Total Electricity'].sum() * 100\n )\n })\n ).reset_index(drop=True)\n \n # Combine country data with global average\n transformed_df = pd.concat([country_data, global_avg], ignore_index=True)\n \n # Convert Year to string for better visualization\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThe code performs the following transformation steps:\n\n1. **Filter data** to include only records from **2000** and **2020**\n2. Calculate **Total Electricity** generation by summing three sources: **fossil fuels**, **nuclear**, and **renewables** (all measured in TWh)\n3. Compute **Renewable Percentage** for each country and year by dividing **Electricity from renewables** by **Total Electricity** and multiplying by 100\n4. Extract relevant columns: **Entity** (country name), **Year**, and **Renewable Percentage**\n5. Calculate **Global Average** renewable percentage for each year by:\n - Summing all countries' **renewable electricity** generation\n - Dividing by the sum of all countries' **total electricity** generation\n - Multiplying by 100 to get percentage\n6. Combine individual country data with the **Global Average** statistics\n7. Convert **Year** values to string format\n8. Sort results by **Entity** name and **Year**\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"Total Electricity\",\n \"explanation\": \"The sum of electricity generated from all three sources (fossil fuels, nuclear, and renewables) measured in terawatt-hours (TWh). This represents the total electricity production capacity for each country.\"\n },\n {\n \"field\": \"Renewable Percentage\",\n \"explanation\": \"The proportion of electricity generated from renewable sources relative to total electricity production, expressed as a percentage: \\\\( \\\\text{Renewable Percentage} = \\\\frac{\\\\text{Electricity from renewables}}{\\\\text{Total Electricity}} \\\\times 100 \\\\). This metric indicates the extent of renewable energy adoption in a country's electricity mix.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-81","displayId":"energy-source1","names":["Energy","Energy Source","Year"],"rows":[{"Energy":7160.71,"Energy Source":"Fossil Fuels","Year":2000},{"Energy":1996.65,"Energy Source":"Nuclear","Year":2000},{"Energy":1742.56,"Energy Source":"Renewables","Year":2000},{"Energy":7273.89,"Energy Source":"Fossil Fuels","Year":2001},{"Energy":2037.54,"Energy Source":"Nuclear","Year":2001},{"Energy":1690.11,"Energy Source":"Renewables","Year":2001},{"Energy":7621.07,"Energy Source":"Fossil Fuels","Year":2002},{"Energy":2042.18,"Energy Source":"Nuclear","Year":2002},{"Energy":1757.63,"Energy Source":"Renewables","Year":2002},{"Energy":8043.86,"Energy Source":"Fossil Fuels","Year":2003},{"Energy":1998.52,"Energy Source":"Nuclear","Year":2003},{"Energy":1804.52,"Energy Source":"Renewables","Year":2003},{"Energy":8399.72,"Energy Source":"Fossil Fuels","Year":2004},{"Energy":2095,"Energy Source":"Nuclear","Year":2004},{"Energy":1952.72,"Energy Source":"Renewables","Year":2004},{"Energy":8828.43,"Energy Source":"Fossil Fuels","Year":2005},{"Energy":2094.4,"Energy Source":"Nuclear","Year":2005},{"Energy":2025.26,"Energy Source":"Renewables","Year":2005},{"Energy":9183.05,"Energy Source":"Fossil Fuels","Year":2006},{"Energy":2120.38,"Energy Source":"Nuclear","Year":2006},{"Energy":2165.94,"Energy Source":"Renewables","Year":2006},{"Energy":9853.09,"Energy Source":"Fossil Fuels","Year":2007},{"Energy":2067.04,"Energy Source":"Nuclear","Year":2007},{"Energy":2256.79,"Energy Source":"Renewables","Year":2007},{"Energy":9817.15,"Energy Source":"Fossil Fuels","Year":2008},{"Energy":2043.94,"Energy Source":"Nuclear","Year":2008},{"Energy":2496.03,"Energy Source":"Renewables","Year":2008},{"Energy":9686.86,"Energy Source":"Fossil Fuels","Year":2009},{"Energy":2017.25,"Energy Source":"Nuclear","Year":2009},{"Energy":2563.95,"Energy Source":"Renewables","Year":2009},{"Energy":10427.03,"Energy Source":"Fossil Fuels","Year":2010},{"Energy":2083.37,"Energy Source":"Nuclear","Year":2010},{"Energy":2802.89,"Energy Source":"Renewables","Year":2010},{"Energy":10974.83,"Energy Source":"Fossil Fuels","Year":2011},{"Energy":1956,"Energy Source":"Nuclear","Year":2011},{"Energy":2997.29,"Energy Source":"Renewables","Year":2011},{"Energy":11277.49,"Energy Source":"Fossil Fuels","Year":2012},{"Energy":1788.26,"Energy Source":"Nuclear","Year":2012},{"Energy":3226.1,"Energy Source":"Renewables","Year":2012},{"Energy":11561.86,"Energy Source":"Fossil Fuels","Year":2013},{"Energy":1813,"Energy Source":"Nuclear","Year":2013},{"Energy":3473.9,"Energy Source":"Renewables","Year":2013},{"Energy":11761.51,"Energy Source":"Fossil Fuels","Year":2014},{"Energy":1847.87,"Energy Source":"Nuclear","Year":2014},{"Energy":3753.03,"Energy Source":"Renewables","Year":2014},{"Energy":11653.61,"Energy Source":"Fossil Fuels","Year":2015},{"Energy":1886.61,"Energy Source":"Nuclear","Year":2015},{"Energy":3903.7,"Energy Source":"Renewables","Year":2015},{"Energy":11785.35,"Energy Source":"Fossil Fuels","Year":2016},{"Energy":1906.92,"Energy Source":"Nuclear","Year":2016},{"Energy":4186.83,"Energy Source":"Renewables","Year":2016},{"Energy":12003.24,"Energy Source":"Fossil Fuels","Year":2017},{"Energy":1942.94,"Energy Source":"Nuclear","Year":2017},{"Energy":4500.22,"Energy Source":"Renewables","Year":2017},{"Energy":12445.24,"Energy Source":"Fossil Fuels","Year":2018},{"Energy":2016.82,"Energy Source":"Nuclear","Year":2018},{"Energy":4834.65,"Energy Source":"Renewables","Year":2018},{"Energy":12355.91,"Energy Source":"Fossil Fuels","Year":2019},{"Energy":2073.87,"Energy Source":"Nuclear","Year":2019},{"Energy":5146.11,"Energy Source":"Renewables","Year":2019},{"Energy":12032.75,"Energy Source":"Fossil Fuels","Year":2020},{"Energy":1974.46,"Energy Source":"Nuclear","Year":2020},{"Energy":5497.73,"Energy Source":"Renewables","Year":2020}],"metadata":{"Energy":{"type":"number","semanticType":"Number"},"Energy Source":{"type":"string","semanticType":"String","levels":["Fossil Fuels","Nuclear","Renewables"]},"Year":{"type":"number","semanticType":"Year"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Group by Year and sum electricity generation from all countries\n global_energy = df_energy.groupby('Year').agg({\n 'Electricity from fossil fuels (TWh)': 'sum',\n 'Electricity from nuclear (TWh)': 'sum',\n 'Electricity from renewables (TWh)': 'sum'\n }).reset_index()\n \n # Rename columns for clarity\n global_energy.columns = ['Year', 'Fossil Fuels', 'Nuclear', 'Renewables']\n \n # Transform to long format\n transformed_df = pd.melt(\n global_energy,\n id_vars=['Year'],\n value_vars=['Fossil Fuels', 'Nuclear', 'Renewables'],\n var_name='Energy Source',\n value_name='Energy'\n )\n \n # Sort by Year and Energy Source\n transformed_df = transformed_df.sort_values(['Year', 'Energy Source']).reset_index(drop=True)\n \n return transformed_df\n","source":["global-energy-20-small.csv"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 117440.0, 146139.9994, 158029.9988, ..., 5736319.824, 5738290.039, nan, 9861099.609\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 61.99, 62.21, 63.02, 63.71\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"compare each countries renewable between 2000 and 2020\",\n \"chart_type\": \"Grouped Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Renewable Energy\",\n \"color\": \"Year\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Compare renewable energy (Electricity from renewables) for each country between the years 2000 and 2020. Filter the data to include only years 2000 and 2020, and prepare the output with country names, year, and renewable energy values for visualization as a grouped bar chart.\",\n \"display_instruction\": \"Compare **renewable energy** by **country** between **2000** and **2020**\",\n \"output_fields\": [\"Entity\", \"Year\", \"Electricity from renewables (TWh)\"],\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Electricity from renewables (TWh)\",\n \"color\": \"Year\"\n },\n \"reason\": \"The user wants to compare renewable energy across countries between 2000 and 2020. The output field 'Renewable Energy' mentioned in the original chart_encodings maps to 'Electricity from renewables (TWh)' in the dataset. To create a grouped bar chart comparing these two years, we need to filter the data to only include years 2000 and 2020, and output the Entity (country), Year, and the renewable energy values. The chart_encodings are updated to use the actual column name from the dataset.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n transformed_df = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Select only the required columns\n transformed_df = transformed_df[['Entity', 'Year', 'Electricity from renewables (TWh)']]\n \n # Convert Year to string for better visualization as a categorical variable\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year for better readability\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n Electricity from renewables (TWh) Entity Year\n0 17.11 Australia 2000\n1 63.99 Australia 2020\n2 308.77 Brazil 2000\n3 520.01 Brazil 2020\n4 363.70 Canada 2000\n5 429.24 Canada 2020\n6 225.56 China 2000\n7 2184.94 China 2020\n8 67.83 France 2000\n9 125.28 France 2020\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show their renewable percentage instead, and add an entity to show global average\",\n \"chart_type\": \"Grouped Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Electricity from renewables (TWh)\",\n \"color\": \"Year\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Calculate the renewable energy percentage (proportion of renewables to total electricity generation from all sources) for each country between 2000 and 2020. Add a 'Global Average' entity that represents the worldwide average renewable percentage for each year. The output should include Entity (with 'Global Average' added), Year, and the renewable percentage.\",\n \"display_instruction\": \"Show **renewable energy percentage** by **country** with **global average** for **2000** and **2020**\",\n \"output_fields\": [\"Entity\", \"Year\", \"Renewable Percentage\"],\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Renewable Percentage\",\n \"color\": \"Year\"\n },\n \"reason\": \"The user wants to see renewable percentage instead of absolute values, and wants to add a global average entity for comparison. Renewable percentage is calculated as the ratio of renewable energy to total electricity generation (fossil fuels + nuclear + renewables). A 'Global Average' entity needs to be computed by aggregating all countries' data for each year. The y-axis encoding is updated to reflect the new metric 'Renewable Percentage'.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n df_filtered = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Calculate total electricity generation\n df_filtered['Total Electricity'] = (\n df_filtered['Electricity from fossil fuels (TWh)'] + \n df_filtered['Electricity from nuclear (TWh)'] + \n df_filtered['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_filtered['Renewable Percentage'] = (\n df_filtered['Electricity from renewables (TWh)'] / df_filtered['Total Electricity'] * 100\n )\n \n # Select required columns for individual countries\n country_data = df_filtered[['Entity', 'Year', 'Renewable Percentage']].copy()\n \n # Calculate global average for each year\n global_avg = df_filtered.groupby('Year').apply(\n lambda x: pd.Series({\n 'Entity': 'Global Average',\n 'Year': x['Year'].iloc[0],\n 'Renewable Percentage': (\n x['Electricity from renewables (TWh)'].sum() / x['Total Electricity'].sum() * 100\n )\n })\n ).reset_index(drop=True)\n \n # Combine country data with global average\n transformed_df = pd.concat([country_data, global_avg], ignore_index=True)\n \n # Convert Year to string for better visualization\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n Entity Renewable Percentage Year\n0 Australia 8.634437 2000\n1 Australia 25.503168 2020\n2 Brazil 90.130772 2000\n3 Brazil 84.641177 2020\n4 Canada 61.809592 2000\n5 Canada 68.779644 2020\n6 China 16.639127 2000\n7 China 28.246461 2020\n8 France 12.711769 2000\n9 France 23.761024 2020\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show global energy composition over time\",\n \"chart_type\": \"Custom Area\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"Energy\",\n \"color\": \"Energy Source\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Show the global energy composition over time by aggregating electricity generation from all countries for each year, broken down by energy source (fossil fuels, nuclear, and renewables). The output should be in long format with Year, Energy Source, and Energy values to support a stacked area chart visualization.\",\n \"display_instruction\": \"Show global **energy composition** by **energy source** over time\",\n \"output_fields\": [\"Year\", \"Energy Source\", \"Energy\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"Energy\",\n \"color\": \"Energy Source\"\n },\n \"reason\": \"The user wants to visualize global energy composition over time, which requires aggregating electricity generation data across all countries for each year and breaking it down by energy source type. The data needs to be transformed into long format where each row represents a year-energy source combination, with the total energy value for that source globally. This follows up from the previous renewable percentage analysis by showing the overall energy mix trends rather than focusing on specific countries or percentages.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Group by Year and sum electricity generation from all countries\n global_energy = df_energy.groupby('Year').agg({\n 'Electricity from fossil fuels (TWh)': 'sum',\n 'Electricity from nuclear (TWh)': 'sum',\n 'Electricity from renewables (TWh)': 'sum'\n }).reset_index()\n \n # Rename columns for clarity\n global_energy.columns = ['Year', 'Fossil Fuels', 'Nuclear', 'Renewables']\n \n # Transform to long format\n transformed_df = pd.melt(\n global_energy,\n id_vars=['Year'],\n value_vars=['Fossil Fuels', 'Nuclear', 'Renewables'],\n var_name='Energy Source',\n value_name='Energy'\n )\n \n # Sort by Year and Energy Source\n transformed_df = transformed_df.sort_values(['Year', 'Energy Source']).reset_index(drop=True)\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"table-27","sourceTableIds":["global-energy-20-small.csv"],"instruction":"show global energy composition over time","displayInstruction":"Show global **energy composition** by **energy source** over time","chart":{"id":"chart-1760738942326","chartType":"Custom Area","encodingMap":{"x":{"channel":"x","bin":false,"fieldID":"original--global-energy-20-small.csv--Year"},"y":{"channel":"y","bin":false,"fieldID":"concept-1760738917347"},"x2":{"channel":"x2","bin":false},"y2":{"channel":"y2","bin":false},"color":{"channel":"color","bin":false,"fieldID":"concept-1760738921927"},"column":{"channel":"column","bin":false},"row":{"channel":"row","bin":false}},"tableRef":"table-27","saved":false,"source":"trigger","unread":false},"resultTableId":"table-81"},"explanation":{"agent":"CodeExplanationAgent","code":"- Group the energy data by **Year** and aggregate electricity generation values across all countries by summing **Electricity from fossil fuels (TWh)**, **Electricity from nuclear (TWh)**, and **Electricity from renewables (TWh)**\n- Rename the aggregated columns to simplified labels: **Fossil Fuels**, **Nuclear**, and **Renewables**\n- Transform the data from wide format to long format by unpivoting the three energy source columns into two columns: **Energy Source** (containing the type of energy) and **Energy** (containing the generation value in TWh)\n- Sort the resulting dataset by **Year** and **Energy Source** for consistent ordering","concepts":[],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small_csv)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 158029.9988, 190729.9957, 209229.9957, ..., nan, 4956060.059, nan, 9861099.609\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., nan, nan, nan, nan\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Group by Year and sum electricity generation from all countries\n global_energy = df_energy.groupby('Year').agg({\n 'Electricity from fossil fuels (TWh)': 'sum',\n 'Electricity from nuclear (TWh)': 'sum',\n 'Electricity from renewables (TWh)': 'sum'\n }).reset_index()\n \n # Rename columns for clarity\n global_energy.columns = ['Year', 'Fossil Fuels', 'Nuclear', 'Renewables']\n \n # Transform to long format\n transformed_df = pd.melt(\n global_energy,\n id_vars=['Year'],\n value_vars=['Fossil Fuels', 'Nuclear', 'Renewables'],\n var_name='Energy Source',\n value_name='Energy'\n )\n \n # Sort by Year and Energy Source\n transformed_df = transformed_df.sort_values(['Year', 'Energy Source']).reset_index(drop=True)\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\n- Group the energy data by **Year** and aggregate electricity generation values across all countries by summing **Electricity from fossil fuels (TWh)**, **Electricity from nuclear (TWh)**, and **Electricity from renewables (TWh)**\n- Rename the aggregated columns to simplified labels: **Fossil Fuels**, **Nuclear**, and **Renewables**\n- Transform the data from wide format to long format by unpivoting the three energy source columns into two columns: **Energy Source** (containing the type of energy) and **Energy** (containing the generation value in TWh)\n- Sort the resulting dataset by **Year** and **Energy Source** for consistent ordering\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-10","displayId":"energy-source2","names":["Energy","Energy Source","Entity","Year"],"rows":[{"Energy":1113.3,"Energy Source":"Fossil Fuels","Entity":"China","Year":2000},{"Energy":16.74,"Energy Source":"Nuclear","Entity":"China","Year":2000},{"Energy":225.56,"Energy Source":"Renewables","Entity":"China","Year":2000},{"Energy":1182.59,"Energy Source":"Fossil Fuels","Entity":"China","Year":2001},{"Energy":17.47,"Energy Source":"Nuclear","Entity":"China","Year":2001},{"Energy":280.73,"Energy Source":"Renewables","Entity":"China","Year":2001},{"Energy":1337.46,"Energy Source":"Fossil Fuels","Entity":"China","Year":2002},{"Energy":25.13,"Energy Source":"Nuclear","Entity":"China","Year":2002},{"Energy":291.41,"Energy Source":"Renewables","Entity":"China","Year":2002},{"Energy":1579.96,"Energy Source":"Fossil Fuels","Entity":"China","Year":2003},{"Energy":43.34,"Energy Source":"Nuclear","Entity":"China","Year":2003},{"Energy":287.28,"Energy Source":"Renewables","Entity":"China","Year":2003},{"Energy":1795.41,"Energy Source":"Fossil Fuels","Entity":"China","Year":2004},{"Energy":50.47,"Energy Source":"Nuclear","Entity":"China","Year":2004},{"Energy":357.43,"Energy Source":"Renewables","Entity":"China","Year":2004},{"Energy":2042.8,"Energy Source":"Fossil Fuels","Entity":"China","Year":2005},{"Energy":53.09,"Energy Source":"Nuclear","Entity":"China","Year":2005},{"Energy":404.37,"Energy Source":"Renewables","Entity":"China","Year":2005},{"Energy":2364.16,"Energy Source":"Fossil Fuels","Entity":"China","Year":2006},{"Energy":54.84,"Energy Source":"Nuclear","Entity":"China","Year":2006},{"Energy":446.72,"Energy Source":"Renewables","Entity":"China","Year":2006},{"Energy":2718.7,"Energy Source":"Fossil Fuels","Entity":"China","Year":2007},{"Energy":62.13,"Energy Source":"Nuclear","Entity":"China","Year":2007},{"Energy":500.71,"Energy Source":"Renewables","Entity":"China","Year":2007},{"Energy":2762.29,"Energy Source":"Fossil Fuels","Entity":"China","Year":2008},{"Energy":68.39,"Energy Source":"Nuclear","Entity":"China","Year":2008},{"Energy":665.08,"Energy Source":"Renewables","Entity":"China","Year":2008},{"Energy":2980.2,"Energy Source":"Fossil Fuels","Entity":"China","Year":2009},{"Energy":70.05,"Energy Source":"Nuclear","Entity":"China","Year":2009},{"Energy":664.39,"Energy Source":"Renewables","Entity":"China","Year":2009},{"Energy":3326.19,"Energy Source":"Fossil Fuels","Entity":"China","Year":2010},{"Energy":74.74,"Energy Source":"Nuclear","Entity":"China","Year":2010},{"Energy":786.38,"Energy Source":"Renewables","Entity":"China","Year":2010},{"Energy":3811.77,"Energy Source":"Fossil Fuels","Entity":"China","Year":2011},{"Energy":87.2,"Energy Source":"Nuclear","Entity":"China","Year":2011},{"Energy":792.38,"Energy Source":"Renewables","Entity":"China","Year":2011},{"Energy":3869.38,"Energy Source":"Fossil Fuels","Entity":"China","Year":2012},{"Energy":98.32,"Energy Source":"Nuclear","Entity":"China","Year":2012},{"Energy":999.56,"Energy Source":"Renewables","Entity":"China","Year":2012},{"Energy":4203.77,"Energy Source":"Fossil Fuels","Entity":"China","Year":2013},{"Energy":111.5,"Energy Source":"Nuclear","Entity":"China","Year":2013},{"Energy":1093.37,"Energy Source":"Renewables","Entity":"China","Year":2013},{"Energy":4345.86,"Energy Source":"Fossil Fuels","Entity":"China","Year":2014},{"Energy":133.22,"Energy Source":"Nuclear","Entity":"China","Year":2014},{"Energy":1289.23,"Energy Source":"Renewables","Entity":"China","Year":2014},{"Energy":4222.76,"Energy Source":"Fossil Fuels","Entity":"China","Year":2015},{"Energy":171.38,"Energy Source":"Nuclear","Entity":"China","Year":2015},{"Energy":1393.66,"Energy Source":"Renewables","Entity":"China","Year":2015},{"Energy":4355,"Energy Source":"Fossil Fuels","Entity":"China","Year":2016},{"Energy":213.18,"Energy Source":"Nuclear","Entity":"China","Year":2016},{"Energy":1522.79,"Energy Source":"Renewables","Entity":"China","Year":2016},{"Energy":4643.1,"Energy Source":"Fossil Fuels","Entity":"China","Year":2017},{"Energy":248.1,"Energy Source":"Nuclear","Entity":"China","Year":2017},{"Energy":1667.06,"Energy Source":"Renewables","Entity":"China","Year":2017},{"Energy":4990.28,"Energy Source":"Fossil Fuels","Entity":"China","Year":2018},{"Energy":295,"Energy Source":"Nuclear","Entity":"China","Year":2018},{"Energy":1835.32,"Energy Source":"Renewables","Entity":"China","Year":2018},{"Energy":5098.22,"Energy Source":"Fossil Fuels","Entity":"China","Year":2019},{"Energy":348.7,"Energy Source":"Nuclear","Entity":"China","Year":2019},{"Energy":2014.57,"Energy Source":"Renewables","Entity":"China","Year":2019},{"Energy":5184.13,"Energy Source":"Fossil Fuels","Entity":"China","Year":2020},{"Energy":366.2,"Energy Source":"Nuclear","Entity":"China","Year":2020},{"Energy":2184.94,"Energy Source":"Renewables","Entity":"China","Year":2020},{"Energy":475.35,"Energy Source":"Fossil Fuels","Entity":"India","Year":2000},{"Energy":15.77,"Energy Source":"Nuclear","Entity":"India","Year":2000},{"Energy":80.27,"Energy Source":"Renewables","Entity":"India","Year":2000},{"Energy":491.01,"Energy Source":"Fossil Fuels","Entity":"India","Year":2001},{"Energy":18.89,"Energy Source":"Nuclear","Entity":"India","Year":2001},{"Energy":76.19,"Energy Source":"Renewables","Entity":"India","Year":2001},{"Energy":517.51,"Energy Source":"Fossil Fuels","Entity":"India","Year":2002},{"Energy":19.35,"Energy Source":"Nuclear","Entity":"India","Year":2002},{"Energy":72.78,"Energy Source":"Renewables","Entity":"India","Year":2002},{"Energy":545.36,"Energy Source":"Fossil Fuels","Entity":"India","Year":2003},{"Energy":18.14,"Energy Source":"Nuclear","Entity":"India","Year":2003},{"Energy":74.63,"Energy Source":"Renewables","Entity":"India","Year":2003},{"Energy":567.86,"Energy Source":"Fossil Fuels","Entity":"India","Year":2004},{"Energy":21.26,"Energy Source":"Nuclear","Entity":"India","Year":2004},{"Energy":109.2,"Energy Source":"Renewables","Entity":"India","Year":2004},{"Energy":579.32,"Energy Source":"Fossil Fuels","Entity":"India","Year":2005},{"Energy":17.73,"Energy Source":"Nuclear","Entity":"India","Year":2005},{"Energy":107.47,"Energy Source":"Renewables","Entity":"India","Year":2005},{"Energy":599.24,"Energy Source":"Fossil Fuels","Entity":"India","Year":2006},{"Energy":17.63,"Energy Source":"Nuclear","Entity":"India","Year":2006},{"Energy":127.56,"Energy Source":"Renewables","Entity":"India","Year":2006},{"Energy":636.68,"Energy Source":"Fossil Fuels","Entity":"India","Year":2007},{"Energy":17.83,"Energy Source":"Nuclear","Entity":"India","Year":2007},{"Energy":141.75,"Energy Source":"Renewables","Entity":"India","Year":2007},{"Energy":674.27,"Energy Source":"Fossil Fuels","Entity":"India","Year":2008},{"Energy":15.23,"Energy Source":"Nuclear","Entity":"India","Year":2008},{"Energy":138.91,"Energy Source":"Renewables","Entity":"India","Year":2008},{"Energy":728.56,"Energy Source":"Fossil Fuels","Entity":"India","Year":2009},{"Energy":16.82,"Energy Source":"Nuclear","Entity":"India","Year":2009},{"Energy":134.33,"Energy Source":"Renewables","Entity":"India","Year":2009},{"Energy":771.78,"Energy Source":"Fossil Fuels","Entity":"India","Year":2010},{"Energy":23.08,"Energy Source":"Nuclear","Entity":"India","Year":2010},{"Energy":142.61,"Energy Source":"Renewables","Entity":"India","Year":2010},{"Energy":828.16,"Energy Source":"Fossil Fuels","Entity":"India","Year":2011},{"Energy":32.22,"Energy Source":"Nuclear","Entity":"India","Year":2011},{"Energy":173.62,"Energy Source":"Renewables","Entity":"India","Year":2011},{"Energy":893.45,"Energy Source":"Fossil Fuels","Entity":"India","Year":2012},{"Energy":33.14,"Energy Source":"Nuclear","Entity":"India","Year":2012},{"Energy":165.25,"Energy Source":"Renewables","Entity":"India","Year":2012},{"Energy":924.93,"Energy Source":"Fossil Fuels","Entity":"India","Year":2013},{"Energy":33.31,"Energy Source":"Nuclear","Entity":"India","Year":2013},{"Energy":187.9,"Energy Source":"Renewables","Entity":"India","Year":2013},{"Energy":1025.29,"Energy Source":"Fossil Fuels","Entity":"India","Year":2014},{"Energy":34.69,"Energy Source":"Nuclear","Entity":"India","Year":2014},{"Energy":202.04,"Energy Source":"Renewables","Entity":"India","Year":2014},{"Energy":1080.44,"Energy Source":"Fossil Fuels","Entity":"India","Year":2015},{"Energy":38.31,"Energy Source":"Nuclear","Entity":"India","Year":2015},{"Energy":203.21,"Energy Source":"Renewables","Entity":"India","Year":2015},{"Energy":1155.52,"Energy Source":"Fossil Fuels","Entity":"India","Year":2016},{"Energy":37.9,"Energy Source":"Nuclear","Entity":"India","Year":2016},{"Energy":208.21,"Energy Source":"Renewables","Entity":"India","Year":2016},{"Energy":1198.85,"Energy Source":"Fossil Fuels","Entity":"India","Year":2017},{"Energy":37.41,"Energy Source":"Nuclear","Entity":"India","Year":2017},{"Energy":234.9,"Energy Source":"Renewables","Entity":"India","Year":2017},{"Energy":1276.32,"Energy Source":"Fossil Fuels","Entity":"India","Year":2018},{"Energy":39.05,"Energy Source":"Nuclear","Entity":"India","Year":2018},{"Energy":263.61,"Energy Source":"Renewables","Entity":"India","Year":2018},{"Energy":1273.59,"Energy Source":"Fossil Fuels","Entity":"India","Year":2019},{"Energy":45.16,"Energy Source":"Nuclear","Entity":"India","Year":2019},{"Energy":303.16,"Energy Source":"Renewables","Entity":"India","Year":2019},{"Energy":1202.34,"Energy Source":"Fossil Fuels","Entity":"India","Year":2020},{"Energy":44.61,"Energy Source":"Nuclear","Entity":"India","Year":2020},{"Energy":315.76,"Energy Source":"Renewables","Entity":"India","Year":2020},{"Energy":2697.28,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2000},{"Energy":753.89,"Energy Source":"Nuclear","Entity":"United States","Year":2000},{"Energy":350.93,"Energy Source":"Renewables","Entity":"United States","Year":2000},{"Energy":2678.68,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2001},{"Energy":768.83,"Energy Source":"Nuclear","Entity":"United States","Year":2001},{"Energy":280.06,"Energy Source":"Renewables","Entity":"United States","Year":2001},{"Energy":2727.83,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2002},{"Energy":780.06,"Energy Source":"Nuclear","Entity":"United States","Year":2002},{"Energy":336.34,"Energy Source":"Renewables","Entity":"United States","Year":2002},{"Energy":2756.03,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2003},{"Energy":763.73,"Energy Source":"Nuclear","Entity":"United States","Year":2003},{"Energy":349.18,"Energy Source":"Renewables","Entity":"United States","Year":2003},{"Energy":2818.28,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2004},{"Energy":788.53,"Energy Source":"Nuclear","Entity":"United States","Year":2004},{"Energy":345.14,"Energy Source":"Renewables","Entity":"United States","Year":2004},{"Energy":2899.96,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2005},{"Energy":781.99,"Energy Source":"Nuclear","Entity":"United States","Year":2005},{"Energy":353.04,"Energy Source":"Renewables","Entity":"United States","Year":2005},{"Energy":2878.56,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2006},{"Energy":787.22,"Energy Source":"Nuclear","Entity":"United States","Year":2006},{"Energy":381.16,"Energy Source":"Renewables","Entity":"United States","Year":2006},{"Energy":2988.24,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2007},{"Energy":806.42,"Energy Source":"Nuclear","Entity":"United States","Year":2007},{"Energy":347.91,"Energy Source":"Renewables","Entity":"United States","Year":2007},{"Energy":2924.21,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2008},{"Energy":806.21,"Energy Source":"Nuclear","Entity":"United States","Year":2008},{"Energy":377.11,"Energy Source":"Renewables","Entity":"United States","Year":2008},{"Energy":2725.41,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2009},{"Energy":798.85,"Energy Source":"Nuclear","Entity":"United States","Year":2009},{"Energy":415.56,"Energy Source":"Renewables","Entity":"United States","Year":2009},{"Energy":2882.49,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2010},{"Energy":806.97,"Energy Source":"Nuclear","Entity":"United States","Year":2010},{"Energy":424.48,"Energy Source":"Renewables","Entity":"United States","Year":2010},{"Energy":2788.93,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2011},{"Energy":790.2,"Energy Source":"Nuclear","Entity":"United States","Year":2011},{"Energy":509.74,"Energy Source":"Renewables","Entity":"United States","Year":2011},{"Energy":2779.02,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2012},{"Energy":769.33,"Energy Source":"Nuclear","Entity":"United States","Year":2012},{"Energy":492.32,"Energy Source":"Renewables","Entity":"United States","Year":2012},{"Energy":2746.21,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2013},{"Energy":789.02,"Energy Source":"Nuclear","Entity":"United States","Year":2013},{"Energy":520.38,"Energy Source":"Renewables","Entity":"United States","Year":2013},{"Energy":2752.01,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2014},{"Energy":797.17,"Energy Source":"Nuclear","Entity":"United States","Year":2014},{"Energy":546.83,"Energy Source":"Renewables","Entity":"United States","Year":2014},{"Energy":2730.32,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2015},{"Energy":797.18,"Energy Source":"Nuclear","Entity":"United States","Year":2015},{"Energy":556.49,"Energy Source":"Renewables","Entity":"United States","Year":2015},{"Energy":2656.96,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2016},{"Energy":805.69,"Energy Source":"Nuclear","Entity":"United States","Year":2016},{"Energy":624.91,"Energy Source":"Renewables","Entity":"United States","Year":2016},{"Energy":2540.17,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2017},{"Energy":804.95,"Energy Source":"Nuclear","Entity":"United States","Year":2017},{"Energy":707.19,"Energy Source":"Renewables","Entity":"United States","Year":2017},{"Energy":2661.3,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2018},{"Energy":807.08,"Energy Source":"Nuclear","Entity":"United States","Year":2018},{"Energy":733.17,"Energy Source":"Renewables","Entity":"United States","Year":2018},{"Energy":2588.21,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2019},{"Energy":809.41,"Energy Source":"Nuclear","Entity":"United States","Year":2019},{"Energy":760.76,"Energy Source":"Renewables","Entity":"United States","Year":2019},{"Energy":2431.9,"Energy Source":"Fossil Fuels","Entity":"United States","Year":2020},{"Energy":789.88,"Energy Source":"Nuclear","Entity":"United States","Year":2020},{"Energy":821.4,"Energy Source":"Renewables","Entity":"United States","Year":2020}],"metadata":{"Energy":{"type":"number","semanticType":"Number"},"Energy Source":{"type":"string","semanticType":"String","levels":["Fossil Fuels","Nuclear","Renewables"]},"Entity":{"type":"string","semanticType":"Location"},"Year":{"type":"number","semanticType":"Year"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total CO2 emissions by country across all years\n total_co2_by_country = df_energy.groupby('Entity')['Value_co2_emissions_kt_by_country'].sum().reset_index()\n \n # Get top 3 CO2 emitting countries\n top3_countries = total_co2_by_country.nlargest(3, 'Value_co2_emissions_kt_by_country')['Entity'].tolist()\n \n # Filter data for top 3 countries\n df_top3 = df_energy[df_energy['Entity'].isin(top3_countries)].copy()\n \n # Select relevant columns and rename for clarity\n df_top3 = df_top3[['Year', 'Entity', 'Electricity from fossil fuels (TWh)', \n 'Electricity from nuclear (TWh)', 'Electricity from renewables (TWh)']].copy()\n \n df_top3.columns = ['Year', 'Entity', 'Fossil Fuels', 'Nuclear', 'Renewables']\n \n # Transform to long format\n transformed_df = pd.melt(\n df_top3,\n id_vars=['Year', 'Entity'],\n value_vars=['Fossil Fuels', 'Nuclear', 'Renewables'],\n var_name='Energy Source',\n value_name='Energy'\n )\n \n # Sort by Entity, Year, and Energy Source\n transformed_df = transformed_df.sort_values(['Entity', 'Year', 'Energy Source']).reset_index(drop=True)\n \n return transformed_df\n","source":["global-energy-20-small.csv"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 117440.0, 146139.9994, 158029.9988, ..., 5736319.824, 5738290.039, nan, 9861099.609\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 61.99, 62.21, 63.02, 63.71\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"compare each countries renewable between 2000 and 2020\",\n \"chart_type\": \"Grouped Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Renewable Energy\",\n \"color\": \"Year\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Compare renewable energy (Electricity from renewables) for each country between the years 2000 and 2020. Filter the data to include only years 2000 and 2020, and prepare the output with country names, year, and renewable energy values for visualization as a grouped bar chart.\",\n \"display_instruction\": \"Compare **renewable energy** by **country** between **2000** and **2020**\",\n \"output_fields\": [\"Entity\", \"Year\", \"Electricity from renewables (TWh)\"],\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Electricity from renewables (TWh)\",\n \"color\": \"Year\"\n },\n \"reason\": \"The user wants to compare renewable energy across countries between 2000 and 2020. The output field 'Renewable Energy' mentioned in the original chart_encodings maps to 'Electricity from renewables (TWh)' in the dataset. To create a grouped bar chart comparing these two years, we need to filter the data to only include years 2000 and 2020, and output the Entity (country), Year, and the renewable energy values. The chart_encodings are updated to use the actual column name from the dataset.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n transformed_df = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Select only the required columns\n transformed_df = transformed_df[['Entity', 'Year', 'Electricity from renewables (TWh)']]\n \n # Convert Year to string for better visualization as a categorical variable\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year for better readability\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n Electricity from renewables (TWh) Entity Year\n0 17.11 Australia 2000\n1 63.99 Australia 2020\n2 308.77 Brazil 2000\n3 520.01 Brazil 2020\n4 363.70 Canada 2000\n5 429.24 Canada 2020\n6 225.56 China 2000\n7 2184.94 China 2020\n8 67.83 France 2000\n9 125.28 France 2020\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show their renewable percentage instead, and add an entity to show global average\",\n \"chart_type\": \"Grouped Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Electricity from renewables (TWh)\",\n \"color\": \"Year\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Calculate the renewable energy percentage (proportion of renewables to total electricity generation from all sources) for each country between 2000 and 2020. Add a 'Global Average' entity that represents the worldwide average renewable percentage for each year. The output should include Entity (with 'Global Average' added), Year, and the renewable percentage.\",\n \"display_instruction\": \"Show **renewable energy percentage** by **country** with **global average** for **2000** and **2020**\",\n \"output_fields\": [\"Entity\", \"Year\", \"Renewable Percentage\"],\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Renewable Percentage\",\n \"color\": \"Year\"\n },\n \"reason\": \"The user wants to see renewable percentage instead of absolute values, and wants to add a global average entity for comparison. Renewable percentage is calculated as the ratio of renewable energy to total electricity generation (fossil fuels + nuclear + renewables). A 'Global Average' entity needs to be computed by aggregating all countries' data for each year. The y-axis encoding is updated to reflect the new metric 'Renewable Percentage'.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n df_filtered = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Calculate total electricity generation\n df_filtered['Total Electricity'] = (\n df_filtered['Electricity from fossil fuels (TWh)'] + \n df_filtered['Electricity from nuclear (TWh)'] + \n df_filtered['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_filtered['Renewable Percentage'] = (\n df_filtered['Electricity from renewables (TWh)'] / df_filtered['Total Electricity'] * 100\n )\n \n # Select required columns for individual countries\n country_data = df_filtered[['Entity', 'Year', 'Renewable Percentage']].copy()\n \n # Calculate global average for each year\n global_avg = df_filtered.groupby('Year').apply(\n lambda x: pd.Series({\n 'Entity': 'Global Average',\n 'Year': x['Year'].iloc[0],\n 'Renewable Percentage': (\n x['Electricity from renewables (TWh)'].sum() / x['Total Electricity'].sum() * 100\n )\n })\n ).reset_index(drop=True)\n \n # Combine country data with global average\n transformed_df = pd.concat([country_data, global_avg], ignore_index=True)\n \n # Convert Year to string for better visualization\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n Entity Renewable Percentage Year\n0 Australia 8.634437 2000\n1 Australia 25.503168 2020\n2 Brazil 90.130772 2000\n3 Brazil 84.641177 2020\n4 Canada 61.809592 2000\n5 Canada 68.779644 2020\n6 China 16.639127 2000\n7 China 28.246461 2020\n8 France 12.711769 2000\n9 France 23.761024 2020\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show global energy composition over time\",\n \"chart_type\": \"Custom Area\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"Energy\",\n \"color\": \"Energy Source\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Show the global energy composition over time by aggregating electricity generation from all countries for each year, broken down by energy source (fossil fuels, nuclear, and renewables). The output should be in long format with Year, Energy Source, and Energy values to support a stacked area chart visualization.\",\n \"display_instruction\": \"Show global **energy composition** by **energy source** over time\",\n \"output_fields\": [\"Year\", \"Energy Source\", \"Energy\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"Energy\",\n \"color\": \"Energy Source\"\n },\n \"reason\": \"The user wants to visualize global energy composition over time, which requires aggregating electricity generation data across all countries for each year and breaking it down by energy source type. The data needs to be transformed into long format where each row represents a year-energy source combination, with the total energy value for that source globally. This follows up from the previous renewable percentage analysis by showing the overall energy mix trends rather than focusing on specific countries or percentages.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Group by Year and sum electricity generation from all countries\n global_energy = df_energy.groupby('Year').agg({\n 'Electricity from fossil fuels (TWh)': 'sum',\n 'Electricity from nuclear (TWh)': 'sum',\n 'Electricity from renewables (TWh)': 'sum'\n }).reset_index()\n \n # Rename columns for clarity\n global_energy.columns = ['Year', 'Fossil Fuels', 'Nuclear', 'Renewables']\n \n # Transform to long format\n transformed_df = pd.melt(\n global_energy,\n id_vars=['Year'],\n value_vars=['Fossil Fuels', 'Nuclear', 'Renewables'],\n var_name='Energy Source',\n value_name='Energy'\n )\n \n # Sort by Year and Energy Source\n transformed_df = transformed_df.sort_values(['Year', 'Energy Source']).reset_index(drop=True)\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n Energy Energy Source Year\n0 7160.71 Fossil Fuels 2000\n1 1996.65 Nuclear 2000\n2 1742.56 Renewables 2000\n3 7273.89 Fossil Fuels 2001\n4 2037.54 Nuclear 2001\n5 1690.11 Renewables 2001\n6 7621.07 Fossil Fuels 2002\n7 2042.18 Nuclear 2002\n8 1757.63 Renewables 2002\n9 8043.86 Fossil Fuels 2003\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show the composition of top 3 CO2 emitting countries\",\n \"chart_type\": \"Custom Area\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"Energy\",\n \"color\": \"Energy Source\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Identify the top 3 CO2 emitting countries based on total CO2 emissions across all years, then show the energy composition (fossil fuels, nuclear, and renewables) for each of these countries over time. The output should be in long format with Year, Entity (country), Energy Source, and Energy values to support faceted area charts.\",\n \"display_instruction\": \"Show **energy composition** of **top 3 CO2 emitting countries** over time\",\n \"output_fields\": [\"Year\", \"Entity\", \"Energy Source\", \"Energy\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"Energy\",\n \"color\": \"Energy Source\",\n \"facet\": \"Entity\"\n },\n \"reason\": \"The user wants to see the energy composition specifically for the top 3 CO2 emitting countries over time, following up from the global energy composition view. We need to identify which countries have the highest total CO2 emissions, filter the data for those countries, and transform it into long format by energy source. A facet encoding is added to show separate area charts for each of the top 3 countries, allowing comparison of their individual energy compositions over time.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total CO2 emissions by country across all years\n total_co2_by_country = df_energy.groupby('Entity')['Value_co2_emissions_kt_by_country'].sum().reset_index()\n \n # Get top 3 CO2 emitting countries\n top3_countries = total_co2_by_country.nlargest(3, 'Value_co2_emissions_kt_by_country')['Entity'].tolist()\n \n # Filter data for top 3 countries\n df_top3 = df_energy[df_energy['Entity'].isin(top3_countries)].copy()\n \n # Select relevant columns and rename for clarity\n df_top3 = df_top3[['Year', 'Entity', 'Electricity from fossil fuels (TWh)', \n 'Electricity from nuclear (TWh)', 'Electricity from renewables (TWh)']].copy()\n \n df_top3.columns = ['Year', 'Entity', 'Fossil Fuels', 'Nuclear', 'Renewables']\n \n # Transform to long format\n transformed_df = pd.melt(\n df_top3,\n id_vars=['Year', 'Entity'],\n value_vars=['Fossil Fuels', 'Nuclear', 'Renewables'],\n var_name='Energy Source',\n value_name='Energy'\n )\n \n # Sort by Entity, Year, and Energy Source\n transformed_df = transformed_df.sort_values(['Entity', 'Year', 'Energy Source']).reset_index(drop=True)\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"table-81","sourceTableIds":["global-energy-20-small.csv"],"instruction":"show the composition of top 3 CO2 emitting countries","displayInstruction":"Show **energy composition** of **top 3 CO2 emitting countries** over time","chart":{"id":"chart-1760739075465","chartType":"Custom Area","encodingMap":{"x":{"channel":"x","bin":false,"fieldID":"original--global-energy-20-small.csv--Year"},"y":{"channel":"y","bin":false,"fieldID":"concept-1760738917347"},"x2":{},"y2":{"channel":"y2","bin":false},"color":{"fieldID":"concept-1760738921927","sortBy":"[\"Fossil Fuels\",\"Nuclear\",\"Renewables\"]"},"column":{},"row":{"channel":"row","bin":false}},"tableRef":"table-81","saved":false,"source":"trigger","unread":false},"resultTableId":"table-10"},"explanation":{"agent":"CodeExplanationAgent","code":"The code performs the following high-level transformations:\n\n1. **Identifies top polluters**: Calculates the total `Value_co2_emissions_kt_by_country` across all years for each `Entity` (country), then selects the **top 3** countries with the highest cumulative CO2 emissions.\n\n2. **Filters the dataset**: Retains only records for the **top 3 CO2-emitting countries** from the original energy data.\n\n3. **Simplifies column structure**: Extracts the relevant energy production columns (`Electricity from fossil fuels (TWh)`, `Electricity from nuclear (TWh)`, `Electricity from renewables (TWh)`) and renames them to **Fossil Fuels**, **Nuclear**, and **Renewables** for clarity.\n\n4. **Reshapes data to long format**: Transforms the data from wide format (separate columns for each energy source) to long format, where each row represents a specific `Year`, `Entity`, and `Energy Source` combination with its corresponding `Energy` production value.\n\n5. **Sorts the output**: Orders the final dataset by `Entity`, `Year`, and `Energy Source` for systematic presentation.","concepts":[],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small_csv)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 117440.0, 146139.9994, 158029.9988, ..., 5593029.785, 5736319.824, 5738290.039, 9861099.609\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 62.21, 63.02, 63.71, 439.73\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total CO2 emissions by country across all years\n total_co2_by_country = df_energy.groupby('Entity')['Value_co2_emissions_kt_by_country'].sum().reset_index()\n \n # Get top 3 CO2 emitting countries\n top3_countries = total_co2_by_country.nlargest(3, 'Value_co2_emissions_kt_by_country')['Entity'].tolist()\n \n # Filter data for top 3 countries\n df_top3 = df_energy[df_energy['Entity'].isin(top3_countries)].copy()\n \n # Select relevant columns and rename for clarity\n df_top3 = df_top3[['Year', 'Entity', 'Electricity from fossil fuels (TWh)', \n 'Electricity from nuclear (TWh)', 'Electricity from renewables (TWh)']].copy()\n \n df_top3.columns = ['Year', 'Entity', 'Fossil Fuels', 'Nuclear', 'Renewables']\n \n # Transform to long format\n transformed_df = pd.melt(\n df_top3,\n id_vars=['Year', 'Entity'],\n value_vars=['Fossil Fuels', 'Nuclear', 'Renewables'],\n var_name='Energy Source',\n value_name='Energy'\n )\n \n # Sort by Entity, Year, and Energy Source\n transformed_df = transformed_df.sort_values(['Entity', 'Year', 'Energy Source']).reset_index(drop=True)\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThe code performs the following high-level transformations:\n\n1. **Identifies top polluters**: Calculates the total `Value_co2_emissions_kt_by_country` across all years for each `Entity` (country), then selects the **top 3** countries with the highest cumulative CO2 emissions.\n\n2. **Filters the dataset**: Retains only records for the **top 3 CO2-emitting countries** from the original energy data.\n\n3. **Simplifies column structure**: Extracts the relevant energy production columns (`Electricity from fossil fuels (TWh)`, `Electricity from nuclear (TWh)`, `Electricity from renewables (TWh)`) and renames them to **Fossil Fuels**, **Nuclear**, and **Renewables** for clarity.\n\n4. **Reshapes data to long format**: Transforms the data from wide format (separate columns for each energy source) to long format, where each row represents a specific `Year`, `Entity`, and `Energy Source` combination with its corresponding `Energy` production value.\n\n5. **Sorts the output**: Orders the final dataset by `Entity`, `Year`, and `Energy Source` for systematic presentation.\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""}],"charts":[{"id":"chart-1760739084455","chartType":"Custom Area","encodingMap":{"x":{"fieldID":"original--global-energy-20-small.csv--Year"},"y":{"fieldID":"concept-1760738917347"},"x2":{},"y2":{"channel":"y2","bin":false},"color":{"fieldID":"concept-1760738921927"},"column":{"fieldID":"original--global-energy-20-small.csv--Entity"},"row":{"channel":"row","bin":false}},"tableRef":"table-10","saved":false,"source":"user","unread":false},{"id":"chart-1760739021201","chartType":"Custom Area","encodingMap":{"x":{"channel":"x","bin":false,"fieldID":"original--global-energy-20-small.csv--Year"},"y":{"channel":"y","bin":false,"fieldID":"concept-1760738917347"},"x2":{},"y2":{"channel":"y2","bin":false},"color":{"fieldID":"concept-1760738921927","sortBy":"[\"Fossil 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Global renewable electricity nearly doubled from 16% to 29% of total generation, signaling meaningful progress in the energy transition.\n\n[IMAGE(chart-1760738819387)]\n\nThe data reveals striking regional variations. Brazil maintained renewable leadership above 84%, while countries like Australia, Germany, and Italy dramatically expanded their renewable capacity—Australia tripling from 9% to 26%. However, some nations like Mexico experienced declining renewable shares, highlighting uneven progress across different energy systems.\n\n**In summary**, while the 20-year period shows encouraging momentum toward cleaner energy, the pace and direction vary significantly by country, suggesting that achieving global renewable energy goals will require sustained, coordinated efforts tailored to each nation's unique energy landscape and policy environment.","style":"short note","selectedChartIds":["chart-1760738819387"],"createdAt":1760831163718},{"id":"report-1760831130105-4063","content":"# The Global Renewable Energy Revolution: Two Decades of Transformation\n\nThe world's energy landscape has undergone a remarkable transformation between 2000 and 2020, with renewable electricity generation emerging as a critical component of the global energy mix. This shift reflects both technological advancement and growing commitment to sustainable energy solutions.\n\n[IMAGE(chart-1760738770100)]\n\nLooking at renewable energy adoption across major economies, the growth has been nothing short of extraordinary. China leads the pack with a staggering increase from 225.56 TWh in 2000 to 2,184.94 TWh in 2020—nearly a tenfold expansion. The United States more than doubled its renewable output from 335.45 TWh to 821.40 TWh, while Brazil grew from 308.77 TWh to 520.01 TWh. Notably, countries like Australia, India, and Germany also demonstrated significant gains, with Australia jumping from just 17.11 TWh to 63.99 TWh during this period.\n\n[IMAGE(chart-1760739084455)]\n\nWhen examining the energy portfolios of the three largest CO2 emitters—China, India, and the United States—a complex picture emerges. While China's total energy consumption has grown exponentially, with fossil fuels still dominating, the renewable sector (shown in red) has expanded substantially. The United States shows a more stable total energy consumption, with renewables gradually claiming a larger share. India's energy growth, though significant, remains heavily reliant on fossil fuels, though renewable adoption is accelerating.\n\n**In summary**, the past two decades reveal a global energy transition in progress. While renewable energy has achieved impressive growth worldwide, fossil fuels continue to dominate electricity generation in major economies. Key questions remain: Can this momentum accelerate sufficiently to meet climate goals? What policies will drive faster renewable adoption in emerging economies?","style":"blog post","selectedChartIds":["chart-1760739084455","chart-1760738770100"],"createdAt":1760831142289},{"id":"report-1760831094231-2424","content":"# Global Renewable Energy: A Tale of Leaders and Laggards\n\nThe global energy landscape has undergone significant transformation over the past two decades, with renewable energy emerging as a critical player in the electricity mix. Analyzing data from 21 major economies between 2000 and 2020 reveals striking disparities in how nations have embraced clean energy alternatives.\n\n[IMAGE(chart-1760738423852)]\n\nThe first visualization reveals a fascinating divergence in renewable energy adoption. **Brazil** stands out as a consistent leader, maintaining renewable electricity percentages between 75-90% throughout the entire period, thanks largely to its robust hydroelectric infrastructure. **Canada** follows a similar trajectory, steadily increasing from about 60% to nearly 70% by 2020. Meanwhile, **Germany, Spain, Italy, and the United Kingdom** show remarkable growth trajectories, climbing from under 20% in the early 2000s to over 40% by 2020—demonstrating that nations can dramatically reshape their energy portfolios within two decades.\n\nOn the opposite end of the spectrum, **South Africa** remains nearly flat at the bottom, showing minimal renewable adoption despite global trends. **Saudi Arabia** and **Poland** also lag significantly, though both show modest upticks in recent years.\n\n[IMAGE(chart-1760738436615)]\n\nThe ranking chart illustrates the competitive dynamics of renewable energy leadership. **Brazil and Canada** maintain their dominance at ranks 1-2 throughout most years, while European nations like **Germany, Spain, and the UK** engage in a dynamic competition for the 3rd-5th positions, particularly after 2010. The volatility in middle rankings reflects the rapid changes in energy policy and investment across different nations, with countries like **China** climbing from lower ranks to break into the top 7 by 2020.\n\n**In summary**, the data reveals a bifurcated global energy transition: a group of progressive nations have successfully scaled renewable electricity to 40-90% of their mix, while others remain heavily dependent on fossil fuels. These patterns suggest that political will, natural resource endowment, and infrastructure investment are key determinants of renewable energy success. Important follow-up questions include: What policy mechanisms enabled top performers to achieve such high renewable percentages? Can lagging nations replicate these successes, or do geographic and economic constraints create insurmountable barriers?","style":"blog post","selectedChartIds":["chart-1760738423852","chart-1760738436615"],"createdAt":1760831110064}],"currentReport":{"id":"report-1760750575650-2619","content":"# Hollywood's Billion-Dollar Hitmakers\n\n*Avatar* stands alone—earning over $2.5B in profit, dwarfing all competition. Action and Adventure films dominate the most profitable titles, with franchises like *Jurassic Park*, *The Dark Knight*, and *Lord of the Rings* proving blockbuster formulas work.\n\n\"Chart\"\n\nSteven Spielberg leads all directors with $7.2B in total profit across his career, showcasing remarkable consistency with hits spanning decades—from *Jurassic Park* to *E.T.* His nearest competitors trail by billions, underlining his unmatched commercial impact.\n\n\"Chart\"\n\n**In summary**, mega-budget Action and Adventure films generate extraordinary returns when they succeed, and a handful of elite directors—led by Spielberg—have mastered the formula for sustained box office dominance.","style":"short note","selectedChartIds":["chart-1760743347871","chart-1760743768741"],"chartImages":{},"createdAt":1760750584189,"title":"Report - 10/17/2025"},"activeChallenges":[],"agentWorkInProgress":[],"_persist":{"version":-1,"rehydrated":true}} \ No newline at end of file +{"tables": [{"kind": "table", "id": "global-energy-20-small.csv", "displayId": "energy-co2", "names": ["Year", "Entity", "Value_co2_emissions_kt_by_country", "Electricity from fossil fuels (TWh)", "Electricity from nuclear (TWh)", "Electricity from renewables (TWh)"], "metadata": {"Year": {"type": "number", "semanticType": "Year"}, "Entity": {"type": "string", "semanticType": "Location"}, "Value_co2_emissions_kt_by_country": {"type": "number", "semanticType": "Number"}, "Electricity from fossil fuels (TWh)": {"type": "number", "semanticType": "Number"}, "Electricity from nuclear (TWh)": {"type": "number", "semanticType": "Number"}, "Electricity from renewables (TWh)": {"type": "number", "semanticType": "Number"}}, "rows": [{"Year": 2000, "Entity": "Australia", "Value_co2_emissions_kt_by_country": 339450, "Electricity from fossil fuels (TWh)": 181.05, "Electricity from nuclear (TWh)": 0, "Electricity from renewables (TWh)": 17.11}, {"Year": 2001, "Entity": "Australia", "Value_co2_emissions_kt_by_country": 345640, "Electricity from fossil fuels (TWh)": 194.33, "Electricity from nuclear (TWh)": 0, "Electricity from renewables (TWh)": 17.4}, {"Year": 2002, "Entity": "Australia", "Value_co2_emissions_kt_by_country": 353369.9951, "Electricity from fossil fuels (TWh)": 197.29, "Electricity from nuclear (TWh)": 0, "Electricity from renewables (TWh)": 17.35}, {"Year": 2003, "Entity": "Australia", "Value_co2_emissions_kt_by_country": 352579.9866, "Electricity from fossil fuels (TWh)": 195.13, "Electricity from nuclear (TWh)": 0, "Electricity from renewables (TWh)": 18.5}, {"Year": 2004, "Entity": "Australia", "Value_co2_emissions_kt_by_country": 365809.9976, "Electricity from fossil fuels (TWh)": 203.66, "Electricity from nuclear (TWh)": 0, "Electricity from renewables (TWh)": 19.41}, {"Year": 2005, "Entity": "Australia", "Value_co2_emissions_kt_by_country": 370089.9963, "Electricity from fossil fuels (TWh)": 195.95, "Electricity from nuclear (TWh)": 0, "Electricity from renewables (TWh)": 19.75}, {"Year": 2006, "Entity": "Australia", "Value_co2_emissions_kt_by_country": 375489.9902, "Electricity from fossil fuels (TWh)": 198.72, "Electricity from nuclear (TWh)": 0, "Electricity from renewables (TWh)": 21.19}, {"Year": 2007, "Entity": "Australia", "Value_co2_emissions_kt_by_country": 385750, "Electricity from fossil fuels (TWh)": 208.59, "Electricity from nuclear (TWh)": 0, "Electricity from renewables (TWh)": 20.93}, {"Year": 2008, "Entity": "Australia", "Value_co2_emissions_kt_by_country": 388940.0024, "Electricity from fossil fuels (TWh)": 211.06, "Electricity from nuclear (TWh)": 0, "Electricity from renewables (TWh)": 18.49}, {"Year": 2009, "Entity": "Australia", "Value_co2_emissions_kt_by_country": 395290.0085, "Electricity from fossil fuels (TWh)": 216.42, "Electricity from nuclear (TWh)": 0, "Electricity from renewables (TWh)": 18.32}, {"Year": 2010, "Entity": "Australia", "Value_co2_emissions_kt_by_country": 387540.0085, "Electricity from fossil fuels (TWh)": 212.5, "Electricity from nuclear (TWh)": 0, "Electricity from renewables (TWh)": 21.13}, {"Year": 2011, "Entity": "Australia", "Value_co2_emissions_kt_by_country": 386380.0049, "Electricity from fossil fuels (TWh)": 213.56, "Electricity from nuclear (TWh)": 0, "Electricity from renewables (TWh)": 27.33}, {"Year": 2012, "Entity": "Australia", "Value_co2_emissions_kt_by_country": 386970.0012, "Electricity from fossil fuels (TWh)": 206.75, "Electricity from nuclear (TWh)": 0, "Electricity from renewables (TWh)": 26.63}, {"Year": 2013, "Entity": "Australia", "Value_co2_emissions_kt_by_country": 380279.9988, "Electricity from fossil fuels (TWh)": 195.78, "Electricity from nuclear (TWh)": 0, "Electricity from renewables (TWh)": 34.2}, {"Year": 2014, "Entity": "Australia", "Value_co2_emissions_kt_by_country": 371630.0049, "Electricity from fossil fuels (TWh)": 205.46, "Electricity from nuclear (TWh)": 0, "Electricity from renewables (TWh)": 36.15}, {"Year": 2015, "Entity": "Australia", "Value_co2_emissions_kt_by_country": 377799.9878, "Electricity from fossil fuels (TWh)": 197.72, "Electricity from nuclear (TWh)": 0, "Electricity from renewables (TWh)": 33.12}, {"Year": 2016, "Entity": "Australia", "Value_co2_emissions_kt_by_country": 384989.9902, "Electricity from fossil fuels (TWh)": 207.66, "Electricity from nuclear (TWh)": 0, "Electricity from renewables (TWh)": 38.41}, {"Year": 2017, "Entity": "Australia", "Value_co2_emissions_kt_by_country": 389160.0037, "Electricity from fossil fuels (TWh)": 209.14, "Electricity from nuclear (TWh)": 0, "Electricity from renewables (TWh)": 40.77}, {"Year": 2018, "Entity": "Australia", "Value_co2_emissions_kt_by_country": 387070.0073, "Electricity from fossil fuels (TWh)": 207.45, "Electricity from nuclear (TWh)": 0, "Electricity from renewables (TWh)": 42.93}, {"Year": 2019, "Entity": "Australia", "Value_co2_emissions_kt_by_country": 386529.9988, "Electricity from fossil fuels (TWh)": 196.45, "Electricity from nuclear (TWh)": 0, "Electricity from renewables (TWh)": 53.41}, {"Year": 2020, "Entity": "Australia", "Value_co2_emissions_kt_by_country": null, "Electricity from fossil fuels (TWh)": 186.92, "Electricity from nuclear (TWh)": 0, "Electricity from renewables (TWh)": 63.99}, {"Year": 2000, "Entity": "Brazil", "Value_co2_emissions_kt_by_country": 313670, "Electricity from fossil fuels (TWh)": 28.87, "Electricity from nuclear (TWh)": 4.94, "Electricity from renewables (TWh)": 308.77}, {"Year": 2001, "Entity": "Brazil", "Value_co2_emissions_kt_by_country": 319380, "Electricity from fossil fuels (TWh)": 35.19, "Electricity from nuclear (TWh)": 14.27, "Electricity from renewables (TWh)": 273.71}, {"Year": 2002, "Entity": "Brazil", "Value_co2_emissions_kt_by_country": 317760.0098, "Electricity from fossil fuels (TWh)": 33.5, "Electricity from nuclear (TWh)": 13.84, "Electricity from renewables (TWh)": 292.95}, {"Year": 2003, "Entity": "Brazil", "Value_co2_emissions_kt_by_country": 310809.9976, "Electricity 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"Entity": "Brazil", "Value_co2_emissions_kt_by_country": 373630.0049, "Electricity from fossil fuels (TWh)": 55.87, "Electricity from nuclear (TWh)": 13.21, "Electricity from renewables (TWh)": 385.61}, {"Year": 2009, "Entity": "Brazil", "Value_co2_emissions_kt_by_country": 350000, "Electricity from fossil fuels (TWh)": 36.32, "Electricity from nuclear (TWh)": 12.22, "Electricity from renewables (TWh)": 410.13}, {"Year": 2010, "Entity": "Brazil", "Value_co2_emissions_kt_by_country": 397929.9927, "Electricity from fossil fuels (TWh)": 61.02, "Electricity from nuclear (TWh)": 13.77, "Electricity from renewables (TWh)": 435.99}, {"Year": 2011, "Entity": "Brazil", "Value_co2_emissions_kt_by_country": 418309.9976, "Electricity from fossil fuels (TWh)": 50.27, "Electricity from nuclear (TWh)": 14.8, "Electricity from renewables (TWh)": 462.32}, {"Year": 2012, "Entity": "Brazil", "Value_co2_emissions_kt_by_country": 454230.011, "Electricity from fossil fuels (TWh)": 77.21, "Electricity from 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"Electricity from renewables (TWh)": 363.7}, {"Year": 2001, "Entity": "Canada", "Value_co2_emissions_kt_by_country": 506620, "Electricity from fossil fuels (TWh)": 159.93, "Electricity from nuclear (TWh)": 72.86, "Electricity from renewables (TWh)": 339.58}, {"Year": 2002, "Entity": "Canada", "Value_co2_emissions_kt_by_country": 524349.9756, "Electricity from fossil fuels (TWh)": 155.12, "Electricity from nuclear (TWh)": 71.75, "Electricity from renewables (TWh)": 357.06}, {"Year": 2003, "Entity": "Canada", "Value_co2_emissions_kt_by_country": 544539.978, "Electricity from fossil fuels (TWh)": 157.35, "Electricity from nuclear (TWh)": 71.15, "Electricity from renewables (TWh)": 343.88}, {"Year": 2004, "Entity": "Canada", "Value_co2_emissions_kt_by_country": 536419.9829, "Electricity from fossil fuels (TWh)": 148.86, "Electricity from nuclear (TWh)": 85.87, "Electricity from renewables (TWh)": 347.68}, {"Year": 2005, "Entity": "Canada", "Value_co2_emissions_kt_by_country": 549030.0293, 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380.24}, {"Year": 2010, "Entity": "Canada", "Value_co2_emissions_kt_by_country": 537010.0098, "Electricity from fossil fuels (TWh)": 130.08, "Electricity from nuclear (TWh)": 85.53, "Electricity from renewables (TWh)": 366.21}, {"Year": 2011, "Entity": "Canada", "Value_co2_emissions_kt_by_country": 549289.978, "Electricity from fossil fuels (TWh)": 131.3, "Electricity from nuclear (TWh)": 88.29, "Electricity from renewables (TWh)": 391.95}, {"Year": 2012, "Entity": "Canada", "Value_co2_emissions_kt_by_country": 546210.022, "Electricity from fossil fuels (TWh)": 124.2, "Electricity from nuclear (TWh)": 89.49, "Electricity from renewables (TWh)": 398.58}, {"Year": 2013, "Entity": "Canada", "Value_co2_emissions_kt_by_country": 555659.9731, "Electricity from fossil fuels (TWh)": 122.87, "Electricity from nuclear (TWh)": 97.58, "Electricity from renewables (TWh)": 417.28}, {"Year": 2014, "Entity": "Canada", "Value_co2_emissions_kt_by_country": 561679.9927, "Electricity from fossil fuels (TWh)": 122.75, "Electricity from nuclear (TWh)": 101.21, "Electricity from renewables (TWh)": 412.13}, {"Year": 2015, "Entity": "Canada", "Value_co2_emissions_kt_by_country": 558700.0122, "Electricity from fossil fuels (TWh)": 125.7, "Electricity from nuclear (TWh)": 96.05, "Electricity from renewables (TWh)": 417.2}, {"Year": 2016, "Entity": "Canada", "Value_co2_emissions_kt_by_country": 556830.0171, "Electricity from fossil fuels (TWh)": 122.35, "Electricity from nuclear (TWh)": 95.69, "Electricity from renewables (TWh)": 426.84}, {"Year": 2017, "Entity": "Canada", "Value_co2_emissions_kt_by_country": 568080.0171, "Electricity from fossil fuels (TWh)": 113.7, "Electricity from nuclear (TWh)": 95.57, "Electricity from renewables (TWh)": 435.43}, {"Year": 2018, "Entity": "Canada", "Value_co2_emissions_kt_by_country": 580090.0269, "Electricity from fossil fuels (TWh)": 112.47, "Electricity from nuclear (TWh)": 95.03, "Electricity from renewables (TWh)": 428.39}, {"Year": 2019, "Entity": "Canada", "Value_co2_emissions_kt_by_country": 580210.022, "Electricity from fossil fuels (TWh)": 110.65, "Electricity from nuclear (TWh)": 95.47, "Electricity from renewables (TWh)": 421.8}, {"Year": 2020, "Entity": "Canada", "Value_co2_emissions_kt_by_country": null, "Electricity from fossil fuels (TWh)": 102.19, "Electricity from nuclear (TWh)": 92.65, "Electricity from renewables (TWh)": 429.24}, {"Year": 2000, "Entity": "China", "Value_co2_emissions_kt_by_country": 3346530, "Electricity from fossil fuels (TWh)": 1113.3, "Electricity from nuclear (TWh)": 16.74, "Electricity from renewables (TWh)": 225.56}, {"Year": 2001, "Entity": "China", "Value_co2_emissions_kt_by_country": 3529080, "Electricity from fossil fuels (TWh)": 1182.59, "Electricity from nuclear (TWh)": 17.47, "Electricity from renewables (TWh)": 280.73}, {"Year": 2002, "Entity": "China", "Value_co2_emissions_kt_by_country": 3810060.059, "Electricity from fossil fuels (TWh)": 1337.46, "Electricity from nuclear (TWh)": 25.13, "Electricity from renewables (TWh)": 291.41}, {"Year": 2003, "Entity": "China", "Value_co2_emissions_kt_by_country": 4415910.156, "Electricity from fossil fuels (TWh)": 1579.96, "Electricity from nuclear (TWh)": 43.34, "Electricity from renewables (TWh)": 287.28}, {"Year": 2004, "Entity": "China", "Value_co2_emissions_kt_by_country": 5124819.824, "Electricity from fossil fuels (TWh)": 1795.41, "Electricity from nuclear (TWh)": 50.47, "Electricity from renewables (TWh)": 357.43}, {"Year": 2005, "Entity": "China", "Value_co2_emissions_kt_by_country": 5824629.883, "Electricity from fossil fuels (TWh)": 2042.8, "Electricity from nuclear (TWh)": 53.09, "Electricity from renewables (TWh)": 404.37}, {"Year": 2006, "Entity": "China", "Value_co2_emissions_kt_by_country": 6437470.215, "Electricity from fossil fuels (TWh)": 2364.16, "Electricity from nuclear (TWh)": 54.84, "Electricity from renewables (TWh)": 446.72}, {"Year": 2007, "Entity": "China", "Value_co2_emissions_kt_by_country": 6993180.176, "Electricity from fossil fuels (TWh)": 2718.7, "Electricity from nuclear (TWh)": 62.13, "Electricity from renewables (TWh)": 500.71}, {"Year": 2008, "Entity": "China", "Value_co2_emissions_kt_by_country": 7199600.098, "Electricity from fossil fuels (TWh)": 2762.29, "Electricity from nuclear (TWh)": 68.39, "Electricity from renewables (TWh)": 665.08}, {"Year": 2009, "Entity": "China", "Value_co2_emissions_kt_by_country": 7719069.824, "Electricity from fossil fuels (TWh)": 2980.2, "Electricity from nuclear (TWh)": 70.05, "Electricity from renewables (TWh)": 664.39}, {"Year": 2010, "Entity": "China", "Value_co2_emissions_kt_by_country": 8474919.922, "Electricity from fossil fuels (TWh)": 3326.19, "Electricity from nuclear (TWh)": 74.74, "Electricity from renewables (TWh)": 786.38}, {"Year": 2011, "Entity": "China", "Value_co2_emissions_kt_by_country": 9282549.805, "Electricity from fossil fuels (TWh)": 3811.77, "Electricity from nuclear 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{"Entity": "Ukraine", "Year": 2003, "energy": 9.27, "source": "renewables"}, {"Entity": "Ukraine", "Year": 2004, "energy": 11.78, "source": "renewables"}, {"Entity": "Ukraine", "Year": 2005, "energy": 12.4, "source": "renewables"}, {"Entity": "Ukraine", "Year": 2006, "energy": 12.92, "source": "renewables"}, {"Entity": "Ukraine", "Year": 2007, "energy": 10.47, "source": "renewables"}, {"Entity": "Ukraine", "Year": 2008, "energy": 11.82, "source": "renewables"}, {"Entity": "Ukraine", "Year": 2009, "energy": 12.12, "source": "renewables"}, {"Entity": "Ukraine", "Year": 2010, "energy": 13.39, "source": "renewables"}, {"Entity": "Ukraine", "Year": 2011, "energy": 11.2, "source": "renewables"}, {"Entity": "Ukraine", "Year": 2012, "energy": 11.23, "source": "renewables"}, {"Entity": "Ukraine", "Year": 2013, "energy": 15.11, "source": "renewables"}, {"Entity": "Ukraine", "Year": 2014, "energy": 10.17, "source": "renewables"}, {"Entity": "Ukraine", "Year": 2015, "energy": 7.1, "source": "renewables"}, {"Entity": "Ukraine", "Year": 2016, "energy": 9.25, "source": "renewables"}, {"Entity": "Ukraine", "Year": 2017, "energy": 10.88, "source": "renewables"}, {"Entity": "Ukraine", "Year": 2018, "energy": 13.02, "source": "renewables"}, {"Entity": "Ukraine", "Year": 2019, "energy": 11.87, "source": "renewables"}, {"Entity": "Ukraine", "Year": 2020, "energy": 17.56, "source": "renewables"}, {"Entity": "United Kingdom", "Year": 2000, "energy": 9.98, "source": "renewables"}, {"Entity": "United Kingdom", "Year": 2001, "energy": 9.56, "source": "renewables"}, {"Entity": "United Kingdom", "Year": 2002, "energy": 11.13, "source": "renewables"}, {"Entity": "United Kingdom", "Year": 2003, "energy": 10.62, "source": "renewables"}, {"Entity": "United Kingdom", "Year": 2004, "energy": 14.14, "source": "renewables"}, {"Entity": "United Kingdom", "Year": 2005, "energy": 16.93, "source": "renewables"}, {"Entity": "United Kingdom", "Year": 2006, "energy": 18.11, "source": "renewables"}, {"Entity": "United Kingdom", "Year": 2007, "energy": 19.69, "source": "renewables"}, {"Entity": "United Kingdom", "Year": 2008, "energy": 21.85, "source": "renewables"}, {"Entity": "United Kingdom", "Year": 2009, "energy": 25.25, "source": "renewables"}, {"Entity": "United Kingdom", "Year": 2010, "energy": 26.18, "source": "renewables"}, {"Entity": "United Kingdom", "Year": 2011, "energy": 35.2, "source": "renewables"}, {"Entity": "United Kingdom", "Year": 2012, "energy": 41.24, "source": "renewables"}, {"Entity": "United Kingdom", "Year": 2013, "energy": 53.21, "source": "renewables"}, {"Entity": "United Kingdom", "Year": 2014, "energy": 64.52, "source": "renewables"}, {"Entity": "United Kingdom", "Year": 2015, "energy": 82.57, "source": "renewables"}, {"Entity": "United Kingdom", "Year": 2016, "energy": 82.99, "source": "renewables"}, {"Entity": "United Kingdom", "Year": 2017, "energy": 98.85, "source": "renewables"}, {"Entity": "United Kingdom", "Year": 2018, "energy": 110.03, "source": "renewables"}, {"Entity": "United Kingdom", "Year": 2019, "energy": 120.48, "source": "renewables"}, {"Entity": "United Kingdom", "Year": 2020, "energy": 131.74, "source": "renewables"}, {"Entity": "United States", "Year": 2000, "energy": 350.93, "source": "renewables"}, {"Entity": "United States", "Year": 2001, "energy": 280.06, "source": "renewables"}, {"Entity": "United States", "Year": 2002, "energy": 336.34, "source": "renewables"}, {"Entity": "United States", "Year": 2003, "energy": 349.18, "source": "renewables"}, {"Entity": "United States", "Year": 2004, "energy": 345.14, "source": "renewables"}, {"Entity": "United States", "Year": 2005, "energy": 353.04, "source": "renewables"}, {"Entity": "United States", "Year": 2006, "energy": 381.16, "source": "renewables"}, {"Entity": "United States", "Year": 2007, "energy": 347.91, "source": "renewables"}, {"Entity": "United States", "Year": 2008, "energy": 377.11, "source": "renewables"}, {"Entity": "United States", "Year": 2009, "energy": 415.56, "source": "renewables"}, {"Entity": "United States", "Year": 2010, "energy": 424.48, "source": "renewables"}, {"Entity": "United States", "Year": 2011, "energy": 509.74, "source": "renewables"}, {"Entity": "United States", "Year": 2012, "energy": 492.32, "source": "renewables"}, {"Entity": "United States", "Year": 2013, "energy": 520.38, "source": "renewables"}, {"Entity": "United States", "Year": 2014, "energy": 546.83, "source": "renewables"}, {"Entity": "United States", "Year": 2015, "energy": 556.49, "source": "renewables"}, {"Entity": "United States", "Year": 2016, "energy": 624.91, "source": "renewables"}, {"Entity": "United States", "Year": 2017, "energy": 707.19, "source": "renewables"}, {"Entity": "United States", "Year": 2018, "energy": 733.17, "source": "renewables"}, {"Entity": "United States", "Year": 2019, "energy": 760.76, "source": "renewables"}, {"Entity": "United States", "Year": 2020, "energy": 821.4, "source": "renewables"}], "metadata": {"Entity": {"type": "string", "semanticType": "Location"}, "Year": {"type": "number", "semanticType": "Year"}, "energy": {"type": "number", "semanticType": "Number"}, "source": {"type": "string", "semanticType": "String", "levels": ["fossil fuels", "nuclear", "renewables"]}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Unpivot the energy source columns into long format\n transformed_df = pd.melt(\n df_energy,\n id_vars=['Year', 'Entity'],\n value_vars=[\n 'Electricity from fossil fuels (TWh)',\n 'Electricity from nuclear (TWh)',\n 'Electricity from renewables (TWh)'\n ],\n var_name='source',\n value_name='energy'\n )\n \n # Clean up the source names to be more readable\n transformed_df['source'] = transformed_df['source'].str.replace('Electricity from ', '').str.replace(' (TWh)', '')\n \n return transformed_df\n", "source": ["global-energy-20-small.csv"], "dialog": [{"content": "You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 158029.9988, 190729.9957, 209229.9957, ..., 6993180.176, 9861099.609, 9874660.156, 10096009.77\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 90.14, 91.79, 92.54, 804.95\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"energy\",\n \"color\": \"Entity\",\n \"column\": \"source\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Transform the data into a long format where different energy sources (fossil fuels, nuclear, renewables) are unpivoted into separate rows. Each row should contain the year, entity (country), energy source type, and the corresponding energy value in TWh. This will enable visualization of energy generation trends over time by source and country.\",\n \"display_instruction\": \"Show **energy generation** trends by **source** and **Entity** over **Year**\",\n \"output_fields\": [\"Year\", \"Entity\", \"source\", \"energy\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"energy\",\n \"color\": \"Entity\",\n \"column\": \"source\"\n },\n \"reason\": \"The user wants to visualize energy data across different sources with a line chart faceted by source. The original data has energy sources in separate columns (fossil fuels, nuclear, renewables), so we need to unpivot these columns into a long format with 'source' and 'energy' fields. The chart_encodings provided by the user are appropriate for this visualization goal, using faceting (column) to separate different energy sources.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Unpivot the energy source columns into long format\n transformed_df = pd.melt(\n df_energy,\n id_vars=['Year', 'Entity'],\n value_vars=[\n 'Electricity from fossil fuels (TWh)',\n 'Electricity from nuclear (TWh)',\n 'Electricity from renewables (TWh)'\n ],\n var_name='source',\n value_name='energy'\n )\n \n # Clean up the source names to be more readable\n transformed_df['source'] = transformed_df['source'].str.replace('Electricity from ', '').str.replace(' (TWh)', '')\n \n return transformed_df\n```", "role": "assistant"}], "trigger": {"tableId": "global-energy-20-small.csv", "resultTableId": "table-82", "chart": {"id": "chart-1760738394155", "chartType": "Line Chart", "encodingMap": {"x": {"fieldID": "original--global-energy-20-small.csv--Year"}, "y": {"channel": "y", "bin": false, "fieldID": "concept-1760738385163"}, "color": {"fieldID": "original--global-energy-20-small.csv--Entity"}, "opacity": {"channel": "opacity", "bin": false}, "column": {"channel": "column", "bin": false, "fieldID": "concept-1760738389404"}, "row": {"channel": "row", "bin": false}}, "tableRef": "global-energy-20-small.csv", "saved": false, "source": "trigger", "unread": false}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "", "displayContent": "Show **energy generation** trends by **source** and **Entity** over **Year**"}]}, "explanation": {"agent": "CodeExplanationAgent", "code": "The code performs a data transformation to restructure the energy data from a wide format to a long format:\n\n1. **Unpivot energy columns**: Uses `pd.melt()` to transform the three energy source columns (**Electricity from fossil fuels**, **Electricity from nuclear**, and **Electricity from renewables**) from separate columns into rows, while keeping **Year** and **Entity** as identifier columns.\n\n2. **Create source classification**: Generates a new **source** column that captures which energy type each row represents, and stores the corresponding energy values in an **energy** column.\n\n3. **Clean source labels**: Removes the prefix \"Electricity from \" and suffix \" (TWh)\" from the source names, resulting in cleaner labels: **fossil fuels**, **nuclear**, and **renewables**.\n\n4. **Return restructured data**: The final dataset has each country-year-energy source combination as a separate row, making it easier to analyze and visualize energy mix across countries and time periods.", "concepts": [], "dialog": [{"content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small_csv)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 190729.9957, 227580.0018, 233600.0061, ..., 5736319.824, 5738290.039, nan, 9861099.609\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 90.14, 91.79, 92.54, nan\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Unpivot the energy source columns into long format\n transformed_df = pd.melt(\n df_energy,\n id_vars=['Year', 'Entity'],\n value_vars=[\n 'Electricity from fossil fuels (TWh)',\n 'Electricity from nuclear (TWh)',\n 'Electricity from renewables (TWh)'\n ],\n var_name='source',\n value_name='energy'\n )\n \n # Clean up the source names to be more readable\n transformed_df['source'] = transformed_df['source'].str.replace('Electricity from ', '').str.replace(' (TWh)', '')\n \n return transformed_df\n\n\n[EXPLANATION]\n", "role": "user"}, {"content": "[CODE EXPLANATION]\n\nThe code performs a data transformation to restructure the energy data from a wide format to a long format:\n\n1. **Unpivot energy columns**: Uses `pd.melt()` to transform the three energy source columns (**Electricity from fossil fuels**, **Electricity from nuclear**, and **Electricity from renewables**) from separate columns into rows, while keeping **Year** and **Entity** as identifier columns.\n\n2. **Create source classification**: Generates a new **source** column that captures which energy type each row represents, and stores the corresponding energy values in an **energy** column.\n\n3. **Clean source labels**: Removes the prefix \"Electricity from \" and suffix \" (TWh)\" from the source names, resulting in cleaner labels: **fossil fuels**, **nuclear**, and **renewables**.\n\n4. **Return restructured data**: The final dataset has each country-year-energy source combination as a separate row, making it easier to analyze and visualize energy mix across countries and time periods.\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```", "role": "assistant"}], "status": "ok"}, "status": "completed"}, "anchored": false, "attachedMetadata": ""}, {"kind": "table", "id": "table-45", "displayId": "renewable-energy", "names": ["Entity", "Year", "renewable_percentage"], "rows": [{"Entity": "Australia", "Year": 2000, "renewable_percentage": 8.6344368187}, {"Entity": "Australia", "Year": 2001, "renewable_percentage": 8.2180135078}, {"Entity": "Australia", "Year": 2002, "renewable_percentage": 8.0833022736}, {"Entity": "Australia", "Year": 2003, "renewable_percentage": 8.6598324205}, {"Entity": "Australia", "Year": 2004, "renewable_percentage": 8.7013045232}, {"Entity": "Australia", "Year": 2005, "renewable_percentage": 9.1562355123}, {"Entity": "Australia", "Year": 2006, "renewable_percentage": 9.6357600837}, {"Entity": "Australia", "Year": 2007, "renewable_percentage": 9.1190310213}, {"Entity": "Australia", "Year": 2008, "renewable_percentage": 8.0548900022}, {"Entity": "Australia", "Year": 2009, "renewable_percentage": 7.8043793133}, {"Entity": "Australia", "Year": 2010, "renewable_percentage": 9.0442152121}, {"Entity": "Australia", "Year": 2011, "renewable_percentage": 11.3454273735}, {"Entity": "Australia", "Year": 2012, "renewable_percentage": 11.4105750279}, {"Entity": "Australia", "Year": 2013, "renewable_percentage": 14.8708583355}, {"Entity": "Australia", "Year": 2014, "renewable_percentage": 14.9621290509}, {"Entity": "Australia", "Year": 2015, "renewable_percentage": 14.3476000693}, {"Entity": "Australia", "Year": 2016, "renewable_percentage": 15.6093794449}, {"Entity": "Australia", "Year": 2017, "renewable_percentage": 16.3138729943}, {"Entity": "Australia", "Year": 2018, "renewable_percentage": 17.145938174}, {"Entity": "Australia", "Year": 2019, "renewable_percentage": 21.3759705435}, {"Entity": "Australia", "Year": 2020, "renewable_percentage": 25.5031684668}, {"Entity": "Brazil", "Year": 2000, "renewable_percentage": 90.1307723743}, {"Entity": "Brazil", "Year": 2001, "renewable_percentage": 84.6953615744}, {"Entity": "Brazil", "Year": 2002, "renewable_percentage": 86.0883364189}, {"Entity": "Brazil", "Year": 2003, "renewable_percentage": 87.4561159097}, {"Entity": "Brazil", "Year": 2004, "renewable_percentage": 86.4260041451}, {"Entity": "Brazil", "Year": 2005, "renewable_percentage": 87.6781562721}, {"Entity": "Brazil", "Year": 2006, "renewable_percentage": 87.2842473236}, {"Entity": "Brazil", "Year": 2007, "renewable_percentage": 88.7252098726}, {"Entity": "Brazil", "Year": 2008, "renewable_percentage": 84.8072313004}, {"Entity": "Brazil", "Year": 2009, "renewable_percentage": 89.4172280725}, {"Entity": "Brazil", "Year": 2010, "renewable_percentage": 85.3576882415}, {"Entity": "Brazil", "Year": 2011, "renewable_percentage": 87.6618820986}, {"Entity": "Brazil", "Year": 2012, "renewable_percentage": 83.1164558813}, {"Entity": "Brazil", "Year": 2013, "renewable_percentage": 77.5240022006}, {"Entity": "Brazil", "Year": 2014, "renewable_percentage": 74.0418657409}, {"Entity": "Brazil", "Year": 2015, "renewable_percentage": 75.0231817625}, {"Entity": "Brazil", "Year": 2016, "renewable_percentage": 81.0938046902}, {"Entity": "Brazil", "Year": 2017, "renewable_percentage": 79.9091472228}, {"Entity": "Brazil", "Year": 2018, "renewable_percentage": 82.9198505403}, {"Entity": "Brazil", "Year": 2019, "renewable_percentage": 82.8548799017}, {"Entity": "Brazil", "Year": 2020, "renewable_percentage": 84.6411771408}, {"Entity": "Canada", "Year": 2000, "renewable_percentage": 61.8095917882}, {"Entity": "Canada", "Year": 2001, "renewable_percentage": 59.3287558747}, {"Entity": "Canada", "Year": 2002, "renewable_percentage": 61.1477403113}, {"Entity": "Canada", "Year": 2003, "renewable_percentage": 60.0789685174}, {"Entity": "Canada", "Year": 2004, 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Arabia", "Year": 2004, "renewable_percentage": null}, {"Entity": "Saudi Arabia", "Year": 2005, "renewable_percentage": null}, {"Entity": "Saudi Arabia", "Year": 2006, "renewable_percentage": null}, {"Entity": "Saudi Arabia", "Year": 2007, "renewable_percentage": null}, {"Entity": "Saudi Arabia", "Year": 2008, "renewable_percentage": null}, {"Entity": "Saudi Arabia", "Year": 2009, "renewable_percentage": null}, {"Entity": "Saudi Arabia", "Year": 2010, "renewable_percentage": null}, {"Entity": "Saudi Arabia", "Year": 2011, "renewable_percentage": null}, {"Entity": "Saudi Arabia", "Year": 2012, "renewable_percentage": null}, {"Entity": "Saudi Arabia", "Year": 2013, "renewable_percentage": null}, {"Entity": "Saudi Arabia", "Year": 2014, "renewable_percentage": null}, {"Entity": "Saudi Arabia", "Year": 2015, "renewable_percentage": null}, {"Entity": "Saudi Arabia", "Year": 2016, "renewable_percentage": null}, {"Entity": "Saudi Arabia", "Year": 2017, "renewable_percentage": null}, {"Entity": "Saudi Arabia", "Year": 2018, "renewable_percentage": null}, {"Entity": "Saudi Arabia", "Year": 2019, "renewable_percentage": null}, {"Entity": "Saudi Arabia", "Year": 2020, "renewable_percentage": null}, {"Entity": "South Africa", "Year": 2000, "renewable_percentage": 0.9110805721}, {"Entity": "South Africa", "Year": 2001, "renewable_percentage": 1.2516536074}, {"Entity": "South Africa", "Year": 2002, "renewable_percentage": 1.3802249619}, {"Entity": "South Africa", "Year": 2003, "renewable_percentage": 0.545271261}, {"Entity": "South Africa", "Year": 2004, "renewable_percentage": 0.5827199439}, {"Entity": "South Africa", "Year": 2005, "renewable_percentage": 0.763458686}, {"Entity": "South Africa", "Year": 2006, "renewable_percentage": 1.3863060017}, {"Entity": "South Africa", "Year": 2007, "renewable_percentage": 0.5267209594}, {"Entity": "South Africa", "Year": 2008, "renewable_percentage": 0.6895692269}, {"Entity": "South Africa", "Year": 2009, "renewable_percentage": 0.8031088083}, {"Entity": "South Africa", "Year": 2010, "renewable_percentage": 1.0330068318}, {"Entity": "South Africa", "Year": 2011, "renewable_percentage": 1.0184465622}, {"Entity": "South Africa", "Year": 2012, "renewable_percentage": 0.6890826069}, {"Entity": "South Africa", "Year": 2013, "renewable_percentage": 0.6792168043}, {"Entity": "South Africa", "Year": 2014, "renewable_percentage": 1.4288129861}, {"Entity": "South Africa", "Year": 2015, "renewable_percentage": 2.6256790549}, {"Entity": "South Africa", "Year": 2016, "renewable_percentage": 3.2586126531}, {"Entity": "South Africa", "Year": 2017, "renewable_percentage": 4.2202606137}, {"Entity": "South Africa", "Year": 2018, "renewable_percentage": 5.1554655529}, {"Entity": "South Africa", "Year": 2019, "renewable_percentage": 5.3589699864}, {"Entity": "South Africa", "Year": 2020, "renewable_percentage": 5.780581212}, {"Entity": "Spain", "Year": 2000, "renewable_percentage": 15.6119862394}, {"Entity": "Spain", "Year": 2001, "renewable_percentage": 21.1524434719}, {"Entity": "Spain", "Year": 2002, "renewable_percentage": 13.8260180901}, {"Entity": "Spain", "Year": 2003, "renewable_percentage": 21.667314419}, {"Entity": "Spain", "Year": 2004, "renewable_percentage": 18.3190206468}, {"Entity": "Spain", "Year": 2005, "renewable_percentage": 14.8597342333}, {"Entity": "Spain", "Year": 2006, "renewable_percentage": 17.6623992413}, {"Entity": "Spain", "Year": 2007, "renewable_percentage": 19.3347262296}, {"Entity": "Spain", "Year": 2008, "renewable_percentage": 20.0051501593}, {"Entity": "Spain", "Year": 2009, "renewable_percentage": 25.4107639008}, {"Entity": "Spain", "Year": 2010, "renewable_percentage": 32.7922186819}, {"Entity": "Spain", "Year": 2011, "renewable_percentage": 30.0408415417}, {"Entity": "Spain", "Year": 2012, "renewable_percentage": 29.6047928652}, {"Entity": "Spain", "Year": 2013, "renewable_percentage": 39.5850357054}, {"Entity": "Spain", "Year": 2014, "renewable_percentage": 40.1032952644}, {"Entity": "Spain", "Year": 2015, "renewable_percentage": 34.9899091826}, {"Entity": "Spain", "Year": 2016, "renewable_percentage": 38.5818061138}, {"Entity": "Spain", "Year": 2017, "renewable_percentage": 32.220593624}, {"Entity": "Spain", "Year": 2018, "renewable_percentage": 38.2080329557}, {"Entity": "Spain", "Year": 2019, "renewable_percentage": 37.280815091}, {"Entity": "Spain", "Year": 2020, "renewable_percentage": 43.8108805298}, {"Entity": "Thailand", "Year": 2000, "renewable_percentage": 7.1261029822}, {"Entity": "Thailand", "Year": 2001, "renewable_percentage": 7.061527212}, {"Entity": "Thailand", "Year": 2002, "renewable_percentage": 7.9444772593}, {"Entity": "Thailand", "Year": 2003, "renewable_percentage": 7.6718362852}, {"Entity": "Thailand", "Year": 2004, "renewable_percentage": 6.5163549406}, {"Entity": "Thailand", "Year": 2005, "renewable_percentage": 6.0325203252}, {"Entity": "Thailand", "Year": 2006, "renewable_percentage": 7.5988547551}, {"Entity": "Thailand", "Year": 2007, "renewable_percentage": 7.7085852479}, {"Entity": "Thailand", "Year": 2008, "renewable_percentage": 6.5625458278}, {"Entity": "Thailand", "Year": 2009, "renewable_percentage": 6.6263303689}, {"Entity": "Thailand", "Year": 2010, "renewable_percentage": 5.7085828343}, {"Entity": "Thailand", "Year": 2011, "renewable_percentage": 8.039961941}, {"Entity": "Thailand", "Year": 2012, "renewable_percentage": 8.5396118358}, {"Entity": "Thailand", "Year": 2013, "renewable_percentage": 7.6765035487}, {"Entity": "Thailand", "Year": 2014, "renewable_percentage": 8.395728489}, {"Entity": "Thailand", "Year": 2015, "renewable_percentage": 7.9949619145}, {"Entity": "Thailand", "Year": 2016, "renewable_percentage": 8.9840234023}, {"Entity": "Thailand", "Year": 2017, "renewable_percentage": 10.9570957096}, {"Entity": "Thailand", "Year": 2018, "renewable_percentage": 14.1900054915}, {"Entity": "Thailand", "Year": 2019, "renewable_percentage": 14.7001731284}, {"Entity": "Thailand", "Year": 2020, "renewable_percentage": 13.7963737796}, {"Entity": "Ukraine", "Year": 2000, "renewable_percentage": 6.5860921352}, {"Entity": "Ukraine", "Year": 2001, "renewable_percentage": 6.9729761009}, {"Entity": "Ukraine", "Year": 2002, "renewable_percentage": 5.5597165409}, {"Entity": "Ukraine", "Year": 2003, "renewable_percentage": 5.1442841287}, {"Entity": "Ukraine", "Year": 2004, "renewable_percentage": 6.4718162839}, {"Entity": "Ukraine", "Year": 2005, "renewable_percentage": 6.6698940347}, {"Entity": "Ukraine", "Year": 2006, "renewable_percentage": 6.68633235}, {"Entity": "Ukraine", "Year": 2007, "renewable_percentage": 5.3380238605}, {"Entity": "Ukraine", "Year": 2008, "renewable_percentage": 6.1377090041}, {"Entity": "Ukraine", "Year": 2009, "renewable_percentage": 6.980762585}, {"Entity": "Ukraine", "Year": 2010, "renewable_percentage": 7.0914098083}, {"Entity": "Ukraine", "Year": 2011, "renewable_percentage": 5.7450628366}, {"Entity": "Ukraine", "Year": 2012, "renewable_percentage": 5.6614236741}, {"Entity": "Ukraine", "Year": 2013, "renewable_percentage": 7.8003200661}, {"Entity": "Ukraine", "Year": 2014, "renewable_percentage": 5.5885262117}, {"Entity": "Ukraine", "Year": 2015, "renewable_percentage": 4.3924771096}, {"Entity": "Ukraine", "Year": 2016, "renewable_percentage": 5.6797249171}, {"Entity": "Ukraine", "Year": 2017, "renewable_percentage": 7.0457194664}, {"Entity": "Ukraine", "Year": 2018, "renewable_percentage": 8.228528092}, {"Entity": "Ukraine", "Year": 2019, "renewable_percentage": 7.7754487096}, {"Entity": "Ukraine", "Year": 2020, "renewable_percentage": 11.8440577364}, {"Entity": "United Kingdom", "Year": 2000, "renewable_percentage": 2.6657406913}, {"Entity": "United Kingdom", "Year": 2001, "renewable_percentage": 2.5001961451}, {"Entity": "United Kingdom", "Year": 2002, "renewable_percentage": 2.8939157566}, {"Entity": "United Kingdom", "Year": 2003, "renewable_percentage": 2.6854802003}, {"Entity": "United Kingdom", "Year": 2004, "renewable_percentage": 3.6136880575}, {"Entity": "United Kingdom", "Year": 2005, "renewable_percentage": 4.2815234434}, {"Entity": "United Kingdom", "Year": 2006, "renewable_percentage": 4.6029890199}, {"Entity": "United Kingdom", "Year": 2007, "renewable_percentage": 5.0104331009}, {"Entity": "United Kingdom", "Year": 2008, "renewable_percentage": 5.6776842324}, {"Entity": "United Kingdom", "Year": 2009, "renewable_percentage": 6.7679854187}, {"Entity": "United Kingdom", "Year": 2010, "renewable_percentage": 6.9092924441}, {"Entity": "United Kingdom", "Year": 2011, "renewable_percentage": 9.6422505889}, {"Entity": "United Kingdom", "Year": 2012, "renewable_percentage": 11.4273047189}, {"Entity": "United Kingdom", "Year": 2013, "renewable_percentage": 14.9727052732}, {"Entity": "United Kingdom", "Year": 2014, "renewable_percentage": 19.2476358104}, {"Entity": "United Kingdom", "Year": 2015, "renewable_percentage": 24.6227709191}, {"Entity": "United Kingdom", "Year": 2016, "renewable_percentage": 24.6788390627}, {"Entity": "United Kingdom", "Year": 2017, "renewable_percentage": 29.4986571173}, {"Entity": "United Kingdom", "Year": 2018, "renewable_percentage": 33.2919818457}, {"Entity": "United Kingdom", "Year": 2019, "renewable_percentage": 37.4568630499}, {"Entity": "United Kingdom", "Year": 2020, "renewable_percentage": 42.8603962651}, {"Entity": "United States", "Year": 2000, "renewable_percentage": 9.2298992662}, {"Entity": "United States", "Year": 2001, "renewable_percentage": 7.5132056541}, {"Entity": "United States", "Year": 2002, "renewable_percentage": 8.749216358}, {"Entity": "United States", "Year": 2003, "renewable_percentage": 9.0252110397}, {"Entity": "United States", "Year": 2004, "renewable_percentage": 8.7334100887}, {"Entity": "United States", "Year": 2005, "renewable_percentage": 8.7494640631}, {"Entity": "United States", "Year": 2006, "renewable_percentage": 9.4184742052}, {"Entity": "United States", "Year": 2007, "renewable_percentage": 8.3984096829}, {"Entity": "United States", "Year": 2008, "renewable_percentage": 9.180943292}, {"Entity": "United States", "Year": 2009, "renewable_percentage": 10.547689996}, {"Entity": "United States", "Year": 2010, "renewable_percentage": 10.3180892283}, {"Entity": "United States", "Year": 2011, "renewable_percentage": 12.4665249812}, {"Entity": "United States", "Year": 2012, "renewable_percentage": 12.1841179804}, {"Entity": "United States", "Year": 2013, "renewable_percentage": 12.8311154179}, {"Entity": "United States", "Year": 2014, "renewable_percentage": 13.3503092033}, {"Entity": "United States", "Year": 2015, "renewable_percentage": 13.6261352256}, {"Entity": "United States", "Year": 2016, "renewable_percentage": 15.2880936304}, {"Entity": "United States", "Year": 2017, "renewable_percentage": 17.4515276472}, {"Entity": "United States", "Year": 2018, "renewable_percentage": 17.4499886946}, {"Entity": "United States", "Year": 2019, "renewable_percentage": 18.2946243489}, {"Entity": "United States", "Year": 2020, "renewable_percentage": 20.3156921037}], "metadata": {"Entity": {"type": "string", "semanticType": "Location"}, "Year": {"type": "number", "semanticType": "Year"}, "renewable_percentage": {"type": "number", "semanticType": "Percentage"}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total energy for each row\n df_energy['total_energy'] = (\n df_energy['Electricity from fossil fuels (TWh)'] + \n df_energy['Electricity from nuclear (TWh)'] + \n df_energy['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_energy['renewable_percentage'] = (\n df_energy['Electricity from renewables (TWh)'] / df_energy['total_energy'] * 100\n )\n \n # Handle division by zero cases\n df_energy['renewable_percentage'] = df_energy['renewable_percentage'].replace([np.inf, -np.inf], np.nan)\n \n # Select only the required columns\n transformed_df = df_energy[['Year', 'Entity', 'renewable_percentage']].copy()\n \n return transformed_df\n", "source": ["global-energy-20-small.csv"], "dialog": [{"content": "You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 158029.9988, 190729.9957, 209229.9957, ..., 6993180.176, 9861099.609, 9874660.156, 10096009.77\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 90.14, 91.79, 92.54, 804.95\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"energy\",\n \"color\": \"Entity\",\n \"column\": \"source\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Transform the data into a long format where different energy sources (fossil fuels, nuclear, renewables) are unpivoted into separate rows. Each row should contain the year, entity (country), energy source type, and the corresponding energy value in TWh. This will enable visualization of energy generation trends over time by source and country.\",\n \"display_instruction\": \"Show **energy generation** trends by **source** and **Entity** over **Year**\",\n \"output_fields\": [\"Year\", \"Entity\", \"source\", \"energy\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"energy\",\n \"color\": \"Entity\",\n \"column\": \"source\"\n },\n \"reason\": \"The user wants to visualize energy data across different sources with a line chart faceted by source. The original data has energy sources in separate columns (fossil fuels, nuclear, renewables), so we need to unpivot these columns into a long format with 'source' and 'energy' fields. The chart_encodings provided by the user are appropriate for this visualization goal, using faceting (column) to separate different energy sources.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Unpivot the energy source columns into long format\n transformed_df = pd.melt(\n df_energy,\n id_vars=['Year', 'Entity'],\n value_vars=[\n 'Electricity from fossil fuels (TWh)',\n 'Electricity from nuclear (TWh)',\n 'Electricity from renewables (TWh)'\n ],\n var_name='source',\n value_name='energy'\n )\n \n # Clean up the source names to be more readable\n transformed_df['source'] = transformed_df['source'].str.replace('Electricity from ', '').str.replace(' (TWh)', '')\n \n return transformed_df\n```", "role": "assistant"}, {"content": "This is the result from the latest python code:\n\n Entity Year energy source\n0 Australia 2000 181.05 fossil fuels\n1 Australia 2001 194.33 fossil fuels\n2 Australia 2002 197.29 fossil fuels\n3 Australia 2003 195.13 fossil fuels\n4 Australia 2004 203.66 fossil fuels\n5 Australia 2005 195.95 fossil fuels\n6 Australia 2006 198.72 fossil fuels\n7 Australia 2007 208.59 fossil fuels\n8 Australia 2008 211.06 fossil fuels\n9 Australia 2009 216.42 fossil fuels\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show renewable percentage for each country\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"energy\",\n \"color\": \"Entity\",\n \"column\": \"source\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Calculate the renewable energy percentage for each country and year. The renewable percentage is computed as the ratio of renewable energy to total energy (sum of fossil fuels, nuclear, and renewables) multiplied by 100. The output should contain year, entity (country), and the renewable percentage value.\",\n \"display_instruction\": \"Show **renewable percentage** for each **Entity** over **Year**\",\n \"output_fields\": [\"Year\", \"Entity\", \"renewable_percentage\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"renewable_percentage\",\n \"color\": \"Entity\"\n },\n \"reason\": \"The user wants to see renewable percentage trends, which requires calculating the proportion of renewable energy relative to total energy generation. This is a single metric per country per year, so we don't need the 'column' faceting by source anymore. The chart_encodings are updated to reflect that we're now showing a percentage metric rather than absolute energy values by source.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total energy for each row\n df_energy['total_energy'] = (\n df_energy['Electricity from fossil fuels (TWh)'] + \n df_energy['Electricity from nuclear (TWh)'] + \n df_energy['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_energy['renewable_percentage'] = (\n df_energy['Electricity from renewables (TWh)'] / df_energy['total_energy'] * 100\n )\n \n # Handle division by zero cases\n df_energy['renewable_percentage'] = df_energy['renewable_percentage'].replace([np.inf, -np.inf], np.nan)\n \n # Select only the required columns\n transformed_df = df_energy[['Year', 'Entity', 'renewable_percentage']].copy()\n \n return transformed_df\n```", "role": "assistant"}], "trigger": {"tableId": "table-82", "resultTableId": "table-45", "chart": {"id": "chart-1760738416626", "chartType": "Line Chart", "encodingMap": {"x": {"fieldID": "original--global-energy-20-small.csv--Year"}, "y": {"fieldID": "concept-1760738385163"}, "color": {"fieldID": "original--global-energy-20-small.csv--Entity"}, "opacity": {"channel": "opacity", "bin": false}, "column": {"fieldID": "concept-1760738389404", "sortBy": "[\"fossil fuels\",\"nuclear\",\"renewables\"]"}, "row": {"channel": "row", "bin": false}}, "tableRef": "table-82", "saved": false, "source": "trigger", "unread": false}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "show renewable percentage for each country", "displayContent": "Show **renewable percentage** for each **Entity** over **Year**"}]}, "explanation": {"agent": "CodeExplanationAgent", "code": "The code transforms global energy data to analyze renewable energy adoption:\n\n- Calculates **total_energy** by summing electricity generation from three sources: **fossil fuels**, **nuclear**, and **renewables** (all measured in TWh)\n- Computes **renewable_percentage** by dividing **Electricity from renewables (TWh)** by **total_energy** and multiplying by **100**\n- Handles edge cases by replacing infinite values (from division by zero) with **NaN**\n- Filters the dataset to retain only **Year**, **Entity** (country), and **renewable_percentage** columns", "concepts": [{"explanation": "The proportion of a country's total electricity generation that comes from renewable sources, expressed as a percentage. Calculated as: \\( \\frac{\\text{Electricity from renewables}}{\\text{Total electricity generation}} \\times 100 \\). This metric indicates the degree of renewable energy adoption in each country's energy mix.", "field": "renewable_percentage"}], "dialog": [{"content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small_csv)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 117440.0, 146139.9994, 158029.9988, ..., 5738290.039, 9861099.609, 10502929.69, 10707219.73\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., nan, nan, nan, nan\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total energy for each row\n df_energy['total_energy'] = (\n df_energy['Electricity from fossil fuels (TWh)'] + \n df_energy['Electricity from nuclear (TWh)'] + \n df_energy['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_energy['renewable_percentage'] = (\n df_energy['Electricity from renewables (TWh)'] / df_energy['total_energy'] * 100\n )\n \n # Handle division by zero cases\n df_energy['renewable_percentage'] = df_energy['renewable_percentage'].replace([np.inf, -np.inf], np.nan)\n \n # Select only the required columns\n transformed_df = df_energy[['Year', 'Entity', 'renewable_percentage']].copy()\n \n return transformed_df\n\n\n[EXPLANATION]\n", "role": "user"}, {"content": "[CODE EXPLANATION]\n\nThe code transforms global energy data to analyze renewable energy adoption:\n\n- Calculates **total_energy** by summing electricity generation from three sources: **fossil fuels**, **nuclear**, and **renewables** (all measured in TWh)\n- Computes **renewable_percentage** by dividing **Electricity from renewables (TWh)** by **total_energy** and multiplying by **100**\n- Handles edge cases by replacing infinite values (from division by zero) with **NaN**\n- Filters the dataset to retain only **Year**, **Entity** (country), and **renewable_percentage** columns\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"renewable_percentage\",\n \"explanation\": \"The proportion of a country's total electricity generation that comes from renewable sources, expressed as a percentage. Calculated as: \\\\( \\\\frac{\\\\text{Electricity from renewables}}{\\\\text{Total electricity generation}} \\\\times 100 \\\\). This metric indicates the degree of renewable energy adoption in each country's energy mix.\"\n }\n]\n```", "role": "assistant"}], "status": "ok"}, "status": "completed"}, "anchored": false, "attachedMetadata": ""}, {"kind": "table", "id": "table-78", "displayId": "renewable-energy-rank", "names": ["Entity", "Year", "rank", "renewable_percentage"], "rows": [{"Entity": "Australia", "Year": 2000, "rank": 11, "renewable_percentage": 8.6344368187}, {"Entity": "Australia", "Year": 2001, "rank": 10, "renewable_percentage": 8.2180135078}, {"Entity": "Australia", "Year": 2002, "rank": 11, "renewable_percentage": 8.0833022736}, {"Entity": "Australia", "Year": 2003, "rank": 11, "renewable_percentage": 8.6598324205}, {"Entity": "Australia", "Year": 2004, "rank": 12, "renewable_percentage": 8.7013045232}, {"Entity": "Australia", "Year": 2005, "rank": 11, "renewable_percentage": 9.1562355123}, {"Entity": "Australia", "Year": 2006, "rank": 11, "renewable_percentage": 9.6357600837}, {"Entity": "Australia", "Year": 2007, "rank": 11, "renewable_percentage": 9.1190310213}, {"Entity": "Australia", "Year": 2008, "rank": 12, "renewable_percentage": 8.0548900022}, {"Entity": "Australia", "Year": 2009, "rank": 12, "renewable_percentage": 7.8043793133}, {"Entity": "Australia", "Year": 2010, "rank": 12, "renewable_percentage": 9.0442152121}, {"Entity": "Australia", "Year": 2011, "rank": 11, "renewable_percentage": 11.3454273735}, {"Entity": "Australia", "Year": 2012, "rank": 12, "renewable_percentage": 11.4105750279}, {"Entity": "Australia", "Year": 2013, "rank": 11, "renewable_percentage": 14.8708583355}, {"Entity": "Australia", "Year": 2014, "rank": 11, "renewable_percentage": 14.9621290509}, {"Entity": "Australia", "Year": 2015, "rank": 12, "renewable_percentage": 14.3476000693}, {"Entity": "Australia", "Year": 2016, "rank": 11, "renewable_percentage": 15.6093794449}, {"Entity": "Australia", "Year": 2017, "rank": 12, "renewable_percentage": 16.3138729943}, {"Entity": "Australia", "Year": 2018, "rank": 12, "renewable_percentage": 17.145938174}, {"Entity": "Australia", "Year": 2019, "rank": 8, "renewable_percentage": 21.3759705435}, {"Entity": "Australia", "Year": 2020, "rank": 8, "renewable_percentage": 25.5031684668}, {"Entity": "Brazil", "Year": 2000, "rank": 1, "renewable_percentage": 90.1307723743}, {"Entity": "Brazil", "Year": 2001, "rank": 1, "renewable_percentage": 84.6953615744}, {"Entity": "Brazil", "Year": 2002, "rank": 1, "renewable_percentage": 86.0883364189}, {"Entity": "Brazil", "Year": 2003, "rank": 1, "renewable_percentage": 87.4561159097}, {"Entity": "Brazil", "Year": 2004, "rank": 1, "renewable_percentage": 86.4260041451}, {"Entity": "Brazil", "Year": 2005, "rank": 1, "renewable_percentage": 87.6781562721}, {"Entity": "Brazil", "Year": 2006, "rank": 1, "renewable_percentage": 87.2842473236}, {"Entity": "Brazil", "Year": 2007, "rank": 1, "renewable_percentage": 88.7252098726}, {"Entity": "Brazil", "Year": 2008, "rank": 1, "renewable_percentage": 84.8072313004}, {"Entity": "Brazil", "Year": 2009, "rank": 1, "renewable_percentage": 89.4172280725}, {"Entity": "Brazil", "Year": 2010, "rank": 1, "renewable_percentage": 85.3576882415}, {"Entity": "Brazil", "Year": 2011, "rank": 1, "renewable_percentage": 87.6618820986}, {"Entity": "Brazil", "Year": 2012, "rank": 1, "renewable_percentage": 83.1164558813}, {"Entity": "Brazil", "Year": 2013, "rank": 1, "renewable_percentage": 77.5240022006}, {"Entity": "Brazil", "Year": 2014, "rank": 1, "renewable_percentage": 74.0418657409}, {"Entity": "Brazil", "Year": 2015, "rank": 1, "renewable_percentage": 75.0231817625}, {"Entity": "Brazil", "Year": 2016, "rank": 1, "renewable_percentage": 81.0938046902}, {"Entity": "Brazil", "Year": 2017, "rank": 1, "renewable_percentage": 79.9091472228}, {"Entity": "Brazil", "Year": 2018, "rank": 1, "renewable_percentage": 82.9198505403}, {"Entity": "Brazil", "Year": 2019, "rank": 1, "renewable_percentage": 82.8548799017}, {"Entity": "Brazil", "Year": 2020, "rank": 1, "renewable_percentage": 84.6411771408}, {"Entity": "Canada", "Year": 2000, "rank": 2, "renewable_percentage": 61.8095917882}, {"Entity": "Canada", "Year": 2001, "rank": 2, "renewable_percentage": 59.3287558747}, {"Entity": "Canada", "Year": 2002, "rank": 2, "renewable_percentage": 61.1477403113}, {"Entity": "Canada", "Year": 2003, "rank": 2, "renewable_percentage": 60.0789685174}, {"Entity": "Canada", "Year": 2004, "rank": 2, "renewable_percentage": 59.6967771845}, {"Entity": "Canada", "Year": 2005, "rank": 2, "renewable_percentage": 60.8208155391}, {"Entity": "Canada", "Year": 2006, "rank": 2, "renewable_percentage": 60.8271602855}, {"Entity": "Canada", "Year": 2007, "rank": 2, "renewable_percentage": 61.2460642446}, {"Entity": "Canada", "Year": 2008, "rank": 2, "renewable_percentage": 62.6520720838}, {"Entity": "Canada", "Year": 2009, "rank": 2, "renewable_percentage": 63.8919227732}, {"Entity": "Canada", "Year": 2010, "rank": 2, "renewable_percentage": 62.9421470558}, {"Entity": "Canada", "Year": 2011, "rank": 2, "renewable_percentage": 64.0922915917}, {"Entity": "Canada", "Year": 2012, "rank": 2, "renewable_percentage": 65.098730952}, {"Entity": "Canada", "Year": 2013, "rank": 2, "renewable_percentage": 65.4320794066}, {"Entity": "Canada", "Year": 2014, "rank": 2, "renewable_percentage": 64.791145907}, {"Entity": "Canada", "Year": 2015, "rank": 2, "renewable_percentage": 65.2946239925}, {"Entity": "Canada", "Year": 2016, "rank": 2, "renewable_percentage": 66.1890584295}, {"Entity": "Canada", "Year": 2017, "rank": 2, "renewable_percentage": 67.5399410579}, {"Entity": "Canada", "Year": 2018, "rank": 2, "renewable_percentage": 67.3685700357}, {"Entity": "Canada", "Year": 2019, "rank": 2, "renewable_percentage": 67.1741623137}, {"Entity": "Canada", "Year": 2020, "rank": 2, "renewable_percentage": 68.7796436354}, {"Entity": "China", "Year": 2000, "rank": 5, "renewable_percentage": 16.639126586}, {"Entity": "China", "Year": 2001, "rank": 6, "renewable_percentage": 18.9581237042}, {"Entity": "China", "Year": 2002, "rank": 3, "renewable_percentage": 17.6185006046}, {"Entity": "China", "Year": 2003, "rank": 6, "renewable_percentage": 15.0362717081}, {"Entity": "China", "Year": 2004, "rank": 6, "renewable_percentage": 16.2224108273}, {"Entity": "China", "Year": 2005, "rank": 5, "renewable_percentage": 16.1731179957}, {"Entity": "China", "Year": 2006, "rank": 7, "renewable_percentage": 15.5884036124}, {"Entity": "China", "Year": 2007, "rank": 7, "renewable_percentage": 15.2583847828}, {"Entity": "China", "Year": 2008, "rank": 5, "renewable_percentage": 19.0253335469}, {"Entity": "China", "Year": 2009, "rank": 5, "renewable_percentage": 17.8857170547}, {"Entity": "China", "Year": 2010, "rank": 6, "renewable_percentage": 18.7800759915}, {"Entity": "China", "Year": 2011, "rank": 7, "renewable_percentage": 16.8902341543}, {"Entity": "China", "Year": 2012, "rank": 6, "renewable_percentage": 20.122965176}, {"Entity": "China", "Year": 2013, "rank": 6, "renewable_percentage": 20.2152481955}, {"Entity": "China", "Year": 2014, "rank": 6, "renewable_percentage": 22.3502204285}, {"Entity": "China", "Year": 2015, "rank": 7, "renewable_percentage": 24.079270189}, {"Entity": "China", "Year": 2016, "rank": 6, "renewable_percentage": 25.0007798429}, {"Entity": "China", "Year": 2017, "rank": 7, "renewable_percentage": 25.419242299}, {"Entity": "China", "Year": 2018, "rank": 7, "renewable_percentage": 25.7747942589}, {"Entity": "China", "Year": 2019, "rank": 7, "renewable_percentage": 26.9995671106}, {"Entity": "China", "Year": 2020, "rank": 7, "renewable_percentage": 28.2464606924}, {"Entity": "France", "Year": 2000, "rank": 8, "renewable_percentage": 12.7117691154}, {"Entity": "France", "Year": 2001, "rank": 7, "renewable_percentage": 13.9961372206}, {"Entity": "France", "Year": 2002, "rank": 8, "renewable_percentage": 11.3544157067}, {"Entity": "France", "Year": 2003, "rank": 9, "renewable_percentage": 10.9783540506}, {"Entity": "France", "Year": 2004, "rank": 9, "renewable_percentage": 11.0051305559}, {"Entity": "France", "Year": 2005, "rank": 10, "renewable_percentage": 9.6479837153}, {"Entity": "France", "Year": 2006, "rank": 10, "renewable_percentage": 10.7235915493}, {"Entity": "France", "Year": 2007, "rank": 9, "renewable_percentage": 11.4370075239}, {"Entity": "France", "Year": 2008, "rank": 9, "renewable_percentage": 12.7487441615}, {"Entity": "France", "Year": 2009, "rank": 9, "renewable_percentage": 12.8776856068}, {"Entity": "France", "Year": 2010, "rank": 9, "renewable_percentage": 13.6240072491}, {"Entity": "France", "Year": 2011, "rank": 10, "renewable_percentage": 11.63553049}, {"Entity": "France", "Year": 2012, "rank": 9, "renewable_percentage": 15.0331522889}, {"Entity": "France", "Year": 2013, "rank": 7, "renewable_percentage": 17.2469424928}, {"Entity": "France", "Year": 2014, "rank": 9, "renewable_percentage": 16.6074992494}, {"Entity": "France", "Year": 2015, "rank": 9, "renewable_percentage": 16.002230276}, {"Entity": "France", "Year": 2016, "rank": 8, "renewable_percentage": 17.7212924013}, {"Entity": "France", "Year": 2017, "rank": 11, "renewable_percentage": 16.6576751547}, {"Entity": "France", "Year": 2018, "rank": 8, "renewable_percentage": 19.7315179827}, {"Entity": "France", "Year": 2019, "rank": 9, "renewable_percentage": 20.0116665488}, {"Entity": "France", "Year": 2020, "rank": 9, "renewable_percentage": 23.7610241821}, {"Entity": "Germany", "Year": 2000, "rank": 14, "renewable_percentage": 6.1977983575}, {"Entity": "Germany", "Year": 2001, "rank": 14, "renewable_percentage": 6.5132585197}, {"Entity": "Germany", "Year": 2002, "rank": 13, "renewable_percentage": 7.6431369854}, {"Entity": "Germany", "Year": 2003, "rank": 12, "renewable_percentage": 7.7455438643}, {"Entity": "Germany", "Year": 2004, "rank": 10, "renewable_percentage": 9.4989185292}, {"Entity": "Germany", "Year": 2005, "rank": 8, "renewable_percentage": 10.3356645637}, {"Entity": "Germany", "Year": 2006, "rank": 8, "renewable_percentage": 11.5129959829}, {"Entity": "Germany", "Year": 2007, "rank": 8, "renewable_percentage": 14.135471525}, {"Entity": "Germany", "Year": 2008, "rank": 8, "renewable_percentage": 14.8894504106}, {"Entity": "Germany", "Year": 2009, "rank": 7, "renewable_percentage": 16.2902842395}, {"Entity": "Germany", "Year": 2010, "rank": 7, "renewable_percentage": 16.8384989754}, {"Entity": "Germany", "Year": 2011, "rank": 5, "renewable_percentage": 20.4967199299}, {"Entity": "Germany", "Year": 2012, "rank": 5, "renewable_percentage": 23.056464482}, {"Entity": "Germany", "Year": 2013, "rank": 5, "renewable_percentage": 24.1368929731}, {"Entity": "Germany", "Year": 2014, "rank": 5, "renewable_percentage": 26.2182434067}, {"Entity": "Germany", "Year": 2015, "rank": 5, "renewable_percentage": 29.4721888318}, {"Entity": "Germany", "Year": 2016, "rank": 5, "renewable_percentage": 29.4990435013}, {"Entity": "Germany", "Year": 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Kingdom", "Year": 2009, "rank": 14, "renewable_percentage": 6.7679854187}, {"Entity": "United Kingdom", "Year": 2010, "rank": 15, "renewable_percentage": 6.9092924441}, {"Entity": "United Kingdom", "Year": 2011, "rank": 13, "renewable_percentage": 9.6422505889}, {"Entity": "United Kingdom", "Year": 2012, "rank": 11, "renewable_percentage": 11.4273047189}, {"Entity": "United Kingdom", "Year": 2013, "rank": 10, "renewable_percentage": 14.9727052732}, {"Entity": "United Kingdom", "Year": 2014, "rank": 8, "renewable_percentage": 19.2476358104}, {"Entity": "United Kingdom", "Year": 2015, "rank": 6, "renewable_percentage": 24.6227709191}, {"Entity": "United Kingdom", "Year": 2016, "rank": 7, "renewable_percentage": 24.6788390627}, {"Entity": "United Kingdom", "Year": 2017, "rank": 6, "renewable_percentage": 29.4986571173}, {"Entity": "United Kingdom", "Year": 2018, "rank": 6, "renewable_percentage": 33.2919818457}, {"Entity": "United Kingdom", "Year": 2019, "rank": 5, "renewable_percentage": 37.4568630499}, {"Entity": "United Kingdom", "Year": 2020, "rank": 5, "renewable_percentage": 42.8603962651}, {"Entity": "United States", "Year": 2000, "rank": 10, "renewable_percentage": 9.2298992662}, {"Entity": "United States", "Year": 2001, "rank": 11, "renewable_percentage": 7.5132056541}, {"Entity": "United States", "Year": 2002, "rank": 10, "renewable_percentage": 8.749216358}, {"Entity": "United States", "Year": 2003, "rank": 10, "renewable_percentage": 9.0252110397}, {"Entity": "United States", "Year": 2004, "rank": 11, "renewable_percentage": 8.7334100887}, {"Entity": "United States", "Year": 2005, "rank": 12, "renewable_percentage": 8.7494640631}, {"Entity": "United States", "Year": 2006, "rank": 12, "renewable_percentage": 9.4184742052}, {"Entity": "United States", "Year": 2007, "rank": 12, "renewable_percentage": 8.3984096829}, {"Entity": "United States", "Year": 2008, "rank": 11, "renewable_percentage": 9.180943292}, {"Entity": "United States", "Year": 2009, "rank": 10, "renewable_percentage": 10.547689996}, {"Entity": "United States", "Year": 2010, "rank": 11, "renewable_percentage": 10.3180892283}, {"Entity": "United States", "Year": 2011, "rank": 9, "renewable_percentage": 12.4665249812}, {"Entity": "United States", "Year": 2012, "rank": 10, "renewable_percentage": 12.1841179804}, {"Entity": "United States", "Year": 2013, "rank": 12, "renewable_percentage": 12.8311154179}, {"Entity": "United States", "Year": 2014, "rank": 12, "renewable_percentage": 13.3503092033}, {"Entity": "United States", "Year": 2015, "rank": 14, "renewable_percentage": 13.6261352256}, {"Entity": "United States", "Year": 2016, "rank": 12, "renewable_percentage": 15.2880936304}, {"Entity": "United States", "Year": 2017, "rank": 9, "renewable_percentage": 17.4515276472}, {"Entity": "United States", "Year": 2018, "rank": 11, "renewable_percentage": 17.4499886946}, {"Entity": "United States", "Year": 2019, "rank": 13, "renewable_percentage": 18.2946243489}, {"Entity": "United States", "Year": 2020, "rank": 12, "renewable_percentage": 20.3156921037}], "metadata": {"Entity": {"type": "string", "semanticType": "Location"}, "Year": {"type": "number", "semanticType": "Year"}, "rank": {"type": "number", "semanticType": "Number"}, "renewable_percentage": {"type": "number", "semanticType": "Percentage"}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total energy for each row\n df_energy['total_energy'] = (\n df_energy['Electricity from fossil fuels (TWh)'] + \n df_energy['Electricity from nuclear (TWh)'] + \n df_energy['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_energy['renewable_percentage'] = (\n df_energy['Electricity from renewables (TWh)'] / df_energy['total_energy'] * 100\n )\n \n # Handle division by zero cases\n df_energy['renewable_percentage'] = df_energy['renewable_percentage'].replace([np.inf, -np.inf], np.nan)\n \n # Rank countries by renewable percentage within each year (highest percentage = rank 1)\n df_energy['rank'] = df_energy.groupby('Year')['renewable_percentage'].rank(\n method='dense', \n ascending=False\n )\n \n # Select only the required columns\n transformed_df = df_energy[['Year', 'Entity', 'renewable_percentage', 'rank']].copy()\n \n return transformed_df\n", "source": ["global-energy-20-small.csv"], "dialog": [{"content": "You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 158029.9988, 190729.9957, 209229.9957, ..., 6993180.176, 9861099.609, 9874660.156, 10096009.77\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 90.14, 91.79, 92.54, 804.95\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"energy\",\n \"color\": \"Entity\",\n \"column\": \"source\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Transform the data into a long format where different energy sources (fossil fuels, nuclear, renewables) are unpivoted into separate rows. Each row should contain the year, entity (country), energy source type, and the corresponding energy value in TWh. This will enable visualization of energy generation trends over time by source and country.\",\n \"display_instruction\": \"Show **energy generation** trends by **source** and **Entity** over **Year**\",\n \"output_fields\": [\"Year\", \"Entity\", \"source\", \"energy\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"energy\",\n \"color\": \"Entity\",\n \"column\": \"source\"\n },\n \"reason\": \"The user wants to visualize energy data across different sources with a line chart faceted by source. The original data has energy sources in separate columns (fossil fuels, nuclear, renewables), so we need to unpivot these columns into a long format with 'source' and 'energy' fields. The chart_encodings provided by the user are appropriate for this visualization goal, using faceting (column) to separate different energy sources.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Unpivot the energy source columns into long format\n transformed_df = pd.melt(\n df_energy,\n id_vars=['Year', 'Entity'],\n value_vars=[\n 'Electricity from fossil fuels (TWh)',\n 'Electricity from nuclear (TWh)',\n 'Electricity from renewables (TWh)'\n ],\n var_name='source',\n value_name='energy'\n )\n \n # Clean up the source names to be more readable\n transformed_df['source'] = transformed_df['source'].str.replace('Electricity from ', '').str.replace(' (TWh)', '')\n \n return transformed_df\n```", "role": "assistant"}, {"content": "This is the result from the latest python code:\n\n Entity Year energy source\n0 Australia 2000 181.05 fossil fuels\n1 Australia 2001 194.33 fossil fuels\n2 Australia 2002 197.29 fossil fuels\n3 Australia 2003 195.13 fossil fuels\n4 Australia 2004 203.66 fossil fuels\n5 Australia 2005 195.95 fossil fuels\n6 Australia 2006 198.72 fossil fuels\n7 Australia 2007 208.59 fossil fuels\n8 Australia 2008 211.06 fossil fuels\n9 Australia 2009 216.42 fossil fuels\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show renewable percentage for each country\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"energy\",\n \"color\": \"Entity\",\n \"column\": \"source\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Calculate the renewable energy percentage for each country and year. The renewable percentage is computed as the ratio of renewable energy to total energy (sum of fossil fuels, nuclear, and renewables) multiplied by 100. The output should contain year, entity (country), and the renewable percentage value.\",\n \"display_instruction\": \"Show **renewable percentage** for each **Entity** over **Year**\",\n \"output_fields\": [\"Year\", \"Entity\", \"renewable_percentage\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"renewable_percentage\",\n \"color\": \"Entity\"\n },\n \"reason\": \"The user wants to see renewable percentage trends, which requires calculating the proportion of renewable energy relative to total energy generation. This is a single metric per country per year, so we don't need the 'column' faceting by source anymore. The chart_encodings are updated to reflect that we're now showing a percentage metric rather than absolute energy values by source.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total energy for each row\n df_energy['total_energy'] = (\n df_energy['Electricity from fossil fuels (TWh)'] + \n df_energy['Electricity from nuclear (TWh)'] + \n df_energy['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_energy['renewable_percentage'] = (\n df_energy['Electricity from renewables (TWh)'] / df_energy['total_energy'] * 100\n )\n \n # Handle division by zero cases\n df_energy['renewable_percentage'] = df_energy['renewable_percentage'].replace([np.inf, -np.inf], np.nan)\n \n # Select only the required columns\n transformed_df = df_energy[['Year', 'Entity', 'renewable_percentage']].copy()\n \n return transformed_df\n```", "role": "assistant"}, {"content": "This is the result from the latest python code:\n\n Entity Year renewable_percentage\n0 Australia 2000 8.634437\n1 Australia 2001 8.218014\n2 Australia 2002 8.083302\n3 Australia 2003 8.659832\n4 Australia 2004 8.701305\n5 Australia 2005 9.156236\n6 Australia 2006 9.635760\n7 Australia 2007 9.119031\n8 Australia 2008 8.054890\n9 Australia 2009 7.804379\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"rank countries by their renewable percentage\",\n \"chart_type\": \"Line Chart\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"renewable_percentage\",\n \"color\": \"Entity\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Calculate the renewable energy percentage for each country and year, then rank countries based on their renewable percentage. The ranking should be computed for each year, showing which countries have the highest to lowest renewable percentage. The output should contain year, entity (country), renewable percentage, and the rank of each country within that year.\",\n \"display_instruction\": \"Show **countries ranked** by **renewable percentage** over **Year**\",\n \"output_fields\": [\"Year\", \"Entity\", \"renewable_percentage\", \"rank\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"renewable_percentage\",\n \"color\": \"Entity\"\n },\n \"reason\": \"The user wants to rank countries by their renewable percentage, which requires adding a rank field that orders countries within each year based on their renewable percentage (highest percentage = rank 1). This allows comparison of countries' relative positions in renewable energy adoption over time. The chart_encodings remain the same as the previous step since we're still visualizing renewable percentage trends, but now with ranking information available.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total energy for each row\n df_energy['total_energy'] = (\n df_energy['Electricity from fossil fuels (TWh)'] + \n df_energy['Electricity from nuclear (TWh)'] + \n df_energy['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_energy['renewable_percentage'] = (\n df_energy['Electricity from renewables (TWh)'] / df_energy['total_energy'] * 100\n )\n \n # Handle division by zero cases\n df_energy['renewable_percentage'] = df_energy['renewable_percentage'].replace([np.inf, -np.inf], np.nan)\n \n # Rank countries by renewable percentage within each year (highest percentage = rank 1)\n df_energy['rank'] = df_energy.groupby('Year')['renewable_percentage'].rank(\n method='dense', \n ascending=False\n )\n \n # Select only the required columns\n transformed_df = df_energy[['Year', 'Entity', 'renewable_percentage', 'rank']].copy()\n \n return transformed_df\n```", "role": "assistant"}], "trigger": {"tableId": "table-45", "resultTableId": "table-78", "chart": {"id": "chart-1760738441793", "chartType": "Line Chart", "encodingMap": {"x": {"fieldID": "original--global-energy-20-small.csv--Year"}, "y": {"fieldID": "concept-renewable_percentage-1760738424337"}, "color": {"fieldID": "original--global-energy-20-small.csv--Entity"}, "opacity": {"channel": "opacity", "bin": false}, "column": {}, "row": {"channel": "row", "bin": false}}, "tableRef": "table-45", "saved": false, "source": "trigger", "unread": false}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "rank countries by their renewable percentage", "displayContent": "Show **countries ranked** by **renewable percentage** over **Year**"}]}, "explanation": {"agent": "CodeExplanationAgent", "code": "- Calculate **total_energy** for each country-year by summing electricity generated from **fossil fuels**, **nuclear**, and **renewables** (all measured in TWh)\n- Compute **renewable_percentage** by dividing **Electricity from renewables (TWh)** by **total_energy** and multiplying by **100**\n- Replace any infinite values in **renewable_percentage** with NaN to handle cases where total energy is zero\n- Assign a **rank** to each country within each **Year** based on **renewable_percentage**, where rank **1** represents the highest renewable percentage (using dense ranking method)\n- Return a dataset containing **Year**, **Entity** (country name), **renewable_percentage**, and **rank**", "concepts": [{"explanation": "The proportion of a country's total electricity generation that comes from renewable sources, expressed as a percentage. Calculated as: \\[ \\text{renewable\\_percentage} = \\frac{\\text{Electricity from renewables (TWh)}}{\\text{total\\_energy}} \\times 100 \\]", "field": "renewable_percentage"}], "dialog": [{"content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small_csv)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 190729.9957, 227580.0018, 233600.0061, ..., 9282549.805, 9861099.609, 9874660.156, 10096009.77\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 88.76, 90.14, 91.79, 92.54\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total energy for each row\n df_energy['total_energy'] = (\n df_energy['Electricity from fossil fuels (TWh)'] + \n df_energy['Electricity from nuclear (TWh)'] + \n df_energy['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_energy['renewable_percentage'] = (\n df_energy['Electricity from renewables (TWh)'] / df_energy['total_energy'] * 100\n )\n \n # Handle division by zero cases\n df_energy['renewable_percentage'] = df_energy['renewable_percentage'].replace([np.inf, -np.inf], np.nan)\n \n # Rank countries by renewable percentage within each year (highest percentage = rank 1)\n df_energy['rank'] = df_energy.groupby('Year')['renewable_percentage'].rank(\n method='dense', \n ascending=False\n )\n \n # Select only the required columns\n transformed_df = df_energy[['Year', 'Entity', 'renewable_percentage', 'rank']].copy()\n \n return transformed_df\n\n\n[EXPLANATION]\n", "role": "user"}, {"content": "[CODE EXPLANATION]\n\n- Calculate **total_energy** for each country-year by summing electricity generated from **fossil fuels**, **nuclear**, and **renewables** (all measured in TWh)\n- Compute **renewable_percentage** by dividing **Electricity from renewables (TWh)** by **total_energy** and multiplying by **100**\n- Replace any infinite values in **renewable_percentage** with NaN to handle cases where total energy is zero\n- Assign a **rank** to each country within each **Year** based on **renewable_percentage**, where rank **1** represents the highest renewable percentage (using dense ranking method)\n- Return a dataset containing **Year**, **Entity** (country name), **renewable_percentage**, and **rank**\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"renewable_percentage\",\n \"explanation\": \"The proportion of a country's total electricity generation that comes from renewable sources, expressed as a percentage. Calculated as: \\\\[ \\\\text{renewable\\\\_percentage} = \\\\frac{\\\\text{Electricity from renewables (TWh)}}{\\\\text{total\\\\_energy}} \\\\times 100 \\\\]\"\n }\n]\n```", "role": "assistant"}], "status": "ok"}, "status": "completed"}, "anchored": false, "attachedMetadata": ""}, {"kind": "table", "id": "table-97", "displayId": "renewable-elec", "names": ["Electricity from renewables (TWh)", "Entity", "Year"], "rows": [{"Electricity from renewables (TWh)": 17.11, "Entity": "Australia", "Year": "2000"}, {"Electricity from renewables (TWh)": 63.99, "Entity": "Australia", "Year": "2020"}, {"Electricity from renewables (TWh)": 308.77, "Entity": "Brazil", "Year": "2000"}, {"Electricity from renewables (TWh)": 520.01, "Entity": "Brazil", "Year": "2020"}, {"Electricity from renewables (TWh)": 363.7, "Entity": "Canada", "Year": "2000"}, {"Electricity from renewables (TWh)": 429.24, "Entity": "Canada", "Year": "2020"}, {"Electricity from renewables (TWh)": 225.56, "Entity": "China", "Year": "2000"}, {"Electricity from renewables (TWh)": 2184.94, "Entity": "China", "Year": "2020"}, {"Electricity from renewables (TWh)": 67.83, "Entity": "France", "Year": "2000"}, {"Electricity from renewables (TWh)": 125.28, "Entity": "France", "Year": "2020"}, {"Electricity from renewables (TWh)": 35.47, "Entity": "Germany", "Year": "2000"}, {"Electricity from renewables (TWh)": 251.48, "Entity": "Germany", "Year": "2020"}, {"Electricity from renewables (TWh)": 80.27, "Entity": "India", "Year": "2000"}, {"Electricity from renewables (TWh)": 315.76, "Entity": "India", "Year": "2020"}, {"Electricity from renewables (TWh)": 19.6, "Entity": "Indonesia", "Year": "2000"}, {"Electricity from renewables (TWh)": 52.91, "Entity": "Indonesia", "Year": "2020"}, {"Electricity from renewables (TWh)": 50.87, "Entity": "Italy", "Year": "2000"}, {"Electricity from renewables (TWh)": 116.9, "Entity": "Italy", "Year": "2020"}, {"Electricity from renewables (TWh)": 104.16, "Entity": "Japan", "Year": "2000"}, {"Electricity from renewables (TWh)": 205.6, "Entity": "Japan", "Year": "2020"}, {"Electricity from renewables (TWh)": 7.53, "Entity": "Kazakhstan", "Year": "2000"}, {"Electricity from renewables (TWh)": 11.94, "Entity": "Kazakhstan", "Year": "2020"}, {"Electricity from renewables (TWh)": 44.51, "Entity": "Mexico", "Year": "2000"}, {"Electricity from renewables (TWh)": 69.19, "Entity": "Mexico", "Year": "2020"}, {"Electricity from renewables (TWh)": 2.33, "Entity": "Poland", "Year": "2000"}, {"Electricity from renewables (TWh)": 28.23, "Entity": "Poland", "Year": "2020"}, {"Electricity from renewables (TWh)": 0, "Entity": "Saudi Arabia", "Year": "2000"}, {"Electricity from renewables (TWh)": 0.21, "Entity": "Saudi Arabia", "Year": "2020"}, {"Electricity from renewables (TWh)": 1.79, "Entity": "South Africa", "Year": "2000"}, {"Electricity from renewables (TWh)": 12.83, "Entity": "South Africa", "Year": "2020"}, {"Electricity from renewables (TWh)": 34.49, "Entity": "Spain", "Year": "2000"}, {"Electricity from renewables (TWh)": 113.79, "Entity": "Spain", "Year": "2020"}, {"Electricity from renewables (TWh)": 6.38, "Entity": "Thailand", "Year": "2000"}, {"Electricity from renewables (TWh)": 24.73, "Entity": "Thailand", "Year": "2020"}, {"Electricity from renewables (TWh)": 11.28, "Entity": "Ukraine", "Year": "2000"}, {"Electricity from renewables (TWh)": 17.56, "Entity": "Ukraine", "Year": "2020"}, {"Electricity from renewables (TWh)": 9.98, "Entity": "United Kingdom", "Year": "2000"}, {"Electricity from renewables (TWh)": 131.74, "Entity": "United Kingdom", "Year": "2020"}, {"Electricity from renewables (TWh)": 350.93, "Entity": "United States", "Year": "2000"}, {"Electricity from renewables (TWh)": 821.4, "Entity": "United States", "Year": "2020"}], "metadata": {"Electricity from renewables (TWh)": {"type": "number", "semanticType": "Number"}, "Entity": {"type": "string", "semanticType": "Location"}, "Year": {"type": "number", "semanticType": "Year"}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n transformed_df = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Select only the required columns\n transformed_df = transformed_df[['Entity', 'Year', 'Electricity from renewables (TWh)']]\n \n # Convert Year to string for better visualization as a categorical variable\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year for better readability\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n", "source": ["global-energy-20-small.csv"], "dialog": [{"content": "You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 117440.0, 146139.9994, 158029.9988, ..., 5736319.824, 5738290.039, nan, 9861099.609\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 61.99, 62.21, 63.02, 63.71\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"compare each countries renewable between 2000 and 2020\",\n \"chart_type\": \"Grouped Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Renewable Energy\",\n \"color\": \"Year\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Compare renewable energy (Electricity from renewables) for each country between the years 2000 and 2020. Filter the data to include only years 2000 and 2020, and prepare the output with country names, year, and renewable energy values for visualization as a grouped bar chart.\",\n \"display_instruction\": \"Compare **renewable energy** by **country** between **2000** and **2020**\",\n \"output_fields\": [\"Entity\", \"Year\", \"Electricity from renewables (TWh)\"],\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Electricity from renewables (TWh)\",\n \"color\": \"Year\"\n },\n \"reason\": \"The user wants to compare renewable energy across countries between 2000 and 2020. The output field 'Renewable Energy' mentioned in the original chart_encodings maps to 'Electricity from renewables (TWh)' in the dataset. To create a grouped bar chart comparing these two years, we need to filter the data to only include years 2000 and 2020, and output the Entity (country), Year, and the renewable energy values. The chart_encodings are updated to use the actual column name from the dataset.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n transformed_df = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Select only the required columns\n transformed_df = transformed_df[['Entity', 'Year', 'Electricity from renewables (TWh)']]\n \n # Convert Year to string for better visualization as a categorical variable\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year for better readability\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n```", "role": "assistant"}], "trigger": {"tableId": "global-energy-20-small.csv", "resultTableId": "table-97", "chart": {"id": "chart-1760738769634", "chartType": "Grouped Bar Chart", "encodingMap": {"x": {"fieldID": "original--global-energy-20-small.csv--Entity"}, "y": {"fieldID": "concept-1760738743125"}, "color": {"channel": "color", "bin": false, "fieldID": "original--global-energy-20-small.csv--Year"}, "column": {"channel": "column", "bin": false}, "row": {"channel": "row", "bin": false}}, "tableRef": "global-energy-20-small.csv", "saved": false, "source": "trigger", "unread": false}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "compare each countries renewable between 2000 and 2020", "displayContent": "Compare **renewable energy** by **country** between **2000** and **2020**"}]}, "explanation": {"agent": "CodeExplanationAgent", "code": "The code performs a straightforward comparison analysis of renewable energy generation across countries:\n\n- **Filters** the dataset to include only data from years **2000** and **2020**, creating a 20-year comparison window\n- **Selects** three key columns: `Entity` (country name), `Year`, and `Electricity from renewables (TWh)` (renewable energy generation)\n- **Converts** the `Year` field to string format to treat it as a categorical variable for visualization purposes\n- **Sorts** the results by `Entity` and `Year` to organize countries alphabetically with their respective year data grouped together\n- **Returns** a cleaned dataset showing how renewable electricity generation has changed for each country between 2000 and 2020", "concepts": [], "dialog": [{"content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small_csv)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 117440.0, 146139.9994, 158029.9988, ..., 10006669.92, 10502929.69, 10707219.73, nan\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 56.18, 61.99, 63.02, 63.71\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n transformed_df = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Select only the required columns\n transformed_df = transformed_df[['Entity', 'Year', 'Electricity from renewables (TWh)']]\n \n # Convert Year to string for better visualization as a categorical variable\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year for better readability\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n\n\n[EXPLANATION]\n", "role": "user"}, {"content": "[CODE EXPLANATION]\n\nThe code performs a straightforward comparison analysis of renewable energy generation across countries:\n\n- **Filters** the dataset to include only data from years **2000** and **2020**, creating a 20-year comparison window\n- **Selects** three key columns: `Entity` (country name), `Year`, and `Electricity from renewables (TWh)` (renewable energy generation)\n- **Converts** the `Year` field to string format to treat it as a categorical variable for visualization purposes\n- **Sorts** the results by `Entity` and `Year` to organize countries alphabetically with their respective year data grouped together\n- **Returns** a cleaned dataset showing how renewable electricity generation has changed for each country between 2000 and 2020\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```", "role": "assistant"}], "status": "ok"}, "status": "completed"}, "anchored": false, "attachedMetadata": ""}, {"kind": "table", "id": "table-27", "displayId": "renewable-energy1", "names": ["Entity", "Renewable Percentage", "Year"], "rows": [{"Entity": "Australia", "Renewable Percentage": 8.6344368187, "Year": "2000"}, {"Entity": "Australia", "Renewable Percentage": 25.5031684668, "Year": "2020"}, {"Entity": "Brazil", "Renewable Percentage": 90.1307723743, "Year": "2000"}, {"Entity": "Brazil", "Renewable Percentage": 84.6411771408, "Year": "2020"}, {"Entity": "Canada", "Renewable Percentage": 61.8095917882, "Year": "2000"}, {"Entity": "Canada", "Renewable Percentage": 68.7796436354, "Year": "2020"}, {"Entity": "China", "Renewable Percentage": 16.639126586, "Year": "2000"}, {"Entity": "China", "Renewable Percentage": 28.2464606924, "Year": "2020"}, {"Entity": "France", "Renewable Percentage": 12.7117691154, "Year": "2000"}, {"Entity": "France", "Renewable Percentage": 23.7610241821, "Year": "2020"}, {"Entity": "Germany", "Renewable Percentage": 6.1977983575, "Year": "2000"}, {"Entity": "Germany", "Renewable Percentage": 44.3324048937, "Year": "2020"}, {"Entity": "Global Average", "Renewable Percentage": 16.4213212559, "Year": "2000"}, {"Entity": "Global Average", "Renewable Percentage": 29.2955247263, "Year": "2020"}, {"Entity": "India", "Renewable Percentage": 14.0481982534, "Year": "2000"}, {"Entity": "India", "Renewable Percentage": 20.2059243238, "Year": "2020"}, {"Entity": "Indonesia", "Renewable Percentage": null, "Year": "2000"}, {"Entity": "Indonesia", "Renewable Percentage": null, "Year": "2020"}, {"Entity": "Italy", "Renewable Percentage": 18.900241501, "Year": "2000"}, {"Entity": "Italy", "Renewable Percentage": 42.0397741576, "Year": "2020"}, {"Entity": "Japan", "Renewable Percentage": 10.5382436261, "Year": "2000"}, {"Entity": "Japan", "Renewable Percentage": 21.324925062, "Year": "2020"}, {"Entity": "Kazakhstan", "Renewable Percentage": null, "Year": "2000"}, {"Entity": "Kazakhstan", "Renewable Percentage": null, "Year": "2020"}, {"Entity": "Mexico", "Renewable Percentage": 22.9291160107, "Year": "2000"}, {"Entity": "Mexico", "Renewable Percentage": 21.2552224134, "Year": "2020"}, {"Entity": "Poland", "Renewable Percentage": 1.6273222517, "Year": "2000"}, {"Entity": "Poland", "Renewable Percentage": 17.9648720886, "Year": "2020"}, {"Entity": "Saudi Arabia", "Renewable Percentage": null, "Year": "2000"}, {"Entity": "Saudi Arabia", "Renewable Percentage": null, "Year": "2020"}, {"Entity": "South Africa", "Renewable Percentage": 0.9110805721, "Year": "2000"}, {"Entity": "South Africa", "Renewable Percentage": 5.780581212, "Year": "2020"}, {"Entity": "Spain", "Renewable Percentage": 15.6119862394, "Year": "2000"}, {"Entity": "Spain", "Renewable Percentage": 43.8108805298, "Year": "2020"}, {"Entity": "Thailand", "Renewable Percentage": 7.1261029822, "Year": "2000"}, {"Entity": "Thailand", "Renewable Percentage": 13.7963737796, "Year": "2020"}, {"Entity": "Ukraine", "Renewable Percentage": 6.5860921352, "Year": "2000"}, {"Entity": "Ukraine", "Renewable Percentage": 11.8440577364, "Year": "2020"}, {"Entity": "United Kingdom", "Renewable Percentage": 2.6657406913, "Year": "2000"}, {"Entity": "United Kingdom", "Renewable Percentage": 42.8603962651, "Year": "2020"}, {"Entity": "United States", "Renewable Percentage": 9.2298992662, "Year": "2000"}, {"Entity": "United States", "Renewable Percentage": 20.3156921037, "Year": "2020"}], "metadata": {"Entity": {"type": "string", "semanticType": "Name"}, "Renewable Percentage": {"type": "number", "semanticType": "Percentage"}, "Year": {"type": "number", "semanticType": "Year"}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n df_filtered = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Calculate total electricity generation\n df_filtered['Total Electricity'] = (\n df_filtered['Electricity from fossil fuels (TWh)'] + \n df_filtered['Electricity from nuclear (TWh)'] + \n df_filtered['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_filtered['Renewable Percentage'] = (\n df_filtered['Electricity from renewables (TWh)'] / df_filtered['Total Electricity'] * 100\n )\n \n # Select required columns for individual countries\n country_data = df_filtered[['Entity', 'Year', 'Renewable Percentage']].copy()\n \n # Calculate global average for each year\n global_avg = df_filtered.groupby('Year').apply(\n lambda x: pd.Series({\n 'Entity': 'Global Average',\n 'Year': x['Year'].iloc[0],\n 'Renewable Percentage': (\n x['Electricity from renewables (TWh)'].sum() / x['Total Electricity'].sum() * 100\n )\n })\n ).reset_index(drop=True)\n \n # Combine country data with global average\n transformed_df = pd.concat([country_data, global_avg], ignore_index=True)\n \n # Convert Year to string for better visualization\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n", "source": ["global-energy-20-small.csv"], "dialog": [{"content": "You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 117440.0, 146139.9994, 158029.9988, ..., 5736319.824, 5738290.039, nan, 9861099.609\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 61.99, 62.21, 63.02, 63.71\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"compare each countries renewable between 2000 and 2020\",\n \"chart_type\": \"Grouped Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Renewable Energy\",\n \"color\": \"Year\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Compare renewable energy (Electricity from renewables) for each country between the years 2000 and 2020. Filter the data to include only years 2000 and 2020, and prepare the output with country names, year, and renewable energy values for visualization as a grouped bar chart.\",\n \"display_instruction\": \"Compare **renewable energy** by **country** between **2000** and **2020**\",\n \"output_fields\": [\"Entity\", \"Year\", \"Electricity from renewables (TWh)\"],\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Electricity from renewables (TWh)\",\n \"color\": \"Year\"\n },\n \"reason\": \"The user wants to compare renewable energy across countries between 2000 and 2020. The output field 'Renewable Energy' mentioned in the original chart_encodings maps to 'Electricity from renewables (TWh)' in the dataset. To create a grouped bar chart comparing these two years, we need to filter the data to only include years 2000 and 2020, and output the Entity (country), Year, and the renewable energy values. The chart_encodings are updated to use the actual column name from the dataset.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n transformed_df = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Select only the required columns\n transformed_df = transformed_df[['Entity', 'Year', 'Electricity from renewables (TWh)']]\n \n # Convert Year to string for better visualization as a categorical variable\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year for better readability\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n```", "role": "assistant"}, {"content": "This is the result from the latest python code:\n\n Electricity from renewables (TWh) Entity Year\n0 17.11 Australia 2000\n1 63.99 Australia 2020\n2 308.77 Brazil 2000\n3 520.01 Brazil 2020\n4 363.70 Canada 2000\n5 429.24 Canada 2020\n6 225.56 China 2000\n7 2184.94 China 2020\n8 67.83 France 2000\n9 125.28 France 2020\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show their renewable percentage instead, and add an entity to show global average\",\n \"chart_type\": \"Grouped Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Electricity from renewables (TWh)\",\n \"color\": \"Year\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Calculate the renewable energy percentage (proportion of renewables to total electricity generation from all sources) for each country between 2000 and 2020. Add a 'Global Average' entity that represents the worldwide average renewable percentage for each year. The output should include Entity (with 'Global Average' added), Year, and the renewable percentage.\",\n \"display_instruction\": \"Show **renewable energy percentage** by **country** with **global average** for **2000** and **2020**\",\n \"output_fields\": [\"Entity\", \"Year\", \"Renewable Percentage\"],\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Renewable Percentage\",\n \"color\": \"Year\"\n },\n \"reason\": \"The user wants to see renewable percentage instead of absolute values, and wants to add a global average entity for comparison. Renewable percentage is calculated as the ratio of renewable energy to total electricity generation (fossil fuels + nuclear + renewables). A 'Global Average' entity needs to be computed by aggregating all countries' data for each year. The y-axis encoding is updated to reflect the new metric 'Renewable Percentage'.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n df_filtered = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Calculate total electricity generation\n df_filtered['Total Electricity'] = (\n df_filtered['Electricity from fossil fuels (TWh)'] + \n df_filtered['Electricity from nuclear (TWh)'] + \n df_filtered['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_filtered['Renewable Percentage'] = (\n df_filtered['Electricity from renewables (TWh)'] / df_filtered['Total Electricity'] * 100\n )\n \n # Select required columns for individual countries\n country_data = df_filtered[['Entity', 'Year', 'Renewable Percentage']].copy()\n \n # Calculate global average for each year\n global_avg = df_filtered.groupby('Year').apply(\n lambda x: pd.Series({\n 'Entity': 'Global Average',\n 'Year': x['Year'].iloc[0],\n 'Renewable Percentage': (\n x['Electricity from renewables (TWh)'].sum() / x['Total Electricity'].sum() * 100\n )\n })\n ).reset_index(drop=True)\n \n # Combine country data with global average\n transformed_df = pd.concat([country_data, global_avg], ignore_index=True)\n \n # Convert Year to string for better visualization\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n```", "role": "assistant"}], "trigger": {"tableId": "table-97", "resultTableId": "table-27", "chart": {"id": "chart-1760738813383", "chartType": "Grouped Bar Chart", "encodingMap": {"x": {"fieldID": "original--global-energy-20-small.csv--Entity"}, "y": {"fieldID": "original--global-energy-20-small.csv--Electricity from renewables (TWh)"}, "color": {"fieldID": "original--global-energy-20-small.csv--Year"}, "column": {"channel": "column", "bin": false}, "row": {"channel": "row", "bin": false}}, "tableRef": "table-97", "saved": false, "source": "trigger", "unread": false}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "show their renewable percentage instead, and add an entity to show global average", "displayContent": "Show **renewable energy percentage** by **country** with **global average** for **2000** and **2020**"}]}, "explanation": {"agent": "CodeExplanationAgent", "code": "The code performs the following transformation steps:\n\n1. **Filter data** to include only records from **2000** and **2020**\n2. Calculate **Total Electricity** generation by summing three sources: **fossil fuels**, **nuclear**, and **renewables** (all measured in TWh)\n3. Compute **Renewable Percentage** for each country and year by dividing **Electricity from renewables** by **Total Electricity** and multiplying by 100\n4. Extract relevant columns: **Entity** (country name), **Year**, and **Renewable Percentage**\n5. Calculate **Global Average** renewable percentage for each year by:\n - Summing all countries' **renewable electricity** generation\n - Dividing by the sum of all countries' **total electricity** generation\n - Multiplying by 100 to get percentage\n6. Combine individual country data with the **Global Average** statistics\n7. Convert **Year** values to string format\n8. Sort results by **Entity** name and **Year**", "concepts": [{"explanation": "The sum of electricity generated from all three sources (fossil fuels, nuclear, and renewables) measured in terawatt-hours (TWh). This represents the total electricity production capacity for each country.", "field": "Total Electricity"}, {"explanation": "The proportion of electricity generated from renewable sources relative to total electricity production, expressed as a percentage: \\( \\text{Renewable Percentage} = \\frac{\\text{Electricity from renewables}}{\\text{Total Electricity}} \\times 100 \\). This metric indicates the extent of renewable energy adoption in a country's electricity mix.", "field": "Renewable Percentage"}], "dialog": [{"content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small_csv)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 117440.0, 146139.9994, 158029.9988, ..., 10096009.77, 10502929.69, 10707219.73, nan\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., nan, nan, nan, nan\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n df_filtered = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Calculate total electricity generation\n df_filtered['Total Electricity'] = (\n df_filtered['Electricity from fossil fuels (TWh)'] + \n df_filtered['Electricity from nuclear (TWh)'] + \n df_filtered['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_filtered['Renewable Percentage'] = (\n df_filtered['Electricity from renewables (TWh)'] / df_filtered['Total Electricity'] * 100\n )\n \n # Select required columns for individual countries\n country_data = df_filtered[['Entity', 'Year', 'Renewable Percentage']].copy()\n \n # Calculate global average for each year\n global_avg = df_filtered.groupby('Year').apply(\n lambda x: pd.Series({\n 'Entity': 'Global Average',\n 'Year': x['Year'].iloc[0],\n 'Renewable Percentage': (\n x['Electricity from renewables (TWh)'].sum() / x['Total Electricity'].sum() * 100\n )\n })\n ).reset_index(drop=True)\n \n # Combine country data with global average\n transformed_df = pd.concat([country_data, global_avg], ignore_index=True)\n \n # Convert Year to string for better visualization\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n\n\n[EXPLANATION]\n", "role": "user"}, {"content": "[CODE EXPLANATION]\n\nThe code performs the following transformation steps:\n\n1. **Filter data** to include only records from **2000** and **2020**\n2. Calculate **Total Electricity** generation by summing three sources: **fossil fuels**, **nuclear**, and **renewables** (all measured in TWh)\n3. Compute **Renewable Percentage** for each country and year by dividing **Electricity from renewables** by **Total Electricity** and multiplying by 100\n4. Extract relevant columns: **Entity** (country name), **Year**, and **Renewable Percentage**\n5. Calculate **Global Average** renewable percentage for each year by:\n - Summing all countries' **renewable electricity** generation\n - Dividing by the sum of all countries' **total electricity** generation\n - Multiplying by 100 to get percentage\n6. Combine individual country data with the **Global Average** statistics\n7. Convert **Year** values to string format\n8. Sort results by **Entity** name and **Year**\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"Total Electricity\",\n \"explanation\": \"The sum of electricity generated from all three sources (fossil fuels, nuclear, and renewables) measured in terawatt-hours (TWh). This represents the total electricity production capacity for each country.\"\n },\n {\n \"field\": \"Renewable Percentage\",\n \"explanation\": \"The proportion of electricity generated from renewable sources relative to total electricity production, expressed as a percentage: \\\\( \\\\text{Renewable Percentage} = \\\\frac{\\\\text{Electricity from renewables}}{\\\\text{Total Electricity}} \\\\times 100 \\\\). This metric indicates the extent of renewable energy adoption in a country's electricity mix.\"\n }\n]\n```", "role": "assistant"}], "status": "ok"}, "status": "completed"}, "anchored": false, "attachedMetadata": ""}, {"kind": "table", "id": "table-81", "displayId": "energy-source1", "names": ["Energy", "Energy Source", "Year"], "rows": [{"Energy": 7160.71, "Energy Source": "Fossil Fuels", "Year": 2000}, {"Energy": 1996.65, "Energy Source": "Nuclear", "Year": 2000}, {"Energy": 1742.56, "Energy Source": "Renewables", "Year": 2000}, {"Energy": 7273.89, "Energy Source": "Fossil Fuels", "Year": 2001}, {"Energy": 2037.54, "Energy Source": "Nuclear", "Year": 2001}, {"Energy": 1690.11, "Energy Source": "Renewables", "Year": 2001}, {"Energy": 7621.07, "Energy Source": "Fossil Fuels", "Year": 2002}, {"Energy": 2042.18, "Energy Source": "Nuclear", "Year": 2002}, {"Energy": 1757.63, "Energy Source": "Renewables", "Year": 2002}, {"Energy": 8043.86, "Energy Source": "Fossil Fuels", "Year": 2003}, {"Energy": 1998.52, "Energy Source": "Nuclear", "Year": 2003}, {"Energy": 1804.52, "Energy Source": "Renewables", "Year": 2003}, {"Energy": 8399.72, "Energy Source": "Fossil Fuels", "Year": 2004}, {"Energy": 2095, "Energy Source": "Nuclear", "Year": 2004}, {"Energy": 1952.72, "Energy Source": "Renewables", "Year": 2004}, {"Energy": 8828.43, "Energy Source": "Fossil Fuels", "Year": 2005}, {"Energy": 2094.4, "Energy Source": "Nuclear", "Year": 2005}, {"Energy": 2025.26, "Energy Source": "Renewables", "Year": 2005}, {"Energy": 9183.05, "Energy Source": "Fossil Fuels", "Year": 2006}, {"Energy": 2120.38, "Energy Source": "Nuclear", "Year": 2006}, {"Energy": 2165.94, "Energy Source": "Renewables", "Year": 2006}, {"Energy": 9853.09, "Energy Source": "Fossil Fuels", "Year": 2007}, {"Energy": 2067.04, "Energy Source": "Nuclear", "Year": 2007}, {"Energy": 2256.79, "Energy Source": "Renewables", "Year": 2007}, {"Energy": 9817.15, "Energy Source": "Fossil Fuels", "Year": 2008}, {"Energy": 2043.94, "Energy Source": "Nuclear", "Year": 2008}, {"Energy": 2496.03, "Energy Source": "Renewables", "Year": 2008}, {"Energy": 9686.86, "Energy Source": "Fossil Fuels", "Year": 2009}, {"Energy": 2017.25, "Energy Source": "Nuclear", "Year": 2009}, {"Energy": 2563.95, "Energy Source": "Renewables", "Year": 2009}, {"Energy": 10427.03, "Energy Source": "Fossil Fuels", "Year": 2010}, {"Energy": 2083.37, "Energy Source": "Nuclear", "Year": 2010}, {"Energy": 2802.89, "Energy Source": "Renewables", "Year": 2010}, {"Energy": 10974.83, "Energy Source": "Fossil Fuels", "Year": 2011}, {"Energy": 1956, "Energy Source": "Nuclear", "Year": 2011}, {"Energy": 2997.29, "Energy Source": "Renewables", "Year": 2011}, {"Energy": 11277.49, "Energy Source": "Fossil Fuels", "Year": 2012}, {"Energy": 1788.26, "Energy Source": "Nuclear", "Year": 2012}, {"Energy": 3226.1, "Energy Source": "Renewables", "Year": 2012}, {"Energy": 11561.86, "Energy Source": "Fossil Fuels", "Year": 2013}, {"Energy": 1813, "Energy Source": "Nuclear", "Year": 2013}, {"Energy": 3473.9, "Energy Source": "Renewables", "Year": 2013}, {"Energy": 11761.51, "Energy Source": "Fossil Fuels", "Year": 2014}, {"Energy": 1847.87, "Energy Source": "Nuclear", "Year": 2014}, {"Energy": 3753.03, "Energy Source": "Renewables", "Year": 2014}, {"Energy": 11653.61, "Energy Source": "Fossil Fuels", "Year": 2015}, {"Energy": 1886.61, "Energy Source": "Nuclear", "Year": 2015}, {"Energy": 3903.7, "Energy Source": "Renewables", "Year": 2015}, {"Energy": 11785.35, "Energy Source": "Fossil Fuels", "Year": 2016}, {"Energy": 1906.92, "Energy Source": "Nuclear", "Year": 2016}, {"Energy": 4186.83, "Energy Source": "Renewables", "Year": 2016}, {"Energy": 12003.24, "Energy Source": "Fossil Fuels", "Year": 2017}, {"Energy": 1942.94, "Energy Source": "Nuclear", "Year": 2017}, {"Energy": 4500.22, "Energy Source": "Renewables", "Year": 2017}, {"Energy": 12445.24, "Energy Source": "Fossil Fuels", "Year": 2018}, {"Energy": 2016.82, "Energy Source": "Nuclear", "Year": 2018}, {"Energy": 4834.65, "Energy Source": "Renewables", "Year": 2018}, {"Energy": 12355.91, "Energy Source": "Fossil Fuels", "Year": 2019}, {"Energy": 2073.87, "Energy Source": "Nuclear", "Year": 2019}, {"Energy": 5146.11, "Energy Source": "Renewables", "Year": 2019}, {"Energy": 12032.75, "Energy Source": "Fossil Fuels", "Year": 2020}, {"Energy": 1974.46, "Energy Source": "Nuclear", "Year": 2020}, {"Energy": 5497.73, "Energy Source": "Renewables", "Year": 2020}], "metadata": {"Energy": {"type": "number", "semanticType": "Number"}, "Energy Source": {"type": "string", "semanticType": "String", "levels": ["Fossil Fuels", "Nuclear", "Renewables"]}, "Year": {"type": "number", "semanticType": "Year"}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Group by Year and sum electricity generation from all countries\n global_energy = df_energy.groupby('Year').agg({\n 'Electricity from fossil fuels (TWh)': 'sum',\n 'Electricity from nuclear (TWh)': 'sum',\n 'Electricity from renewables (TWh)': 'sum'\n }).reset_index()\n \n # Rename columns for clarity\n global_energy.columns = ['Year', 'Fossil Fuels', 'Nuclear', 'Renewables']\n \n # Transform to long format\n transformed_df = pd.melt(\n global_energy,\n id_vars=['Year'],\n value_vars=['Fossil Fuels', 'Nuclear', 'Renewables'],\n var_name='Energy Source',\n value_name='Energy'\n )\n \n # Sort by Year and Energy Source\n transformed_df = transformed_df.sort_values(['Year', 'Energy Source']).reset_index(drop=True)\n \n return transformed_df\n", "source": ["global-energy-20-small.csv"], "dialog": [{"content": "You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 117440.0, 146139.9994, 158029.9988, ..., 5736319.824, 5738290.039, nan, 9861099.609\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 61.99, 62.21, 63.02, 63.71\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"compare each countries renewable between 2000 and 2020\",\n \"chart_type\": \"Grouped Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Renewable Energy\",\n \"color\": \"Year\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Compare renewable energy (Electricity from renewables) for each country between the years 2000 and 2020. Filter the data to include only years 2000 and 2020, and prepare the output with country names, year, and renewable energy values for visualization as a grouped bar chart.\",\n \"display_instruction\": \"Compare **renewable energy** by **country** between **2000** and **2020**\",\n \"output_fields\": [\"Entity\", \"Year\", \"Electricity from renewables (TWh)\"],\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Electricity from renewables (TWh)\",\n \"color\": \"Year\"\n },\n \"reason\": \"The user wants to compare renewable energy across countries between 2000 and 2020. The output field 'Renewable Energy' mentioned in the original chart_encodings maps to 'Electricity from renewables (TWh)' in the dataset. To create a grouped bar chart comparing these two years, we need to filter the data to only include years 2000 and 2020, and output the Entity (country), Year, and the renewable energy values. The chart_encodings are updated to use the actual column name from the dataset.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n transformed_df = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Select only the required columns\n transformed_df = transformed_df[['Entity', 'Year', 'Electricity from renewables (TWh)']]\n \n # Convert Year to string for better visualization as a categorical variable\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year for better readability\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n```", "role": "assistant"}, {"content": "This is the result from the latest python code:\n\n Electricity from renewables (TWh) Entity Year\n0 17.11 Australia 2000\n1 63.99 Australia 2020\n2 308.77 Brazil 2000\n3 520.01 Brazil 2020\n4 363.70 Canada 2000\n5 429.24 Canada 2020\n6 225.56 China 2000\n7 2184.94 China 2020\n8 67.83 France 2000\n9 125.28 France 2020\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show their renewable percentage instead, and add an entity to show global average\",\n \"chart_type\": \"Grouped Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Electricity from renewables (TWh)\",\n \"color\": \"Year\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Calculate the renewable energy percentage (proportion of renewables to total electricity generation from all sources) for each country between 2000 and 2020. Add a 'Global Average' entity that represents the worldwide average renewable percentage for each year. The output should include Entity (with 'Global Average' added), Year, and the renewable percentage.\",\n \"display_instruction\": \"Show **renewable energy percentage** by **country** with **global average** for **2000** and **2020**\",\n \"output_fields\": [\"Entity\", \"Year\", \"Renewable Percentage\"],\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Renewable Percentage\",\n \"color\": \"Year\"\n },\n \"reason\": \"The user wants to see renewable percentage instead of absolute values, and wants to add a global average entity for comparison. Renewable percentage is calculated as the ratio of renewable energy to total electricity generation (fossil fuels + nuclear + renewables). A 'Global Average' entity needs to be computed by aggregating all countries' data for each year. The y-axis encoding is updated to reflect the new metric 'Renewable Percentage'.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n df_filtered = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Calculate total electricity generation\n df_filtered['Total Electricity'] = (\n df_filtered['Electricity from fossil fuels (TWh)'] + \n df_filtered['Electricity from nuclear (TWh)'] + \n df_filtered['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_filtered['Renewable Percentage'] = (\n df_filtered['Electricity from renewables (TWh)'] / df_filtered['Total Electricity'] * 100\n )\n \n # Select required columns for individual countries\n country_data = df_filtered[['Entity', 'Year', 'Renewable Percentage']].copy()\n \n # Calculate global average for each year\n global_avg = df_filtered.groupby('Year').apply(\n lambda x: pd.Series({\n 'Entity': 'Global Average',\n 'Year': x['Year'].iloc[0],\n 'Renewable Percentage': (\n x['Electricity from renewables (TWh)'].sum() / x['Total Electricity'].sum() * 100\n )\n })\n ).reset_index(drop=True)\n \n # Combine country data with global average\n transformed_df = pd.concat([country_data, global_avg], ignore_index=True)\n \n # Convert Year to string for better visualization\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n```", "role": "assistant"}, {"content": "This is the result from the latest python code:\n\n Entity Renewable Percentage Year\n0 Australia 8.634437 2000\n1 Australia 25.503168 2020\n2 Brazil 90.130772 2000\n3 Brazil 84.641177 2020\n4 Canada 61.809592 2000\n5 Canada 68.779644 2020\n6 China 16.639127 2000\n7 China 28.246461 2020\n8 France 12.711769 2000\n9 France 23.761024 2020\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show global energy composition over time\",\n \"chart_type\": \"Custom Area\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"Energy\",\n \"color\": \"Energy Source\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Show the global energy composition over time by aggregating electricity generation from all countries for each year, broken down by energy source (fossil fuels, nuclear, and renewables). The output should be in long format with Year, Energy Source, and Energy values to support a stacked area chart visualization.\",\n \"display_instruction\": \"Show global **energy composition** by **energy source** over time\",\n \"output_fields\": [\"Year\", \"Energy Source\", \"Energy\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"Energy\",\n \"color\": \"Energy Source\"\n },\n \"reason\": \"The user wants to visualize global energy composition over time, which requires aggregating electricity generation data across all countries for each year and breaking it down by energy source type. The data needs to be transformed into long format where each row represents a year-energy source combination, with the total energy value for that source globally. This follows up from the previous renewable percentage analysis by showing the overall energy mix trends rather than focusing on specific countries or percentages.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Group by Year and sum electricity generation from all countries\n global_energy = df_energy.groupby('Year').agg({\n 'Electricity from fossil fuels (TWh)': 'sum',\n 'Electricity from nuclear (TWh)': 'sum',\n 'Electricity from renewables (TWh)': 'sum'\n }).reset_index()\n \n # Rename columns for clarity\n global_energy.columns = ['Year', 'Fossil Fuels', 'Nuclear', 'Renewables']\n \n # Transform to long format\n transformed_df = pd.melt(\n global_energy,\n id_vars=['Year'],\n value_vars=['Fossil Fuels', 'Nuclear', 'Renewables'],\n var_name='Energy Source',\n value_name='Energy'\n )\n \n # Sort by Year and Energy Source\n transformed_df = transformed_df.sort_values(['Year', 'Energy Source']).reset_index(drop=True)\n \n return transformed_df\n```", "role": "assistant"}], "trigger": {"tableId": "table-27", "resultTableId": "table-81", "chart": {"id": "chart-1760738942326", "chartType": "Custom Area", "encodingMap": {"x": {"channel": "x", "bin": false, "fieldID": "original--global-energy-20-small.csv--Year"}, "y": {"channel": "y", "bin": false, "fieldID": "concept-1760738917347"}, "x2": {"channel": "x2", "bin": false}, "y2": {"channel": "y2", "bin": false}, "color": {"channel": "color", "bin": false, "fieldID": "concept-1760738921927"}, "column": {"channel": "column", "bin": false}, "row": {"channel": "row", "bin": false}}, "tableRef": "table-27", "saved": false, "source": "trigger", "unread": false}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "show global energy composition over time", "displayContent": "Show global **energy composition** by **energy source** over time"}]}, "explanation": {"agent": "CodeExplanationAgent", "code": "- Group the energy data by **Year** and aggregate electricity generation values across all countries by summing **Electricity from fossil fuels (TWh)**, **Electricity from nuclear (TWh)**, and **Electricity from renewables (TWh)**\n- Rename the aggregated columns to simplified labels: **Fossil Fuels**, **Nuclear**, and **Renewables**\n- Transform the data from wide format to long format by unpivoting the three energy source columns into two columns: **Energy Source** (containing the type of energy) and **Energy** (containing the generation value in TWh)\n- Sort the resulting dataset by **Year** and **Energy Source** for consistent ordering", "concepts": [], "dialog": [{"content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small_csv)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 158029.9988, 190729.9957, 209229.9957, ..., nan, 4956060.059, nan, 9861099.609\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., nan, nan, nan, nan\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Group by Year and sum electricity generation from all countries\n global_energy = df_energy.groupby('Year').agg({\n 'Electricity from fossil fuels (TWh)': 'sum',\n 'Electricity from nuclear (TWh)': 'sum',\n 'Electricity from renewables (TWh)': 'sum'\n }).reset_index()\n \n # Rename columns for clarity\n global_energy.columns = ['Year', 'Fossil Fuels', 'Nuclear', 'Renewables']\n \n # Transform to long format\n transformed_df = pd.melt(\n global_energy,\n id_vars=['Year'],\n value_vars=['Fossil Fuels', 'Nuclear', 'Renewables'],\n var_name='Energy Source',\n value_name='Energy'\n )\n \n # Sort by Year and Energy Source\n transformed_df = transformed_df.sort_values(['Year', 'Energy Source']).reset_index(drop=True)\n \n return transformed_df\n\n\n[EXPLANATION]\n", "role": "user"}, {"content": "[CODE EXPLANATION]\n\n- Group the energy data by **Year** and aggregate electricity generation values across all countries by summing **Electricity from fossil fuels (TWh)**, **Electricity from nuclear (TWh)**, and **Electricity from renewables (TWh)**\n- Rename the aggregated columns to simplified labels: **Fossil Fuels**, **Nuclear**, and **Renewables**\n- Transform the data from wide format to long format by unpivoting the three energy source columns into two columns: **Energy Source** (containing the type of energy) and **Energy** (containing the generation value in TWh)\n- Sort the resulting dataset by **Year** and **Energy Source** for consistent ordering\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```", "role": "assistant"}], "status": "ok"}, "status": "completed"}, "anchored": false, "attachedMetadata": ""}, {"kind": "table", "id": "table-10", "displayId": "energy-source2", "names": ["Energy", "Energy Source", "Entity", "Year"], "rows": [{"Energy": 1113.3, "Energy Source": "Fossil Fuels", "Entity": "China", "Year": 2000}, {"Energy": 16.74, "Energy Source": "Nuclear", "Entity": "China", "Year": 2000}, {"Energy": 225.56, "Energy Source": "Renewables", "Entity": "China", "Year": 2000}, {"Energy": 1182.59, "Energy Source": "Fossil Fuels", "Entity": "China", "Year": 2001}, {"Energy": 17.47, "Energy Source": "Nuclear", "Entity": "China", "Year": 2001}, {"Energy": 280.73, "Energy Source": "Renewables", "Entity": "China", "Year": 2001}, {"Energy": 1337.46, "Energy Source": "Fossil Fuels", "Entity": "China", "Year": 2002}, {"Energy": 25.13, "Energy Source": "Nuclear", "Entity": "China", "Year": 2002}, {"Energy": 291.41, "Energy Source": "Renewables", "Entity": "China", "Year": 2002}, {"Energy": 1579.96, "Energy Source": "Fossil Fuels", "Entity": "China", "Year": 2003}, {"Energy": 43.34, "Energy Source": "Nuclear", "Entity": "China", "Year": 2003}, {"Energy": 287.28, "Energy Source": "Renewables", "Entity": "China", "Year": 2003}, {"Energy": 1795.41, "Energy Source": "Fossil Fuels", "Entity": "China", "Year": 2004}, {"Energy": 50.47, "Energy Source": "Nuclear", "Entity": "China", "Year": 2004}, {"Energy": 357.43, "Energy Source": "Renewables", "Entity": "China", "Year": 2004}, {"Energy": 2042.8, "Energy Source": "Fossil Fuels", "Entity": "China", "Year": 2005}, {"Energy": 53.09, "Energy Source": "Nuclear", "Entity": "China", "Year": 2005}, {"Energy": 404.37, "Energy Source": "Renewables", "Entity": "China", "Year": 2005}, {"Energy": 2364.16, "Energy Source": "Fossil Fuels", "Entity": "China", "Year": 2006}, {"Energy": 54.84, "Energy Source": "Nuclear", "Entity": "China", "Year": 2006}, {"Energy": 446.72, "Energy Source": "Renewables", "Entity": "China", "Year": 2006}, {"Energy": 2718.7, "Energy Source": "Fossil Fuels", "Entity": "China", "Year": 2007}, {"Energy": 62.13, "Energy Source": "Nuclear", "Entity": "China", "Year": 2007}, {"Energy": 500.71, "Energy Source": "Renewables", "Entity": "China", "Year": 2007}, {"Energy": 2762.29, "Energy Source": "Fossil Fuels", "Entity": "China", "Year": 2008}, {"Energy": 68.39, "Energy Source": "Nuclear", "Entity": "China", "Year": 2008}, {"Energy": 665.08, "Energy Source": "Renewables", "Entity": "China", "Year": 2008}, {"Energy": 2980.2, "Energy Source": "Fossil Fuels", "Entity": "China", "Year": 2009}, {"Energy": 70.05, "Energy Source": "Nuclear", "Entity": "China", "Year": 2009}, {"Energy": 664.39, "Energy Source": "Renewables", "Entity": "China", "Year": 2009}, {"Energy": 3326.19, "Energy Source": "Fossil Fuels", "Entity": "China", "Year": 2010}, {"Energy": 74.74, "Energy Source": "Nuclear", "Entity": "China", "Year": 2010}, {"Energy": 786.38, "Energy Source": "Renewables", "Entity": "China", "Year": 2010}, {"Energy": 3811.77, "Energy Source": "Fossil Fuels", "Entity": "China", "Year": 2011}, {"Energy": 87.2, "Energy Source": "Nuclear", "Entity": "China", "Year": 2011}, {"Energy": 792.38, "Energy Source": "Renewables", "Entity": "China", "Year": 2011}, {"Energy": 3869.38, "Energy Source": "Fossil Fuels", "Entity": "China", "Year": 2012}, {"Energy": 98.32, "Energy Source": "Nuclear", "Entity": "China", "Year": 2012}, {"Energy": 999.56, "Energy Source": "Renewables", "Entity": "China", "Year": 2012}, {"Energy": 4203.77, "Energy Source": "Fossil Fuels", "Entity": "China", "Year": 2013}, {"Energy": 111.5, "Energy Source": "Nuclear", "Entity": "China", "Year": 2013}, {"Energy": 1093.37, "Energy Source": "Renewables", "Entity": "China", "Year": 2013}, {"Energy": 4345.86, "Energy Source": "Fossil Fuels", "Entity": "China", "Year": 2014}, {"Energy": 133.22, "Energy Source": "Nuclear", "Entity": "China", "Year": 2014}, {"Energy": 1289.23, "Energy Source": "Renewables", "Entity": "China", "Year": 2014}, {"Energy": 4222.76, "Energy Source": "Fossil Fuels", "Entity": "China", "Year": 2015}, {"Energy": 171.38, "Energy Source": "Nuclear", "Entity": "China", "Year": 2015}, {"Energy": 1393.66, "Energy Source": "Renewables", "Entity": "China", "Year": 2015}, {"Energy": 4355, "Energy Source": "Fossil Fuels", "Entity": "China", "Year": 2016}, {"Energy": 213.18, "Energy Source": "Nuclear", "Entity": "China", "Year": 2016}, {"Energy": 1522.79, "Energy Source": "Renewables", "Entity": "China", "Year": 2016}, {"Energy": 4643.1, "Energy Source": "Fossil Fuels", "Entity": "China", "Year": 2017}, {"Energy": 248.1, "Energy Source": "Nuclear", "Entity": "China", "Year": 2017}, {"Energy": 1667.06, "Energy Source": "Renewables", "Entity": "China", "Year": 2017}, {"Energy": 4990.28, "Energy Source": "Fossil Fuels", "Entity": "China", "Year": 2018}, {"Energy": 295, "Energy Source": "Nuclear", "Entity": "China", "Year": 2018}, {"Energy": 1835.32, "Energy Source": "Renewables", "Entity": "China", "Year": 2018}, {"Energy": 5098.22, "Energy Source": "Fossil Fuels", "Entity": "China", "Year": 2019}, {"Energy": 348.7, "Energy Source": "Nuclear", "Entity": "China", "Year": 2019}, {"Energy": 2014.57, "Energy Source": "Renewables", "Entity": "China", "Year": 2019}, {"Energy": 5184.13, "Energy Source": "Fossil Fuels", "Entity": "China", "Year": 2020}, {"Energy": 366.2, "Energy Source": "Nuclear", "Entity": "China", "Year": 2020}, {"Energy": 2184.94, "Energy Source": "Renewables", "Entity": "China", "Year": 2020}, {"Energy": 475.35, "Energy Source": "Fossil Fuels", "Entity": "India", "Year": 2000}, {"Energy": 15.77, "Energy Source": "Nuclear", "Entity": "India", "Year": 2000}, {"Energy": 80.27, "Energy Source": "Renewables", "Entity": "India", "Year": 2000}, {"Energy": 491.01, "Energy Source": "Fossil Fuels", "Entity": "India", "Year": 2001}, {"Energy": 18.89, "Energy Source": "Nuclear", "Entity": "India", "Year": 2001}, {"Energy": 76.19, "Energy Source": "Renewables", "Entity": "India", "Year": 2001}, {"Energy": 517.51, "Energy Source": "Fossil Fuels", "Entity": "India", "Year": 2002}, {"Energy": 19.35, "Energy Source": "Nuclear", "Entity": "India", "Year": 2002}, {"Energy": 72.78, "Energy Source": "Renewables", "Entity": "India", "Year": 2002}, {"Energy": 545.36, "Energy Source": "Fossil Fuels", "Entity": "India", "Year": 2003}, {"Energy": 18.14, "Energy Source": "Nuclear", "Entity": "India", "Year": 2003}, {"Energy": 74.63, "Energy Source": "Renewables", "Entity": "India", "Year": 2003}, {"Energy": 567.86, "Energy Source": "Fossil Fuels", "Entity": "India", "Year": 2004}, {"Energy": 21.26, "Energy Source": "Nuclear", "Entity": "India", "Year": 2004}, {"Energy": 109.2, "Energy Source": "Renewables", "Entity": "India", "Year": 2004}, {"Energy": 579.32, "Energy Source": "Fossil Fuels", "Entity": "India", "Year": 2005}, {"Energy": 17.73, "Energy Source": "Nuclear", "Entity": "India", "Year": 2005}, {"Energy": 107.47, "Energy Source": "Renewables", "Entity": "India", "Year": 2005}, {"Energy": 599.24, "Energy Source": "Fossil Fuels", "Entity": "India", "Year": 2006}, {"Energy": 17.63, "Energy Source": "Nuclear", "Entity": "India", "Year": 2006}, {"Energy": 127.56, "Energy Source": "Renewables", "Entity": "India", "Year": 2006}, {"Energy": 636.68, "Energy Source": "Fossil Fuels", "Entity": "India", "Year": 2007}, {"Energy": 17.83, "Energy Source": "Nuclear", "Entity": "India", "Year": 2007}, {"Energy": 141.75, "Energy Source": "Renewables", "Entity": "India", "Year": 2007}, {"Energy": 674.27, "Energy Source": "Fossil Fuels", "Entity": "India", "Year": 2008}, {"Energy": 15.23, "Energy Source": "Nuclear", "Entity": "India", "Year": 2008}, {"Energy": 138.91, "Energy Source": "Renewables", "Entity": "India", "Year": 2008}, {"Energy": 728.56, "Energy Source": "Fossil Fuels", "Entity": "India", "Year": 2009}, {"Energy": 16.82, "Energy Source": "Nuclear", "Entity": "India", "Year": 2009}, {"Energy": 134.33, "Energy Source": "Renewables", "Entity": "India", "Year": 2009}, {"Energy": 771.78, "Energy Source": "Fossil Fuels", "Entity": "India", "Year": 2010}, {"Energy": 23.08, "Energy Source": "Nuclear", "Entity": "India", "Year": 2010}, {"Energy": 142.61, "Energy Source": "Renewables", "Entity": "India", "Year": 2010}, {"Energy": 828.16, "Energy Source": "Fossil Fuels", "Entity": "India", "Year": 2011}, {"Energy": 32.22, "Energy Source": "Nuclear", "Entity": "India", "Year": 2011}, {"Energy": 173.62, "Energy Source": "Renewables", "Entity": "India", "Year": 2011}, {"Energy": 893.45, "Energy Source": "Fossil Fuels", "Entity": "India", "Year": 2012}, {"Energy": 33.14, "Energy Source": "Nuclear", "Entity": "India", "Year": 2012}, {"Energy": 165.25, "Energy Source": "Renewables", "Entity": "India", "Year": 2012}, {"Energy": 924.93, "Energy Source": "Fossil Fuels", "Entity": "India", "Year": 2013}, {"Energy": 33.31, "Energy Source": "Nuclear", "Entity": "India", "Year": 2013}, {"Energy": 187.9, "Energy Source": "Renewables", "Entity": "India", "Year": 2013}, {"Energy": 1025.29, "Energy Source": "Fossil Fuels", "Entity": "India", "Year": 2014}, {"Energy": 34.69, "Energy Source": "Nuclear", "Entity": "India", "Year": 2014}, {"Energy": 202.04, "Energy Source": "Renewables", "Entity": "India", "Year": 2014}, {"Energy": 1080.44, "Energy Source": "Fossil Fuels", "Entity": "India", "Year": 2015}, {"Energy": 38.31, "Energy Source": "Nuclear", "Entity": "India", "Year": 2015}, {"Energy": 203.21, "Energy Source": "Renewables", "Entity": "India", "Year": 2015}, {"Energy": 1155.52, "Energy Source": "Fossil Fuels", "Entity": "India", "Year": 2016}, {"Energy": 37.9, "Energy Source": "Nuclear", "Entity": "India", "Year": 2016}, {"Energy": 208.21, "Energy Source": "Renewables", "Entity": "India", "Year": 2016}, {"Energy": 1198.85, "Energy Source": "Fossil Fuels", "Entity": "India", "Year": 2017}, {"Energy": 37.41, "Energy Source": "Nuclear", "Entity": "India", "Year": 2017}, {"Energy": 234.9, "Energy Source": "Renewables", "Entity": "India", "Year": 2017}, {"Energy": 1276.32, "Energy Source": "Fossil Fuels", "Entity": "India", "Year": 2018}, {"Energy": 39.05, "Energy Source": "Nuclear", "Entity": "India", "Year": 2018}, {"Energy": 263.61, "Energy Source": "Renewables", "Entity": "India", "Year": 2018}, {"Energy": 1273.59, "Energy Source": "Fossil Fuels", "Entity": "India", "Year": 2019}, {"Energy": 45.16, "Energy Source": "Nuclear", "Entity": "India", "Year": 2019}, {"Energy": 303.16, "Energy Source": "Renewables", "Entity": "India", "Year": 2019}, {"Energy": 1202.34, "Energy Source": "Fossil Fuels", "Entity": "India", "Year": 2020}, {"Energy": 44.61, "Energy Source": "Nuclear", "Entity": "India", "Year": 2020}, {"Energy": 315.76, "Energy Source": "Renewables", "Entity": "India", "Year": 2020}, {"Energy": 2697.28, "Energy Source": "Fossil Fuels", "Entity": "United States", "Year": 2000}, {"Energy": 753.89, "Energy Source": "Nuclear", "Entity": "United States", "Year": 2000}, {"Energy": 350.93, "Energy Source": "Renewables", "Entity": "United States", "Year": 2000}, {"Energy": 2678.68, "Energy Source": "Fossil Fuels", "Entity": "United States", "Year": 2001}, {"Energy": 768.83, "Energy Source": "Nuclear", "Entity": "United States", "Year": 2001}, {"Energy": 280.06, "Energy Source": "Renewables", "Entity": "United States", "Year": 2001}, {"Energy": 2727.83, "Energy Source": "Fossil Fuels", "Entity": "United States", "Year": 2002}, {"Energy": 780.06, "Energy Source": "Nuclear", "Entity": "United States", "Year": 2002}, {"Energy": 336.34, "Energy Source": "Renewables", "Entity": "United States", "Year": 2002}, {"Energy": 2756.03, "Energy Source": "Fossil Fuels", "Entity": "United States", "Year": 2003}, {"Energy": 763.73, "Energy Source": "Nuclear", "Entity": "United States", "Year": 2003}, {"Energy": 349.18, "Energy Source": "Renewables", "Entity": "United States", "Year": 2003}, {"Energy": 2818.28, "Energy Source": "Fossil Fuels", "Entity": "United States", "Year": 2004}, {"Energy": 788.53, "Energy Source": "Nuclear", "Entity": "United States", "Year": 2004}, {"Energy": 345.14, "Energy Source": "Renewables", "Entity": "United States", "Year": 2004}, {"Energy": 2899.96, "Energy Source": "Fossil Fuels", "Entity": "United States", "Year": 2005}, {"Energy": 781.99, "Energy Source": "Nuclear", "Entity": "United States", "Year": 2005}, {"Energy": 353.04, "Energy Source": "Renewables", "Entity": "United States", "Year": 2005}, {"Energy": 2878.56, "Energy Source": "Fossil Fuels", "Entity": "United States", "Year": 2006}, {"Energy": 787.22, "Energy Source": "Nuclear", "Entity": "United States", "Year": 2006}, {"Energy": 381.16, "Energy Source": "Renewables", "Entity": "United States", "Year": 2006}, {"Energy": 2988.24, "Energy Source": "Fossil Fuels", "Entity": "United States", "Year": 2007}, {"Energy": 806.42, "Energy Source": "Nuclear", "Entity": "United States", "Year": 2007}, {"Energy": 347.91, "Energy Source": "Renewables", "Entity": "United States", "Year": 2007}, {"Energy": 2924.21, "Energy Source": "Fossil Fuels", "Entity": "United States", "Year": 2008}, {"Energy": 806.21, "Energy Source": "Nuclear", "Entity": "United States", "Year": 2008}, {"Energy": 377.11, "Energy Source": "Renewables", "Entity": "United States", "Year": 2008}, {"Energy": 2725.41, "Energy Source": "Fossil Fuels", "Entity": "United States", "Year": 2009}, {"Energy": 798.85, "Energy Source": "Nuclear", "Entity": "United States", "Year": 2009}, {"Energy": 415.56, "Energy Source": "Renewables", "Entity": "United States", "Year": 2009}, {"Energy": 2882.49, "Energy Source": "Fossil Fuels", "Entity": "United States", "Year": 2010}, {"Energy": 806.97, "Energy Source": "Nuclear", "Entity": "United States", "Year": 2010}, {"Energy": 424.48, "Energy Source": "Renewables", "Entity": "United States", "Year": 2010}, {"Energy": 2788.93, "Energy Source": "Fossil Fuels", "Entity": "United States", "Year": 2011}, {"Energy": 790.2, "Energy Source": "Nuclear", "Entity": "United States", "Year": 2011}, {"Energy": 509.74, "Energy Source": "Renewables", "Entity": "United States", "Year": 2011}, {"Energy": 2779.02, "Energy Source": "Fossil Fuels", "Entity": "United States", "Year": 2012}, {"Energy": 769.33, "Energy Source": "Nuclear", "Entity": "United States", "Year": 2012}, {"Energy": 492.32, "Energy Source": "Renewables", "Entity": "United States", "Year": 2012}, {"Energy": 2746.21, "Energy Source": "Fossil Fuels", "Entity": "United States", "Year": 2013}, {"Energy": 789.02, "Energy Source": "Nuclear", "Entity": "United States", "Year": 2013}, {"Energy": 520.38, "Energy Source": "Renewables", "Entity": "United States", "Year": 2013}, {"Energy": 2752.01, "Energy Source": "Fossil Fuels", "Entity": "United States", "Year": 2014}, {"Energy": 797.17, "Energy Source": "Nuclear", "Entity": "United States", "Year": 2014}, {"Energy": 546.83, "Energy Source": "Renewables", "Entity": "United States", "Year": 2014}, {"Energy": 2730.32, "Energy Source": "Fossil Fuels", "Entity": "United States", "Year": 2015}, {"Energy": 797.18, "Energy Source": "Nuclear", "Entity": "United States", "Year": 2015}, {"Energy": 556.49, "Energy Source": "Renewables", "Entity": "United States", "Year": 2015}, {"Energy": 2656.96, "Energy Source": "Fossil Fuels", "Entity": "United States", "Year": 2016}, {"Energy": 805.69, "Energy Source": "Nuclear", "Entity": "United States", "Year": 2016}, {"Energy": 624.91, "Energy Source": "Renewables", "Entity": "United States", "Year": 2016}, {"Energy": 2540.17, "Energy Source": "Fossil Fuels", "Entity": "United States", "Year": 2017}, {"Energy": 804.95, "Energy Source": "Nuclear", "Entity": "United States", "Year": 2017}, {"Energy": 707.19, "Energy Source": "Renewables", "Entity": "United States", "Year": 2017}, {"Energy": 2661.3, "Energy Source": "Fossil Fuels", "Entity": "United States", "Year": 2018}, {"Energy": 807.08, "Energy Source": "Nuclear", "Entity": "United States", "Year": 2018}, {"Energy": 733.17, "Energy Source": "Renewables", "Entity": "United States", "Year": 2018}, {"Energy": 2588.21, "Energy Source": "Fossil Fuels", "Entity": "United States", "Year": 2019}, {"Energy": 809.41, "Energy Source": "Nuclear", "Entity": "United States", "Year": 2019}, {"Energy": 760.76, "Energy Source": "Renewables", "Entity": "United States", "Year": 2019}, {"Energy": 2431.9, "Energy Source": "Fossil Fuels", "Entity": "United States", "Year": 2020}, {"Energy": 789.88, "Energy Source": "Nuclear", "Entity": "United States", "Year": 2020}, {"Energy": 821.4, "Energy Source": "Renewables", "Entity": "United States", "Year": 2020}], "metadata": {"Energy": {"type": "number", "semanticType": "Number"}, "Energy Source": {"type": "string", "semanticType": "String", "levels": ["Fossil Fuels", "Nuclear", "Renewables"]}, "Entity": {"type": "string", "semanticType": "Location"}, "Year": {"type": "number", "semanticType": "Year"}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total CO2 emissions by country across all years\n total_co2_by_country = df_energy.groupby('Entity')['Value_co2_emissions_kt_by_country'].sum().reset_index()\n \n # Get top 3 CO2 emitting countries\n top3_countries = total_co2_by_country.nlargest(3, 'Value_co2_emissions_kt_by_country')['Entity'].tolist()\n \n # Filter data for top 3 countries\n df_top3 = df_energy[df_energy['Entity'].isin(top3_countries)].copy()\n \n # Select relevant columns and rename for clarity\n df_top3 = df_top3[['Year', 'Entity', 'Electricity from fossil fuels (TWh)', \n 'Electricity from nuclear (TWh)', 'Electricity from renewables (TWh)']].copy()\n \n df_top3.columns = ['Year', 'Entity', 'Fossil Fuels', 'Nuclear', 'Renewables']\n \n # Transform to long format\n transformed_df = pd.melt(\n df_top3,\n id_vars=['Year', 'Entity'],\n value_vars=['Fossil Fuels', 'Nuclear', 'Renewables'],\n var_name='Energy Source',\n value_name='Energy'\n )\n \n # Sort by Entity, Year, and Energy Source\n transformed_df = transformed_df.sort_values(['Entity', 'Year', 'Energy Source']).reset_index(drop=True)\n \n return transformed_df\n", "source": ["global-energy-20-small.csv"], "dialog": [{"content": "You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 117440.0, 146139.9994, 158029.9988, ..., 5736319.824, 5738290.039, nan, 9861099.609\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 61.99, 62.21, 63.02, 63.71\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"compare each countries renewable between 2000 and 2020\",\n \"chart_type\": \"Grouped Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Renewable Energy\",\n \"color\": \"Year\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Compare renewable energy (Electricity from renewables) for each country between the years 2000 and 2020. Filter the data to include only years 2000 and 2020, and prepare the output with country names, year, and renewable energy values for visualization as a grouped bar chart.\",\n \"display_instruction\": \"Compare **renewable energy** by **country** between **2000** and **2020**\",\n \"output_fields\": [\"Entity\", \"Year\", \"Electricity from renewables (TWh)\"],\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Electricity from renewables (TWh)\",\n \"color\": \"Year\"\n },\n \"reason\": \"The user wants to compare renewable energy across countries between 2000 and 2020. The output field 'Renewable Energy' mentioned in the original chart_encodings maps to 'Electricity from renewables (TWh)' in the dataset. To create a grouped bar chart comparing these two years, we need to filter the data to only include years 2000 and 2020, and output the Entity (country), Year, and the renewable energy values. The chart_encodings are updated to use the actual column name from the dataset.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n transformed_df = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Select only the required columns\n transformed_df = transformed_df[['Entity', 'Year', 'Electricity from renewables (TWh)']]\n \n # Convert Year to string for better visualization as a categorical variable\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year for better readability\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n```", "role": "assistant"}, {"content": "This is the result from the latest python code:\n\n Electricity from renewables (TWh) Entity Year\n0 17.11 Australia 2000\n1 63.99 Australia 2020\n2 308.77 Brazil 2000\n3 520.01 Brazil 2020\n4 363.70 Canada 2000\n5 429.24 Canada 2020\n6 225.56 China 2000\n7 2184.94 China 2020\n8 67.83 France 2000\n9 125.28 France 2020\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show their renewable percentage instead, and add an entity to show global average\",\n \"chart_type\": \"Grouped Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Electricity from renewables (TWh)\",\n \"color\": \"Year\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Calculate the renewable energy percentage (proportion of renewables to total electricity generation from all sources) for each country between 2000 and 2020. Add a 'Global Average' entity that represents the worldwide average renewable percentage for each year. The output should include Entity (with 'Global Average' added), Year, and the renewable percentage.\",\n \"display_instruction\": \"Show **renewable energy percentage** by **country** with **global average** for **2000** and **2020**\",\n \"output_fields\": [\"Entity\", \"Year\", \"Renewable Percentage\"],\n \"chart_encodings\": {\n \"x\": \"Entity\",\n \"y\": \"Renewable Percentage\",\n \"color\": \"Year\"\n },\n \"reason\": \"The user wants to see renewable percentage instead of absolute values, and wants to add a global average entity for comparison. Renewable percentage is calculated as the ratio of renewable energy to total electricity generation (fossil fuels + nuclear + renewables). A 'Global Average' entity needs to be computed by aggregating all countries' data for each year. The y-axis encoding is updated to reflect the new metric 'Renewable Percentage'.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Filter data for years 2000 and 2020\n df_filtered = df_energy[df_energy['Year'].isin([2000, 2020])].copy()\n \n # Calculate total electricity generation\n df_filtered['Total Electricity'] = (\n df_filtered['Electricity from fossil fuels (TWh)'] + \n df_filtered['Electricity from nuclear (TWh)'] + \n df_filtered['Electricity from renewables (TWh)']\n )\n \n # Calculate renewable percentage\n df_filtered['Renewable Percentage'] = (\n df_filtered['Electricity from renewables (TWh)'] / df_filtered['Total Electricity'] * 100\n )\n \n # Select required columns for individual countries\n country_data = df_filtered[['Entity', 'Year', 'Renewable Percentage']].copy()\n \n # Calculate global average for each year\n global_avg = df_filtered.groupby('Year').apply(\n lambda x: pd.Series({\n 'Entity': 'Global Average',\n 'Year': x['Year'].iloc[0],\n 'Renewable Percentage': (\n x['Electricity from renewables (TWh)'].sum() / x['Total Electricity'].sum() * 100\n )\n })\n ).reset_index(drop=True)\n \n # Combine country data with global average\n transformed_df = pd.concat([country_data, global_avg], ignore_index=True)\n \n # Convert Year to string for better visualization\n transformed_df['Year'] = transformed_df['Year'].astype(str)\n \n # Sort by Entity and Year\n transformed_df = transformed_df.sort_values(['Entity', 'Year']).reset_index(drop=True)\n \n return transformed_df\n```", "role": "assistant"}, {"content": "This is the result from the latest python code:\n\n Entity Renewable Percentage Year\n0 Australia 8.634437 2000\n1 Australia 25.503168 2020\n2 Brazil 90.130772 2000\n3 Brazil 84.641177 2020\n4 Canada 61.809592 2000\n5 Canada 68.779644 2020\n6 China 16.639127 2000\n7 China 28.246461 2020\n8 France 12.711769 2000\n9 France 23.761024 2020\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show global energy composition over time\",\n \"chart_type\": \"Custom Area\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"Energy\",\n \"color\": \"Energy Source\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Show the global energy composition over time by aggregating electricity generation from all countries for each year, broken down by energy source (fossil fuels, nuclear, and renewables). The output should be in long format with Year, Energy Source, and Energy values to support a stacked area chart visualization.\",\n \"display_instruction\": \"Show global **energy composition** by **energy source** over time\",\n \"output_fields\": [\"Year\", \"Energy Source\", \"Energy\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"Energy\",\n \"color\": \"Energy Source\"\n },\n \"reason\": \"The user wants to visualize global energy composition over time, which requires aggregating electricity generation data across all countries for each year and breaking it down by energy source type. The data needs to be transformed into long format where each row represents a year-energy source combination, with the total energy value for that source globally. This follows up from the previous renewable percentage analysis by showing the overall energy mix trends rather than focusing on specific countries or percentages.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Group by Year and sum electricity generation from all countries\n global_energy = df_energy.groupby('Year').agg({\n 'Electricity from fossil fuels (TWh)': 'sum',\n 'Electricity from nuclear (TWh)': 'sum',\n 'Electricity from renewables (TWh)': 'sum'\n }).reset_index()\n \n # Rename columns for clarity\n global_energy.columns = ['Year', 'Fossil Fuels', 'Nuclear', 'Renewables']\n \n # Transform to long format\n transformed_df = pd.melt(\n global_energy,\n id_vars=['Year'],\n value_vars=['Fossil Fuels', 'Nuclear', 'Renewables'],\n var_name='Energy Source',\n value_name='Energy'\n )\n \n # Sort by Year and Energy Source\n transformed_df = transformed_df.sort_values(['Year', 'Energy Source']).reset_index(drop=True)\n \n return transformed_df\n```", "role": "assistant"}, {"content": "This is the result from the latest python code:\n\n Energy Energy Source Year\n0 7160.71 Fossil Fuels 2000\n1 1996.65 Nuclear 2000\n2 1742.56 Renewables 2000\n3 7273.89 Fossil Fuels 2001\n4 2037.54 Nuclear 2001\n5 1690.11 Renewables 2001\n6 7621.07 Fossil Fuels 2002\n7 2042.18 Nuclear 2002\n8 1757.63 Renewables 2002\n9 8043.86 Fossil Fuels 2003\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show the composition of top 3 CO2 emitting countries\",\n \"chart_type\": \"Custom Area\",\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"Energy\",\n \"color\": \"Energy Source\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Identify the top 3 CO2 emitting countries based on total CO2 emissions across all years, then show the energy composition (fossil fuels, nuclear, and renewables) for each of these countries over time. The output should be in long format with Year, Entity (country), Energy Source, and Energy values to support faceted area charts.\",\n \"display_instruction\": \"Show **energy composition** of **top 3 CO2 emitting countries** over time\",\n \"output_fields\": [\"Year\", \"Entity\", \"Energy Source\", \"Energy\"],\n \"chart_encodings\": {\n \"x\": \"Year\",\n \"y\": \"Energy\",\n \"color\": \"Energy Source\",\n \"facet\": \"Entity\"\n },\n \"reason\": \"The user wants to see the energy composition specifically for the top 3 CO2 emitting countries over time, following up from the global energy composition view. We need to identify which countries have the highest total CO2 emissions, filter the data for those countries, and transform it into long format by energy source. A facet encoding is added to show separate area charts for each of the top 3 countries, allowing comparison of their individual energy compositions over time.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total CO2 emissions by country across all years\n total_co2_by_country = df_energy.groupby('Entity')['Value_co2_emissions_kt_by_country'].sum().reset_index()\n \n # Get top 3 CO2 emitting countries\n top3_countries = total_co2_by_country.nlargest(3, 'Value_co2_emissions_kt_by_country')['Entity'].tolist()\n \n # Filter data for top 3 countries\n df_top3 = df_energy[df_energy['Entity'].isin(top3_countries)].copy()\n \n # Select relevant columns and rename for clarity\n df_top3 = df_top3[['Year', 'Entity', 'Electricity from fossil fuels (TWh)', \n 'Electricity from nuclear (TWh)', 'Electricity from renewables (TWh)']].copy()\n \n df_top3.columns = ['Year', 'Entity', 'Fossil Fuels', 'Nuclear', 'Renewables']\n \n # Transform to long format\n transformed_df = pd.melt(\n df_top3,\n id_vars=['Year', 'Entity'],\n value_vars=['Fossil Fuels', 'Nuclear', 'Renewables'],\n var_name='Energy Source',\n value_name='Energy'\n )\n \n # Sort by Entity, Year, and Energy Source\n transformed_df = transformed_df.sort_values(['Entity', 'Year', 'Energy Source']).reset_index(drop=True)\n \n return transformed_df\n```", "role": "assistant"}], "trigger": {"tableId": "table-81", "resultTableId": "table-10", "chart": {"id": "chart-1760739075465", "chartType": "Custom Area", "encodingMap": {"x": {"channel": "x", "bin": false, "fieldID": "original--global-energy-20-small.csv--Year"}, "y": {"channel": "y", "bin": false, "fieldID": "concept-1760738917347"}, "x2": {}, "y2": {"channel": "y2", "bin": false}, "color": {"fieldID": "concept-1760738921927", "sortBy": "[\"Fossil Fuels\",\"Nuclear\",\"Renewables\"]"}, "column": {}, "row": {"channel": "row", "bin": false}}, "tableRef": "table-81", "saved": false, "source": "trigger", "unread": false}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "show the composition of top 3 CO2 emitting countries", "displayContent": "Show **energy composition** of **top 3 CO2 emitting countries** over time"}]}, "explanation": {"agent": "CodeExplanationAgent", "code": "The code performs the following high-level transformations:\n\n1. **Identifies top polluters**: Calculates the total `Value_co2_emissions_kt_by_country` across all years for each `Entity` (country), then selects the **top 3** countries with the highest cumulative CO2 emissions.\n\n2. **Filters the dataset**: Retains only records for the **top 3 CO2-emitting countries** from the original energy data.\n\n3. **Simplifies column structure**: Extracts the relevant energy production columns (`Electricity from fossil fuels (TWh)`, `Electricity from nuclear (TWh)`, `Electricity from renewables (TWh)`) and renames them to **Fossil Fuels**, **Nuclear**, and **Renewables** for clarity.\n\n4. **Reshapes data to long format**: Transforms the data from wide format (separate columns for each energy source) to long format, where each row represents a specific `Year`, `Entity`, and `Energy Source` combination with its corresponding `Energy` production value.\n\n5. **Sorts the output**: Orders the final dataset by `Entity`, `Year`, and `Energy Source` for systematic presentation.", "concepts": [], "dialog": [{"content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (global_energy_20_small_csv)\n\n## fields\n\t*Year -- type: int64, values: 2000, 2001, 2002, ..., 2017, 2018, 2019, 2020\n\t*Entity -- type: object, values: Australia, Brazil, Canada, ..., Thailand, Ukraine, United Kingdom, United States\n\t*Value_co2_emissions_kt_by_country -- type: float64, values: 117440.0, 146139.9994, 158029.9988, ..., 5593029.785, 5736319.824, 5738290.039, 9861099.609\n\t*Electricity from fossil fuels (TWh) -- type: float64, values: 28.87, 31.62, 33.5, ..., 4643.1, 4990.28, 5098.22, 5184.13\n\t*Electricity from nuclear (TWh) -- type: float64, values: 0.0, 3.24, 4.94, ..., 62.21, 63.02, 63.71, 439.73\n\t*Electricity from renewables (TWh) -- type: float64, values: 0.0, 0.01, 0.03, ..., 1667.06, 1835.32, 2014.57, 2184.94\n\n## sample\n Year Entity Value_co2_emissions_kt_by_country Electricity from fossil fuels (TWh) Electricity from nuclear (TWh) Electricity from renewables (TWh)\n0 2000 Australia 339450.0000 181.05 0 17.11\n1 2001 Australia 345640.0000 194.33 0 17.40\n2 2002 Australia 353369.9951 197.29 0 17.35\n3 2003 Australia 352579.9866 195.13 0 18.50\n4 2004 Australia 365809.9976 203.66 0 19.41\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_energy):\n # Calculate total CO2 emissions by country across all years\n total_co2_by_country = df_energy.groupby('Entity')['Value_co2_emissions_kt_by_country'].sum().reset_index()\n \n # Get top 3 CO2 emitting countries\n top3_countries = total_co2_by_country.nlargest(3, 'Value_co2_emissions_kt_by_country')['Entity'].tolist()\n \n # Filter data for top 3 countries\n df_top3 = df_energy[df_energy['Entity'].isin(top3_countries)].copy()\n \n # Select relevant columns and rename for clarity\n df_top3 = df_top3[['Year', 'Entity', 'Electricity from fossil fuels (TWh)', \n 'Electricity from nuclear (TWh)', 'Electricity from renewables (TWh)']].copy()\n \n df_top3.columns = ['Year', 'Entity', 'Fossil Fuels', 'Nuclear', 'Renewables']\n \n # Transform to long format\n transformed_df = pd.melt(\n df_top3,\n id_vars=['Year', 'Entity'],\n value_vars=['Fossil Fuels', 'Nuclear', 'Renewables'],\n var_name='Energy Source',\n value_name='Energy'\n )\n \n # Sort by Entity, Year, and Energy Source\n transformed_df = transformed_df.sort_values(['Entity', 'Year', 'Energy Source']).reset_index(drop=True)\n \n return transformed_df\n\n\n[EXPLANATION]\n", "role": "user"}, {"content": "[CODE EXPLANATION]\n\nThe code performs the following high-level transformations:\n\n1. **Identifies top polluters**: Calculates the total `Value_co2_emissions_kt_by_country` across all years for each `Entity` (country), then selects the **top 3** countries with the highest cumulative CO2 emissions.\n\n2. **Filters the dataset**: Retains only records for the **top 3 CO2-emitting countries** from the original energy data.\n\n3. **Simplifies column structure**: Extracts the relevant energy production columns (`Electricity from fossil fuels (TWh)`, `Electricity from nuclear (TWh)`, `Electricity from renewables (TWh)`) and renames them to **Fossil Fuels**, **Nuclear**, and **Renewables** for clarity.\n\n4. **Reshapes data to long format**: Transforms the data from wide format (separate columns for each energy source) to long format, where each row represents a specific `Year`, `Entity`, and `Energy Source` combination with its corresponding `Energy` production value.\n\n5. **Sorts the output**: Orders the final dataset by `Entity`, `Year`, and `Energy Source` for systematic presentation.\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```", "role": "assistant"}], "status": "ok"}, "status": "completed"}, "anchored": false, "attachedMetadata": ""}], 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"type": "number", "source": "original", "description": "", "tableRef": "global-energy-20-small.csv"}, {"id": "original--global-energy-20-small.csv--Electricity from renewables (TWh)", "name": "Electricity from renewables (TWh)", "type": "number", "source": "original", "description": "", "tableRef": "global-energy-20-small.csv"}], "messages": [{"timestamp": 1760831081885, "type": "success", "component": "data formulator", "value": "Successfully loaded Global Energy"}], "displayedMessageIdx": 0, "viewMode": "report", "chartSynthesisInProgress": [], "config": {"formulateTimeoutSeconds": 60, "maxRepairAttempts": 1, "defaultChartWidth": 300, "defaultChartHeight": 300}, "dataCleanBlocks": [], "cleanInProgress": false, "generatedReports": [{"id": "report-1760831156182-8277", "content": "# Global Renewable Energy Shift: 2000 to 2020\n\nBetween 2000 and 2020, the world witnessed a notable transformation in renewable energy adoption. Global renewable electricity nearly doubled from 16% to 29% of total generation, signaling meaningful progress in the energy transition.\n\n[IMAGE(chart-1760738819387)]\n\nThe data reveals striking regional variations. Brazil maintained renewable leadership above 84%, while countries like Australia, Germany, and Italy dramatically expanded their renewable capacity—Australia tripling from 9% to 26%. However, some nations like Mexico experienced declining renewable shares, highlighting uneven progress across different energy systems.\n\n**In summary**, while the 20-year period shows encouraging momentum toward cleaner energy, the pace and direction vary significantly by country, suggesting that achieving global renewable energy goals will require sustained, coordinated efforts tailored to each nation's unique energy landscape and policy environment.", "style": "short note", "selectedChartIds": ["chart-1760738819387"], "createdAt": 1760831163718, "status": "completed", "title": "Global Renewable Energy Shift: 2000 to 2020", "anchorChartId": "chart-1760738819387"}, {"id": "report-1760831130105-4063", "content": "# The Global Renewable Energy Revolution: Two Decades of Transformation\n\nThe world's energy landscape has undergone a remarkable transformation between 2000 and 2020, with renewable electricity generation emerging as a critical component of the global energy mix. This shift reflects both technological advancement and growing commitment to sustainable energy solutions.\n\n[IMAGE(chart-1760738770100)]\n\nLooking at renewable energy adoption across major economies, the growth has been nothing short of extraordinary. China leads the pack with a staggering increase from 225.56 TWh in 2000 to 2,184.94 TWh in 2020—nearly a tenfold expansion. The United States more than doubled its renewable output from 335.45 TWh to 821.40 TWh, while Brazil grew from 308.77 TWh to 520.01 TWh. Notably, countries like Australia, India, and Germany also demonstrated significant gains, with Australia jumping from just 17.11 TWh to 63.99 TWh during this period.\n\n[IMAGE(chart-1760739084455)]\n\nWhen examining the energy portfolios of the three largest CO2 emitters—China, India, and the United States—a complex picture emerges. While China's total energy consumption has grown exponentially, with fossil fuels still dominating, the renewable sector (shown in red) has expanded substantially. The United States shows a more stable total energy consumption, with renewables gradually claiming a larger share. India's energy growth, though significant, remains heavily reliant on fossil fuels, though renewable adoption is accelerating.\n\n**In summary**, the past two decades reveal a global energy transition in progress. While renewable energy has achieved impressive growth worldwide, fossil fuels continue to dominate electricity generation in major economies. Key questions remain: Can this momentum accelerate sufficiently to meet climate goals? What policies will drive faster renewable adoption in emerging economies?", "style": "blog post", "selectedChartIds": ["chart-1760739084455", "chart-1760738770100"], "createdAt": 1760831142289, "status": "completed", "title": "The Global Renewable Energy Revolution: Two Decades of Transformation", "anchorChartId": "chart-1760739084455"}, {"id": "report-1760831094231-2424", "content": "# Global Renewable Energy: A Tale of Leaders and Laggards\n\nThe global energy landscape has undergone significant transformation over the past two decades, with renewable energy emerging as a critical player in the electricity mix. Analyzing data from 21 major economies between 2000 and 2020 reveals striking disparities in how nations have embraced clean energy alternatives.\n\n[IMAGE(chart-1760738423852)]\n\nThe first visualization reveals a fascinating divergence in renewable energy adoption. **Brazil** stands out as a consistent leader, maintaining renewable electricity percentages between 75-90% throughout the entire period, thanks largely to its robust hydroelectric infrastructure. **Canada** follows a similar trajectory, steadily increasing from about 60% to nearly 70% by 2020. Meanwhile, **Germany, Spain, Italy, and the United Kingdom** show remarkable growth trajectories, climbing from under 20% in the early 2000s to over 40% by 2020—demonstrating that nations can dramatically reshape their energy portfolios within two decades.\n\nOn the opposite end of the spectrum, **South Africa** remains nearly flat at the bottom, showing minimal renewable adoption despite global trends. **Saudi Arabia** and **Poland** also lag significantly, though both show modest upticks in recent years.\n\n[IMAGE(chart-1760738436615)]\n\nThe ranking chart illustrates the competitive dynamics of renewable energy leadership. **Brazil and Canada** maintain their dominance at ranks 1-2 throughout most years, while European nations like **Germany, Spain, and the UK** engage in a dynamic competition for the 3rd-5th positions, particularly after 2010. The volatility in middle rankings reflects the rapid changes in energy policy and investment across different nations, with countries like **China** climbing from lower ranks to break into the top 7 by 2020.\n\n**In summary**, the data reveals a bifurcated global energy transition: a group of progressive nations have successfully scaled renewable electricity to 40-90% of their mix, while others remain heavily dependent on fossil fuels. These patterns suggest that political will, natural resource endowment, and infrastructure investment are key determinants of renewable energy success. Important follow-up questions include: What policy mechanisms enabled top performers to achieve such high renewable percentages? Can lagging nations replicate these successes, or do geographic and economic constraints create insurmountable barriers?", "style": "blog post", "selectedChartIds": ["chart-1760738423852", "chart-1760738436615"], "createdAt": 1760831110064, "status": "completed", "title": "Global Renewable Energy: A Tale of Leaders and Laggards", "anchorChartId": "chart-1760738423852"}], "currentReport": {"id": "report-1760750575650-2619", "content": "# Hollywood's Billion-Dollar Hitmakers\n\n*Avatar* stands alone—earning over $2.5B in profit, dwarfing all competition. Action and Adventure films dominate the most profitable titles, with franchises like *Jurassic Park*, *The Dark Knight*, and *Lord of the Rings* proving blockbuster formulas work.\n\n\"Chart\"\n\nSteven Spielberg leads all directors with $7.2B in total profit across his career, showcasing remarkable consistency with hits spanning decades—from *Jurassic Park* to *E.T.* His nearest competitors trail by billions, underlining his unmatched commercial impact.\n\n\"Chart\"\n\n**In summary**, mega-budget Action and Adventure films generate extraordinary returns when they succeed, and a handful of elite directors—led by Spielberg—have mastered the formula for sustained box office dominance.", "style": "short note", "selectedChartIds": ["chart-1760743347871", "chart-1760743768741"], "chartImages": {}, "createdAt": 1760750584189, "title": "Report - 10/17/2025"}, "activeChallenges": [], "_persist": {"version": -1, "rehydrated": true}, "draftNodes": [], "focusedId": {"type": "report", "reportId": "report-1760831156182-8277"}} \ No newline at end of file diff --git a/public/df_movies.json b/public/df_movies.json index 0e826836..3014d6dc 100644 --- a/public/df_movies.json +++ b/public/df_movies.json @@ -1 +1 @@ -{"tables":[{"id":"movies","displayId":"movies","names":["Title","US Gross","Worldwide Gross","US DVD Sales","Production Budget","Release Date","MPAA Rating","Running Time min","Distributor","Source","Major Genre","Creative Type","Director","Rotten Tomatoes Rating","IMDB Rating","IMDB Votes"],"metadata":{"Title":{"type":"string","semanticType":"Name"},"US Gross":{"type":"number","semanticType":"Number"},"Worldwide Gross":{"type":"number","semanticType":"Number"},"US DVD Sales":{"type":"number","semanticType":"Number"},"Production Budget":{"type":"number","semanticType":"Number"},"Release Date":{"type":"date","semanticType":"Date"},"MPAA Rating":{"type":"string","semanticType":"String","levels":["G","PG","PG-13","R","NC-17","Not Rated","Open"]},"Running Time 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III: Revenge of the Sith","Worldwide Gross":848998877},{"Production Budget":75000000,"Profit":711686679,"Title":"The Lost World: Jurassic Park","Worldwide Gross":786686679},{"Production Budget":79300000,"Profit":704539505,"Title":"The Lion King","Worldwide Gross":783839505},{"Production Budget":250000000,"Profit":687499905,"Title":"Harry Potter and the Half-Blood Prince","Worldwide Gross":937499905},{"Production Budget":139000000,"Profit":682708551,"Title":"Spider-Man","Worldwide Gross":821708551},{"Production Budget":130000000,"Profit":665538952,"Title":"Harry Potter and the Prisoner of Azkaban","Worldwide Gross":795538952},{"Production Budget":300000000,"Profit":660996492,"Title":"Pirates of the Caribbean: At World's End","Worldwide Gross":960996492},{"Production Budget":50000000,"Profit":652623634,"Title":"The Twilight Saga: New Moon","Worldwide Gross":702623634},{"Production Budget":160000000,"Profit":638958162,"Title":"Shrek the Third","Worldwide Gross":798958162}],"metadata":{"Production Budget":{"type":"number","semanticType":"Number"},"Profit":{"type":"number","semanticType":"Number"},"Title":{"type":"string","semanticType":"Name"},"Worldwide Gross":{"type":"number","semanticType":"Number"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df_movies['Profit'] = df_movies['Worldwide Gross'] - df_movies['Production Budget']\n \n # Remove rows with missing values in required columns\n df_filtered = df_movies.dropna(subset=['Profit', 'Production Budget', 'Worldwide Gross', 'Title'])\n \n # Sort by profit in descending order and select top 30\n df_top30 = df_filtered.nlargest(30, 'Profit')\n \n # Select only the required output fields\n transformed_df = df_top30[['Title', 'Production Budget', 'Worldwide Gross', 'Profit']].reset_index(drop=True)\n \n return transformed_df\n","source":["movies"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 380270577.0, 402111870.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 1133027325.0, 1842879955.0, 2767891499.0\n\t*US DVD Sales -- type: float64, values: 33619971.0, 140689412.0, nan, ..., nan, nan, 46260220.0, nan\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 23000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, nan, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.0, 2.5, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 237477.0, 417703.0, 519541.0, nan\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"show top 30 movies based on their profit\",\n \"chart_type\": \"Scatter Plot\",\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Worldwide Gross\",\n \"color\": \"Title\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Calculate the profit for each movie (Worldwide Gross minus Production Budget), then select the top 30 movies with the highest profit. Display these movies with their Production Budget, Worldwide Gross, and Title to visualize the relationship between budget and revenue for the most profitable films.\",\n \"display_instruction\": \"Show top 30 movies by **profit** with their **Production Budget** and **Worldwide Gross**\",\n \"output_fields\": [\"Title\", \"Production Budget\", \"Worldwide Gross\", \"Profit\"],\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Worldwide Gross\",\n \"color\": \"Title\"\n },\n \"reason\": \"The user wants to show top 30 movies based on profit, which requires calculating profit as the difference between Worldwide Gross and Production Budget. The chart encodings provided are sufficient to visualize the most profitable movies, showing the relationship between their budget and gross revenue. An intermediate field 'Profit' is added to output_fields to enable the top 30 selection, though it won't be directly visualized in the chart.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df_movies['Profit'] = df_movies['Worldwide Gross'] - df_movies['Production Budget']\n \n # Remove rows with missing values in required columns\n df_filtered = df_movies.dropna(subset=['Profit', 'Production Budget', 'Worldwide Gross', 'Title'])\n \n # Sort by profit in descending order and select top 30\n df_top30 = df_filtered.nlargest(30, 'Profit')\n \n # Select only the required output fields\n transformed_df = df_top30[['Title', 'Production Budget', 'Worldwide Gross', 'Profit']].reset_index(drop=True)\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"movies","sourceTableIds":["movies"],"instruction":"show top 30 movies based on their profit","displayInstruction":"Show top 30 movies by **profit** with their **Production Budget** and **Worldwide Gross**","chart":{"id":"chart-1760743154092","chartType":"Scatter Plot","encodingMap":{"x":{"fieldID":"original--movies--Production Budget"},"y":{"fieldID":"original--movies--Worldwide Gross"},"color":{"fieldID":"original--movies--Title"},"size":{"channel":"size","bin":false},"opacity":{"channel":"opacity","bin":false},"column":{"channel":"column","bin":false},"row":{"channel":"row","bin":false}},"tableRef":"movies","saved":false,"source":"trigger","unread":false},"resultTableId":"table-78"},"explanation":{"agent":"CodeExplanationAgent","code":"The code identifies the most financially successful movies by analyzing their profitability:\n\n- Calculate **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n- Filter out movies with missing values in **Profit**, **Production Budget**, **Worldwide Gross**, or **Title** fields\n- Sort all movies by **Profit** in descending order and select the **top 30** most profitable movies\n- Return a simplified dataset containing only **Title**, **Production Budget**, **Worldwide Gross**, and **Profit** for these top performers","concepts":[{"explanation":"The net financial gain from a movie, calculated as the difference between worldwide box office revenue and production costs: \\( \\text{Profit} = \\text{Worldwide\\_Gross} - \\text{Production\\_Budget} \\). This metric represents the pure theatrical profitability before accounting for marketing costs, distribution fees, or ancillary revenue streams like DVD sales.","field":"Profit"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 441226247.0, 533345358.0, 600788188.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 1065659812.0, 1133027325.0, 1842879955.0, 2767891499.0\n\t*US DVD Sales -- type: float64, values: 2638319.0, 3907625.0, 4653193.0, ..., 124058348.0, 153389976.0, 234119058.0, 320830925.0\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 23000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: 46.0, 72.0, 74.0, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.5, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 261439.0, 292562.0, 417703.0, 519541.0\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df_movies['Profit'] = df_movies['Worldwide Gross'] - df_movies['Production Budget']\n \n # Remove rows with missing values in required columns\n df_filtered = df_movies.dropna(subset=['Profit', 'Production Budget', 'Worldwide Gross', 'Title'])\n \n # Sort by profit in descending order and select top 30\n df_top30 = df_filtered.nlargest(30, 'Profit')\n \n # Select only the required output fields\n transformed_df = df_top30[['Title', 'Production Budget', 'Worldwide Gross', 'Profit']].reset_index(drop=True)\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThe code identifies the most financially successful movies by analyzing their profitability:\n\n- Calculate **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n- Filter out movies with missing values in **Profit**, **Production Budget**, **Worldwide Gross**, or **Title** fields\n- Sort all movies by **Profit** in descending order and select the **top 30** most profitable movies\n- Return a simplified dataset containing only **Title**, **Production Budget**, **Worldwide Gross**, and **Profit** for these top performers\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"Profit\",\n \"explanation\": \"The net financial gain from a movie, calculated as the difference between worldwide box office revenue and production costs: \\\\( \\\\text{Profit} = \\\\text{Worldwide\\\\_Gross} - \\\\text{Production\\\\_Budget} \\\\). This metric represents the pure theatrical profitability before accounting for marketing costs, distribution fees, or ancillary revenue streams like DVD sales.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-77","displayId":"movie-profits1","names":["Major Genre","Production Budget","Profit","Title","Worldwide Gross"],"rows":[{"Major Genre":"Action","Production Budget":237000000,"Profit":2530891499,"Title":"Avatar","Worldwide Gross":2767891499},{"Major Genre":"Action","Production Budget":63000000,"Profit":860067947,"Title":"Jurassic Park","Worldwide Gross":923067947},{"Major Genre":"Action","Production Budget":185000000,"Profit":837345358,"Title":"The Dark Knight","Worldwide Gross":1022345358},{"Major Genre":"Adventure","Production Budget":94000000,"Profit":1039027325,"Title":"The Lord of the Rings: The Return of the King","Worldwide Gross":1133027325},{"Major Genre":"Adventure","Production Budget":125000000,"Profit":851457891,"Title":"Harry Potter and the Sorcerer's Stone","Worldwide Gross":976457891},{"Major Genre":"Adventure","Production Budget":70000000,"Profit":849838758,"Title":"Shrek 2","Worldwide Gross":919838758},{"Major Genre":"Black Comedy","Production Budget":37000000,"Profit":126415735,"Title":"Burn After Reading","Worldwide Gross":163415735},{"Major Genre":"Black Comedy","Production Budget":10000000,"Profit":73593107,"Title":"Snatch","Worldwide Gross":83593107},{"Major Genre":"Black Comedy","Production Budget":28000000,"Profit":43430876,"Title":"The Royal Tenenbaums","Worldwide Gross":71430876},{"Major Genre":"Comedy","Production Budget":28000000,"Profit":476050219,"Title":"Aladdin","Worldwide Gross":504050219},{"Major Genre":"Comedy","Production Budget":150000000,"Profit":470495432,"Title":"Ratatouille","Worldwide Gross":620495432},{"Major Genre":"Comedy","Production Budget":110000000,"Profit":464480841,"Title":"Night at the Museum","Worldwide Gross":574480841},{"Major Genre":"Concert/Performance","Production Budget":6500000,"Profit":64781781,"Title":"Hannah Montana/Miley Cyrus: Best of Both Worlds Concert Tour","Worldwide Gross":71281781},{"Major Genre":"Concert/Performance","Production Budget":3000000,"Profit":35236338,"Title":"The Original Kings of Comedy","Worldwide Gross":38236338},{"Major Genre":"Concert/Performance","Production Budget":3000000,"Profit":16184820,"Title":"Martin Lawrence Live: RunTelDat","Worldwide Gross":19184820},{"Major Genre":"Documentary","Production Budget":6000000,"Profit":216414517,"Title":"Fahrenheit 9/11","Worldwide Gross":222414517},{"Major Genre":"Documentary","Production Budget":3400000,"Profit":126037223,"Title":"La marche de l'empereur","Worldwide Gross":129437223},{"Major Genre":"Documentary","Production Budget":3000000,"Profit":55576018,"Title":"Bowling for Columbine","Worldwide Gross":58576018},{"Major Genre":"Drama","Production Budget":10500000,"Profit":782410554,"Title":"ET: The Extra-Terrestrial","Worldwide Gross":792910554},{"Major Genre":"Drama","Production Budget":50000000,"Profit":652623634,"Title":"The Twilight Saga: New Moon","Worldwide Gross":702623634},{"Major Genre":"Drama","Production Budget":55000000,"Profit":624400525,"Title":"Forrest Gump","Worldwide Gross":679400525},{"Major Genre":"Horror","Production Budget":12000000,"Profit":458700000,"Title":"Jaws","Worldwide Gross":470700000},{"Major Genre":"Horror","Production Budget":150000000,"Profit":435055701,"Title":"I am Legend","Worldwide Gross":585055701},{"Major Genre":"Horror","Production Budget":12000000,"Profit":390500000,"Title":"The Exorcist","Worldwide Gross":402500000},{"Major Genre":"Musical","Production Budget":20000000,"Profit":383476931,"Title":"Beauty and the Beast","Worldwide Gross":403476931},{"Major Genre":"Musical","Production Budget":8200000,"Profit":278014286,"Title":"The Sound of Music","Worldwide Gross":286214286},{"Major Genre":"Musical","Production Budget":30000000,"Profit":277687518,"Title":"Chicago","Worldwide Gross":307687518},{"Major Genre":"Romantic Comedy","Production Budget":14000000,"Profit":449400000,"Title":"Pretty Woman","Worldwide Gross":463400000},{"Major Genre":"Romantic Comedy","Production Budget":5000000,"Profit":363744044,"Title":"My Big Fat Greek Wedding","Worldwide Gross":368744044},{"Major Genre":"Romantic Comedy","Production Budget":22000000,"Profit":338099999,"Title":"There's Something About Mary","Worldwide Gross":360099999},{"Major Genre":"Thriller/Suspense","Production Budget":200000000,"Profit":1642879955,"Title":"Titanic","Worldwide Gross":1842879955},{"Major Genre":"Thriller/Suspense","Production Budget":40000000,"Profit":632806292,"Title":"The Sixth Sense","Worldwide Gross":672806292},{"Major Genre":"Thriller/Suspense","Production Budget":125000000,"Profit":632236138,"Title":"The Da Vinci Code","Worldwide Gross":757236138},{"Major Genre":"Western","Production Budget":19000000,"Profit":405200000,"Title":"Dances with Wolves","Worldwide Gross":424200000},{"Major Genre":"Western","Production Budget":6000000,"Profit":96308900,"Title":"Butch Cassidy and the Sundance Kid","Worldwide Gross":102308900},{"Major Genre":"Western","Production Budget":26000000,"Profit":42293719,"Title":"Open Range","Worldwide Gross":68293719}],"metadata":{"Major Genre":{"type":"string","semanticType":"String"},"Production Budget":{"type":"number","semanticType":"Number"},"Profit":{"type":"number","semanticType":"Number"},"Title":{"type":"string","semanticType":"Name"},"Worldwide Gross":{"type":"number","semanticType":"Number"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df_movies['Profit'] = df_movies['Worldwide Gross'] - df_movies['Production Budget']\n \n # Remove rows with missing values in required columns\n df_filtered = df_movies.dropna(subset=['Profit', 'Production Budget', 'Worldwide Gross', 'Title', 'Major Genre'])\n \n # Group by Major Genre and select top 3 most profitable movies from each genre\n transformed_df = df_filtered.groupby('Major Genre', group_keys=False).apply(\n lambda x: x.nlargest(3, 'Profit')\n ).reset_index(drop=True)\n \n # Select only the required output fields\n transformed_df = transformed_df[['Title', 'Major Genre', 'Profit', 'Production Budget', 'Worldwide Gross']]\n \n return transformed_df\n","source":["movies"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 380270577.0, 402111870.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 1133027325.0, 1842879955.0, 2767891499.0\n\t*US DVD Sales -- type: float64, values: 33619971.0, 140689412.0, nan, ..., nan, nan, 46260220.0, nan\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 23000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, nan, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.0, 2.5, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 237477.0, 417703.0, 519541.0, nan\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"show top 30 movies based on their profit\",\n \"chart_type\": \"Scatter Plot\",\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Worldwide Gross\",\n \"color\": \"Title\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Calculate the profit for each movie (Worldwide Gross minus Production Budget), then select the top 30 movies with the highest profit. Display these movies with their Production Budget, Worldwide Gross, and Title to visualize the relationship between budget and revenue for the most profitable films.\",\n \"display_instruction\": \"Show top 30 movies by **profit** with their **Production Budget** and **Worldwide Gross**\",\n \"output_fields\": [\"Title\", \"Production Budget\", \"Worldwide Gross\", \"Profit\"],\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Worldwide Gross\",\n \"color\": \"Title\"\n },\n \"reason\": \"The user wants to show top 30 movies based on profit, which requires calculating profit as the difference between Worldwide Gross and Production Budget. The chart encodings provided are sufficient to visualize the most profitable movies, showing the relationship between their budget and gross revenue. An intermediate field 'Profit' is added to output_fields to enable the top 30 selection, though it won't be directly visualized in the chart.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df_movies['Profit'] = df_movies['Worldwide Gross'] - df_movies['Production Budget']\n \n # Remove rows with missing values in required columns\n df_filtered = df_movies.dropna(subset=['Profit', 'Production Budget', 'Worldwide Gross', 'Title'])\n \n # Sort by profit in descending order and select top 30\n df_top30 = df_filtered.nlargest(30, 'Profit')\n \n # Select only the required output fields\n transformed_df = df_top30[['Title', 'Production Budget', 'Worldwide Gross', 'Profit']].reset_index(drop=True)\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n Production Budget Profit Title Worldwide Gross\n0 237000000 2530891499 Avatar 2767891499\n1 200000000 1642879955 Titanic 1842879955\n2 94000000 1039027325 The Lord of the Rings: The Return of the King 1133027325\n3 63000000 860067947 Jurassic Park 923067947\n4 125000000 851457891 Harry Potter and the Sorcerer's Stone 976457891\n5 70000000 849838758 Shrek 2 919838758\n6 200000000 846340665 Toy Story 3 1046340665\n7 225000000 840659812 Pirates of the Caribbean: Dead Man's Chest 1065659812\n8 185000000 837345358 The Dark Knight 1022345358\n9 94000000 832284377 The Lord of the Rings: The Two Towers 926284377\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show the top 3 most profitable movies from each genre\",\n \"chart_type\": \"Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Title\",\n \"y\": \"Profit\",\n \"color\": \"Production Budget\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Calculate the profit for each movie (Worldwide Gross minus Production Budget), then group movies by their Major Genre and select the top 3 most profitable movies from each genre. Display these movies with their Title, Profit, Production Budget, Major Genre, and Worldwide Gross.\",\n \"display_instruction\": \"Show top 3 most profitable movies from each **Major Genre**\",\n \"output_fields\": [\"Title\", \"Major Genre\", \"Profit\", \"Production Budget\", \"Worldwide Gross\"],\n \"chart_encodings\": {\n \"x\": \"Title\",\n \"y\": \"Profit\",\n \"color\": \"Major Genre\"\n },\n \"reason\": \"The user wants to see the top 3 most profitable movies from each genre, which builds upon the previous profit calculation. The chart encodings are updated to use 'Major Genre' for color instead of 'Production Budget' because when showing top movies by genre, it's more meaningful to distinguish them by genre in the visualization. The Major Genre field is added to output_fields to enable grouping and visualization by genre.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df_movies['Profit'] = df_movies['Worldwide Gross'] - df_movies['Production Budget']\n \n # Remove rows with missing values in required columns\n df_filtered = df_movies.dropna(subset=['Profit', 'Production Budget', 'Worldwide Gross', 'Title', 'Major Genre'])\n \n # Group by Major Genre and select top 3 most profitable movies from each genre\n transformed_df = df_filtered.groupby('Major Genre', group_keys=False).apply(\n lambda x: x.nlargest(3, 'Profit')\n ).reset_index(drop=True)\n \n # Select only the required output fields\n transformed_df = transformed_df[['Title', 'Major Genre', 'Profit', 'Production Budget', 'Worldwide Gross']]\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"table-78","sourceTableIds":["movies"],"instruction":"show the top 3 most profitable movies from each genre","displayInstruction":"Show top 3 most profitable movies from each **Major Genre**","chart":{"id":"chart-1760743349594","chartType":"Bar Chart","encodingMap":{"x":{"fieldID":"original--movies--Title"},"y":{"fieldID":"concept-Profit-1760742653681"},"color":{"fieldID":"original--movies--Production Budget"},"opacity":{"channel":"opacity","bin":false},"column":{"channel":"column","bin":false},"row":{"channel":"row","bin":false}},"tableRef":"table-78","saved":false,"source":"trigger","unread":false},"resultTableId":"table-77"},"explanation":{"agent":"CodeExplanationAgent","code":"This code performs a profitability analysis to identify the most successful movies in each genre:\n\n- **Calculate Profit**: Computes the **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n- **Filter Data**: Removes movies with missing values in **Profit**, **Production Budget**, **Worldwide Gross**, **Title**, or **Major Genre** to ensure complete data for analysis\n- **Group by Genre**: Groups all movies by their **Major Genre** (e.g., Action, Comedy, Drama)\n- **Select Top Performers**: Within each genre, identifies the **top 3** movies with the highest **Profit**\n- **Format Output**: Returns a focused dataset containing only **Title**, **Major Genre**, **Profit**, **Production Budget**, and **Worldwide Gross** for these top-performing movies","concepts":[{"explanation":"The net financial gain from a movie's theatrical release, calculated as the difference between worldwide box office revenue and production costs: \\( \\text{Profit} = \\text{Worldwide\\_Gross} - \\text{Production\\_Budget} \\). This metric indicates the commercial success of a film before accounting for marketing costs and revenue sharing.","field":"Profit"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 403706375.0, 441226247.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 976457891.0, 1133027325.0, 1842879955.0\n\t*US DVD Sales -- type: float64, values: nan, 140689412.0, nan, ..., 44145849.0, nan, nan, 46260220.0\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 5000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, nan, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.5, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 237477.0, 417703.0, nan, nan\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df_movies['Profit'] = df_movies['Worldwide Gross'] - df_movies['Production Budget']\n \n # Remove rows with missing values in required columns\n df_filtered = df_movies.dropna(subset=['Profit', 'Production Budget', 'Worldwide Gross', 'Title', 'Major Genre'])\n \n # Group by Major Genre and select top 3 most profitable movies from each genre\n transformed_df = df_filtered.groupby('Major Genre', group_keys=False).apply(\n lambda x: x.nlargest(3, 'Profit')\n ).reset_index(drop=True)\n \n # Select only the required output fields\n transformed_df = transformed_df[['Title', 'Major Genre', 'Profit', 'Production Budget', 'Worldwide Gross']]\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThis code performs a profitability analysis to identify the most successful movies in each genre:\n\n- **Calculate Profit**: Computes the **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n- **Filter Data**: Removes movies with missing values in **Profit**, **Production Budget**, **Worldwide Gross**, **Title**, or **Major Genre** to ensure complete data for analysis\n- **Group by Genre**: Groups all movies by their **Major Genre** (e.g., Action, Comedy, Drama)\n- **Select Top Performers**: Within each genre, identifies the **top 3** movies with the highest **Profit**\n- **Format Output**: Returns a focused dataset containing only **Title**, **Major Genre**, **Profit**, **Production Budget**, and **Worldwide Gross** for these top-performing movies\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"Profit\",\n \"explanation\": \"The net financial gain from a movie's theatrical release, calculated as the difference between worldwide box office revenue and production costs: \\\\( \\\\text{Profit} = \\\\text{Worldwide\\\\_Gross} - \\\\text{Production\\\\_Budget} \\\\). This metric indicates the commercial success of a film before accounting for marketing costs and revenue sharing.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-770727","displayId":"director-profit","names":["Director","Profit","Title","Total_Director_Profit"],"rows":[{"Director":"Steven Spielberg","Profit":860067947,"Title":"Jurassic Park","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":782410554,"Title":"ET: The Extra-Terrestrial","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":711686679,"Title":"The Lost World: Jurassic Park","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":601558145,"Title":"Indiana Jones and the Kingdom of the Crystal Skull","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":459745532,"Title":"The War of the Worlds","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":458700000,"Title":"Jaws","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":426171806,"Title":"Indiana Jones and the Last Crusade","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":416635085,"Title":"Saving Private Ryan","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":366800358,"Title":"Raiders of the Lost Ark","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":317700000,"Title":"Close Encounters of the Third Kind","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":305080271,"Title":"Indiana Jones and the Temple of Doom","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":299106800,"Title":"Catch Me if You Can","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":296200000,"Title":"Schindler's List","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":256824714,"Title":"Minority Report","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":230854823,"Title":"Hook","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":145900000,"Title":"Artificial Intelligence: AI","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":143673959,"Title":"The Terminal","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":78589701,"Title":"The Color Purple","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":62875000,"Title":1941,"Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":55279090,"Title":"Munich","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":19500000,"Title":"Twilight Zone: The Movie","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":4212592,"Title":"Amistad","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Profit":-130000000,"Title":"The Adventures of Tintin: Secret of the Unicorn","Total_Director_Profit":7169573056},{"Director":"James Cameron","Profit":2530891499,"Title":"Avatar","Total_Director_Profit":5078066216},{"Director":"James Cameron","Profit":1642879955,"Title":"Titanic","Total_Director_Profit":5078066216},{"Director":"James Cameron","Profit":416816151,"Title":"Terminator 2: Judgment Day","Total_Director_Profit":5078066216},{"Director":"James Cameron","Profit":265300000,"Title":"True Lies","Total_Director_Profit":5078066216},{"Director":"James Cameron","Profit":166316455,"Title":"Aliens","Total_Director_Profit":5078066216},{"Director":"James Cameron","Profit":71619031,"Title":"The Terminator","Total_Director_Profit":5078066216},{"Director":"James Cameron","Profit":-15756875,"Title":"The Abyss","Total_Director_Profit":5078066216},{"Director":"Chris Columbus","Profit":851457891,"Title":"Harry Potter and the Sorcerer's Stone","Total_Director_Profit":3036030427},{"Director":"Chris Columbus","Profit":778987880,"Title":"Harry Potter and the Chamber of Secrets","Total_Director_Profit":3036030427},{"Director":"Chris Columbus","Profit":461684675,"Title":"Home Alone","Total_Director_Profit":3036030427},{"Director":"Chris Columbus","Profit":416286003,"Title":"Mrs. Doubtfire","Total_Director_Profit":3036030427},{"Director":"Chris Columbus","Profit":338994850,"Title":"Home Alone 2: Lost in New York","Total_Director_Profit":3036030427},{"Director":"Chris Columbus","Profit":131435277,"Title":"Percy Jackson & the Olympians: The Lightning Thief","Total_Director_Profit":3036030427},{"Director":"Chris Columbus","Profit":69709917,"Title":"Stepmom","Total_Director_Profit":3036030427},{"Director":"Chris Columbus","Profit":-1617462,"Title":"I Love You, Beth Cooper","Total_Director_Profit":3036030427},{"Director":"Chris Columbus","Profit":-2579224,"Title":"Bicentennial Man","Total_Director_Profit":3036030427},{"Director":"Chris Columbus","Profit":-8329380,"Title":"Rent","Total_Director_Profit":3036030427},{"Director":"George Lucas","Profit":809288297,"Title":"Star Wars Ep. I: The Phantom Menace","Total_Director_Profit":3011105789},{"Director":"George Lucas","Profit":786900000,"Title":"Star Wars Ep. IV: A New Hope","Total_Director_Profit":3011105789},{"Director":"George Lucas","Profit":733998877,"Title":"Star Wars Ep. III: Revenge of the Sith","Total_Director_Profit":3011105789},{"Director":"George Lucas","Profit":541695615,"Title":"Star Wars Ep. II: Attack of the Clones","Total_Director_Profit":3011105789},{"Director":"George Lucas","Profit":139223000,"Title":"American Graffiti","Total_Director_Profit":3011105789},{"Director":"Peter Jackson","Profit":1039027325,"Title":"The Lord of the Rings: The Return of the King","Total_Director_Profit":3001395936},{"Director":"Peter Jackson","Profit":832284377,"Title":"The Lord of the Rings: The Two Towers","Total_Director_Profit":3001395936},{"Director":"Peter Jackson","Profit":759621686,"Title":"The Lord of the Rings: The Fellowship of the Ring","Total_Director_Profit":3001395936},{"Director":"Peter Jackson","Profit":343517357,"Title":"King Kong","Total_Director_Profit":3001395936},{"Director":"Peter Jackson","Profit":29702568,"Title":"The Lovely Bones","Total_Director_Profit":3001395936},{"Director":"Peter Jackson","Profit":-2757377,"Title":"Braindead","Total_Director_Profit":3001395936},{"Director":"Robert Zemeckis","Profit":624400525,"Title":"Forrest Gump","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Profit":362109762,"Title":"Back to the Future","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Profit":342230516,"Title":"Cast Away","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Profit":292000000,"Title":"Back to the Future Part II","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Profit":281500000,"Title":"Who Framed Roger Rabbit?","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Profit":203700000,"Title":"Back to the Future Part III","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Profit":198693989,"Title":"What Lies Beneath","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Profit":135420597,"Title":"The Polar Express","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Profit":133743744,"Title":"Disney's A Christmas Carol","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Profit":94022650,"Title":"Death Becomes Her","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Profit":75900000,"Title":"Contact","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Profit":44995215,"Title":"Beowulf","Total_Director_Profit":2788716998},{"Director":"Michael Bay","Profit":626303693,"Title":"Transformers: Revenge of the Fallen","Total_Director_Profit":2461192847},{"Director":"Michael Bay","Profit":557272592,"Title":"Transformers","Total_Director_Profit":2461192847},{"Director":"Michael Bay","Profit":414600000,"Title":"Armageddon","Total_Director_Profit":2461192847},{"Director":"Michael Bay","Profit":297739855,"Title":"Pearl Harbor","Total_Director_Profit":2461192847},{"Director":"Michael Bay","Profit":261069511,"Title":"The Rock","Total_Director_Profit":2461192847},{"Director":"Michael Bay","Profit":142940870,"Title":"Bad Boys II","Total_Director_Profit":2461192847},{"Director":"Michael Bay","Profit":118247413,"Title":"Bad Boys","Total_Director_Profit":2461192847},{"Director":"Michael Bay","Profit":43018913,"Title":"The Island","Total_Director_Profit":2461192847},{"Director":"Roland Emmerich","Profit":742400878,"Title":"Independence Day","Total_Director_Profit":2390416794},{"Director":"Roland Emmerich","Profit":566812167,"Title":2012,"Total_Director_Profit":2390416794},{"Director":"Roland Emmerich","Profit":419272402,"Title":"The Day After Tomorrow","Total_Director_Profit":2390416794},{"Director":"Roland Emmerich","Profit":251000000,"Title":"Godzilla","Total_Director_Profit":2390416794},{"Director":"Roland Emmerich","Profit":164065678,"Title":"10,000 B.C.","Total_Director_Profit":2390416794},{"Director":"Roland Emmerich","Profit":141565669,"Title":"Stargate","Total_Director_Profit":2390416794},{"Director":"Roland Emmerich","Profit":105300000,"Title":"The Patriot","Total_Director_Profit":2390416794},{"Director":"Gore Verbinski","Profit":840659812,"Title":"Pirates of the Caribbean: Dead Man's Chest","Total_Director_Profit":2359931719},{"Director":"Gore Verbinski","Profit":660996492,"Title":"Pirates of the Caribbean: At World's End","Total_Director_Profit":2359931719},{"Director":"Gore Verbinski","Profit":530011224,"Title":"Pirates of the Caribbean: The Curse of the Black Pearl","Total_Director_Profit":2359931719},{"Director":"Gore Verbinski","Profit":201094024,"Title":"The Ring","Total_Director_Profit":2359931719},{"Director":"Gore Verbinski","Profit":107808615,"Title":"The Mexican","Total_Director_Profit":2359931719},{"Director":"Gore Verbinski","Profit":23894591,"Title":"Mouse Hunt","Total_Director_Profit":2359931719},{"Director":"Gore Verbinski","Profit":-4533039,"Title":"The Weather Man","Total_Director_Profit":2359931719},{"Director":"Tim Burton","Profit":823291110,"Title":"Alice in Wonderland","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Profit":376348924,"Title":"Batman","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Profit":324459076,"Title":"Charlie and the Chocolate Factory","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Profit":262211740,"Title":"Planet of the Apes","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Profit":186822354,"Title":"Batman Returns","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Profit":137068340,"Title":"Sleepy Hollow","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Profit":87359111,"Title":"The Corpse Bride","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Profit":58326666,"Title":"Beetle Juice","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Profit":53432867,"Title":"Big Fish","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Profit":33976987,"Title":"Edward Scissorhands","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Profit":21371017,"Title":"Mars Attacks!","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Profit":-12171534,"Title":"Ed Wood","Total_Director_Profit":2352496658},{"Director":"Andrew Adamson","Profit":849838758,"Title":"Shrek 2","Total_Director_Profit":2047535219},{"Director":"Andrew Adamson","Profit":568806957,"Title":"The Chronicles of Narnia: The Lion, the Witch and the Wardrobe","Total_Director_Profit":2047535219},{"Director":"Andrew Adamson","Profit":434399218,"Title":"Shrek","Total_Director_Profit":2047535219},{"Director":"Andrew Adamson","Profit":194490286,"Title":"The Chronicles of Narnia: Prince Caspian","Total_Director_Profit":2047535219},{"Director":"Sam Raimi","Profit":682708551,"Title":"Spider-Man","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Profit":632871626,"Title":"Spider-Man 3","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Profit":583705001,"Title":"Spider-Man 2","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Profit":55724728,"Title":"Drag Me To Hell","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Profit":34567606,"Title":"The Gift","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Profit":29025000,"Title":"The Evil Dead","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Profit":10502976,"Title":"Army of Darkness","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Profit":2423044,"Title":"Evil Dead II","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Profit":-683727,"Title":"A Simple Plan","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Profit":-3887360,"Title":"For Love of the Game","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Profit":-13447540,"Title":"The Quick and the Dead","Total_Director_Profit":2013509905},{"Director":"Ron Howard","Profit":632236138,"Title":"The Da Vinci Code","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Profit":335975846,"Title":"Angels & Demons","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Profit":269100000,"Title":"Apollo 13","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Profit":238708996,"Title":"A Beautiful Mind","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Profit":238700000,"Title":"Ransom","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Profit":222141403,"Title":"How the Grinch Stole Christmas","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Profit":54599495,"Title":"Splash","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Profit":20539911,"Title":"Cinderella Man","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Profit":-855414,"Title":"Frost/Nixon","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Profit":-24680311,"Title":"EDtv","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Profit":-26746567,"Title":"The Missing","Total_Director_Profit":1959719497},{"Director":"Christopher Nolan","Profit":837345358,"Title":"The Dark Knight","Total_Director_Profit":1823751815},{"Director":"Christopher Nolan","Profit":593830280,"Title":"Inception","Total_Director_Profit":1823751815},{"Director":"Christopher Nolan","Profit":222353017,"Title":"Batman Begins","Total_Director_Profit":1823751815},{"Director":"Christopher Nolan","Profit":67896006,"Title":"The Prestige","Total_Director_Profit":1823751815},{"Director":"Christopher Nolan","Profit":67622499,"Title":"Insomnia","Total_Director_Profit":1823751815},{"Director":"Christopher Nolan","Profit":34665950,"Title":"Memento","Total_Director_Profit":1823751815},{"Director":"Christopher Nolan","Profit":38705,"Title":"Following","Total_Director_Profit":1823751815},{"Director":"M. Night Shyamalan","Profit":632806292,"Title":"The Sixth Sense","Total_Director_Profit":1575120870},{"Director":"M. Night Shyamalan","Profit":337563071,"Title":"Signs","Total_Director_Profit":1575120870},{"Director":"M. Night Shyamalan","Profit":188514545,"Title":"The Village","Total_Director_Profit":1575120870},{"Director":"M. Night Shyamalan","Profit":174856037,"Title":"Unbreakable","Total_Director_Profit":1575120870},{"Director":"M. Night Shyamalan","Profit":140191957,"Title":"The Last Airbender","Total_Director_Profit":1575120870},{"Director":"M. Night Shyamalan","Profit":103403799,"Title":"The Happening","Total_Director_Profit":1575120870},{"Director":"M. Night Shyamalan","Profit":-2214831,"Title":"Lady in the Water","Total_Director_Profit":1575120870},{"Director":"David Yates","Profit":788468864,"Title":"Harry Potter and the Order of the Phoenix","Total_Director_Profit":1475968769},{"Director":"David Yates","Profit":687499905,"Title":"Harry Potter and the Half-Blood Prince","Total_Director_Profit":1475968769},{"Director":"John Lasseter","Profit":394966906,"Title":"Toy Story 2","Total_Director_Profit":1436948978},{"Director":"John Lasseter","Profit":391923762,"Title":"Cars","Total_Director_Profit":1436948978},{"Director":"John Lasseter","Profit":331948825,"Title":"Toy Story","Total_Director_Profit":1436948978},{"Director":"John Lasseter","Profit":318109485,"Title":"A Bug's Life","Total_Director_Profit":1436948978},{"Director":"Carlos Saldanha","Profit":796685941,"Title":"Ice Age: Dawn of the Dinosaurs","Total_Director_Profit":1373585223},{"Director":"Carlos Saldanha","Profit":576899282,"Title":"Ice Age: The Meltdown","Total_Director_Profit":1373585223},{"Director":"Andy Wachowski","Profit":611576929,"Title":"The Matrix Reloaded","Total_Director_Profit":1296311080},{"Director":"Andy Wachowski","Profit":395279930,"Title":"The Matrix","Total_Director_Profit":1296311080},{"Director":"Andy Wachowski","Profit":314259759,"Title":"The Matrix Revolutions","Total_Director_Profit":1296311080},{"Director":"Andy Wachowski","Profit":1800000,"Title":"Bound","Total_Director_Profit":1296311080},{"Director":"Andy Wachowski","Profit":-26605538,"Title":"Speed Racer","Total_Director_Profit":1296311080},{"Director":"Ridley Scott","Profit":354683805,"Title":"Gladiator","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Profit":263100280,"Title":"Hannibal","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Profit":194630630,"Title":"Alien","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Profit":165697825,"Title":"American Gangster","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Profit":101398413,"Title":"Kingdom of Heaven","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Profit":100885538,"Title":"Robin Hood","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Profit":78638745,"Title":"Black Hawk Down","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Profit":40894666,"Title":"Body of Lies","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Profit":15892212,"Title":"Black Rain","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Profit":7064105,"Title":"A Good Year","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Profit":5139618,"Title":"Blade Runner","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Profit":-1830844,"Title":"G.I.Jane","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Profit":-9497888,"Title":"Legend","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Profit":-27770700,"Title":"White Squall","Total_Director_Profit":1288926405}],"metadata":{"Director":{"type":"string","semanticType":"Name"},"Profit":{"type":"number","semanticType":"Number"},"Title":{"type":"string","semanticType":"Name"},"Total_Director_Profit":{"type":"number","semanticType":"Number"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df = df_movies.copy()\n df['Profit'] = df['Worldwide Gross'] - df['Production Budget']\n \n # Remove rows with missing Director or Profit values\n df = df.dropna(subset=['Director', 'Profit'])\n \n # Calculate total profit per director\n director_profit = df.groupby('Director')['Profit'].sum().reset_index()\n director_profit.columns = ['Director', 'Total_Director_Profit']\n \n # Get top 20 directors by total profit\n top_20_directors = director_profit.nlargest(20, 'Total_Director_Profit')['Director'].tolist()\n \n # Filter movies by top 20 directors\n df_filtered = df[df['Director'].isin(top_20_directors)].copy()\n \n # Add total director profit to each movie row\n df_filtered = df_filtered.merge(director_profit, on='Director', how='left')\n \n # Select and order columns\n transformed_df = df_filtered[['Director', 'Title', 'Profit', 'Total_Director_Profit']].copy()\n \n # Sort by total director profit (descending) and then by movie profit (descending)\n transformed_df = transformed_df.sort_values(['Total_Director_Profit', 'Profit'], ascending=[False, False])\n \n return transformed_df\n","source":["movies"],"dialog":[{"content":"You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 441226247.0, 533345358.0, 600788188.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 976457891.0, 1133027325.0, 1842879955.0\n\t*US DVD Sales -- type: float64, values: nan, 1978111.0, 140689412.0, ..., 100178981.0, 104578578.0, 234119058.0, 320830925.0\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 15000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, nan, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.5, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 245271.0, 261439.0, 326950.0, 417703.0\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[GOAL]\n\nshow top 20 directories based on profit breakdown by their movies","role":"user"},{"content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"Show top 20 directors based on total profit, with a breakdown of profit by their individual movies\",\n \"display_instruction\": \"Show top 20 **directors** by total **profit** with movie breakdown\",\n \"recommendation\": \"Calculate profit (Worldwide Gross - Production Budget) for each movie, then aggregate by director to find top 20 directors by total profit. Display individual movie profits for each director to show the breakdown.\",\n \"output_fields\": [\"Director\", \"Title\", \"Profit\", \"Total_Director_Profit\"],\n \"chart_type\": \"group_bar\",\n \"chart_encodings\": {\n \"x\": \"Director\",\n \"y\": \"Profit\",\n \"color\": \"Title\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df = df_movies.copy()\n df['Profit'] = df['Worldwide Gross'] - df['Production Budget']\n \n # Remove rows with missing Director or Profit values\n df = df.dropna(subset=['Director', 'Profit'])\n \n # Calculate total profit per director\n director_profit = df.groupby('Director')['Profit'].sum().reset_index()\n director_profit.columns = ['Director', 'Total_Director_Profit']\n \n # Get top 20 directors by total profit\n top_20_directors = director_profit.nlargest(20, 'Total_Director_Profit')['Director'].tolist()\n \n # Filter movies by top 20 directors\n df_filtered = df[df['Director'].isin(top_20_directors)].copy()\n \n # Add total director profit to each movie row\n df_filtered = df_filtered.merge(director_profit, on='Director', how='left')\n \n # Select and order columns\n transformed_df = df_filtered[['Director', 'Title', 'Profit', 'Total_Director_Profit']].copy()\n \n # Sort by total director profit (descending) and then by movie profit (descending)\n transformed_df = transformed_df.sort_values(['Total_Director_Profit', 'Profit'], ascending=[False, False])\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"movies","sourceTableIds":["movies"],"instruction":"show top 20 directories based on profit breakdown by their movies","displayInstruction":"Show top 20 **directors** by total **profit** with movie breakdown","chart":{"id":"chart-1760743765735","chartType":"Auto","encodingMap":{},"tableRef":"movies","saved":false,"source":"trigger","unread":true},"resultTableId":"table-770727"},"explanation":{"agent":"CodeExplanationAgent","code":"This code performs a director profitability analysis:\n\n1. Calculate **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n\n2. Remove movies with missing **Director** or **Profit** information to ensure clean analysis\n\n3. Aggregate movies by **Director** to compute **Total_Director_Profit** (sum of all profits from their movies)\n\n4. Identify the **top 20 directors** ranked by their total profit across all films\n\n5. Filter the dataset to include only movies directed by these **top 20 directors**\n\n6. Enrich each movie record with its director's **Total_Director_Profit** for comparison context\n\n7. Sort results first by **Total_Director_Profit** (descending) to group directors by overall success, then by individual movie **Profit** (descending) to rank films within each director's portfolio","concepts":[{"explanation":"Net financial return for each movie, calculated as: \\( \\text{Profit} = \\text{Worldwide\\_Gross} - \\text{Production\\_Budget} \\). Positive values indicate profitable films, while negative values represent financial losses.","field":"Profit"},{"explanation":"Cumulative profit generated by a director across all their films in the dataset, computed as: \\( \\text{Total\\_Director\\_Profit} = \\sum \\text{Profit}_{\\text{all movies by director}} \\). This metric measures a director's overall commercial track record and financial value to studios.","field":"Total_Director_Profit"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 402111870.0, 403706375.0, 600788188.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 976457891.0, 1133027325.0, 1842879955.0\n\t*US DVD Sales -- type: float64, values: 893953.0, 2638319.0, 3507046.0, ..., 104578578.0, 124058348.0, 234119058.0, 320830925.0\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 23000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: 46.0, 72.0, 74.0, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: nan, 1.5, 2.5, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 212985.0, 237477.0, 417703.0, nan\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df = df_movies.copy()\n df['Profit'] = df['Worldwide Gross'] - df['Production Budget']\n \n # Remove rows with missing Director or Profit values\n df = df.dropna(subset=['Director', 'Profit'])\n \n # Calculate total profit per director\n director_profit = df.groupby('Director')['Profit'].sum().reset_index()\n director_profit.columns = ['Director', 'Total_Director_Profit']\n \n # Get top 20 directors by total profit\n top_20_directors = director_profit.nlargest(20, 'Total_Director_Profit')['Director'].tolist()\n \n # Filter movies by top 20 directors\n df_filtered = df[df['Director'].isin(top_20_directors)].copy()\n \n # Add total director profit to each movie row\n df_filtered = df_filtered.merge(director_profit, on='Director', how='left')\n \n # Select and order columns\n transformed_df = df_filtered[['Director', 'Title', 'Profit', 'Total_Director_Profit']].copy()\n \n # Sort by total director profit (descending) and then by movie profit (descending)\n transformed_df = transformed_df.sort_values(['Total_Director_Profit', 'Profit'], ascending=[False, False])\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThis code performs a director profitability analysis:\n\n1. Calculate **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n\n2. Remove movies with missing **Director** or **Profit** information to ensure clean analysis\n\n3. Aggregate movies by **Director** to compute **Total_Director_Profit** (sum of all profits from their movies)\n\n4. Identify the **top 20 directors** ranked by their total profit across all films\n\n5. Filter the dataset to include only movies directed by these **top 20 directors**\n\n6. Enrich each movie record with its director's **Total_Director_Profit** for comparison context\n\n7. Sort results first by **Total_Director_Profit** (descending) to group directors by overall success, then by individual movie **Profit** (descending) to rank films within each director's portfolio\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"Profit\",\n \"explanation\": \"Net financial return for each movie, calculated as: \\\\( \\\\text{Profit} = \\\\text{Worldwide\\\\_Gross} - \\\\text{Production\\\\_Budget} \\\\). Positive values indicate profitable films, while negative values represent financial losses.\"\n },\n {\n \"field\": \"Total_Director_Profit\",\n \"explanation\": \"Cumulative profit generated by a director across all their films in the dataset, computed as: \\\\( \\\\text{Total\\\\_Director\\\\_Profit} = \\\\sum \\\\text{Profit}_{\\\\text{all movies by director}} \\\\). This metric measures a director's overall commercial track record and financial value to studios.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-89","displayId":"director-genre-profit","names":["Director","Genre_Profit","Major Genre","Total_Director_Profit"],"rows":[{"Director":"Steven Spielberg","Genre_Profit":2288324872,"Major Genre":"Action","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Genre_Profit":2222007781,"Major Genre":"Drama","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Genre_Profit":2118165403,"Major Genre":"Adventure","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Genre_Profit":478200000,"Major Genre":"Horror","Total_Director_Profit":7169573056},{"Director":"Steven Spielberg","Genre_Profit":62875000,"Major Genre":"Comedy","Total_Director_Profit":7169573056},{"Director":"James Cameron","Genre_Profit":3435186261,"Major Genre":"Action","Total_Director_Profit":5078066216},{"Director":"James Cameron","Genre_Profit":1642879955,"Major Genre":"Thriller/Suspense","Total_Director_Profit":5078066216},{"Director":"Chris Columbus","Genre_Profit":1761881048,"Major Genre":"Adventure","Total_Director_Profit":3036030427},{"Director":"Chris Columbus","Genre_Profit":1215348066,"Major Genre":"Comedy","Total_Director_Profit":3036030427},{"Director":"Chris Columbus","Genre_Profit":67130693,"Major Genre":"Drama","Total_Director_Profit":3036030427},{"Director":"Chris Columbus","Genre_Profit":-8329380,"Major Genre":"Musical","Total_Director_Profit":3036030427},{"Director":"George Lucas","Genre_Profit":2871882789,"Major Genre":"Adventure","Total_Director_Profit":3011105789},{"Director":"George Lucas","Genre_Profit":139223000,"Major Genre":"Comedy","Total_Director_Profit":3011105789},{"Director":"Peter Jackson","Genre_Profit":2974450745,"Major Genre":"Adventure","Total_Director_Profit":3001395936},{"Director":"Peter Jackson","Genre_Profit":29702568,"Major Genre":"Drama","Total_Director_Profit":3001395936},{"Director":"Peter Jackson","Genre_Profit":-2757377,"Major Genre":"Horror","Total_Director_Profit":3001395936},{"Director":"Robert Zemeckis","Genre_Profit":1176274785,"Major Genre":"Drama","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Genre_Profit":1038225574,"Major Genre":"Adventure","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Genre_Profit":375522650,"Major Genre":"Comedy","Total_Director_Profit":2788716998},{"Director":"Robert Zemeckis","Genre_Profit":198693989,"Major Genre":"Thriller/Suspense","Total_Director_Profit":2788716998},{"Director":"Michael Bay","Genre_Profit":2046592847,"Major Genre":"Action","Total_Director_Profit":2461192847},{"Director":"Michael Bay","Genre_Profit":414600000,"Major Genre":"Adventure","Total_Director_Profit":2461192847},{"Director":"Roland Emmerich","Genre_Profit":1325738958,"Major Genre":"Adventure","Total_Director_Profit":2390416794},{"Director":"Roland Emmerich","Genre_Profit":959377836,"Major Genre":"Action","Total_Director_Profit":2390416794},{"Director":"Roland Emmerich","Genre_Profit":105300000,"Major Genre":"Drama","Total_Director_Profit":2390416794},{"Director":"Gore Verbinski","Genre_Profit":2031667528,"Major Genre":"Adventure","Total_Director_Profit":2359931719},{"Director":"Gore Verbinski","Genre_Profit":201094024,"Major Genre":"Horror","Total_Director_Profit":2359931719},{"Director":"Gore Verbinski","Genre_Profit":107808615,"Major Genre":"Action","Total_Director_Profit":2359931719},{"Director":"Gore Verbinski","Genre_Profit":23894591,"Major Genre":"Comedy","Total_Director_Profit":2359931719},{"Director":"Gore Verbinski","Genre_Profit":-4533039,"Major Genre":"Drama","Total_Director_Profit":2359931719},{"Director":"Tim Burton","Genre_Profit":1172861961,"Major Genre":"Adventure","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Genre_Profit":563171278,"Major Genre":"Action","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Genre_Profit":425962212,"Major Genre":"Comedy","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Genre_Profit":137068340,"Major Genre":"Horror","Total_Director_Profit":2352496658},{"Director":"Tim Burton","Genre_Profit":53432867,"Major Genre":"Drama","Total_Director_Profit":2352496658},{"Director":"Andrew Adamson","Genre_Profit":2047535219,"Major Genre":"Adventure","Total_Director_Profit":2047535219},{"Director":"Sam Raimi","Genre_Profit":1899285178,"Major Genre":"Adventure","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Genre_Profit":97675748,"Major Genre":"Horror","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Genre_Profit":34567606,"Major Genre":"Thriller/Suspense","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Genre_Profit":-4571087,"Major Genre":"Drama","Total_Director_Profit":2013509905},{"Director":"Sam Raimi","Genre_Profit":-13447540,"Major Genre":"Western","Total_Director_Profit":2013509905},{"Director":"Ron Howard","Genre_Profit":968211984,"Major Genre":"Thriller/Suspense","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Genre_Profit":527493493,"Major Genre":"Drama","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Genre_Profit":252060587,"Major Genre":"Comedy","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Genre_Profit":238700000,"Major Genre":"Action","Total_Director_Profit":1959719497},{"Director":"Ron Howard","Genre_Profit":-26746567,"Major Genre":"Western","Total_Director_Profit":1959719497},{"Director":"Christopher Nolan","Genre_Profit":1059698375,"Major Genre":"Action","Total_Director_Profit":1823713110},{"Director":"Christopher Nolan","Genre_Profit":729348785,"Major Genre":"Thriller/Suspense","Total_Director_Profit":1823713110},{"Director":"Christopher Nolan","Genre_Profit":34665950,"Major Genre":"Drama","Total_Director_Profit":1823713110},{"Director":"M. Night Shyamalan","Genre_Profit":1437143744,"Major Genre":"Thriller/Suspense","Total_Director_Profit":1575120870},{"Director":"M. Night Shyamalan","Genre_Profit":140191957,"Major Genre":"Adventure","Total_Director_Profit":1575120870},{"Director":"M. Night Shyamalan","Genre_Profit":-2214831,"Major Genre":"Drama","Total_Director_Profit":1575120870},{"Director":"David Yates","Genre_Profit":1475968769,"Major Genre":"Adventure","Total_Director_Profit":1475968769},{"Director":"John Lasseter","Genre_Profit":1045025216,"Major Genre":"Adventure","Total_Director_Profit":1436948978},{"Director":"John Lasseter","Genre_Profit":391923762,"Major Genre":"Comedy","Total_Director_Profit":1436948978},{"Director":"Carlos Saldanha","Genre_Profit":1373585223,"Major Genre":"Adventure","Total_Director_Profit":1373585223},{"Director":"Andy Wachowski","Genre_Profit":1294511080,"Major Genre":"Action","Total_Director_Profit":1296311080},{"Director":"Andy Wachowski","Genre_Profit":1800000,"Major Genre":"Thriller/Suspense","Total_Director_Profit":1296311080},{"Director":"Ridley Scott","Genre_Profit":534208088,"Major Genre":"Action","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Genre_Profit":457730910,"Major Genre":"Horror","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Genre_Profit":170931086,"Major Genre":"Drama","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Genre_Profit":64129825,"Major Genre":"Adventure","Total_Director_Profit":1288926405},{"Director":"Ridley Scott","Genre_Profit":61926496,"Major Genre":"Thriller/Suspense","Total_Director_Profit":1288926405}],"metadata":{"Director":{"type":"string","semanticType":"Name"},"Genre_Profit":{"type":"number","semanticType":"Number"},"Major Genre":{"type":"string","semanticType":"String"},"Total_Director_Profit":{"type":"number","semanticType":"Number"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df = df_movies.copy()\n df['Profit'] = df['Worldwide Gross'] - df['Production Budget']\n \n # Remove rows with missing Director, Profit, or Major Genre values\n df = df.dropna(subset=['Director', 'Profit', 'Major Genre'])\n \n # Calculate total profit per director\n director_profit = df.groupby('Director')['Profit'].sum().reset_index()\n director_profit.columns = ['Director', 'Total_Director_Profit']\n \n # Get top 20 directors by total profit\n top_20_directors = director_profit.nlargest(20, 'Total_Director_Profit')['Director'].tolist()\n \n # Filter movies by top 20 directors\n df_filtered = df[df['Director'].isin(top_20_directors)].copy()\n \n # Aggregate profit by Director and Major Genre\n genre_breakdown = df_filtered.groupby(['Director', 'Major Genre'])['Profit'].sum().reset_index()\n genre_breakdown.columns = ['Director', 'Major Genre', 'Genre_Profit']\n \n # Add total director profit to each row\n genre_breakdown = genre_breakdown.merge(director_profit, on='Director', how='left')\n \n # Sort by total director profit (descending) and then by genre profit (descending)\n transformed_df = genre_breakdown.sort_values(['Total_Director_Profit', 'Genre_Profit'], ascending=[False, False])\n \n return transformed_df\n","source":["movies"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 441226247.0, 533345358.0, 600788188.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 976457891.0, 1133027325.0, 1842879955.0\n\t*US DVD Sales -- type: float64, values: nan, 1978111.0, 140689412.0, ..., 100178981.0, 104578578.0, 234119058.0, 320830925.0\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 15000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, nan, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.5, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 245271.0, 261439.0, 326950.0, 417703.0\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[GOAL]\n\nshow top 20 directories based on profit breakdown by their movies","role":"user"},{"content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"Show top 20 directors based on total profit, with a breakdown of profit by their individual movies\",\n \"display_instruction\": \"Show top 20 **directors** by total **profit** with movie breakdown\",\n \"recommendation\": \"Calculate profit (Worldwide Gross - Production Budget) for each movie, then aggregate by director to find top 20 directors by total profit. Display individual movie profits for each director to show the breakdown.\",\n \"output_fields\": [\"Director\", \"Title\", \"Profit\", \"Total_Director_Profit\"],\n \"chart_type\": \"group_bar\",\n \"chart_encodings\": {\n \"x\": \"Director\",\n \"y\": \"Profit\",\n \"color\": \"Title\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df = df_movies.copy()\n df['Profit'] = df['Worldwide Gross'] - df['Production Budget']\n \n # Remove rows with missing Director or Profit values\n df = df.dropna(subset=['Director', 'Profit'])\n \n # Calculate total profit per director\n director_profit = df.groupby('Director')['Profit'].sum().reset_index()\n director_profit.columns = ['Director', 'Total_Director_Profit']\n \n # Get top 20 directors by total profit\n top_20_directors = director_profit.nlargest(20, 'Total_Director_Profit')['Director'].tolist()\n \n # Filter movies by top 20 directors\n df_filtered = df[df['Director'].isin(top_20_directors)].copy()\n \n # Add total director profit to each movie row\n df_filtered = df_filtered.merge(director_profit, on='Director', how='left')\n \n # Select and order columns\n transformed_df = df_filtered[['Director', 'Title', 'Profit', 'Total_Director_Profit']].copy()\n \n # Sort by total director profit (descending) and then by movie profit (descending)\n transformed_df = transformed_df.sort_values(['Total_Director_Profit', 'Profit'], ascending=[False, False])\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n Director Profit Title Total_Director_Profit\n0 Steven Spielberg 860067947 Jurassic Park 7169573056\n1 Steven Spielberg 782410554 ET: The Extra-Terrestrial 7169573056\n2 Steven Spielberg 711686679 The Lost World: Jurassic Park 7169573056\n3 Steven Spielberg 601558145 Indiana Jones and the Kingdom of the Crystal Skull 7169573056\n4 Steven Spielberg 459745532 The War of the Worlds 7169573056\n5 Steven Spielberg 458700000 Jaws 7169573056\n6 Steven Spielberg 426171806 Indiana Jones and the Last Crusade 7169573056\n7 Steven Spielberg 416635085 Saving Private Ryan 7169573056\n8 Steven Spielberg 366800358 Raiders of the Lost Ark 7169573056\n9 Steven Spielberg 317700000 Close Encounters of the Third Kind 7169573056\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"break down by movie genre instead\",\n \"chart_type\": \"Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Director\",\n \"y\": \"Profit\",\n \"color\": \"Title\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Show top 20 directors based on total profit, with a breakdown by the major genre of their movies instead of individual movie titles. This will show how each director's profit is distributed across different genres.\",\n \"display_instruction\": \"Show top 20 **directors** by total **profit** with **genre** breakdown\",\n \"output_fields\": [\"Director\", \"Major Genre\", \"Genre_Profit\", \"Total_Director_Profit\"],\n \"chart_encodings\": {\n \"x\": \"Director\",\n \"y\": \"Genre_Profit\",\n \"color\": \"Major Genre\"\n },\n \"reason\": \"The user wants to change the breakdown from individual movies to movie genres. Instead of showing profit by Title, we now aggregate profit by Major Genre for each director. The chart_encodings are updated to use 'Major Genre' for color instead of 'Title', and y-axis now shows 'Genre_Profit' (aggregated by genre) instead of individual movie profit.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df = df_movies.copy()\n df['Profit'] = df['Worldwide Gross'] - df['Production Budget']\n \n # Remove rows with missing Director, Profit, or Major Genre values\n df = df.dropna(subset=['Director', 'Profit', 'Major Genre'])\n \n # Calculate total profit per director\n director_profit = df.groupby('Director')['Profit'].sum().reset_index()\n director_profit.columns = ['Director', 'Total_Director_Profit']\n \n # Get top 20 directors by total profit\n top_20_directors = director_profit.nlargest(20, 'Total_Director_Profit')['Director'].tolist()\n \n # Filter movies by top 20 directors\n df_filtered = df[df['Director'].isin(top_20_directors)].copy()\n \n # Aggregate profit by Director and Major Genre\n genre_breakdown = df_filtered.groupby(['Director', 'Major Genre'])['Profit'].sum().reset_index()\n genre_breakdown.columns = ['Director', 'Major Genre', 'Genre_Profit']\n \n # Add total director profit to each row\n genre_breakdown = genre_breakdown.merge(director_profit, on='Director', how='left')\n \n # Sort by total director profit (descending) and then by genre profit (descending)\n transformed_df = genre_breakdown.sort_values(['Total_Director_Profit', 'Genre_Profit'], ascending=[False, False])\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"table-770727","sourceTableIds":["movies"],"instruction":"break down by movie genre instead","displayInstruction":"Show top 20 **directors** by total **profit** with **genre** breakdown","chart":{"id":"chart-1760743802354","chartType":"Bar Chart","encodingMap":{"x":{"fieldID":"original--movies--Director"},"y":{"fieldID":"concept-Profit-1760742653681"},"color":{"fieldID":"original--movies--Title"},"opacity":{"channel":"opacity","bin":false},"column":{"channel":"column","bin":false},"row":{"channel":"row","bin":false}},"tableRef":"table-770727","saved":false,"source":"trigger","unread":false},"resultTableId":"table-89"},"explanation":{"agent":"CodeExplanationAgent","code":"- Calculate **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n- Remove movies with missing **Director**, **Profit**, or **Major Genre** information\n- Aggregate the **Profit** for each **Director** to calculate their **Total_Director_Profit**\n- Identify the **top 20 directors** based on their **Total_Director_Profit**\n- Filter the dataset to include only movies made by these **top 20 directors**\n- Group the filtered movies by **Director** and **Major Genre**, summing the **Profit** for each combination to create **Genre_Profit**\n- Merge the **Total_Director_Profit** back into the genre breakdown data\n- Sort the results first by **Total_Director_Profit** (highest to lowest), then by **Genre_Profit** (highest to lowest) within each director","concepts":[{"explanation":"The financial return of a movie, calculated as \\( \\text{Profit} = \\text{Worldwide\\_Gross} - \\text{Production\\_Budget} \\). This represents the net revenue after accounting for production costs.","field":"Profit"},{"explanation":"The cumulative profit generated by all movies directed by a specific director. This metric aggregates the Profit across all films in their portfolio to measure overall financial success.","field":"Total_Director_Profit"},{"explanation":"The total profit generated by a director within a specific Major Genre. This breaks down a director's Total_Director_Profit by genre category, showing which genres contribute most to their financial success.","field":"Genre_Profit"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 403706375.0, 441226247.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., nan, 1133027325.0, 1842879955.0, 2767891499.0\n\t*US DVD Sales -- type: float64, values: 7166365.0, 33619971.0, 140689412.0, ..., nan, nan, 46260220.0, nan\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 15000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.5, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., nan, nan, 212985.0, 411088.0\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df = df_movies.copy()\n df['Profit'] = df['Worldwide Gross'] - df['Production Budget']\n \n # Remove rows with missing Director, Profit, or Major Genre values\n df = df.dropna(subset=['Director', 'Profit', 'Major Genre'])\n \n # Calculate total profit per director\n director_profit = df.groupby('Director')['Profit'].sum().reset_index()\n director_profit.columns = ['Director', 'Total_Director_Profit']\n \n # Get top 20 directors by total profit\n top_20_directors = director_profit.nlargest(20, 'Total_Director_Profit')['Director'].tolist()\n \n # Filter movies by top 20 directors\n df_filtered = df[df['Director'].isin(top_20_directors)].copy()\n \n # Aggregate profit by Director and Major Genre\n genre_breakdown = df_filtered.groupby(['Director', 'Major Genre'])['Profit'].sum().reset_index()\n genre_breakdown.columns = ['Director', 'Major Genre', 'Genre_Profit']\n \n # Add total director profit to each row\n genre_breakdown = genre_breakdown.merge(director_profit, on='Director', how='left')\n \n # Sort by total director profit (descending) and then by genre profit (descending)\n transformed_df = genre_breakdown.sort_values(['Total_Director_Profit', 'Genre_Profit'], ascending=[False, False])\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\n- Calculate **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n- Remove movies with missing **Director**, **Profit**, or **Major Genre** information\n- Aggregate the **Profit** for each **Director** to calculate their **Total_Director_Profit**\n- Identify the **top 20 directors** based on their **Total_Director_Profit**\n- Filter the dataset to include only movies made by these **top 20 directors**\n- Group the filtered movies by **Director** and **Major Genre**, summing the **Profit** for each combination to create **Genre_Profit**\n- Merge the **Total_Director_Profit** back into the genre breakdown data\n- Sort the results first by **Total_Director_Profit** (highest to lowest), then by **Genre_Profit** (highest to lowest) within each director\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"Profit\",\n \"explanation\": \"The financial return of a movie, calculated as \\\\( \\\\text{Profit} = \\\\text{Worldwide\\\\_Gross} - \\\\text{Production\\\\_Budget} \\\\). This represents the net revenue after accounting for production costs.\"\n },\n {\n \"field\": \"Total_Director_Profit\",\n \"explanation\": \"The cumulative profit generated by all movies directed by a specific director. This metric aggregates the Profit across all films in their portfolio to measure overall financial success.\"\n },\n {\n \"field\": \"Genre_Profit\",\n \"explanation\": \"The total profit generated by a director within a specific Major Genre. This breaks down a director's Total_Director_Profit by genre category, showing which genres contribute most to their financial success.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-456490","displayId":"movie-ratings","names":["IMDB Rating","Production Budget","Rotten Tomatoes Rating","Title"],"rows":[{"IMDB Rating":6.1,"Production Budget":8000000,"Rotten Tomatoes Rating":null,"Title":"The Land Girls"},{"IMDB Rating":6.9,"Production Budget":300000,"Rotten Tomatoes Rating":null,"Title":"First Love, Last Rites"},{"IMDB Rating":6.8,"Production Budget":250000,"Rotten Tomatoes Rating":null,"Title":"I Married a Strange Person"},{"IMDB Rating":null,"Production Budget":300000,"Rotten Tomatoes Rating":13,"Title":"Let's Talk About Sex"},{"IMDB Rating":3.4,"Production Budget":1000000,"Rotten Tomatoes Rating":62,"Title":"Slam"},{"IMDB Rating":7.7,"Production Budget":6000,"Rotten Tomatoes Rating":null,"Title":"Following"},{"IMDB Rating":3.8,"Production Budget":1600000,"Rotten Tomatoes Rating":null,"Title":"Foolish"},{"IMDB Rating":5.8,"Production Budget":40000000,"Rotten Tomatoes Rating":25,"Title":"Pirates"},{"IMDB Rating":7,"Production Budget":6000000,"Rotten Tomatoes Rating":86,"Title":"Duel in the Sun"},{"IMDB Rating":7,"Production Budget":1000000,"Rotten Tomatoes Rating":81,"Title":"Tom Jones"},{"IMDB Rating":7.5,"Production Budget":10000000,"Rotten Tomatoes Rating":84,"Title":"Oliver!"},{"IMDB Rating":8.4,"Production Budget":2000000,"Rotten Tomatoes Rating":97,"Title":"To Kill A Mockingbird"},{"IMDB Rating":6.8,"Production Budget":100000,"Rotten Tomatoes Rating":87,"Title":"Hollywood Shuffle"},{"IMDB Rating":7,"Production Budget":5200000,"Rotten Tomatoes Rating":null,"Title":"Wilson"},{"IMDB Rating":6.1,"Production Budget":22000000,"Rotten Tomatoes Rating":null,"Title":"Darling Lili"},{"IMDB Rating":2.5,"Production Budget":13500000,"Rotten Tomatoes Rating":90,"Title":"The Ten Commandments"},{"IMDB Rating":8.9,"Production Budget":340000,"Rotten Tomatoes Rating":null,"Title":"12 Angry Men"},{"IMDB Rating":8.1,"Production Budget":29000000,"Rotten Tomatoes Rating":null,"Title":"Twelve Monkeys"},{"IMDB Rating":7,"Production Budget":4000000,"Rotten Tomatoes Rating":57,"Title":1776},{"IMDB Rating":5.6,"Production Budget":32000000,"Rotten Tomatoes Rating":33,"Title":1941},{"IMDB Rating":6.3,"Production Budget":1900000,"Rotten Tomatoes Rating":null,"Title":"Chacun sa nuit"},{"IMDB Rating":8.4,"Production Budget":10500000,"Rotten Tomatoes Rating":96,"Title":"2001: A Space Odyssey"},{"IMDB Rating":null,"Production Budget":5000000,"Rotten Tomatoes Rating":92,"Title":"20,000 Leagues Under the Sea"},{"IMDB Rating":6.9,"Production Budget":2000000,"Rotten Tomatoes Rating":null,"Title":"24 7: Twenty Four Seven"},{"IMDB Rating":7.1,"Production Budget":500000,"Rotten Tomatoes Rating":77,"Title":"Twin Falls Idaho"},{"IMDB Rating":5.7,"Production 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Can Count on Me"},{"IMDB Rating":5,"Production Budget":60000000,"Rotten Tomatoes Rating":14,"Title":"Year One"},{"IMDB Rating":7,"Production Budget":50000000,"Rotten Tomatoes Rating":43,"Title":"Yes Man"},{"IMDB Rating":6.7,"Production Budget":4000000,"Rotten Tomatoes Rating":78,"Title":"You Kill Me"},{"IMDB Rating":7.6,"Production Budget":45000000,"Rotten Tomatoes Rating":null,"Title":"Yours, Mine and Ours"},{"IMDB Rating":6.2,"Production Budget":65000000,"Rotten Tomatoes Rating":68,"Title":"You've Got Mail"},{"IMDB Rating":6.7,"Production Budget":18000000,"Rotten Tomatoes Rating":null,"Title":"Youth in Revolt"},{"IMDB Rating":4.5,"Production Budget":800000,"Rotten Tomatoes Rating":null,"Title":"The Young Unknowns"},{"IMDB Rating":7.2,"Production Budget":35000000,"Rotten Tomatoes Rating":76,"Title":"The Young Victoria"},{"IMDB Rating":null,"Production Budget":65000000,"Rotten Tomatoes Rating":75,"Title":"Zathura"},{"IMDB Rating":6.8,"Production Budget":5000000,"Rotten Tomatoes Rating":66,"Title":"Zero Effect"},{"IMDB Rating":6.4,"Production Budget":28000000,"Rotten Tomatoes Rating":62,"Title":"Zoolander"},{"IMDB Rating":7.8,"Production Budget":23600000,"Rotten Tomatoes Rating":89,"Title":"Zombieland"},{"IMDB Rating":7,"Production Budget":24000000,"Rotten Tomatoes Rating":65,"Title":"Zack and Miri Make a Porno"},{"IMDB Rating":null,"Production Budget":85000000,"Rotten Tomatoes Rating":89,"Title":"Zodiac"},{"IMDB Rating":3.4,"Production Budget":35000000,"Rotten Tomatoes Rating":3,"Title":"Zoom"},{"IMDB Rating":5.7,"Production Budget":80000000,"Rotten Tomatoes Rating":26,"Title":"The Legend of Zorro"},{"IMDB Rating":6.7,"Production Budget":65000000,"Rotten Tomatoes Rating":82,"Title":"The Mask of Zorro"}],"metadata":{"IMDB Rating":{"type":"number","semanticType":"Number"},"Production Budget":{"type":"number","semanticType":"Number"},"Rotten Tomatoes Rating":{"type":"number","semanticType":"Number"},"Title":{"type":"string","semanticType":"Name"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values in key fields\n transformed_df = df_movies[['Title', 'Production Budget', 'Rotten Tomatoes Rating', 'IMDB Rating']].copy()\n \n # Remove rows where Production Budget is missing\n transformed_df = transformed_df[transformed_df['Production Budget'].notna()]\n \n # Keep rows where at least one rating is available\n transformed_df = transformed_df[\n (transformed_df['Rotten Tomatoes Rating'].notna()) | \n (transformed_df['IMDB Rating'].notna())\n ]\n \n return transformed_df\n","source":["movies"],"dialog":[{"content":"You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 403706375.0, 441226247.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 976457891.0, 1133027325.0, 1842879955.0\n\t*US DVD Sales -- type: float64, values: 15442818.0, 17031573.0, 33619971.0, ..., nan, nan, 46260220.0, nan\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 5000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.0, 2.4, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 212985.0, 237477.0, 417703.0, nan\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[GOAL]\n\nDo higher production budgets correlate with better ratings?","role":"user"},{"content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"The user wants to analyze the correlation between production budgets and movie ratings to understand if higher budgets lead to better ratings.\",\n \"display_instruction\": \"Show correlation between **Production Budget** and **ratings**\",\n \"recommendation\": \"Create a scatter plot visualization to show the relationship between Production Budget and ratings. Since there are two rating systems (Rotten Tomatoes and IMDB), we'll analyze both and include them as separate dimensions in the output data.\",\n \"output_fields\": [\"Title\", \"Production Budget\", \"Rotten Tomatoes Rating\", \"IMDB Rating\"],\n \"chart_type\": \"point\",\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Rotten Tomatoes Rating\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values in key fields\n transformed_df = df_movies[['Title', 'Production Budget', 'Rotten Tomatoes Rating', 'IMDB Rating']].copy()\n \n # Remove rows where Production Budget is missing\n transformed_df = transformed_df[transformed_df['Production Budget'].notna()]\n \n # Keep rows where at least one rating is available\n transformed_df = transformed_df[\n (transformed_df['Rotten Tomatoes Rating'].notna()) | \n (transformed_df['IMDB Rating'].notna())\n ]\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"movies","sourceTableIds":["movies"],"instruction":"Do higher production budgets correlate with better ratings?","displayInstruction":"Show correlation between **Production Budget** and **ratings**","chart":{"id":"chart-1760744462348","chartType":"Auto","encodingMap":{},"tableRef":"movies","saved":false,"source":"trigger","unread":true},"resultTableId":"table-456490"},"explanation":{"agent":"CodeExplanationAgent","code":"- Select key fields from the movies dataset: **Title**, **Production Budget**, **Rotten Tomatoes Rating**, and **IMDB Rating**\n- Filter out movies with missing **Production Budget** values to ensure all movies have known budget information\n- Retain only movies that have at least one rating available (either **Rotten Tomatoes Rating** or **IMDB Rating**), ensuring each movie has some form of critical assessment\n- Return a cleaned dataset containing only movies with complete budget information and at least one rating metric","concepts":[],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 403706375.0, 441226247.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., nan, 1133027325.0, 1842879955.0, 2767891499.0\n\t*US DVD Sales -- type: float64, values: nan, 853973.0, 13180936.0, ..., nan, 46260220.0, 124058348.0, nan\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 23000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.0, 2.5, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 212985.0, 229156.0, 237477.0, 417703.0\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values in key fields\n transformed_df = df_movies[['Title', 'Production Budget', 'Rotten Tomatoes Rating', 'IMDB Rating']].copy()\n \n # Remove rows where Production Budget is missing\n transformed_df = transformed_df[transformed_df['Production Budget'].notna()]\n \n # Keep rows where at least one rating is available\n transformed_df = transformed_df[\n (transformed_df['Rotten Tomatoes Rating'].notna()) | \n (transformed_df['IMDB Rating'].notna())\n ]\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\n- Select key fields from the movies dataset: **Title**, **Production Budget**, **Rotten Tomatoes Rating**, and **IMDB Rating**\n- Filter out movies with missing **Production Budget** values to ensure all movies have known budget information\n- Retain only movies that have at least one rating available (either **Rotten Tomatoes Rating** or **IMDB Rating**), ensuring each movie has some form of critical assessment\n- Return a cleaned dataset containing only movies with complete budget information and at least one rating metric\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-37","displayId":"movie-budget-ratings","names":["Number of Movies","Production Budget Bin","Rotten Tomatoes Rating Bin"],"rows":[{"Number of Movies":6,"Production Budget Bin":"<$1M","Rotten Tomatoes Rating Bin":"0-20"},{"Number of Movies":9,"Production Budget Bin":"<$1M","Rotten Tomatoes Rating Bin":"21-40"},{"Number of Movies":12,"Production Budget Bin":"<$1M","Rotten Tomatoes Rating Bin":"41-60"},{"Number of Movies":19,"Production Budget Bin":"<$1M","Rotten Tomatoes Rating Bin":"61-80"},{"Number of Movies":54,"Production Budget Bin":"<$1M","Rotten Tomatoes Rating Bin":"81-100"},{"Number of Movies":61,"Production Budget Bin":"$1M-$10M","Rotten Tomatoes Rating Bin":"0-20"},{"Number of Movies":72,"Production Budget Bin":"$1M-$10M","Rotten Tomatoes Rating Bin":"21-40"},{"Number of Movies":58,"Production Budget Bin":"$1M-$10M","Rotten Tomatoes Rating Bin":"41-60"},{"Number of Movies":104,"Production Budget Bin":"$1M-$10M","Rotten Tomatoes Rating Bin":"61-80"},{"Number of Movies":219,"Production Budget Bin":"$1M-$10M","Rotten Tomatoes Rating Bin":"81-100"},{"Number of Movies":229,"Production Budget Bin":"$10M-$50M","Rotten Tomatoes Rating Bin":"0-20"},{"Number of Movies":257,"Production Budget Bin":"$10M-$50M","Rotten Tomatoes Rating Bin":"21-40"},{"Number of Movies":241,"Production Budget Bin":"$10M-$50M","Rotten Tomatoes Rating Bin":"41-60"},{"Number of Movies":245,"Production Budget Bin":"$10M-$50M","Rotten Tomatoes Rating Bin":"61-80"},{"Number of Movies":210,"Production Budget Bin":"$10M-$50M","Rotten Tomatoes Rating Bin":"81-100"},{"Number of Movies":62,"Production Budget Bin":"$50M-$100M","Rotten Tomatoes Rating Bin":"0-20"},{"Number of Movies":99,"Production Budget Bin":"$50M-$100M","Rotten Tomatoes Rating Bin":"21-40"},{"Number of Movies":98,"Production Budget Bin":"$50M-$100M","Rotten Tomatoes Rating Bin":"41-60"},{"Number of Movies":95,"Production Budget Bin":"$50M-$100M","Rotten Tomatoes Rating Bin":"61-80"},{"Number of Movies":54,"Production Budget Bin":"$50M-$100M","Rotten Tomatoes Rating Bin":"81-100"},{"Number of Movies":9,"Production Budget Bin":">$100M","Rotten Tomatoes Rating Bin":"0-20"},{"Number of Movies":24,"Production Budget Bin":">$100M","Rotten Tomatoes Rating Bin":"21-40"},{"Number of Movies":26,"Production Budget Bin":">$100M","Rotten Tomatoes Rating Bin":"41-60"},{"Number of Movies":26,"Production Budget Bin":">$100M","Rotten Tomatoes Rating Bin":"61-80"},{"Number of Movies":31,"Production Budget Bin":">$100M","Rotten Tomatoes Rating Bin":"81-100"}],"metadata":{"Number of Movies":{"type":"number","semanticType":"Number"},"Production Budget Bin":{"type":"string","semanticType":"Range","levels":["<$1M","$1M-$10M","$10M-$50M","$50M-$100M",">$100M"]},"Rotten Tomatoes Rating Bin":{"type":"string","semanticType":"Range","levels":["0-20","21-40","41-60","61-80","81-100"]}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values\n df_filtered = df_movies[['Production Budget', 'Rotten Tomatoes Rating']].copy()\n df_filtered = df_filtered[\n (df_filtered['Production Budget'].notna()) & \n (df_filtered['Rotten Tomatoes Rating'].notna())\n ]\n \n # Create budget bins (in millions for readability)\n budget_bins = [0, 1000000, 10000000, 50000000, 100000000, float('inf')]\n budget_labels = ['<$1M', '$1M-$10M', '$10M-$50M', '$50M-$100M', '>$100M']\n df_filtered['Production Budget Bin'] = pd.cut(\n df_filtered['Production Budget'], \n bins=budget_bins, \n labels=budget_labels,\n include_lowest=True\n )\n \n # Create rating bins\n rating_bins = [0, 20, 40, 60, 80, 100]\n rating_labels = ['0-20', '21-40', '41-60', '61-80', '81-100']\n df_filtered['Rotten Tomatoes Rating Bin'] = pd.cut(\n df_filtered['Rotten Tomatoes Rating'], \n bins=rating_bins, \n labels=rating_labels,\n include_lowest=True\n )\n \n # Count movies in each bin combination\n transformed_df = df_filtered.groupby(\n ['Production Budget Bin', 'Rotten Tomatoes Rating Bin'], \n observed=True\n ).size().reset_index(name='Number of Movies')\n \n # Convert categorical to string for better display\n transformed_df['Production Budget Bin'] = transformed_df['Production Budget Bin'].astype(str)\n transformed_df['Rotten Tomatoes Rating Bin'] = transformed_df['Rotten Tomatoes Rating Bin'].astype(str)\n \n return transformed_df\n","source":["movies"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 403706375.0, 441226247.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 976457891.0, 1133027325.0, 1842879955.0\n\t*US DVD Sales -- type: float64, values: 15442818.0, 17031573.0, 33619971.0, ..., nan, nan, 46260220.0, nan\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 5000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.0, 2.4, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 212985.0, 237477.0, 417703.0, nan\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[GOAL]\n\nDo higher production budgets correlate with better ratings?","role":"user"},{"content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"The user wants to analyze the correlation between production budgets and movie ratings to understand if higher budgets lead to better ratings.\",\n \"display_instruction\": \"Show correlation between **Production Budget** and **ratings**\",\n \"recommendation\": \"Create a scatter plot visualization to show the relationship between Production Budget and ratings. Since there are two rating systems (Rotten Tomatoes and IMDB), we'll analyze both and include them as separate dimensions in the output data.\",\n \"output_fields\": [\"Title\", \"Production Budget\", \"Rotten Tomatoes Rating\", \"IMDB Rating\"],\n \"chart_type\": \"point\",\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Rotten Tomatoes Rating\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values in key fields\n transformed_df = df_movies[['Title', 'Production Budget', 'Rotten Tomatoes Rating', 'IMDB Rating']].copy()\n \n # Remove rows where Production Budget is missing\n transformed_df = transformed_df[transformed_df['Production Budget'].notna()]\n \n # Keep rows where at least one rating is available\n transformed_df = transformed_df[\n (transformed_df['Rotten Tomatoes Rating'].notna()) | \n (transformed_df['IMDB Rating'].notna())\n ]\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n IMDB Rating Production Budget Rotten Tomatoes Rating Title\n0 6.1 8000000 NaN The Land Girls\n1 6.9 300000 NaN First Love, Last Rites\n2 6.8 250000 NaN I Married a Strange Person\n3 NaN 300000 13.0 Let's Talk About Sex\n4 3.4 1000000 62.0 Slam\n5 7.7 6000 NaN Following\n6 3.8 1600000 NaN Foolish\n7 5.8 40000000 25.0 Pirates\n8 7.0 6000000 86.0 Duel in the Sun\n9 7.0 1000000 81.0 Tom Jones\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"bin rt rating and budget into bins to show number of movies in each range\",\n \"chart_type\": \"Scatter Plot\",\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Rotten Tomatoes Rating\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Bin the Production Budget and Rotten Tomatoes Rating into categorical ranges, then count the number of movies that fall into each combination of budget and rating bins to create a heatmap-style aggregation.\",\n \"display_instruction\": \"Show **number of movies** in each **Production Budget** and **Rotten Tomatoes Rating** bin\",\n \"output_fields\": [\"Production Budget Bin\", \"Rotten Tomatoes Rating Bin\", \"Number of Movies\"],\n \"chart_encodings\": {\n \"x\": \"Production Budget Bin\",\n \"y\": \"Rotten Tomatoes Rating Bin\",\n \"color\": \"Number of Movies\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n },\n \"reason\": \"The user wants to bin both Production Budget and Rotten Tomatoes Rating into ranges and count movies in each combination. This requires creating categorical bins for both dimensions and aggregating counts. A heatmap visualization (using color encoding for count) would be more appropriate than a scatter plot for showing binned data.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values\n df_filtered = df_movies[['Production Budget', 'Rotten Tomatoes Rating']].copy()\n df_filtered = df_filtered[\n (df_filtered['Production Budget'].notna()) & \n (df_filtered['Rotten Tomatoes Rating'].notna())\n ]\n \n # Create budget bins (in millions for readability)\n budget_bins = [0, 1000000, 10000000, 50000000, 100000000, float('inf')]\n budget_labels = ['<$1M', '$1M-$10M', '$10M-$50M', '$50M-$100M', '>$100M']\n df_filtered['Production Budget Bin'] = pd.cut(\n df_filtered['Production Budget'], \n bins=budget_bins, \n labels=budget_labels,\n include_lowest=True\n )\n \n # Create rating bins\n rating_bins = [0, 20, 40, 60, 80, 100]\n rating_labels = ['0-20', '21-40', '41-60', '61-80', '81-100']\n df_filtered['Rotten Tomatoes Rating Bin'] = pd.cut(\n df_filtered['Rotten Tomatoes Rating'], \n bins=rating_bins, \n labels=rating_labels,\n include_lowest=True\n )\n \n # Count movies in each bin combination\n transformed_df = df_filtered.groupby(\n ['Production Budget Bin', 'Rotten Tomatoes Rating Bin'], \n observed=True\n ).size().reset_index(name='Number of Movies')\n \n # Convert categorical to string for better display\n transformed_df['Production Budget Bin'] = transformed_df['Production Budget Bin'].astype(str)\n transformed_df['Rotten Tomatoes Rating Bin'] = transformed_df['Rotten Tomatoes Rating Bin'].astype(str)\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"table-456490","sourceTableIds":["movies"],"instruction":"bin rt rating and budget into bins to show number of movies in each range","displayInstruction":"Show **number of movies** in each **Production Budget** and **Rotten Tomatoes Rating** bin","chart":{"id":"chart-1760744514854","chartType":"Scatter Plot","encodingMap":{"x":{"fieldID":"original--movies--Production Budget"},"y":{"fieldID":"original--movies--Rotten Tomatoes Rating"},"color":{"channel":"color","bin":false},"size":{"channel":"size","bin":false},"opacity":{"channel":"opacity","bin":false},"column":{"channel":"column","bin":false},"row":{"channel":"row","bin":false}},"tableRef":"table-456490","saved":false,"source":"trigger","unread":false},"resultTableId":"table-37"},"explanation":{"agent":"CodeExplanationAgent","code":"- Filter the dataset to include only movies with both **Production Budget** and **Rotten Tomatoes Rating** values available\n- Create **budget categories** by grouping Production Budget into five bins: **<$1M**, **$1M-$10M**, **$10M-$50M**, **$50M-$100M**, and **>$100M**\n- Create **rating categories** by grouping Rotten Tomatoes Rating into five bins: **0-20**, **21-40**, **41-60**, **61-80**, and **81-100**\n- **Count** the number of movies that fall into each combination of budget category and rating category\n- Return a summary table showing the distribution of movies across budget and rating bins","concepts":[{"explanation":"Categorical bins created through discretization of continuous variables. Production Budget is divided into 5 ranges based on million-dollar thresholds, while Rotten Tomatoes Rating is divided into 5 equal-width intervals of 20 points each. This binning approach enables analysis of the relationship between production investment levels and critical reception patterns.","field":"Production Budget Bin, Rotten Tomatoes Rating Bin"},{"explanation":"This analysis employs a **two-dimensional frequency distribution** (contingency table approach) to explore the relationship between production budget and critical reception. The transformation creates a discrete joint distribution by binning two continuous variables and counting occurrences in each cell. This approach is useful for identifying patterns such as whether higher-budget films tend to receive better or worse critical ratings. **Alternative modeling approaches** could include: (1) correlation analysis between raw budget and rating values, (2) regression modeling to predict ratings from budget while controlling for other factors, (3) chi-square tests of independence to formally test whether budget and rating categories are associated, or (4) visualization through heatmaps to reveal concentration patterns across the budget-rating space.","field":"Statistical Analysis"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 402111870.0, 441226247.0, 600788188.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 1133027325.0, 1842879955.0, 2767891499.0\n\t*US DVD Sales -- type: float64, values: 618454.0, 853973.0, 1877732.0, ..., nan, nan, 46260220.0, nan\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 5000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, nan, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: nan, 1.5, 2.5, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 57313.0, nan, 73720.0, 212985.0\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values\n df_filtered = df_movies[['Production Budget', 'Rotten Tomatoes Rating']].copy()\n df_filtered = df_filtered[\n (df_filtered['Production Budget'].notna()) & \n (df_filtered['Rotten Tomatoes Rating'].notna())\n ]\n \n # Create budget bins (in millions for readability)\n budget_bins = [0, 1000000, 10000000, 50000000, 100000000, float('inf')]\n budget_labels = ['<$1M', '$1M-$10M', '$10M-$50M', '$50M-$100M', '>$100M']\n df_filtered['Production Budget Bin'] = pd.cut(\n df_filtered['Production Budget'], \n bins=budget_bins, \n labels=budget_labels,\n include_lowest=True\n )\n \n # Create rating bins\n rating_bins = [0, 20, 40, 60, 80, 100]\n rating_labels = ['0-20', '21-40', '41-60', '61-80', '81-100']\n df_filtered['Rotten Tomatoes Rating Bin'] = pd.cut(\n df_filtered['Rotten Tomatoes Rating'], \n bins=rating_bins, \n labels=rating_labels,\n include_lowest=True\n )\n \n # Count movies in each bin combination\n transformed_df = df_filtered.groupby(\n ['Production Budget Bin', 'Rotten Tomatoes Rating Bin'], \n observed=True\n ).size().reset_index(name='Number of Movies')\n \n # Convert categorical to string for better display\n transformed_df['Production Budget Bin'] = transformed_df['Production Budget Bin'].astype(str)\n transformed_df['Rotten Tomatoes Rating Bin'] = transformed_df['Rotten Tomatoes Rating Bin'].astype(str)\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\n- Filter the dataset to include only movies with both **Production Budget** and **Rotten Tomatoes Rating** values available\n- Create **budget categories** by grouping Production Budget into five bins: **<$1M**, **$1M-$10M**, **$10M-$50M**, **$50M-$100M**, and **>$100M**\n- Create **rating categories** by grouping Rotten Tomatoes Rating into five bins: **0-20**, **21-40**, **41-60**, **61-80**, and **81-100**\n- **Count** the number of movies that fall into each combination of budget category and rating category\n- Return a summary table showing the distribution of movies across budget and rating bins\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"Production Budget Bin, Rotten Tomatoes Rating Bin\",\n \"explanation\": \"Categorical bins created through discretization of continuous variables. Production Budget is divided into 5 ranges based on million-dollar thresholds, while Rotten Tomatoes Rating is divided into 5 equal-width intervals of 20 points each. This binning approach enables analysis of the relationship between production investment levels and critical reception patterns.\"\n },\n {\n \"field\": \"Statistical Analysis\",\n \"explanation\": \"This analysis employs a **two-dimensional frequency distribution** (contingency table approach) to explore the relationship between production budget and critical reception. The transformation creates a discrete joint distribution by binning two continuous variables and counting occurrences in each cell. This approach is useful for identifying patterns such as whether higher-budget films tend to receive better or worse critical ratings. **Alternative modeling approaches** could include: (1) correlation analysis between raw budget and rating values, (2) regression modeling to predict ratings from budget while controlling for other factors, (3) chi-square tests of independence to formally test whether budget and rating categories are associated, or (4) visualization through heatmaps to reveal concentration patterns across the budget-rating space.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-54","displayId":"film-profit-analysis","names":["Average Profit","Production Budget Bin","Rotten Tomatoes Rating Bin"],"rows":[{"Average Profit":1265816.3333333333,"Production Budget Bin":"<$1M","Rotten Tomatoes Rating Bin":"0-20"},{"Average Profit":6494134,"Production Budget Bin":"<$1M","Rotten Tomatoes Rating Bin":"21-40"},{"Average Profit":7866065.833333333,"Production Budget Bin":"<$1M","Rotten Tomatoes Rating Bin":"41-60"},{"Average Profit":6749073.2105263155,"Production Budget Bin":"<$1M","Rotten Tomatoes Rating Bin":"61-80"},{"Average Profit":20121581.403846152,"Production Budget Bin":"<$1M","Rotten Tomatoes Rating Bin":"81-100"},{"Average Profit":9285520.68852459,"Production Budget Bin":"$1M-$10M","Rotten Tomatoes Rating Bin":"0-20"},{"Average Profit":17386269.305555556,"Production Budget Bin":"$1M-$10M","Rotten Tomatoes Rating Bin":"21-40"},{"Average Profit":13840988.827586208,"Production Budget Bin":"$1M-$10M","Rotten Tomatoes Rating Bin":"41-60"},{"Average Profit":20654143.125,"Production Budget Bin":"$1M-$10M","Rotten Tomatoes Rating Bin":"61-80"},{"Average Profit":37024663.62962963,"Production Budget Bin":"$1M-$10M","Rotten Tomatoes Rating Bin":"81-100"},{"Average Profit":11168889.11790393,"Production Budget Bin":"$10M-$50M","Rotten Tomatoes Rating Bin":"0-20"},{"Average Profit":32529317.311284047,"Production Budget Bin":"$10M-$50M","Rotten Tomatoes Rating Bin":"21-40"},{"Average Profit":36640084.60165975,"Production Budget Bin":"$10M-$50M","Rotten Tomatoes Rating Bin":"41-60"},{"Average Profit":49506279.571428575,"Production Budget Bin":"$10M-$50M","Rotten Tomatoes Rating Bin":"61-80"},{"Average Profit":93010923.90476191,"Production Budget Bin":"$10M-$50M","Rotten Tomatoes Rating Bin":"81-100"},{"Average Profit":11584648.806451613,"Production Budget Bin":"$50M-$100M","Rotten Tomatoes Rating Bin":"0-20"},{"Average Profit":59680849.09090909,"Production Budget Bin":"$50M-$100M","Rotten Tomatoes Rating Bin":"21-40"},{"Average Profit":104106140.86734694,"Production Budget Bin":"$50M-$100M","Rotten Tomatoes Rating Bin":"41-60"},{"Average Profit":121416300.37894736,"Production Budget Bin":"$50M-$100M","Rotten Tomatoes Rating Bin":"61-80"},{"Average Profit":226983168.4814815,"Production Budget Bin":"$50M-$100M","Rotten Tomatoes Rating Bin":"81-100"},{"Average Profit":132877326,"Production Budget Bin":">$100M","Rotten Tomatoes Rating Bin":"0-20"},{"Average Profit":148619038.95833334,"Production Budget Bin":">$100M","Rotten Tomatoes Rating Bin":"21-40"},{"Average Profit":297717419.34615386,"Production Budget Bin":">$100M","Rotten Tomatoes Rating Bin":"41-60"},{"Average Profit":289246367.34615386,"Production Budget Bin":">$100M","Rotten Tomatoes Rating Bin":"61-80"},{"Average Profit":515989525.483871,"Production Budget Bin":">$100M","Rotten Tomatoes Rating Bin":"81-100"}],"metadata":{"Average Profit":{"type":"number","semanticType":"Number"},"Production Budget Bin":{"type":"string","semanticType":"Range","levels":["<$1M","$1M-$10M","$10M-$50M","$50M-$100M",">$100M"]},"Rotten Tomatoes Rating Bin":{"type":"string","semanticType":"Range","levels":["0-20","21-40","41-60","61-80","81-100"]}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values\n df_filtered = df_movies[['Production Budget', 'Rotten Tomatoes Rating', 'Worldwide Gross']].copy()\n df_filtered = df_filtered[\n (df_filtered['Production Budget'].notna()) & \n (df_filtered['Rotten Tomatoes Rating'].notna()) &\n (df_filtered['Worldwide Gross'].notna())\n ]\n \n # Calculate profit\n df_filtered['Profit'] = df_filtered['Worldwide Gross'] - df_filtered['Production Budget']\n \n # Create budget bins (in millions for readability)\n budget_bins = [0, 1000000, 10000000, 50000000, 100000000, float('inf')]\n budget_labels = ['<$1M', '$1M-$10M', '$10M-$50M', '$50M-$100M', '>$100M']\n df_filtered['Production Budget Bin'] = pd.cut(\n df_filtered['Production Budget'], \n bins=budget_bins, \n labels=budget_labels,\n include_lowest=True\n )\n \n # Create rating bins\n rating_bins = [0, 20, 40, 60, 80, 100]\n rating_labels = ['0-20', '21-40', '41-60', '61-80', '81-100']\n df_filtered['Rotten Tomatoes Rating Bin'] = pd.cut(\n df_filtered['Rotten Tomatoes Rating'], \n bins=rating_bins, \n labels=rating_labels,\n include_lowest=True\n )\n \n # Calculate average profit in each bin combination\n transformed_df = df_filtered.groupby(\n ['Production Budget Bin', 'Rotten Tomatoes Rating Bin'], \n observed=True\n )['Profit'].mean().reset_index(name='Average Profit')\n \n # Convert categorical to string for better display\n transformed_df['Production Budget Bin'] = transformed_df['Production Budget Bin'].astype(str)\n transformed_df['Rotten Tomatoes Rating Bin'] = transformed_df['Rotten Tomatoes Rating Bin'].astype(str)\n \n return transformed_df\n","source":["movies"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 403706375.0, 441226247.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 976457891.0, 1133027325.0, 1842879955.0\n\t*US DVD Sales -- type: float64, values: 15442818.0, 17031573.0, 33619971.0, ..., nan, nan, 46260220.0, nan\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 5000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.0, 2.4, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 212985.0, 237477.0, 417703.0, nan\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[GOAL]\n\nDo higher production budgets correlate with better ratings?","role":"user"},{"content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"The user wants to analyze the correlation between production budgets and movie ratings to understand if higher budgets lead to better ratings.\",\n \"display_instruction\": \"Show correlation between **Production Budget** and **ratings**\",\n \"recommendation\": \"Create a scatter plot visualization to show the relationship between Production Budget and ratings. Since there are two rating systems (Rotten Tomatoes and IMDB), we'll analyze both and include them as separate dimensions in the output data.\",\n \"output_fields\": [\"Title\", \"Production Budget\", \"Rotten Tomatoes Rating\", \"IMDB Rating\"],\n \"chart_type\": \"point\",\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Rotten Tomatoes Rating\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values in key fields\n transformed_df = df_movies[['Title', 'Production Budget', 'Rotten Tomatoes Rating', 'IMDB Rating']].copy()\n \n # Remove rows where Production Budget is missing\n transformed_df = transformed_df[transformed_df['Production Budget'].notna()]\n \n # Keep rows where at least one rating is available\n transformed_df = transformed_df[\n (transformed_df['Rotten Tomatoes Rating'].notna()) | \n (transformed_df['IMDB Rating'].notna())\n ]\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n IMDB Rating Production Budget Rotten Tomatoes Rating Title\n0 6.1 8000000 NaN The Land Girls\n1 6.9 300000 NaN First Love, Last Rites\n2 6.8 250000 NaN I Married a Strange Person\n3 NaN 300000 13.0 Let's Talk About Sex\n4 3.4 1000000 62.0 Slam\n5 7.7 6000 NaN Following\n6 3.8 1600000 NaN Foolish\n7 5.8 40000000 25.0 Pirates\n8 7.0 6000000 86.0 Duel in the Sun\n9 7.0 1000000 81.0 Tom Jones\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"bin rt rating and budget into bins to show number of movies in each range\",\n \"chart_type\": \"Scatter Plot\",\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Rotten Tomatoes Rating\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Bin the Production Budget and Rotten Tomatoes Rating into categorical ranges, then count the number of movies that fall into each combination of budget and rating bins to create a heatmap-style aggregation.\",\n \"display_instruction\": \"Show **number of movies** in each **Production Budget** and **Rotten Tomatoes Rating** bin\",\n \"output_fields\": [\"Production Budget Bin\", \"Rotten Tomatoes Rating Bin\", \"Number of Movies\"],\n \"chart_encodings\": {\n \"x\": \"Production Budget Bin\",\n \"y\": \"Rotten Tomatoes Rating Bin\",\n \"color\": \"Number of Movies\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n },\n \"reason\": \"The user wants to bin both Production Budget and Rotten Tomatoes Rating into ranges and count movies in each combination. This requires creating categorical bins for both dimensions and aggregating counts. A heatmap visualization (using color encoding for count) would be more appropriate than a scatter plot for showing binned data.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values\n df_filtered = df_movies[['Production Budget', 'Rotten Tomatoes Rating']].copy()\n df_filtered = df_filtered[\n (df_filtered['Production Budget'].notna()) & \n (df_filtered['Rotten Tomatoes Rating'].notna())\n ]\n \n # Create budget bins (in millions for readability)\n budget_bins = [0, 1000000, 10000000, 50000000, 100000000, float('inf')]\n budget_labels = ['<$1M', '$1M-$10M', '$10M-$50M', '$50M-$100M', '>$100M']\n df_filtered['Production Budget Bin'] = pd.cut(\n df_filtered['Production Budget'], \n bins=budget_bins, \n labels=budget_labels,\n include_lowest=True\n )\n \n # Create rating bins\n rating_bins = [0, 20, 40, 60, 80, 100]\n rating_labels = ['0-20', '21-40', '41-60', '61-80', '81-100']\n df_filtered['Rotten Tomatoes Rating Bin'] = pd.cut(\n df_filtered['Rotten Tomatoes Rating'], \n bins=rating_bins, \n labels=rating_labels,\n include_lowest=True\n )\n \n # Count movies in each bin combination\n transformed_df = df_filtered.groupby(\n ['Production Budget Bin', 'Rotten Tomatoes Rating Bin'], \n observed=True\n ).size().reset_index(name='Number of Movies')\n \n # Convert categorical to string for better display\n transformed_df['Production Budget Bin'] = transformed_df['Production Budget Bin'].astype(str)\n transformed_df['Rotten Tomatoes Rating Bin'] = transformed_df['Rotten Tomatoes Rating Bin'].astype(str)\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n Number of Movies Production Budget Bin Rotten Tomatoes Rating Bin\n0 6 <$1M 0-20\n1 9 <$1M 21-40\n2 12 <$1M 41-60\n3 19 <$1M 61-80\n4 54 <$1M 81-100\n5 61 $1M-$10M 0-20\n6 72 $1M-$10M 21-40\n7 58 $1M-$10M 41-60\n8 104 $1M-$10M 61-80\n9 219 $1M-$10M 81-100\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show profit relation to budget ratings bin\",\n \"chart_type\": \"Heatmap\",\n \"chart_encodings\": {\n \"x\": \"Rotten Tomatoes Rating Bin\",\n \"y\": \"Production Budget Bin\",\n \"color\": \"Number of Movies\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Calculate profit (Worldwide Gross minus Production Budget) for movies, then bin the Production Budget and Rotten Tomatoes Rating into categorical ranges, and compute the average profit for movies in each combination of budget and rating bins.\",\n \"display_instruction\": \"Show **average profit** in each **Production Budget** and **Rotten Tomatoes Rating** bin\",\n \"output_fields\": [\"Production Budget Bin\", \"Rotten Tomatoes Rating Bin\", \"Average Profit\"],\n \"chart_encodings\": {\n \"x\": \"Rotten Tomatoes Rating Bin\",\n \"y\": \"Production Budget Bin\",\n \"color\": \"Average Profit\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n },\n \"reason\": \"The user wants to analyze the relationship between profit and the budget/rating bins. This requires calculating profit (Worldwide Gross - Production Budget) and then aggregating it by the budget and rating bins. The chart encodings are swapped to match the user's preference (x=Rating, y=Budget).\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values\n df_filtered = df_movies[['Production Budget', 'Rotten Tomatoes Rating', 'Worldwide Gross']].copy()\n df_filtered = df_filtered[\n (df_filtered['Production Budget'].notna()) & \n (df_filtered['Rotten Tomatoes Rating'].notna()) &\n (df_filtered['Worldwide Gross'].notna())\n ]\n \n # Calculate profit\n df_filtered['Profit'] = df_filtered['Worldwide Gross'] - df_filtered['Production Budget']\n \n # Create budget bins (in millions for readability)\n budget_bins = [0, 1000000, 10000000, 50000000, 100000000, float('inf')]\n budget_labels = ['<$1M', '$1M-$10M', '$10M-$50M', '$50M-$100M', '>$100M']\n df_filtered['Production Budget Bin'] = pd.cut(\n df_filtered['Production Budget'], \n bins=budget_bins, \n labels=budget_labels,\n include_lowest=True\n )\n \n # Create rating bins\n rating_bins = [0, 20, 40, 60, 80, 100]\n rating_labels = ['0-20', '21-40', '41-60', '61-80', '81-100']\n df_filtered['Rotten Tomatoes Rating Bin'] = pd.cut(\n df_filtered['Rotten Tomatoes Rating'], \n bins=rating_bins, \n labels=rating_labels,\n include_lowest=True\n )\n \n # Calculate average profit in each bin combination\n transformed_df = df_filtered.groupby(\n ['Production Budget Bin', 'Rotten Tomatoes Rating Bin'], \n observed=True\n )['Profit'].mean().reset_index(name='Average Profit')\n \n # Convert categorical to string for better display\n transformed_df['Production Budget Bin'] = transformed_df['Production Budget Bin'].astype(str)\n transformed_df['Rotten Tomatoes Rating Bin'] = transformed_df['Rotten Tomatoes Rating Bin'].astype(str)\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"table-37","sourceTableIds":["movies"],"instruction":"show profit relation to budget ratings bin","displayInstruction":"Show **average profit** in each **Production Budget** and **Rotten Tomatoes Rating** bin","chart":{"id":"chart-1760744581372","chartType":"Heatmap","encodingMap":{"x":{"fieldID":"concept-Rotten Tomatoes Rating Bin-1760744520448","sortBy":"[\"0-20\",\"21-40\",\"41-60\",\"61-80\",\"81-100\"]"},"y":{"fieldID":"concept-Production Budget Bin-1760744338462","sortBy":"[\"<$1M\",\"$1M-$10M\",\"$10M-$50M\",\"$50M-$100M\",\">$100M\"]"},"color":{"fieldID":"concept-Number of Movies-1760744520448"},"column":{"channel":"column","bin":false},"row":{"channel":"row","bin":false}},"tableRef":"table-37","saved":false,"source":"trigger","unread":false},"resultTableId":"table-54"},"explanation":{"agent":"CodeExplanationAgent","code":"This code analyzes the relationship between movie **production budgets**, **critical ratings**, and **profitability**:\n\n- Filters the dataset to include only movies with complete data for **Production Budget**, **Rotten Tomatoes Rating**, and **Worldwide Gross**\n- Calculates **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n- Creates **Production Budget Bins** to categorize movies into five budget ranges: **<$1M**, **$1M-$10M**, **$10M-$50M**, **$50M-$100M**, and **>$100M**\n- Creates **Rotten Tomatoes Rating Bins** to categorize movies into five rating ranges: **0-20**, **21-40**, **41-60**, **61-80**, and **81-100**\n- Groups movies by their **budget bin** and **rating bin** combinations\n- Calculates the **Average Profit** for each budget-rating combination to reveal profitability patterns across different production scales and critical reception levels","concepts":[{"explanation":"The net financial return of a movie, calculated as: \\[ \\text{Profit} = \\text{Worldwide\\_Gross} - \\text{Production\\_Budget} \\] This represents the total revenue minus the initial investment, indicating whether a movie was financially successful.","field":"Profit"},{"explanation":"This analysis uses a **two-dimensional binning approach** to examine how movie profitability varies across production budget levels and critical reception scores. The model segments movies into categorical bins based on **Production Budget** (five budget tiers from under $1M to over $100M) and **Rotten Tomatoes Rating** (five quality tiers from 0-100), then calculates **Average Profit** within each budget-rating combination. This binning strategy helps identify optimal investment strategies by revealing which budget-quality combinations yield the highest returns. Alternative modeling approaches could include: **multiple linear regression** to model profit as a continuous function of budget and rating, **polynomial regression** to capture non-linear relationships, **interaction effect models** to test whether budget impact varies by rating level, or **quantile regression** to analyze profitability patterns at different profit percentiles rather than just averages.","field":"Statistical Analysis"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 673.0, ..., 403706375.0, 441226247.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 976457891.0, 1133027325.0, 1842879955.0\n\t*US DVD Sales -- type: float64, values: 13180936.0, 33619971.0, 140689412.0, ..., nan, nan, 46260220.0, 83571732.0\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 23000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, nan, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.0, 2.5, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 237477.0, 417703.0, nan, nan\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values\n df_filtered = df_movies[['Production Budget', 'Rotten Tomatoes Rating', 'Worldwide Gross']].copy()\n df_filtered = df_filtered[\n (df_filtered['Production Budget'].notna()) & \n (df_filtered['Rotten Tomatoes Rating'].notna()) &\n (df_filtered['Worldwide Gross'].notna())\n ]\n \n # Calculate profit\n df_filtered['Profit'] = df_filtered['Worldwide Gross'] - df_filtered['Production Budget']\n \n # Create budget bins (in millions for readability)\n budget_bins = [0, 1000000, 10000000, 50000000, 100000000, float('inf')]\n budget_labels = ['<$1M', '$1M-$10M', '$10M-$50M', '$50M-$100M', '>$100M']\n df_filtered['Production Budget Bin'] = pd.cut(\n df_filtered['Production Budget'], \n bins=budget_bins, \n labels=budget_labels,\n include_lowest=True\n )\n \n # Create rating bins\n rating_bins = [0, 20, 40, 60, 80, 100]\n rating_labels = ['0-20', '21-40', '41-60', '61-80', '81-100']\n df_filtered['Rotten Tomatoes Rating Bin'] = pd.cut(\n df_filtered['Rotten Tomatoes Rating'], \n bins=rating_bins, \n labels=rating_labels,\n include_lowest=True\n )\n \n # Calculate average profit in each bin combination\n transformed_df = df_filtered.groupby(\n ['Production Budget Bin', 'Rotten Tomatoes Rating Bin'], \n observed=True\n )['Profit'].mean().reset_index(name='Average Profit')\n \n # Convert categorical to string for better display\n transformed_df['Production Budget Bin'] = transformed_df['Production Budget Bin'].astype(str)\n transformed_df['Rotten Tomatoes Rating Bin'] = transformed_df['Rotten Tomatoes Rating Bin'].astype(str)\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\nThis code analyzes the relationship between movie **production budgets**, **critical ratings**, and **profitability**:\n\n- Filters the dataset to include only movies with complete data for **Production Budget**, **Rotten Tomatoes Rating**, and **Worldwide Gross**\n- Calculates **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n- Creates **Production Budget Bins** to categorize movies into five budget ranges: **<$1M**, **$1M-$10M**, **$10M-$50M**, **$50M-$100M**, and **>$100M**\n- Creates **Rotten Tomatoes Rating Bins** to categorize movies into five rating ranges: **0-20**, **21-40**, **41-60**, **61-80**, and **81-100**\n- Groups movies by their **budget bin** and **rating bin** combinations\n- Calculates the **Average Profit** for each budget-rating combination to reveal profitability patterns across different production scales and critical reception levels\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"Profit\",\n \"explanation\": \"The net financial return of a movie, calculated as: \\\\[ \\\\text{Profit} = \\\\text{Worldwide\\\\_Gross} - \\\\text{Production\\\\_Budget} \\\\] This represents the total revenue minus the initial investment, indicating whether a movie was financially successful.\"\n },\n {\n \"field\": \"Statistical Analysis\",\n \"explanation\": \"This analysis uses a **two-dimensional binning approach** to examine how movie profitability varies across production budget levels and critical reception scores. The model segments movies into categorical bins based on **Production Budget** (five budget tiers from under $1M to over $100M) and **Rotten Tomatoes Rating** (five quality tiers from 0-100), then calculates **Average Profit** within each budget-rating combination. This binning strategy helps identify optimal investment strategies by revealing which budget-quality combinations yield the highest returns. Alternative modeling approaches could include: **multiple linear regression** to model profit as a continuous function of budget and rating, **polynomial regression** to capture non-linear relationships, **interaction effect models** to test whether budget impact varies by rating level, or **quantile regression** to analyze profitability patterns at different profit percentiles rather than just averages.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""}],"charts":[{"id":"chart-1760744573683","chartType":"Heatmap","encodingMap":{"x":{"fieldID":"concept-Rotten Tomatoes Rating Bin-1760744520448","sortBy":"[\"0-20\",\"21-40\",\"41-60\",\"61-80\",\"81-100\"]"},"y":{"fieldID":"concept-Production Budget Bin-1760744338462","sortBy":"[\"<$1M\",\"$1M-$10M\",\"$10M-$50M\",\"$50M-$100M\",\">$100M\"]"},"color":{"fieldID":"concept-Average 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Movies"}],"displayedMessageIdx":0,"focusedTableId":"table-89","focusedChartId":"chart-1760743804092","viewMode":"report","chartSynthesisInProgress":[],"config":{"formulateTimeoutSeconds":60,"maxRepairAttempts":1,"defaultChartWidth":300,"defaultChartHeight":300},"agentActions":[],"dataCleanBlocks":[],"cleanInProgress":false,"generatedReports":[{"id":"report-1760831318093-4967","content":"# Bigger Budgets Win with Great Reviews\n\nLow-budget films struggle regardless of reviews, averaging just $1.3M profit even with poor ratings. The real story emerges with blockbusters: films budgeted over $100M earn an average of $516M profit when they achieve 81-100% on Rotten Tomatoes—a staggering return that dwarfs all other categories.\n\n[IMAGE(chart-1760744573683)]\n\nMid-budget films ($10M-$50M) show steady but modest profits across all rating tiers, rarely exceeding $30M in average returns. The data reveals a clear pattern: critical acclaim matters most when paired with substantial production investment.\n\n**In summary**, the film industry's most profitable strategy combines massive budgets with quality execution—blockbusters that earn top critic scores generate extraordinary returns, while smaller productions face profit ceilings regardless of critical reception.","style":"short note","selectedChartIds":["chart-1760744573683"],"createdAt":1760831325401},{"id":"report-1760831241705-9562","content":"# Top Directors Dominate with Action & Adventure Films\n\nSteven Spielberg leads all directors with over $7 billion in total profit, driven primarily by action ($2.3B) and drama ($2.2B) films. James Cameron follows at $5.1B, focusing almost exclusively on action blockbusters.\n\n[IMAGE(chart-1760743804092)]\n\nAdventure films emerge as the most profitable genre across multiple directors—Chris Columbus, George Lucas, and Peter Jackson each generated $3B+ primarily from adventure franchises. Meanwhile, directors like Michael Bay and Gore Verbinski found success mixing action with adventure content.\n\n**In summary**, the most profitable directors concentrate on action and adventure genres, with Spielberg's genre diversity being the exception among top earners. This suggests blockbuster franchises in these genres offer the most reliable path to commercial success.","style":"short note","selectedChartIds":["chart-1760743804092"],"createdAt":1760831249446},{"id":"report-1760831215537-1231","content":"# Movie Magic: When Big Budgets Pay Off (and When They Don't)\n\nEver wonder if spending more money guarantees box office gold? The data tells a fascinating story about Hollywood's biggest gambles.\n\n[IMAGE(chart-1760742454293)]\n\nLooking at the relationship between production budgets and worldwide gross, there's a clear trend: **bigger budgets *can* lead to bigger returns**, but it's far from guaranteed. The sweet spot appears to be around $200-250M, where blockbusters like *Avatar* ($2.77B worldwide) dominate. However, the chart reveals something crucial—**most films cluster in the lower-left corner**, suggesting that moderate budgets between $25-100M are the industry standard, though they rarely break the billion-dollar barrier.\n\n[IMAGE(chart-1760743347871)]\n\nWhen we examine the **top performers by genre**, the winners become crystal clear. *Avatar* leads the pack with an astounding $2.53B profit, followed by adventure franchises like *Jurassic Park* and *The Lord of the Rings*. Action and adventure films dominate profitability, while genres like comedy, horror, and romantic comedies show more modest—but still impressive—returns in the $300-450M range. Musicals like *The Sound of Music* prove timeless appeal still pays dividends.\n\n**In summary**, while throwing money at a film doesn't guarantee success, the blockbuster strategy works when executed well—particularly for action and adventure franchises. The real question: *Are mid-budget films becoming an endangered species in modern Hollywood?*","style":"social post","selectedChartIds":["chart-1760743347871","chart-1760742454293"],"createdAt":1760831228624}],"currentReport":{"id":"report-1760750575650-2619","content":"# Hollywood's Billion-Dollar Hitmakers\n\n*Avatar* stands alone—earning over $2.5B in profit, dwarfing all competition. Action and Adventure films dominate the most profitable titles, with franchises like *Jurassic Park*, *The Dark Knight*, and *Lord of the Rings* proving blockbuster formulas work.\n\n\"Chart\"\n\nSteven Spielberg leads all directors with $7.2B in total profit across his career, showcasing remarkable consistency with hits spanning decades—from *Jurassic Park* to *E.T.* His nearest competitors trail by billions, underlining his unmatched commercial impact.\n\n\"Chart\"\n\n**In summary**, mega-budget Action and Adventure films generate extraordinary returns when they succeed, and a handful of elite directors—led by Spielberg—have mastered the formula for sustained box office dominance.","style":"short note","selectedChartIds":["chart-1760743347871","chart-1760743768741"],"chartImages":{},"createdAt":1760750584189,"title":"Report - 10/17/2025"},"activeChallenges":[],"agentWorkInProgress":[],"_persist":{"version":-1,"rehydrated":true}} \ No newline at end of file +{"tables": [{"kind": "table", "id": "movies", "displayId": "movies", "names": ["Title", "US Gross", "Worldwide Gross", "US DVD Sales", "Production Budget", "Release Date", "MPAA Rating", "Running Time min", "Distributor", "Source", "Major Genre", "Creative Type", "Director", "Rotten Tomatoes Rating", "IMDB Rating", "IMDB Votes"], "metadata": {"Title": {"type": "string", "semanticType": "Name"}, "US Gross": {"type": "number", "semanticType": "Number"}, "Worldwide Gross": {"type": "number", "semanticType": "Number"}, "US DVD Sales": {"type": "number", "semanticType": "Number"}, "Production Budget": {"type": "number", "semanticType": "Number"}, "Release Date": {"type": "date", "semanticType": "Date"}, "MPAA Rating": {"type": "string", "semanticType": "String", "levels": ["G", "PG", "PG-13", "R", "NC-17", "Not Rated", "Open"]}, "Running Time min": {"type": "number", "semanticType": "Duration"}, "Distributor": {"type": "string", "semanticType": "Name"}, "Source": {"type": "string", "semanticType": "String"}, 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"Source": null, "Major Genre": "Drama", "Creative Type": null, "Director": null, "Rotten Tomatoes Rating": null, "IMDB Rating": 6.9, "IMDB Votes": 207}, {"Title": "I Married a Strange Person", "US Gross": 203134, "Worldwide Gross": 203134, "US DVD Sales": null, "Production Budget": 250000, "Release Date": "Aug 28 1998", "MPAA Rating": null, "Running Time min": null, "Distributor": "Lionsgate", "Source": null, "Major Genre": "Comedy", "Creative Type": null, "Director": null, "Rotten Tomatoes Rating": null, "IMDB Rating": 6.8, "IMDB Votes": 865}, {"Title": "Let's Talk About Sex", "US Gross": 373615, "Worldwide Gross": 373615, "US DVD Sales": null, "Production Budget": 300000, "Release Date": "Sep 11 1998", "MPAA Rating": null, "Running Time min": null, "Distributor": "Fine Line", "Source": null, "Major Genre": "Comedy", "Creative Type": null, "Director": null, "Rotten Tomatoes Rating": 13, "IMDB Rating": null, "IMDB Votes": null}, {"Title": "Slam", "US Gross": 1009819, "Worldwide Gross": 1087521, "US DVD Sales": null, "Production Budget": 1000000, "Release Date": "Oct 09 1998", "MPAA Rating": "R", "Running Time min": null, "Distributor": "Trimark", "Source": "Original Screenplay", "Major Genre": "Drama", "Creative Type": "Contemporary Fiction", "Director": null, "Rotten Tomatoes Rating": 62, "IMDB Rating": 3.4, "IMDB Votes": 165}, {"Title": "Mississippi Mermaid", "US Gross": 24551, "Worldwide Gross": 2624551, "US DVD Sales": null, "Production Budget": 1600000, "Release Date": "Jan 15 1999", "MPAA Rating": null, "Running Time min": null, "Distributor": "MGM", "Source": null, "Major Genre": null, "Creative Type": null, "Director": null, "Rotten Tomatoes Rating": null, "IMDB Rating": null, "IMDB Votes": null}, {"Title": "Following", "US Gross": 44705, "Worldwide Gross": 44705, "US DVD Sales": null, "Production Budget": 6000, "Release Date": "Apr 04 1999", "MPAA Rating": "R", "Running Time min": null, "Distributor": "Zeitgeist", "Source": null, "Major Genre": null, "Creative Type": null, "Director": "Christopher Nolan", "Rotten Tomatoes Rating": null, "IMDB Rating": 7.7, "IMDB Votes": 15133}, {"Title": "Foolish", "US Gross": 6026908, "Worldwide Gross": 6026908, "US DVD Sales": null, "Production Budget": 1600000, "Release Date": "Apr 09 1999", "MPAA Rating": "R", "Running Time min": null, "Distributor": "Artisan", "Source": "Original Screenplay", "Major Genre": "Comedy", "Creative Type": "Contemporary Fiction", "Director": null, "Rotten Tomatoes Rating": null, "IMDB Rating": 3.8, "IMDB Votes": 353}, {"Title": "Pirates", "US Gross": 1641825, "Worldwide Gross": 6341825, "US DVD Sales": null, "Production Budget": 40000000, "Release Date": "Jul 01 1986", "MPAA Rating": "R", "Running Time min": null, "Distributor": null, "Source": null, "Major Genre": null, "Creative Type": null, "Director": "Roman Polanski", "Rotten Tomatoes Rating": 25, "IMDB Rating": 5.8, "IMDB Votes": 3275}, {"Title": "Duel in the Sun", "US Gross": 20400000, "Worldwide Gross": 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"Source": "Original Screenplay", "Major Genre": "Comedy", "Creative Type": "Historical Fiction", "Director": "Harold Ramis", "Rotten Tomatoes Rating": 14, "IMDB Rating": 5, "IMDB Votes": 23091}, {"Title": "Yes", "US Gross": 396035, "Worldwide Gross": 661221, "US DVD Sales": null, "Production Budget": 1700000, "Release Date": "Jun 24 2005", "MPAA Rating": "R", "Running Time min": null, "Distributor": "Sony Pictures Classics", "Source": "Original Screenplay", "Major Genre": "Drama", "Creative Type": null, "Director": null, "Rotten Tomatoes Rating": null, "IMDB Rating": null, "IMDB Votes": null}, {"Title": "Yes Man", "US Gross": 97690976, "Worldwide Gross": 225990976, "US DVD Sales": 26601131, "Production Budget": 50000000, "Release Date": "Dec 19 2008", "MPAA Rating": "PG-13", "Running Time min": 104, "Distributor": "Warner Bros.", "Source": "Based on Book/Short Story", "Major Genre": "Comedy", "Creative Type": "Contemporary Fiction", "Director": "Peyton Reed", "Rotten Tomatoes Rating": 43, "IMDB Rating": 7, "IMDB Votes": 62150}, {"Title": "You Kill Me", "US Gross": 2426851, "Worldwide Gross": 2426851, "US DVD Sales": null, "Production Budget": 4000000, "Release Date": "Jun 22 2007", "MPAA Rating": "R", "Running Time min": 92, "Distributor": "IFC Films", "Source": "Original Screenplay", "Major Genre": "Black Comedy", "Creative Type": "Contemporary Fiction", "Director": "John Dahl", "Rotten Tomatoes Rating": 78, "IMDB Rating": 6.7, "IMDB Votes": 9498}, {"Title": "Yours, Mine and Ours", "US Gross": 53359917, "Worldwide Gross": 72359917, "US DVD Sales": 26979166, "Production Budget": 45000000, "Release Date": "Nov 23 2005", "MPAA Rating": "PG", "Running Time min": 90, "Distributor": "Paramount Pictures", "Source": "Remake", "Major Genre": "Comedy", "Creative Type": "Contemporary Fiction", "Director": "Raja Gosnell", "Rotten Tomatoes Rating": null, "IMDB Rating": 7.6, "IMDB Votes": 259}, {"Title": "You've Got Mail", "US Gross": 115821495, "Worldwide Gross": 250800000, "US 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"R", "Running Time min": null, "Distributor": "IFC Films", "Source": "Original Screenplay", "Major Genre": "Drama", "Creative Type": "Contemporary Fiction", "Director": "Alfonso Cuaron", "Rotten Tomatoes Rating": null, "IMDB Rating": null, "IMDB Votes": null}, {"Title": "Yu-Gi-Oh", "US Gross": 19762690, "Worldwide Gross": 28762690, "US DVD Sales": null, "Production Budget": 20000000, "Release Date": "Aug 13 2004", "MPAA Rating": "PG", "Running Time min": null, "Distributor": "Warner Bros.", "Source": "Based on TV", "Major Genre": "Adventure", "Creative Type": "Kids Fiction", "Director": null, "Rotten Tomatoes Rating": null, "IMDB Rating": null, "IMDB Votes": null}, {"Title": "The Young Unknowns", "US Gross": 58163, "Worldwide Gross": 58163, "US DVD Sales": null, "Production Budget": 800000, "Release Date": "Apr 11 2003", "MPAA Rating": "R", "Running Time min": null, "Distributor": "Indican Pictures", "Source": null, "Major Genre": null, "Creative Type": null, "Director": null, "Rotten Tomatoes Rating": null, "IMDB Rating": 4.5, "IMDB Votes": 98}, {"Title": "The Young Victoria", "US Gross": 11001272, "Worldwide Gross": 11001272, "US DVD Sales": 3273039, "Production Budget": 35000000, "Release Date": "Dec 18 2009", "MPAA Rating": "PG", "Running Time min": null, "Distributor": "Apparition", "Source": "Based on Real Life Events", "Major Genre": "Drama", "Creative Type": "Dramatization", "Director": null, "Rotten Tomatoes Rating": 76, "IMDB Rating": 7.2, "IMDB Votes": 8408}, {"Title": "Zathura", "US Gross": 28045540, "Worldwide Gross": 58545540, "US DVD Sales": 22025352, "Production Budget": 65000000, "Release Date": "Nov 11 2005", "MPAA Rating": "PG", "Running Time min": 113, "Distributor": "Sony Pictures", "Source": "Based on Book/Short Story", "Major Genre": "Adventure", "Creative Type": "Fantasy", "Director": "Jon Favreau", "Rotten Tomatoes Rating": 75, "IMDB Rating": null, "IMDB Votes": null}, {"Title": "Zero Effect", "US Gross": 2080693, "Worldwide Gross": 2080693, "US DVD Sales": null, "Production Budget": 5000000, "Release Date": "Jan 30 1998", "MPAA Rating": "R", "Running Time min": null, "Distributor": "Sony Pictures", "Source": "Original Screenplay", "Major Genre": "Comedy", "Creative Type": "Contemporary Fiction", "Director": null, "Rotten Tomatoes Rating": 66, "IMDB Rating": 6.8, "IMDB Votes": 8489}, {"Title": "Zoolander", "US Gross": 45172250, "Worldwide Gross": 60780981, "US DVD Sales": null, "Production Budget": 28000000, "Release Date": "Sep 28 2001", "MPAA Rating": "PG-13", "Running Time min": 89, "Distributor": "Paramount Pictures", "Source": "Original Screenplay", "Major Genre": "Comedy", "Creative Type": "Contemporary Fiction", "Director": "Ben Stiller", "Rotten Tomatoes Rating": 62, "IMDB Rating": 6.4, "IMDB Votes": 69296}, {"Title": "Zombieland", "US Gross": 75590286, "Worldwide Gross": 98690286, "US DVD Sales": 28281155, "Production Budget": 23600000, "Release Date": "Oct 02 2009", "MPAA Rating": "R", "Running Time min": 87, 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Type": "Dramatization", "Director": "David Fincher", "Rotten Tomatoes Rating": 89, "IMDB Rating": null, "IMDB Votes": null}, {"Title": "Zoom", "US Gross": 11989328, "Worldwide Gross": 12506188, "US DVD Sales": 6679409, "Production Budget": 35000000, "Release Date": "Aug 11 2006", "MPAA Rating": "PG", "Running Time min": null, "Distributor": "Sony Pictures", "Source": "Based on Comic/Graphic Novel", "Major Genre": "Adventure", "Creative Type": "Super Hero", "Director": "Peter Hewitt", "Rotten Tomatoes Rating": 3, "IMDB Rating": 3.4, "IMDB Votes": 7424}, {"Title": "The Legend of Zorro", "US Gross": 45575336, "Worldwide Gross": 141475336, "US DVD Sales": null, "Production Budget": 80000000, "Release Date": "Oct 28 2005", "MPAA Rating": "PG", "Running Time min": 129, "Distributor": "Sony Pictures", "Source": "Remake", "Major Genre": "Adventure", "Creative Type": "Historical Fiction", "Director": "Martin Campbell", "Rotten Tomatoes Rating": 26, "IMDB Rating": 5.7, "IMDB Votes": 21161}, {"Title": "The Mask of Zorro", "US Gross": 93828745, "Worldwide Gross": 233700000, "US DVD Sales": null, "Production Budget": 65000000, "Release Date": "Jul 17 1998", "MPAA Rating": "PG-13", "Running Time min": 136, "Distributor": "Sony Pictures", "Source": "Remake", "Major Genre": "Adventure", "Creative Type": "Historical Fiction", "Director": "Martin Campbell", "Rotten Tomatoes Rating": 82, "IMDB Rating": 6.7, "IMDB Votes": 4789}], "anchored": true, "attachedMetadata": ""}, {"kind": "table", "id": "table-78", "displayId": "movie-profits", "names": ["Production Budget", "Profit", "Title", "Worldwide Gross"], "rows": [{"Production Budget": 237000000, "Profit": 2530891499, "Title": "Avatar", "Worldwide Gross": 2767891499}, {"Production Budget": 200000000, "Profit": 1642879955, "Title": "Titanic", "Worldwide Gross": 1842879955}, {"Production Budget": 94000000, "Profit": 1039027325, "Title": "The Lord of the Rings: The Return of the King", "Worldwide Gross": 1133027325}, {"Production Budget": 63000000, "Profit": 860067947, "Title": "Jurassic Park", "Worldwide Gross": 923067947}, {"Production Budget": 125000000, "Profit": 851457891, "Title": "Harry Potter and the Sorcerer's Stone", "Worldwide Gross": 976457891}, {"Production Budget": 70000000, "Profit": 849838758, "Title": "Shrek 2", "Worldwide Gross": 919838758}, {"Production Budget": 200000000, "Profit": 846340665, "Title": "Toy Story 3", "Worldwide Gross": 1046340665}, {"Production Budget": 225000000, "Profit": 840659812, "Title": "Pirates of the Caribbean: Dead Man's Chest", "Worldwide Gross": 1065659812}, {"Production Budget": 185000000, "Profit": 837345358, "Title": "The Dark Knight", "Worldwide Gross": 1022345358}, {"Production Budget": 94000000, "Profit": 832284377, "Title": "The Lord of the Rings: The Two Towers", "Worldwide Gross": 926284377}, {"Production Budget": 200000000, "Profit": 823291110, "Title": "Alice in Wonderland", "Worldwide Gross": 1023291110}, {"Production Budget": 115000000, "Profit": 809288297, "Title": "Star Wars Ep. I: The Phantom Menace", "Worldwide Gross": 924288297}, {"Production Budget": 90000000, "Profit": 796685941, "Title": "Ice Age: Dawn of the Dinosaurs", "Worldwide Gross": 886685941}, {"Production Budget": 150000000, "Profit": 788468864, "Title": "Harry Potter and the Order of the Phoenix", "Worldwide Gross": 938468864}, {"Production Budget": 11000000, "Profit": 786900000, "Title": "Star Wars Ep. IV: A New Hope", "Worldwide Gross": 797900000}, {"Production Budget": 10500000, "Profit": 782410554, "Title": "ET: The Extra-Terrestrial", "Worldwide Gross": 792910554}, {"Production Budget": 100000000, "Profit": 778987880, "Title": "Harry Potter and the Chamber of Secrets", "Worldwide Gross": 878987880}, {"Production Budget": 94000000, "Profit": 773894287, "Title": "Finding Nemo", "Worldwide Gross": 867894287}, {"Production Budget": 109000000, "Profit": 759621686, "Title": "The Lord of the Rings: The Fellowship of the Ring", "Worldwide Gross": 868621686}, {"Production Budget": 150000000, "Profit": 746013036, "Title": "Harry Potter and the Goblet of Fire", "Worldwide Gross": 896013036}, {"Production Budget": 75000000, "Profit": 742400878, "Title": "Independence Day", "Worldwide Gross": 817400878}, {"Production Budget": 115000000, "Profit": 733998877, "Title": "Star Wars Ep. III: Revenge of the Sith", "Worldwide Gross": 848998877}, {"Production Budget": 75000000, "Profit": 711686679, "Title": "The Lost World: Jurassic Park", "Worldwide Gross": 786686679}, {"Production Budget": 79300000, "Profit": 704539505, "Title": "The Lion King", "Worldwide Gross": 783839505}, {"Production Budget": 250000000, "Profit": 687499905, "Title": "Harry Potter and the Half-Blood Prince", "Worldwide Gross": 937499905}, {"Production Budget": 139000000, "Profit": 682708551, "Title": "Spider-Man", "Worldwide Gross": 821708551}, {"Production Budget": 130000000, "Profit": 665538952, "Title": "Harry Potter and the Prisoner of Azkaban", "Worldwide Gross": 795538952}, {"Production Budget": 300000000, "Profit": 660996492, "Title": "Pirates of the Caribbean: At World's End", "Worldwide Gross": 960996492}, {"Production Budget": 50000000, "Profit": 652623634, "Title": "The Twilight Saga: New Moon", "Worldwide Gross": 702623634}, {"Production Budget": 160000000, "Profit": 638958162, "Title": "Shrek the Third", "Worldwide Gross": 798958162}], "metadata": {"Production Budget": {"type": "number", "semanticType": "Number"}, "Profit": {"type": "number", "semanticType": "Number"}, "Title": {"type": "string", "semanticType": "Name"}, "Worldwide Gross": {"type": "number", "semanticType": "Number"}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df_movies['Profit'] = df_movies['Worldwide Gross'] - df_movies['Production Budget']\n \n # Remove rows with missing values in required columns\n df_filtered = df_movies.dropna(subset=['Profit', 'Production Budget', 'Worldwide Gross', 'Title'])\n \n # Sort by profit in descending order and select top 30\n df_top30 = df_filtered.nlargest(30, 'Profit')\n \n # Select only the required output fields\n transformed_df = df_top30[['Title', 'Production Budget', 'Worldwide Gross', 'Profit']].reset_index(drop=True)\n \n return transformed_df\n", "source": ["movies"], "dialog": [{"content": "You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 380270577.0, 402111870.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 1133027325.0, 1842879955.0, 2767891499.0\n\t*US DVD Sales -- type: float64, values: 33619971.0, 140689412.0, nan, ..., nan, nan, 46260220.0, nan\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 23000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, nan, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.0, 2.5, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 237477.0, 417703.0, 519541.0, nan\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"show top 30 movies based on their profit\",\n \"chart_type\": \"Scatter Plot\",\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Worldwide Gross\",\n \"color\": \"Title\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Calculate the profit for each movie (Worldwide Gross minus Production Budget), then select the top 30 movies with the highest profit. Display these movies with their Production Budget, Worldwide Gross, and Title to visualize the relationship between budget and revenue for the most profitable films.\",\n \"display_instruction\": \"Show top 30 movies by **profit** with their **Production Budget** and **Worldwide Gross**\",\n \"output_fields\": [\"Title\", \"Production Budget\", \"Worldwide Gross\", \"Profit\"],\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Worldwide Gross\",\n \"color\": \"Title\"\n },\n \"reason\": \"The user wants to show top 30 movies based on profit, which requires calculating profit as the difference between Worldwide Gross and Production Budget. The chart encodings provided are sufficient to visualize the most profitable movies, showing the relationship between their budget and gross revenue. An intermediate field 'Profit' is added to output_fields to enable the top 30 selection, though it won't be directly visualized in the chart.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df_movies['Profit'] = df_movies['Worldwide Gross'] - df_movies['Production Budget']\n \n # Remove rows with missing values in required columns\n df_filtered = df_movies.dropna(subset=['Profit', 'Production Budget', 'Worldwide Gross', 'Title'])\n \n # Sort by profit in descending order and select top 30\n df_top30 = df_filtered.nlargest(30, 'Profit')\n \n # Select only the required output fields\n transformed_df = df_top30[['Title', 'Production Budget', 'Worldwide Gross', 'Profit']].reset_index(drop=True)\n \n return transformed_df\n```", "role": "assistant"}], "trigger": {"tableId": "movies", "resultTableId": "table-78", "chart": {"id": "chart-1760743154092", "chartType": "Scatter Plot", "encodingMap": {"x": {"fieldID": "original--movies--Production Budget"}, "y": {"fieldID": "original--movies--Worldwide Gross"}, "color": {"fieldID": "original--movies--Title"}, "size": {"channel": "size", "bin": false}, "opacity": {"channel": "opacity", "bin": false}, "column": {"channel": "column", "bin": false}, "row": {"channel": "row", "bin": false}}, "tableRef": "movies", "saved": false, "source": "trigger", "unread": false}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "show top 30 movies based on their profit", "displayContent": "Show top 30 movies by **profit** with their **Production Budget** and **Worldwide Gross**"}]}, "explanation": {"agent": "CodeExplanationAgent", "code": "The code identifies the most financially successful movies by analyzing their profitability:\n\n- Calculate **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n- Filter out movies with missing values in **Profit**, **Production Budget**, **Worldwide Gross**, or **Title** fields\n- Sort all movies by **Profit** in descending order and select the **top 30** most profitable movies\n- Return a simplified dataset containing only **Title**, **Production Budget**, **Worldwide Gross**, and **Profit** for these top performers", "concepts": [{"explanation": "The net financial gain from a movie, calculated as the difference between worldwide box office revenue and production costs: \\( \\text{Profit} = \\text{Worldwide\\_Gross} - \\text{Production\\_Budget} \\). This metric represents the pure theatrical profitability before accounting for marketing costs, distribution fees, or ancillary revenue streams like DVD sales.", "field": "Profit"}], "dialog": [{"content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 441226247.0, 533345358.0, 600788188.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 1065659812.0, 1133027325.0, 1842879955.0, 2767891499.0\n\t*US DVD Sales -- type: float64, values: 2638319.0, 3907625.0, 4653193.0, ..., 124058348.0, 153389976.0, 234119058.0, 320830925.0\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 23000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: 46.0, 72.0, 74.0, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.5, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 261439.0, 292562.0, 417703.0, 519541.0\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df_movies['Profit'] = df_movies['Worldwide Gross'] - df_movies['Production Budget']\n \n # Remove rows with missing values in required columns\n df_filtered = df_movies.dropna(subset=['Profit', 'Production Budget', 'Worldwide Gross', 'Title'])\n \n # Sort by profit in descending order and select top 30\n df_top30 = df_filtered.nlargest(30, 'Profit')\n \n # Select only the required output fields\n transformed_df = df_top30[['Title', 'Production Budget', 'Worldwide Gross', 'Profit']].reset_index(drop=True)\n \n return transformed_df\n\n\n[EXPLANATION]\n", "role": "user"}, {"content": "[CODE EXPLANATION]\n\nThe code identifies the most financially successful movies by analyzing their profitability:\n\n- Calculate **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n- Filter out movies with missing values in **Profit**, **Production Budget**, **Worldwide Gross**, or **Title** fields\n- Sort all movies by **Profit** in descending order and select the **top 30** most profitable movies\n- Return a simplified dataset containing only **Title**, **Production Budget**, **Worldwide Gross**, and **Profit** for these top performers\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"Profit\",\n \"explanation\": \"The net financial gain from a movie, calculated as the difference between worldwide box office revenue and production costs: \\\\( \\\\text{Profit} = \\\\text{Worldwide\\\\_Gross} - \\\\text{Production\\\\_Budget} \\\\). This metric represents the pure theatrical profitability before accounting for marketing costs, distribution fees, or ancillary revenue streams like DVD sales.\"\n }\n]\n```", "role": "assistant"}], "status": "ok"}, "status": "completed"}, "anchored": false, "attachedMetadata": ""}, {"kind": "table", "id": "table-77", "displayId": "movie-profits1", "names": ["Major Genre", "Production Budget", "Profit", "Title", "Worldwide Gross"], "rows": [{"Major Genre": "Action", "Production Budget": 237000000, "Profit": 2530891499, "Title": "Avatar", "Worldwide Gross": 2767891499}, {"Major Genre": "Action", "Production Budget": 63000000, "Profit": 860067947, "Title": "Jurassic Park", "Worldwide Gross": 923067947}, {"Major Genre": "Action", "Production Budget": 185000000, "Profit": 837345358, "Title": "The Dark Knight", "Worldwide Gross": 1022345358}, {"Major Genre": "Adventure", "Production Budget": 94000000, "Profit": 1039027325, "Title": "The Lord of the Rings: The Return of the King", "Worldwide Gross": 1133027325}, {"Major Genre": "Adventure", "Production Budget": 125000000, "Profit": 851457891, "Title": "Harry Potter and the Sorcerer's Stone", "Worldwide Gross": 976457891}, {"Major Genre": "Adventure", "Production Budget": 70000000, "Profit": 849838758, "Title": "Shrek 2", "Worldwide Gross": 919838758}, {"Major Genre": "Black Comedy", "Production Budget": 37000000, "Profit": 126415735, "Title": "Burn After Reading", "Worldwide Gross": 163415735}, {"Major Genre": "Black Comedy", "Production Budget": 10000000, "Profit": 73593107, "Title": "Snatch", "Worldwide Gross": 83593107}, {"Major Genre": "Black Comedy", "Production Budget": 28000000, "Profit": 43430876, "Title": "The Royal Tenenbaums", "Worldwide Gross": 71430876}, {"Major Genre": "Comedy", "Production Budget": 28000000, "Profit": 476050219, "Title": "Aladdin", "Worldwide Gross": 504050219}, {"Major Genre": "Comedy", "Production Budget": 150000000, "Profit": 470495432, "Title": "Ratatouille", "Worldwide Gross": 620495432}, {"Major Genre": "Comedy", "Production Budget": 110000000, "Profit": 464480841, "Title": "Night at the Museum", "Worldwide Gross": 574480841}, {"Major Genre": "Concert/Performance", "Production Budget": 6500000, "Profit": 64781781, "Title": "Hannah Montana/Miley Cyrus: Best of Both Worlds Concert Tour", "Worldwide Gross": 71281781}, {"Major Genre": "Concert/Performance", "Production Budget": 3000000, "Profit": 35236338, "Title": "The Original Kings of Comedy", "Worldwide Gross": 38236338}, {"Major Genre": "Concert/Performance", "Production Budget": 3000000, "Profit": 16184820, "Title": "Martin Lawrence Live: RunTelDat", "Worldwide Gross": 19184820}, {"Major Genre": "Documentary", "Production Budget": 6000000, "Profit": 216414517, "Title": "Fahrenheit 9/11", "Worldwide Gross": 222414517}, {"Major Genre": "Documentary", "Production Budget": 3400000, "Profit": 126037223, "Title": "La marche de l'empereur", "Worldwide Gross": 129437223}, {"Major Genre": "Documentary", "Production Budget": 3000000, "Profit": 55576018, "Title": "Bowling for Columbine", "Worldwide Gross": 58576018}, {"Major Genre": "Drama", "Production Budget": 10500000, "Profit": 782410554, "Title": "ET: The Extra-Terrestrial", "Worldwide Gross": 792910554}, {"Major Genre": "Drama", "Production Budget": 50000000, "Profit": 652623634, "Title": "The Twilight Saga: New Moon", "Worldwide Gross": 702623634}, {"Major Genre": "Drama", "Production Budget": 55000000, "Profit": 624400525, "Title": "Forrest Gump", "Worldwide Gross": 679400525}, {"Major Genre": "Horror", "Production Budget": 12000000, "Profit": 458700000, "Title": "Jaws", "Worldwide Gross": 470700000}, {"Major Genre": "Horror", "Production Budget": 150000000, "Profit": 435055701, "Title": "I am Legend", "Worldwide Gross": 585055701}, {"Major Genre": "Horror", "Production Budget": 12000000, "Profit": 390500000, "Title": "The Exorcist", "Worldwide Gross": 402500000}, {"Major Genre": "Musical", "Production Budget": 20000000, "Profit": 383476931, "Title": "Beauty and the Beast", "Worldwide Gross": 403476931}, {"Major Genre": "Musical", "Production Budget": 8200000, "Profit": 278014286, "Title": "The Sound of Music", "Worldwide Gross": 286214286}, {"Major Genre": "Musical", "Production Budget": 30000000, "Profit": 277687518, "Title": "Chicago", "Worldwide Gross": 307687518}, {"Major Genre": "Romantic Comedy", "Production Budget": 14000000, "Profit": 449400000, "Title": "Pretty Woman", "Worldwide Gross": 463400000}, {"Major Genre": "Romantic Comedy", "Production Budget": 5000000, "Profit": 363744044, "Title": "My Big Fat Greek Wedding", "Worldwide Gross": 368744044}, {"Major Genre": "Romantic Comedy", "Production Budget": 22000000, "Profit": 338099999, "Title": "There's Something About Mary", "Worldwide Gross": 360099999}, {"Major Genre": "Thriller/Suspense", "Production Budget": 200000000, "Profit": 1642879955, "Title": "Titanic", "Worldwide Gross": 1842879955}, {"Major Genre": "Thriller/Suspense", "Production Budget": 40000000, "Profit": 632806292, "Title": "The Sixth Sense", "Worldwide Gross": 672806292}, {"Major Genre": "Thriller/Suspense", "Production Budget": 125000000, "Profit": 632236138, "Title": "The Da Vinci Code", "Worldwide Gross": 757236138}, {"Major Genre": "Western", "Production Budget": 19000000, "Profit": 405200000, "Title": "Dances with Wolves", "Worldwide Gross": 424200000}, {"Major Genre": "Western", "Production Budget": 6000000, "Profit": 96308900, "Title": "Butch Cassidy and the Sundance Kid", "Worldwide Gross": 102308900}, {"Major Genre": "Western", "Production Budget": 26000000, "Profit": 42293719, "Title": "Open Range", "Worldwide Gross": 68293719}], "metadata": {"Major Genre": {"type": "string", "semanticType": "String"}, "Production Budget": {"type": "number", "semanticType": "Number"}, "Profit": {"type": "number", "semanticType": "Number"}, "Title": {"type": "string", "semanticType": "Name"}, "Worldwide Gross": {"type": "number", "semanticType": "Number"}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df_movies['Profit'] = df_movies['Worldwide Gross'] - df_movies['Production Budget']\n \n # Remove rows with missing values in required columns\n df_filtered = df_movies.dropna(subset=['Profit', 'Production Budget', 'Worldwide Gross', 'Title', 'Major Genre'])\n \n # Group by Major Genre and select top 3 most profitable movies from each genre\n transformed_df = df_filtered.groupby('Major Genre', group_keys=False).apply(\n lambda x: x.nlargest(3, 'Profit')\n ).reset_index(drop=True)\n \n # Select only the required output fields\n transformed_df = transformed_df[['Title', 'Major Genre', 'Profit', 'Production Budget', 'Worldwide Gross']]\n \n return transformed_df\n", "source": ["movies"], "dialog": [{"content": "You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 380270577.0, 402111870.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 1133027325.0, 1842879955.0, 2767891499.0\n\t*US DVD Sales -- type: float64, values: 33619971.0, 140689412.0, nan, ..., nan, nan, 46260220.0, nan\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 23000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, nan, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.0, 2.5, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 237477.0, 417703.0, 519541.0, nan\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[GOAL]\n\n{\n \"instruction\": \"show top 30 movies based on their profit\",\n \"chart_type\": \"Scatter Plot\",\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Worldwide Gross\",\n \"color\": \"Title\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Calculate the profit for each movie (Worldwide Gross minus Production Budget), then select the top 30 movies with the highest profit. Display these movies with their Production Budget, Worldwide Gross, and Title to visualize the relationship between budget and revenue for the most profitable films.\",\n \"display_instruction\": \"Show top 30 movies by **profit** with their **Production Budget** and **Worldwide Gross**\",\n \"output_fields\": [\"Title\", \"Production Budget\", \"Worldwide Gross\", \"Profit\"],\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Worldwide Gross\",\n \"color\": \"Title\"\n },\n \"reason\": \"The user wants to show top 30 movies based on profit, which requires calculating profit as the difference between Worldwide Gross and Production Budget. The chart encodings provided are sufficient to visualize the most profitable movies, showing the relationship between their budget and gross revenue. An intermediate field 'Profit' is added to output_fields to enable the top 30 selection, though it won't be directly visualized in the chart.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df_movies['Profit'] = df_movies['Worldwide Gross'] - df_movies['Production Budget']\n \n # Remove rows with missing values in required columns\n df_filtered = df_movies.dropna(subset=['Profit', 'Production Budget', 'Worldwide Gross', 'Title'])\n \n # Sort by profit in descending order and select top 30\n df_top30 = df_filtered.nlargest(30, 'Profit')\n \n # Select only the required output fields\n transformed_df = df_top30[['Title', 'Production Budget', 'Worldwide Gross', 'Profit']].reset_index(drop=True)\n \n return transformed_df\n```", "role": "assistant"}, {"content": "This is the result from the latest python code:\n\n Production Budget Profit Title Worldwide Gross\n0 237000000 2530891499 Avatar 2767891499\n1 200000000 1642879955 Titanic 1842879955\n2 94000000 1039027325 The Lord of the Rings: The Return of the King 1133027325\n3 63000000 860067947 Jurassic Park 923067947\n4 125000000 851457891 Harry Potter and the Sorcerer's Stone 976457891\n5 70000000 849838758 Shrek 2 919838758\n6 200000000 846340665 Toy Story 3 1046340665\n7 225000000 840659812 Pirates of the Caribbean: Dead Man's Chest 1065659812\n8 185000000 837345358 The Dark Knight 1022345358\n9 94000000 832284377 The Lord of the Rings: The Two Towers 926284377\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show the top 3 most profitable movies from each genre\",\n \"chart_type\": \"Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Title\",\n \"y\": \"Profit\",\n \"color\": \"Production Budget\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Calculate the profit for each movie (Worldwide Gross minus Production Budget), then group movies by their Major Genre and select the top 3 most profitable movies from each genre. Display these movies with their Title, Profit, Production Budget, Major Genre, and Worldwide Gross.\",\n \"display_instruction\": \"Show top 3 most profitable movies from each **Major Genre**\",\n \"output_fields\": [\"Title\", \"Major Genre\", \"Profit\", \"Production Budget\", \"Worldwide Gross\"],\n \"chart_encodings\": {\n \"x\": \"Title\",\n \"y\": \"Profit\",\n \"color\": \"Major Genre\"\n },\n \"reason\": \"The user wants to see the top 3 most profitable movies from each genre, which builds upon the previous profit calculation. The chart encodings are updated to use 'Major Genre' for color instead of 'Production Budget' because when showing top movies by genre, it's more meaningful to distinguish them by genre in the visualization. The Major Genre field is added to output_fields to enable grouping and visualization by genre.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df_movies['Profit'] = df_movies['Worldwide Gross'] - df_movies['Production Budget']\n \n # Remove rows with missing values in required columns\n df_filtered = df_movies.dropna(subset=['Profit', 'Production Budget', 'Worldwide Gross', 'Title', 'Major Genre'])\n \n # Group by Major Genre and select top 3 most profitable movies from each genre\n transformed_df = df_filtered.groupby('Major Genre', group_keys=False).apply(\n lambda x: x.nlargest(3, 'Profit')\n ).reset_index(drop=True)\n \n # Select only the required output fields\n transformed_df = transformed_df[['Title', 'Major Genre', 'Profit', 'Production Budget', 'Worldwide Gross']]\n \n return transformed_df\n```", "role": "assistant"}], "trigger": {"tableId": "table-78", "resultTableId": "table-77", "chart": {"id": "chart-1760743349594", "chartType": "Bar Chart", "encodingMap": {"x": {"fieldID": "original--movies--Title"}, "y": {"fieldID": "concept-Profit-1760742653681"}, "color": {"fieldID": "original--movies--Production Budget"}, "opacity": {"channel": "opacity", "bin": false}, "column": {"channel": "column", "bin": false}, "row": {"channel": "row", "bin": false}}, "tableRef": "table-78", "saved": false, "source": "trigger", "unread": false}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "show the top 3 most profitable movies from each genre", "displayContent": "Show top 3 most profitable movies from each **Major Genre**"}]}, "explanation": {"agent": "CodeExplanationAgent", "code": "This code performs a profitability analysis to identify the most successful movies in each genre:\n\n- **Calculate Profit**: Computes the **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n- **Filter Data**: Removes movies with missing values in **Profit**, **Production Budget**, **Worldwide Gross**, **Title**, or **Major Genre** to ensure complete data for analysis\n- **Group by Genre**: Groups all movies by their **Major Genre** (e.g., Action, Comedy, Drama)\n- **Select Top Performers**: Within each genre, identifies the **top 3** movies with the highest **Profit**\n- **Format Output**: Returns a focused dataset containing only **Title**, **Major Genre**, **Profit**, **Production Budget**, and **Worldwide Gross** for these top-performing movies", "concepts": [{"explanation": "The net financial gain from a movie's theatrical release, calculated as the difference between worldwide box office revenue and production costs: \\( \\text{Profit} = \\text{Worldwide\\_Gross} - \\text{Production\\_Budget} \\). This metric indicates the commercial success of a film before accounting for marketing costs and revenue sharing.", "field": "Profit"}], "dialog": [{"content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 403706375.0, 441226247.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 976457891.0, 1133027325.0, 1842879955.0\n\t*US DVD Sales -- type: float64, values: nan, 140689412.0, nan, ..., 44145849.0, nan, nan, 46260220.0\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 5000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, nan, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.5, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 237477.0, 417703.0, nan, nan\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df_movies['Profit'] = df_movies['Worldwide Gross'] - df_movies['Production Budget']\n \n # Remove rows with missing values in required columns\n df_filtered = df_movies.dropna(subset=['Profit', 'Production Budget', 'Worldwide Gross', 'Title', 'Major Genre'])\n \n # Group by Major Genre and select top 3 most profitable movies from each genre\n transformed_df = df_filtered.groupby('Major Genre', group_keys=False).apply(\n lambda x: x.nlargest(3, 'Profit')\n ).reset_index(drop=True)\n \n # Select only the required output fields\n transformed_df = transformed_df[['Title', 'Major Genre', 'Profit', 'Production Budget', 'Worldwide Gross']]\n \n return transformed_df\n\n\n[EXPLANATION]\n", "role": "user"}, {"content": "[CODE EXPLANATION]\n\nThis code performs a profitability analysis to identify the most successful movies in each genre:\n\n- **Calculate Profit**: Computes the **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n- **Filter Data**: Removes movies with missing values in **Profit**, **Production Budget**, **Worldwide Gross**, **Title**, or **Major Genre** to ensure complete data for analysis\n- **Group by Genre**: Groups all movies by their **Major Genre** (e.g., Action, Comedy, Drama)\n- **Select Top Performers**: Within each genre, identifies the **top 3** movies with the highest **Profit**\n- **Format Output**: Returns a focused dataset containing only **Title**, **Major Genre**, **Profit**, **Production Budget**, and **Worldwide Gross** for these top-performing movies\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"Profit\",\n \"explanation\": \"The net financial gain from a movie's theatrical release, calculated as the difference between worldwide box office revenue and production costs: \\\\( \\\\text{Profit} = \\\\text{Worldwide\\\\_Gross} - \\\\text{Production\\\\_Budget} \\\\). This metric indicates the commercial success of a film before accounting for marketing costs and revenue sharing.\"\n }\n]\n```", "role": "assistant"}], "status": "ok"}, "status": "completed"}, "anchored": false, "attachedMetadata": ""}, {"kind": "table", "id": "table-770727", "displayId": "director-profit", "names": ["Director", "Profit", "Title", "Total_Director_Profit"], "rows": [{"Director": "Steven Spielberg", "Profit": 860067947, "Title": "Jurassic Park", "Total_Director_Profit": 7169573056}, {"Director": "Steven Spielberg", "Profit": 782410554, "Title": "ET: The Extra-Terrestrial", "Total_Director_Profit": 7169573056}, {"Director": "Steven Spielberg", "Profit": 711686679, "Title": "The Lost World: Jurassic Park", "Total_Director_Profit": 7169573056}, {"Director": "Steven Spielberg", "Profit": 601558145, "Title": "Indiana Jones and the Kingdom of the Crystal Skull", "Total_Director_Profit": 7169573056}, {"Director": "Steven Spielberg", "Profit": 459745532, "Title": "The War of the Worlds", "Total_Director_Profit": 7169573056}, {"Director": "Steven Spielberg", "Profit": 458700000, "Title": "Jaws", "Total_Director_Profit": 7169573056}, {"Director": "Steven Spielberg", "Profit": 426171806, "Title": "Indiana Jones and the Last Crusade", "Total_Director_Profit": 7169573056}, {"Director": "Steven Spielberg", "Profit": 416635085, "Title": "Saving Private Ryan", "Total_Director_Profit": 7169573056}, {"Director": "Steven Spielberg", "Profit": 366800358, "Title": "Raiders of the Lost Ark", "Total_Director_Profit": 7169573056}, {"Director": "Steven Spielberg", "Profit": 317700000, "Title": "Close Encounters of the Third Kind", "Total_Director_Profit": 7169573056}, {"Director": "Steven Spielberg", "Profit": 305080271, "Title": "Indiana Jones and the Temple of Doom", "Total_Director_Profit": 7169573056}, {"Director": "Steven Spielberg", "Profit": 299106800, "Title": "Catch Me if You Can", "Total_Director_Profit": 7169573056}, {"Director": "Steven Spielberg", "Profit": 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3036030427}, {"Director": "Chris Columbus", "Profit": -1617462, "Title": "I Love You, Beth Cooper", "Total_Director_Profit": 3036030427}, {"Director": "Chris Columbus", "Profit": -2579224, "Title": "Bicentennial Man", "Total_Director_Profit": 3036030427}, {"Director": "Chris Columbus", "Profit": -8329380, "Title": "Rent", "Total_Director_Profit": 3036030427}, {"Director": "George Lucas", "Profit": 809288297, "Title": "Star Wars Ep. 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624400525, "Title": "Forrest Gump", "Total_Director_Profit": 2788716998}, {"Director": "Robert Zemeckis", "Profit": 362109762, "Title": "Back to the Future", "Total_Director_Profit": 2788716998}, {"Director": "Robert Zemeckis", "Profit": 342230516, "Title": "Cast Away", "Total_Director_Profit": 2788716998}, {"Director": "Robert Zemeckis", "Profit": 292000000, "Title": "Back to the Future Part II", "Total_Director_Profit": 2788716998}, {"Director": "Robert Zemeckis", "Profit": 281500000, "Title": "Who Framed Roger Rabbit?", "Total_Director_Profit": 2788716998}, {"Director": "Robert Zemeckis", "Profit": 203700000, "Title": "Back to the Future Part III", "Total_Director_Profit": 2788716998}, {"Director": "Robert Zemeckis", "Profit": 198693989, "Title": "What Lies Beneath", "Total_Director_Profit": 2788716998}, {"Director": "Robert Zemeckis", "Profit": 135420597, "Title": "The Polar Express", "Total_Director_Profit": 2788716998}, {"Director": "Robert Zemeckis", "Profit": 133743744, "Title": "Disney's A Christmas Carol", "Total_Director_Profit": 2788716998}, {"Director": "Robert Zemeckis", "Profit": 94022650, "Title": "Death Becomes Her", "Total_Director_Profit": 2788716998}, {"Director": "Robert Zemeckis", "Profit": 75900000, "Title": "Contact", "Total_Director_Profit": 2788716998}, {"Director": "Robert Zemeckis", "Profit": 44995215, "Title": "Beowulf", "Total_Director_Profit": 2788716998}, {"Director": "Michael Bay", "Profit": 626303693, "Title": "Transformers: Revenge of the Fallen", "Total_Director_Profit": 2461192847}, {"Director": "Michael Bay", "Profit": 557272592, "Title": "Transformers", "Total_Director_Profit": 2461192847}, {"Director": "Michael Bay", "Profit": 414600000, "Title": "Armageddon", "Total_Director_Profit": 2461192847}, {"Director": "Michael Bay", "Profit": 297739855, "Title": "Pearl Harbor", "Total_Director_Profit": 2461192847}, {"Director": "Michael Bay", "Profit": 261069511, "Title": "The Rock", "Total_Director_Profit": 2461192847}, {"Director": "Michael Bay", "Profit": 142940870, "Title": "Bad Boys II", "Total_Director_Profit": 2461192847}, {"Director": "Michael Bay", "Profit": 118247413, "Title": "Bad Boys", "Total_Director_Profit": 2461192847}, {"Director": "Michael Bay", "Profit": 43018913, "Title": "The Island", "Total_Director_Profit": 2461192847}, {"Director": "Roland Emmerich", "Profit": 742400878, "Title": "Independence Day", "Total_Director_Profit": 2390416794}, {"Director": "Roland Emmerich", "Profit": 566812167, "Title": 2012, "Total_Director_Profit": 2390416794}, {"Director": "Roland Emmerich", "Profit": 419272402, "Title": "The Day After Tomorrow", "Total_Director_Profit": 2390416794}, {"Director": "Roland Emmerich", "Profit": 251000000, "Title": "Godzilla", "Total_Director_Profit": 2390416794}, {"Director": "Roland Emmerich", "Profit": 164065678, "Title": "10,000 B.C.", "Total_Director_Profit": 2390416794}, {"Director": "Roland Emmerich", "Profit": 141565669, "Title": "Stargate", "Total_Director_Profit": 2390416794}, {"Director": "Roland Emmerich", "Profit": 105300000, "Title": "The Patriot", "Total_Director_Profit": 2390416794}, {"Director": "Gore Verbinski", "Profit": 840659812, "Title": "Pirates of the Caribbean: Dead Man's Chest", "Total_Director_Profit": 2359931719}, {"Director": "Gore Verbinski", "Profit": 660996492, "Title": "Pirates of the Caribbean: At World's End", "Total_Director_Profit": 2359931719}, {"Director": "Gore Verbinski", "Profit": 530011224, "Title": "Pirates of the Caribbean: The Curse of the Black Pearl", "Total_Director_Profit": 2359931719}, {"Director": "Gore Verbinski", "Profit": 201094024, "Title": "The Ring", "Total_Director_Profit": 2359931719}, {"Director": "Gore Verbinski", "Profit": 107808615, "Title": "The Mexican", "Total_Director_Profit": 2359931719}, {"Director": "Gore Verbinski", "Profit": 23894591, "Title": "Mouse Hunt", "Total_Director_Profit": 2359931719}, {"Director": "Gore Verbinski", "Profit": -4533039, "Title": "The Weather Man", "Total_Director_Profit": 2359931719}, {"Director": "Tim Burton", "Profit": 823291110, "Title": "Alice in Wonderland", "Total_Director_Profit": 2352496658}, {"Director": "Tim Burton", "Profit": 376348924, "Title": "Batman", "Total_Director_Profit": 2352496658}, {"Director": "Tim Burton", "Profit": 324459076, "Title": "Charlie and the Chocolate Factory", "Total_Director_Profit": 2352496658}, {"Director": "Tim Burton", "Profit": 262211740, "Title": "Planet of the Apes", "Total_Director_Profit": 2352496658}, {"Director": "Tim Burton", "Profit": 186822354, "Title": "Batman Returns", "Total_Director_Profit": 2352496658}, {"Director": "Tim Burton", "Profit": 137068340, "Title": "Sleepy Hollow", "Total_Director_Profit": 2352496658}, {"Director": "Tim Burton", "Profit": 87359111, "Title": "The Corpse Bride", "Total_Director_Profit": 2352496658}, {"Director": "Tim Burton", "Profit": 58326666, "Title": "Beetle Juice", "Total_Director_Profit": 2352496658}, {"Director": "Tim Burton", "Profit": 53432867, "Title": "Big Fish", "Total_Director_Profit": 2352496658}, {"Director": "Tim Burton", "Profit": 33976987, "Title": "Edward Scissorhands", "Total_Director_Profit": 2352496658}, {"Director": "Tim Burton", "Profit": 21371017, "Title": "Mars Attacks!", "Total_Director_Profit": 2352496658}, {"Director": "Tim Burton", "Profit": -12171534, "Title": "Ed Wood", "Total_Director_Profit": 2352496658}, {"Director": "Andrew Adamson", "Profit": 849838758, "Title": "Shrek 2", "Total_Director_Profit": 2047535219}, {"Director": "Andrew Adamson", "Profit": 568806957, "Title": "The Chronicles of Narnia: The Lion, the Witch and the Wardrobe", "Total_Director_Profit": 2047535219}, {"Director": "Andrew Adamson", "Profit": 434399218, "Title": "Shrek", "Total_Director_Profit": 2047535219}, {"Director": "Andrew Adamson", "Profit": 194490286, "Title": "The Chronicles of Narnia: Prince Caspian", "Total_Director_Profit": 2047535219}, {"Director": "Sam Raimi", "Profit": 682708551, "Title": "Spider-Man", "Total_Director_Profit": 2013509905}, {"Director": "Sam Raimi", "Profit": 632871626, "Title": "Spider-Man 3", "Total_Director_Profit": 2013509905}, {"Director": "Sam Raimi", "Profit": 583705001, "Title": "Spider-Man 2", "Total_Director_Profit": 2013509905}, {"Director": "Sam Raimi", "Profit": 55724728, "Title": "Drag Me To Hell", "Total_Director_Profit": 2013509905}, {"Director": "Sam Raimi", "Profit": 34567606, "Title": "The Gift", "Total_Director_Profit": 2013509905}, {"Director": "Sam Raimi", "Profit": 29025000, "Title": "The Evil Dead", "Total_Director_Profit": 2013509905}, {"Director": "Sam Raimi", "Profit": 10502976, "Title": "Army of Darkness", "Total_Director_Profit": 2013509905}, {"Director": "Sam Raimi", "Profit": 2423044, "Title": "Evil Dead II", "Total_Director_Profit": 2013509905}, {"Director": "Sam Raimi", "Profit": -683727, "Title": "A Simple Plan", "Total_Director_Profit": 2013509905}, {"Director": "Sam Raimi", "Profit": -3887360, "Title": "For Love of the Game", "Total_Director_Profit": 2013509905}, {"Director": "Sam Raimi", "Profit": -13447540, "Title": "The Quick and the Dead", "Total_Director_Profit": 2013509905}, {"Director": "Ron Howard", "Profit": 632236138, "Title": "The Da Vinci Code", "Total_Director_Profit": 1959719497}, {"Director": "Ron Howard", "Profit": 335975846, "Title": "Angels & Demons", "Total_Director_Profit": 1959719497}, {"Director": "Ron Howard", "Profit": 269100000, "Title": "Apollo 13", "Total_Director_Profit": 1959719497}, {"Director": "Ron Howard", "Profit": 238708996, "Title": "A Beautiful Mind", "Total_Director_Profit": 1959719497}, {"Director": "Ron Howard", "Profit": 238700000, "Title": "Ransom", "Total_Director_Profit": 1959719497}, {"Director": "Ron Howard", "Profit": 222141403, "Title": "How the Grinch Stole Christmas", "Total_Director_Profit": 1959719497}, {"Director": "Ron Howard", "Profit": 54599495, "Title": "Splash", "Total_Director_Profit": 1959719497}, {"Director": "Ron Howard", "Profit": 20539911, "Title": "Cinderella Man", "Total_Director_Profit": 1959719497}, {"Director": "Ron Howard", "Profit": -855414, "Title": "Frost/Nixon", "Total_Director_Profit": 1959719497}, {"Director": "Ron Howard", "Profit": -24680311, "Title": "EDtv", "Total_Director_Profit": 1959719497}, {"Director": "Ron Howard", "Profit": -26746567, "Title": "The Missing", "Total_Director_Profit": 1959719497}, {"Director": "Christopher Nolan", "Profit": 837345358, "Title": "The Dark Knight", "Total_Director_Profit": 1823751815}, {"Director": "Christopher Nolan", "Profit": 593830280, "Title": "Inception", "Total_Director_Profit": 1823751815}, {"Director": "Christopher Nolan", "Profit": 222353017, "Title": "Batman Begins", "Total_Director_Profit": 1823751815}, {"Director": "Christopher Nolan", "Profit": 67896006, "Title": "The Prestige", "Total_Director_Profit": 1823751815}, {"Director": "Christopher Nolan", "Profit": 67622499, "Title": "Insomnia", "Total_Director_Profit": 1823751815}, {"Director": "Christopher Nolan", "Profit": 34665950, "Title": "Memento", "Total_Director_Profit": 1823751815}, {"Director": "Christopher Nolan", "Profit": 38705, "Title": "Following", "Total_Director_Profit": 1823751815}, {"Director": "M. Night Shyamalan", "Profit": 632806292, "Title": "The Sixth Sense", "Total_Director_Profit": 1575120870}, {"Director": "M. Night Shyamalan", "Profit": 337563071, "Title": "Signs", "Total_Director_Profit": 1575120870}, {"Director": "M. Night Shyamalan", "Profit": 188514545, "Title": "The Village", "Total_Director_Profit": 1575120870}, {"Director": "M. Night Shyamalan", "Profit": 174856037, "Title": "Unbreakable", "Total_Director_Profit": 1575120870}, {"Director": "M. Night Shyamalan", "Profit": 140191957, "Title": "The Last Airbender", "Total_Director_Profit": 1575120870}, {"Director": "M. Night Shyamalan", "Profit": 103403799, "Title": "The Happening", "Total_Director_Profit": 1575120870}, {"Director": "M. Night Shyamalan", "Profit": -2214831, "Title": "Lady in the Water", "Total_Director_Profit": 1575120870}, {"Director": "David Yates", "Profit": 788468864, "Title": "Harry Potter and the Order of the Phoenix", "Total_Director_Profit": 1475968769}, {"Director": "David Yates", "Profit": 687499905, "Title": "Harry Potter and the Half-Blood Prince", "Total_Director_Profit": 1475968769}, {"Director": "John Lasseter", "Profit": 394966906, "Title": "Toy Story 2", "Total_Director_Profit": 1436948978}, {"Director": "John Lasseter", "Profit": 391923762, "Title": "Cars", "Total_Director_Profit": 1436948978}, {"Director": "John Lasseter", "Profit": 331948825, "Title": "Toy Story", "Total_Director_Profit": 1436948978}, {"Director": "John Lasseter", "Profit": 318109485, "Title": "A Bug's Life", "Total_Director_Profit": 1436948978}, {"Director": "Carlos Saldanha", "Profit": 796685941, "Title": "Ice Age: Dawn of the Dinosaurs", "Total_Director_Profit": 1373585223}, {"Director": "Carlos Saldanha", "Profit": 576899282, "Title": "Ice Age: The Meltdown", "Total_Director_Profit": 1373585223}, {"Director": "Andy Wachowski", "Profit": 611576929, "Title": "The Matrix Reloaded", "Total_Director_Profit": 1296311080}, {"Director": "Andy Wachowski", "Profit": 395279930, "Title": "The Matrix", "Total_Director_Profit": 1296311080}, {"Director": "Andy Wachowski", "Profit": 314259759, "Title": "The Matrix Revolutions", "Total_Director_Profit": 1296311080}, {"Director": "Andy Wachowski", "Profit": 1800000, "Title": "Bound", "Total_Director_Profit": 1296311080}, {"Director": "Andy Wachowski", "Profit": -26605538, "Title": "Speed Racer", "Total_Director_Profit": 1296311080}, {"Director": "Ridley Scott", "Profit": 354683805, "Title": "Gladiator", "Total_Director_Profit": 1288926405}, {"Director": "Ridley Scott", "Profit": 263100280, "Title": "Hannibal", "Total_Director_Profit": 1288926405}, {"Director": "Ridley Scott", "Profit": 194630630, "Title": "Alien", "Total_Director_Profit": 1288926405}, {"Director": "Ridley Scott", "Profit": 165697825, "Title": "American Gangster", "Total_Director_Profit": 1288926405}, {"Director": "Ridley Scott", "Profit": 101398413, "Title": "Kingdom of Heaven", "Total_Director_Profit": 1288926405}, {"Director": "Ridley Scott", "Profit": 100885538, "Title": "Robin Hood", "Total_Director_Profit": 1288926405}, {"Director": "Ridley Scott", "Profit": 78638745, "Title": "Black Hawk Down", "Total_Director_Profit": 1288926405}, {"Director": "Ridley Scott", "Profit": 40894666, "Title": "Body of Lies", "Total_Director_Profit": 1288926405}, {"Director": "Ridley Scott", "Profit": 15892212, "Title": "Black Rain", "Total_Director_Profit": 1288926405}, {"Director": "Ridley Scott", "Profit": 7064105, "Title": "A Good Year", "Total_Director_Profit": 1288926405}, {"Director": "Ridley Scott", "Profit": 5139618, "Title": "Blade Runner", "Total_Director_Profit": 1288926405}, {"Director": "Ridley Scott", "Profit": -1830844, "Title": "G.I.Jane", "Total_Director_Profit": 1288926405}, {"Director": "Ridley Scott", "Profit": -9497888, "Title": "Legend", "Total_Director_Profit": 1288926405}, {"Director": "Ridley Scott", "Profit": -27770700, "Title": "White Squall", "Total_Director_Profit": 1288926405}], "metadata": {"Director": {"type": "string", "semanticType": "Name"}, "Profit": {"type": "number", "semanticType": "Number"}, "Title": {"type": "string", "semanticType": "Name"}, "Total_Director_Profit": {"type": "number", "semanticType": "Number"}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df = df_movies.copy()\n df['Profit'] = df['Worldwide Gross'] - df['Production Budget']\n \n # Remove rows with missing Director or Profit values\n df = df.dropna(subset=['Director', 'Profit'])\n \n # Calculate total profit per director\n director_profit = df.groupby('Director')['Profit'].sum().reset_index()\n director_profit.columns = ['Director', 'Total_Director_Profit']\n \n # Get top 20 directors by total profit\n top_20_directors = director_profit.nlargest(20, 'Total_Director_Profit')['Director'].tolist()\n \n # Filter movies by top 20 directors\n df_filtered = df[df['Director'].isin(top_20_directors)].copy()\n \n # Add total director profit to each movie row\n df_filtered = df_filtered.merge(director_profit, on='Director', how='left')\n \n # Select and order columns\n transformed_df = df_filtered[['Director', 'Title', 'Profit', 'Total_Director_Profit']].copy()\n \n # Sort by total director profit (descending) and then by movie profit (descending)\n transformed_df = transformed_df.sort_values(['Total_Director_Profit', 'Profit'], ascending=[False, False])\n \n return transformed_df\n", "source": ["movies"], "dialog": [{"content": "You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 441226247.0, 533345358.0, 600788188.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 976457891.0, 1133027325.0, 1842879955.0\n\t*US DVD Sales -- type: float64, values: nan, 1978111.0, 140689412.0, ..., 100178981.0, 104578578.0, 234119058.0, 320830925.0\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 15000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, nan, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.5, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 245271.0, 261439.0, 326950.0, 417703.0\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[GOAL]\n\nshow top 20 directories based on profit breakdown by their movies", "role": "user"}, {"content": "```json\n{\n \"mode\": \"infer\",\n \"recap\": \"Show top 20 directors based on total profit, with a breakdown of profit by their individual movies\",\n \"display_instruction\": \"Show top 20 **directors** by total **profit** with movie breakdown\",\n \"recommendation\": \"Calculate profit (Worldwide Gross - Production Budget) for each movie, then aggregate by director to find top 20 directors by total profit. Display individual movie profits for each director to show the breakdown.\",\n \"output_fields\": [\"Director\", \"Title\", \"Profit\", \"Total_Director_Profit\"],\n \"chart_type\": \"group_bar\",\n \"chart_encodings\": {\n \"x\": \"Director\",\n \"y\": \"Profit\",\n \"color\": \"Title\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df = df_movies.copy()\n df['Profit'] = df['Worldwide Gross'] - df['Production Budget']\n \n # Remove rows with missing Director or Profit values\n df = df.dropna(subset=['Director', 'Profit'])\n \n # Calculate total profit per director\n director_profit = df.groupby('Director')['Profit'].sum().reset_index()\n director_profit.columns = ['Director', 'Total_Director_Profit']\n \n # Get top 20 directors by total profit\n top_20_directors = director_profit.nlargest(20, 'Total_Director_Profit')['Director'].tolist()\n \n # Filter movies by top 20 directors\n df_filtered = df[df['Director'].isin(top_20_directors)].copy()\n \n # Add total director profit to each movie row\n df_filtered = df_filtered.merge(director_profit, on='Director', how='left')\n \n # Select and order columns\n transformed_df = df_filtered[['Director', 'Title', 'Profit', 'Total_Director_Profit']].copy()\n \n # Sort by total director profit (descending) and then by movie profit (descending)\n transformed_df = transformed_df.sort_values(['Total_Director_Profit', 'Profit'], ascending=[False, False])\n \n return transformed_df\n```", "role": "assistant"}], "trigger": {"tableId": "movies", "resultTableId": "table-770727", "chart": {"id": "chart-1760743765735", "chartType": "Auto", "encodingMap": {}, "tableRef": "movies", "saved": false, "source": "trigger", "unread": true}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "show top 20 directories based on profit breakdown by their movies", "displayContent": "Show top 20 **directors** by total **profit** with movie breakdown"}]}, "explanation": {"agent": "CodeExplanationAgent", "code": "This code performs a director profitability analysis:\n\n1. Calculate **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n\n2. Remove movies with missing **Director** or **Profit** information to ensure clean analysis\n\n3. Aggregate movies by **Director** to compute **Total_Director_Profit** (sum of all profits from their movies)\n\n4. Identify the **top 20 directors** ranked by their total profit across all films\n\n5. Filter the dataset to include only movies directed by these **top 20 directors**\n\n6. Enrich each movie record with its director's **Total_Director_Profit** for comparison context\n\n7. Sort results first by **Total_Director_Profit** (descending) to group directors by overall success, then by individual movie **Profit** (descending) to rank films within each director's portfolio", "concepts": [{"explanation": "Net financial return for each movie, calculated as: \\( \\text{Profit} = \\text{Worldwide\\_Gross} - \\text{Production\\_Budget} \\). Positive values indicate profitable films, while negative values represent financial losses.", "field": "Profit"}, {"explanation": "Cumulative profit generated by a director across all their films in the dataset, computed as: \\( \\text{Total\\_Director\\_Profit} = \\sum \\text{Profit}_{\\text{all movies by director}} \\). This metric measures a director's overall commercial track record and financial value to studios.", "field": "Total_Director_Profit"}], "dialog": [{"content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 402111870.0, 403706375.0, 600788188.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 976457891.0, 1133027325.0, 1842879955.0\n\t*US DVD Sales -- type: float64, values: 893953.0, 2638319.0, 3507046.0, ..., 104578578.0, 124058348.0, 234119058.0, 320830925.0\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 23000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: 46.0, 72.0, 74.0, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: nan, 1.5, 2.5, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 212985.0, 237477.0, 417703.0, nan\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df = df_movies.copy()\n df['Profit'] = df['Worldwide Gross'] - df['Production Budget']\n \n # Remove rows with missing Director or Profit values\n df = df.dropna(subset=['Director', 'Profit'])\n \n # Calculate total profit per director\n director_profit = df.groupby('Director')['Profit'].sum().reset_index()\n director_profit.columns = ['Director', 'Total_Director_Profit']\n \n # Get top 20 directors by total profit\n top_20_directors = director_profit.nlargest(20, 'Total_Director_Profit')['Director'].tolist()\n \n # Filter movies by top 20 directors\n df_filtered = df[df['Director'].isin(top_20_directors)].copy()\n \n # Add total director profit to each movie row\n df_filtered = df_filtered.merge(director_profit, on='Director', how='left')\n \n # Select and order columns\n transformed_df = df_filtered[['Director', 'Title', 'Profit', 'Total_Director_Profit']].copy()\n \n # Sort by total director profit (descending) and then by movie profit (descending)\n transformed_df = transformed_df.sort_values(['Total_Director_Profit', 'Profit'], ascending=[False, False])\n \n return transformed_df\n\n\n[EXPLANATION]\n", "role": "user"}, {"content": "[CODE EXPLANATION]\n\nThis code performs a director profitability analysis:\n\n1. Calculate **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n\n2. Remove movies with missing **Director** or **Profit** information to ensure clean analysis\n\n3. Aggregate movies by **Director** to compute **Total_Director_Profit** (sum of all profits from their movies)\n\n4. Identify the **top 20 directors** ranked by their total profit across all films\n\n5. Filter the dataset to include only movies directed by these **top 20 directors**\n\n6. Enrich each movie record with its director's **Total_Director_Profit** for comparison context\n\n7. Sort results first by **Total_Director_Profit** (descending) to group directors by overall success, then by individual movie **Profit** (descending) to rank films within each director's portfolio\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"Profit\",\n \"explanation\": \"Net financial return for each movie, calculated as: \\\\( \\\\text{Profit} = \\\\text{Worldwide\\\\_Gross} - \\\\text{Production\\\\_Budget} \\\\). Positive values indicate profitable films, while negative values represent financial losses.\"\n },\n {\n \"field\": \"Total_Director_Profit\",\n \"explanation\": \"Cumulative profit generated by a director across all their films in the dataset, computed as: \\\\( \\\\text{Total\\\\_Director\\\\_Profit} = \\\\sum \\\\text{Profit}_{\\\\text{all movies by director}} \\\\). This metric measures a director's overall commercial track record and financial value to studios.\"\n }\n]\n```", "role": "assistant"}], "status": "ok"}, "status": "completed"}, "anchored": false, "attachedMetadata": ""}, {"kind": "table", "id": "table-89", "displayId": "director-genre-profit", "names": ["Director", "Genre_Profit", "Major Genre", "Total_Director_Profit"], "rows": [{"Director": "Steven Spielberg", "Genre_Profit": 2288324872, "Major Genre": "Action", "Total_Director_Profit": 7169573056}, {"Director": "Steven Spielberg", "Genre_Profit": 2222007781, "Major Genre": "Drama", "Total_Director_Profit": 7169573056}, {"Director": "Steven Spielberg", "Genre_Profit": 2118165403, "Major Genre": "Adventure", "Total_Director_Profit": 7169573056}, {"Director": "Steven Spielberg", "Genre_Profit": 478200000, "Major Genre": "Horror", "Total_Director_Profit": 7169573056}, {"Director": "Steven Spielberg", "Genre_Profit": 62875000, "Major Genre": "Comedy", "Total_Director_Profit": 7169573056}, {"Director": "James Cameron", "Genre_Profit": 3435186261, "Major Genre": "Action", "Total_Director_Profit": 5078066216}, {"Director": "James Cameron", "Genre_Profit": 1642879955, "Major Genre": "Thriller/Suspense", "Total_Director_Profit": 5078066216}, {"Director": "Chris Columbus", "Genre_Profit": 1761881048, "Major Genre": "Adventure", "Total_Director_Profit": 3036030427}, {"Director": "Chris Columbus", "Genre_Profit": 1215348066, "Major Genre": "Comedy", "Total_Director_Profit": 3036030427}, {"Director": "Chris Columbus", "Genre_Profit": 67130693, "Major Genre": "Drama", "Total_Director_Profit": 3036030427}, {"Director": "Chris Columbus", "Genre_Profit": -8329380, "Major Genre": "Musical", "Total_Director_Profit": 3036030427}, {"Director": "George Lucas", "Genre_Profit": 2871882789, "Major Genre": "Adventure", "Total_Director_Profit": 3011105789}, {"Director": "George Lucas", "Genre_Profit": 139223000, "Major Genre": "Comedy", "Total_Director_Profit": 3011105789}, {"Director": "Peter Jackson", "Genre_Profit": 2974450745, "Major Genre": "Adventure", "Total_Director_Profit": 3001395936}, {"Director": "Peter Jackson", "Genre_Profit": 29702568, "Major Genre": "Drama", "Total_Director_Profit": 3001395936}, {"Director": "Peter Jackson", "Genre_Profit": -2757377, "Major Genre": "Horror", "Total_Director_Profit": 3001395936}, {"Director": "Robert Zemeckis", "Genre_Profit": 1176274785, "Major Genre": "Drama", "Total_Director_Profit": 2788716998}, {"Director": "Robert Zemeckis", "Genre_Profit": 1038225574, "Major Genre": "Adventure", "Total_Director_Profit": 2788716998}, {"Director": "Robert Zemeckis", "Genre_Profit": 375522650, "Major Genre": "Comedy", "Total_Director_Profit": 2788716998}, {"Director": "Robert Zemeckis", "Genre_Profit": 198693989, "Major Genre": "Thriller/Suspense", "Total_Director_Profit": 2788716998}, {"Director": "Michael Bay", "Genre_Profit": 2046592847, "Major Genre": "Action", "Total_Director_Profit": 2461192847}, {"Director": "Michael Bay", "Genre_Profit": 414600000, "Major Genre": "Adventure", "Total_Director_Profit": 2461192847}, {"Director": "Roland Emmerich", "Genre_Profit": 1325738958, "Major Genre": "Adventure", "Total_Director_Profit": 2390416794}, {"Director": "Roland Emmerich", "Genre_Profit": 959377836, "Major Genre": "Action", "Total_Director_Profit": 2390416794}, {"Director": "Roland Emmerich", "Genre_Profit": 105300000, "Major Genre": "Drama", "Total_Director_Profit": 2390416794}, {"Director": "Gore Verbinski", "Genre_Profit": 2031667528, "Major Genre": "Adventure", "Total_Director_Profit": 2359931719}, {"Director": "Gore Verbinski", "Genre_Profit": 201094024, "Major Genre": "Horror", "Total_Director_Profit": 2359931719}, {"Director": "Gore Verbinski", "Genre_Profit": 107808615, "Major Genre": "Action", "Total_Director_Profit": 2359931719}, {"Director": "Gore Verbinski", "Genre_Profit": 23894591, "Major Genre": "Comedy", "Total_Director_Profit": 2359931719}, {"Director": "Gore Verbinski", "Genre_Profit": -4533039, "Major Genre": "Drama", "Total_Director_Profit": 2359931719}, {"Director": "Tim Burton", "Genre_Profit": 1172861961, "Major Genre": "Adventure", "Total_Director_Profit": 2352496658}, {"Director": "Tim Burton", "Genre_Profit": 563171278, "Major Genre": "Action", "Total_Director_Profit": 2352496658}, {"Director": "Tim Burton", "Genre_Profit": 425962212, "Major Genre": "Comedy", "Total_Director_Profit": 2352496658}, {"Director": "Tim Burton", "Genre_Profit": 137068340, "Major Genre": "Horror", "Total_Director_Profit": 2352496658}, {"Director": "Tim Burton", "Genre_Profit": 53432867, "Major Genre": "Drama", "Total_Director_Profit": 2352496658}, {"Director": "Andrew Adamson", "Genre_Profit": 2047535219, "Major Genre": "Adventure", "Total_Director_Profit": 2047535219}, {"Director": "Sam Raimi", "Genre_Profit": 1899285178, "Major Genre": "Adventure", "Total_Director_Profit": 2013509905}, {"Director": "Sam Raimi", "Genre_Profit": 97675748, "Major Genre": "Horror", "Total_Director_Profit": 2013509905}, {"Director": "Sam Raimi", "Genre_Profit": 34567606, "Major Genre": "Thriller/Suspense", "Total_Director_Profit": 2013509905}, {"Director": "Sam Raimi", "Genre_Profit": -4571087, "Major Genre": "Drama", "Total_Director_Profit": 2013509905}, {"Director": "Sam Raimi", "Genre_Profit": -13447540, "Major Genre": "Western", "Total_Director_Profit": 2013509905}, {"Director": "Ron Howard", "Genre_Profit": 968211984, "Major Genre": "Thriller/Suspense", "Total_Director_Profit": 1959719497}, {"Director": "Ron Howard", "Genre_Profit": 527493493, "Major Genre": "Drama", "Total_Director_Profit": 1959719497}, {"Director": "Ron Howard", "Genre_Profit": 252060587, "Major Genre": "Comedy", "Total_Director_Profit": 1959719497}, {"Director": "Ron Howard", "Genre_Profit": 238700000, "Major Genre": "Action", "Total_Director_Profit": 1959719497}, {"Director": "Ron Howard", "Genre_Profit": -26746567, "Major Genre": "Western", "Total_Director_Profit": 1959719497}, {"Director": "Christopher Nolan", "Genre_Profit": 1059698375, "Major Genre": "Action", "Total_Director_Profit": 1823713110}, {"Director": "Christopher Nolan", "Genre_Profit": 729348785, "Major Genre": "Thriller/Suspense", "Total_Director_Profit": 1823713110}, {"Director": "Christopher Nolan", "Genre_Profit": 34665950, "Major Genre": "Drama", "Total_Director_Profit": 1823713110}, {"Director": "M. Night Shyamalan", "Genre_Profit": 1437143744, "Major Genre": "Thriller/Suspense", "Total_Director_Profit": 1575120870}, {"Director": "M. Night Shyamalan", "Genre_Profit": 140191957, "Major Genre": "Adventure", "Total_Director_Profit": 1575120870}, {"Director": "M. Night Shyamalan", "Genre_Profit": -2214831, "Major Genre": "Drama", "Total_Director_Profit": 1575120870}, {"Director": "David Yates", "Genre_Profit": 1475968769, "Major Genre": "Adventure", "Total_Director_Profit": 1475968769}, {"Director": "John Lasseter", "Genre_Profit": 1045025216, "Major Genre": "Adventure", "Total_Director_Profit": 1436948978}, {"Director": "John Lasseter", "Genre_Profit": 391923762, "Major Genre": "Comedy", "Total_Director_Profit": 1436948978}, {"Director": "Carlos Saldanha", "Genre_Profit": 1373585223, "Major Genre": "Adventure", "Total_Director_Profit": 1373585223}, {"Director": "Andy Wachowski", "Genre_Profit": 1294511080, "Major Genre": "Action", "Total_Director_Profit": 1296311080}, {"Director": "Andy Wachowski", "Genre_Profit": 1800000, "Major Genre": "Thriller/Suspense", "Total_Director_Profit": 1296311080}, {"Director": "Ridley Scott", "Genre_Profit": 534208088, "Major Genre": "Action", "Total_Director_Profit": 1288926405}, {"Director": "Ridley Scott", "Genre_Profit": 457730910, "Major Genre": "Horror", "Total_Director_Profit": 1288926405}, {"Director": "Ridley Scott", "Genre_Profit": 170931086, "Major Genre": "Drama", "Total_Director_Profit": 1288926405}, {"Director": "Ridley Scott", "Genre_Profit": 64129825, "Major Genre": "Adventure", "Total_Director_Profit": 1288926405}, {"Director": "Ridley Scott", "Genre_Profit": 61926496, "Major Genre": "Thriller/Suspense", "Total_Director_Profit": 1288926405}], "metadata": {"Director": {"type": "string", "semanticType": "Name"}, "Genre_Profit": {"type": "number", "semanticType": "Number"}, "Major Genre": {"type": "string", "semanticType": "String"}, "Total_Director_Profit": {"type": "number", "semanticType": "Number"}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df = df_movies.copy()\n df['Profit'] = df['Worldwide Gross'] - df['Production Budget']\n \n # Remove rows with missing Director, Profit, or Major Genre values\n df = df.dropna(subset=['Director', 'Profit', 'Major Genre'])\n \n # Calculate total profit per director\n director_profit = df.groupby('Director')['Profit'].sum().reset_index()\n director_profit.columns = ['Director', 'Total_Director_Profit']\n \n # Get top 20 directors by total profit\n top_20_directors = director_profit.nlargest(20, 'Total_Director_Profit')['Director'].tolist()\n \n # Filter movies by top 20 directors\n df_filtered = df[df['Director'].isin(top_20_directors)].copy()\n \n # Aggregate profit by Director and Major Genre\n genre_breakdown = df_filtered.groupby(['Director', 'Major Genre'])['Profit'].sum().reset_index()\n genre_breakdown.columns = ['Director', 'Major Genre', 'Genre_Profit']\n \n # Add total director profit to each row\n genre_breakdown = genre_breakdown.merge(director_profit, on='Director', how='left')\n \n # Sort by total director profit (descending) and then by genre profit (descending)\n transformed_df = genre_breakdown.sort_values(['Total_Director_Profit', 'Genre_Profit'], ascending=[False, False])\n \n return transformed_df\n", "source": ["movies"], "dialog": [{"content": "You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 441226247.0, 533345358.0, 600788188.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 976457891.0, 1133027325.0, 1842879955.0\n\t*US DVD Sales -- type: float64, values: nan, 1978111.0, 140689412.0, ..., 100178981.0, 104578578.0, 234119058.0, 320830925.0\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 15000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, nan, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.5, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 245271.0, 261439.0, 326950.0, 417703.0\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[GOAL]\n\nshow top 20 directories based on profit breakdown by their movies", "role": "user"}, {"content": "```json\n{\n \"mode\": \"infer\",\n \"recap\": \"Show top 20 directors based on total profit, with a breakdown of profit by their individual movies\",\n \"display_instruction\": \"Show top 20 **directors** by total **profit** with movie breakdown\",\n \"recommendation\": \"Calculate profit (Worldwide Gross - Production Budget) for each movie, then aggregate by director to find top 20 directors by total profit. Display individual movie profits for each director to show the breakdown.\",\n \"output_fields\": [\"Director\", \"Title\", \"Profit\", \"Total_Director_Profit\"],\n \"chart_type\": \"group_bar\",\n \"chart_encodings\": {\n \"x\": \"Director\",\n \"y\": \"Profit\",\n \"color\": \"Title\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df = df_movies.copy()\n df['Profit'] = df['Worldwide Gross'] - df['Production Budget']\n \n # Remove rows with missing Director or Profit values\n df = df.dropna(subset=['Director', 'Profit'])\n \n # Calculate total profit per director\n director_profit = df.groupby('Director')['Profit'].sum().reset_index()\n director_profit.columns = ['Director', 'Total_Director_Profit']\n \n # Get top 20 directors by total profit\n top_20_directors = director_profit.nlargest(20, 'Total_Director_Profit')['Director'].tolist()\n \n # Filter movies by top 20 directors\n df_filtered = df[df['Director'].isin(top_20_directors)].copy()\n \n # Add total director profit to each movie row\n df_filtered = df_filtered.merge(director_profit, on='Director', how='left')\n \n # Select and order columns\n transformed_df = df_filtered[['Director', 'Title', 'Profit', 'Total_Director_Profit']].copy()\n \n # Sort by total director profit (descending) and then by movie profit (descending)\n transformed_df = transformed_df.sort_values(['Total_Director_Profit', 'Profit'], ascending=[False, False])\n \n return transformed_df\n```", "role": "assistant"}, {"content": "This is the result from the latest python code:\n\n Director Profit Title Total_Director_Profit\n0 Steven Spielberg 860067947 Jurassic Park 7169573056\n1 Steven Spielberg 782410554 ET: The Extra-Terrestrial 7169573056\n2 Steven Spielberg 711686679 The Lost World: Jurassic Park 7169573056\n3 Steven Spielberg 601558145 Indiana Jones and the Kingdom of the Crystal Skull 7169573056\n4 Steven Spielberg 459745532 The War of the Worlds 7169573056\n5 Steven Spielberg 458700000 Jaws 7169573056\n6 Steven Spielberg 426171806 Indiana Jones and the Last Crusade 7169573056\n7 Steven Spielberg 416635085 Saving Private Ryan 7169573056\n8 Steven Spielberg 366800358 Raiders of the Lost Ark 7169573056\n9 Steven Spielberg 317700000 Close Encounters of the Third Kind 7169573056\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"break down by movie genre instead\",\n \"chart_type\": \"Bar Chart\",\n \"chart_encodings\": {\n \"x\": \"Director\",\n \"y\": \"Profit\",\n \"color\": \"Title\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Show top 20 directors based on total profit, with a breakdown by the major genre of their movies instead of individual movie titles. This will show how each director's profit is distributed across different genres.\",\n \"display_instruction\": \"Show top 20 **directors** by total **profit** with **genre** breakdown\",\n \"output_fields\": [\"Director\", \"Major Genre\", \"Genre_Profit\", \"Total_Director_Profit\"],\n \"chart_encodings\": {\n \"x\": \"Director\",\n \"y\": \"Genre_Profit\",\n \"color\": \"Major Genre\"\n },\n \"reason\": \"The user wants to change the breakdown from individual movies to movie genres. Instead of showing profit by Title, we now aggregate profit by Major Genre for each director. The chart_encodings are updated to use 'Major Genre' for color instead of 'Title', and y-axis now shows 'Genre_Profit' (aggregated by genre) instead of individual movie profit.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df = df_movies.copy()\n df['Profit'] = df['Worldwide Gross'] - df['Production Budget']\n \n # Remove rows with missing Director, Profit, or Major Genre values\n df = df.dropna(subset=['Director', 'Profit', 'Major Genre'])\n \n # Calculate total profit per director\n director_profit = df.groupby('Director')['Profit'].sum().reset_index()\n director_profit.columns = ['Director', 'Total_Director_Profit']\n \n # Get top 20 directors by total profit\n top_20_directors = director_profit.nlargest(20, 'Total_Director_Profit')['Director'].tolist()\n \n # Filter movies by top 20 directors\n df_filtered = df[df['Director'].isin(top_20_directors)].copy()\n \n # Aggregate profit by Director and Major Genre\n genre_breakdown = df_filtered.groupby(['Director', 'Major Genre'])['Profit'].sum().reset_index()\n genre_breakdown.columns = ['Director', 'Major Genre', 'Genre_Profit']\n \n # Add total director profit to each row\n genre_breakdown = genre_breakdown.merge(director_profit, on='Director', how='left')\n \n # Sort by total director profit (descending) and then by genre profit (descending)\n transformed_df = genre_breakdown.sort_values(['Total_Director_Profit', 'Genre_Profit'], ascending=[False, False])\n \n return transformed_df\n```", "role": "assistant"}], "trigger": {"tableId": "table-770727", "resultTableId": "table-89", "chart": {"id": "chart-1760743802354", "chartType": "Bar Chart", "encodingMap": {"x": {"fieldID": "original--movies--Director"}, "y": {"fieldID": "concept-Profit-1760742653681"}, "color": {"fieldID": "original--movies--Title"}, "opacity": {"channel": "opacity", "bin": false}, "column": {"channel": "column", "bin": false}, "row": {"channel": "row", "bin": false}}, "tableRef": "table-770727", "saved": false, "source": "trigger", "unread": false}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "break down by movie genre instead", "displayContent": "Show top 20 **directors** by total **profit** with **genre** breakdown"}]}, "explanation": {"agent": "CodeExplanationAgent", "code": "- Calculate **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n- Remove movies with missing **Director**, **Profit**, or **Major Genre** information\n- Aggregate the **Profit** for each **Director** to calculate their **Total_Director_Profit**\n- Identify the **top 20 directors** based on their **Total_Director_Profit**\n- Filter the dataset to include only movies made by these **top 20 directors**\n- Group the filtered movies by **Director** and **Major Genre**, summing the **Profit** for each combination to create **Genre_Profit**\n- Merge the **Total_Director_Profit** back into the genre breakdown data\n- Sort the results first by **Total_Director_Profit** (highest to lowest), then by **Genre_Profit** (highest to lowest) within each director", "concepts": [{"explanation": "The financial return of a movie, calculated as \\( \\text{Profit} = \\text{Worldwide\\_Gross} - \\text{Production\\_Budget} \\). This represents the net revenue after accounting for production costs.", "field": "Profit"}, {"explanation": "The cumulative profit generated by all movies directed by a specific director. This metric aggregates the Profit across all films in their portfolio to measure overall financial success.", "field": "Total_Director_Profit"}, {"explanation": "The total profit generated by a director within a specific Major Genre. This breaks down a director's Total_Director_Profit by genre category, showing which genres contribute most to their financial success.", "field": "Genre_Profit"}], "dialog": [{"content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 403706375.0, 441226247.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., nan, 1133027325.0, 1842879955.0, 2767891499.0\n\t*US DVD Sales -- type: float64, values: 7166365.0, 33619971.0, 140689412.0, ..., nan, nan, 46260220.0, nan\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 15000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.5, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., nan, nan, 212985.0, 411088.0\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Calculate profit for each movie\n df = df_movies.copy()\n df['Profit'] = df['Worldwide Gross'] - df['Production Budget']\n \n # Remove rows with missing Director, Profit, or Major Genre values\n df = df.dropna(subset=['Director', 'Profit', 'Major Genre'])\n \n # Calculate total profit per director\n director_profit = df.groupby('Director')['Profit'].sum().reset_index()\n director_profit.columns = ['Director', 'Total_Director_Profit']\n \n # Get top 20 directors by total profit\n top_20_directors = director_profit.nlargest(20, 'Total_Director_Profit')['Director'].tolist()\n \n # Filter movies by top 20 directors\n df_filtered = df[df['Director'].isin(top_20_directors)].copy()\n \n # Aggregate profit by Director and Major Genre\n genre_breakdown = df_filtered.groupby(['Director', 'Major Genre'])['Profit'].sum().reset_index()\n genre_breakdown.columns = ['Director', 'Major Genre', 'Genre_Profit']\n \n # Add total director profit to each row\n genre_breakdown = genre_breakdown.merge(director_profit, on='Director', how='left')\n \n # Sort by total director profit (descending) and then by genre profit (descending)\n transformed_df = genre_breakdown.sort_values(['Total_Director_Profit', 'Genre_Profit'], ascending=[False, False])\n \n return transformed_df\n\n\n[EXPLANATION]\n", "role": "user"}, {"content": "[CODE EXPLANATION]\n\n- Calculate **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n- Remove movies with missing **Director**, **Profit**, or **Major Genre** information\n- Aggregate the **Profit** for each **Director** to calculate their **Total_Director_Profit**\n- Identify the **top 20 directors** based on their **Total_Director_Profit**\n- Filter the dataset to include only movies made by these **top 20 directors**\n- Group the filtered movies by **Director** and **Major Genre**, summing the **Profit** for each combination to create **Genre_Profit**\n- Merge the **Total_Director_Profit** back into the genre breakdown data\n- Sort the results first by **Total_Director_Profit** (highest to lowest), then by **Genre_Profit** (highest to lowest) within each director\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"Profit\",\n \"explanation\": \"The financial return of a movie, calculated as \\\\( \\\\text{Profit} = \\\\text{Worldwide\\\\_Gross} - \\\\text{Production\\\\_Budget} \\\\). This represents the net revenue after accounting for production costs.\"\n },\n {\n \"field\": \"Total_Director_Profit\",\n \"explanation\": \"The cumulative profit generated by all movies directed by a specific director. This metric aggregates the Profit across all films in their portfolio to measure overall financial success.\"\n },\n {\n \"field\": \"Genre_Profit\",\n \"explanation\": \"The total profit generated by a director within a specific Major Genre. This breaks down a director's Total_Director_Profit by genre category, showing which genres contribute most to their financial success.\"\n }\n]\n```", "role": "assistant"}], "status": "ok"}, "status": "completed"}, "anchored": false, "attachedMetadata": ""}, {"kind": "table", "id": "table-456490", "displayId": "movie-ratings", "names": ["IMDB Rating", "Production Budget", "Rotten Tomatoes Rating", "Title"], "rows": [{"IMDB Rating": 6.1, "Production Budget": 8000000, "Rotten Tomatoes Rating": null, "Title": "The Land Girls"}, {"IMDB Rating": 6.9, "Production Budget": 300000, "Rotten Tomatoes Rating": null, "Title": "First Love, Last Rites"}, {"IMDB Rating": 6.8, "Production Budget": 250000, "Rotten Tomatoes Rating": null, "Title": "I Married a Strange Person"}, {"IMDB Rating": null, "Production Budget": 300000, "Rotten Tomatoes Rating": 13, "Title": "Let's Talk About Sex"}, {"IMDB Rating": 3.4, "Production Budget": 1000000, "Rotten Tomatoes Rating": 62, "Title": "Slam"}, {"IMDB Rating": 7.7, "Production Budget": 6000, "Rotten Tomatoes Rating": null, "Title": "Following"}, {"IMDB Rating": 3.8, "Production Budget": 1600000, "Rotten Tomatoes Rating": null, "Title": "Foolish"}, {"IMDB Rating": 5.8, "Production Budget": 40000000, "Rotten Tomatoes Rating": 25, "Title": "Pirates"}, {"IMDB Rating": 7, "Production Budget": 6000000, "Rotten Tomatoes Rating": 86, "Title": "Duel in the Sun"}, {"IMDB Rating": 7, "Production Budget": 1000000, "Rotten Tomatoes Rating": 81, "Title": "Tom Jones"}, {"IMDB Rating": 7.5, "Production Budget": 10000000, "Rotten Tomatoes Rating": 84, "Title": "Oliver!"}, {"IMDB Rating": 8.4, "Production Budget": 2000000, "Rotten Tomatoes Rating": 97, "Title": "To Kill A Mockingbird"}, {"IMDB Rating": 6.8, "Production Budget": 100000, "Rotten Tomatoes Rating": 87, "Title": "Hollywood Shuffle"}, {"IMDB Rating": 7, "Production Budget": 5200000, "Rotten Tomatoes Rating": null, "Title": "Wilson"}, {"IMDB Rating": 6.1, "Production Budget": 22000000, "Rotten Tomatoes Rating": null, "Title": "Darling Lili"}, {"IMDB Rating": 2.5, "Production Budget": 13500000, "Rotten Tomatoes Rating": 90, "Title": "The Ten Commandments"}, {"IMDB Rating": 8.9, "Production Budget": 340000, "Rotten Tomatoes Rating": null, "Title": "12 Angry Men"}, {"IMDB Rating": 8.1, "Production Budget": 29000000, "Rotten Tomatoes Rating": null, "Title": "Twelve Monkeys"}, {"IMDB Rating": 7, "Production Budget": 4000000, "Rotten Tomatoes Rating": 57, "Title": 1776}, {"IMDB Rating": 5.6, "Production Budget": 32000000, "Rotten Tomatoes Rating": 33, "Title": 1941}, {"IMDB Rating": 6.3, "Production Budget": 1900000, "Rotten Tomatoes Rating": null, "Title": "Chacun sa nuit"}, {"IMDB Rating": 8.4, "Production Budget": 10500000, "Rotten Tomatoes Rating": 96, "Title": "2001: A Space Odyssey"}, {"IMDB Rating": null, "Production Budget": 5000000, "Rotten Tomatoes Rating": 92, "Title": "20,000 Leagues Under the Sea"}, {"IMDB Rating": 6.9, "Production Budget": 2000000, "Rotten Tomatoes Rating": null, "Title": "24 7: Twenty Four Seven"}, {"IMDB Rating": 7.1, "Production Budget": 500000, "Rotten Tomatoes Rating": 77, "Title": "Twin Falls Idaho"}, {"IMDB Rating": 5.7, "Production Budget": 15000000, "Rotten Tomatoes Rating": null, "Title": "3 Men and a Baby"}, {"IMDB Rating": 3.2, "Production Budget": 20000000, "Rotten Tomatoes Rating": 17, "Title": "3 Ninjas Kick Back"}, {"IMDB Rating": 6, "Production Budget": 1500000, "Rotten Tomatoes Rating": null, "Title": "Forty Shades of Blue"}, {"IMDB Rating": 7.7, "Production Budget": 439000, "Rotten Tomatoes Rating": 95, "Title": "42nd Street"}, {"IMDB Rating": 6.4, "Production Budget": 4000000, "Rotten Tomatoes Rating": 14, "Title": "Four Rooms"}, {"IMDB Rating": 7, "Production Budget": 6500000, "Rotten Tomatoes Rating": 71, "Title": "The Four Seasons"}, {"IMDB Rating": 7.1, "Production Budget": 4500000, "Rotten Tomatoes Rating": 96, "Title": "Four Weddings and a Funeral"}, {"IMDB Rating": 7.4, "Production Budget": 350000, "Rotten Tomatoes Rating": 97, "Title": "51 Birch Street"}, {"IMDB Rating": 6.8, "Production Budget": 17000000, "Rotten Tomatoes Rating": 57, "Title": "55 Days at Peking"}, {"IMDB Rating": 5.4, "Production Budget": 18000000, "Rotten Tomatoes Rating": null, "Title": "Nine 1/2 Weeks"}, {"IMDB Rating": 4.9, "Production Budget": 113500000, "Rotten Tomatoes Rating": null, "Title": "AstÈrix aux Jeux Olympiques"}, {"IMDB Rating": 7.6, "Production Budget": 70000000, "Rotten Tomatoes Rating": 88, "Title": "The Abyss"}, {"IMDB Rating": 4.6, "Production Budget": 7000000, "Rotten Tomatoes Rating": 10, "Title": "Action Jackson"}, {"IMDB Rating": 6.6, "Production Budget": 12000000, "Rotten Tomatoes Rating": 49, "Title": "Ace Ventura: Pet Detective"}, {"IMDB Rating": 5.6, "Production Budget": 30000000, "Rotten Tomatoes Rating": null, "Title": "Ace Ventura: When Nature Calls"}, {"IMDB Rating": null, "Production Budget": 5000000, "Rotten Tomatoes Rating": 31, "Title": "April Fool's Day"}, {"IMDB Rating": 5.7, "Production Budget": 4000000, "Rotten Tomatoes Rating": null, "Title": "Among Giants"}, {"IMDB Rating": 7.1, "Production Budget": 3768785, "Rotten Tomatoes Rating": 100, "Title": "Annie Get Your Gun"}, {"IMDB Rating": 6.7, "Production Budget": 3000000, "Rotten Tomatoes Rating": 20, "Title": "Alice in Wonderland"}, {"IMDB Rating": 7.3, "Production Budget": 24000000, "Rotten Tomatoes Rating": null, "Title": "The Princess and the Cobbler"}, {"IMDB Rating": 5.9, "Production Budget": 12000000, "Rotten Tomatoes Rating": 54, "Title": "The Alamo"}, {"IMDB Rating": 3.2, "Production Budget": 32000000, "Rotten Tomatoes Rating": 71, "Title": "Alive"}, {"IMDB Rating": 7.4, "Production Budget": 14000000, "Rotten Tomatoes Rating": null, "Title": "Amen"}, {"IMDB Rating": 7.6, "Production Budget": 777000, "Rotten Tomatoes Rating": 97, "Title": "American Graffiti"}, {"IMDB Rating": 3.7, "Production Budget": 350000, "Rotten Tomatoes Rating": null, "Title": "American Ninja 2: The Confrontation"}, {"IMDB Rating": 6.8, "Production Budget": 62000000, "Rotten Tomatoes Rating": 90, "Title": "The American President"}, {"IMDB Rating": 8.2, "Production Budget": 4000000, "Rotten Tomatoes Rating": 98, "Title": "Annie Hall"}, {"IMDB Rating": 6.1, "Production Budget": 2500000, "Rotten Tomatoes Rating": null, "Title": "Anatomie"}, {"IMDB Rating": 5.8, "Production Budget": 6500000, "Rotten Tomatoes Rating": 62, "Title": "The Adventures of Huck Finn"}, {"IMDB Rating": 8.4, "Production Budget": 3000000, "Rotten Tomatoes Rating": 91, "Title": "The Apartment"}, {"IMDB Rating": 8.6, "Production Budget": 31500000, "Rotten Tomatoes Rating": 98, "Title": "Apocalypse Now"}, {"IMDB Rating": 6.2, "Production Budget": 31000000, "Rotten Tomatoes Rating": 85, "Title": "Arachnophobia"}, {"IMDB Rating": 6.4, "Production Budget": 16500000, "Rotten Tomatoes Rating": null, "Title": "Arn - Tempelriddaren"}, {"IMDB Rating": 5.1, "Production Budget": 600000, "Rotten Tomatoes Rating": null, "Title": "Arnolds Park"}, {"IMDB Rating": 5.6, "Production Budget": 150000, "Rotten Tomatoes Rating": null, "Title": "Sweet Sweetback's Baad Asssss Song"}, {"IMDB Rating": 4.4, "Production Budget": 989000, "Rotten Tomatoes Rating": 17, "Title": "And Then Came Love"}, {"IMDB Rating": 5.6, "Production Budget": 6000000, "Rotten Tomatoes Rating": 73, "Title": "Around the World in 80 Days"}, {"IMDB Rating": 5.7, "Production Budget": 9000000, "Rotten Tomatoes Rating": 74, "Title": "Barbarella"}, {"IMDB Rating": 8.1, "Production Budget": 11000000, "Rotten Tomatoes Rating": 94, "Title": "Barry Lyndon"}, {"IMDB Rating": 5.4, "Production Budget": 4000000, "Rotten Tomatoes Rating": null, "Title": "Barbarians, The"}, {"IMDB Rating": 7.3, "Production Budget": 30000000, "Rotten Tomatoes Rating": 98, "Title": "Babe"}, {"IMDB Rating": 5, "Production Budget": 50000000, "Rotten Tomatoes Rating": 21, "Title": "Baby's Day Out"}, {"IMDB Rating": 7.7, "Production Budget": 35000000, "Rotten Tomatoes Rating": 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"Title": "The Expendables"}, {"IMDB Rating": 4.1, "Production Budget": 60000000, "Rotten Tomatoes Rating": null, "Title": "XXX: State of the Union"}, {"IMDB Rating": 6.3, "Production Budget": 20000000, "Rotten Tomatoes Rating": 64, "Title": "The Yards"}, {"IMDB Rating": 7.7, "Production Budget": 1200000, "Rotten Tomatoes Rating": 95, "Title": "You Can Count on Me"}, {"IMDB Rating": 5, "Production Budget": 60000000, "Rotten Tomatoes Rating": 14, "Title": "Year One"}, {"IMDB Rating": 7, "Production Budget": 50000000, "Rotten Tomatoes Rating": 43, "Title": "Yes Man"}, {"IMDB Rating": 6.7, "Production Budget": 4000000, "Rotten Tomatoes Rating": 78, "Title": "You Kill Me"}, {"IMDB Rating": 7.6, "Production Budget": 45000000, "Rotten Tomatoes Rating": null, "Title": "Yours, Mine and Ours"}, {"IMDB Rating": 6.2, "Production Budget": 65000000, "Rotten Tomatoes Rating": 68, "Title": "You've Got Mail"}, {"IMDB Rating": 6.7, "Production Budget": 18000000, "Rotten Tomatoes Rating": null, "Title": "Youth in Revolt"}, {"IMDB Rating": 4.5, "Production Budget": 800000, "Rotten Tomatoes Rating": null, "Title": "The Young Unknowns"}, {"IMDB Rating": 7.2, "Production Budget": 35000000, "Rotten Tomatoes Rating": 76, "Title": "The Young Victoria"}, {"IMDB Rating": null, "Production Budget": 65000000, "Rotten Tomatoes Rating": 75, "Title": "Zathura"}, {"IMDB Rating": 6.8, "Production Budget": 5000000, "Rotten Tomatoes Rating": 66, "Title": "Zero Effect"}, {"IMDB Rating": 6.4, "Production Budget": 28000000, "Rotten Tomatoes Rating": 62, "Title": "Zoolander"}, {"IMDB Rating": 7.8, "Production Budget": 23600000, "Rotten Tomatoes Rating": 89, "Title": "Zombieland"}, {"IMDB Rating": 7, "Production Budget": 24000000, "Rotten Tomatoes Rating": 65, "Title": "Zack and Miri Make a Porno"}, {"IMDB Rating": null, "Production Budget": 85000000, "Rotten Tomatoes Rating": 89, "Title": "Zodiac"}, {"IMDB Rating": 3.4, "Production Budget": 35000000, "Rotten Tomatoes Rating": 3, "Title": "Zoom"}, {"IMDB Rating": 5.7, "Production Budget": 80000000, "Rotten Tomatoes Rating": 26, "Title": "The Legend of Zorro"}, {"IMDB Rating": 6.7, "Production Budget": 65000000, "Rotten Tomatoes Rating": 82, "Title": "The Mask of Zorro"}], "metadata": {"IMDB Rating": {"type": "number", "semanticType": "Number"}, "Production Budget": {"type": "number", "semanticType": "Number"}, "Rotten Tomatoes Rating": {"type": "number", "semanticType": "Number"}, "Title": {"type": "string", "semanticType": "Name"}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values in key fields\n transformed_df = df_movies[['Title', 'Production Budget', 'Rotten Tomatoes Rating', 'IMDB Rating']].copy()\n \n # Remove rows where Production Budget is missing\n transformed_df = transformed_df[transformed_df['Production Budget'].notna()]\n \n # Keep rows where at least one rating is available\n transformed_df = transformed_df[\n (transformed_df['Rotten Tomatoes Rating'].notna()) | \n (transformed_df['IMDB Rating'].notna())\n ]\n \n return transformed_df\n", "source": ["movies"], "dialog": [{"content": "You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 403706375.0, 441226247.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 976457891.0, 1133027325.0, 1842879955.0\n\t*US DVD Sales -- type: float64, values: 15442818.0, 17031573.0, 33619971.0, ..., nan, nan, 46260220.0, nan\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 5000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.0, 2.4, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 212985.0, 237477.0, 417703.0, nan\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[GOAL]\n\nDo higher production budgets correlate with better ratings?", "role": "user"}, {"content": "```json\n{\n \"mode\": \"infer\",\n \"recap\": \"The user wants to analyze the correlation between production budgets and movie ratings to understand if higher budgets lead to better ratings.\",\n \"display_instruction\": \"Show correlation between **Production Budget** and **ratings**\",\n \"recommendation\": \"Create a scatter plot visualization to show the relationship between Production Budget and ratings. Since there are two rating systems (Rotten Tomatoes and IMDB), we'll analyze both and include them as separate dimensions in the output data.\",\n \"output_fields\": [\"Title\", \"Production Budget\", \"Rotten Tomatoes Rating\", \"IMDB Rating\"],\n \"chart_type\": \"point\",\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Rotten Tomatoes Rating\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values in key fields\n transformed_df = df_movies[['Title', 'Production Budget', 'Rotten Tomatoes Rating', 'IMDB Rating']].copy()\n \n # Remove rows where Production Budget is missing\n transformed_df = transformed_df[transformed_df['Production Budget'].notna()]\n \n # Keep rows where at least one rating is available\n transformed_df = transformed_df[\n (transformed_df['Rotten Tomatoes Rating'].notna()) | \n (transformed_df['IMDB Rating'].notna())\n ]\n \n return transformed_df\n```", "role": "assistant"}], "trigger": {"tableId": "movies", "resultTableId": "table-456490", "chart": {"id": "chart-1760744462348", "chartType": "Auto", "encodingMap": {}, "tableRef": "movies", "saved": false, "source": "trigger", "unread": true}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "Do higher production budgets correlate with better ratings?", "displayContent": "Show correlation between **Production Budget** and **ratings**"}]}, "explanation": {"agent": "CodeExplanationAgent", "code": "- Select key fields from the movies dataset: **Title**, **Production Budget**, **Rotten Tomatoes Rating**, and **IMDB Rating**\n- Filter out movies with missing **Production Budget** values to ensure all movies have known budget information\n- Retain only movies that have at least one rating available (either **Rotten Tomatoes Rating** or **IMDB Rating**), ensuring each movie has some form of critical assessment\n- Return a cleaned dataset containing only movies with complete budget information and at least one rating metric", "concepts": [], "dialog": [{"content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 403706375.0, 441226247.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., nan, 1133027325.0, 1842879955.0, 2767891499.0\n\t*US DVD Sales -- type: float64, values: nan, 853973.0, 13180936.0, ..., nan, 46260220.0, 124058348.0, nan\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 23000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.0, 2.5, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 212985.0, 229156.0, 237477.0, 417703.0\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values in key fields\n transformed_df = df_movies[['Title', 'Production Budget', 'Rotten Tomatoes Rating', 'IMDB Rating']].copy()\n \n # Remove rows where Production Budget is missing\n transformed_df = transformed_df[transformed_df['Production Budget'].notna()]\n \n # Keep rows where at least one rating is available\n transformed_df = transformed_df[\n (transformed_df['Rotten Tomatoes Rating'].notna()) | \n (transformed_df['IMDB Rating'].notna())\n ]\n \n return transformed_df\n\n\n[EXPLANATION]\n", "role": "user"}, {"content": "[CODE EXPLANATION]\n\n- Select key fields from the movies dataset: **Title**, **Production Budget**, **Rotten Tomatoes Rating**, and **IMDB Rating**\n- Filter out movies with missing **Production Budget** values to ensure all movies have known budget information\n- Retain only movies that have at least one rating available (either **Rotten Tomatoes Rating** or **IMDB Rating**), ensuring each movie has some form of critical assessment\n- Return a cleaned dataset containing only movies with complete budget information and at least one rating metric\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```", "role": "assistant"}], "status": "ok"}, "status": "completed"}, "anchored": false, "attachedMetadata": ""}, {"kind": "table", "id": "table-37", "displayId": "movie-budget-ratings", "names": ["Number of Movies", "Production Budget Bin", "Rotten Tomatoes Rating Bin"], "rows": [{"Number of Movies": 6, "Production Budget Bin": "<$1M", "Rotten Tomatoes Rating Bin": "0-20"}, {"Number of Movies": 9, "Production Budget Bin": "<$1M", "Rotten Tomatoes Rating Bin": "21-40"}, {"Number of Movies": 12, "Production Budget Bin": "<$1M", "Rotten Tomatoes Rating Bin": "41-60"}, {"Number of Movies": 19, "Production Budget Bin": "<$1M", "Rotten Tomatoes Rating Bin": "61-80"}, {"Number of Movies": 54, "Production Budget Bin": "<$1M", "Rotten Tomatoes Rating Bin": "81-100"}, {"Number of Movies": 61, "Production Budget Bin": "$1M-$10M", "Rotten Tomatoes Rating Bin": "0-20"}, {"Number of Movies": 72, "Production Budget Bin": "$1M-$10M", "Rotten Tomatoes Rating Bin": "21-40"}, {"Number of Movies": 58, "Production Budget Bin": "$1M-$10M", "Rotten Tomatoes Rating Bin": "41-60"}, {"Number of Movies": 104, "Production Budget Bin": "$1M-$10M", "Rotten Tomatoes Rating Bin": "61-80"}, {"Number of Movies": 219, "Production Budget Bin": "$1M-$10M", "Rotten Tomatoes Rating Bin": "81-100"}, {"Number of Movies": 229, "Production Budget Bin": "$10M-$50M", "Rotten Tomatoes Rating Bin": "0-20"}, {"Number of Movies": 257, "Production Budget Bin": "$10M-$50M", "Rotten Tomatoes Rating Bin": "21-40"}, {"Number of Movies": 241, "Production Budget Bin": "$10M-$50M", "Rotten Tomatoes Rating Bin": "41-60"}, {"Number of Movies": 245, "Production Budget Bin": "$10M-$50M", "Rotten Tomatoes Rating Bin": "61-80"}, {"Number of Movies": 210, "Production Budget Bin": "$10M-$50M", "Rotten Tomatoes Rating Bin": "81-100"}, {"Number of Movies": 62, "Production Budget Bin": "$50M-$100M", "Rotten Tomatoes Rating Bin": "0-20"}, {"Number of Movies": 99, "Production Budget Bin": "$50M-$100M", "Rotten Tomatoes Rating Bin": "21-40"}, {"Number of Movies": 98, "Production Budget Bin": "$50M-$100M", "Rotten Tomatoes Rating Bin": "41-60"}, {"Number of Movies": 95, "Production Budget Bin": "$50M-$100M", "Rotten Tomatoes Rating Bin": "61-80"}, {"Number of Movies": 54, "Production Budget Bin": "$50M-$100M", "Rotten Tomatoes Rating Bin": "81-100"}, {"Number of Movies": 9, "Production Budget Bin": ">$100M", "Rotten Tomatoes Rating Bin": "0-20"}, {"Number of Movies": 24, "Production Budget Bin": ">$100M", "Rotten Tomatoes Rating Bin": "21-40"}, {"Number of Movies": 26, "Production Budget Bin": ">$100M", "Rotten Tomatoes Rating Bin": "41-60"}, {"Number of Movies": 26, "Production Budget Bin": ">$100M", "Rotten Tomatoes Rating Bin": "61-80"}, {"Number of Movies": 31, "Production Budget Bin": ">$100M", "Rotten Tomatoes Rating Bin": "81-100"}], "metadata": {"Number of Movies": {"type": "number", "semanticType": "Number"}, "Production Budget Bin": {"type": "string", "semanticType": "Range", "levels": ["<$1M", "$1M-$10M", "$10M-$50M", "$50M-$100M", ">$100M"]}, "Rotten Tomatoes Rating Bin": {"type": "string", "semanticType": "Range", "levels": ["0-20", "21-40", "41-60", "61-80", "81-100"]}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values\n df_filtered = df_movies[['Production Budget', 'Rotten Tomatoes Rating']].copy()\n df_filtered = df_filtered[\n (df_filtered['Production Budget'].notna()) & \n (df_filtered['Rotten Tomatoes Rating'].notna())\n ]\n \n # Create budget bins (in millions for readability)\n budget_bins = [0, 1000000, 10000000, 50000000, 100000000, float('inf')]\n budget_labels = ['<$1M', '$1M-$10M', '$10M-$50M', '$50M-$100M', '>$100M']\n df_filtered['Production Budget Bin'] = pd.cut(\n df_filtered['Production Budget'], \n bins=budget_bins, \n labels=budget_labels,\n include_lowest=True\n )\n \n # Create rating bins\n rating_bins = [0, 20, 40, 60, 80, 100]\n rating_labels = ['0-20', '21-40', '41-60', '61-80', '81-100']\n df_filtered['Rotten Tomatoes Rating Bin'] = pd.cut(\n df_filtered['Rotten Tomatoes Rating'], \n bins=rating_bins, \n labels=rating_labels,\n include_lowest=True\n )\n \n # Count movies in each bin combination\n transformed_df = df_filtered.groupby(\n ['Production Budget Bin', 'Rotten Tomatoes Rating Bin'], \n observed=True\n ).size().reset_index(name='Number of Movies')\n \n # Convert categorical to string for better display\n transformed_df['Production Budget Bin'] = transformed_df['Production Budget Bin'].astype(str)\n transformed_df['Rotten Tomatoes Rating Bin'] = transformed_df['Rotten Tomatoes Rating Bin'].astype(str)\n \n return transformed_df\n", "source": ["movies"], "dialog": [{"content": "You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 403706375.0, 441226247.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 976457891.0, 1133027325.0, 1842879955.0\n\t*US DVD Sales -- type: float64, values: 15442818.0, 17031573.0, 33619971.0, ..., nan, nan, 46260220.0, nan\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 5000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.0, 2.4, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 212985.0, 237477.0, 417703.0, nan\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[GOAL]\n\nDo higher production budgets correlate with better ratings?", "role": "user"}, {"content": "```json\n{\n \"mode\": \"infer\",\n \"recap\": \"The user wants to analyze the correlation between production budgets and movie ratings to understand if higher budgets lead to better ratings.\",\n \"display_instruction\": \"Show correlation between **Production Budget** and **ratings**\",\n \"recommendation\": \"Create a scatter plot visualization to show the relationship between Production Budget and ratings. Since there are two rating systems (Rotten Tomatoes and IMDB), we'll analyze both and include them as separate dimensions in the output data.\",\n \"output_fields\": [\"Title\", \"Production Budget\", \"Rotten Tomatoes Rating\", \"IMDB Rating\"],\n \"chart_type\": \"point\",\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Rotten Tomatoes Rating\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values in key fields\n transformed_df = df_movies[['Title', 'Production Budget', 'Rotten Tomatoes Rating', 'IMDB Rating']].copy()\n \n # Remove rows where Production Budget is missing\n transformed_df = transformed_df[transformed_df['Production Budget'].notna()]\n \n # Keep rows where at least one rating is available\n transformed_df = transformed_df[\n (transformed_df['Rotten Tomatoes Rating'].notna()) | \n (transformed_df['IMDB Rating'].notna())\n ]\n \n return transformed_df\n```", "role": "assistant"}, {"content": "This is the result from the latest python code:\n\n IMDB Rating Production Budget Rotten Tomatoes Rating Title\n0 6.1 8000000 NaN The Land Girls\n1 6.9 300000 NaN First Love, Last Rites\n2 6.8 250000 NaN I Married a Strange Person\n3 NaN 300000 13.0 Let's Talk About Sex\n4 3.4 1000000 62.0 Slam\n5 7.7 6000 NaN Following\n6 3.8 1600000 NaN Foolish\n7 5.8 40000000 25.0 Pirates\n8 7.0 6000000 86.0 Duel in the Sun\n9 7.0 1000000 81.0 Tom Jones\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"bin rt rating and budget into bins to show number of movies in each range\",\n \"chart_type\": \"Scatter Plot\",\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Rotten Tomatoes Rating\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Bin the Production Budget and Rotten Tomatoes Rating into categorical ranges, then count the number of movies that fall into each combination of budget and rating bins to create a heatmap-style aggregation.\",\n \"display_instruction\": \"Show **number of movies** in each **Production Budget** and **Rotten Tomatoes Rating** bin\",\n \"output_fields\": [\"Production Budget Bin\", \"Rotten Tomatoes Rating Bin\", \"Number of Movies\"],\n \"chart_encodings\": {\n \"x\": \"Production Budget Bin\",\n \"y\": \"Rotten Tomatoes Rating Bin\",\n \"color\": \"Number of Movies\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n },\n \"reason\": \"The user wants to bin both Production Budget and Rotten Tomatoes Rating into ranges and count movies in each combination. This requires creating categorical bins for both dimensions and aggregating counts. A heatmap visualization (using color encoding for count) would be more appropriate than a scatter plot for showing binned data.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values\n df_filtered = df_movies[['Production Budget', 'Rotten Tomatoes Rating']].copy()\n df_filtered = df_filtered[\n (df_filtered['Production Budget'].notna()) & \n (df_filtered['Rotten Tomatoes Rating'].notna())\n ]\n \n # Create budget bins (in millions for readability)\n budget_bins = [0, 1000000, 10000000, 50000000, 100000000, float('inf')]\n budget_labels = ['<$1M', '$1M-$10M', '$10M-$50M', '$50M-$100M', '>$100M']\n df_filtered['Production Budget Bin'] = pd.cut(\n df_filtered['Production Budget'], \n bins=budget_bins, \n labels=budget_labels,\n include_lowest=True\n )\n \n # Create rating bins\n rating_bins = [0, 20, 40, 60, 80, 100]\n rating_labels = ['0-20', '21-40', '41-60', '61-80', '81-100']\n df_filtered['Rotten Tomatoes Rating Bin'] = pd.cut(\n df_filtered['Rotten Tomatoes Rating'], \n bins=rating_bins, \n labels=rating_labels,\n include_lowest=True\n )\n \n # Count movies in each bin combination\n transformed_df = df_filtered.groupby(\n ['Production Budget Bin', 'Rotten Tomatoes Rating Bin'], \n observed=True\n ).size().reset_index(name='Number of Movies')\n \n # Convert categorical to string for better display\n transformed_df['Production Budget Bin'] = transformed_df['Production Budget Bin'].astype(str)\n transformed_df['Rotten Tomatoes Rating Bin'] = transformed_df['Rotten Tomatoes Rating Bin'].astype(str)\n \n return transformed_df\n```", "role": "assistant"}], "trigger": {"tableId": "table-456490", "resultTableId": "table-37", "chart": {"id": "chart-1760744514854", "chartType": "Scatter Plot", "encodingMap": {"x": {"fieldID": "original--movies--Production Budget"}, "y": {"fieldID": "original--movies--Rotten Tomatoes Rating"}, "color": {"channel": "color", "bin": false}, "size": {"channel": "size", "bin": false}, "opacity": {"channel": "opacity", "bin": false}, "column": {"channel": "column", "bin": false}, "row": {"channel": "row", "bin": false}}, "tableRef": "table-456490", "saved": false, "source": "trigger", "unread": false}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "bin rt rating and budget into bins to show number of movies in each range", "displayContent": "Show **number of movies** in each **Production Budget** and **Rotten Tomatoes Rating** bin"}]}, "explanation": {"agent": "CodeExplanationAgent", "code": "- Filter the dataset to include only movies with both **Production Budget** and **Rotten Tomatoes Rating** values available\n- Create **budget categories** by grouping Production Budget into five bins: **<$1M**, **$1M-$10M**, **$10M-$50M**, **$50M-$100M**, and **>$100M**\n- Create **rating categories** by grouping Rotten Tomatoes Rating into five bins: **0-20**, **21-40**, **41-60**, **61-80**, and **81-100**\n- **Count** the number of movies that fall into each combination of budget category and rating category\n- Return a summary table showing the distribution of movies across budget and rating bins", "concepts": [{"explanation": "Categorical bins created through discretization of continuous variables. Production Budget is divided into 5 ranges based on million-dollar thresholds, while Rotten Tomatoes Rating is divided into 5 equal-width intervals of 20 points each. This binning approach enables analysis of the relationship between production investment levels and critical reception patterns.", "field": "Production Budget Bin, Rotten Tomatoes Rating Bin"}, {"explanation": "This analysis employs a **two-dimensional frequency distribution** (contingency table approach) to explore the relationship between production budget and critical reception. The transformation creates a discrete joint distribution by binning two continuous variables and counting occurrences in each cell. This approach is useful for identifying patterns such as whether higher-budget films tend to receive better or worse critical ratings. **Alternative modeling approaches** could include: (1) correlation analysis between raw budget and rating values, (2) regression modeling to predict ratings from budget while controlling for other factors, (3) chi-square tests of independence to formally test whether budget and rating categories are associated, or (4) visualization through heatmaps to reveal concentration patterns across the budget-rating space.", "field": "Statistical Analysis"}], "dialog": [{"content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 402111870.0, 441226247.0, 600788188.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 1133027325.0, 1842879955.0, 2767891499.0\n\t*US DVD Sales -- type: float64, values: 618454.0, 853973.0, 1877732.0, ..., nan, nan, 46260220.0, nan\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 5000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, nan, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: nan, 1.5, 2.5, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 57313.0, nan, 73720.0, 212985.0\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values\n df_filtered = df_movies[['Production Budget', 'Rotten Tomatoes Rating']].copy()\n df_filtered = df_filtered[\n (df_filtered['Production Budget'].notna()) & \n (df_filtered['Rotten Tomatoes Rating'].notna())\n ]\n \n # Create budget bins (in millions for readability)\n budget_bins = [0, 1000000, 10000000, 50000000, 100000000, float('inf')]\n budget_labels = ['<$1M', '$1M-$10M', '$10M-$50M', '$50M-$100M', '>$100M']\n df_filtered['Production Budget Bin'] = pd.cut(\n df_filtered['Production Budget'], \n bins=budget_bins, \n labels=budget_labels,\n include_lowest=True\n )\n \n # Create rating bins\n rating_bins = [0, 20, 40, 60, 80, 100]\n rating_labels = ['0-20', '21-40', '41-60', '61-80', '81-100']\n df_filtered['Rotten Tomatoes Rating Bin'] = pd.cut(\n df_filtered['Rotten Tomatoes Rating'], \n bins=rating_bins, \n labels=rating_labels,\n include_lowest=True\n )\n \n # Count movies in each bin combination\n transformed_df = df_filtered.groupby(\n ['Production Budget Bin', 'Rotten Tomatoes Rating Bin'], \n observed=True\n ).size().reset_index(name='Number of Movies')\n \n # Convert categorical to string for better display\n transformed_df['Production Budget Bin'] = transformed_df['Production Budget Bin'].astype(str)\n transformed_df['Rotten Tomatoes Rating Bin'] = transformed_df['Rotten Tomatoes Rating Bin'].astype(str)\n \n return transformed_df\n\n\n[EXPLANATION]\n", "role": "user"}, {"content": "[CODE EXPLANATION]\n\n- Filter the dataset to include only movies with both **Production Budget** and **Rotten Tomatoes Rating** values available\n- Create **budget categories** by grouping Production Budget into five bins: **<$1M**, **$1M-$10M**, **$10M-$50M**, **$50M-$100M**, and **>$100M**\n- Create **rating categories** by grouping Rotten Tomatoes Rating into five bins: **0-20**, **21-40**, **41-60**, **61-80**, and **81-100**\n- **Count** the number of movies that fall into each combination of budget category and rating category\n- Return a summary table showing the distribution of movies across budget and rating bins\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"Production Budget Bin, Rotten Tomatoes Rating Bin\",\n \"explanation\": \"Categorical bins created through discretization of continuous variables. Production Budget is divided into 5 ranges based on million-dollar thresholds, while Rotten Tomatoes Rating is divided into 5 equal-width intervals of 20 points each. This binning approach enables analysis of the relationship between production investment levels and critical reception patterns.\"\n },\n {\n \"field\": \"Statistical Analysis\",\n \"explanation\": \"This analysis employs a **two-dimensional frequency distribution** (contingency table approach) to explore the relationship between production budget and critical reception. The transformation creates a discrete joint distribution by binning two continuous variables and counting occurrences in each cell. This approach is useful for identifying patterns such as whether higher-budget films tend to receive better or worse critical ratings. **Alternative modeling approaches** could include: (1) correlation analysis between raw budget and rating values, (2) regression modeling to predict ratings from budget while controlling for other factors, (3) chi-square tests of independence to formally test whether budget and rating categories are associated, or (4) visualization through heatmaps to reveal concentration patterns across the budget-rating space.\"\n }\n]\n```", "role": "assistant"}], "status": "ok"}, "status": "completed"}, "anchored": false, "attachedMetadata": ""}, {"kind": "table", "id": "table-54", "displayId": "film-profit-analysis", "names": ["Average Profit", "Production Budget Bin", "Rotten Tomatoes Rating Bin"], "rows": [{"Average Profit": 1265816.3333333333, "Production Budget Bin": "<$1M", "Rotten Tomatoes Rating Bin": "0-20"}, {"Average Profit": 6494134, "Production Budget Bin": "<$1M", "Rotten Tomatoes Rating Bin": "21-40"}, {"Average Profit": 7866065.833333333, "Production Budget Bin": "<$1M", "Rotten Tomatoes Rating Bin": "41-60"}, {"Average Profit": 6749073.2105263155, "Production Budget Bin": "<$1M", "Rotten Tomatoes Rating Bin": "61-80"}, {"Average Profit": 20121581.403846152, "Production Budget Bin": "<$1M", "Rotten Tomatoes Rating Bin": "81-100"}, {"Average Profit": 9285520.68852459, "Production Budget Bin": "$1M-$10M", "Rotten Tomatoes Rating Bin": "0-20"}, {"Average Profit": 17386269.305555556, "Production Budget Bin": "$1M-$10M", "Rotten Tomatoes Rating Bin": "21-40"}, {"Average Profit": 13840988.827586208, "Production Budget Bin": "$1M-$10M", "Rotten Tomatoes Rating Bin": "41-60"}, {"Average Profit": 20654143.125, "Production Budget Bin": "$1M-$10M", "Rotten Tomatoes Rating Bin": "61-80"}, {"Average Profit": 37024663.62962963, "Production Budget Bin": "$1M-$10M", "Rotten Tomatoes Rating Bin": "81-100"}, {"Average Profit": 11168889.11790393, "Production Budget Bin": "$10M-$50M", "Rotten Tomatoes Rating Bin": "0-20"}, {"Average Profit": 32529317.311284047, "Production Budget Bin": "$10M-$50M", "Rotten Tomatoes Rating Bin": "21-40"}, {"Average Profit": 36640084.60165975, "Production Budget Bin": "$10M-$50M", "Rotten Tomatoes Rating Bin": "41-60"}, {"Average Profit": 49506279.571428575, "Production Budget Bin": "$10M-$50M", "Rotten Tomatoes Rating Bin": "61-80"}, {"Average Profit": 93010923.90476191, "Production Budget Bin": "$10M-$50M", "Rotten Tomatoes Rating Bin": "81-100"}, {"Average Profit": 11584648.806451613, "Production Budget Bin": "$50M-$100M", "Rotten Tomatoes Rating Bin": "0-20"}, {"Average Profit": 59680849.09090909, "Production Budget Bin": "$50M-$100M", "Rotten Tomatoes Rating Bin": "21-40"}, {"Average Profit": 104106140.86734694, "Production Budget Bin": "$50M-$100M", "Rotten Tomatoes Rating Bin": "41-60"}, {"Average Profit": 121416300.37894736, "Production Budget Bin": "$50M-$100M", "Rotten Tomatoes Rating Bin": "61-80"}, {"Average Profit": 226983168.4814815, "Production Budget Bin": "$50M-$100M", "Rotten Tomatoes Rating Bin": "81-100"}, {"Average Profit": 132877326, "Production Budget Bin": ">$100M", "Rotten Tomatoes Rating Bin": "0-20"}, {"Average Profit": 148619038.95833334, "Production Budget Bin": ">$100M", "Rotten Tomatoes Rating Bin": "21-40"}, {"Average Profit": 297717419.34615386, "Production Budget Bin": ">$100M", "Rotten Tomatoes Rating Bin": "41-60"}, {"Average Profit": 289246367.34615386, "Production Budget Bin": ">$100M", "Rotten Tomatoes Rating Bin": "61-80"}, {"Average Profit": 515989525.483871, "Production Budget Bin": ">$100M", "Rotten Tomatoes Rating Bin": "81-100"}], "metadata": {"Average Profit": {"type": "number", "semanticType": "Number"}, "Production Budget Bin": {"type": "string", "semanticType": "Range", "levels": ["<$1M", "$1M-$10M", "$10M-$50M", "$50M-$100M", ">$100M"]}, "Rotten Tomatoes Rating Bin": {"type": "string", "semanticType": "Range", "levels": ["0-20", "21-40", "41-60", "61-80", "81-100"]}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values\n df_filtered = df_movies[['Production Budget', 'Rotten Tomatoes Rating', 'Worldwide Gross']].copy()\n df_filtered = df_filtered[\n (df_filtered['Production Budget'].notna()) & \n (df_filtered['Rotten Tomatoes Rating'].notna()) &\n (df_filtered['Worldwide Gross'].notna())\n ]\n \n # Calculate profit\n df_filtered['Profit'] = df_filtered['Worldwide Gross'] - df_filtered['Production Budget']\n \n # Create budget bins (in millions for readability)\n budget_bins = [0, 1000000, 10000000, 50000000, 100000000, float('inf')]\n budget_labels = ['<$1M', '$1M-$10M', '$10M-$50M', '$50M-$100M', '>$100M']\n df_filtered['Production Budget Bin'] = pd.cut(\n df_filtered['Production Budget'], \n bins=budget_bins, \n labels=budget_labels,\n include_lowest=True\n )\n \n # Create rating bins\n rating_bins = [0, 20, 40, 60, 80, 100]\n rating_labels = ['0-20', '21-40', '41-60', '61-80', '81-100']\n df_filtered['Rotten Tomatoes Rating Bin'] = pd.cut(\n df_filtered['Rotten Tomatoes Rating'], \n bins=rating_bins, \n labels=rating_labels,\n include_lowest=True\n )\n \n # Calculate average profit in each bin combination\n transformed_df = df_filtered.groupby(\n ['Production Budget Bin', 'Rotten Tomatoes Rating Bin'], \n observed=True\n )['Profit'].mean().reset_index(name='Average Profit')\n \n # Convert categorical to string for better display\n transformed_df['Production Budget Bin'] = transformed_df['Production Budget Bin'].astype(str)\n transformed_df['Rotten Tomatoes Rating Bin'] = transformed_df['Rotten Tomatoes Rating Bin'].astype(str)\n \n return transformed_df\n", "source": ["movies"], "dialog": [{"content": "You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 403706375.0, 441226247.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 976457891.0, 1133027325.0, 1842879955.0\n\t*US DVD Sales -- type: float64, values: 15442818.0, 17031573.0, 33619971.0, ..., nan, nan, 46260220.0, nan\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 5000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, 3.0, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.0, 2.4, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 212985.0, 237477.0, 417703.0, nan\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[GOAL]\n\nDo higher production budgets correlate with better ratings?", "role": "user"}, {"content": "```json\n{\n \"mode\": \"infer\",\n \"recap\": \"The user wants to analyze the correlation between production budgets and movie ratings to understand if higher budgets lead to better ratings.\",\n \"display_instruction\": \"Show correlation between **Production Budget** and **ratings**\",\n \"recommendation\": \"Create a scatter plot visualization to show the relationship between Production Budget and ratings. Since there are two rating systems (Rotten Tomatoes and IMDB), we'll analyze both and include them as separate dimensions in the output data.\",\n \"output_fields\": [\"Title\", \"Production Budget\", \"Rotten Tomatoes Rating\", \"IMDB Rating\"],\n \"chart_type\": \"point\",\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Rotten Tomatoes Rating\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values in key fields\n transformed_df = df_movies[['Title', 'Production Budget', 'Rotten Tomatoes Rating', 'IMDB Rating']].copy()\n \n # Remove rows where Production Budget is missing\n transformed_df = transformed_df[transformed_df['Production Budget'].notna()]\n \n # Keep rows where at least one rating is available\n transformed_df = transformed_df[\n (transformed_df['Rotten Tomatoes Rating'].notna()) | \n (transformed_df['IMDB Rating'].notna())\n ]\n \n return transformed_df\n```", "role": "assistant"}, {"content": "This is the result from the latest python code:\n\n IMDB Rating Production Budget Rotten Tomatoes Rating Title\n0 6.1 8000000 NaN The Land Girls\n1 6.9 300000 NaN First Love, Last Rites\n2 6.8 250000 NaN I Married a Strange Person\n3 NaN 300000 13.0 Let's Talk About Sex\n4 3.4 1000000 62.0 Slam\n5 7.7 6000 NaN Following\n6 3.8 1600000 NaN Foolish\n7 5.8 40000000 25.0 Pirates\n8 7.0 6000000 86.0 Duel in the Sun\n9 7.0 1000000 81.0 Tom Jones\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"bin rt rating and budget into bins to show number of movies in each range\",\n \"chart_type\": \"Scatter Plot\",\n \"chart_encodings\": {\n \"x\": \"Production Budget\",\n \"y\": \"Rotten Tomatoes Rating\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Bin the Production Budget and Rotten Tomatoes Rating into categorical ranges, then count the number of movies that fall into each combination of budget and rating bins to create a heatmap-style aggregation.\",\n \"display_instruction\": \"Show **number of movies** in each **Production Budget** and **Rotten Tomatoes Rating** bin\",\n \"output_fields\": [\"Production Budget Bin\", \"Rotten Tomatoes Rating Bin\", \"Number of Movies\"],\n \"chart_encodings\": {\n \"x\": \"Production Budget Bin\",\n \"y\": \"Rotten Tomatoes Rating Bin\",\n \"color\": \"Number of Movies\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n },\n \"reason\": \"The user wants to bin both Production Budget and Rotten Tomatoes Rating into ranges and count movies in each combination. This requires creating categorical bins for both dimensions and aggregating counts. A heatmap visualization (using color encoding for count) would be more appropriate than a scatter plot for showing binned data.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values\n df_filtered = df_movies[['Production Budget', 'Rotten Tomatoes Rating']].copy()\n df_filtered = df_filtered[\n (df_filtered['Production Budget'].notna()) & \n (df_filtered['Rotten Tomatoes Rating'].notna())\n ]\n \n # Create budget bins (in millions for readability)\n budget_bins = [0, 1000000, 10000000, 50000000, 100000000, float('inf')]\n budget_labels = ['<$1M', '$1M-$10M', '$10M-$50M', '$50M-$100M', '>$100M']\n df_filtered['Production Budget Bin'] = pd.cut(\n df_filtered['Production Budget'], \n bins=budget_bins, \n labels=budget_labels,\n include_lowest=True\n )\n \n # Create rating bins\n rating_bins = [0, 20, 40, 60, 80, 100]\n rating_labels = ['0-20', '21-40', '41-60', '61-80', '81-100']\n df_filtered['Rotten Tomatoes Rating Bin'] = pd.cut(\n df_filtered['Rotten Tomatoes Rating'], \n bins=rating_bins, \n labels=rating_labels,\n include_lowest=True\n )\n \n # Count movies in each bin combination\n transformed_df = df_filtered.groupby(\n ['Production Budget Bin', 'Rotten Tomatoes Rating Bin'], \n observed=True\n ).size().reset_index(name='Number of Movies')\n \n # Convert categorical to string for better display\n transformed_df['Production Budget Bin'] = transformed_df['Production Budget Bin'].astype(str)\n transformed_df['Rotten Tomatoes Rating Bin'] = transformed_df['Rotten Tomatoes Rating Bin'].astype(str)\n \n return transformed_df\n```", "role": "assistant"}, {"content": "This is the result from the latest python code:\n\n Number of Movies Production Budget Bin Rotten Tomatoes Rating Bin\n0 6 <$1M 0-20\n1 9 <$1M 21-40\n2 12 <$1M 41-60\n3 19 <$1M 61-80\n4 54 <$1M 81-100\n5 61 $1M-$10M 0-20\n6 72 $1M-$10M 21-40\n7 58 $1M-$10M 41-60\n8 104 $1M-$10M 61-80\n9 219 $1M-$10M 81-100\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show profit relation to budget ratings bin\",\n \"chart_type\": \"Heatmap\",\n \"chart_encodings\": {\n \"x\": \"Rotten Tomatoes Rating Bin\",\n \"y\": \"Production Budget Bin\",\n \"color\": \"Number of Movies\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Calculate profit (Worldwide Gross minus Production Budget) for movies, then bin the Production Budget and Rotten Tomatoes Rating into categorical ranges, and compute the average profit for movies in each combination of budget and rating bins.\",\n \"display_instruction\": \"Show **average profit** in each **Production Budget** and **Rotten Tomatoes Rating** bin\",\n \"output_fields\": [\"Production Budget Bin\", \"Rotten Tomatoes Rating Bin\", \"Average Profit\"],\n \"chart_encodings\": {\n \"x\": \"Rotten Tomatoes Rating Bin\",\n \"y\": \"Production Budget Bin\",\n \"color\": \"Average Profit\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n },\n \"reason\": \"The user wants to analyze the relationship between profit and the budget/rating bins. This requires calculating profit (Worldwide Gross - Production Budget) and then aggregating it by the budget and rating bins. The chart encodings are swapped to match the user's preference (x=Rating, y=Budget).\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values\n df_filtered = df_movies[['Production Budget', 'Rotten Tomatoes Rating', 'Worldwide Gross']].copy()\n df_filtered = df_filtered[\n (df_filtered['Production Budget'].notna()) & \n (df_filtered['Rotten Tomatoes Rating'].notna()) &\n (df_filtered['Worldwide Gross'].notna())\n ]\n \n # Calculate profit\n df_filtered['Profit'] = df_filtered['Worldwide Gross'] - df_filtered['Production Budget']\n \n # Create budget bins (in millions for readability)\n budget_bins = [0, 1000000, 10000000, 50000000, 100000000, float('inf')]\n budget_labels = ['<$1M', '$1M-$10M', '$10M-$50M', '$50M-$100M', '>$100M']\n df_filtered['Production Budget Bin'] = pd.cut(\n df_filtered['Production Budget'], \n bins=budget_bins, \n labels=budget_labels,\n include_lowest=True\n )\n \n # Create rating bins\n rating_bins = [0, 20, 40, 60, 80, 100]\n rating_labels = ['0-20', '21-40', '41-60', '61-80', '81-100']\n df_filtered['Rotten Tomatoes Rating Bin'] = pd.cut(\n df_filtered['Rotten Tomatoes Rating'], \n bins=rating_bins, \n labels=rating_labels,\n include_lowest=True\n )\n \n # Calculate average profit in each bin combination\n transformed_df = df_filtered.groupby(\n ['Production Budget Bin', 'Rotten Tomatoes Rating Bin'], \n observed=True\n )['Profit'].mean().reset_index(name='Average Profit')\n \n # Convert categorical to string for better display\n transformed_df['Production Budget Bin'] = transformed_df['Production Budget Bin'].astype(str)\n transformed_df['Rotten Tomatoes Rating Bin'] = transformed_df['Rotten Tomatoes Rating Bin'].astype(str)\n \n return transformed_df\n```", "role": "assistant"}], "trigger": {"tableId": "table-37", "resultTableId": "table-54", "chart": {"id": "chart-1760744581372", "chartType": "Heatmap", "encodingMap": {"x": {"fieldID": "concept-Rotten Tomatoes Rating Bin-1760744520448", "sortBy": "[\"0-20\",\"21-40\",\"41-60\",\"61-80\",\"81-100\"]"}, "y": {"fieldID": "concept-Production Budget Bin-1760744338462", "sortBy": "[\"<$1M\",\"$1M-$10M\",\"$10M-$50M\",\"$50M-$100M\",\">$100M\"]"}, "color": {"fieldID": "concept-Number of Movies-1760744520448"}, "column": {"channel": "column", "bin": false}, "row": {"channel": "row", "bin": false}}, "tableRef": "table-37", "saved": false, "source": "trigger", "unread": false}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "show profit relation to budget ratings bin", "displayContent": "Show **average profit** in each **Production Budget** and **Rotten Tomatoes Rating** bin"}]}, "explanation": {"agent": "CodeExplanationAgent", "code": "This code analyzes the relationship between movie **production budgets**, **critical ratings**, and **profitability**:\n\n- Filters the dataset to include only movies with complete data for **Production Budget**, **Rotten Tomatoes Rating**, and **Worldwide Gross**\n- Calculates **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n- Creates **Production Budget Bins** to categorize movies into five budget ranges: **<$1M**, **$1M-$10M**, **$10M-$50M**, **$50M-$100M**, and **>$100M**\n- Creates **Rotten Tomatoes Rating Bins** to categorize movies into five rating ranges: **0-20**, **21-40**, **41-60**, **61-80**, and **81-100**\n- Groups movies by their **budget bin** and **rating bin** combinations\n- Calculates the **Average Profit** for each budget-rating combination to reveal profitability patterns across different production scales and critical reception levels", "concepts": [{"explanation": "The net financial return of a movie, calculated as: \\[ \\text{Profit} = \\text{Worldwide\\_Gross} - \\text{Production\\_Budget} \\] This represents the total revenue minus the initial investment, indicating whether a movie was financially successful.", "field": "Profit"}, {"explanation": "This analysis uses a **two-dimensional binning approach** to examine how movie profitability varies across production budget levels and critical reception scores. The model segments movies into categorical bins based on **Production Budget** (five budget tiers from under $1M to over $100M) and **Rotten Tomatoes Rating** (five quality tiers from 0-100), then calculates **Average Profit** within each budget-rating combination. This binning strategy helps identify optimal investment strategies by revealing which budget-quality combinations yield the highest returns. Alternative modeling approaches could include: **multiple linear regression** to model profit as a continuous function of budget and rating, **polynomial regression** to capture non-linear relationships, **interaction effect models** to test whether budget impact varies by rating level, or **quantile regression** to analyze profitability patterns at different profit percentiles rather than just averages.", "field": "Statistical Analysis"}], "dialog": [{"content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (movies)\n\n## fields\n\t*Title -- type: object, values: Silent Movie, 9, Rapa Nui, ..., A Night at the Roxbury, Fighting Tommy Riley, Gridiron Gang, Journey from the Fall\n\t*US Gross -- type: float64, values: 0.0, 401.0, 673.0, ..., 403706375.0, 441226247.0, 533345358.0, 760167650.0\n\t*Worldwide Gross -- type: float64, values: 0.0, 401.0, 423.0, ..., 937499905.0, 976457891.0, 1133027325.0, 1842879955.0\n\t*US DVD Sales -- type: float64, values: 13180936.0, 33619971.0, 140689412.0, ..., nan, nan, 46260220.0, 83571732.0\n\t*Production Budget -- type: float64, values: 218.0, 1100.0, 23000.0, ..., 237000000.0, 250000000.0, 258000000.0, 300000000.0\n\t*Release Date -- type: object, values: Apr 01 1965, Apr 01 1975, Apr 01 1986, ..., Sep 30 1983, Sep 30 1994, Sep 30 2005, Sep 30 2006\n\t*MPAA Rating -- type: object, values: G, NC-17, Not Rated, Open, PG, PG-13, R\n\t*Running Time min -- type: float64, values: nan, nan, nan, ..., nan, nan, nan, nan\n\t*Distributor -- type: object, values: 20th Century Fox, 3D Entertainment, 8X Entertainment, ..., Women Make Movies, Yari Film Group Releasing, Yash Raj Films, Zeitgeist\n\t*Source -- type: object, values: Based on Book/Short Story, Based on Comic/Graphic Novel, Based on Factual Book/Article, ..., Original Screenplay, Remake, Spin-Off, Traditional/Legend/Fairytale\n\t*Major Genre -- type: object, values: Action, Adventure, Black Comedy, ..., Musical, Romantic Comedy, Thriller/Suspense, Western\n\t*Creative Type -- type: object, values: Contemporary Fiction, Dramatization, Factual, ..., Kids Fiction, Multiple Creative Types, Science Fiction, Super Hero\n\t*Director -- type: object, values: Abel Ferrara, Adam McKay, Adam Shankman, ..., Yimou Zhang, Zach Braff, Zack Snyder, Zak Penn\n\t*Rotten Tomatoes Rating -- type: float64, values: 1.0, 2.0, nan, ..., nan, nan, nan, nan\n\t*IMDB Rating -- type: float64, values: 1.5, 2.0, 2.5, ..., nan, nan, nan, nan\n\t*IMDB Votes -- type: float64, values: 18.0, 25.0, 26.0, ..., 237477.0, 417703.0, nan, nan\n\n## sample\n Title US Gross Worldwide Gross US DVD Sales Production Budget Release Date MPAA Rating Running Time min Distributor Source Major Genre Creative Type Director Rotten Tomatoes Rating IMDB Rating IMDB Votes\n0 The Land Girls 146083 146083 None 8000000 Jun 12 1998 R None Gramercy None None None None NaN 6.1 1071.0\n1 First Love, Last Rites 10876 10876 None 300000 Aug 07 1998 R None Strand None Drama None None NaN 6.9 207.0\n2 I Married a Strange Person 203134 203134 None 250000 Aug 28 1998 None None Lionsgate None Comedy None None NaN 6.8 865.0\n3 Let's Talk About Sex 373615 373615 None 300000 Sep 11 1998 None None Fine Line None Comedy None None 13.0 NaN NaN\n4 Slam 1009819 1087521 None 1000000 Oct 09 1998 R None Trimark Original Screenplay Drama Contemporary Fiction None 62.0 3.4 165.0\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_movies):\n # Select relevant columns and filter out rows with missing values\n df_filtered = df_movies[['Production Budget', 'Rotten Tomatoes Rating', 'Worldwide Gross']].copy()\n df_filtered = df_filtered[\n (df_filtered['Production Budget'].notna()) & \n (df_filtered['Rotten Tomatoes Rating'].notna()) &\n (df_filtered['Worldwide Gross'].notna())\n ]\n \n # Calculate profit\n df_filtered['Profit'] = df_filtered['Worldwide Gross'] - df_filtered['Production Budget']\n \n # Create budget bins (in millions for readability)\n budget_bins = [0, 1000000, 10000000, 50000000, 100000000, float('inf')]\n budget_labels = ['<$1M', '$1M-$10M', '$10M-$50M', '$50M-$100M', '>$100M']\n df_filtered['Production Budget Bin'] = pd.cut(\n df_filtered['Production Budget'], \n bins=budget_bins, \n labels=budget_labels,\n include_lowest=True\n )\n \n # Create rating bins\n rating_bins = [0, 20, 40, 60, 80, 100]\n rating_labels = ['0-20', '21-40', '41-60', '61-80', '81-100']\n df_filtered['Rotten Tomatoes Rating Bin'] = pd.cut(\n df_filtered['Rotten Tomatoes Rating'], \n bins=rating_bins, \n labels=rating_labels,\n include_lowest=True\n )\n \n # Calculate average profit in each bin combination\n transformed_df = df_filtered.groupby(\n ['Production Budget Bin', 'Rotten Tomatoes Rating Bin'], \n observed=True\n )['Profit'].mean().reset_index(name='Average Profit')\n \n # Convert categorical to string for better display\n transformed_df['Production Budget Bin'] = transformed_df['Production Budget Bin'].astype(str)\n transformed_df['Rotten Tomatoes Rating Bin'] = transformed_df['Rotten Tomatoes Rating Bin'].astype(str)\n \n return transformed_df\n\n\n[EXPLANATION]\n", "role": "user"}, {"content": "[CODE EXPLANATION]\n\nThis code analyzes the relationship between movie **production budgets**, **critical ratings**, and **profitability**:\n\n- Filters the dataset to include only movies with complete data for **Production Budget**, **Rotten Tomatoes Rating**, and **Worldwide Gross**\n- Calculates **Profit** for each movie by subtracting **Production Budget** from **Worldwide Gross**\n- Creates **Production Budget Bins** to categorize movies into five budget ranges: **<$1M**, **$1M-$10M**, **$10M-$50M**, **$50M-$100M**, and **>$100M**\n- Creates **Rotten Tomatoes Rating Bins** to categorize movies into five rating ranges: **0-20**, **21-40**, **41-60**, **61-80**, and **81-100**\n- Groups movies by their **budget bin** and **rating bin** combinations\n- Calculates the **Average Profit** for each budget-rating combination to reveal profitability patterns across different production scales and critical reception levels\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"Profit\",\n \"explanation\": \"The net financial return of a movie, calculated as: \\\\[ \\\\text{Profit} = \\\\text{Worldwide\\\\_Gross} - \\\\text{Production\\\\_Budget} \\\\] This represents the total revenue minus the initial investment, indicating whether a movie was financially successful.\"\n },\n {\n \"field\": \"Statistical Analysis\",\n \"explanation\": \"This analysis uses a **two-dimensional binning approach** to examine how movie profitability varies across production budget levels and critical reception scores. The model segments movies into categorical bins based on **Production Budget** (five budget tiers from under $1M to over $100M) and **Rotten Tomatoes Rating** (five quality tiers from 0-100), then calculates **Average Profit** within each budget-rating combination. This binning strategy helps identify optimal investment strategies by revealing which budget-quality combinations yield the highest returns. Alternative modeling approaches could include: **multiple linear regression** to model profit as a continuous function of budget and rating, **polynomial regression** to capture non-linear relationships, **interaction effect models** to test whether budget impact varies by rating level, or **quantile regression** to analyze profitability patterns at different profit percentiles rather than just averages.\"\n }\n]\n```", "role": "assistant"}], "status": "ok"}, "status": "completed"}, "anchored": false, "attachedMetadata": ""}], "charts": [{"id": "chart-1760744573683", "chartType": "Heatmap", "encodingMap": {"x": {"fieldID": "concept-Rotten Tomatoes Rating Bin-1760744520448", "sortBy": "[\"0-20\",\"21-40\",\"41-60\",\"61-80\",\"81-100\"]"}, "y": {"fieldID": "concept-Production Budget Bin-1760744338462", "sortBy": "[\"<$1M\",\"$1M-$10M\",\"$10M-$50M\",\"$50M-$100M\",\">$100M\"]"}, "color": {"fieldID": "concept-Average Profit-1760744582466"}, "column": {"channel": "column", "bin": false}, 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"tableRef": "movies"}], "messages": [{"timestamp": 1760831191070, "type": "success", "component": "data formulator", "value": "Successfully loaded Movies"}], "displayedMessageIdx": 0, "viewMode": "report", "chartSynthesisInProgress": [], "config": {"formulateTimeoutSeconds": 60, "maxRepairAttempts": 1, "defaultChartWidth": 300, "defaultChartHeight": 300}, "dataCleanBlocks": [], "cleanInProgress": false, "generatedReports": [{"id": "report-1760831318093-4967", "content": "# Bigger Budgets Win with Great Reviews\n\nLow-budget films struggle regardless of reviews, averaging just $1.3M profit even with poor ratings. The real story emerges with blockbusters: films budgeted over $100M earn an average of $516M profit when they achieve 81-100% on Rotten Tomatoes—a staggering return that dwarfs all other categories.\n\n[IMAGE(chart-1760744573683)]\n\nMid-budget films ($10M-$50M) show steady but modest profits across all rating tiers, rarely exceeding $30M in average returns. The data reveals a clear pattern: critical acclaim matters most when paired with substantial production investment.\n\n**In summary**, the film industry's most profitable strategy combines massive budgets with quality execution—blockbusters that earn top critic scores generate extraordinary returns, while smaller productions face profit ceilings regardless of critical reception.", "style": "short note", "selectedChartIds": ["chart-1760744573683"], "createdAt": 1760831325401, "status": "completed", "title": "Bigger Budgets Win with Great Reviews", "anchorChartId": "chart-1760744573683"}, {"id": "report-1760831241705-9562", "content": "# Top Directors Dominate with Action & Adventure Films\n\nSteven Spielberg leads all directors with over $7 billion in total profit, driven primarily by action ($2.3B) and drama ($2.2B) films. James Cameron follows at $5.1B, focusing almost exclusively on action blockbusters.\n\n[IMAGE(chart-1760743804092)]\n\nAdventure films emerge as the most profitable genre across multiple directors—Chris Columbus, George Lucas, and Peter Jackson each generated $3B+ primarily from adventure franchises. Meanwhile, directors like Michael Bay and Gore Verbinski found success mixing action with adventure content.\n\n**In summary**, the most profitable directors concentrate on action and adventure genres, with Spielberg's genre diversity being the exception among top earners. This suggests blockbuster franchises in these genres offer the most reliable path to commercial success.", "style": "short note", "selectedChartIds": ["chart-1760743804092"], "createdAt": 1760831249446, "status": "completed", "title": "Top Directors Dominate with Action & Adventure Films", "anchorChartId": "chart-1760743804092"}, {"id": "report-1760831215537-1231", "content": "# Movie Magic: When Big Budgets Pay Off (and When They Don't)\n\nEver wonder if spending more money guarantees box office gold? The data tells a fascinating story about Hollywood's biggest gambles.\n\n[IMAGE(chart-1760742454293)]\n\nLooking at the relationship between production budgets and worldwide gross, there's a clear trend: **bigger budgets *can* lead to bigger returns**, but it's far from guaranteed. The sweet spot appears to be around $200-250M, where blockbusters like *Avatar* ($2.77B worldwide) dominate. However, the chart reveals something crucial—**most films cluster in the lower-left corner**, suggesting that moderate budgets between $25-100M are the industry standard, though they rarely break the billion-dollar barrier.\n\n[IMAGE(chart-1760743347871)]\n\nWhen we examine the **top performers by genre**, the winners become crystal clear. *Avatar* leads the pack with an astounding $2.53B profit, followed by adventure franchises like *Jurassic Park* and *The Lord of the Rings*. Action and adventure films dominate profitability, while genres like comedy, horror, and romantic comedies show more modest—but still impressive—returns in the $300-450M range. Musicals like *The Sound of Music* prove timeless appeal still pays dividends.\n\n**In summary**, while throwing money at a film doesn't guarantee success, the blockbuster strategy works when executed well—particularly for action and adventure franchises. The real question: *Are mid-budget films becoming an endangered species in modern Hollywood?*", "style": "social post", "selectedChartIds": ["chart-1760743347871", "chart-1760742454293"], "createdAt": 1760831228624, "status": "completed", "title": "Movie Magic: When Big Budgets Pay Off (and When They Don't)", "anchorChartId": "chart-1760743347871"}], "currentReport": {"id": "report-1760750575650-2619", "content": "# Hollywood's Billion-Dollar Hitmakers\n\n*Avatar* stands alone—earning over $2.5B in profit, dwarfing all competition. Action and Adventure films dominate the most profitable titles, with franchises like *Jurassic Park*, *The Dark Knight*, and *Lord of the Rings* proving blockbuster formulas work.\n\n\"Chart\"\n\nSteven Spielberg leads all directors with $7.2B in total profit across his career, showcasing remarkable consistency with hits spanning decades—from *Jurassic Park* to *E.T.* His nearest competitors trail by billions, underlining his unmatched commercial impact.\n\n\"Chart\"\n\n**In summary**, mega-budget Action and Adventure films generate extraordinary returns when they succeed, and a handful of elite directors—led by Spielberg—have mastered the formula for sustained box office dominance.", "style": "short note", "selectedChartIds": ["chart-1760743347871", "chart-1760743768741"], "chartImages": {}, "createdAt": 1760750584189, "title": "Report - 10/17/2025"}, "activeChallenges": [], "_persist": {"version": -1, "rehydrated": true}, "draftNodes": [], "focusedId": {"type": "report", "reportId": 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pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n","source":["history"],"dialog":[{"role":"system","content":"You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"input_tables\": [...] // string[], describe names of the input tables that will be used in the transformation.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\", 'boxplot'. \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"input_tables\", the names of a subset of input tables from [CONTEXT] section that will be used to achieve the user's goal.\n - **IMPORTANT** Note that the Table 1 in [CONTEXT] section is the table the user is currently viewing, it should take precedence if the user refers to insights about the \"current table\".\n - At the same time, leverage table information to determine which tables are relevant to the user's goal and should be used.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\", \"boxplot\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - boxplot: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - (boxplot) Box plots: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical (optional for creating grouped boxplots), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the boxplot will be grouped automatically (items with the same x values will be grouped).\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE]. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n# from sklearn import ... # import from sklearn if you need it.\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- decide the function signature based on the number of tables you decided in the previous step \"input_tables\":\n - if you decide there will only be one input table, then function signature should be `def transform_data(df1)`\n - if you decided there will be k input tables, then function signature should be `def transform_data(df_1, df_2, ..., df_k)`.\n - instead of using generic names like df1, df2, ..., try to use intuitive table names for function arguments, for example, if you have input_tables: [\"City\", \"Weather\"]`, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes.\n - **VERY IMPORTANT** the number of arguments in the function signature must be the same as the number of tables provided in \"input_tables\", and the order of arguments must match the order of tables provided in \"input_tables\".\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"},{"role":"user","content":"[CONTEXT]\n\n## Table 1: history (738 rows × 8 columns)\n\n### Schema (8 fields)\n - symbol -- type: object, values: AAPL, AMZN, GOOGL, META, MSFT, NVDA\n - date -- type: object, values: 2025-07-30, 2025-07-31, 2025-08-01, ..., 2026-01-20, 2026-01-21, 2026-01-22, 2026-01-23\n - open -- type: float64, values: 167.53, 168.01, 169.07, ..., 781.21, 782.98, 785.25, 789.97\n - high -- type: float64, values: 169.01, 170.94, 170.96, ..., 789.62, 792.49, 794.28, 795.06\n - low -- type: float64, values: 164.05, 166.72, 167.2, ..., 771.36, 772.21, 777.07, 779.66\n - close -- type: float64, values: 167.0, 168.29, 170.28, ..., 779.09, 780.97, 784.06, 788.82\n - volume -- type: int64, values: 5627500, 5855900, 6861200, ..., 320600300, 324925900, 343504800, 346926200\n - fetched_at -- type: object, values: 2026-01-26T23:46:42.347312Z\n\n### Sample Data (first 5 rows)\n```\n symbol date open high low close volume fetched_at\n0 AAPL 2025-07-30 211.45 211.94 207.28 208.61 45512500 2026-01-26T23:46:42.347312Z\n1 AAPL 2025-07-31 208.05 209.40 206.72 207.13 80698400 2026-01-26T23:46:42.347312Z\n2 AAPL 2025-08-01 210.43 213.13 201.08 201.95 104434500 2026-01-26T23:46:42.347312Z\n3 AAPL 2025-08-04 204.08 207.44 201.26 202.92 75109300 2026-01-26T23:46:42.347312Z\n4 AAPL 2025-08-05 202.97 204.91 201.74 202.49 44155100 2026-01-26T23:46:42.347312Z\n```\n\n\n[GOAL]\n\n1) First, plot the overall **closing price** trends for each **symbol** over **time** on a single line chart to compare their broad trajectories and volatility."},{"role":"assistant","content":"{\n \"mode\": \"infer\",\n \"recap\": \"Compare overall closing price trends and volatility over time for each stock symbol on a single line chart.\",\n \"display_instruction\": \"Visualize **closing price** trends by **symbol** over **date**\",\n \"recommendation\": \"A line chart with time on the x-axis and closing price on the y-axis, colored by symbol, best shows comparative trajectories and volatility over time. The data only needs column selection, date normalization, and sorting to be visualization-ready.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"close\",\n \"color\": \"symbol\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n```"}],"trigger":{"tableId":"history","sourceTableIds":["history"],"instruction":"1) First, plot the overall **closing price** trends for each **symbol** over **time** on a single line chart to compare their broad trajectories and volatility.","displayInstruction":"Visualize **closing price** trends by **symbol** over **date**","chart":{"id":"chart-1769471233291","chartType":"Auto","encodingMap":{},"tableRef":"history","saved":false,"source":"trigger","unread":true},"resultTableId":"table-233476"},"explanation":{"status":"ok","concepts":[],"code":"- **Keep only the key fields**: selects the columns **`\"date\"`**, **`\"symbol\"`**, and **`\"close\"`** from the input data and **drops all other columns**.\n- **Standardize the date format**: converts **`date`** into a **string** in the **`\"YYYY-MM-DD\"`** format to ensure consistency (no time component, no different date formats).\n- **Order the data for time-series use**: **sorts** the rows first by **`symbol`** and then by **`date`** so that each stock’s closing prices are in **chronological order**, which is important for **line charts** or time-based analysis.\n- **Return the cleaned dataset**: outputs this **simplified and ordered** table as the final transformed data.","dialog":[{"role":"system","content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n"},{"role":"user","content":"[CONTEXT]\n\n## Table 1: history (738 rows × 8 columns)\n\n### Schema (8 fields)\n - symbol -- type: object, values: AAPL, AMZN, GOOGL, META, MSFT, NVDA\n - date -- type: object, values: 2025-07-30, 2025-07-31, 2025-08-01, ..., 2026-01-20, 2026-01-21, 2026-01-22, 2026-01-23\n - open -- type: float64, values: 167.53, 168.01, 169.07, ..., 781.21, 782.98, 785.25, 789.97\n - high -- type: float64, values: 169.01, 170.94, 170.96, ..., 789.62, 792.49, 794.28, 795.06\n - low -- type: float64, values: 164.05, 166.72, 167.2, ..., 771.36, 772.21, 777.07, 779.66\n - close -- type: float64, values: 167.0, 168.29, 170.28, ..., 779.09, 780.97, 784.06, 788.82\n - volume -- type: int64, values: 5627500, 5855900, 6861200, ..., 320600300, 324925900, 343504800, 346926200\n - fetched_at -- type: object, values: 2026-01-26T23:46:42.347312Z\n\n### Sample Data (first 5 rows)\n```\n symbol date open high low close volume fetched_at\n0 AAPL 2025-07-30 211.45 211.94 207.28 208.61 45512500 2026-01-26T23:46:42.347312Z\n1 AAPL 2025-07-31 208.05 209.40 206.72 207.13 80698400 2026-01-26T23:46:42.347312Z\n2 AAPL 2025-08-01 210.43 213.13 201.08 201.95 104434500 2026-01-26T23:46:42.347312Z\n3 AAPL 2025-08-04 204.08 207.44 201.26 202.92 75109300 2026-01-26T23:46:42.347312Z\n4 AAPL 2025-08-05 202.97 204.91 201.74 202.49 44155100 2026-01-26T23:46:42.347312Z\n```\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n\n\n[EXPLANATION]\n"},{"role":"assistant","content":"[CODE EXPLANATION]\n\n- **Keep only the key fields**: selects the columns **`\"date\"`**, **`\"symbol\"`**, and **`\"close\"`** from the input data and **drops all other columns**.\n- **Standardize the date format**: converts **`date`** into a **string** in the **`\"YYYY-MM-DD\"`** format to ensure consistency (no time component, no different date formats).\n- **Order the data for time-series use**: **sorts** the rows first by **`symbol`** and then by **`date`** so that each stock’s closing prices are in **chronological order**, which is important for **line charts** or time-based analysis.\n- **Return the cleaned dataset**: outputs this **simplified and ordered** table as the final transformed data.\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```"}],"agent":"CodeExplanationAgent"}},"anchored":false,"createdBy":"user","attachedMetadata":"","contentHash":"a2a0adc6"},{"id":"table-265247","displayId":"stock-ma","names":["date","symbol","close","value","ma_window"],"rows":[{"date":"2025-07-31","symbol":"AAPL","close":207.13,"value":207.13,"ma_window":20},{"date":"2025-08-01","symbol":"AAPL","close":201.95,"value":204.54,"ma_window":20},{"date":"2025-08-04","symbol":"AAPL","close":202.92,"value":204,"ma_window":20},{"date":"2025-08-05","symbol":"AAPL","close":202.49,"value":203.6225,"ma_window":20},{"date":"2025-08-06","symbol":"AAPL","close":212.8,"value":205.458,"ma_window":20},{"date":"2025-08-07","symbol":"AAPL","close":219.57,"value":207.81,"ma_window":20},{"date":"2025-08-08","symbol":"AAPL","close":228.87,"value":210.8185714286,"ma_window":20},{"date":"2025-08-11","symbol":"AAPL","close":226.96,"value":212.83625,"ma_window":20},{"date":"2025-08-12","symbol":"AAPL","close":229.43,"value":214.68,"ma_window":20},{"date":"2025-08-13","symbol":"AAPL","close":233.1,"value":216.522,"ma_window":20},{"date":"2025-08-14","symbol":"AAPL","close":232.55,"value":217.9790909091,"ma_window":20},{"date":"2025-08-15","symbol":"AAPL","close":231.37,"value":219.095,"ma_window":20},{"date":"2025-08-18","symbol":"AAPL","close":230.67,"value":219.9853846154,"ma_window":20},{"date":"2025-08-19","symbol":"AAPL","close":230.34,"value":220.725,"ma_window":20},{"date":"2025-08-20","symbol":"AAPL","close":225.79,"value":221.0626666667,"ma_window":20},{"date":"2025-08-21","symbol":"AAPL","close":224.68,"value":221.28875,"ma_window":20},{"date":"2025-08-22","symbol":"AAPL","close":227.54,"value":221.6564705882,"ma_window":20},{"date":"2025-08-25","symbol":"AAPL","close":226.94,"value":221.95,"ma_window":20},{"date":"2025-08-26","symbol":"AAPL","close":229.09,"value":222.3257894737,"ma_window":20},{"date":"2025-08-27","symbol":"AAPL","close":230.27,"value":222.723,"ma_window":20},{"date":"2025-08-28","symbol":"AAPL","close":232.33,"value":223.983,"ma_window":20},{"date":"2025-08-29","symbol":"AAPL","close":231.92,"value":225.4815,"ma_window":20},{"date":"2025-09-02","symbol":"AAPL","close":229.5,"value":226.8105,"ma_window":20},{"date":"2025-09-03","symbol":"AAPL","close":238.24,"value":228.598,"ma_window":20},{"date":"2025-09-04","symbol":"AAPL","close":239.55,"value":229.9355,"ma_window":20},{"date":"2025-09-05","symbol":"AAPL","close":239.46,"value":230.93,"ma_window":20},{"date":"2025-09-08","symbol":"AAPL","close":237.65,"value":231.369,"ma_window":20},{"date":"2025-09-09","symbol":"AAPL","close":234.12,"value":231.727,"ma_window":20},{"date":"2025-09-10","symbol":"AAPL","close":226.57,"value":231.584,"ma_window":20},{"date":"2025-09-11","symbol":"AAPL","close":229.81,"value":231.4195,"ma_window":20},{"date":"2025-09-12","symbol":"AAPL","close":233.84,"value":231.484,"ma_window":20},{"date":"2025-09-15","symbol":"AAPL","close":236.47,"value":231.739,"ma_window":20},{"date":"2025-09-16","symbol":"AAPL","close":237.92,"value":232.1015,"ma_window":20},{"date":"2025-09-17","symbol":"AAPL","close":238.76,"value":232.5225,"ma_window":20},{"date":"2025-09-18","symbol":"AAPL","close":237.65,"value":233.1155,"ma_window":20},{"date":"2025-09-19","symbol":"AAPL","close":245.26,"value":234.1445,"ma_window":20},{"date":"2025-09-22","symbol":"AAPL","close":255.83,"value":235.559,"ma_window":20},{"date":"2025-09-23","symbol":"AAPL","close":254.18,"value":236.921,"ma_window":20},{"date":"2025-09-24","symbol":"AAPL","close":252.07,"value":238.07,"ma_window":20},{"date":"2025-09-25","symbol":"AAPL","close":256.62,"value":239.3875,"ma_window":20},{"date":"2025-09-26","symbol":"AAPL","close":255.21,"value":240.5315,"ma_window":20},{"date":"2025-09-29","symbol":"AAPL","close":254.18,"value":241.6445,"ma_window":20},{"date":"2025-09-30","symbol":"AAPL","close":254.38,"value":242.8885,"ma_window":20},{"date":"2025-10-01","symbol":"AAPL","close":255.2,"value":243.7365,"ma_window":20},{"date":"2025-10-02","symbol":"AAPL","close":256.88,"value":244.603,"ma_window":20},{"date":"2025-10-03","symbol":"AAPL","close":257.77,"value":245.5185,"ma_window":20},{"date":"2025-10-06","symbol":"AAPL","close":256.44,"value":246.458,"ma_window":20},{"date":"2025-10-07","symbol":"AAPL","close":256.23,"value":247.5635,"ma_window":20},{"date":"2025-10-08","symbol":"AAPL","close":257.81,"value":249.1255,"ma_window":20},{"date":"2025-10-09","symbol":"AAPL","close":253.79,"value":250.3245,"ma_window":20},{"date":"2025-10-10","symbol":"AAPL","close":245.03,"value":250.884,"ma_window":20},{"date":"2025-10-13","symbol":"AAPL","close":247.42,"value":251.4315,"ma_window":20},{"date":"2025-10-14","symbol":"AAPL","close":247.53,"value":251.912,"ma_window":20},{"date":"2025-10-15","symbol":"AAPL","close":249.1,"value":252.429,"ma_window":20},{"date":"2025-10-16","symbol":"AAPL","close":247.21,"value":252.907,"ma_window":20},{"date":"2025-10-17","symbol":"AAPL","close":252.05,"value":253.2465,"ma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pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Work on a copy and keep only needed columns if present\n base_cols = [c for c in [\"date\", \"symbol\", \"close\"] if c in df_history.columns]\n df = df_history[base_cols].copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper rolling computation\n df = df.sort_values([\"symbol\", \"date\"])\n\n # Ensure we have a clean, simple index to avoid alignment issues\n df = df.reset_index(drop=True)\n\n # Compute rolling means using transform to preserve index alignment\n df[\"ma_20\"] = (\n df.groupby(\"symbol\")[\"close\"]\n .transform(lambda s: s.rolling(window=20, min_periods=1).mean())\n )\n df[\"ma_60\"] = (\n df.groupby(\"symbol\")[\"close\"]\n .transform(lambda s: s.rolling(window=60, min_periods=1).mean())\n )\n\n # Reshape to long format for the moving averages\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\", \"close\"],\n value_vars=[\"ma_20\", \"ma_60\"],\n var_name=\"ma_window\",\n value_name=\"value\",\n )\n\n # Map ma_window to numeric window size (20 or 60)\n window_map = {\"ma_20\": 20, \"ma_60\": 60}\n df_long[\"ma_window\"] = df_long[\"ma_window\"].map(window_map).astype(int)\n\n # Final tidy output\n transformed_df = df_long[[\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"]].reset_index(drop=True)\n return transformed_df\n","source":["history"],"dialog":[{"role":"system","content":"You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"input_tables\": [...] // string[], describe names of the input tables that will be used in the transformation.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\", 'boxplot'. \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"input_tables\", the names of a subset of input tables from [CONTEXT] section that will be used to achieve the user's goal.\n - **IMPORTANT** Note that the Table 1 in [CONTEXT] section is the table the user is currently viewing, it should take precedence if the user refers to insights about the \"current table\".\n - At the same time, leverage table information to determine which tables are relevant to the user's goal and should be used.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\", \"boxplot\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - boxplot: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - (boxplot) Box plots: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical (optional for creating grouped boxplots), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the boxplot will be grouped automatically (items with the same x values will be grouped).\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE]. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n# from sklearn import ... # import from sklearn if you need it.\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- decide the function signature based on the number of tables you decided in the previous step \"input_tables\":\n - if you decide there will only be one input table, then function signature should be `def transform_data(df1)`\n - if you decided there will be k input tables, then function signature should be `def transform_data(df_1, df_2, ..., df_k)`.\n - instead of using generic names like df1, df2, ..., try to use intuitive table names for function arguments, for example, if you have input_tables: [\"City\", \"Weather\"]`, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes.\n - **VERY IMPORTANT** the number of arguments in the function signature must be the same as the number of tables provided in \"input_tables\", and the order of arguments must match the order of tables provided in \"input_tables\".\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"},{"role":"user","content":"[CONTEXT]\n\n## Table 1: history (738 rows × 8 columns)\n\n### Schema (8 fields)\n - symbol -- type: object, values: AAPL, AMZN, GOOGL, META, MSFT, NVDA\n - date -- type: object, values: 2025-07-30, 2025-07-31, 2025-08-01, ..., 2026-01-20, 2026-01-21, 2026-01-22, 2026-01-23\n - open -- type: float64, values: 167.53, 168.01, 169.07, ..., 781.21, 782.98, 785.25, 789.97\n - high -- type: float64, values: 169.01, 170.94, 170.96, ..., 789.62, 792.49, 794.28, 795.06\n - low -- type: float64, values: 164.05, 166.72, 167.2, ..., 771.36, 772.21, 777.07, 779.66\n - close -- type: float64, values: 167.0, 168.29, 170.28, ..., 779.09, 780.97, 784.06, 788.82\n - volume -- type: int64, values: 5627500, 5855900, 6861200, ..., 320600300, 324925900, 343504800, 346926200\n - fetched_at -- type: object, values: 2026-01-26T23:46:42.347312Z\n\n### Sample Data (first 5 rows)\n```\n symbol date open high low close volume fetched_at\n0 AAPL 2025-07-30 211.45 211.94 207.28 208.61 45512500 2026-01-26T23:46:42.347312Z\n1 AAPL 2025-07-31 208.05 209.40 206.72 207.13 80698400 2026-01-26T23:46:42.347312Z\n2 AAPL 2025-08-01 210.43 213.13 201.08 201.95 104434500 2026-01-26T23:46:42.347312Z\n3 AAPL 2025-08-04 204.08 207.44 201.26 202.92 75109300 2026-01-26T23:46:42.347312Z\n4 AAPL 2025-08-05 202.97 204.91 201.74 202.49 44155100 2026-01-26T23:46:42.347312Z\n```\n\n\n[GOAL]\n\n1) First, plot the overall **closing price** trends for each **symbol** over **time** on a single line chart to compare their broad trajectories and volatility."},{"role":"assistant","content":"{\n \"mode\": \"infer\",\n \"recap\": \"Compare overall closing price trends and volatility over time for each stock symbol on a single line chart.\",\n \"display_instruction\": \"Visualize **closing price** trends by **symbol** over **date**\",\n \"recommendation\": \"A line chart with time on the x-axis and closing price on the y-axis, colored by symbol, best shows comparative trajectories and volatility over time. The data only needs column selection, date normalization, and sorting to be visualization-ready.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"close\",\n \"color\": \"symbol\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n```"},{"role":"user","content":"This is the result from the latest python code:\n\n date symbol close\n0 2025-07-30 AAPL 208.61\n1 2025-07-31 AAPL 207.13\n2 2025-08-01 AAPL 201.95\n3 2025-08-04 AAPL 202.92\n4 2025-08-05 AAPL 202.49\n5 2025-08-06 AAPL 212.80\n6 2025-08-07 AAPL 219.57\n7 2025-08-08 AAPL 228.87\n8 2025-08-11 AAPL 226.96\n9 2025-08-12 AAPL 229.43\n......\n\nUpdate the code above based on the following instruction:\n\n2) For each symbol, compute 20-day and 60-day rolling averages of close price (columns: date, symbol, value, ma_window with values 20 or 60) and plot them over time alongside the actual close to compare short- vs long-term trends and reversals."},{"role":"assistant","content":"{\n \"mode\": \"infer\",\n \"recap\": \"Compute 20-day and 60-day rolling averages of closing prices for each symbol in tidy format for plotting alongside actual closes over time.\",\n \"display_instruction\": \"Add **20/60-day moving averages** of **close** by **symbol** over **date**\",\n \"recommendation\": \"To compare short- and long-term trends per symbol, compute 20- and 60-day rolling means of close, reshape them into a long format with one row per date-symbol-window, and keep the original close for plotting alongside the moving averages.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"value\",\n \"color\": \"ma_window\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"symbol\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Work on a copy\n df = df_history.copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper rolling computation\n df = df.sort_values([\"symbol\", \"date\"])\n\n # Group by symbol and compute rolling means on close\n group = df.groupby(\"symbol\", group_keys=False)[\"close\"]\n df[\"ma_20\"] = group.rolling(20, min_periods=1).mean()\n df[\"ma_60\"] = group.rolling(60, min_periods=1).mean()\n\n # Reshape to long format for the moving averages\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\", \"close\"],\n value_vars=[\"ma_20\", \"ma_60\"],\n var_name=\"ma_window\",\n value_name=\"value\",\n )\n\n # Map ma_window to numeric window size (20 or 60)\n window_map = {\"ma_20\": 20, \"ma_60\": 60}\n df_long[\"ma_window\"] = df_long[\"ma_window\"].map(window_map)\n\n # Ensure correct column order and types\n df_long[\"ma_window\"] = df_long[\"ma_window\"].astype(int)\n\n transformed_df = df_long[[\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"]].reset_index(drop=True)\n return transformed_df\n```"},{"role":"user","content":"This is the result from the latest python code:\n\nEmpty DataFrame\nColumns: []\nIndex: []\n......\n\nUpdate the code above based on the following instruction:\n\nWe run into the following problem executing the code, please fix it:\n\nError: TypeError - incompatible index of inserted column with frame index\n\nPlease think step by step, reflect why the error happens and fix the code so that no more errors would occur."},{"role":"assistant","content":"{\n \"mode\": \"infer\",\n \"recap\": \"Compute 20-day and 60-day rolling averages of closing prices for each symbol in tidy format for plotting alongside actual closes over time.\",\n \"display_instruction\": \"Compute **20/60-day moving averages** of **close** by **symbol** over **date**\",\n \"recommendation\": \"The previous implementation used groupby().rolling() and assigned the result directly to new columns, which can create an index mismatch and raise a TypeError. Using groupby().transform(...) with an inner rolling mean keeps the index aligned with the original DataFrame, avoiding incompatible index issues. After computing the moving averages, reshape to long format for visualization.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"value\",\n \"color\": \"ma_window\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"symbol\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Work on a copy and keep only needed columns if present\n base_cols = [c for c in [\"date\", \"symbol\", \"close\"] if c in df_history.columns]\n df = df_history[base_cols].copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper rolling computation\n df = df.sort_values([\"symbol\", \"date\"])\n\n # Ensure we have a clean, simple index to avoid alignment issues\n df = df.reset_index(drop=True)\n\n # Compute rolling means using transform to preserve index alignment\n df[\"ma_20\"] = (\n df.groupby(\"symbol\")[\"close\"]\n .transform(lambda s: s.rolling(window=20, min_periods=1).mean())\n )\n df[\"ma_60\"] = (\n df.groupby(\"symbol\")[\"close\"]\n .transform(lambda s: s.rolling(window=60, min_periods=1).mean())\n )\n\n # Reshape to long format for the moving averages\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\", \"close\"],\n value_vars=[\"ma_20\", \"ma_60\"],\n var_name=\"ma_window\",\n value_name=\"value\",\n )\n\n # Map ma_window to numeric window size (20 or 60)\n window_map = {\"ma_20\": 20, \"ma_60\": 60}\n df_long[\"ma_window\"] = df_long[\"ma_window\"].map(window_map).astype(int)\n\n # Final tidy output\n transformed_df = df_long[[\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"]].reset_index(drop=True)\n return transformed_df\n```"}],"trigger":{"tableId":"table-233476","sourceTableIds":["history"],"instruction":"2) For each symbol, compute 20-day and 60-day rolling averages of close price (columns: date, symbol, value, ma_window with values 20 or 60) and plot them over time alongside the actual close to compare short- vs long-term trends and reversals.","displayInstruction":"Compute **20/60-day moving averages** of **close** by **symbol** over **date**","chart":{"id":"chart-1769471261112","chartType":"Auto","encodingMap":{},"tableRef":"history","saved":false,"source":"trigger","unread":true},"resultTableId":"table-265247"},"explanation":{"status":"ok","concepts":[{"field":"ma_20, ma_60, value","explanation":"These fields represent a rolling (moving) average of the closing price over a fixed number of recent trading days for each stock symbol. For a window of N days (N = 20 or 60), the moving average on a given date is the average of the last N closing prices up to that date. Mathematically, if close_t is the closing price on day t, then the N-day moving average MA_N(t) is: MA_N(t) = (close_t + close_{t-1} + ... + close_{t-N+1}) / N. At the start of the series, when fewer than N days are available, the average is taken over all available past days instead."},{"field":"ma_window","explanation":"This field indicates the length of the moving-average window, in days. A value of 20 means the corresponding 'value' is a 20-day moving average; a value of 60 means it is a 60-day moving average."}],"code":"- Start from the original **history** table but keep only the **`date`**, **`symbol`**, and **`close`** fields to simplify the data.\n- Convert **`date`** into a standardized text format **`YYYY-MM-DD`** so all dates look consistent.\n- **Sort** the data by **`symbol`** and then by **`date`** to ensure the time series for each stock is in the correct order.\n- For each **`symbol`**, **compute**:\n - **`ma_20`**: a **20-day moving average** of the **`close`** price (uses up to the last **20** days; for the first few days it uses all available past days).\n - **`ma_60`**: a **60-day moving average** of the **`close`** price (uses up to the last **60** days; similarly relaxed at the start).\n- **Reshape** the data so that instead of having separate columns **`ma_20`** and **`ma_60`**, each row represents **one moving-average value**:\n - Keep **`date`**, **`symbol`**, and **`close`**.\n - Put the moving-average values into a single column **`value`**.\n - Use a column **`ma_window`** to indicate whether **`value`** is for **20** days or **60** days.\n- **Map** the labels **`ma_20`** and **`ma_60`** into numeric values **20** and **60** in **`ma_window`**, and keep only:\n - **`date`**, **`symbol`**, **`close`**, **`value`** (moving average), **`ma_window`** (window length in days).","dialog":[{"role":"system","content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n"},{"role":"user","content":"[CONTEXT]\n\n## Table 1: history (738 rows × 8 columns)\n\n### Schema (8 fields)\n - symbol -- type: object, values: AAPL, AMZN, GOOGL, META, MSFT, NVDA\n - date -- type: object, values: 2025-07-30, 2025-07-31, 2025-08-01, ..., 2026-01-20, 2026-01-21, 2026-01-22, 2026-01-23\n - open -- type: float64, values: 167.53, 168.01, 169.07, ..., 781.21, 782.98, 785.25, 789.97\n - high -- type: float64, values: 169.01, 170.94, 170.96, ..., 789.62, 792.49, 794.28, 795.06\n - low -- type: float64, values: 164.05, 166.72, 167.2, ..., 771.36, 772.21, 777.07, 779.66\n - close -- type: float64, values: 167.0, 168.29, 170.28, ..., 779.09, 780.97, 784.06, 788.82\n - volume -- type: int64, values: 5627500, 5855900, 6861200, ..., 320600300, 324925900, 343504800, 346926200\n - fetched_at -- type: object, values: 2026-01-26T23:46:42.347312Z\n\n### Sample Data (first 5 rows)\n```\n symbol date open high low close volume fetched_at\n0 AAPL 2025-07-30 211.45 211.94 207.28 208.61 45512500 2026-01-26T23:46:42.347312Z\n1 AAPL 2025-07-31 208.05 209.40 206.72 207.13 80698400 2026-01-26T23:46:42.347312Z\n2 AAPL 2025-08-01 210.43 213.13 201.08 201.95 104434500 2026-01-26T23:46:42.347312Z\n3 AAPL 2025-08-04 204.08 207.44 201.26 202.92 75109300 2026-01-26T23:46:42.347312Z\n4 AAPL 2025-08-05 202.97 204.91 201.74 202.49 44155100 2026-01-26T23:46:42.347312Z\n```\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Work on a copy and keep only needed columns if present\n base_cols = [c for c in [\"date\", \"symbol\", \"close\"] if c in df_history.columns]\n df = df_history[base_cols].copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper rolling computation\n df = df.sort_values([\"symbol\", \"date\"])\n\n # Ensure we have a clean, simple index to avoid alignment issues\n df = df.reset_index(drop=True)\n\n # Compute rolling means using transform to preserve index alignment\n df[\"ma_20\"] = (\n df.groupby(\"symbol\")[\"close\"]\n .transform(lambda s: s.rolling(window=20, min_periods=1).mean())\n )\n df[\"ma_60\"] = (\n df.groupby(\"symbol\")[\"close\"]\n .transform(lambda s: s.rolling(window=60, min_periods=1).mean())\n )\n\n # Reshape to long format for the moving averages\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\", \"close\"],\n value_vars=[\"ma_20\", \"ma_60\"],\n var_name=\"ma_window\",\n value_name=\"value\",\n )\n\n # Map ma_window to numeric window size (20 or 60)\n window_map = {\"ma_20\": 20, \"ma_60\": 60}\n df_long[\"ma_window\"] = df_long[\"ma_window\"].map(window_map).astype(int)\n\n # Final tidy output\n transformed_df = df_long[[\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"]].reset_index(drop=True)\n return transformed_df\n\n\n[EXPLANATION]\n"},{"role":"assistant","content":"[CODE EXPLANATION]\n\n- Start from the original **history** table but keep only the **`date`**, **`symbol`**, and **`close`** fields to simplify the data.\n- Convert **`date`** into a standardized text format **`YYYY-MM-DD`** so all dates look consistent.\n- **Sort** the data by **`symbol`** and then by **`date`** to ensure the time series for each stock is in the correct order.\n- For each **`symbol`**, **compute**:\n - **`ma_20`**: a **20-day moving average** of the **`close`** price (uses up to the last **20** days; for the first few days it uses all available past days).\n - **`ma_60`**: a **60-day moving average** of the **`close`** price (uses up to the last **60** days; similarly relaxed at the start).\n- **Reshape** the data so that instead of having separate columns **`ma_20`** and **`ma_60`**, each row represents **one moving-average value**:\n - Keep **`date`**, **`symbol`**, and **`close`**.\n - Put the moving-average values into a single column **`value`**.\n - Use a column **`ma_window`** to indicate whether **`value`** is for **20** days or **60** days.\n- **Map** the labels **`ma_20`** and **`ma_60`** into numeric values **20** and **60** in **`ma_window`**, and keep only:\n - **`date`**, **`symbol`**, **`close`**, **`value`** (moving average), **`ma_window`** (window length in days).\n\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"ma_20, ma_60, value\",\n \"explanation\": \"These fields represent a rolling (moving) average of the closing price over a fixed number of recent trading days for each stock symbol. For a window of N days (N = 20 or 60), the moving average on a given date is the average of the last N closing prices up to that date. Mathematically, if close_t is the closing price on day t, then the N-day moving average MA_N(t) is: MA_N(t) = (close_t + close_{t-1} + ... + close_{t-N+1}) / N. At the start of the series, when fewer than N days are available, the average is taken over all available past days instead.\"\n },\n {\n \"field\": \"ma_window\",\n \"explanation\": \"This field indicates the length of the moving-average window, in days. A value of 20 means the corresponding 'value' is a 20-day moving average; a value of 60 means it is a 60-day moving average.\"\n }\n]\n```"}],"agent":"CodeExplanationAgent"}},"anchored":false,"createdBy":"user","attachedMetadata":"","contentHash":"63c48b66"},{"id":"table-277926","displayId":"stock-metr","names":["date","symbol","metric","value"],"rows":[{"date":"2025-07-31","symbol":"AAPL","metric":"return","value":null},{"date":"2025-08-01","symbol":"AAPL","metric":"return","value":-0.0250084488},{"date":"2025-08-04","symbol":"AAPL","metric":"return","value":0.0048031691},{"date":"2025-08-05","symbol":"AAPL","metric":"return","value":-0.0021190617},{"date":"2025-08-06","symbol":"AAPL","metric":"return","value":0.0509160946},{"date":"2025-08-07","symbol":"AAPL","metric":"return","value":0.0318139098},{"date":"2025-08-08","symbol":"AAPL","metric":"return","value":0.042355513},{"date":"2025-08-11","symbol":"AAPL","metric":"return","value":-0.0083453489},{"date":"2025-08-12","symbol":"AAPL","metric":"return","value":0.010882975},{"date":"2025-08-13","symbol":"AAPL","metric":"return","value":0.0159961644},{"date":"2025-08-14","symbol":"AAPL","metric":"return","value":-0.0023595024},{"date":"2025-08-15","symbol":"AAPL","metric":"return","value":-0.0050741776},{"date":"2025-08-18","symbol":"AAPL","metric":"return","value":-0.0030254571},{"date":"2025-08-19","symbol":"AAPL","metric":"return","value":-0.0014306152},{"date":"2025-08-20","symbol":"AAPL","metric":"return","value":-0.019753408},{"date":"2025-08-21","symbol":"AAPL","metric":"return","value":-0.0049160725},{"date":"2025-08-22","symbol":"AAPL","metric":"return","value":0.0127292149},{"date":"2025-08-25","symbol":"AAPL","metric":"return","value":-0.002636899},{"date":"2025-08-26","symbol":"AAPL","metric":"return","value":0.0094738697},{"date":"2025-08-27","symbol":"AAPL","metric":"return","value":0.0051508141},{"date":"2025-08-28","symbol":"AAPL","metric":"return","value":0.0089460199},{"date":"2025-08-29","symbol":"AAPL","metric":"return","value":-0.0017647312},{"date":"2025-09-02","symbol":"AAPL","metric":"return","value":-0.0104346326},{"date":"2025-09-03","symbol":"AAPL","metric":"return","value":0.0380827887},{"date":"2025-09-04","symbol":"AAPL","metric":"return","value":0.0054986568},{"date":"2025-09-05","symbol":"AAPL","metric":"return","value":-0.0003757044},{"date":"2025-09-08","symbol":"AAPL","metric":"return","value":-0.0075586737},{"date":"2025-09-09","symbol":"AAPL","metric":"return","value":-0.0148537766},{"date":"2025-09-10","symbol":"AAPL","metric":"return","value":-0.0322484196},{"date":"2025-09-11","symbol":"AAPL","metric":"return","value":0.0143002163},{"date":"2025-09-12","symbol":"AAPL","metric":"return","value":0.0175362256},{"date":"2025-09-15","symbol":"AAPL","metric":"return","value":0.0112470065},{"date":"2025-09-16","symbol":"AAPL","metric":"return","value":0.006131856},{"date":"2025-09-17","symbol":"AAPL","metric":"return","value":0.0035305985},{"date":"2025-09-18","symbol":"AAPL","metric":"return","value":-0.0046490199},{"date":"2025-09-19","symbol":"AAPL","metric":"return","value":0.0320218809},{"date":"2025-09-22","symbol":"AAPL","metric":"return","value":0.0430971214},{"date":"2025-09-23","symbol":"AAPL","metric":"return","value":-0.0064495954},{"date":"2025-09-24","symbol":"AAPL","metric":"return","value":-0.0083012039},{"date":"2025-09-25","symbol":"AAPL","metric":"return","value":0.0180505415},{"date":"2025-09-26","symbol":"AAPL","metric":"return","value":-0.0054945055},{"date":"2025-09-29","symbol":"AAPL","metric":"return","value":-0.004035892},{"date":"2025-09-30","symbol":"AAPL","metric":"return","value":0.000786844},{"date":"2025-10-01","symbol":"AAPL","metric":"return","value":0.0032235239},{"date":"2025-10-02","symbol":"AAPL","metric":"return","value":0.0065830721},{"date":"2025-10-03","symbol":"AAPL","metric":"return","value":0.0034646528},{"date":"2025-10-06","symbol":"AAPL","metric":"return","value":-0.0051596384},{"date":"2025-10-07","symbol":"AAPL","metric":"return","value":-0.000818905},{"date":"2025-10-08","symbol":"AAPL","metric":"return","value":0.0061663349},{"date":"2025-10-09","symbol":"AAPL","metric":"return","value":-0.0155928785},{"date":"2025-10-10","symbol":"AAPL","metric":"return","value":-0.0345167264},{"date":"2025-10-13","symbol":"AAPL","metric":"return","value":0.0097539077},{"date":"2025-10-14","symbol":"AAPL","metric":"return","value":0.0004445881},{"date":"2025-10-15","symbol":"AAPL","metric":"return","value":0.0063426655},{"date":"2025-10-16","symbol":"AAPL","metric":"return","value":-0.0075873143},{"date":"2025-10-17","symbol":"AAPL","metric":"return","value":0.019578496},{"date":"2025-10-20","symbol":"AAPL","metric":"return","value":0.0394366197},{"date":"2025-10-21","symbol":"AAPL","metric":"return","value":0.002022978},{"date":"2025-10-22","symbol":"AAPL","metric":"return","value":-0.0164558891},{"date":"2025-10-23","symbol":"AAPL","metric":"return","value":0.0043764524},{"date":"2025-10-24","symbol":"AAPL","metric":"return","value":0.0124937339},{"date":"2025-10-27","symbol":"AAPL","metric":"return","value":0.02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pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Keep only necessary columns\n cols = [c for c in [\"date\", \"symbol\", \"close\", \"volume\"] if c in df_history.columns]\n df = df_history[cols].copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for time-series operations\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n # Compute daily returns as pct_change of close within each symbol\n df[\"return\"] = df.groupby(\"symbol\")[\"close\"].pct_change()\n\n # Compute 20-day rolling volatility (std of returns) within each symbol\n df[\"vol20\"] = (\n df.groupby(\"symbol\")[\"return\"]\n .transform(lambda s: s.rolling(window=20, min_periods=1).std())\n )\n\n # Reshape to long format: metrics = return, vol20, volume\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\"],\n value_vars=[\"return\", \"vol20\", \"volume\"],\n var_name=\"metric\",\n value_name=\"value\",\n )\n\n # Final tidy output\n transformed_df = df_long[[\"date\", \"symbol\", \"metric\", \"value\"]].reset_index(drop=True)\n return transformed_df\n","source":["history"],"dialog":[{"role":"system","content":"You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"input_tables\": [...] // string[], describe names of the input tables that will be used in the transformation.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\", 'boxplot'. \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"input_tables\", the names of a subset of input tables from [CONTEXT] section that will be used to achieve the user's goal.\n - **IMPORTANT** Note that the Table 1 in [CONTEXT] section is the table the user is currently viewing, it should take precedence if the user refers to insights about the \"current table\".\n - At the same time, leverage table information to determine which tables are relevant to the user's goal and should be used.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\", \"boxplot\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - boxplot: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - (boxplot) Box plots: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical (optional for creating grouped boxplots), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the boxplot will be grouped automatically (items with the same x values will be grouped).\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE]. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n# from sklearn import ... # import from sklearn if you need it.\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- decide the function signature based on the number of tables you decided in the previous step \"input_tables\":\n - if you decide there will only be one input table, then function signature should be `def transform_data(df1)`\n - if you decided there will be k input tables, then function signature should be `def transform_data(df_1, df_2, ..., df_k)`.\n - instead of using generic names like df1, df2, ..., try to use intuitive table names for function arguments, for example, if you have input_tables: [\"City\", \"Weather\"]`, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes.\n - **VERY IMPORTANT** the number of arguments in the function signature must be the same as the number of tables provided in \"input_tables\", and the order of arguments must match the order of tables provided in \"input_tables\".\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"},{"role":"user","content":"[CONTEXT]\n\n## Table 1: history (738 rows × 8 columns)\n\n### Schema (8 fields)\n - symbol -- type: object, values: AAPL, AMZN, GOOGL, META, MSFT, NVDA\n - date -- type: object, values: 2025-07-30, 2025-07-31, 2025-08-01, ..., 2026-01-20, 2026-01-21, 2026-01-22, 2026-01-23\n - open -- type: float64, values: 167.53, 168.01, 169.07, ..., 781.21, 782.98, 785.25, 789.97\n - high -- type: float64, values: 169.01, 170.94, 170.96, ..., 789.62, 792.49, 794.28, 795.06\n - low -- type: float64, values: 164.05, 166.72, 167.2, ..., 771.36, 772.21, 777.07, 779.66\n - close -- type: float64, values: 167.0, 168.29, 170.28, ..., 779.09, 780.97, 784.06, 788.82\n - volume -- type: int64, values: 5627500, 5855900, 6861200, ..., 320600300, 324925900, 343504800, 346926200\n - fetched_at -- type: object, values: 2026-01-26T23:46:42.347312Z\n\n### Sample Data (first 5 rows)\n```\n symbol date open high low close volume fetched_at\n0 AAPL 2025-07-30 211.45 211.94 207.28 208.61 45512500 2026-01-26T23:46:42.347312Z\n1 AAPL 2025-07-31 208.05 209.40 206.72 207.13 80698400 2026-01-26T23:46:42.347312Z\n2 AAPL 2025-08-01 210.43 213.13 201.08 201.95 104434500 2026-01-26T23:46:42.347312Z\n3 AAPL 2025-08-04 204.08 207.44 201.26 202.92 75109300 2026-01-26T23:46:42.347312Z\n4 AAPL 2025-08-05 202.97 204.91 201.74 202.49 44155100 2026-01-26T23:46:42.347312Z\n```\n\n\n[GOAL]\n\n1) First, plot the overall **closing price** trends for each **symbol** over **time** on a single line chart to compare their broad trajectories and volatility."},{"role":"assistant","content":"{\n \"mode\": \"infer\",\n \"recap\": \"Compare overall closing price trends and volatility over time for each stock symbol on a single line chart.\",\n \"display_instruction\": \"Visualize **closing price** trends by **symbol** over **date**\",\n \"recommendation\": \"A line chart with time on the x-axis and closing price on the y-axis, colored by symbol, best shows comparative trajectories and volatility over time. The data only needs column selection, date normalization, and sorting to be visualization-ready.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"close\",\n \"color\": \"symbol\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n```"},{"role":"user","content":"This is the result from the latest python code:\n\n date symbol close\n0 2025-07-30 AAPL 208.61\n1 2025-07-31 AAPL 207.13\n2 2025-08-01 AAPL 201.95\n3 2025-08-04 AAPL 202.92\n4 2025-08-05 AAPL 202.49\n5 2025-08-06 AAPL 212.80\n6 2025-08-07 AAPL 219.57\n7 2025-08-08 AAPL 228.87\n8 2025-08-11 AAPL 226.96\n9 2025-08-12 AAPL 229.43\n......\n\nUpdate the code above based on the following instruction:\n\n2) For each symbol, compute 20-day and 60-day rolling averages of close price (columns: date, symbol, value, ma_window with values 20 or 60) and plot them over time alongside the actual close to compare short- vs long-term trends and reversals."},{"role":"assistant","content":"{\n \"mode\": \"infer\",\n \"recap\": \"Compute 20-day and 60-day rolling averages of closing prices for each symbol in tidy format for plotting alongside actual closes over time.\",\n \"display_instruction\": \"Add **20/60-day moving averages** of **close** by **symbol** over **date**\",\n \"recommendation\": \"To compare short- and long-term trends per symbol, compute 20- and 60-day rolling means of close, reshape them into a long format with one row per date-symbol-window, and keep the original close for plotting alongside the moving averages.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"value\",\n \"color\": \"ma_window\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"symbol\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Work on a copy\n df = df_history.copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper rolling computation\n df = df.sort_values([\"symbol\", \"date\"])\n\n # Group by symbol and compute rolling means on close\n group = df.groupby(\"symbol\", group_keys=False)[\"close\"]\n df[\"ma_20\"] = group.rolling(20, min_periods=1).mean()\n df[\"ma_60\"] = group.rolling(60, min_periods=1).mean()\n\n # Reshape to long format for the moving averages\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\", \"close\"],\n value_vars=[\"ma_20\", \"ma_60\"],\n var_name=\"ma_window\",\n value_name=\"value\",\n )\n\n # Map ma_window to numeric window size (20 or 60)\n window_map = {\"ma_20\": 20, \"ma_60\": 60}\n df_long[\"ma_window\"] = df_long[\"ma_window\"].map(window_map)\n\n # Ensure correct column order and types\n df_long[\"ma_window\"] = df_long[\"ma_window\"].astype(int)\n\n transformed_df = df_long[[\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"]].reset_index(drop=True)\n return transformed_df\n```"},{"role":"user","content":"This is the result from the latest python code:\n\nEmpty DataFrame\nColumns: []\nIndex: []\n......\n\nUpdate the code above based on the following instruction:\n\nWe run into the following problem executing the code, please fix it:\n\nError: TypeError - incompatible index of inserted column with frame index\n\nPlease think step by step, reflect why the error happens and fix the code so that no more errors would occur."},{"role":"assistant","content":"{\n \"mode\": \"infer\",\n \"recap\": \"Compute 20-day and 60-day rolling averages of closing prices for each symbol in tidy format for plotting alongside actual closes over time.\",\n \"display_instruction\": \"Compute **20/60-day moving averages** of **close** by **symbol** over **date**\",\n \"recommendation\": \"The previous implementation used groupby().rolling() and assigned the result directly to new columns, which can create an index mismatch and raise a TypeError. Using groupby().transform(...) with an inner rolling mean keeps the index aligned with the original DataFrame, avoiding incompatible index issues. After computing the moving averages, reshape to long format for visualization.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"value\",\n \"color\": \"ma_window\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"symbol\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Work on a copy and keep only needed columns if present\n base_cols = [c for c in [\"date\", \"symbol\", \"close\"] if c in df_history.columns]\n df = df_history[base_cols].copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper rolling computation\n df = df.sort_values([\"symbol\", \"date\"])\n\n # Ensure we have a clean, simple index to avoid alignment issues\n df = df.reset_index(drop=True)\n\n # Compute rolling means using transform to preserve index alignment\n df[\"ma_20\"] = (\n df.groupby(\"symbol\")[\"close\"]\n .transform(lambda s: s.rolling(window=20, min_periods=1).mean())\n )\n df[\"ma_60\"] = (\n df.groupby(\"symbol\")[\"close\"]\n .transform(lambda s: s.rolling(window=60, min_periods=1).mean())\n )\n\n # Reshape to long format for the moving averages\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\", \"close\"],\n value_vars=[\"ma_20\", \"ma_60\"],\n var_name=\"ma_window\",\n value_name=\"value\",\n )\n\n # Map ma_window to numeric window size (20 or 60)\n window_map = {\"ma_20\": 20, \"ma_60\": 60}\n df_long[\"ma_window\"] = df_long[\"ma_window\"].map(window_map).astype(int)\n\n # Final tidy output\n transformed_df = df_long[[\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"]].reset_index(drop=True)\n return transformed_df\n```"},{"role":"user","content":"This is the result from the latest python code:\n\n date symbol close value ma_window\n0 2025-07-30 AAPL 208.61 208.610000 20\n1 2025-07-31 AAPL 207.13 207.870000 20\n2 2025-08-01 AAPL 201.95 205.896667 20\n3 2025-08-04 AAPL 202.92 205.152500 20\n4 2025-08-05 AAPL 202.49 204.620000 20\n5 2025-08-06 AAPL 212.80 205.983333 20\n6 2025-08-07 AAPL 219.57 207.924286 20\n7 2025-08-08 AAPL 228.87 210.542500 20\n8 2025-08-11 AAPL 226.96 212.366667 20\n9 2025-08-12 AAPL 229.43 214.073000 20\n......\n\nUpdate the code above based on the following instruction:\n\n3) For each symbol, compute daily returns as pct_change of close, then 20-day rolling volatility as the rolling std of these returns; keep volume. Reshape to long format with columns [date, symbol, metric, value], where metric ∈ {\"return\", \"vol20\", \"volume\"}, and visualize over time (e.g., faceted by symbol with separate lines for each metric) to highlight periods of strong trends with elevated volatility or volume."},{"role":"assistant","content":"{\n \"mode\": \"infer\",\n \"recap\": \"Compute daily returns and 20-day rolling volatility for each symbol, keep volume, and reshape to a long format for multi-metric time-series visualization.\",\n \"display_instruction\": \"Show **daily return**, **20-day volatility**, and **volume** by **symbol** over **date**\",\n \"recommendation\": \"To highlight periods of strong trends with elevated volatility or volume, compute per-symbol daily returns as pct_change of close and 20-day rolling volatility as the rolling standard deviation of those returns. Keep volume, then reshape into a long table with one metric per row so time-series lines for return, volatility, and volume can be plotted and compared per symbol.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"metric\", \"value\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"value\",\n \"color\": \"metric\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"symbol\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Keep only necessary columns\n cols = [c for c in [\"date\", \"symbol\", \"close\", \"volume\"] if c in df_history.columns]\n df = df_history[cols].copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for time-series operations\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n # Compute daily returns as pct_change of close within each symbol\n df[\"return\"] = df.groupby(\"symbol\")[\"close\"].pct_change()\n\n # Compute 20-day rolling volatility (std of returns) within each symbol\n df[\"vol20\"] = (\n df.groupby(\"symbol\")[\"return\"]\n .transform(lambda s: s.rolling(window=20, min_periods=1).std())\n )\n\n # Reshape to long format: metrics = return, vol20, volume\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\"],\n value_vars=[\"return\", \"vol20\", \"volume\"],\n var_name=\"metric\",\n value_name=\"value\",\n )\n\n # Final tidy output\n transformed_df = df_long[[\"date\", \"symbol\", \"metric\", \"value\"]].reset_index(drop=True)\n return transformed_df\n```"}],"trigger":{"tableId":"table-265247","sourceTableIds":["history"],"instruction":"3) For each symbol, compute daily returns as pct_change of close, then 20-day rolling volatility as the rolling std of these returns; keep volume. Reshape to long format with columns [date, symbol, metric, value], where metric ∈ {\"return\", \"vol20\", \"volume\"}, and visualize over time (e.g., faceted by symbol with separate lines for each metric) to highlight periods of strong trends with elevated volatility or volume.","displayInstruction":"Show **daily return**, **20-day volatility**, and **volume** by **symbol** over **date**","chart":{"id":"chart-1769471280073","chartType":"Auto","encodingMap":{},"tableRef":"history","saved":false,"source":"trigger","unread":true},"resultTableId":"table-277926"},"explanation":{"status":"ok","concepts":[{"field":"return","explanation":"Daily return measures how much the closing price changes in percentage terms from one trading day to the next for the same symbol. For day t, return_t = (Close_t - Close_{t-1}) / Close_{t-1}."},{"field":"vol20","explanation":"20-day rolling volatility is a short-term risk measure that summarizes how much the daily returns have fluctuated over roughly the past month. For each date, vol20 is the standard deviation of the last up-to-20 'return' values for that symbol; higher vol20 indicates more unstable (more volatile) recent price movements."},{"field":"metric, value","explanation":"These two fields reorganize different measures into a single tidy structure: 'metric' tells you which quantity is being reported (return, vol20, or volume), and 'value' stores the corresponding number. This makes it easy to filter and compare different metrics in the same table."},{"field":"Statistical Analysis","explanation":"The transformation introduces two statistical concepts: (1) daily returns, which quantify relative price changes over time, and (2) rolling volatility, which applies a moving standard deviation to recent returns to estimate short-term risk. The model is implicit: for each symbol, returns are treated as a time series, and volatility is computed over a 20-day rolling window. Other possible analyses on this data could include moving averages of price or volume, exponential moving volatility, correlation between different symbols' returns, or time-series models such as ARIMA or GARCH to forecast returns and volatility."}],"code":"- **Selects** only the key fields from the original data: **\"date\"**, **\"symbol\"**, **\"close\"**, and **\"volume\"**.\n- **Standardizes** the **\"date\"** format to **\"YYYY-MM-DD\"** so all dates are consistent.\n- **Sorts** the data by **\"symbol\"** and then by **\"date\"** to put each stock’s prices in correct time order.\n- For each **symbol**, **calculates daily price change**:\n - Adds a new field **\"return\"** = the **percentage change of the closing price** from the previous trading day for that same symbol.\n- For each **symbol**, **measures recent risk/variability**:\n - Adds a new field **\"vol20\"** = the **20‑day rolling volatility**, i.e., the **standard deviation of the last up-to-20 daily returns** (a larger value means the stock has been more volatile recently).\n- **Restructures** the data into a “long” tidy format:\n - Keeps **\"date\"** and **\"symbol\"**.\n - Converts the three measures **\"return\"**, **\"vol20\"**, and **\"volume\"** into rows under a single **\"metric\"** column, with their numbers stored in a **\"value\"** column.\n- **Outputs** a clean table with columns **\"date\"**, **\"symbol\"**, **\"metric\"**, and **\"value\"**, where each row represents **one metric for one stock on one date**.","dialog":[{"role":"system","content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n"},{"role":"user","content":"[CONTEXT]\n\n## Table 1: history (738 rows × 8 columns)\n\n### Schema (8 fields)\n - symbol -- type: object, values: AAPL, AMZN, GOOGL, META, MSFT, NVDA\n - date -- type: object, values: 2025-07-30, 2025-07-31, 2025-08-01, ..., 2026-01-20, 2026-01-21, 2026-01-22, 2026-01-23\n - open -- type: float64, values: 167.53, 168.01, 169.07, ..., 781.21, 782.98, 785.25, 789.97\n - high -- type: float64, values: 169.01, 170.94, 170.96, ..., 789.62, 792.49, 794.28, 795.06\n - low -- type: float64, values: 164.05, 166.72, 167.2, ..., 771.36, 772.21, 777.07, 779.66\n - close -- type: float64, values: 167.0, 168.29, 170.28, ..., 779.09, 780.97, 784.06, 788.82\n - volume -- type: int64, values: 5627500, 5855900, 6861200, ..., 320600300, 324925900, 343504800, 346926200\n - fetched_at -- type: object, values: 2026-01-26T23:46:42.347312Z\n\n### Sample Data (first 5 rows)\n```\n symbol date open high low close volume fetched_at\n0 AAPL 2025-07-30 211.45 211.94 207.28 208.61 45512500 2026-01-26T23:46:42.347312Z\n1 AAPL 2025-07-31 208.05 209.40 206.72 207.13 80698400 2026-01-26T23:46:42.347312Z\n2 AAPL 2025-08-01 210.43 213.13 201.08 201.95 104434500 2026-01-26T23:46:42.347312Z\n3 AAPL 2025-08-04 204.08 207.44 201.26 202.92 75109300 2026-01-26T23:46:42.347312Z\n4 AAPL 2025-08-05 202.97 204.91 201.74 202.49 44155100 2026-01-26T23:46:42.347312Z\n```\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Keep only necessary columns\n cols = [c for c in [\"date\", \"symbol\", \"close\", \"volume\"] if c in df_history.columns]\n df = df_history[cols].copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for time-series operations\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n # Compute daily returns as pct_change of close within each symbol\n df[\"return\"] = df.groupby(\"symbol\")[\"close\"].pct_change()\n\n # Compute 20-day rolling volatility (std of returns) within each symbol\n df[\"vol20\"] = (\n df.groupby(\"symbol\")[\"return\"]\n .transform(lambda s: s.rolling(window=20, min_periods=1).std())\n )\n\n # Reshape to long format: metrics = return, vol20, volume\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\"],\n value_vars=[\"return\", \"vol20\", \"volume\"],\n var_name=\"metric\",\n value_name=\"value\",\n )\n\n # Final tidy output\n transformed_df = df_long[[\"date\", \"symbol\", \"metric\", \"value\"]].reset_index(drop=True)\n return transformed_df\n\n\n[EXPLANATION]\n"},{"role":"assistant","content":"[CODE EXPLANATION]\n\n- **Selects** only the key fields from the original data: **\"date\"**, **\"symbol\"**, **\"close\"**, and **\"volume\"**.\n- **Standardizes** the **\"date\"** format to **\"YYYY-MM-DD\"** so all dates are consistent.\n- **Sorts** the data by **\"symbol\"** and then by **\"date\"** to put each stock’s prices in correct time order.\n- For each **symbol**, **calculates daily price change**:\n - Adds a new field **\"return\"** = the **percentage change of the closing price** from the previous trading day for that same symbol.\n- For each **symbol**, **measures recent risk/variability**:\n - Adds a new field **\"vol20\"** = the **20‑day rolling volatility**, i.e., the **standard deviation of the last up-to-20 daily returns** (a larger value means the stock has been more volatile recently).\n- **Restructures** the data into a “long” tidy format:\n - Keeps **\"date\"** and **\"symbol\"**.\n - Converts the three measures **\"return\"**, **\"vol20\"**, and **\"volume\"** into rows under a single **\"metric\"** column, with their numbers stored in a **\"value\"** column.\n- **Outputs** a clean table with columns **\"date\"**, **\"symbol\"**, **\"metric\"**, and **\"value\"**, where each row represents **one metric for one stock on one date**.\n\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"return\",\n \"explanation\": \"Daily return measures how much the closing price changes in percentage terms from one trading day to the next for the same symbol. For day t, return_t = (Close_t - Close_{t-1}) / Close_{t-1}.\"\n },\n {\n \"field\": \"vol20\",\n \"explanation\": \"20-day rolling volatility is a short-term risk measure that summarizes how much the daily returns have fluctuated over roughly the past month. For each date, vol20 is the standard deviation of the last up-to-20 'return' values for that symbol; higher vol20 indicates more unstable (more volatile) recent price movements.\"\n },\n {\n \"field\": \"metric, value\",\n \"explanation\": \"These two fields reorganize different measures into a single tidy structure: 'metric' tells you which quantity is being reported (return, vol20, or volume), and 'value' stores the corresponding number. This makes it easy to filter and compare different metrics in the same table.\"\n },\n {\n \"field\": \"Statistical Analysis\",\n \"explanation\": \"The transformation introduces two statistical concepts: (1) daily returns, which quantify relative price changes over time, and (2) rolling volatility, which applies a moving standard deviation to recent returns to estimate short-term risk. The model is implicit: for each symbol, returns are treated as a time series, and volatility is computed over a 20-day rolling window. Other possible analyses on this data could include moving averages of price or volume, exponential moving volatility, correlation between different symbols' returns, or time-series models such as ARIMA or GARCH to forecast returns and volatility.\"\n 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pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Filter for Microsoft (MSFT) and Google (GOOGL)\n target_symbols = [\"MSFT\", \"GOOGL\"]\n df = df[df[\"symbol\"].isin(target_symbols)].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n","source":["history"],"dialog":[{"role":"system","content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"input_tables\", the names of a subset of input tables from [CONTEXT] section that will be used to achieve the user's goal.\n - **IMPORTANT** Note that the Table 1 in [CONTEXT] section is the table the user is currently viewing, it should take precedence if the user refers to insights about the \"current table\".\n - At the same time, leverage table information to determine which tables are relevant to the user's goal and should be used.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, latitude, longitude, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - if the user provides latitude and longitude as visual channels, use \"latitude\" and \"longitude\" as visual channels in \"chart_encodings\" as opposed to \"x\" and \"y\".\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\".\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"input_tables\": [...] // string[], describe names of the input tables that will be used in the transformation.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables described in \"input_tables\") and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], only import libraries allowed in the template, do not modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n# from sklearn import ... # import from sklearn if you need it.\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- decide the function signature based on the number of tables you decided in the previous step \"input_tables\":\n - if you decide there will only be one input table, then function signature should be `def transform_data(df1)`\n - if you decided there will be k input tables, then function signature should be `def transform_data(df_1, df_2, ..., df_k)`.\n - instead of using generic names like df1, df2, ..., try to use intuitive table names for function arguments, for example, if you have input_tables: [\"City\", \"Weather\"]`, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes.\n - **VERY IMPORTANT** the number of arguments in the function signature must be the same as the number of tables provided in \"input_tables\", and the order of arguments must match the order of tables provided in \"input_tables\".\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n"},{"role":"user","content":"[CONTEXT]\n\n## Table 1: history (738 rows × 8 columns)\n\n### Schema (8 fields)\n - symbol -- type: object, values: AAPL, AMZN, GOOGL, META, MSFT, NVDA\n - date -- type: object, values: 2025-07-30, 2025-07-31, 2025-08-01, ..., 2026-01-20, 2026-01-21, 2026-01-22, 2026-01-23\n - open -- type: float64, values: 167.53, 168.01, 169.07, ..., 781.21, 782.98, 785.25, 789.97\n - high -- type: float64, values: 169.01, 170.94, 170.96, ..., 789.62, 792.49, 794.28, 795.06\n - low -- type: float64, values: 164.05, 166.72, 167.2, ..., 771.36, 772.21, 777.07, 779.66\n - close -- type: float64, values: 167.0, 168.29, 170.28, ..., 779.09, 780.97, 784.06, 788.82\n - volume -- type: int64, values: 5627500, 5855900, 6861200, ..., 320600300, 324925900, 343504800, 346926200\n - fetched_at -- type: object, values: 2026-01-26T23:46:42.347312Z\n\n### Sample Data (first 5 rows)\n```\n symbol date open high low close volume fetched_at\n0 AAPL 2025-07-30 211.45 211.94 207.28 208.61 45512500 2026-01-26T23:46:42.347312Z\n1 AAPL 2025-07-31 208.05 209.40 206.72 207.13 80698400 2026-01-26T23:46:42.347312Z\n2 AAPL 2025-08-01 210.43 213.13 201.08 201.95 104434500 2026-01-26T23:46:42.347312Z\n3 AAPL 2025-08-04 204.08 207.44 201.26 202.92 75109300 2026-01-26T23:46:42.347312Z\n4 AAPL 2025-08-05 202.97 204.91 201.74 202.49 44155100 2026-01-26T23:46:42.347312Z\n```\n\n\n[GOAL]\n\n1) First, plot the overall **closing price** trends for each **symbol** over **time** on a single line chart to compare their broad trajectories and volatility."},{"role":"assistant","content":"{\n \"mode\": \"infer\",\n \"recap\": \"Compare overall closing price trends and volatility over time for each stock symbol on a single line chart.\",\n \"display_instruction\": \"Visualize **closing price** trends by **symbol** over **date**\",\n \"recommendation\": \"A line chart with time on the x-axis and closing price on the y-axis, colored by symbol, best shows comparative trajectories and volatility over time. The data only needs column selection, date normalization, and sorting to be visualization-ready.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"close\",\n \"color\": \"symbol\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n```"},{"role":"user","content":"This is the result from the latest python code:\n\n date symbol close\n0 2025-07-30 AAPL 208.61\n1 2025-07-31 AAPL 207.13\n2 2025-08-01 AAPL 201.95\n3 2025-08-04 AAPL 202.92\n4 2025-08-05 AAPL 202.49\n5 2025-08-06 AAPL 212.80\n6 2025-08-07 AAPL 219.57\n7 2025-08-08 AAPL 228.87\n8 2025-08-11 AAPL 226.96\n9 2025-08-12 AAPL 229.43\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"compare microsoft and google\",\n \"chart_type\": \"\",\n \"chart_encodings\": {}\n}"},{"role":"assistant","content":"{\n \"detailed_instruction\": \"Filter the historical price data to only include Microsoft (MSFT) and Google (GOOGL) and prepare it for a line chart that compares their closing price trends over time on the same plot.\",\n \"display_instruction\": \"Compare **closing price** trends of **MSFT** and **GOOGL** over **date**\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\"],\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"close\",\n \"color\": \"symbol\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n },\n \"reason\": \"The follow-up focuses the existing trend comparison on only Microsoft and Google. This requires filtering the original prepared data to those two symbols while keeping the same line chart structure: date on x, close on y, and symbol as the color encoding.\"\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Filter for Microsoft (MSFT) and Google (GOOGL)\n target_symbols = [\"MSFT\", \"GOOGL\"]\n df = df[df[\"symbol\"].isin(target_symbols)].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n```"}],"trigger":{"tableId":"table-233476","sourceTableIds":["history"],"instruction":"compare microsoft and google","displayInstruction":"Compare **closing price** trends of **MSFT** and **GOOGL** over **date**","chart":{"id":"chart-1769471462237","chartType":"Auto","encodingMap":{},"tableRef":"table-233476","saved":false,"source":"trigger","unread":true},"resultTableId":"table-457008"},"explanation":{"status":"ok","concepts":[],"code":"- **Keep only** the **`date`**, **`symbol`**, and **`close`** price columns from the original data, discarding all others.\n- **Filter rows** to include **only** the two companies: **`MSFT` (Microsoft)** and **`GOOGL` (Google)**.\n- **Standardize the date format** by converting **`date`** into a **string** formatted as **`YYYY-MM-DD`** (year-month-day).\n- **Sort the data** by **`symbol`** and then by **`date`** so that each company’s prices are in **chronological order**, making it ready for **line plotting over time**.\n- **Return** this cleaned and filtered dataset as the final result.","dialog":[{"role":"system","content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n"},{"role":"user","content":"[CONTEXT]\n\n## Table 1: history (738 rows × 8 columns)\n\n### Schema (8 fields)\n - symbol -- type: object, values: AAPL, AMZN, GOOGL, META, MSFT, NVDA\n - date -- type: object, values: 2025-07-30, 2025-07-31, 2025-08-01, ..., 2026-01-20, 2026-01-21, 2026-01-22, 2026-01-23\n - open -- type: float64, values: 167.53, 168.01, 169.07, ..., 781.21, 782.98, 785.25, 789.97\n - high -- type: float64, values: 169.01, 170.94, 170.96, ..., 789.62, 792.49, 794.28, 795.06\n - low -- type: float64, values: 164.05, 166.72, 167.2, ..., 771.36, 772.21, 777.07, 779.66\n - close -- type: float64, values: 167.0, 168.29, 170.28, ..., 779.09, 780.97, 784.06, 788.82\n - volume -- type: int64, values: 5627500, 5855900, 6861200, ..., 320600300, 324925900, 343504800, 346926200\n - fetched_at -- type: object, values: 2026-01-26T23:46:42.347312Z\n\n### Sample Data (first 5 rows)\n```\n symbol date open high low close volume fetched_at\n0 AAPL 2025-07-30 211.45 211.94 207.28 208.61 45512500 2026-01-26T23:46:42.347312Z\n1 AAPL 2025-07-31 208.05 209.40 206.72 207.13 80698400 2026-01-26T23:46:42.347312Z\n2 AAPL 2025-08-01 210.43 213.13 201.08 201.95 104434500 2026-01-26T23:46:42.347312Z\n3 AAPL 2025-08-04 204.08 207.44 201.26 202.92 75109300 2026-01-26T23:46:42.347312Z\n4 AAPL 2025-08-05 202.97 204.91 201.74 202.49 44155100 2026-01-26T23:46:42.347312Z\n```\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Filter for Microsoft (MSFT) and Google (GOOGL)\n target_symbols = [\"MSFT\", \"GOOGL\"]\n df = df[df[\"symbol\"].isin(target_symbols)].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n\n\n[EXPLANATION]\n"},{"role":"assistant","content":"[CODE EXPLANATION]\n\n- **Keep only** the **`date`**, **`symbol`**, and **`close`** price columns from the original data, discarding all others.\n- **Filter rows** to include **only** the two companies: **`MSFT` (Microsoft)** and **`GOOGL` (Google)**.\n- **Standardize the date format** by converting **`date`** into a **string** formatted as **`YYYY-MM-DD`** (year-month-day).\n- **Sort the data** by **`symbol`** and then by **`date`** so that each company’s prices are in **chronological order**, making it ready for **line plotting over time**.\n- **Return** this cleaned and filtered dataset as the final result.\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n]\n```"}],"agent":"CodeExplanationAgent"}},"anchored":false,"createdBy":"user","attachedMetadata":"","contentHash":"903aa346"}],"charts":[{"id":"chart-1769471461230","chartType":"Scatter Plot","encodingMap":{"x":{"fieldID":"original--history--date"},"y":{"fieldID":"original--history--close"},"color":{"fieldID":"original--history--symbol"},"size":{"channel":"size","bin":false},"opacity":{"channel":"opacity","bin":false},"column":{"channel":"column","bin":false},"row":{"channel":"row","bin":false}},"tableRef":"table-457008","saved":false,"source":"user","unread":false},{"id":"chart-1769471279744","chartType":"Line 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view_ushx"}],"displayedMessageIdx":8,"focusedTableId":"table-233476","focusedChartId":"chart-1769471233467","viewMode":"editor","chartSynthesisInProgress":[],"config":{"formulateTimeoutSeconds":60,"maxRepairAttempts":1,"defaultChartWidth":300,"defaultChartHeight":300},"agentActions":[{"actionId":"exploreDataFromNL_1769471236248","tableId":"table-294972","description":"- Compared raw close-price trajectories across six mega-cap stocks.\n- Analyzed 20/60-day moving averages plus returns, volatility, and volume.\n- Summarized phase (quarterly) performance: avg/cumulative returns and volatility by symbol.","status":"completed","hidden":false,"lastUpdate":1769471309407}],"dataCleanBlocks":[],"cleanInProgress":false,"generatedReports":[{"id":"report-1769471498009-9657","content":"# Monitoring Mega-Cap Tech: Price, Trends, and Risk at a Glance\n\nThis live report is built to help you track six large tech stocks (AAPL, AMZN, GOOGL, META, MSFT, NVDA) across price, trends, and risk. Each chart updates as new rows are added to the `history` table.\n\n[IMAGE(chart-1769471233467)]\n\nThe first chart shows daily closing prices over time for all six symbols. Use it to spot broad moves, compare relative price levels, and see when one stock starts to diverge from the group.\n\n[IMAGE(chart-1769471263442)]\n\nThe second chart adds 20‑day and 60‑day moving averages. Watch how actual prices relate to these smoother trend lines: crossovers, sustained gaps, or trend flattening can signal shifting momentum.\n\n[IMAGE(chart-1769471279744)]\n\nThe third set of panels tracks three risk and activity metrics: daily returns, 20‑day return volatility, and trading volume. Look for periods where volatility or volume spikes, or where returns cluster on one side.\n\n[IMAGE(chart-1769471461230)]\n\nThe final chart zooms in on GOOGL and MSFT closing prices, making it easier to compare their day‑to‑day paths without the distraction of other symbols.\n\n**In summary**, use these views together: price levels and trends, plus returns, volatility, and volume, to monitor how each stock is behaving and how relationships between them evolve over time. 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{"date": "2026-01-09", "symbol": "NVDA", "close": 184.86}, {"date": "2026-01-12", "symbol": "NVDA", "close": 184.94}, {"date": "2026-01-13", "symbol": "NVDA", "close": 185.81}, {"date": "2026-01-14", "symbol": "NVDA", "close": 183.14}, {"date": "2026-01-15", "symbol": "NVDA", "close": 187.05}, {"date": "2026-01-16", "symbol": "NVDA", "close": 186.23}, {"date": "2026-01-20", "symbol": "NVDA", "close": 178.07}, {"date": "2026-01-21", "symbol": "NVDA", "close": 183.32}, {"date": "2026-01-22", "symbol": "NVDA", "close": 184.84}, {"date": "2026-01-23", "symbol": "NVDA", "close": 187.67}, {"date": "2026-01-26", "symbol": "NVDA", "close": 186.47}], "metadata": {"date": {"type": "date", "semanticType": "Date"}, "symbol": {"type": "string", "semanticType": "String"}, "close": {"type": "number", "semanticType": "Number"}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n", "source": ["history"], "dialog": [{"role": "system", "content": "You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"input_tables\": [...] // string[], describe names of the input tables that will be used in the transformation.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\", 'boxplot'. \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"input_tables\", the names of a subset of input tables from [CONTEXT] section that will be used to achieve the user's goal.\n - **IMPORTANT** Note that the Table 1 in [CONTEXT] section is the table the user is currently viewing, it should take precedence if the user refers to insights about the \"current table\".\n - At the same time, leverage table information to determine which tables are relevant to the user's goal and should be used.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\", \"boxplot\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - boxplot: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - (boxplot) Box plots: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical (optional for creating grouped boxplots), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the boxplot will be grouped automatically (items with the same x values will be grouped).\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE]. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n# from sklearn import ... # import from sklearn if you need it.\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- decide the function signature based on the number of tables you decided in the previous step \"input_tables\":\n - if you decide there will only be one input table, then function signature should be `def transform_data(df1)`\n - if you decided there will be k input tables, then function signature should be `def transform_data(df_1, df_2, ..., df_k)`.\n - instead of using generic names like df1, df2, ..., try to use intuitive table names for function arguments, for example, if you have input_tables: [\"City\", \"Weather\"]`, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes.\n - **VERY IMPORTANT** the number of arguments in the function signature must be the same as the number of tables provided in \"input_tables\", and the order of arguments must match the order of tables provided in \"input_tables\".\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"}, {"role": "user", "content": "[CONTEXT]\n\n## Table 1: history (738 rows × 8 columns)\n\n### Schema (8 fields)\n - symbol -- type: object, values: AAPL, AMZN, GOOGL, META, MSFT, NVDA\n - date -- type: object, values: 2025-07-30, 2025-07-31, 2025-08-01, ..., 2026-01-20, 2026-01-21, 2026-01-22, 2026-01-23\n - open -- type: float64, values: 167.53, 168.01, 169.07, ..., 781.21, 782.98, 785.25, 789.97\n - high -- type: float64, values: 169.01, 170.94, 170.96, ..., 789.62, 792.49, 794.28, 795.06\n - low -- type: float64, values: 164.05, 166.72, 167.2, ..., 771.36, 772.21, 777.07, 779.66\n - close -- type: float64, values: 167.0, 168.29, 170.28, ..., 779.09, 780.97, 784.06, 788.82\n - volume -- type: int64, values: 5627500, 5855900, 6861200, ..., 320600300, 324925900, 343504800, 346926200\n - fetched_at -- type: object, values: 2026-01-26T23:46:42.347312Z\n\n### Sample Data (first 5 rows)\n```\n symbol date open high low close volume fetched_at\n0 AAPL 2025-07-30 211.45 211.94 207.28 208.61 45512500 2026-01-26T23:46:42.347312Z\n1 AAPL 2025-07-31 208.05 209.40 206.72 207.13 80698400 2026-01-26T23:46:42.347312Z\n2 AAPL 2025-08-01 210.43 213.13 201.08 201.95 104434500 2026-01-26T23:46:42.347312Z\n3 AAPL 2025-08-04 204.08 207.44 201.26 202.92 75109300 2026-01-26T23:46:42.347312Z\n4 AAPL 2025-08-05 202.97 204.91 201.74 202.49 44155100 2026-01-26T23:46:42.347312Z\n```\n\n\n[GOAL]\n\n1) First, plot the overall **closing price** trends for each **symbol** over **time** on a single line chart to compare their broad trajectories and volatility."}, {"role": "assistant", "content": "{\n \"mode\": \"infer\",\n \"recap\": \"Compare overall closing price trends and volatility over time for each stock symbol on a single line chart.\",\n \"display_instruction\": \"Visualize **closing price** trends by **symbol** over **date**\",\n \"recommendation\": \"A line chart with time on the x-axis and closing price on the y-axis, colored by symbol, best shows comparative trajectories and volatility over time. The data only needs column selection, date normalization, and sorting to be visualization-ready.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"close\",\n \"color\": \"symbol\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n```"}], "trigger": {"tableId": "history", "resultTableId": "table-233476", "chart": {"id": "chart-1769471233291", "chartType": "Auto", "encodingMap": {}, "tableRef": "history", "saved": false, "source": "trigger", "unread": true}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "1) First, plot the overall **closing price** trends for each **symbol** over **time** on a single line chart to compare their broad trajectories and volatility.", "displayContent": "Visualize **closing price** trends by **symbol** over **date**"}]}, "explanation": {"status": "ok", "concepts": [], "code": "- **Keep only the key fields**: selects the columns **`\"date\"`**, **`\"symbol\"`**, and **`\"close\"`** from the input data and **drops all other columns**.\n- **Standardize the date format**: converts **`date`** into a **string** in the **`\"YYYY-MM-DD\"`** format to ensure consistency (no time component, no different date formats).\n- **Order the data for time-series use**: **sorts** the rows first by **`symbol`** and then by **`date`** so that each stock’s closing prices are in **chronological order**, which is important for **line charts** or time-based analysis.\n- **Return the cleaned dataset**: outputs this **simplified and ordered** table as the final transformed data.", "dialog": [{"role": "system", "content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n"}, {"role": "user", "content": "[CONTEXT]\n\n## Table 1: history (738 rows × 8 columns)\n\n### Schema (8 fields)\n - symbol -- type: object, values: AAPL, AMZN, GOOGL, META, MSFT, NVDA\n - date -- type: object, values: 2025-07-30, 2025-07-31, 2025-08-01, ..., 2026-01-20, 2026-01-21, 2026-01-22, 2026-01-23\n - open -- type: float64, values: 167.53, 168.01, 169.07, ..., 781.21, 782.98, 785.25, 789.97\n - high -- type: float64, values: 169.01, 170.94, 170.96, ..., 789.62, 792.49, 794.28, 795.06\n - low -- type: float64, values: 164.05, 166.72, 167.2, ..., 771.36, 772.21, 777.07, 779.66\n - close -- type: float64, values: 167.0, 168.29, 170.28, ..., 779.09, 780.97, 784.06, 788.82\n - volume -- type: int64, values: 5627500, 5855900, 6861200, ..., 320600300, 324925900, 343504800, 346926200\n - fetched_at -- type: object, values: 2026-01-26T23:46:42.347312Z\n\n### Sample Data (first 5 rows)\n```\n symbol date open high low close volume fetched_at\n0 AAPL 2025-07-30 211.45 211.94 207.28 208.61 45512500 2026-01-26T23:46:42.347312Z\n1 AAPL 2025-07-31 208.05 209.40 206.72 207.13 80698400 2026-01-26T23:46:42.347312Z\n2 AAPL 2025-08-01 210.43 213.13 201.08 201.95 104434500 2026-01-26T23:46:42.347312Z\n3 AAPL 2025-08-04 204.08 207.44 201.26 202.92 75109300 2026-01-26T23:46:42.347312Z\n4 AAPL 2025-08-05 202.97 204.91 201.74 202.49 44155100 2026-01-26T23:46:42.347312Z\n```\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n\n\n[EXPLANATION]\n"}, {"role": "assistant", "content": "[CODE EXPLANATION]\n\n- **Keep only the key fields**: selects the columns **`\"date\"`**, **`\"symbol\"`**, and **`\"close\"`** from the input data and **drops all other columns**.\n- **Standardize the date format**: converts **`date`** into a **string** in the **`\"YYYY-MM-DD\"`** format to ensure consistency (no time component, no different date formats).\n- **Order the data for time-series use**: **sorts** the rows first by **`symbol`** and then by **`date`** so that each stock’s closing prices are in **chronological order**, which is important for **line charts** or time-based analysis.\n- **Return the cleaned dataset**: outputs this **simplified and ordered** table as the final transformed data.\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```"}], "agent": "CodeExplanationAgent"}, "status": 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"symbol": "NVDA", "close": 202.88, "value": 181.2235, "ma_window": 60}, {"date": "2025-10-31", "symbol": "NVDA", "close": 202.48, "value": 181.5856666667, "ma_window": 60}, {"date": "2025-11-03", "symbol": "NVDA", "close": 206.87, "value": 181.9888333333, "ma_window": 60}, {"date": "2025-11-04", "symbol": "NVDA", "close": 198.68, "value": 182.2661666667, "ma_window": 60}, {"date": "2025-11-05", "symbol": "NVDA", "close": 195.2, "value": 182.4671666667, "ma_window": 60}, {"date": "2025-11-06", "symbol": "NVDA", "close": 188.07, "value": 182.5755, "ma_window": 60}, {"date": "2025-11-07", "symbol": "NVDA", "close": 188.14, "value": 182.6778333333, "ma_window": 60}, {"date": "2025-11-10", "symbol": "NVDA", "close": 199.04, "value": 182.988, "ma_window": 60}, {"date": "2025-11-11", "symbol": "NVDA", "close": 193.15, "value": 183.174, "ma_window": 60}, {"date": "2025-11-12", "symbol": "NVDA", "close": 193.79, "value": 183.4768333333, "ma_window": 60}, {"date": "2025-11-13", "symbol": "NVDA", "close": 186.85, "value": 183.668, "ma_window": 60}, {"date": "2025-11-14", "symbol": "NVDA", "close": 190.16, "value": 183.9213333333, "ma_window": 60}, {"date": "2025-11-17", "symbol": "NVDA", "close": 186.59, "value": 184.065, "ma_window": 60}, {"date": "2025-11-18", "symbol": "NVDA", "close": 181.35, "value": 184.091, "ma_window": 60}, {"date": "2025-11-19", "symbol": "NVDA", "close": 186.51, "value": 184.1703333333, "ma_window": 60}, {"date": "2025-11-20", "symbol": "NVDA", "close": 180.63, "value": 184.1545, "ma_window": 60}, {"date": "2025-11-21", "symbol": "NVDA", "close": 178.87, "value": 184.1331666667, "ma_window": 60}, {"date": "2025-11-24", "symbol": "NVDA", "close": 182.54, "value": 184.2728333333, "ma_window": 60}, {"date": "2025-11-25", "symbol": "NVDA", "close": 177.81, "value": 184.3903333333, "ma_window": 60}, {"date": "2025-11-26", "symbol": "NVDA", "close": 180.25, "value": 184.5511666667, "ma_window": 60}, {"date": "2025-11-28", "symbol": "NVDA", "close": 176.99, "value": 184.6403333333, "ma_window": 60}, {"date": "2025-12-01", "symbol": "NVDA", "close": 179.91, "value": 184.8555, "ma_window": 60}, {"date": "2025-12-02", "symbol": "NVDA", "close": 181.45, "value": 185.0748333333, "ma_window": 60}, {"date": "2025-12-03", "symbol": "NVDA", "close": 179.58, "value": 185.2221666667, "ma_window": 60}, {"date": "2025-12-04", "symbol": "NVDA", "close": 183.38, "value": 185.3233333333, "ma_window": 60}, {"date": "2025-12-05", "symbol": "NVDA", "close": 182.41, "value": 185.4108333333, "ma_window": 60}, {"date": "2025-12-08", "symbol": "NVDA", "close": 185.55, "value": 185.5398333333, "ma_window": 60}, {"date": "2025-12-09", "symbol": "NVDA", "close": 184.97, "value": 185.6603333333, "ma_window": 60}, {"date": "2025-12-10", "symbol": "NVDA", "close": 183.78, "value": 185.8088333333, "ma_window": 60}, {"date": "2025-12-11", "symbol": "NVDA", "close": 180.93, "value": 185.9863333333, "ma_window": 60}, {"date": "2025-12-12", "symbol": "NVDA", "close": 175.02, "value": 185.9661666667, "ma_window": 60}, {"date": "2025-12-15", "symbol": "NVDA", "close": 176.29, "value": 185.96, "ma_window": 60}, {"date": "2025-12-16", "symbol": "NVDA", "close": 177.72, "value": 185.862, "ma_window": 60}, {"date": "2025-12-17", "symbol": "NVDA", "close": 170.94, "value": 185.7373333333, "ma_window": 60}, {"date": "2025-12-18", "symbol": "NVDA", "close": 174.14, "value": 185.6903333333, "ma_window": 60}, {"date": "2025-12-19", "symbol": "NVDA", "close": 180.99, "value": 185.7455, "ma_window": 60}, {"date": "2025-12-22", "symbol": "NVDA", "close": 183.69, "value": 185.8373333333, "ma_window": 60}, {"date": "2025-12-23", "symbol": "NVDA", "close": 189.21, "value": 185.9601666667, "ma_window": 60}, {"date": "2025-12-24", "symbol": "NVDA", "close": 188.61, "value": 185.9941666667, "ma_window": 60}, {"date": "2025-12-26", "symbol": "NVDA", "close": 190.53, "value": 186.0491666667, "ma_window": 60}, {"date": "2025-12-29", "symbol": "NVDA", "close": 188.22, "value": 186.0381666667, "ma_window": 60}, {"date": "2025-12-30", "symbol": "NVDA", "close": 187.54, "value": 186.037, "ma_window": 60}, {"date": "2025-12-31", "symbol": "NVDA", "close": 186.5, "value": 186.0531666667, "ma_window": 60}, {"date": "2026-01-02", "symbol": "NVDA", "close": 188.85, "value": 186.1168333333, "ma_window": 60}, {"date": "2026-01-05", "symbol": "NVDA", "close": 188.12, "value": 186.1005, "ma_window": 60}, {"date": "2026-01-06", "symbol": "NVDA", "close": 187.24, "value": 186.0118333333, "ma_window": 60}, {"date": "2026-01-07", "symbol": "NVDA", "close": 189.11, "value": 186.1111666667, "ma_window": 60}, {"date": "2026-01-08", "symbol": "NVDA", "close": 185.04, "value": 186.0566666667, "ma_window": 60}, {"date": "2026-01-09", "symbol": "NVDA", "close": 184.86, "value": 186.1373333333, "ma_window": 60}, {"date": "2026-01-12", "symbol": "NVDA", "close": 184.94, "value": 186.2226666667, "ma_window": 60}, {"date": "2026-01-13", "symbol": "NVDA", "close": 185.81, "value": 186.2895, "ma_window": 60}, {"date": "2026-01-14", "symbol": "NVDA", "close": 183.14, "value": 186.2883333333, "ma_window": 60}, {"date": "2026-01-15", "symbol": "NVDA", "close": 187.05, "value": 186.362, "ma_window": 60}, {"date": "2026-01-16", "symbol": "NVDA", "close": 186.23, "value": 186.4466666667, "ma_window": 60}, {"date": "2026-01-20", "symbol": "NVDA", "close": 178.07, "value": 186.41, "ma_window": 60}, {"date": "2026-01-21", "symbol": "NVDA", "close": 183.32, "value": 186.4295, "ma_window": 60}, {"date": "2026-01-22", "symbol": "NVDA", "close": 184.84, "value": 186.406, "ma_window": 60}, {"date": "2026-01-23", "symbol": "NVDA", "close": 187.67, "value": 186.3425, "ma_window": 60}, {"date": "2026-01-26", "symbol": "NVDA", "close": 186.47, "value": 186.1, "ma_window": 60}], "metadata": {"date": {"type": "date", "semanticType": "Date"}, "symbol": {"type": "string", "semanticType": "String"}, "close": {"type": "number", "semanticType": "Number"}, "value": {"type": "number", "semanticType": "Number"}, "ma_window": {"type": "integer", "semanticType": "Duration"}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Work on a copy and keep only needed columns if present\n base_cols = [c for c in [\"date\", \"symbol\", \"close\"] if c in df_history.columns]\n df = df_history[base_cols].copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper rolling computation\n df = df.sort_values([\"symbol\", \"date\"])\n\n # Ensure we have a clean, simple index to avoid alignment issues\n df = df.reset_index(drop=True)\n\n # Compute rolling means using transform to preserve index alignment\n df[\"ma_20\"] = (\n df.groupby(\"symbol\")[\"close\"]\n .transform(lambda s: s.rolling(window=20, min_periods=1).mean())\n )\n df[\"ma_60\"] = (\n df.groupby(\"symbol\")[\"close\"]\n .transform(lambda s: s.rolling(window=60, min_periods=1).mean())\n )\n\n # Reshape to long format for the moving averages\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\", \"close\"],\n value_vars=[\"ma_20\", \"ma_60\"],\n var_name=\"ma_window\",\n value_name=\"value\",\n )\n\n # Map ma_window to numeric window size (20 or 60)\n window_map = {\"ma_20\": 20, \"ma_60\": 60}\n df_long[\"ma_window\"] = df_long[\"ma_window\"].map(window_map).astype(int)\n\n # Final tidy output\n transformed_df = df_long[[\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"]].reset_index(drop=True)\n return transformed_df\n", "source": ["history"], "dialog": [{"role": "system", "content": "You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"input_tables\": [...] // string[], describe names of the input tables that will be used in the transformation.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\", 'boxplot'. \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"input_tables\", the names of a subset of input tables from [CONTEXT] section that will be used to achieve the user's goal.\n - **IMPORTANT** Note that the Table 1 in [CONTEXT] section is the table the user is currently viewing, it should take precedence if the user refers to insights about the \"current table\".\n - At the same time, leverage table information to determine which tables are relevant to the user's goal and should be used.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\", \"boxplot\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - boxplot: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - (boxplot) Box plots: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical (optional for creating grouped boxplots), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the boxplot will be grouped automatically (items with the same x values will be grouped).\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE]. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n# from sklearn import ... # import from sklearn if you need it.\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- decide the function signature based on the number of tables you decided in the previous step \"input_tables\":\n - if you decide there will only be one input table, then function signature should be `def transform_data(df1)`\n - if you decided there will be k input tables, then function signature should be `def transform_data(df_1, df_2, ..., df_k)`.\n - instead of using generic names like df1, df2, ..., try to use intuitive table names for function arguments, for example, if you have input_tables: [\"City\", \"Weather\"]`, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes.\n - **VERY IMPORTANT** the number of arguments in the function signature must be the same as the number of tables provided in \"input_tables\", and the order of arguments must match the order of tables provided in \"input_tables\".\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"}, {"role": "user", "content": "[CONTEXT]\n\n## Table 1: history (738 rows × 8 columns)\n\n### Schema (8 fields)\n - symbol -- type: object, values: AAPL, AMZN, GOOGL, META, MSFT, NVDA\n - date -- type: object, values: 2025-07-30, 2025-07-31, 2025-08-01, ..., 2026-01-20, 2026-01-21, 2026-01-22, 2026-01-23\n - open -- type: float64, values: 167.53, 168.01, 169.07, ..., 781.21, 782.98, 785.25, 789.97\n - high -- type: float64, values: 169.01, 170.94, 170.96, ..., 789.62, 792.49, 794.28, 795.06\n - low -- type: float64, values: 164.05, 166.72, 167.2, ..., 771.36, 772.21, 777.07, 779.66\n - close -- type: float64, values: 167.0, 168.29, 170.28, ..., 779.09, 780.97, 784.06, 788.82\n - volume -- type: int64, values: 5627500, 5855900, 6861200, ..., 320600300, 324925900, 343504800, 346926200\n - fetched_at -- type: object, values: 2026-01-26T23:46:42.347312Z\n\n### Sample Data (first 5 rows)\n```\n symbol date open high low close volume fetched_at\n0 AAPL 2025-07-30 211.45 211.94 207.28 208.61 45512500 2026-01-26T23:46:42.347312Z\n1 AAPL 2025-07-31 208.05 209.40 206.72 207.13 80698400 2026-01-26T23:46:42.347312Z\n2 AAPL 2025-08-01 210.43 213.13 201.08 201.95 104434500 2026-01-26T23:46:42.347312Z\n3 AAPL 2025-08-04 204.08 207.44 201.26 202.92 75109300 2026-01-26T23:46:42.347312Z\n4 AAPL 2025-08-05 202.97 204.91 201.74 202.49 44155100 2026-01-26T23:46:42.347312Z\n```\n\n\n[GOAL]\n\n1) First, plot the overall **closing price** trends for each **symbol** over **time** on a single line chart to compare their broad trajectories and volatility."}, {"role": "assistant", "content": "{\n \"mode\": \"infer\",\n \"recap\": \"Compare overall closing price trends and volatility over time for each stock symbol on a single line chart.\",\n \"display_instruction\": \"Visualize **closing price** trends by **symbol** over **date**\",\n \"recommendation\": \"A line chart with time on the x-axis and closing price on the y-axis, colored by symbol, best shows comparative trajectories and volatility over time. The data only needs column selection, date normalization, and sorting to be visualization-ready.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"close\",\n \"color\": \"symbol\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n```"}, {"role": "user", "content": "This is the result from the latest python code:\n\n date symbol close\n0 2025-07-30 AAPL 208.61\n1 2025-07-31 AAPL 207.13\n2 2025-08-01 AAPL 201.95\n3 2025-08-04 AAPL 202.92\n4 2025-08-05 AAPL 202.49\n5 2025-08-06 AAPL 212.80\n6 2025-08-07 AAPL 219.57\n7 2025-08-08 AAPL 228.87\n8 2025-08-11 AAPL 226.96\n9 2025-08-12 AAPL 229.43\n......\n\nUpdate the code above based on the following instruction:\n\n2) For each symbol, compute 20-day and 60-day rolling averages of close price (columns: date, symbol, value, ma_window with values 20 or 60) and plot them over time alongside the actual close to compare short- vs long-term trends and reversals."}, {"role": "assistant", "content": "{\n \"mode\": \"infer\",\n \"recap\": \"Compute 20-day and 60-day rolling averages of closing prices for each symbol in tidy format for plotting alongside actual closes over time.\",\n \"display_instruction\": \"Add **20/60-day moving averages** of **close** by **symbol** over **date**\",\n \"recommendation\": \"To compare short- and long-term trends per symbol, compute 20- and 60-day rolling means of close, reshape them into a long format with one row per date-symbol-window, and keep the original close for plotting alongside the moving averages.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"value\",\n \"color\": \"ma_window\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"symbol\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Work on a copy\n df = df_history.copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper rolling computation\n df = df.sort_values([\"symbol\", \"date\"])\n\n # Group by symbol and compute rolling means on close\n group = df.groupby(\"symbol\", group_keys=False)[\"close\"]\n df[\"ma_20\"] = group.rolling(20, min_periods=1).mean()\n df[\"ma_60\"] = group.rolling(60, min_periods=1).mean()\n\n # Reshape to long format for the moving averages\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\", \"close\"],\n value_vars=[\"ma_20\", \"ma_60\"],\n var_name=\"ma_window\",\n value_name=\"value\",\n )\n\n # Map ma_window to numeric window size (20 or 60)\n window_map = {\"ma_20\": 20, \"ma_60\": 60}\n df_long[\"ma_window\"] = df_long[\"ma_window\"].map(window_map)\n\n # Ensure correct column order and types\n df_long[\"ma_window\"] = df_long[\"ma_window\"].astype(int)\n\n transformed_df = df_long[[\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"]].reset_index(drop=True)\n return transformed_df\n```"}, {"role": "user", "content": "This is the result from the latest python code:\n\nEmpty DataFrame\nColumns: []\nIndex: []\n......\n\nUpdate the code above based on the following instruction:\n\nWe run into the following problem executing the code, please fix it:\n\nError: TypeError - incompatible index of inserted column with frame index\n\nPlease think step by step, reflect why the error happens and fix the code so that no more errors would occur."}, {"role": "assistant", "content": "{\n \"mode\": \"infer\",\n \"recap\": \"Compute 20-day and 60-day rolling averages of closing prices for each symbol in tidy format for plotting alongside actual closes over time.\",\n \"display_instruction\": \"Compute **20/60-day moving averages** of **close** by **symbol** over **date**\",\n \"recommendation\": \"The previous implementation used groupby().rolling() and assigned the result directly to new columns, which can create an index mismatch and raise a TypeError. Using groupby().transform(...) with an inner rolling mean keeps the index aligned with the original DataFrame, avoiding incompatible index issues. After computing the moving averages, reshape to long format for visualization.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"value\",\n \"color\": \"ma_window\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"symbol\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Work on a copy and keep only needed columns if present\n base_cols = [c for c in [\"date\", \"symbol\", \"close\"] if c in df_history.columns]\n df = df_history[base_cols].copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper rolling computation\n df = df.sort_values([\"symbol\", \"date\"])\n\n # Ensure we have a clean, simple index to avoid alignment issues\n df = df.reset_index(drop=True)\n\n # Compute rolling means using transform to preserve index alignment\n df[\"ma_20\"] = (\n df.groupby(\"symbol\")[\"close\"]\n .transform(lambda s: s.rolling(window=20, min_periods=1).mean())\n )\n df[\"ma_60\"] = (\n df.groupby(\"symbol\")[\"close\"]\n .transform(lambda s: s.rolling(window=60, min_periods=1).mean())\n )\n\n # Reshape to long format for the moving averages\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\", \"close\"],\n value_vars=[\"ma_20\", \"ma_60\"],\n var_name=\"ma_window\",\n value_name=\"value\",\n )\n\n # Map ma_window to numeric window size (20 or 60)\n window_map = {\"ma_20\": 20, \"ma_60\": 60}\n df_long[\"ma_window\"] = df_long[\"ma_window\"].map(window_map).astype(int)\n\n # Final tidy output\n transformed_df = df_long[[\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"]].reset_index(drop=True)\n return transformed_df\n```"}], "trigger": {"tableId": "table-233476", "resultTableId": "table-265247", "chart": {"id": "chart-1769471261112", "chartType": "Auto", "encodingMap": {}, "tableRef": "history", "saved": false, "source": "trigger", "unread": true}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "2) For each symbol, compute 20-day and 60-day rolling averages of close price (columns: date, symbol, value, ma_window with values 20 or 60) and plot them over time alongside the actual close to compare short- vs long-term trends and reversals.", "displayContent": "Compute **20/60-day moving averages** of **close** by **symbol** over **date**"}]}, "explanation": {"status": "ok", "concepts": [{"field": "ma_20, ma_60, value", "explanation": "These fields represent a rolling (moving) average of the closing price over a fixed number of recent trading days for each stock symbol. For a window of N days (N = 20 or 60), the moving average on a given date is the average of the last N closing prices up to that date. Mathematically, if close_t is the closing price on day t, then the N-day moving average MA_N(t) is: MA_N(t) = (close_t + close_{t-1} + ... + close_{t-N+1}) / N. At the start of the series, when fewer than N days are available, the average is taken over all available past days instead."}, {"field": "ma_window", "explanation": "This field indicates the length of the moving-average window, in days. A value of 20 means the corresponding 'value' is a 20-day moving average; a value of 60 means it is a 60-day moving average."}], "code": "- Start from the original **history** table but keep only the **`date`**, **`symbol`**, and **`close`** fields to simplify the data.\n- Convert **`date`** into a standardized text format **`YYYY-MM-DD`** so all dates look consistent.\n- **Sort** the data by **`symbol`** and then by **`date`** to ensure the time series for each stock is in the correct order.\n- For each **`symbol`**, **compute**:\n - **`ma_20`**: a **20-day moving average** of the **`close`** price (uses up to the last **20** days; for the first few days it uses all available past days).\n - **`ma_60`**: a **60-day moving average** of the **`close`** price (uses up to the last **60** days; similarly relaxed at the start).\n- **Reshape** the data so that instead of having separate columns **`ma_20`** and **`ma_60`**, each row represents **one moving-average value**:\n - Keep **`date`**, **`symbol`**, and **`close`**.\n - Put the moving-average values into a single column **`value`**.\n - Use a column **`ma_window`** to indicate whether **`value`** is for **20** days or **60** days.\n- **Map** the labels **`ma_20`** and **`ma_60`** into numeric values **20** and **60** in **`ma_window`**, and keep only:\n - **`date`**, **`symbol`**, **`close`**, **`value`** (moving average), **`ma_window`** (window length in days).", "dialog": [{"role": "system", "content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n"}, {"role": "user", "content": "[CONTEXT]\n\n## Table 1: history (738 rows × 8 columns)\n\n### Schema (8 fields)\n - symbol -- type: object, values: AAPL, AMZN, GOOGL, META, MSFT, NVDA\n - date -- type: object, values: 2025-07-30, 2025-07-31, 2025-08-01, ..., 2026-01-20, 2026-01-21, 2026-01-22, 2026-01-23\n - open -- type: float64, values: 167.53, 168.01, 169.07, ..., 781.21, 782.98, 785.25, 789.97\n - high -- type: float64, values: 169.01, 170.94, 170.96, ..., 789.62, 792.49, 794.28, 795.06\n - low -- type: float64, values: 164.05, 166.72, 167.2, ..., 771.36, 772.21, 777.07, 779.66\n - close -- type: float64, values: 167.0, 168.29, 170.28, ..., 779.09, 780.97, 784.06, 788.82\n - volume -- type: int64, values: 5627500, 5855900, 6861200, ..., 320600300, 324925900, 343504800, 346926200\n - fetched_at -- type: object, values: 2026-01-26T23:46:42.347312Z\n\n### Sample Data (first 5 rows)\n```\n symbol date open high low close volume fetched_at\n0 AAPL 2025-07-30 211.45 211.94 207.28 208.61 45512500 2026-01-26T23:46:42.347312Z\n1 AAPL 2025-07-31 208.05 209.40 206.72 207.13 80698400 2026-01-26T23:46:42.347312Z\n2 AAPL 2025-08-01 210.43 213.13 201.08 201.95 104434500 2026-01-26T23:46:42.347312Z\n3 AAPL 2025-08-04 204.08 207.44 201.26 202.92 75109300 2026-01-26T23:46:42.347312Z\n4 AAPL 2025-08-05 202.97 204.91 201.74 202.49 44155100 2026-01-26T23:46:42.347312Z\n```\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Work on a copy and keep only needed columns if present\n base_cols = [c for c in [\"date\", \"symbol\", \"close\"] if c in df_history.columns]\n df = df_history[base_cols].copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper rolling computation\n df = df.sort_values([\"symbol\", \"date\"])\n\n # Ensure we have a clean, simple index to avoid alignment issues\n df = df.reset_index(drop=True)\n\n # Compute rolling means using transform to preserve index alignment\n df[\"ma_20\"] = (\n df.groupby(\"symbol\")[\"close\"]\n .transform(lambda s: s.rolling(window=20, min_periods=1).mean())\n )\n df[\"ma_60\"] = (\n df.groupby(\"symbol\")[\"close\"]\n .transform(lambda s: s.rolling(window=60, min_periods=1).mean())\n )\n\n # Reshape to long format for the moving averages\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\", \"close\"],\n value_vars=[\"ma_20\", \"ma_60\"],\n var_name=\"ma_window\",\n value_name=\"value\",\n )\n\n # Map ma_window to numeric window size (20 or 60)\n window_map = {\"ma_20\": 20, \"ma_60\": 60}\n df_long[\"ma_window\"] = df_long[\"ma_window\"].map(window_map).astype(int)\n\n # Final tidy output\n transformed_df = df_long[[\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"]].reset_index(drop=True)\n return transformed_df\n\n\n[EXPLANATION]\n"}, {"role": "assistant", "content": "[CODE EXPLANATION]\n\n- Start from the original **history** table but keep only the **`date`**, **`symbol`**, and **`close`** fields to simplify the data.\n- Convert **`date`** into a standardized text format **`YYYY-MM-DD`** so all dates look consistent.\n- **Sort** the data by **`symbol`** and then by **`date`** to ensure the time series for each stock is in the correct order.\n- For each **`symbol`**, **compute**:\n - **`ma_20`**: a **20-day moving average** of the **`close`** price (uses up to the last **20** days; for the first few days it uses all available past days).\n - **`ma_60`**: a **60-day moving average** of the **`close`** price (uses up to the last **60** days; similarly relaxed at the start).\n- **Reshape** the data so that instead of having separate columns **`ma_20`** and **`ma_60`**, each row represents **one moving-average value**:\n - Keep **`date`**, **`symbol`**, and **`close`**.\n - Put the moving-average values into a single column **`value`**.\n - Use a column **`ma_window`** to indicate whether **`value`** is for **20** days or **60** days.\n- **Map** the labels **`ma_20`** and **`ma_60`** into numeric values **20** and **60** in **`ma_window`**, and keep only:\n - **`date`**, **`symbol`**, **`close`**, **`value`** (moving average), **`ma_window`** (window length in days).\n\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"ma_20, ma_60, value\",\n \"explanation\": \"These fields represent a rolling (moving) average of the closing price over a fixed number of recent trading days for each stock symbol. For a window of N days (N = 20 or 60), the moving average on a given date is the average of the last N closing prices up to that date. Mathematically, if close_t is the closing price on day t, then the N-day moving average MA_N(t) is: MA_N(t) = (close_t + close_{t-1} + ... + close_{t-N+1}) / N. At the start of the series, when fewer than N days are available, the average is taken over all available past days instead.\"\n },\n {\n \"field\": \"ma_window\",\n \"explanation\": \"This field indicates the length of the moving-average window, in days. A value of 20 means the corresponding 'value' is a 20-day moving average; a value of 60 means it is a 60-day moving average.\"\n }\n]\n```"}], "agent": "CodeExplanationAgent"}, "status": "completed"}, "anchored": false, "attachedMetadata": "", "contentHash": "63c48b66"}, {"kind": "table", "id": "table-277926", "displayId": "stock-metr", "names": ["date", "symbol", "metric", "value"], "rows": [{"date": "2025-07-31", "symbol": "AAPL", "metric": "return", "value": null}, {"date": "2025-08-01", "symbol": "AAPL", "metric": "return", "value": -0.0250084488}, {"date": "2025-08-04", "symbol": "AAPL", "metric": "return", "value": 0.0048031691}, {"date": "2025-08-05", "symbol": "AAPL", "metric": "return", "value": -0.0021190617}, {"date": "2025-08-06", "symbol": "AAPL", "metric": "return", "value": 0.0509160946}, {"date": "2025-08-07", "symbol": "AAPL", "metric": "return", "value": 0.0318139098}, {"date": "2025-08-08", "symbol": "AAPL", "metric": "return", "value": 0.042355513}, {"date": "2025-08-11", "symbol": "AAPL", "metric": "return", "value": -0.0083453489}, {"date": "2025-08-12", "symbol": "AAPL", "metric": "return", "value": 0.010882975}, {"date": "2025-08-13", "symbol": "AAPL", "metric": "return", "value": 0.0159961644}, {"date": "2025-08-14", "symbol": "AAPL", "metric": "return", "value": -0.0023595024}, {"date": "2025-08-15", "symbol": "AAPL", "metric": "return", "value": -0.0050741776}, {"date": "2025-08-18", "symbol": "AAPL", "metric": "return", "value": -0.0030254571}, {"date": "2025-08-19", "symbol": "AAPL", "metric": "return", "value": -0.0014306152}, {"date": "2025-08-20", "symbol": "AAPL", "metric": "return", "value": -0.019753408}, {"date": "2025-08-21", "symbol": "AAPL", "metric": "return", "value": -0.0049160725}, {"date": "2025-08-22", "symbol": "AAPL", "metric": "return", "value": 0.0127292149}, {"date": "2025-08-25", "symbol": "AAPL", "metric": "return", "value": -0.002636899}, {"date": "2025-08-26", "symbol": "AAPL", "metric": "return", "value": 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"volume", "value": 130168900}, {"date": "2025-10-09", "symbol": "NVDA", "metric": "volume", "value": 182997200}, {"date": "2025-10-10", "symbol": "NVDA", "metric": "volume", "value": 268774400}, {"date": "2025-10-13", "symbol": "NVDA", "metric": "volume", "value": 153482800}, {"date": "2025-10-14", "symbol": "NVDA", "metric": "volume", "value": 205641400}, {"date": "2025-10-15", "symbol": "NVDA", "metric": "volume", "value": 214450500}, {"date": "2025-10-16", "symbol": "NVDA", "metric": "volume", "value": 179723300}, {"date": "2025-10-17", "symbol": "NVDA", "metric": "volume", "value": 173135200}, {"date": "2025-10-20", "symbol": "NVDA", "metric": "volume", "value": 128544700}, {"date": "2025-10-21", "symbol": "NVDA", "metric": "volume", "value": 124240200}, {"date": "2025-10-22", "symbol": "NVDA", "metric": "volume", "value": 162249600}, {"date": "2025-10-23", "symbol": "NVDA", "metric": "volume", "value": 111363700}, {"date": "2025-10-24", "symbol": "NVDA", "metric": "volume", "value": 131296700}, {"date": "2025-10-27", "symbol": "NVDA", "metric": "volume", "value": 153452700}, {"date": "2025-10-28", "symbol": "NVDA", "metric": "volume", "value": 297986200}, {"date": "2025-10-29", "symbol": "NVDA", "metric": "volume", "value": 308829600}, {"date": "2025-10-30", "symbol": "NVDA", "metric": "volume", "value": 178864400}, {"date": "2025-10-31", "symbol": "NVDA", "metric": "volume", "value": 179802200}, {"date": "2025-11-03", "symbol": "NVDA", "metric": "volume", "value": 180267300}, {"date": "2025-11-04", "symbol": "NVDA", "metric": "volume", "value": 188919300}, {"date": "2025-11-05", "symbol": "NVDA", "metric": "volume", "value": 171350300}, {"date": "2025-11-06", "symbol": "NVDA", "metric": "volume", "value": 223029800}, {"date": "2025-11-07", "symbol": "NVDA", "metric": "volume", "value": 264942300}, {"date": "2025-11-10", "symbol": "NVDA", "metric": "volume", "value": 198897100}, {"date": "2025-11-11", "symbol": "NVDA", "metric": "volume", "value": 176483300}, {"date": "2025-11-12", "symbol": "NVDA", "metric": "volume", "value": 154935300}, {"date": "2025-11-13", "symbol": "NVDA", "metric": "volume", "value": 207423100}, {"date": "2025-11-14", "symbol": "NVDA", "metric": "volume", "value": 186591900}, {"date": "2025-11-17", "symbol": "NVDA", "metric": "volume", "value": 173628900}, {"date": "2025-11-18", "symbol": "NVDA", "metric": "volume", "value": 213598900}, {"date": "2025-11-19", "symbol": "NVDA", "metric": "volume", "value": 247246400}, {"date": "2025-11-20", "symbol": "NVDA", "metric": "volume", "value": 343504800}, {"date": "2025-11-21", "symbol": "NVDA", "metric": "volume", "value": 346926200}, {"date": "2025-11-24", "symbol": "NVDA", "metric": "volume", "value": 256618300}, {"date": "2025-11-25", "symbol": "NVDA", "metric": "volume", "value": 320600300}, {"date": "2025-11-26", "symbol": "NVDA", "metric": "volume", "value": 183852000}, {"date": "2025-11-28", "symbol": "NVDA", "metric": "volume", "value": 121332800}, {"date": "2025-12-01", "symbol": "NVDA", "metric": "volume", "value": 188131000}, {"date": "2025-12-02", "symbol": "NVDA", "metric": "volume", "value": 182632200}, {"date": "2025-12-03", "symbol": "NVDA", "metric": "volume", "value": 165138000}, {"date": "2025-12-04", "symbol": "NVDA", "metric": "volume", "value": 167364900}, {"date": "2025-12-05", "symbol": "NVDA", "metric": "volume", "value": 143971100}, {"date": "2025-12-08", "symbol": "NVDA", "metric": "volume", "value": 204378100}, {"date": "2025-12-09", "symbol": "NVDA", "metric": "volume", "value": 144719700}, {"date": "2025-12-10", "symbol": "NVDA", "metric": "volume", "value": 162785400}, {"date": "2025-12-11", "symbol": "NVDA", "metric": "volume", "value": 182136600}, {"date": "2025-12-12", "symbol": "NVDA", "metric": "volume", "value": 204274900}, {"date": "2025-12-15", "symbol": "NVDA", "metric": "volume", "value": 164775600}, {"date": "2025-12-16", "symbol": "NVDA", "metric": "volume", "value": 148588100}, {"date": "2025-12-17", "symbol": "NVDA", "metric": "volume", "value": 222775500}, {"date": "2025-12-18", "symbol": "NVDA", "metric": "volume", "value": 176096000}, {"date": "2025-12-19", "symbol": "NVDA", "metric": "volume", "value": 324925900}, {"date": "2025-12-22", "symbol": "NVDA", "metric": "volume", "value": 129064400}, {"date": "2025-12-23", "symbol": "NVDA", "metric": "volume", "value": 174873600}, {"date": "2025-12-24", "symbol": "NVDA", "metric": "volume", "value": 65528500}, {"date": "2025-12-26", "symbol": "NVDA", "metric": "volume", "value": 139740300}, {"date": "2025-12-29", "symbol": "NVDA", "metric": "volume", "value": 120006100}, {"date": "2025-12-30", "symbol": "NVDA", "metric": "volume", "value": 97687300}, {"date": "2025-12-31", "symbol": "NVDA", "metric": "volume", "value": 120100500}, {"date": "2026-01-02", "symbol": "NVDA", "metric": "volume", "value": 148240500}, {"date": "2026-01-05", "symbol": "NVDA", "metric": "volume", "value": 183529700}, {"date": "2026-01-06", "symbol": "NVDA", "metric": "volume", "value": 176862600}, {"date": "2026-01-07", "symbol": "NVDA", "metric": "volume", "value": 153543200}, {"date": "2026-01-08", "symbol": "NVDA", "metric": "volume", "value": 172457000}, {"date": "2026-01-09", "symbol": "NVDA", "metric": "volume", "value": 131327500}, {"date": "2026-01-12", "symbol": "NVDA", "metric": "volume", "value": 137968500}, {"date": "2026-01-13", "symbol": "NVDA", "metric": "volume", "value": 160128900}, {"date": "2026-01-14", "symbol": "NVDA", "metric": "volume", "value": 159586100}, {"date": "2026-01-15", "symbol": "NVDA", "metric": "volume", "value": 206188600}, {"date": "2026-01-16", "symbol": "NVDA", "metric": "volume", "value": 187967200}, {"date": "2026-01-20", "symbol": "NVDA", "metric": "volume", "value": 223345300}, {"date": "2026-01-21", "symbol": "NVDA", "metric": "volume", "value": 200381000}, {"date": "2026-01-22", "symbol": "NVDA", "metric": "volume", "value": 139636600}, {"date": "2026-01-23", "symbol": "NVDA", "metric": "volume", "value": 142748100}, {"date": "2026-01-26", "symbol": "NVDA", "metric": "volume", "value": 124489200}], "metadata": {"date": {"type": "date", "semanticType": "Date"}, "symbol": {"type": "string", "semanticType": "String"}, "metric": {"type": "string", "semanticType": "String"}, "value": {"type": "number", "semanticType": "Number"}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Keep only necessary columns\n cols = [c for c in [\"date\", \"symbol\", \"close\", \"volume\"] if c in df_history.columns]\n df = df_history[cols].copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for time-series operations\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n # Compute daily returns as pct_change of close within each symbol\n df[\"return\"] = df.groupby(\"symbol\")[\"close\"].pct_change()\n\n # Compute 20-day rolling volatility (std of returns) within each symbol\n df[\"vol20\"] = (\n df.groupby(\"symbol\")[\"return\"]\n .transform(lambda s: s.rolling(window=20, min_periods=1).std())\n )\n\n # Reshape to long format: metrics = return, vol20, volume\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\"],\n value_vars=[\"return\", \"vol20\", \"volume\"],\n var_name=\"metric\",\n value_name=\"value\",\n )\n\n # Final tidy output\n transformed_df = df_long[[\"date\", \"symbol\", \"metric\", \"value\"]].reset_index(drop=True)\n return transformed_df\n", "source": ["history"], "dialog": [{"role": "system", "content": "You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"input_tables\": [...] // string[], describe names of the input tables that will be used in the transformation.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\", 'boxplot'. \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"input_tables\", the names of a subset of input tables from [CONTEXT] section that will be used to achieve the user's goal.\n - **IMPORTANT** Note that the Table 1 in [CONTEXT] section is the table the user is currently viewing, it should take precedence if the user refers to insights about the \"current table\".\n - At the same time, leverage table information to determine which tables are relevant to the user's goal and should be used.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\", \"boxplot\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - boxplot: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - (boxplot) Box plots: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical (optional for creating grouped boxplots), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the boxplot will be grouped automatically (items with the same x values will be grouped).\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE]. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n# from sklearn import ... # import from sklearn if you need it.\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- decide the function signature based on the number of tables you decided in the previous step \"input_tables\":\n - if you decide there will only be one input table, then function signature should be `def transform_data(df1)`\n - if you decided there will be k input tables, then function signature should be `def transform_data(df_1, df_2, ..., df_k)`.\n - instead of using generic names like df1, df2, ..., try to use intuitive table names for function arguments, for example, if you have input_tables: [\"City\", \"Weather\"]`, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes.\n - **VERY IMPORTANT** the number of arguments in the function signature must be the same as the number of tables provided in \"input_tables\", and the order of arguments must match the order of tables provided in \"input_tables\".\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"}, {"role": "user", "content": "[CONTEXT]\n\n## Table 1: history (738 rows × 8 columns)\n\n### Schema (8 fields)\n - symbol -- type: object, values: AAPL, AMZN, GOOGL, META, MSFT, NVDA\n - date -- type: object, values: 2025-07-30, 2025-07-31, 2025-08-01, ..., 2026-01-20, 2026-01-21, 2026-01-22, 2026-01-23\n - open -- type: float64, values: 167.53, 168.01, 169.07, ..., 781.21, 782.98, 785.25, 789.97\n - high -- type: float64, values: 169.01, 170.94, 170.96, ..., 789.62, 792.49, 794.28, 795.06\n - low -- type: float64, values: 164.05, 166.72, 167.2, ..., 771.36, 772.21, 777.07, 779.66\n - close -- type: float64, values: 167.0, 168.29, 170.28, ..., 779.09, 780.97, 784.06, 788.82\n - volume -- type: int64, values: 5627500, 5855900, 6861200, ..., 320600300, 324925900, 343504800, 346926200\n - fetched_at -- type: object, values: 2026-01-26T23:46:42.347312Z\n\n### Sample Data (first 5 rows)\n```\n symbol date open high low close volume fetched_at\n0 AAPL 2025-07-30 211.45 211.94 207.28 208.61 45512500 2026-01-26T23:46:42.347312Z\n1 AAPL 2025-07-31 208.05 209.40 206.72 207.13 80698400 2026-01-26T23:46:42.347312Z\n2 AAPL 2025-08-01 210.43 213.13 201.08 201.95 104434500 2026-01-26T23:46:42.347312Z\n3 AAPL 2025-08-04 204.08 207.44 201.26 202.92 75109300 2026-01-26T23:46:42.347312Z\n4 AAPL 2025-08-05 202.97 204.91 201.74 202.49 44155100 2026-01-26T23:46:42.347312Z\n```\n\n\n[GOAL]\n\n1) First, plot the overall **closing price** trends for each **symbol** over **time** on a single line chart to compare their broad trajectories and volatility."}, {"role": "assistant", "content": "{\n \"mode\": \"infer\",\n \"recap\": \"Compare overall closing price trends and volatility over time for each stock symbol on a single line chart.\",\n \"display_instruction\": \"Visualize **closing price** trends by **symbol** over **date**\",\n \"recommendation\": \"A line chart with time on the x-axis and closing price on the y-axis, colored by symbol, best shows comparative trajectories and volatility over time. The data only needs column selection, date normalization, and sorting to be visualization-ready.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"close\",\n \"color\": \"symbol\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n```"}, {"role": "user", "content": "This is the result from the latest python code:\n\n date symbol close\n0 2025-07-30 AAPL 208.61\n1 2025-07-31 AAPL 207.13\n2 2025-08-01 AAPL 201.95\n3 2025-08-04 AAPL 202.92\n4 2025-08-05 AAPL 202.49\n5 2025-08-06 AAPL 212.80\n6 2025-08-07 AAPL 219.57\n7 2025-08-08 AAPL 228.87\n8 2025-08-11 AAPL 226.96\n9 2025-08-12 AAPL 229.43\n......\n\nUpdate the code above based on the following instruction:\n\n2) For each symbol, compute 20-day and 60-day rolling averages of close price (columns: date, symbol, value, ma_window with values 20 or 60) and plot them over time alongside the actual close to compare short- vs long-term trends and reversals."}, {"role": "assistant", "content": "{\n \"mode\": \"infer\",\n \"recap\": \"Compute 20-day and 60-day rolling averages of closing prices for each symbol in tidy format for plotting alongside actual closes over time.\",\n \"display_instruction\": \"Add **20/60-day moving averages** of **close** by **symbol** over **date**\",\n \"recommendation\": \"To compare short- and long-term trends per symbol, compute 20- and 60-day rolling means of close, reshape them into a long format with one row per date-symbol-window, and keep the original close for plotting alongside the moving averages.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"value\",\n \"color\": \"ma_window\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"symbol\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Work on a copy\n df = df_history.copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper rolling computation\n df = df.sort_values([\"symbol\", \"date\"])\n\n # Group by symbol and compute rolling means on close\n group = df.groupby(\"symbol\", group_keys=False)[\"close\"]\n df[\"ma_20\"] = group.rolling(20, min_periods=1).mean()\n df[\"ma_60\"] = group.rolling(60, min_periods=1).mean()\n\n # Reshape to long format for the moving averages\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\", \"close\"],\n value_vars=[\"ma_20\", \"ma_60\"],\n var_name=\"ma_window\",\n value_name=\"value\",\n )\n\n # Map ma_window to numeric window size (20 or 60)\n window_map = {\"ma_20\": 20, \"ma_60\": 60}\n df_long[\"ma_window\"] = df_long[\"ma_window\"].map(window_map)\n\n # Ensure correct column order and types\n df_long[\"ma_window\"] = df_long[\"ma_window\"].astype(int)\n\n transformed_df = df_long[[\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"]].reset_index(drop=True)\n return transformed_df\n```"}, {"role": "user", "content": "This is the result from the latest python code:\n\nEmpty DataFrame\nColumns: []\nIndex: []\n......\n\nUpdate the code above based on the following instruction:\n\nWe run into the following problem executing the code, please fix it:\n\nError: TypeError - incompatible index of inserted column with frame index\n\nPlease think step by step, reflect why the error happens and fix the code so that no more errors would occur."}, {"role": "assistant", "content": "{\n \"mode\": \"infer\",\n \"recap\": \"Compute 20-day and 60-day rolling averages of closing prices for each symbol in tidy format for plotting alongside actual closes over time.\",\n \"display_instruction\": \"Compute **20/60-day moving averages** of **close** by **symbol** over **date**\",\n \"recommendation\": \"The previous implementation used groupby().rolling() and assigned the result directly to new columns, which can create an index mismatch and raise a TypeError. Using groupby().transform(...) with an inner rolling mean keeps the index aligned with the original DataFrame, avoiding incompatible index issues. After computing the moving averages, reshape to long format for visualization.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"value\",\n \"color\": \"ma_window\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"symbol\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Work on a copy and keep only needed columns if present\n base_cols = [c for c in [\"date\", \"symbol\", \"close\"] if c in df_history.columns]\n df = df_history[base_cols].copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper rolling computation\n df = df.sort_values([\"symbol\", \"date\"])\n\n # Ensure we have a clean, simple index to avoid alignment issues\n df = df.reset_index(drop=True)\n\n # Compute rolling means using transform to preserve index alignment\n df[\"ma_20\"] = (\n df.groupby(\"symbol\")[\"close\"]\n .transform(lambda s: s.rolling(window=20, min_periods=1).mean())\n )\n df[\"ma_60\"] = (\n df.groupby(\"symbol\")[\"close\"]\n .transform(lambda s: s.rolling(window=60, min_periods=1).mean())\n )\n\n # Reshape to long format for the moving averages\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\", \"close\"],\n value_vars=[\"ma_20\", \"ma_60\"],\n var_name=\"ma_window\",\n value_name=\"value\",\n )\n\n # Map ma_window to numeric window size (20 or 60)\n window_map = {\"ma_20\": 20, \"ma_60\": 60}\n df_long[\"ma_window\"] = df_long[\"ma_window\"].map(window_map).astype(int)\n\n # Final tidy output\n transformed_df = df_long[[\"date\", \"symbol\", \"close\", \"value\", \"ma_window\"]].reset_index(drop=True)\n return transformed_df\n```"}, {"role": "user", "content": "This is the result from the latest python code:\n\n date symbol close value ma_window\n0 2025-07-30 AAPL 208.61 208.610000 20\n1 2025-07-31 AAPL 207.13 207.870000 20\n2 2025-08-01 AAPL 201.95 205.896667 20\n3 2025-08-04 AAPL 202.92 205.152500 20\n4 2025-08-05 AAPL 202.49 204.620000 20\n5 2025-08-06 AAPL 212.80 205.983333 20\n6 2025-08-07 AAPL 219.57 207.924286 20\n7 2025-08-08 AAPL 228.87 210.542500 20\n8 2025-08-11 AAPL 226.96 212.366667 20\n9 2025-08-12 AAPL 229.43 214.073000 20\n......\n\nUpdate the code above based on the following instruction:\n\n3) For each symbol, compute daily returns as pct_change of close, then 20-day rolling volatility as the rolling std of these returns; keep volume. Reshape to long format with columns [date, symbol, metric, value], where metric ∈ {\"return\", \"vol20\", \"volume\"}, and visualize over time (e.g., faceted by symbol with separate lines for each metric) to highlight periods of strong trends with elevated volatility or volume."}, {"role": "assistant", "content": "{\n \"mode\": \"infer\",\n \"recap\": \"Compute daily returns and 20-day rolling volatility for each symbol, keep volume, and reshape to a long format for multi-metric time-series visualization.\",\n \"display_instruction\": \"Show **daily return**, **20-day volatility**, and **volume** by **symbol** over **date**\",\n \"recommendation\": \"To highlight periods of strong trends with elevated volatility or volume, compute per-symbol daily returns as pct_change of close and 20-day rolling volatility as the rolling standard deviation of those returns. Keep volume, then reshape into a long table with one metric per row so time-series lines for return, volatility, and volume can be plotted and compared per symbol.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"metric\", \"value\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"value\",\n \"color\": \"metric\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"symbol\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Keep only necessary columns\n cols = [c for c in [\"date\", \"symbol\", \"close\", \"volume\"] if c in df_history.columns]\n df = df_history[cols].copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for time-series operations\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n # Compute daily returns as pct_change of close within each symbol\n df[\"return\"] = df.groupby(\"symbol\")[\"close\"].pct_change()\n\n # Compute 20-day rolling volatility (std of returns) within each symbol\n df[\"vol20\"] = (\n df.groupby(\"symbol\")[\"return\"]\n .transform(lambda s: s.rolling(window=20, min_periods=1).std())\n )\n\n # Reshape to long format: metrics = return, vol20, volume\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\"],\n value_vars=[\"return\", \"vol20\", \"volume\"],\n var_name=\"metric\",\n value_name=\"value\",\n )\n\n # Final tidy output\n transformed_df = df_long[[\"date\", \"symbol\", \"metric\", \"value\"]].reset_index(drop=True)\n return transformed_df\n```"}], "trigger": {"tableId": "table-265247", "resultTableId": "table-277926", "chart": {"id": "chart-1769471280073", "chartType": "Auto", "encodingMap": {}, "tableRef": "history", "saved": false, "source": "trigger", "unread": true}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "3) For each symbol, compute daily returns as pct_change of close, then 20-day rolling volatility as the rolling std of these returns; keep volume. Reshape to long format with columns [date, symbol, metric, value], where metric ∈ {\"return\", \"vol20\", \"volume\"}, and visualize over time (e.g., faceted by symbol with separate lines for each metric) to highlight periods of strong trends with elevated volatility or volume.", "displayContent": "Show **daily return**, **20-day volatility**, and **volume** by **symbol** over **date**"}]}, "explanation": {"status": "ok", "concepts": [{"field": "return", "explanation": "Daily return measures how much the closing price changes in percentage terms from one trading day to the next for the same symbol. For day t, return_t = (Close_t - Close_{t-1}) / Close_{t-1}."}, {"field": "vol20", "explanation": "20-day rolling volatility is a short-term risk measure that summarizes how much the daily returns have fluctuated over roughly the past month. For each date, vol20 is the standard deviation of the last up-to-20 'return' values for that symbol; higher vol20 indicates more unstable (more volatile) recent price movements."}, {"field": "metric, value", "explanation": "These two fields reorganize different measures into a single tidy structure: 'metric' tells you which quantity is being reported (return, vol20, or volume), and 'value' stores the corresponding number. This makes it easy to filter and compare different metrics in the same table."}, {"field": "Statistical Analysis", "explanation": "The transformation introduces two statistical concepts: (1) daily returns, which quantify relative price changes over time, and (2) rolling volatility, which applies a moving standard deviation to recent returns to estimate short-term risk. The model is implicit: for each symbol, returns are treated as a time series, and volatility is computed over a 20-day rolling window. Other possible analyses on this data could include moving averages of price or volume, exponential moving volatility, correlation between different symbols' returns, or time-series models such as ARIMA or GARCH to forecast returns and volatility."}], "code": "- **Selects** only the key fields from the original data: **\"date\"**, **\"symbol\"**, **\"close\"**, and **\"volume\"**.\n- **Standardizes** the **\"date\"** format to **\"YYYY-MM-DD\"** so all dates are consistent.\n- **Sorts** the data by **\"symbol\"** and then by **\"date\"** to put each stock’s prices in correct time order.\n- For each **symbol**, **calculates daily price change**:\n - Adds a new field **\"return\"** = the **percentage change of the closing price** from the previous trading day for that same symbol.\n- For each **symbol**, **measures recent risk/variability**:\n - Adds a new field **\"vol20\"** = the **20‑day rolling volatility**, i.e., the **standard deviation of the last up-to-20 daily returns** (a larger value means the stock has been more volatile recently).\n- **Restructures** the data into a “long” tidy format:\n - Keeps **\"date\"** and **\"symbol\"**.\n - Converts the three measures **\"return\"**, **\"vol20\"**, and **\"volume\"** into rows under a single **\"metric\"** column, with their numbers stored in a **\"value\"** column.\n- **Outputs** a clean table with columns **\"date\"**, **\"symbol\"**, **\"metric\"**, and **\"value\"**, where each row represents **one metric for one stock on one date**.", "dialog": [{"role": "system", "content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n"}, {"role": "user", "content": "[CONTEXT]\n\n## Table 1: history (738 rows × 8 columns)\n\n### Schema (8 fields)\n - symbol -- type: object, values: AAPL, AMZN, GOOGL, META, MSFT, NVDA\n - date -- type: object, values: 2025-07-30, 2025-07-31, 2025-08-01, ..., 2026-01-20, 2026-01-21, 2026-01-22, 2026-01-23\n - open -- type: float64, values: 167.53, 168.01, 169.07, ..., 781.21, 782.98, 785.25, 789.97\n - high -- type: float64, values: 169.01, 170.94, 170.96, ..., 789.62, 792.49, 794.28, 795.06\n - low -- type: float64, values: 164.05, 166.72, 167.2, ..., 771.36, 772.21, 777.07, 779.66\n - close -- type: float64, values: 167.0, 168.29, 170.28, ..., 779.09, 780.97, 784.06, 788.82\n - volume -- type: int64, values: 5627500, 5855900, 6861200, ..., 320600300, 324925900, 343504800, 346926200\n - fetched_at -- type: object, values: 2026-01-26T23:46:42.347312Z\n\n### Sample Data (first 5 rows)\n```\n symbol date open high low close volume fetched_at\n0 AAPL 2025-07-30 211.45 211.94 207.28 208.61 45512500 2026-01-26T23:46:42.347312Z\n1 AAPL 2025-07-31 208.05 209.40 206.72 207.13 80698400 2026-01-26T23:46:42.347312Z\n2 AAPL 2025-08-01 210.43 213.13 201.08 201.95 104434500 2026-01-26T23:46:42.347312Z\n3 AAPL 2025-08-04 204.08 207.44 201.26 202.92 75109300 2026-01-26T23:46:42.347312Z\n4 AAPL 2025-08-05 202.97 204.91 201.74 202.49 44155100 2026-01-26T23:46:42.347312Z\n```\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Keep only necessary columns\n cols = [c for c in [\"date\", \"symbol\", \"close\", \"volume\"] if c in df_history.columns]\n df = df_history[cols].copy()\n\n # Normalize date to string YYYY-MM-DD\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for time-series operations\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n # Compute daily returns as pct_change of close within each symbol\n df[\"return\"] = df.groupby(\"symbol\")[\"close\"].pct_change()\n\n # Compute 20-day rolling volatility (std of returns) within each symbol\n df[\"vol20\"] = (\n df.groupby(\"symbol\")[\"return\"]\n .transform(lambda s: s.rolling(window=20, min_periods=1).std())\n )\n\n # Reshape to long format: metrics = return, vol20, volume\n df_long = df.melt(\n id_vars=[\"date\", \"symbol\"],\n value_vars=[\"return\", \"vol20\", \"volume\"],\n var_name=\"metric\",\n value_name=\"value\",\n )\n\n # Final tidy output\n transformed_df = df_long[[\"date\", \"symbol\", \"metric\", \"value\"]].reset_index(drop=True)\n return transformed_df\n\n\n[EXPLANATION]\n"}, {"role": "assistant", "content": "[CODE EXPLANATION]\n\n- **Selects** only the key fields from the original data: **\"date\"**, **\"symbol\"**, **\"close\"**, and **\"volume\"**.\n- **Standardizes** the **\"date\"** format to **\"YYYY-MM-DD\"** so all dates are consistent.\n- **Sorts** the data by **\"symbol\"** and then by **\"date\"** to put each stock’s prices in correct time order.\n- For each **symbol**, **calculates daily price change**:\n - Adds a new field **\"return\"** = the **percentage change of the closing price** from the previous trading day for that same symbol.\n- For each **symbol**, **measures recent risk/variability**:\n - Adds a new field **\"vol20\"** = the **20‑day rolling volatility**, i.e., the **standard deviation of the last up-to-20 daily returns** (a larger value means the stock has been more volatile recently).\n- **Restructures** the data into a “long” tidy format:\n - Keeps **\"date\"** and **\"symbol\"**.\n - Converts the three measures **\"return\"**, **\"vol20\"**, and **\"volume\"** into rows under a single **\"metric\"** column, with their numbers stored in a **\"value\"** column.\n- **Outputs** a clean table with columns **\"date\"**, **\"symbol\"**, **\"metric\"**, and **\"value\"**, where each row represents **one metric for one stock on one date**.\n\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"return\",\n \"explanation\": \"Daily return measures how much the closing price changes in percentage terms from one trading day to the next for the same symbol. For day t, return_t = (Close_t - Close_{t-1}) / Close_{t-1}.\"\n },\n {\n \"field\": \"vol20\",\n \"explanation\": \"20-day rolling volatility is a short-term risk measure that summarizes how much the daily returns have fluctuated over roughly the past month. For each date, vol20 is the standard deviation of the last up-to-20 'return' values for that symbol; higher vol20 indicates more unstable (more volatile) recent price movements.\"\n },\n {\n \"field\": \"metric, value\",\n \"explanation\": \"These two fields reorganize different measures into a single tidy structure: 'metric' tells you which quantity is being reported (return, vol20, or volume), and 'value' stores the corresponding number. This makes it easy to filter and compare different metrics in the same table.\"\n },\n {\n \"field\": \"Statistical Analysis\",\n \"explanation\": \"The transformation introduces two statistical concepts: (1) daily returns, which quantify relative price changes over time, and (2) rolling volatility, which applies a moving standard deviation to recent returns to estimate short-term risk. The model is implicit: for each symbol, returns are treated as a time series, and volatility is computed over a 20-day rolling window. Other possible analyses on this data could include moving averages of price or volume, exponential moving volatility, correlation between different symbols' returns, or time-series models such as ARIMA or GARCH to forecast returns and volatility.\"\n }\n]\n```"}], "agent": "CodeExplanationAgent"}, "status": "completed"}, "anchored": false, "attachedMetadata": "", "contentHash": "d0e7201e"}, {"kind": "table", "id": "table-457008", "displayId": "stock-close1", "names": ["date", "symbol", "close"], "rows": [{"date": "2025-07-31", "symbol": "GOOGL", "close": 191.6}, {"date": "2025-08-01", "symbol": "GOOGL", "close": 188.84}, {"date": "2025-08-04", "symbol": "GOOGL", "close": 194.74}, {"date": "2025-08-05", "symbol": "GOOGL", "close": 194.37}, {"date": "2025-08-06", "symbol": "GOOGL", "close": 195.79}, {"date": "2025-08-07", "symbol": "GOOGL", "close": 196.22}, {"date": "2025-08-08", "symbol": "GOOGL", "close": 201.11}, {"date": "2025-08-11", "symbol": "GOOGL", "close": 200.69}, {"date": "2025-08-12", "symbol": "GOOGL", "close": 203.03}, {"date": "2025-08-13", "symbol": "GOOGL", "close": 201.65}, {"date": "2025-08-14", "symbol": "GOOGL", "close": 202.63}, {"date": "2025-08-15", "symbol": "GOOGL", "close": 203.58}, {"date": "2025-08-18", "symbol": "GOOGL", "close": 203.19}, {"date": "2025-08-19", "symbol": "GOOGL", "close": 201.26}, {"date": "2025-08-20", "symbol": "GOOGL", "close": 199.01}, {"date": "2025-08-21", "symbol": "GOOGL", "close": 199.44}, {"date": "2025-08-22", "symbol": "GOOGL", "close": 205.77}, {"date": "2025-08-25", "symbol": "GOOGL", "close": 208.17}, {"date": "2025-08-26", "symbol": "GOOGL", "close": 206.82}, {"date": "2025-08-27", "symbol": "GOOGL", "close": 207.16}, {"date": "2025-08-28", "symbol": "GOOGL", "close": 211.31}, {"date": "2025-08-29", "symbol": "GOOGL", "close": 212.58}, {"date": "2025-09-02", "symbol": "GOOGL", "close": 211.02}, {"date": "2025-09-03", "symbol": "GOOGL", "close": 230.3}, {"date": "2025-09-04", "symbol": "GOOGL", "close": 231.94}, {"date": "2025-09-05", "symbol": "GOOGL", "close": 234.64}, {"date": "2025-09-08", "symbol": "GOOGL", "close": 233.89}, {"date": "2025-09-09", "symbol": "GOOGL", "close": 239.47}, {"date": "2025-09-10", "symbol": "GOOGL", "close": 239.01}, {"date": "2025-09-11", "symbol": "GOOGL", "close": 240.21}, {"date": "2025-09-12", "symbol": "GOOGL", "close": 240.64}, {"date": "2025-09-15", "symbol": "GOOGL", "close": 251.45}, {"date": "2025-09-16", "symbol": "GOOGL", "close": 251}, {"date": "2025-09-17", "symbol": "GOOGL", "close": 249.37}, {"date": "2025-09-18", "symbol": "GOOGL", "close": 251.87}, {"date": "2025-09-19", "symbol": "GOOGL", "close": 254.55}, {"date": "2025-09-22", "symbol": "GOOGL", "close": 252.36}, {"date": "2025-09-23", "symbol": "GOOGL", "close": 251.5}, {"date": "2025-09-24", "symbol": "GOOGL", "close": 246.98}, {"date": "2025-09-25", "symbol": "GOOGL", "close": 245.63}, {"date": "2025-09-26", "symbol": "GOOGL", "close": 246.38}, {"date": "2025-09-29", "symbol": "GOOGL", "close": 243.89}, {"date": "2025-09-30", "symbol": "GOOGL", "close": 242.94}, {"date": "2025-10-01", "symbol": "GOOGL", "close": 244.74}, {"date": "2025-10-02", "symbol": "GOOGL", "close": 245.53}, {"date": "2025-10-03", "symbol": "GOOGL", "close": 245.19}, {"date": "2025-10-06", "symbol": "GOOGL", "close": 250.27}, {"date": "2025-10-07", "symbol": "GOOGL", "close": 245.6}, {"date": "2025-10-08", "symbol": "GOOGL", "close": 244.46}, {"date": "2025-10-09", "symbol": "GOOGL", "close": 241.37}, {"date": "2025-10-10", "symbol": "GOOGL", "close": 236.42}, {"date": "2025-10-13", "symbol": "GOOGL", "close": 243.99}, {"date": "2025-10-14", "symbol": "GOOGL", "close": 245.29}, {"date": "2025-10-15", "symbol": "GOOGL", "close": 250.87}, {"date": "2025-10-16", "symbol": "GOOGL", "close": 251.3}, {"date": "2025-10-17", "symbol": "GOOGL", "close": 253.13}, {"date": "2025-10-20", "symbol": "GOOGL", "close": 256.38}, {"date": "2025-10-21", "symbol": "GOOGL", "close": 250.3}, {"date": "2025-10-22", "symbol": "GOOGL", "close": 251.53}, {"date": "2025-10-23", "symbol": "GOOGL", "close": 252.91}, {"date": "2025-10-24", "symbol": "GOOGL", "close": 259.75}, {"date": "2025-10-27", "symbol": "GOOGL", "close": 269.09}, {"date": "2025-10-28", "symbol": "GOOGL", "close": 267.3}, {"date": "2025-10-29", "symbol": "GOOGL", "close": 274.39}, {"date": "2025-10-30", "symbol": "GOOGL", "close": 281.3}, {"date": "2025-10-31", "symbol": "GOOGL", "close": 281.01}, {"date": "2025-11-03", "symbol": "GOOGL", "close": 283.53}, {"date": "2025-11-04", "symbol": "GOOGL", "close": 277.36}, {"date": "2025-11-05", "symbol": "GOOGL", "close": 284.12}, {"date": "2025-11-06", "symbol": "GOOGL", "close": 284.56}, {"date": "2025-11-07", "symbol": "GOOGL", "close": 278.65}, {"date": "2025-11-10", "symbol": "GOOGL", "close": 289.91}, {"date": "2025-11-11", "symbol": "GOOGL", "close": 291.12}, {"date": "2025-11-12", "symbol": "GOOGL", "close": 286.52}, {"date": "2025-11-13", "symbol": "GOOGL", "close": 278.39}, {"date": "2025-11-14", "symbol": "GOOGL", "close": 276.23}, {"date": "2025-11-17", "symbol": "GOOGL", "close": 284.83}, {"date": "2025-11-18", "symbol": "GOOGL", "close": 284.09}, {"date": "2025-11-19", "symbol": "GOOGL", "close": 292.62}, {"date": "2025-11-20", "symbol": "GOOGL", "close": 289.26}, {"date": "2025-11-21", "symbol": "GOOGL", "close": 299.46}, {"date": "2025-11-24", "symbol": "GOOGL", "close": 318.37}, {"date": "2025-11-25", "symbol": "GOOGL", "close": 323.23}, {"date": "2025-11-26", "symbol": "GOOGL", "close": 319.74}, {"date": "2025-11-28", "symbol": "GOOGL", "close": 319.97}, {"date": "2025-12-01", "symbol": "GOOGL", "close": 314.68}, {"date": "2025-12-02", "symbol": "GOOGL", "close": 315.6}, {"date": "2025-12-03", "symbol": "GOOGL", "close": 319.42}, {"date": "2025-12-04", "symbol": "GOOGL", "close": 317.41}, {"date": "2025-12-05", "symbol": "GOOGL", "close": 321.06}, {"date": "2025-12-08", "symbol": "GOOGL", "close": 313.72}, {"date": "2025-12-09", "symbol": "GOOGL", "close": 317.08}, {"date": "2025-12-10", "symbol": "GOOGL", "close": 320.21}, {"date": "2025-12-11", "symbol": "GOOGL", "close": 312.43}, {"date": "2025-12-12", "symbol": "GOOGL", "close": 309.29}, {"date": "2025-12-15", "symbol": "GOOGL", "close": 308.22}, {"date": "2025-12-16", "symbol": "GOOGL", "close": 306.57}, {"date": "2025-12-17", "symbol": "GOOGL", "close": 296.72}, {"date": "2025-12-18", "symbol": "GOOGL", "close": 302.46}, {"date": "2025-12-19", "symbol": "GOOGL", "close": 307.16}, {"date": "2025-12-22", "symbol": "GOOGL", "close": 309.78}, {"date": "2025-12-23", "symbol": "GOOGL", "close": 314.35}, {"date": "2025-12-24", "symbol": "GOOGL", "close": 314.09}, {"date": "2025-12-26", "symbol": "GOOGL", "close": 313.51}, {"date": "2025-12-29", "symbol": "GOOGL", "close": 313.56}, {"date": "2025-12-30", "symbol": "GOOGL", "close": 313.85}, {"date": "2025-12-31", "symbol": "GOOGL", "close": 313}, {"date": "2026-01-02", "symbol": "GOOGL", "close": 315.15}, {"date": "2026-01-05", "symbol": "GOOGL", "close": 316.54}, {"date": "2026-01-06", "symbol": "GOOGL", "close": 314.34}, {"date": "2026-01-07", "symbol": "GOOGL", "close": 321.98}, {"date": "2026-01-08", "symbol": "GOOGL", "close": 325.44}, {"date": "2026-01-09", "symbol": "GOOGL", "close": 328.57}, {"date": "2026-01-12", "symbol": "GOOGL", "close": 331.86}, {"date": "2026-01-13", "symbol": "GOOGL", "close": 335.97}, {"date": "2026-01-14", "symbol": "GOOGL", "close": 335.84}, {"date": "2026-01-15", "symbol": "GOOGL", "close": 332.78}, {"date": "2026-01-16", "symbol": "GOOGL", "close": 330}, {"date": "2026-01-20", "symbol": "GOOGL", "close": 322}, {"date": "2026-01-21", "symbol": "GOOGL", "close": 328.38}, {"date": "2026-01-22", "symbol": "GOOGL", "close": 330.54}, {"date": "2026-01-23", "symbol": "GOOGL", "close": 327.93}, {"date": "2026-01-26", "symbol": "GOOGL", "close": 333.26}, {"date": "2025-07-31", "symbol": "MSFT", "close": 531.63}, {"date": "2025-08-01", "symbol": "MSFT", "close": 522.27}, {"date": "2025-08-04", "symbol": "MSFT", "close": 533.76}, {"date": "2025-08-05", "symbol": "MSFT", "close": 525.9}, {"date": "2025-08-06", "symbol": "MSFT", "close": 523.1}, {"date": "2025-08-07", "symbol": "MSFT", "close": 519.01}, {"date": "2025-08-08", "symbol": "MSFT", "close": 520.21}, {"date": "2025-08-11", "symbol": "MSFT", "close": 519.94}, {"date": "2025-08-12", "symbol": "MSFT", "close": 527.38}, {"date": "2025-08-13", "symbol": "MSFT", "close": 518.75}, {"date": "2025-08-14", "symbol": "MSFT", "close": 520.65}, {"date": "2025-08-15", "symbol": "MSFT", "close": 518.35}, {"date": "2025-08-18", "symbol": "MSFT", "close": 515.29}, {"date": "2025-08-19", "symbol": "MSFT", "close": 507.98}, {"date": "2025-08-20", "symbol": "MSFT", "close": 503.95}, {"date": "2025-08-21", "symbol": "MSFT", "close": 503.3}, {"date": "2025-08-22", "symbol": "MSFT", "close": 506.28}, {"date": "2025-08-25", "symbol": "MSFT", "close": 503.32}, {"date": "2025-08-26", "symbol": "MSFT", "close": 501.1}, {"date": "2025-08-27", "symbol": "MSFT", "close": 505.79}, {"date": "2025-08-28", "symbol": "MSFT", "close": 508.69}, {"date": "2025-08-29", "symbol": "MSFT", "close": 505.74}, {"date": "2025-09-02", "symbol": "MSFT", "close": 504.18}, {"date": "2025-09-03", "symbol": "MSFT", "close": 504.41}, {"date": "2025-09-04", "symbol": "MSFT", "close": 507.02}, {"date": "2025-09-05", "symbol": "MSFT", "close": 494.08}, {"date": "2025-09-08", "symbol": "MSFT", "close": 497.27}, {"date": "2025-09-09", "symbol": "MSFT", "close": 497.48}, {"date": "2025-09-10", "symbol": "MSFT", "close": 499.44}, {"date": "2025-09-11", "symbol": "MSFT", "close": 500.07}, {"date": "2025-09-12", "symbol": "MSFT", "close": 508.95}, {"date": "2025-09-15", "symbol": "MSFT", "close": 514.4}, {"date": "2025-09-16", "symbol": "MSFT", "close": 508.09}, {"date": "2025-09-17", "symbol": "MSFT", "close": 509.07}, {"date": "2025-09-18", "symbol": "MSFT", "close": 507.5}, {"date": "2025-09-19", "symbol": "MSFT", "close": 516.96}, {"date": "2025-09-22", "symbol": "MSFT", "close": 513.49}, {"date": "2025-09-23", "symbol": "MSFT", "close": 508.28}, {"date": "2025-09-24", "symbol": "MSFT", "close": 509.2}, {"date": "2025-09-25", "symbol": "MSFT", "close": 506.08}, {"date": "2025-09-26", "symbol": "MSFT", "close": 510.5}, {"date": "2025-09-29", "symbol": "MSFT", "close": 513.64}, {"date": "2025-09-30", "symbol": "MSFT", "close": 516.98}, {"date": "2025-10-01", "symbol": "MSFT", "close": 518.74}, {"date": "2025-10-02", "symbol": "MSFT", "close": 514.78}, {"date": "2025-10-03", "symbol": "MSFT", "close": 516.38}, {"date": "2025-10-06", "symbol": "MSFT", "close": 527.58}, {"date": "2025-10-07", "symbol": "MSFT", "close": 523}, {"date": "2025-10-08", "symbol": "MSFT", "close": 523.87}, {"date": "2025-10-09", "symbol": "MSFT", "close": 521.42}, {"date": "2025-10-10", "symbol": "MSFT", "close": 510.01}, {"date": "2025-10-13", "symbol": "MSFT", "close": 513.09}, {"date": "2025-10-14", "symbol": "MSFT", "close": 512.61}, {"date": "2025-10-15", "symbol": "MSFT", "close": 512.47}, {"date": "2025-10-16", "symbol": "MSFT", "close": 510.65}, {"date": "2025-10-17", "symbol": "MSFT", "close": 512.62}, {"date": "2025-10-20", "symbol": "MSFT", "close": 515.82}, {"date": "2025-10-21", "symbol": "MSFT", "close": 516.69}, {"date": "2025-10-22", "symbol": "MSFT", "close": 519.57}, {"date": "2025-10-23", "symbol": "MSFT", "close": 519.59}, {"date": "2025-10-24", "symbol": "MSFT", "close": 522.63}, {"date": "2025-10-27", "symbol": "MSFT", "close": 530.53}, {"date": "2025-10-28", "symbol": "MSFT", "close": 541.06}, {"date": "2025-10-29", "symbol": "MSFT", "close": 540.54}, {"date": "2025-10-30", "symbol": "MSFT", "close": 524.78}, {"date": "2025-10-31", "symbol": "MSFT", "close": 516.84}, {"date": "2025-11-03", "symbol": "MSFT", "close": 516.06}, {"date": "2025-11-04", "symbol": "MSFT", "close": 513.37}, {"date": "2025-11-05", "symbol": "MSFT", "close": 506.21}, {"date": "2025-11-06", "symbol": "MSFT", "close": 496.17}, {"date": "2025-11-07", "symbol": "MSFT", "close": 495.89}, {"date": "2025-11-10", "symbol": "MSFT", "close": 505.05}, {"date": "2025-11-11", "symbol": "MSFT", "close": 507.73}, {"date": "2025-11-12", "symbol": "MSFT", "close": 510.19}, {"date": "2025-11-13", "symbol": "MSFT", "close": 502.35}, {"date": "2025-11-14", "symbol": "MSFT", "close": 509.23}, {"date": "2025-11-17", "symbol": "MSFT", "close": 506.54}, {"date": "2025-11-18", "symbol": "MSFT", "close": 492.87}, {"date": "2025-11-19", "symbol": "MSFT", "close": 486.21}, {"date": "2025-11-20", "symbol": "MSFT", "close": 478.43}, {"date": "2025-11-21", "symbol": "MSFT", "close": 472.12}, {"date": "2025-11-24", "symbol": "MSFT", "close": 474}, {"date": "2025-11-25", "symbol": "MSFT", "close": 476.99}, {"date": "2025-11-26", "symbol": "MSFT", "close": 485.5}, {"date": "2025-11-28", "symbol": "MSFT", "close": 492.01}, {"date": "2025-12-01", "symbol": "MSFT", "close": 486.74}, {"date": "2025-12-02", "symbol": "MSFT", "close": 490}, {"date": "2025-12-03", "symbol": "MSFT", "close": 477.73}, {"date": "2025-12-04", "symbol": "MSFT", "close": 480.84}, {"date": "2025-12-05", "symbol": "MSFT", "close": 483.16}, {"date": "2025-12-08", "symbol": "MSFT", "close": 491.02}, {"date": "2025-12-09", "symbol": "MSFT", "close": 492.02}, {"date": "2025-12-10", "symbol": "MSFT", "close": 478.56}, {"date": "2025-12-11", "symbol": "MSFT", "close": 483.47}, {"date": "2025-12-12", "symbol": "MSFT", "close": 478.53}, {"date": "2025-12-15", "symbol": "MSFT", "close": 474.82}, {"date": "2025-12-16", "symbol": "MSFT", "close": 476.39}, {"date": "2025-12-17", "symbol": "MSFT", "close": 476.12}, {"date": "2025-12-18", "symbol": "MSFT", "close": 483.98}, {"date": "2025-12-19", "symbol": "MSFT", "close": 485.92}, {"date": "2025-12-22", "symbol": "MSFT", "close": 484.92}, {"date": "2025-12-23", "symbol": "MSFT", "close": 486.85}, {"date": "2025-12-24", "symbol": "MSFT", "close": 488.02}, {"date": "2025-12-26", "symbol": "MSFT", "close": 487.71}, {"date": "2025-12-29", "symbol": "MSFT", "close": 487.1}, {"date": "2025-12-30", "symbol": "MSFT", "close": 487.48}, {"date": "2025-12-31", "symbol": "MSFT", "close": 483.62}, {"date": "2026-01-02", "symbol": "MSFT", "close": 472.94}, {"date": "2026-01-05", "symbol": "MSFT", "close": 472.85}, {"date": "2026-01-06", "symbol": "MSFT", "close": 478.51}, {"date": "2026-01-07", "symbol": "MSFT", "close": 483.47}, {"date": "2026-01-08", "symbol": "MSFT", "close": 478.11}, {"date": "2026-01-09", "symbol": "MSFT", "close": 479.28}, {"date": "2026-01-12", "symbol": "MSFT", "close": 477.18}, {"date": "2026-01-13", "symbol": "MSFT", "close": 470.67}, {"date": "2026-01-14", "symbol": "MSFT", "close": 459.38}, {"date": "2026-01-15", "symbol": "MSFT", "close": 456.66}, {"date": "2026-01-16", "symbol": "MSFT", "close": 459.86}, {"date": "2026-01-20", "symbol": "MSFT", "close": 454.52}, {"date": "2026-01-21", "symbol": "MSFT", "close": 444.11}, {"date": "2026-01-22", "symbol": "MSFT", "close": 451.14}, {"date": "2026-01-23", "symbol": "MSFT", "close": 465.95}, {"date": "2026-01-26", "symbol": "MSFT", "close": 470.28}], "metadata": {"date": {"type": "date", "semanticType": "Date", "levels": []}, "symbol": {"type": "string", "semanticType": "String"}, "close": {"type": "number", "semanticType": "Number"}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Filter for Microsoft (MSFT) and Google (GOOGL)\n target_symbols = [\"MSFT\", \"GOOGL\"]\n df = df[df[\"symbol\"].isin(target_symbols)].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n", "source": ["history"], "dialog": [{"role": "system", "content": "You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"input_tables\", the names of a subset of input tables from [CONTEXT] section that will be used to achieve the user's goal.\n - **IMPORTANT** Note that the Table 1 in [CONTEXT] section is the table the user is currently viewing, it should take precedence if the user refers to insights about the \"current table\".\n - At the same time, leverage table information to determine which tables are relevant to the user's goal and should be used.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, latitude, longitude, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - if the user provides latitude and longitude as visual channels, use \"latitude\" and \"longitude\" as visual channels in \"chart_encodings\" as opposed to \"x\" and \"y\".\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\".\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"input_tables\": [...] // string[], describe names of the input tables that will be used in the transformation.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables described in \"input_tables\") and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], only import libraries allowed in the template, do not modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n# from sklearn import ... # import from sklearn if you need it.\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- decide the function signature based on the number of tables you decided in the previous step \"input_tables\":\n - if you decide there will only be one input table, then function signature should be `def transform_data(df1)`\n - if you decided there will be k input tables, then function signature should be `def transform_data(df_1, df_2, ..., df_k)`.\n - instead of using generic names like df1, df2, ..., try to use intuitive table names for function arguments, for example, if you have input_tables: [\"City\", \"Weather\"]`, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes.\n - **VERY IMPORTANT** the number of arguments in the function signature must be the same as the number of tables provided in \"input_tables\", and the order of arguments must match the order of tables provided in \"input_tables\".\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n"}, {"role": "user", "content": "[CONTEXT]\n\n## Table 1: history (738 rows × 8 columns)\n\n### Schema (8 fields)\n - symbol -- type: object, values: AAPL, AMZN, GOOGL, META, MSFT, NVDA\n - date -- type: object, values: 2025-07-30, 2025-07-31, 2025-08-01, ..., 2026-01-20, 2026-01-21, 2026-01-22, 2026-01-23\n - open -- type: float64, values: 167.53, 168.01, 169.07, ..., 781.21, 782.98, 785.25, 789.97\n - high -- type: float64, values: 169.01, 170.94, 170.96, ..., 789.62, 792.49, 794.28, 795.06\n - low -- type: float64, values: 164.05, 166.72, 167.2, ..., 771.36, 772.21, 777.07, 779.66\n - close -- type: float64, values: 167.0, 168.29, 170.28, ..., 779.09, 780.97, 784.06, 788.82\n - volume -- type: int64, values: 5627500, 5855900, 6861200, ..., 320600300, 324925900, 343504800, 346926200\n - fetched_at -- type: object, values: 2026-01-26T23:46:42.347312Z\n\n### Sample Data (first 5 rows)\n```\n symbol date open high low close volume fetched_at\n0 AAPL 2025-07-30 211.45 211.94 207.28 208.61 45512500 2026-01-26T23:46:42.347312Z\n1 AAPL 2025-07-31 208.05 209.40 206.72 207.13 80698400 2026-01-26T23:46:42.347312Z\n2 AAPL 2025-08-01 210.43 213.13 201.08 201.95 104434500 2026-01-26T23:46:42.347312Z\n3 AAPL 2025-08-04 204.08 207.44 201.26 202.92 75109300 2026-01-26T23:46:42.347312Z\n4 AAPL 2025-08-05 202.97 204.91 201.74 202.49 44155100 2026-01-26T23:46:42.347312Z\n```\n\n\n[GOAL]\n\n1) First, plot the overall **closing price** trends for each **symbol** over **time** on a single line chart to compare their broad trajectories and volatility."}, {"role": "assistant", "content": "{\n \"mode\": \"infer\",\n \"recap\": \"Compare overall closing price trends and volatility over time for each stock symbol on a single line chart.\",\n \"display_instruction\": \"Visualize **closing price** trends by **symbol** over **date**\",\n \"recommendation\": \"A line chart with time on the x-axis and closing price on the y-axis, colored by symbol, best shows comparative trajectories and volatility over time. The data only needs column selection, date normalization, and sorting to be visualization-ready.\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"close\",\n \"color\": \"symbol\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n```"}, {"role": "user", "content": "This is the result from the latest python code:\n\n date symbol close\n0 2025-07-30 AAPL 208.61\n1 2025-07-31 AAPL 207.13\n2 2025-08-01 AAPL 201.95\n3 2025-08-04 AAPL 202.92\n4 2025-08-05 AAPL 202.49\n5 2025-08-06 AAPL 212.80\n6 2025-08-07 AAPL 219.57\n7 2025-08-08 AAPL 228.87\n8 2025-08-11 AAPL 226.96\n9 2025-08-12 AAPL 229.43\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"compare microsoft and google\",\n \"chart_type\": \"\",\n \"chart_encodings\": {}\n}"}, {"role": "assistant", "content": "{\n \"detailed_instruction\": \"Filter the historical price data to only include Microsoft (MSFT) and Google (GOOGL) and prepare it for a line chart that compares their closing price trends over time on the same plot.\",\n \"display_instruction\": \"Compare **closing price** trends of **MSFT** and **GOOGL** over **date**\",\n \"input_tables\": [\"history\"],\n \"output_fields\": [\"date\", \"symbol\", \"close\"],\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"close\",\n \"color\": \"symbol\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n },\n \"reason\": \"The follow-up focuses the existing trend comparison on only Microsoft and Google. This requires filtering the original prepared data to those two symbols while keeping the same line chart structure: date on x, close on y, and symbol as the color encoding.\"\n}\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Filter for Microsoft (MSFT) and Google (GOOGL)\n target_symbols = [\"MSFT\", \"GOOGL\"]\n df = df[df[\"symbol\"].isin(target_symbols)].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n```"}], "trigger": {"tableId": "table-233476", "resultTableId": "table-457008", "chart": {"id": "chart-1769471462237", "chartType": "Auto", "encodingMap": {}, "tableRef": "table-233476", "saved": false, "source": "trigger", "unread": true}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "compare microsoft and google", "displayContent": "Compare **closing price** trends of **MSFT** and **GOOGL** over **date**"}]}, "explanation": {"status": "ok", "concepts": [], "code": "- **Keep only** the **`date`**, **`symbol`**, and **`close`** price columns from the original data, discarding all others.\n- **Filter rows** to include **only** the two companies: **`MSFT` (Microsoft)** and **`GOOGL` (Google)**.\n- **Standardize the date format** by converting **`date`** into a **string** formatted as **`YYYY-MM-DD`** (year-month-day).\n- **Sort the data** by **`symbol`** and then by **`date`** so that each company’s prices are in **chronological order**, making it ready for **line plotting over time**.\n- **Return** this cleaned and filtered dataset as the final result.", "dialog": [{"role": "system", "content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n"}, {"role": "user", "content": "[CONTEXT]\n\n## Table 1: history (738 rows × 8 columns)\n\n### Schema (8 fields)\n - symbol -- type: object, values: AAPL, AMZN, GOOGL, META, MSFT, NVDA\n - date -- type: object, values: 2025-07-30, 2025-07-31, 2025-08-01, ..., 2026-01-20, 2026-01-21, 2026-01-22, 2026-01-23\n - open -- type: float64, values: 167.53, 168.01, 169.07, ..., 781.21, 782.98, 785.25, 789.97\n - high -- type: float64, values: 169.01, 170.94, 170.96, ..., 789.62, 792.49, 794.28, 795.06\n - low -- type: float64, values: 164.05, 166.72, 167.2, ..., 771.36, 772.21, 777.07, 779.66\n - close -- type: float64, values: 167.0, 168.29, 170.28, ..., 779.09, 780.97, 784.06, 788.82\n - volume -- type: int64, values: 5627500, 5855900, 6861200, ..., 320600300, 324925900, 343504800, 346926200\n - fetched_at -- type: object, values: 2026-01-26T23:46:42.347312Z\n\n### Sample Data (first 5 rows)\n```\n symbol date open high low close volume fetched_at\n0 AAPL 2025-07-30 211.45 211.94 207.28 208.61 45512500 2026-01-26T23:46:42.347312Z\n1 AAPL 2025-07-31 208.05 209.40 206.72 207.13 80698400 2026-01-26T23:46:42.347312Z\n2 AAPL 2025-08-01 210.43 213.13 201.08 201.95 104434500 2026-01-26T23:46:42.347312Z\n3 AAPL 2025-08-04 204.08 207.44 201.26 202.92 75109300 2026-01-26T23:46:42.347312Z\n4 AAPL 2025-08-05 202.97 204.91 201.74 202.49 44155100 2026-01-26T23:46:42.347312Z\n```\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_history: pd.DataFrame) -> pd.DataFrame:\n # Select only needed columns\n cols = [\"date\", \"symbol\", \"close\"]\n df = df_history[cols].copy()\n\n # Filter for Microsoft (MSFT) and Google (GOOGL)\n target_symbols = [\"MSFT\", \"GOOGL\"]\n df = df[df[\"symbol\"].isin(target_symbols)].copy()\n\n # Ensure date is a string in YYYY-MM-DD format (no datetime objects)\n df[\"date\"] = pd.to_datetime(df[\"date\"]).dt.strftime(\"%Y-%m-%d\")\n\n # Sort for proper line plotting\n df = df.sort_values([\"symbol\", \"date\"]).reset_index(drop=True)\n\n transformed_df = df\n return transformed_df\n\n\n[EXPLANATION]\n"}, {"role": "assistant", "content": "[CODE EXPLANATION]\n\n- **Keep only** the **`date`**, **`symbol`**, and **`close`** price columns from the original data, discarding all others.\n- **Filter rows** to include **only** the two companies: **`MSFT` (Microsoft)** and **`GOOGL` (Google)**.\n- **Standardize the date format** by converting **`date`** into a **string** formatted as **`YYYY-MM-DD`** (year-month-day).\n- **Sort the data** by **`symbol`** and then by **`date`** so that each company’s prices are in **chronological order**, making it ready for **line plotting over time**.\n- **Return** this cleaned and filtered dataset as the final result.\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n]\n```"}], "agent": "CodeExplanationAgent"}, "status": "completed"}, "anchored": false, "attachedMetadata": "", "contentHash": "903aa346"}], "charts": [{"id": "chart-1769471461230", "chartType": "Scatter Plot", "encodingMap": {"x": {"fieldID": "original--history--date"}, "y": {"fieldID": "original--history--close"}, "color": {"fieldID": "original--history--symbol"}, "size": {"channel": "size", "bin": false}, "opacity": {"channel": "opacity", "bin": false}, "column": {"channel": "column", "bin": false}, "row": {"channel": "row", "bin": false}}, "tableRef": "table-457008", "saved": false, "source": "user", "unread": false}, {"id": "chart-1769471279744", "chartType": "Line Chart", "encodingMap": {"x": {"fieldID": "original--history--date"}, "y": {"fieldID": "concept-value-1769471267162-0.9509557182892663"}, "color": {"fieldID": "original--history--symbol"}, "opacity": {"channel": "opacity", "bin": false}, "column": {"fieldID": "concept-metric-1769471284653-0.6683773975324122"}, "row": {"channel": "row", "bin": false}}, "tableRef": "table-277926", "saved": false, "source": "user", "unread": false}, {"id": "chart-1769471263442", "chartType": "Line Chart", "encodingMap": {"x": {"fieldID": "original--history--date"}, "y": {"fieldID": "concept-value-1769471267162-0.9509557182892663"}, "color": {"fieldID": "original--history--symbol"}, "opacity": {}, "column": {"fieldID": "concept-ma_window-1769471267162-0.4410018093789688"}, "row": {"channel": "row", "bin": false}}, "tableRef": "table-265247", "saved": false, "source": "user", "unread": false}, {"id": "chart-1769471233467", "chartType": "Line Chart", "encodingMap": {"x": {"fieldID": "original--history--date"}, "y": {"fieldID": "original--history--close"}, "color": {"fieldID": "original--history--symbol"}, "opacity": {"channel": "opacity", "bin": false}, "column": {"channel": "column", "bin": false}, "row": {"channel": "row", "bin": false}}, "tableRef": "table-233476", "saved": false, "source": "user", "unread": false}], "conceptShelfItems": [{"id": "concept-phase-1769471302041-0.7083067598600609", "name": "phase", "type": "auto", "description": "", "source": "custom", "tableRef": "custom", "temporary": true}, {"id": "concept-metric-1769471284653-0.6683773975324122", "name": "metric", "type": "auto", "description": "", "source": "custom", "tableRef": "custom", "temporary": true}, {"id": "concept-value-1769471267162-0.9509557182892663", "name": "value", "type": "auto", "description": "", "source": "custom", "tableRef": "custom", "temporary": true}, {"id": "concept-ma_window-1769471267162-0.4410018093789688", "name": "ma_window", "type": "auto", "description": "", "source": "custom", "tableRef": "custom", "temporary": true}, {"id": "original--history--symbol", "name": "symbol", "type": "string", "source": "original", "description": "", "tableRef": "history"}, {"id": "original--history--date", "name": "date", "type": "date", "source": "original", "description": "", "tableRef": "history"}, {"id": "original--history--open", "name": "open", "type": "number", "source": "original", "description": "", "tableRef": "history"}, {"id": "original--history--high", "name": "high", "type": "number", "source": "original", "description": "", "tableRef": "history"}, {"id": "original--history--low", "name": "low", "type": "number", "source": "original", "description": "", "tableRef": "history"}, {"id": "original--history--close", "name": "close", "type": "number", "source": "original", "description": "", "tableRef": "history"}, {"id": "original--history--volume", "name": "volume", "type": "integer", "source": "original", "description": "", "tableRef": "history"}, {"id": "original--history--fetched_at", "name": "fetched_at", "type": "date", "source": "original", "description": "", "tableRef": "history"}], "messages": [{"timestamp": 1769535027918, "type": "success", "component": "data formulator", "value": "Successfully loaded Stock Prices (Live)"}, {"timestamp": 1769535028897, "component": "Data Refresh", "type": "info", "value": "Table \"stock-hist\" data refreshed (738 rows)"}, {"timestamp": 1769535029034, "component": "Data Refresh", "type": "info", "value": "Table \"stock-hist\" data refreshed (738 rows)"}, {"timestamp": 1769535029121, "component": "Data Refresh", "type": "info", "value": "Derived table \"stock-close\" refreshed (738 rows)"}, {"timestamp": 1769535041128, "component": "Data Refresh", "type": "info", "value": "Table \"stock-hist\" data refreshed (738 rows)"}, {"timestamp": 1769535041339, "component": "Data Refresh", "type": "info", "value": "Table \"stock-hist\" data refreshed (738 rows)"}, {"timestamp": 1769535041452, "component": "Data Refresh", "type": "info", "value": "Derived table \"stock-close\" refreshed (738 rows)"}, {"timestamp": 1769535048535, "component": "DB manager", "type": "success", "value": "Deleted 1 unreferenced derived views: view_ushx"}], "displayedMessageIdx": 8, "viewMode": "editor", "chartSynthesisInProgress": [], "config": {"formulateTimeoutSeconds": 60, "maxRepairAttempts": 1, "defaultChartWidth": 300, "defaultChartHeight": 300}, "dataCleanBlocks": [], "cleanInProgress": false, "generatedReports": [{"id": "report-1769471498009-9657", "content": "# Monitoring Mega-Cap Tech: Price, Trends, and Risk at a Glance\n\nThis live report is built to help you track six large tech stocks (AAPL, AMZN, GOOGL, META, MSFT, NVDA) across price, trends, and risk. Each chart updates as new rows are added to the `history` table.\n\n[IMAGE(chart-1769471233467)]\n\nThe first chart shows daily closing prices over time for all six symbols. Use it to spot broad moves, compare relative price levels, and see when one stock starts to diverge from the group.\n\n[IMAGE(chart-1769471263442)]\n\nThe second chart adds 20‑day and 60‑day moving averages. Watch how actual prices relate to these smoother trend lines: crossovers, sustained gaps, or trend flattening can signal shifting momentum.\n\n[IMAGE(chart-1769471279744)]\n\nThe third set of panels tracks three risk and activity metrics: daily returns, 20‑day return volatility, and trading volume. Look for periods where volatility or volume spikes, or where returns cluster on one side.\n\n[IMAGE(chart-1769471461230)]\n\nThe final chart zooms in on GOOGL and MSFT closing prices, making it easier to compare their day‑to‑day paths without the distraction of other symbols.\n\n**In summary**, use these views together: price levels and trends, plus returns, volatility, and volume, to monitor how each stock is behaving and how relationships between them evolve over time. Possible follow‑ups: add alerts for threshold moves, overlay events (earnings, macro news), or include benchmark indices for context.", "style": "live report", "selectedChartIds": ["chart-1769471279744", "chart-1769471233467", "chart-1769471461230", "chart-1769471263442"], "createdAt": 1769471511269, "status": "completed", "title": "Monitoring Mega-Cap Tech: Price, Trends, and Risk at a Glance", "anchorChartId": "chart-1769471279744"}], "_persist": {"version": -1, "rehydrated": true}, "draftNodes": [], "focusedId": {"type": "chart", "chartId": "chart-1769471461230"}} \ No newline at end of file diff --git a/public/df_unemployment.json b/public/df_unemployment.json index 7c85672b..45e87559 100644 --- a/public/df_unemployment.json +++ b/public/df_unemployment.json @@ -1 +1 @@ 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rate":6.5}],"metadata":{"date":{"type":"date","semanticType":"YearMonth"},"series":{"type":"string","semanticType":"String"},"rate":{"type":"number","semanticType":"Percentage"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime and then to string format for visualization\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date']).dt.strftime('%Y-%m-%d')\n \n # Select only the necessary columns for visualization\n transformed_df = df_unemployment[['date', 'series', 'rate']].copy()\n \n return transformed_df\n","source":["unemployment-across-industries"],"dialog":[{"role":"system","content":"You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"},{"role":"user","content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[GOAL]\n\nCompare **unemployment trends** across all industries from 2000-2010"},{"role":"assistant","content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to compare unemployment trends across all industries from 2000-2010 to understand how different sectors were affected over time.\",\n \"display_instruction\": \"Compare **unemployment rates** across **industries** over time\",\n \"recommendation\": \"A line chart with time on x-axis and unemployment rate on y-axis, with different colored lines for each industry series, will effectively show the trends and allow comparison of how unemployment evolved across different sectors during this period.\",\n \"output_fields\": [\"date\", \"series\", \"rate\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"rate\",\n \"color\": \"series\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime and then to string format for visualization\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date']).dt.strftime('%Y-%m-%d')\n \n # Select only the necessary columns for visualization\n transformed_df = df_unemployment[['date', 'series', 'rate']].copy()\n \n return transformed_df\n```"}],"trigger":{"tableId":"unemployment-across-industries","sourceTableIds":["unemployment-across-industries"],"instruction":"Compare **unemployment trends** across all industries from 2000-2010","displayInstruction":"Compare **unemployment rates** across **industries** over time","chart":{"id":"chart-1760745546913","chartType":"Auto","encodingMap":{},"tableRef":"unemployment-across-industries","saved":false,"source":"trigger","unread":true},"resultTableId":"table-544555"},"explanation":{"agent":"CodeExplanationAgent","code":"- Convert the **date** field from timestamp format to a standardized string format (`YYYY-MM-DD`) for cleaner visualization\n- Select and retain only three key fields: **date**, **series** (industry name), and **rate** (unemployment rate percentage)\n- Return a simplified dataset focused on tracking unemployment rates across different industries over time","concepts":[],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime and then to string format for visualization\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date']).dt.strftime('%Y-%m-%d')\n \n # Select only the necessary columns for visualization\n transformed_df = df_unemployment[['date', 'series', 'rate']].copy()\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\n- Convert the **date** field from timestamp format to a standardized string format (`YYYY-MM-DD`) for cleaner visualization\n- Select and retain only three key fields: **date**, **series** (industry name), and **rate** (unemployment rate percentage)\n- Return a simplified dataset focused on tracking unemployment rates across different industries over time\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-540763","displayId":"unemployment-rate","names":["avg_rate","month","month_name","series"],"rows":[{"avg_rate":13.7636363636,"month":1,"month_name":"Jan","series":"Agriculture"},{"avg_rate":13.6454545455,"month":2,"month_name":"Feb","series":"Agriculture"},{"avg_rate":13.83,"month":3,"month_name":"Mar","series":"Agriculture"},{"avg_rate":9.13,"month":4,"month_name":"Apr","series":"Agriculture"},{"avg_rate":7.1,"month":5,"month_name":"May","series":"Agriculture"},{"avg_rate":6.77,"month":6,"month_name":"Jun","series":"Agriculture"},{"avg_rate":7.01,"month":7,"month_name":"Jul","series":"Agriculture"},{"avg_rate":8.08,"month":8,"month_name":"Aug","series":"Agriculture"},{"avg_rate":7.09,"month":9,"month_name":"Sep","series":"Agriculture"},{"avg_rate":7.49,"month":10,"month_name":"Oct","series":"Agriculture"},{"avg_rate":10.47,"month":11,"month_name":"Nov","series":"Agriculture"},{"avg_rate":12.94,"month":12,"month_name":"Dec","series":"Agriculture"},{"avg_rate":7.8636363636,"month":1,"month_name":"Jan","series":"Business services"},{"avg_rate":7.6454545455,"month":2,"month_name":"Feb","series":"Business services"},{"avg_rate":7.13,"month":3,"month_name":"Mar","series":"Business services"},{"avg_rate":6.27,"month":4,"month_name":"Apr","series":"Business services"},{"avg_rate":6.6,"month":5,"month_name":"May","series":"Business services"},{"avg_rate":6.72,"month":6,"month_name":"Jun","series":"Business services"},{"avg_rate":6.74,"month":7,"month_name":"Jul","series":"Business services"},{"avg_rate":6.57,"month":8,"month_name":"Aug","series":"Business services"},{"avg_rate":6.79,"month":9,"month_name":"Sep","series":"Business services"},{"avg_rate":6.71,"month":10,"month_name":"Oct","series":"Business services"},{"avg_rate":6.75,"month":11,"month_name":"Nov","series":"Business services"},{"avg_rate":7.08,"month":12,"month_name":"Dec","series":"Business services"},{"avg_rate":12.9090909091,"month":1,"month_name":"Jan","series":"Construction"},{"avg_rate":13.6,"month":2,"month_name":"Feb","series":"Construction"},{"avg_rate":11.29,"month":3,"month_name":"Mar","series":"Construction"},{"avg_rate":9.45,"month":4,"month_name":"Apr","series":"Construction"},{"avg_rate":8.12,"month":5,"month_name":"May","series":"Construction"},{"avg_rate":7.43,"month":6,"month_name":"Jun","series":"Construction"},{"avg_rate":7.35,"month":7,"month_name":"Jul","series":"Construction"},{"avg_rate":7.3,"month":8,"month_name":"Aug","series":"Construction"},{"avg_rate":7.56,"month":9,"month_name":"Sep","series":"Construction"},{"avg_rate":7.84,"month":10,"month_name":"Oct","series":"Construction"},{"avg_rate":8.7,"month":11,"month_name":"Nov","series":"Construction"},{"avg_rate":10.8,"month":12,"month_name":"Dec","series":"Construction"},{"avg_rate":3.3636363636,"month":1,"month_name":"Jan","series":"Education and 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and Utilities"},{"avg_rate":5.6181818182,"month":2,"month_name":"Feb","series":"Transportation and Utilities"},{"avg_rate":5.1,"month":3,"month_name":"Mar","series":"Transportation and Utilities"},{"avg_rate":4.79,"month":4,"month_name":"Apr","series":"Transportation and Utilities"},{"avg_rate":4.5,"month":5,"month_name":"May","series":"Transportation and Utilities"},{"avg_rate":4.82,"month":6,"month_name":"Jun","series":"Transportation and Utilities"},{"avg_rate":5.04,"month":7,"month_name":"Jul","series":"Transportation and Utilities"},{"avg_rate":4.58,"month":8,"month_name":"Aug","series":"Transportation and Utilities"},{"avg_rate":4.65,"month":9,"month_name":"Sep","series":"Transportation and Utilities"},{"avg_rate":4.8,"month":10,"month_name":"Oct","series":"Transportation and Utilities"},{"avg_rate":4.58,"month":11,"month_name":"Nov","series":"Transportation and Utilities"},{"avg_rate":4.8,"month":12,"month_name":"Dec","series":"Transportation and Utilities"},{"avg_rate":6.4818181818,"month":1,"month_name":"Jan","series":"Wholesale and Retail Trade"},{"avg_rate":6.3727272727,"month":2,"month_name":"Feb","series":"Wholesale and Retail Trade"},{"avg_rate":5.86,"month":3,"month_name":"Mar","series":"Wholesale and Retail Trade"},{"avg_rate":5.46,"month":4,"month_name":"Apr","series":"Wholesale and Retail Trade"},{"avg_rate":5.49,"month":5,"month_name":"May","series":"Wholesale and Retail Trade"},{"avg_rate":5.84,"month":6,"month_name":"Jun","series":"Wholesale and Retail Trade"},{"avg_rate":5.75,"month":7,"month_name":"Jul","series":"Wholesale and Retail Trade"},{"avg_rate":5.61,"month":8,"month_name":"Aug","series":"Wholesale and Retail Trade"},{"avg_rate":5.63,"month":9,"month_name":"Sep","series":"Wholesale and Retail Trade"},{"avg_rate":5.56,"month":10,"month_name":"Oct","series":"Wholesale and Retail Trade"},{"avg_rate":5.52,"month":11,"month_name":"Nov","series":"Wholesale and Retail Trade"},{"avg_rate":5.49,"month":12,"month_name":"Dec","series":"Wholesale and Retail Trade"}],"metadata":{"avg_rate":{"type":"number","semanticType":"Percentage"},"month":{"type":"number","semanticType":"Month"},"month_name":{"type":"string","semanticType":"Month","levels":["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"]},"series":{"type":"string","semanticType":"String"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df1):\n # Calculate average unemployment rate by month and series (industry)\n transformed_df = df1.groupby(['series', 'month'], as_index=False).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Add month name for better readability\n month_names = {\n 1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr', 5: 'May', 6: 'Jun',\n 7: 'Jul', 8: 'Aug', 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'\n }\n transformed_df['month_name'] = transformed_df['month'].map(month_names)\n \n return transformed_df\n","source":["unemployment-across-industries"],"dialog":[{"content":"You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[GOAL]\n\nHow does **seasonal variation** in unemployment differ across **industries**?","role":"user"},{"content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"Analyze how seasonal variation in unemployment differs across industries by examining the cyclical patterns in unemployment rates throughout the year for different industry sectors.\",\n \"display_instruction\": \"Compare seasonal unemployment patterns across **industries**\",\n \"recommendation\": \"To visualize seasonal variation across industries, we should calculate average unemployment rates by month for each industry series. This will reveal the typical seasonal patterns and allow comparison of how different industries experience seasonal fluctuations in unemployment throughout the year.\",\n \"output_fields\": [\"series\", \"month\", \"avg_rate\", \"month_name\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"month\",\n \"y\": \"avg_rate\",\n \"color\": \"series\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df1):\n # Calculate average unemployment rate by month and series (industry)\n transformed_df = df1.groupby(['series', 'month'], as_index=False).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Add month name for better readability\n month_names = {\n 1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr', 5: 'May', 6: 'Jun',\n 7: 'Jul', 8: 'Aug', 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'\n }\n transformed_df['month_name'] = transformed_df['month'].map(month_names)\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"unemployment-across-industries","sourceTableIds":["unemployment-across-industries"],"instruction":"How does **seasonal variation** in unemployment differ across **industries**?","displayInstruction":"Compare seasonal unemployment patterns across **industries**","chart":{"id":"chart-1760745541760","chartType":"Auto","encodingMap":{},"tableRef":"unemployment-across-industries","saved":false,"source":"trigger","unread":true},"resultTableId":"table-540763"},"explanation":{"agent":"CodeExplanationAgent","code":"- Group the unemployment data by **series** (industry) and **month**\n- Calculate the **mean** of the **rate** field for each industry-month combination, creating a new field **avg_rate**\n- Map numeric **month** values (1-12) to their abbreviated names (**Jan**, **Feb**, **Mar**, etc.) and store in **month_name** field for improved readability\n- Return the aggregated dataset containing average unemployment rates per industry per month across all years in the data","concepts":[],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df1):\n # Calculate average unemployment rate by month and series (industry)\n transformed_df = df1.groupby(['series', 'month'], as_index=False).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Add month name for better readability\n month_names = {\n 1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr', 5: 'May', 6: 'Jun',\n 7: 'Jul', 8: 'Aug', 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'\n }\n transformed_df['month_name'] = transformed_df['month'].map(month_names)\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\n- Group the unemployment data by **series** (industry) and **month**\n- Calculate the **mean** of the **rate** field for each industry-month combination, creating a new field **avg_rate**\n- Map numeric **month** values (1-12) to their abbreviated names (**Jan**, **Feb**, **Mar**, etc.) and store in **month_name** field for improved readability\n- Return the aggregated dataset containing average unemployment rates per industry per month across all years in the data\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-552276","displayId":"unemp-by-sector","names":["series","rate_2008","rate_2009","rate_increase"],"rows":[{"series":"Mining and Extraction","rate_2008":3.0833333333,"rate_2009":11.6583333333,"rate_increase":8.575},{"series":"Construction","rate_2008":10.6,"rate_2009":19.05,"rate_increase":8.45},{"series":"Manufacturing","rate_2008":5.7583333333,"rate_2009":12.075,"rate_increase":6.3166666667},{"series":"Agriculture","rate_2008":9.2666666667,"rate_2009":14.3916666667,"rate_increase":5.125},{"series":"Business services","rate_2008":6.5583333333,"rate_2009":10.8,"rate_increase":4.2416666667},{"series":"Information","rate_2008":5.0166666667,"rate_2009":9.225,"rate_increase":4.2083333333},{"series":"Transportation and Utilities","rate_2008":5.1333333333,"rate_2009":8.8833333333,"rate_increase":3.75},{"series":"Wholesale and Retail Trade","rate_2008":5.8416666667,"rate_2009":9.0333333333,"rate_increase":3.1916666667},{"series":"Leisure and hospitality","rate_2008":8.6416666667,"rate_2009":11.6833333333,"rate_increase":3.0416666667},{"series":"Finance","rate_2008":3.95,"rate_2009":6.4,"rate_increase":2.45},{"series":"Other","rate_2008":5.2666666667,"rate_2009":7.5083333333,"rate_increase":2.2416666667},{"series":"Self-employed","rate_2008":3.6416666667,"rate_2009":5.5,"rate_increase":1.8583333333},{"series":"Education and Health","rate_2008":3.4916666667,"rate_2009":5.2666666667,"rate_increase":1.775},{"series":"Government","rate_2008":2.4416666667,"rate_2009":3.6166666667,"rate_increase":1.175}],"metadata":{"series":{"type":"string","semanticType":"String"},"rate_2008":{"type":"number","semanticType":"Percentage"},"rate_2009":{"type":"number","semanticType":"Percentage"},"rate_increase":{"type":"number","semanticType":"Percentage"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date'])\n \n # Filter data for years 2008 and 2009\n df_2008_2009 = df_unemployment[df_unemployment['year'].isin([2008, 2009])].copy()\n \n # Calculate average rate for each industry in 2008 and 2009\n df_2008 = df_2008_2009[df_2008_2009['year'] == 2008].groupby('series')['rate'].mean().reset_index()\n df_2008.columns = ['series', 'rate_2008']\n \n df_2009 = df_2008_2009[df_2008_2009['year'] == 2009].groupby('series')['rate'].mean().reset_index()\n df_2009.columns = ['series', 'rate_2009']\n \n # Merge and calculate the increase\n transformed_df = df_2008.merge(df_2009, on='series')\n transformed_df['rate_increase'] = transformed_df['rate_2009'] - transformed_df['rate_2008']\n \n # Sort by rate increase in descending order\n transformed_df = transformed_df.sort_values('rate_increase', ascending=False).reset_index(drop=True)\n \n return transformed_df\n","source":["unemployment-across-industries"],"dialog":[{"role":"system","content":"You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"},{"role":"user","content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[GOAL]\n\nCompare **unemployment trends** across all industries from 2000-2010"},{"role":"assistant","content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to compare unemployment trends across all industries from 2000-2010 to understand how different sectors were affected over time.\",\n \"display_instruction\": \"Compare **unemployment rates** across **industries** over time\",\n \"recommendation\": \"A line chart with time on x-axis and unemployment rate on y-axis, with different colored lines for each industry series, will effectively show the trends and allow comparison of how unemployment evolved across different sectors during this period.\",\n \"output_fields\": [\"date\", \"series\", \"rate\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"rate\",\n \"color\": \"series\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime and then to string format for visualization\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date']).dt.strftime('%Y-%m-%d')\n \n # Select only the necessary columns for visualization\n transformed_df = df_unemployment[['date', 'series', 'rate']].copy()\n \n return transformed_df\n```"},{"role":"user","content":"This is the result from the latest python code:\n\n date series rate\n0 2000-01-01 Government 2.1\n1 2000-02-01 Government 2.0\n2 2000-03-01 Government 1.5\n3 2000-04-01 Government 1.3\n4 2000-05-01 Government 1.9\n5 2000-06-01 Government 3.1\n6 2000-07-01 Government 2.9\n7 2000-08-01 Government 3.1\n8 2000-09-01 Government 2.1\n9 2000-10-01 Government 2.0\n......\n\nUpdate the code above based on the following instruction:\n\nWhich industries showed the **sharpest increases** in unemployment during **2008-2009**?"},{"role":"assistant","content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to identify which industries experienced the sharpest increases in unemployment rates during the 2008-2009 financial crisis period by calculating the change in unemployment rate for each industry.\",\n \"display_instruction\": \"Identify industries with **sharpest unemployment rate increases** during **2008-2009**\",\n \"recommendation\": \"A bar chart showing the change in unemployment rate from 2008 to 2009 for each industry will clearly visualize which sectors were hit hardest during the financial crisis. The industries should be sorted by the magnitude of increase to make comparison easier.\",\n \"output_fields\": [\"series\", \"rate_2008\", \"rate_2009\", \"rate_increase\"],\n \"chart_type\": \"bar\",\n \"chart_encodings\": {\n \"x\": \"series\",\n \"y\": \"rate_increase\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date'])\n \n # Filter data for years 2008 and 2009\n df_2008_2009 = df_unemployment[df_unemployment['year'].isin([2008, 2009])].copy()\n \n # Calculate average rate for each industry in 2008 and 2009\n df_2008 = df_2008_2009[df_2008_2009['year'] == 2008].groupby('series')['rate'].mean().reset_index()\n df_2008.columns = ['series', 'rate_2008']\n \n df_2009 = df_2008_2009[df_2008_2009['year'] == 2009].groupby('series')['rate'].mean().reset_index()\n df_2009.columns = ['series', 'rate_2009']\n \n # Merge and calculate the increase\n transformed_df = df_2008.merge(df_2009, on='series')\n transformed_df['rate_increase'] = transformed_df['rate_2009'] - transformed_df['rate_2008']\n \n # Sort by rate increase in descending order\n transformed_df = transformed_df.sort_values('rate_increase', ascending=False).reset_index(drop=True)\n \n return transformed_df\n```"}],"trigger":{"tableId":"table-544555","sourceTableIds":["unemployment-across-industries"],"instruction":"Which industries showed the **sharpest increases** in unemployment during **2008-2009**?","displayInstruction":"Identify industries with **sharpest unemployment rate increases** during **2008-2009**","chart":{"id":"chart-1760745556114","chartType":"Auto","encodingMap":{},"tableRef":"unemployment-across-industries","saved":false,"source":"trigger","unread":true},"resultTableId":"table-552276"},"explanation":{"agent":"CodeExplanationAgent","code":"- Convert the **date** field to datetime format for proper date handling\n- Filter the dataset to include only records from **2008** and **2009**\n- Calculate the **average unemployment rate** for each **industry (series)** in **2008**\n- Calculate the **average unemployment rate** for each **industry (series)** in **2009**\n- Merge the two yearly averages and compute the **rate increase** from 2008 to 2009\n- Sort industries by **rate_increase** in descending order to identify which industries experienced the largest growth in unemployment","concepts":[{"explanation":"The difference in average unemployment rate between 2009 and 2008 for each industry, calculated as \\( \\text{rate\\_increase} = \\text{rate\\_2009} - \\text{rate\\_2008} \\). This metric indicates how much unemployment worsened (positive values) or improved (negative values) during the financial crisis period.","field":"rate_increase"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date'])\n \n # Filter data for years 2008 and 2009\n df_2008_2009 = df_unemployment[df_unemployment['year'].isin([2008, 2009])].copy()\n \n # Calculate average rate for each industry in 2008 and 2009\n df_2008 = df_2008_2009[df_2008_2009['year'] == 2008].groupby('series')['rate'].mean().reset_index()\n df_2008.columns = ['series', 'rate_2008']\n \n df_2009 = df_2008_2009[df_2008_2009['year'] == 2009].groupby('series')['rate'].mean().reset_index()\n df_2009.columns = ['series', 'rate_2009']\n \n # Merge and calculate the increase\n transformed_df = df_2008.merge(df_2009, on='series')\n transformed_df['rate_increase'] = transformed_df['rate_2009'] - transformed_df['rate_2008']\n \n # Sort by rate increase in descending order\n transformed_df = transformed_df.sort_values('rate_increase', ascending=False).reset_index(drop=True)\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\n- Convert the **date** field to datetime format for proper date handling\n- Filter the dataset to include only records from **2008** and **2009**\n- Calculate the **average unemployment rate** for each **industry (series)** in **2008**\n- Calculate the **average unemployment rate** for each **industry (series)** in **2009**\n- Merge the two yearly averages and compute the **rate increase** from 2008 to 2009\n- Sort industries by **rate_increase** in descending order to identify which industries experienced the largest growth in unemployment\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"rate_increase\",\n \"explanation\": \"The difference in average unemployment rate between 2009 and 2008 for each industry, calculated as \\\\( \\\\text{rate\\\\_increase} = \\\\text{rate\\\\_2009} - \\\\text{rate\\\\_2008} \\\\). This metric indicates how much unemployment worsened (positive values) or improved (negative values) during the financial crisis period.\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-570117","displayId":"unemp-rate-ind1","names":["date","series","rate"],"rows":[{"date":"2008-01-01","series":"Construction","rate":11},{"date":"2008-02-01","series":"Construction","rate":11.4},{"date":"2008-03-01","series":"Construction","rate":12},{"date":"2008-04-01","series":"Construction","rate":11.1},{"date":"2008-05-01","series":"Construction","rate":8.6},{"date":"2008-06-01","series":"Construction","rate":8.2},{"date":"2008-07-01","series":"Construction","rate":8},{"date":"2008-08-01","series":"Construction","rate":8.2},{"date":"2008-09-01","series":"Construction","rate":9.9},{"date":"2008-10-01","series":"Construction","rate":10.8},{"date":"2008-11-01","series":"Construction","rate":12.7},{"date":"2008-12-01","series":"Construction","rate":15.3},{"date":"2009-01-01","series":"Construction","rate":18.2},{"date":"2009-02-01","series":"Construction","rate":21.4},{"date":"2009-03-01","series":"Construction","rate":21.1},{"date":"2009-04-01","series":"Construction","rate":18.7},{"date":"2009-05-01","series":"Construction","rate":19.2},{"date":"2009-06-01","series":"Construction","rate":17.4},{"date":"2009-07-01","series":"Construction","rate":18.2},{"date":"2009-08-01","series":"Construction","rate":16.5},{"date":"2009-09-01","series":"Construction","rate":17.1},{"date":"2009-10-01","series":"Construction","rate":18.7},{"date":"2009-11-01","series":"Construction","rate":19.4},{"date":"2009-12-01","series":"Construction","rate":22.7},{"date":"2010-01-01","series":"Construction","rate":24.7},{"date":"2010-02-01","series":"Construction","rate":27.1},{"date":"2008-01-01","series":"Manufacturing","rate":5.1},{"date":"2008-02-01","series":"Manufacturing","rate":5},{"date":"2008-03-01","series":"Manufacturing","rate":5},{"date":"2008-04-01","series":"Manufacturing","rate":4.8},{"date":"2008-05-01","series":"Manufacturing","rate":5.3},{"date":"2008-06-01","series":"Manufacturing","rate":5.2},{"date":"2008-07-01","series":"Manufacturing","rate":5.5},{"date":"2008-08-01","series":"Manufacturing","rate":5.7},{"date":"2008-09-01","series":"Manufacturing","rate":6},{"date":"2008-10-01","series":"Manufacturing","rate":6.2},{"date":"2008-11-01","series":"Manufacturing","rate":7},{"date":"2008-12-01","series":"Manufacturing","rate":8.3},{"date":"2009-01-01","series":"Manufacturing","rate":10.9},{"date":"2009-02-01","series":"Manufacturing","rate":11.5},{"date":"2009-03-01","series":"Manufacturing","rate":12.2},{"date":"2009-04-01","series":"Manufacturing","rate":12.4},{"date":"2009-05-01","series":"Manufacturing","rate":12.6},{"date":"2009-06-01","series":"Manufacturing","rate":12.6},{"date":"2009-07-01","series":"Manufacturing","rate":12.4},{"date":"2009-08-01","series":"Manufacturing","rate":11.8},{"date":"2009-09-01","series":"Manufacturing","rate":11.9},{"date":"2009-10-01","series":"Manufacturing","rate":12.2},{"date":"2009-11-01","series":"Manufacturing","rate":12.5},{"date":"2009-12-01","series":"Manufacturing","rate":11.9},{"date":"2010-01-01","series":"Manufacturing","rate":13},{"date":"2010-02-01","series":"Manufacturing","rate":12.1},{"date":"2008-01-01","series":"Mining and Extraction","rate":4},{"date":"2008-02-01","series":"Mining and Extraction","rate":2.2},{"date":"2008-03-01","series":"Mining and Extraction","rate":3.7},{"date":"2008-04-01","series":"Mining and Extraction","rate":3.6},{"date":"2008-05-01","series":"Mining and Extraction","rate":3.4},{"date":"2008-06-01","series":"Mining and Extraction","rate":3.3},{"date":"2008-07-01","series":"Mining and Extraction","rate":1.5},{"date":"2008-08-01","series":"Mining and Extraction","rate":1.9},{"date":"2008-09-01","series":"Mining and Extraction","rate":2.8},{"date":"2008-10-01","series":"Mining and Extraction","rate":1.7},{"date":"2008-11-01","series":"Mining and Extraction","rate":3.7},{"date":"2008-12-01","series":"Mining and Extraction","rate":5.2},{"date":"2009-01-01","series":"Mining and Extraction","rate":7},{"date":"2009-02-01","series":"Mining and Extraction","rate":7.6},{"date":"2009-03-01","series":"Mining and Extraction","rate":12.6},{"date":"2009-04-01","series":"Mining and Extraction","rate":16.1},{"date":"2009-05-01","series":"Mining and Extraction","rate":13.3},{"date":"2009-06-01","series":"Mining and Extraction","rate":13.6},{"date":"2009-07-01","series":"Mining and Extraction","rate":12.6},{"date":"2009-08-01","series":"Mining and Extraction","rate":11.8},{"date":"2009-09-01","series":"Mining and Extraction","rate":10.7},{"date":"2009-10-01","series":"Mining and Extraction","rate":10.8},{"date":"2009-11-01","series":"Mining and Extraction","rate":12},{"date":"2009-12-01","series":"Mining and Extraction","rate":11.8},{"date":"2010-01-01","series":"Mining and Extraction","rate":9.1},{"date":"2010-02-01","series":"Mining and Extraction","rate":10.7}],"metadata":{"date":{"type":"date","semanticType":"YearMonth"},"series":{"type":"string","semanticType":"String"},"rate":{"type":"number","semanticType":"Percentage"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime and then to string format for visualization\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date'])\n \n # Filter for years 2008-2010\n df_filtered = df_unemployment[df_unemployment['year'].isin([2008, 2009, 2010])].copy()\n \n # Filter for top 3 most affected industries\n top_3_industries = ['Mining and Extraction', 'Construction', 'Manufacturing']\n df_filtered = df_filtered[df_filtered['series'].isin(top_3_industries)].copy()\n \n # Convert date to string format for visualization\n df_filtered['date'] = df_filtered['date'].dt.strftime('%Y-%m-%d')\n \n # Select only the necessary columns\n transformed_df = df_filtered[['date', 'series', 'rate']].copy()\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values(['series', 'date']).reset_index(drop=True)\n \n return transformed_df\n","source":["unemployment-across-industries"],"dialog":[{"role":"system","content":"You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"},{"role":"user","content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[GOAL]\n\nCompare **unemployment trends** across all industries from 2000-2010"},{"role":"assistant","content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to compare unemployment trends across all industries from 2000-2010 to understand how different sectors were affected over time.\",\n \"display_instruction\": \"Compare **unemployment rates** across **industries** over time\",\n \"recommendation\": \"A line chart with time on x-axis and unemployment rate on y-axis, with different colored lines for each industry series, will effectively show the trends and allow comparison of how unemployment evolved across different sectors during this period.\",\n \"output_fields\": [\"date\", \"series\", \"rate\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"rate\",\n \"color\": \"series\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime and then to string format for visualization\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date']).dt.strftime('%Y-%m-%d')\n \n # Select only the necessary columns for visualization\n transformed_df = df_unemployment[['date', 'series', 'rate']].copy()\n \n return transformed_df\n```"},{"role":"user","content":"This is the result from the latest python code:\n\n date series rate\n0 2000-01-01 Government 2.1\n1 2000-02-01 Government 2.0\n2 2000-03-01 Government 1.5\n3 2000-04-01 Government 1.3\n4 2000-05-01 Government 1.9\n5 2000-06-01 Government 3.1\n6 2000-07-01 Government 2.9\n7 2000-08-01 Government 3.1\n8 2000-09-01 Government 2.1\n9 2000-10-01 Government 2.0\n......\n\nUpdate the code above based on the following instruction:\n\nWhich industries showed the **sharpest increases** in unemployment during **2008-2009**?"},{"role":"assistant","content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to identify which industries experienced the sharpest increases in unemployment rates during the 2008-2009 financial crisis period by calculating the change in unemployment rate for each industry.\",\n \"display_instruction\": \"Identify industries with **sharpest unemployment rate increases** during **2008-2009**\",\n \"recommendation\": \"A bar chart showing the change in unemployment rate from 2008 to 2009 for each industry will clearly visualize which sectors were hit hardest during the financial crisis. The industries should be sorted by the magnitude of increase to make comparison easier.\",\n \"output_fields\": [\"series\", \"rate_2008\", \"rate_2009\", \"rate_increase\"],\n \"chart_type\": \"bar\",\n \"chart_encodings\": {\n \"x\": \"series\",\n \"y\": \"rate_increase\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date'])\n \n # Filter data for years 2008 and 2009\n df_2008_2009 = df_unemployment[df_unemployment['year'].isin([2008, 2009])].copy()\n \n # Calculate average rate for each industry in 2008 and 2009\n df_2008 = df_2008_2009[df_2008_2009['year'] == 2008].groupby('series')['rate'].mean().reset_index()\n df_2008.columns = ['series', 'rate_2008']\n \n df_2009 = df_2008_2009[df_2008_2009['year'] == 2009].groupby('series')['rate'].mean().reset_index()\n df_2009.columns = ['series', 'rate_2009']\n \n # Merge and calculate the increase\n transformed_df = df_2008.merge(df_2009, on='series')\n transformed_df['rate_increase'] = transformed_df['rate_2009'] - transformed_df['rate_2008']\n \n # Sort by rate increase in descending order\n transformed_df = transformed_df.sort_values('rate_increase', ascending=False).reset_index(drop=True)\n \n return transformed_df\n```"},{"role":"user","content":"This is the result from the latest python code:\n\n series rate_2008 rate_2009 rate_increase\n0 Mining and Extraction 3.083333 11.658333 8.575000\n1 Construction 10.600000 19.050000 8.450000\n2 Manufacturing 5.758333 12.075000 6.316667\n3 Agriculture 9.266667 14.391667 5.125000\n4 Business services 6.558333 10.800000 4.241667\n5 Information 5.016667 9.225000 4.208333\n6 Transportation and Utilities 5.133333 8.883333 3.750000\n7 Wholesale and Retail Trade 5.841667 9.033333 3.191667\n8 Leisure and hospitality 8.641667 11.683333 3.041667\n9 Finance 3.950000 6.400000 2.450000\n......\n\nUpdate the code above based on the following instruction:\n\nFor the **top 3 most affected industries** (Mining and Extraction, Construction, Manufacturing), compare their **monthly unemployment rate trends** during the **2008-2010 period** to analyze how quickly each industry recovered after the 2009 peak"},{"role":"assistant","content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to analyze the monthly unemployment rate trends for the top 3 most affected industries (Mining and Extraction, Construction, Manufacturing) during 2008-2010 to understand their recovery patterns after the 2009 peak.\",\n \"display_instruction\": \"Compare **monthly unemployment trends** for **top 3 affected industries** during **2008-2010 recovery period**\",\n \"recommendation\": \"A line chart with time on x-axis and unemployment rate on y-axis, with different colored lines for the three most affected industries, will effectively show how each industry's unemployment rate evolved during and after the crisis, revealing their different recovery trajectories.\",\n \"output_fields\": [\"date\", \"series\", \"rate\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"rate\",\n \"color\": \"series\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime and then to string format for visualization\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date'])\n \n # Filter for years 2008-2010\n df_filtered = df_unemployment[df_unemployment['year'].isin([2008, 2009, 2010])].copy()\n \n # Filter for top 3 most affected industries\n top_3_industries = ['Mining and Extraction', 'Construction', 'Manufacturing']\n df_filtered = df_filtered[df_filtered['series'].isin(top_3_industries)].copy()\n \n # Convert date to string format for visualization\n df_filtered['date'] = df_filtered['date'].dt.strftime('%Y-%m-%d')\n \n # Select only the necessary columns\n transformed_df = df_filtered[['date', 'series', 'rate']].copy()\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values(['series', 'date']).reset_index(drop=True)\n \n return transformed_df\n```"}],"trigger":{"tableId":"table-552276","sourceTableIds":["unemployment-across-industries"],"instruction":"For the **top 3 most affected industries** (Mining and Extraction, Construction, Manufacturing), compare their **monthly unemployment rate trends** during the **2008-2010 period** to analyze how quickly each industry recovered after the 2009 peak","displayInstruction":"Compare **monthly unemployment trends** for **top 3 affected industries** during **2008-2010 recovery period**","chart":{"id":"chart-1760745567484","chartType":"Auto","encodingMap":{},"tableRef":"unemployment-across-industries","saved":false,"source":"trigger","unread":true},"resultTableId":"table-570117"},"explanation":{"agent":"CodeExplanationAgent","code":"- Convert the **date** field from string to datetime format, then back to a standardized string format (`YYYY-MM-DD`)\n- **Filter** the data to include only the years **2008**, **2009**, and **2010** (the period around the financial crisis)\n- **Select** the top 3 most affected industries: **Mining and Extraction**, **Construction**, and **Manufacturing**\n- **Extract** only the essential columns: **date**, **series** (industry name), and **rate** (unemployment rate)\n- **Sort** the data by **series** and **date** to ensure proper chronological ordering for time series visualization","concepts":[],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime and then to string format for visualization\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date'])\n \n # Filter for years 2008-2010\n df_filtered = df_unemployment[df_unemployment['year'].isin([2008, 2009, 2010])].copy()\n \n # Filter for top 3 most affected industries\n top_3_industries = ['Mining and Extraction', 'Construction', 'Manufacturing']\n df_filtered = df_filtered[df_filtered['series'].isin(top_3_industries)].copy()\n \n # Convert date to string format for visualization\n df_filtered['date'] = df_filtered['date'].dt.strftime('%Y-%m-%d')\n \n # Select only the necessary columns\n transformed_df = df_filtered[['date', 'series', 'rate']].copy()\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values(['series', 'date']).reset_index(drop=True)\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\n- Convert the **date** field from string to datetime format, then back to a standardized string format (`YYYY-MM-DD`)\n- **Filter** the data to include only the years **2008**, **2009**, and **2010** (the period around the financial crisis)\n- **Select** the top 3 most affected industries: **Mining and Extraction**, **Construction**, and **Manufacturing**\n- **Extract** only the essential columns: **date**, **series** (industry name), and **rate** (unemployment rate)\n- **Sort** the data by **series** and **date** to ensure proper chronological ordering for time series visualization\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""},{"id":"table-53","displayId":"industry-rates","names":["avg_rate","month","series","year"],"rows":[{"avg_rate":10.3,"month":1,"series":"Agriculture","year":"2000"},{"avg_rate":11.5,"month":2,"series":"Agriculture","year":"2000"},{"avg_rate":10.4,"month":3,"series":"Agriculture","year":"2000"},{"avg_rate":8.9,"month":4,"series":"Agriculture","year":"2000"},{"avg_rate":5.1,"month":5,"series":"Agriculture","year":"2000"},{"avg_rate":6.7,"month":6,"series":"Agriculture","year":"2000"},{"avg_rate":5,"month":7,"series":"Agriculture","year":"2000"},{"avg_rate":7,"month":8,"series":"Agriculture","year":"2000"},{"avg_rate":8.2,"month":9,"series":"Agriculture","year":"2000"},{"avg_rate":8,"month":10,"series":"Agriculture","year":"2000"},{"avg_rate":13.3,"month":11,"series":"Agriculture","year":"2000"},{"avg_rate":13.9,"month":12,"series":"Agriculture","year":"2000"},{"avg_rate":13.8,"month":1,"series":"Agriculture","year":"2001"},{"avg_rate":15.1,"month":2,"series":"Agriculture","year":"2001"},{"avg_rate":19.2,"month":3,"series":"Agriculture","year":"2001"},{"avg_rate":10.4,"month":4,"series":"Agriculture","year":"2001"},{"avg_rate":7.7,"month":5,"series":"Agriculture","year":"2001"},{"avg_rate":9.7,"month":6,"series":"Agriculture","year":"2001"},{"avg_rate":7.6,"month":7,"series":"Agriculture","year":"2001"},{"avg_rate":9.3,"month":8,"series":"Agriculture","year":"2001"},{"avg_rate":7.2,"month":9,"series":"Agriculture","year":"2001"},{"avg_rate":8.7,"month":10,"series":"Agriculture","year":"2001"},{"avg_rate":11.6,"month":11,"series":"Agriculture","year":"2001"},{"avg_rate":15.1,"month":12,"series":"Agriculture","year":"2001"},{"avg_rate":14.8,"month":1,"series":"Agriculture","year":"2002"},{"avg_rate":14.8,"month":2,"series":"Agriculture","year":"2002"},{"avg_rate":19.6,"month":3,"series":"Agriculture","year":"2002"},{"avg_rate":10.8,"month":4,"series":"Agriculture","year":"2002"},{"avg_rate":6.8,"month":5,"series":"Agriculture","year":"2002"},{"avg_rate":6.3,"month":6,"series":"Agriculture","year":"2002"},{"avg_rate":7.3,"month":7,"series":"Agriculture","year":"2002"},{"avg_rate":9,"month":8,"series":"Agriculture","year":"2002"},{"avg_rate":6.3,"month":9,"series":"Agriculture","year":"2002"},{"avg_rate":6.6,"month":10,"series":"Agriculture","year":"2002"},{"avg_rate":11.1,"month":11,"series":"Agriculture","year":"2002"},{"avg_rate":9.8,"month":12,"series":"Agriculture","year":"2002"},{"avg_rate":13.2,"month":1,"series":"Agriculture","year":"2003"},{"avg_rate":14.7,"month":2,"series":"Agriculture","year":"2003"},{"avg_rate":12.9,"month":3,"series":"Agriculture","year":"2003"},{"avg_rate":12,"month":4,"series":"Agriculture","year":"2003"},{"avg_rate":10.2,"month":5,"series":"Agriculture","year":"2003"},{"avg_rate":6.9,"month":6,"series":"Agriculture","year":"2003"},{"avg_rate":8.2,"month":7,"series":"Agriculture","year":"2003"},{"avg_rate":10.7,"month":8,"series":"Agriculture","year":"2003"},{"avg_rate":6.2,"month":9,"series":"Agriculture","year":"2003"},{"avg_rate":8.5,"month":10,"series":"Agriculture","year":"2003"},{"avg_rate":10.3,"month":11,"series":"Agriculture","year":"2003"},{"avg_rate":10.9,"month":12,"series":"Agriculture","year":"2003"},{"avg_rate":15.1,"month":1,"series":"Agriculture","year":"2004"},{"avg_rate":14.2,"month":2,"series":"Agriculture","year":"2004"},{"avg_rate":12.7,"month":3,"series":"Agriculture","year":"2004"},{"avg_rate":8.3,"month":4,"series":"Agriculture","year":"2004"},{"avg_rate":7.4,"month":5,"series":"Agriculture","year":"2004"},{"avg_rate":7.6,"month":6,"series":"Agriculture","year":"2004"},{"avg_rate":10,"month":7,"series":"Agriculture","year":"2004"},{"avg_rate":7,"month":8,"series":"Agriculture","year":"2004"},{"avg_rate":6.4,"month":9,"series":"Agriculture","year":"2004"},{"avg_rate":7.7,"month":10,"series":"Agriculture","year":"2004"},{"avg_rate":10.5,"month":11,"series":"Agriculture","year":"2004"},{"avg_rate":14,"month":12,"series":"Agriculture","year":"2004"},{"avg_rate":13.2,"month":1,"series":"Agriculture","year":"2005"},{"avg_rate":9.9,"month":2,"series":"Agriculture","year":"2005"},{"avg_rate":11.8,"month":3,"series":"Agriculture","year":"2005"},{"avg_rate":6.9,"month":4,"series":"Agriculture","year":"2005"},{"avg_rate":5.3,"month":5,"series":"Agriculture","year":"2005"},{"avg_rate":5.2,"month":6,"series":"Agriculture","year":"2005"},{"avg_rate":4.7,"month":7,"series":"Agriculture","year":"2005"},{"avg_rate":7.1,"month":8,"series":"Agriculture","year":"2005"},{"avg_rate":9.5,"month":9,"series":"Agriculture","year":"2005"},{"avg_rate":6.7,"month":10,"series":"Agriculture","year":"2005"},{"avg_rate":9.6,"month":11,"series":"Agriculture","year":"2005"},{"avg_rate":11.1,"month":12,"series":"Agriculture","year":"2005"},{"avg_rate":11.5,"month":1,"series":"Agriculture","year":"2006"},{"avg_rate":11.8,"month":2,"series":"Agriculture","year":"2006"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Extraction","year":"2007"},{"avg_rate":3.2,"month":9,"series":"Mining and Extraction","year":"2007"},{"avg_rate":1.3,"month":10,"series":"Mining and Extraction","year":"2007"},{"avg_rate":2.3,"month":11,"series":"Mining and Extraction","year":"2007"},{"avg_rate":3.4,"month":12,"series":"Mining and Extraction","year":"2007"},{"avg_rate":4,"month":1,"series":"Mining and Extraction","year":"2008"},{"avg_rate":2.2,"month":2,"series":"Mining and Extraction","year":"2008"},{"avg_rate":3.7,"month":3,"series":"Mining and Extraction","year":"2008"},{"avg_rate":3.6,"month":4,"series":"Mining and Extraction","year":"2008"},{"avg_rate":3.4,"month":5,"series":"Mining and Extraction","year":"2008"},{"avg_rate":3.3,"month":6,"series":"Mining and Extraction","year":"2008"},{"avg_rate":1.5,"month":7,"series":"Mining and Extraction","year":"2008"},{"avg_rate":1.9,"month":8,"series":"Mining and Extraction","year":"2008"},{"avg_rate":2.8,"month":9,"series":"Mining and Extraction","year":"2008"},{"avg_rate":1.7,"month":10,"series":"Mining and Extraction","year":"2008"},{"avg_rate":3.7,"month":11,"series":"Mining and Extraction","year":"2008"},{"avg_rate":5.2,"month":12,"series":"Mining and Extraction","year":"2008"},{"avg_rate":7,"month":1,"series":"Mining and Extraction","year":"2009"},{"avg_rate":7.6,"month":2,"series":"Mining and Extraction","year":"2009"},{"avg_rate":12.6,"month":3,"series":"Mining and Extraction","year":"2009"},{"avg_rate":16.1,"month":4,"series":"Mining and Extraction","year":"2009"},{"avg_rate":13.3,"month":5,"series":"Mining and Extraction","year":"2009"},{"avg_rate":13.6,"month":6,"series":"Mining and Extraction","year":"2009"},{"avg_rate":12.6,"month":7,"series":"Mining and Extraction","year":"2009"},{"avg_rate":11.8,"month":8,"series":"Mining and Extraction","year":"2009"},{"avg_rate":10.7,"month":9,"series":"Mining and Extraction","year":"2009"},{"avg_rate":10.8,"month":10,"series":"Mining and Extraction","year":"2009"},{"avg_rate":12,"month":11,"series":"Mining and Extraction","year":"2009"},{"avg_rate":11.8,"month":12,"series":"Mining and Extraction","year":"2009"},{"avg_rate":9.1,"month":1,"series":"Mining and Extraction","year":"2010"},{"avg_rate":10.7,"month":2,"series":"Mining and Extraction","year":"2010"}],"metadata":{"avg_rate":{"type":"number","semanticType":"Number"},"month":{"type":"number","semanticType":"Month"},"series":{"type":"string","semanticType":"String"},"year":{"type":"number","semanticType":"Year"}},"derive":{"code":"\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df1):\n # Calculate average unemployment rate by month and series\n monthly_avg = df1.groupby(['series', 'month'], as_index=False).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Calculate seasonal variation (standard deviation across months) for each series\n seasonal_variation = monthly_avg.groupby('series')['avg_rate'].std().reset_index()\n seasonal_variation.columns = ['series', 'seasonal_std']\n \n # Get top 3 most seasonal industries\n top_3_seasonal = seasonal_variation.nlargest(3, 'seasonal_std')['series'].tolist()\n \n # Filter original data for top 3 industries and aggregate by year, month, series\n transformed_df = df1[df1['series'].isin(top_3_seasonal)].groupby(\n ['series', 'year', 'month'], as_index=False\n ).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Convert year to string for color encoding\n transformed_df['year'] = transformed_df['year'].astype(str)\n \n return transformed_df\n","source":["unemployment-across-industries"],"dialog":[{"content":"You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[GOAL]\n\nHow does **seasonal variation** in unemployment differ across **industries**?","role":"user"},{"content":"```json\n{\n \"mode\": \"infer\",\n \"recap\": \"Analyze how seasonal variation in unemployment differs across industries by examining the cyclical patterns in unemployment rates throughout the year for different industry sectors.\",\n \"display_instruction\": \"Compare seasonal unemployment patterns across **industries**\",\n \"recommendation\": \"To visualize seasonal variation across industries, we should calculate average unemployment rates by month for each industry series. This will reveal the typical seasonal patterns and allow comparison of how different industries experience seasonal fluctuations in unemployment throughout the year.\",\n \"output_fields\": [\"series\", \"month\", \"avg_rate\", \"month_name\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"month\",\n \"y\": \"avg_rate\",\n \"color\": \"series\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df1):\n # Calculate average unemployment rate by month and series (industry)\n transformed_df = df1.groupby(['series', 'month'], as_index=False).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Add month name for better readability\n month_names = {\n 1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr', 5: 'May', 6: 'Jun',\n 7: 'Jul', 8: 'Aug', 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'\n }\n transformed_df['month_name'] = transformed_df['month'].map(month_names)\n \n return transformed_df\n```","role":"assistant"},{"content":"This is the result from the latest python code:\n\n avg_rate month month_name series\n0 13.763636 1 Jan Agriculture\n1 13.645455 2 Feb Agriculture\n2 13.830000 3 Mar Agriculture\n3 9.130000 4 Apr Agriculture\n4 7.100000 5 May Agriculture\n5 6.770000 6 Jun Agriculture\n6 7.010000 7 Jul Agriculture\n7 8.080000 8 Aug Agriculture\n8 7.090000 9 Sep Agriculture\n9 7.490000 10 Oct Agriculture\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show the seasonal trends for top 3 industries that are most seaonal\",\n \"chart_type\": \"Custom Area\",\n \"chart_encodings\": {\n \"x\": \"month\",\n \"y\": \"avg_rate\",\n \"color\": \"year\",\n \"column\": \"series\"\n }\n}","role":"user"},{"content":"```json\n{\n \"detailed_instruction\": \"Identify the top 3 industries with the highest seasonal variation in unemployment rates, then show their seasonal trends over time with monthly unemployment rates for each year, faceted by industry.\",\n \"display_instruction\": \"Show seasonal trends for top 3 most seasonal **industries** over **years**\",\n \"output_fields\": [\"series\", \"year\", \"month\", \"avg_rate\"],\n \"chart_encodings\": {\n \"x\": \"month\",\n \"y\": \"avg_rate\",\n \"color\": \"year\",\n \"facet\": \"series\"\n },\n \"reason\": \"To identify the most seasonal industries, we need to calculate the seasonal variation (e.g., standard deviation or range of monthly rates) for each industry. Then select the top 3 industries with highest variation. The output should include year-month-series level data to show trends over time, with faceting by the top 3 most seasonal industries.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df1):\n # Calculate average unemployment rate by month and series\n monthly_avg = df1.groupby(['series', 'month'], as_index=False).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Calculate seasonal variation (standard deviation across months) for each series\n seasonal_variation = monthly_avg.groupby('series')['avg_rate'].std().reset_index()\n seasonal_variation.columns = ['series', 'seasonal_std']\n \n # Get top 3 most seasonal industries\n top_3_seasonal = seasonal_variation.nlargest(3, 'seasonal_std')['series'].tolist()\n \n # Filter original data for top 3 industries and aggregate by year, month, series\n transformed_df = df1[df1['series'].isin(top_3_seasonal)].groupby(\n ['series', 'year', 'month'], as_index=False\n ).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Convert year to string for color encoding\n transformed_df['year'] = transformed_df['year'].astype(str)\n \n return transformed_df\n```","role":"assistant"}],"trigger":{"tableId":"table-540763","sourceTableIds":["unemployment-across-industries"],"instruction":"show the seasonal trends for top 3 industries that are most seaonal","displayInstruction":"Show seasonal trends for top 3 most seasonal **industries** over **years**","chart":{"id":"chart-1760745840104","chartType":"Custom Area","encodingMap":{"x":{"fieldID":"original--unemployment-across-industries--month"},"y":{"fieldID":"concept-avg_rate-1760745549715"},"x2":{},"y2":{},"color":{"fieldID":"original--unemployment-across-industries--year"},"column":{"fieldID":"original--unemployment-across-industries--series"},"row":{"channel":"row","bin":false}},"tableRef":"table-540763","saved":false,"source":"trigger","unread":false},"resultTableId":"table-53"},"explanation":{"agent":"CodeExplanationAgent","code":"- Calculate the **average unemployment rate** (`avg_rate`) for each combination of **series** (industry) and **month** across all years\n- Compute the **seasonal variation** for each **series** by calculating the standard deviation of `avg_rate` across the 12 months\n- Identify the **top 3 industries** with the highest `seasonal_std` (most seasonal unemployment patterns)\n- Filter the original data to include only these **top 3 most seasonal industries**\n- Aggregate the filtered data by **series**, **year**, and **month**, computing the mean unemployment **rate** as `avg_rate`\n- Convert **year** to string format for categorical visualization purposes","concepts":[{"explanation":"Standard deviation of average monthly unemployment rates across the 12 months for each industry. This metric quantifies the degree of seasonal variation in unemployment - higher values indicate stronger seasonal patterns (e.g., construction might have higher unemployment in winter months), while lower values indicate more stable year-round employment patterns. Mathematically: \\( \\text{seasonal\\_std} = \\sigma(\\text{avg\\_rate}_{\\text{month}=1..12}) \\)","field":"seasonal_std"}],"dialog":[{"content":"You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n","role":"system"},{"content":"[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df1):\n # Calculate average unemployment rate by month and series\n monthly_avg = df1.groupby(['series', 'month'], as_index=False).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Calculate seasonal variation (standard deviation across months) for each series\n seasonal_variation = monthly_avg.groupby('series')['avg_rate'].std().reset_index()\n seasonal_variation.columns = ['series', 'seasonal_std']\n \n # Get top 3 most seasonal industries\n top_3_seasonal = seasonal_variation.nlargest(3, 'seasonal_std')['series'].tolist()\n \n # Filter original data for top 3 industries and aggregate by year, month, series\n transformed_df = df1[df1['series'].isin(top_3_seasonal)].groupby(\n ['series', 'year', 'month'], as_index=False\n ).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Convert year to string for color encoding\n transformed_df['year'] = transformed_df['year'].astype(str)\n \n return transformed_df\n\n\n[EXPLANATION]\n","role":"user"},{"content":"[CODE EXPLANATION]\n\n- Calculate the **average unemployment rate** (`avg_rate`) for each combination of **series** (industry) and **month** across all years\n- Compute the **seasonal variation** for each **series** by calculating the standard deviation of `avg_rate` across the 12 months\n- Identify the **top 3 industries** with the highest `seasonal_std` (most seasonal unemployment patterns)\n- Filter the original data to include only these **top 3 most seasonal industries**\n- Aggregate the filtered data by **series**, **year**, and **month**, computing the mean unemployment **rate** as `avg_rate`\n- Convert **year** to string format for categorical visualization purposes\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"seasonal_std\",\n \"explanation\": \"Standard deviation of average monthly unemployment rates across the 12 months for each industry. This metric quantifies the degree of seasonal variation in unemployment - higher values indicate stronger seasonal patterns (e.g., construction might have higher unemployment in winter months), while lower values indicate more stable year-round employment patterns. Mathematically: \\\\( \\\\text{seasonal\\\\_std} = \\\\sigma(\\\\text{avg\\\\_rate}_{\\\\text{month}=1..12}) \\\\)\"\n }\n]\n```","role":"assistant"}],"status":"ok"}},"anchored":false,"createdBy":"user","attachedMetadata":""}],"charts":[{"id":"chart-1760745839180","chartType":"Line Chart","encodingMap":{"x":{"fieldID":"original--unemployment-across-industries--month"},"y":{"fieldID":"concept-avg_rate-1760745549715"},"color":{"fieldID":"original--unemployment-across-industries--year"},"opacity":{"channel":"opacity","bin":false},"column":{"fieldID":"original--unemployment-across-industries--series"},"row":{"channel":"row","bin":false}},"tableRef":"table-53","saved":false,"source":"user","unread":false},{"id":"chart-1760745566172","chartType":"Line 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crisis.","status":"completed","hidden":false,"lastUpdate":1760745581829}],"dataCleanBlocks":[],"cleanInProgress":false,"generatedReports":[{"id":"report-1760831469874-4137","content":"# The 2008-2010 Recession Hit Construction Hardest\n\nDuring the 2008-2010 financial crisis, unemployment surged dramatically across key industries. Construction workers bore the brunt, with unemployment rates skyrocketing from 11% to a staggering 27% by early 2010—nearly tripling in just two years.\n\n[IMAGE(chart-1760745566172)]\n\nManufacturing and Mining sectors also suffered significant job losses, though less severe than Construction. Manufacturing unemployment doubled from 5% to 13%, while Mining spiked from under 4% to 16% before showing signs of recovery by 2010.\n\n**In summary**, the recession's impact was far from uniform: Construction faced catastrophic job losses, suggesting the housing market collapse disproportionately affected building trades. What factors enabled Mining's earlier recovery? How can vulnerable sectors build resilience against future economic shocks?","style":"short note","selectedChartIds":["chart-1760745566172"],"createdAt":1760831476855},{"id":"report-1760831405296-2186","content":"# Seasonal Employment Patterns Reveal Industry Vulnerabilities Across Economic Cycles\n\nThe unemployment landscape across U.S. industries from 2000-2010 reveals striking seasonal patterns and differential impacts during economic downturns. Analysis of 14 industry sectors demonstrates that certain industries face predictable cyclical challenges while others maintain relative stability throughout the year.\n\n[IMAGE(chart-1760745546664)]\n\n**Cross-industry seasonal dynamics** show distinct unemployment patterns throughout the calendar year. Key findings include:\n\n- **Agriculture** experiences the most dramatic seasonal swings, with unemployment rates peaking above 13% in winter months (January-March) and dropping to approximately 7% during summer harvest seasons\n- **Construction** exhibits similar seasonality, with rates near 13% in January declining to 7-8% during peak building months (May-September)\n- **Government and Finance** sectors demonstrate remarkable stability, maintaining unemployment rates between 2-4% year-round, suggesting these sectors are largely insulated from seasonal fluctuations\n\n[IMAGE(chart-1760745839180)]\n\n**Year-over-year trends for the most seasonal industries** reveal how economic conditions compound seasonal effects. The 2008-2009 recession created unprecedented spikes:\n\n- Construction unemployment surged from typical winter peaks of 10-13% to over 25% by early 2010\n- Agriculture showed dramatic year-to-year variation, with 2009-2010 winter unemployment reaching 19-21%, nearly double the levels seen in 2000\n- Mining and Extraction maintained lower overall rates (2-7%) but still exhibited seasonal patterns, with late-year increases across most periods\n\n**In summary**, industries reliant on weather-dependent operations face significant seasonal unemployment challenges, which are dramatically amplified during economic recessions. While sectors like Government and Finance maintain stable employment year-round, Agriculture and Construction workers experience predictable winter unemployment that can more than double during economic downturns. These patterns suggest the need for targeted workforce development and social safety net programs that account for both seasonal and cyclical employment disruptions. Further investigation should examine whether these patterns have evolved post-2010 and assess the effectiveness of countercyclical policies in stabilizing employment in vulnerable sectors.","style":"executive summary","selectedChartIds":["chart-1760745546664","chart-1760745839180"],"createdAt":1760831421331},{"id":"report-1760831364867-9457","content":"# Unemployment Surge Across Industries: The 2008-2009 Economic Crisis Impact\n\nThe decade from 2000 to 2010 witnessed significant volatility in unemployment rates across major industry sectors, with a dramatic escalation during the 2008-2009 financial crisis that fundamentally reshaped the American labor market.\n\n[IMAGE(chart-1760745539559)]\n\nThe longitudinal analysis reveals that while most industries maintained relatively stable unemployment rates between 2000 and 2007—typically fluctuating between 2% and 8%—the landscape changed dramatically beginning in late 2008. **Construction** emerged as the most severely impacted sector, with unemployment rates soaring to approximately **27% by early 2010**. Similarly, **Agriculture** experienced unprecedented spikes reaching above **22%**, demonstrating the crisis's far-reaching effects beyond traditional white-collar sectors. Even traditionally stable sectors like Government and Finance, which historically maintained rates below 3%, saw significant increases, reaching 5-7% during the peak crisis period.\n\n[IMAGE(chart-1760745556213)]\n\nExamining the year-over-year impact between 2008 and 2009 reveals the crisis's differential industry effects:\n\n- **Mining and Extraction** and **Construction** led all sectors with unemployment rate increases exceeding **8.5 percentage points**\n- **Manufacturing** and **Agriculture** followed with increases above **5 percentage points**\n- **Business services** and **Information** sectors experienced moderate increases of approximately **4 percentage points**\n- **Government** showed the smallest increase at just **1.2 percentage points**, demonstrating relative stability during economic turbulence\n\n**In summary**, the 2008-2009 financial crisis created an unprecedented unemployment shock across American industries, with resource extraction, construction, and manufacturing bearing the heaviest burden. The data reveals that sectors tied to physical production and cyclical economic activity experienced the most severe dislocations, while government employment remained comparatively insulated. Key follow-up questions include: What were the long-term recovery trajectories for these industries post-2010? Did the workers displaced from heavily impacted sectors successfully transition to more stable industries? How did policy interventions differentially affect recovery across these sectors?","style":"executive summary","selectedChartIds":["chart-1760745556213","chart-1760745539559"],"createdAt":1760831379076}],"currentReport":{"id":"report-1760750575650-2619","content":"# Hollywood's Billion-Dollar Hitmakers\n\n*Avatar* stands alone—earning over $2.5B in profit, dwarfing all competition. Action and Adventure films dominate the most profitable titles, with franchises like *Jurassic Park*, *The Dark Knight*, and *Lord of the Rings* proving blockbuster formulas work.\n\n\"Chart\"\n\nSteven Spielberg leads all directors with $7.2B in total profit across his career, showcasing remarkable consistency with hits spanning decades—from *Jurassic Park* to *E.T.* His nearest competitors trail by billions, underlining his unmatched commercial impact.\n\n\"Chart\"\n\n**In summary**, mega-budget Action and Adventure films generate extraordinary returns when they succeed, and a handful of elite directors—led by Spielberg—have mastered the formula for sustained box office dominance.","style":"short note","selectedChartIds":["chart-1760743347871","chart-1760743768741"],"chartImages":{},"createdAt":1760750584189,"title":"Report - 10/17/2025"},"activeChallenges":[],"agentWorkInProgress":[],"_persist":{"version":-1,"rehydrated":true}} \ No newline at end of file +{"tables": [{"kind": "table", "id": "unemployment-across-industries", "displayId": "unemp-by-ind", "names": ["series", "year", "month", "count", "rate", "date"], "metadata": {"series": {"type": "string", "semanticType": "String"}, "year": {"type": "number", "semanticType": "Year"}, "month": {"type": "number", "semanticType": "Month"}, "count": {"type": "number", "semanticType": "Number"}, "rate": {"type": "number", "semanticType": "Percentage"}, "date": {"type": "date", "semanticType": "DateTime"}}, "rows": [{"series": "Government", "year": 2000, "month": 1, "count": 430, "rate": 2.1, "date": "2000-01-01T08:00:00.000Z"}, {"series": "Government", "year": 2000, "month": 2, "count": 409, "rate": 2, "date": "2000-02-01T08:00:00.000Z"}, {"series": "Government", "year": 2000, "month": 3, "count": 311, "rate": 1.5, "date": "2000-03-01T08:00:00.000Z"}, {"series": "Government", "year": 2000, "month": 4, "count": 269, "rate": 1.3, "date": "2000-04-01T08:00:00.000Z"}, {"series": "Government", "year": 2000, "month": 5, "count": 370, "rate": 1.9, "date": "2000-05-01T07:00:00.000Z"}, {"series": "Government", "year": 2000, "month": 6, "count": 603, "rate": 3.1, "date": "2000-06-01T07:00:00.000Z"}, {"series": "Government", "year": 2000, "month": 7, "count": 545, "rate": 2.9, "date": "2000-07-01T07:00:00.000Z"}, {"series": "Government", "year": 2000, "month": 8, "count": 583, "rate": 3.1, "date": "2000-08-01T07:00:00.000Z"}, {"series": "Government", "year": 2000, "month": 9, "count": 408, "rate": 2.1, "date": "2000-09-01T07:00:00.000Z"}, {"series": "Government", "year": 2000, "month": 10, "count": 391, "rate": 2, "date": "2000-10-01T07:00:00.000Z"}, {"series": "Government", "year": 2000, "month": 11, "count": 384, "rate": 1.9, "date": "2000-11-01T08:00:00.000Z"}, {"series": "Government", "year": 2000, "month": 12, "count": 365, "rate": 1.8, "date": "2000-12-01T08:00:00.000Z"}, {"series": "Government", "year": 2001, "month": 1, "count": 463, "rate": 2.3, "date": "2001-01-01T08:00:00.000Z"}, {"series": "Government", "year": 2001, "month": 2, "count": 298, "rate": 1.5, "date": "2001-02-01T08:00:00.000Z"}, {"series": "Government", "year": 2001, "month": 3, "count": 355, "rate": 1.8, "date": "2001-03-01T08:00:00.000Z"}, {"series": "Government", "year": 2001, "month": 4, "count": 369, "rate": 1.9, "date": "2001-04-01T08:00:00.000Z"}, {"series": "Government", "year": 2001, "month": 5, "count": 361, "rate": 1.8, "date": "2001-05-01T07:00:00.000Z"}, {"series": "Government", "year": 2001, "month": 6, "count": 525, "rate": 2.7, "date": "2001-06-01T07:00:00.000Z"}, {"series": "Government", "year": 2001, "month": 7, "count": 548, "rate": 2.8, "date": "2001-07-01T07:00:00.000Z"}, {"series": "Government", "year": 2001, "month": 8, "count": 540, "rate": 2.8, "date": "2001-08-01T07:00:00.000Z"}, {"series": "Government", "year": 2001, "month": 9, "count": 438, "rate": 2.2, "date": "2001-09-01T07:00:00.000Z"}, {"series": "Government", "year": 2001, "month": 10, "count": 429, "rate": 2.2, "date": "2001-10-01T07:00:00.000Z"}, {"series": "Government", "year": 2001, "month": 11, "count": 420, "rate": 2.1, "date": "2001-11-01T08:00:00.000Z"}, {"series": "Government", "year": 2001, "month": 12, "count": 419, "rate": 2.1, "date": "2001-12-01T08:00:00.000Z"}, {"series": "Government", "year": 2002, "month": 1, "count": 486, "rate": 2.4, "date": "2002-01-01T08:00:00.000Z"}, {"series": "Government", "year": 2002, "month": 2, "count": 508, "rate": 2.5, "date": "2002-02-01T08:00:00.000Z"}, {"series": "Government", "year": 2002, "month": 3, "count": 477, "rate": 2.4, "date": "2002-03-01T08:00:00.000Z"}, {"series": "Government", "year": 2002, "month": 4, "count": 447, "rate": 2.2, "date": "2002-04-01T08:00:00.000Z"}, {"series": "Government", "year": 2002, "month": 5, "count": 484, "rate": 2.3, "date": "2002-05-01T07:00:00.000Z"}, {"series": "Government", "year": 2002, "month": 6, "count": 561, "rate": 2.8, "date": "2002-06-01T07:00:00.000Z"}, {"series": "Government", "year": 2002, "month": 7, "count": 645, "rate": 3.2, "date": "2002-07-01T07:00:00.000Z"}, {"series": "Government", "year": 2002, "month": 8, "count": 596, "rate": 3, "date": "2002-08-01T07:00:00.000Z"}, {"series": "Government", "year": 2002, "month": 9, "count": 530, "rate": 2.6, "date": "2002-09-01T07:00:00.000Z"}, {"series": "Government", "year": 2002, "month": 10, "count": 499, "rate": 2.5, "date": "2002-10-01T07:00:00.000Z"}, {"series": "Government", "year": 2002, "month": 11, "count": 468, "rate": 2.3, "date": "2002-11-01T08:00:00.000Z"}, {"series": "Government", "year": 2002, "month": 12, "count": 446, "rate": 2.2, "date": "2002-12-01T08:00:00.000Z"}, {"series": "Government", "year": 2003, "month": 1, "count": 571, "rate": 2.8, "date": "2003-01-01T08:00:00.000Z"}, {"series": "Government", "year": 2003, "month": 2, "count": 483, "rate": 2.4, "date": "2003-02-01T08:00:00.000Z"}, {"series": "Government", "year": 2003, "month": 3, "count": 526, "rate": 2.6, "date": "2003-03-01T08:00:00.000Z"}, {"series": "Government", "year": 2003, "month": 4, "count": 440, "rate": 2.2, "date": "2003-04-01T08:00:00.000Z"}, {"series": "Government", "year": 2003, "month": 5, "count": 478, "rate": 2.4, "date": "2003-05-01T07:00:00.000Z"}, {"series": "Government", "year": 2003, "month": 6, "count": 704, "rate": 3.5, "date": "2003-06-01T07:00:00.000Z"}, {"series": "Government", "year": 2003, "month": 7, "count": 749, "rate": 3.8, "date": "2003-07-01T07:00:00.000Z"}, {"series": "Government", "year": 2003, "month": 8, "count": 745, "rate": 3.7, "date": "2003-08-01T07:00:00.000Z"}, {"series": "Government", "year": 2003, "month": 9, "count": 556, "rate": 2.7, "date": "2003-09-01T07:00:00.000Z"}, {"series": "Government", "year": 2003, "month": 10, "count": 500, "rate": 2.4, "date": "2003-10-01T07:00:00.000Z"}, {"series": "Government", "year": 2003, "month": 11, "count": 542, "rate": 2.7, "date": "2003-11-01T08:00:00.000Z"}, {"series": "Government", "year": 2003, "month": 12, "count": 516, "rate": 2.5, "date": 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"2000-03-01", "series": "Education and Health", "rate": 2.5}, {"date": "2000-04-01", "series": "Education and Health", "rate": 2.1}, {"date": "2000-05-01", "series": "Education and Health", "rate": 2.7}, {"date": "2000-06-01", "series": "Education and Health", "rate": 2.9}, {"date": "2000-07-01", "series": "Education and Health", "rate": 3.1}, {"date": "2000-08-01", "series": "Education and Health", "rate": 2.9}, {"date": "2000-09-01", "series": "Education and Health", "rate": 2.6}, {"date": "2000-10-01", "series": "Education and Health", "rate": 2.1}, {"date": "2000-11-01", "series": "Education and Health", "rate": 2.2}, {"date": "2000-12-01", "series": "Education and Health", "rate": 1.8}, {"date": "2001-01-01", "series": "Education and Health", "rate": 2.6}, {"date": "2001-02-01", "series": "Education and Health", "rate": 2.6}, {"date": "2001-03-01", "series": "Education and Health", "rate": 2.8}, {"date": "2001-04-01", "series": "Education and Health", "rate": 2.1}, {"date": "2001-05-01", "series": "Education and Health", "rate": 2.4}, {"date": "2001-06-01", "series": "Education and Health", "rate": 3}, {"date": "2001-07-01", "series": "Education and Health", "rate": 3.1}, {"date": "2001-08-01", "series": "Education and Health", "rate": 3.7}, {"date": "2001-09-01", "series": "Education and Health", "rate": 2.8}, {"date": "2001-10-01", "series": "Education and Health", "rate": 2.9}, {"date": "2001-11-01", "series": "Education and Health", "rate": 3.1}, {"date": "2001-12-01", "series": "Education and Health", "rate": 2.9}, {"date": "2002-01-01", "series": "Education and Health", "rate": 3.5}, {"date": "2002-02-01", "series": "Education and Health", "rate": 3.5}, {"date": "2002-03-01", "series": "Education and Health", "rate": 3.2}, {"date": "2002-04-01", "series": "Education and Health", "rate": 2.9}, {"date": "2002-05-01", "series": "Education and Health", "rate": 3.2}, {"date": "2002-06-01", "series": "Education and Health", "rate": 3.9}, {"date": "2002-07-01", "series": "Education and Health", "rate": 4}, {"date": "2002-08-01", "series": "Education and Health", "rate": 3.9}, {"date": "2002-09-01", "series": "Education and Health", "rate": 3.2}, {"date": "2002-10-01", "series": "Education and Health", "rate": 3}, {"date": "2002-11-01", "series": "Education and Health", "rate": 2.8}, {"date": "2002-12-01", "series": "Education and Health", "rate": 3.2}, {"date": "2003-01-01", "series": "Education and Health", "rate": 3.2}, {"date": "2003-02-01", "series": "Education and Health", "rate": 3.2}, {"date": "2003-03-01", "series": "Education and Health", "rate": 2.9}, {"date": "2003-04-01", "series": "Education and Health", "rate": 3.4}, {"date": "2003-05-01", "series": "Education and Health", "rate": 3.5}, {"date": "2003-06-01", "series": "Education and Health", "rate": 4.4}, {"date": "2003-07-01", "series": "Education and Health", "rate": 4}, {"date": "2003-08-01", "series": "Education and Health", "rate": 4.3}, {"date": "2003-09-01", "series": "Education and Health", "rate": 3.7}, {"date": "2003-10-01", "series": "Education and Health", "rate": 3.6}, {"date": "2003-11-01", "series": "Education and Health", "rate": 3.8}, {"date": "2003-12-01", "series": "Education and Health", "rate": 3.5}, {"date": "2004-01-01", "series": "Education and Health", "rate": 3.7}, {"date": "2004-02-01", "series": "Education and Health", "rate": 3.4}, {"date": "2004-03-01", "series": "Education and Health", "rate": 3.2}, {"date": "2004-04-01", "series": "Education and Health", "rate": 3.3}, {"date": "2004-05-01", "series": "Education and Health", "rate": 3.2}, {"date": "2004-06-01", "series": "Education and Health", "rate": 4.2}, {"date": "2004-07-01", "series": "Education and Health", "rate": 4}, {"date": "2004-08-01", "series": "Education and Health", "rate": 3.7}, {"date": "2004-09-01", "series": "Education and Health", "rate": 3.3}, {"date": "2004-10-01", "series": "Education and Health", "rate": 2.9}, {"date": "2004-11-01", "series": "Education and Health", "rate": 3.2}, {"date": "2004-12-01", "series": "Education and Health", "rate": 3.1}, {"date": "2005-01-01", "series": "Education and Health", "rate": 3.4}, {"date": "2005-02-01", "series": "Education and Health", "rate": 3.4}, {"date": "2005-03-01", "series": "Education and Health", "rate": 3.4}, {"date": "2005-04-01", "series": "Education and Health", "rate": 3.3}, {"date": "2005-05-01", "series": "Education and Health", "rate": 3.6}, {"date": "2005-06-01", "series": "Education and Health", "rate": 3.6}, {"date": "2005-07-01", "series": "Education and Health", "rate": 3.5}, {"date": "2005-08-01", "series": "Education and Health", "rate": 3.5}, {"date": "2005-09-01", "series": "Education and Health", "rate": 3.5}, {"date": "2005-10-01", "series": "Education and Health", "rate": 3.4}, {"date": "2005-11-01", "series": "Education and Health", "rate": 3.6}, {"date": "2005-12-01", "series": "Education and Health", "rate": 2.8}, {"date": "2006-01-01", "series": "Education and Health", "rate": 3.2}, {"date": "2006-02-01", "series": "Education and Health", "rate": 2.8}, {"date": "2006-03-01", "series": "Education and Health", "rate": 3}, {"date": "2006-04-01", "series": "Education and Health", "rate": 3}, {"date": "2006-05-01", "series": "Education and Health", "rate": 2.9}, {"date": "2006-06-01", "series": "Education and Health", "rate": 3.3}, {"date": "2006-07-01", "series": "Education and Health", "rate": 3.5}, {"date": "2006-08-01", "series": "Education and Health", "rate": 3.2}, {"date": "2006-09-01", "series": "Education and Health", "rate": 3}, {"date": "2006-10-01", "series": "Education and Health", "rate": 2.8}, {"date": "2006-11-01", "series": "Education and Health", "rate": 2.8}, {"date": "2006-12-01", "series": "Education and Health", "rate": 2.6}, {"date": "2007-01-01", "series": "Education and Health", "rate": 2.9}, {"date": "2007-02-01", "series": "Education and Health", "rate": 2.5}, {"date": "2007-03-01", "series": "Education and Health", "rate": 2.5}, {"date": "2007-04-01", "series": "Education and Health", "rate": 2.9}, {"date": "2007-05-01", "series": "Education and Health", "rate": 3.3}, {"date": "2007-06-01", "series": "Education and Health", "rate": 3.4}, {"date": "2007-07-01", "series": "Education and Health", "rate": 3.5}, {"date": "2007-08-01", "series": "Education and Health", "rate": 3.4}, {"date": "2007-09-01", "series": "Education and Health", "rate": 3.2}, {"date": "2007-10-01", "series": "Education and Health", "rate": 2.7}, {"date": "2007-11-01", "series": "Education and Health", "rate": 2.7}, {"date": "2007-12-01", "series": "Education and Health", "rate": 2.6}, {"date": "2008-01-01", "series": "Education and Health", "rate": 2.9}, {"date": "2008-02-01", "series": "Education and Health", "rate": 2.9}, {"date": "2008-03-01", "series": "Education and Health", "rate": 3.1}, {"date": "2008-04-01", "series": "Education and Health", "rate": 2.8}, {"date": "2008-05-01", "series": "Education and Health", "rate": 3.2}, {"date": "2008-06-01", "series": "Education and Health", "rate": 3.4}, {"date": "2008-07-01", "series": "Education and Health", "rate": 3.9}, {"date": "2008-08-01", "series": "Education and Health", "rate": 4.3}, {"date": "2008-09-01", "series": "Education and Health", "rate": 4.1}, {"date": "2008-10-01", "series": "Education and Health", "rate": 3.9}, {"date": "2008-11-01", "series": "Education and Health", "rate": 3.6}, {"date": "2008-12-01", "series": "Education and Health", "rate": 3.8}, {"date": "2009-01-01", "series": "Education and Health", "rate": 3.8}, {"date": "2009-02-01", "series": "Education and Health", "rate": 4.1}, {"date": "2009-03-01", "series": "Education and Health", "rate": 4.5}, {"date": "2009-04-01", "series": "Education and Health", "rate": 4.6}, {"date": "2009-05-01", "series": "Education and Health", "rate": 4.9}, {"date": "2009-06-01", "series": "Education and Health", "rate": 6.1}, {"date": 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{"date": "2000-07-01", "series": "Leisure and hospitality", "rate": 6.8}, {"date": "2000-08-01", "series": "Leisure and hospitality", "rate": 6}, {"date": "2000-09-01", "series": "Leisure and hospitality", "rate": 5.9}, {"date": "2000-10-01", "series": "Leisure and hospitality", "rate": 6.5}, {"date": "2000-11-01", "series": "Leisure and hospitality", "rate": 6.5}, {"date": "2000-12-01", "series": "Leisure and hospitality", "rate": 5.9}, {"date": "2001-01-01", "series": "Leisure and hospitality", "rate": 7.7}, {"date": "2001-02-01", "series": "Leisure and hospitality", "rate": 7.5}, {"date": "2001-03-01", "series": "Leisure and hospitality", "rate": 7.4}, {"date": "2001-04-01", "series": "Leisure and hospitality", "rate": 6.8}, {"date": "2001-05-01", "series": "Leisure and hospitality", "rate": 6.7}, {"date": "2001-06-01", "series": "Leisure and hospitality", "rate": 7}, {"date": "2001-07-01", "series": "Leisure and hospitality", "rate": 6.8}, {"date": "2001-08-01", "series": "Leisure 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"2002-10-01", "series": "Leisure and hospitality", "rate": 8.5}, {"date": "2002-11-01", "series": "Leisure and hospitality", "rate": 8.9}, {"date": "2002-12-01", "series": "Leisure and hospitality", "rate": 8.2}, {"date": "2003-01-01", "series": "Leisure and hospitality", "rate": 9.3}, {"date": "2003-02-01", "series": "Leisure and hospitality", "rate": 10}, {"date": "2003-03-01", "series": "Leisure and hospitality", "rate": 8.9}, {"date": "2003-04-01", "series": "Leisure and hospitality", "rate": 8.5}, {"date": "2003-05-01", "series": "Leisure and hospitality", "rate": 7.9}, {"date": "2003-06-01", "series": "Leisure and hospitality", "rate": 8.6}, {"date": "2003-07-01", "series": "Leisure and hospitality", "rate": 8.4}, {"date": "2003-08-01", "series": "Leisure and hospitality", "rate": 9}, {"date": "2003-09-01", "series": "Leisure and hospitality", "rate": 8.8}, {"date": "2003-10-01", "series": "Leisure and hospitality", "rate": 8.3}, {"date": "2003-11-01", "series": "Leisure and 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"2005-01-01", "series": "Leisure and hospitality", "rate": 8.7}, {"date": "2005-02-01", "series": "Leisure and hospitality", "rate": 8.8}, {"date": "2005-03-01", "series": "Leisure and hospitality", "rate": 8.3}, {"date": "2005-04-01", "series": "Leisure and hospitality", "rate": 7.7}, {"date": "2005-05-01", "series": "Leisure and hospitality", "rate": 7.7}, {"date": "2005-06-01", "series": "Leisure and hospitality", "rate": 7.6}, {"date": "2005-07-01", "series": "Leisure and hospitality", "rate": 7.4}, {"date": "2005-08-01", "series": "Leisure and hospitality", "rate": 6.8}, {"date": "2005-09-01", "series": "Leisure and hospitality", "rate": 7.3}, {"date": "2005-10-01", "series": "Leisure and hospitality", "rate": 6.8}, {"date": "2005-11-01", "series": "Leisure and hospitality", "rate": 8.1}, {"date": "2005-12-01", "series": "Leisure and hospitality", "rate": 7.9}, {"date": "2006-01-01", "series": "Leisure and hospitality", "rate": 8.1}, {"date": "2006-02-01", "series": "Leisure and 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"2007-04-01", "series": "Leisure and hospitality", "rate": 6.9}, {"date": "2007-05-01", "series": "Leisure and hospitality", "rate": 6.8}, {"date": "2007-06-01", "series": "Leisure and hospitality", "rate": 7.2}, {"date": "2007-07-01", "series": "Leisure and hospitality", "rate": 7.3}, {"date": "2007-08-01", "series": "Leisure and hospitality", "rate": 7.1}, {"date": "2007-09-01", "series": "Leisure and hospitality", "rate": 7.4}, {"date": "2007-10-01", "series": "Leisure and hospitality", "rate": 7.5}, {"date": "2007-11-01", "series": "Leisure and hospitality", "rate": 8.1}, {"date": "2007-12-01", "series": "Leisure and hospitality", "rate": 7.9}, {"date": "2008-01-01", "series": "Leisure and hospitality", "rate": 9.4}, {"date": "2008-02-01", "series": "Leisure and hospitality", "rate": 8.5}, {"date": "2008-03-01", "series": "Leisure and hospitality", "rate": 7.6}, {"date": "2008-04-01", "series": "Leisure and hospitality", "rate": 6.9}, {"date": "2008-05-01", "series": "Leisure and 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"series": "Self-employed", "rate": 2.8}, {"date": "2003-12-01", "series": "Self-employed", "rate": 2.8}, {"date": "2004-01-01", "series": "Self-employed", "rate": 2.8}, {"date": "2004-02-01", "series": "Self-employed", "rate": 2.5}, {"date": "2004-03-01", "series": "Self-employed", "rate": 2.5}, {"date": "2004-04-01", "series": "Self-employed", "rate": 2.3}, {"date": "2004-05-01", "series": "Self-employed", "rate": 2.7}, {"date": "2004-06-01", "series": "Self-employed", "rate": 2.8}, {"date": "2004-07-01", "series": "Self-employed", "rate": 2.6}, {"date": "2004-08-01", "series": "Self-employed", "rate": 2.9}, {"date": "2004-09-01", "series": "Self-employed", "rate": 3.3}, {"date": "2004-10-01", "series": "Self-employed", "rate": 2.7}, {"date": "2004-11-01", "series": "Self-employed", "rate": 3.2}, {"date": "2004-12-01", "series": "Self-employed", "rate": 3.2}, {"date": "2005-01-01", "series": "Self-employed", "rate": 3.2}, {"date": "2005-02-01", "series": "Self-employed", "rate": 3.4}, {"date": "2005-03-01", "series": "Self-employed", "rate": 2.9}, {"date": "2005-04-01", "series": "Self-employed", "rate": 2.4}, {"date": "2005-05-01", "series": "Self-employed", "rate": 2.7}, {"date": "2005-06-01", "series": "Self-employed", "rate": 2.4}, {"date": "2005-07-01", "series": "Self-employed", "rate": 2.5}, {"date": "2005-08-01", "series": "Self-employed", "rate": 2.3}, {"date": "2005-09-01", "series": "Self-employed", "rate": 2.6}, {"date": "2005-10-01", "series": "Self-employed", "rate": 2.3}, {"date": "2005-11-01", "series": "Self-employed", "rate": 3}, {"date": "2005-12-01", "series": "Self-employed", "rate": 3.1}, {"date": "2006-01-01", "series": "Self-employed", "rate": 3.2}, {"date": "2006-02-01", "series": "Self-employed", "rate": 3.1}, {"date": "2006-03-01", "series": "Self-employed", "rate": 2.8}, {"date": "2006-04-01", "series": "Self-employed", "rate": 3.1}, {"date": "2006-05-01", "series": "Self-employed", "rate": 2.3}, {"date": "2006-06-01", "series": "Self-employed", "rate": 2.2}, {"date": "2006-07-01", "series": "Self-employed", "rate": 2.6}, {"date": "2006-08-01", "series": "Self-employed", "rate": 2.7}, {"date": "2006-09-01", "series": "Self-employed", "rate": 2.7}, {"date": "2006-10-01", "series": "Self-employed", "rate": 2.5}, {"date": "2006-11-01", "series": "Self-employed", "rate": 2.3}, {"date": "2006-12-01", "series": "Self-employed", "rate": 2.6}, {"date": "2007-01-01", "series": "Self-employed", "rate": 3.5}, {"date": "2007-02-01", "series": "Self-employed", "rate": 2.8}, {"date": "2007-03-01", "series": "Self-employed", "rate": 2.8}, {"date": "2007-04-01", "series": "Self-employed", "rate": 2.2}, {"date": "2007-05-01", "series": "Self-employed", "rate": 2.5}, {"date": "2007-06-01", "series": "Self-employed", "rate": 2.3}, {"date": "2007-07-01", "series": "Self-employed", "rate": 2.9}, {"date": "2007-08-01", "series": "Self-employed", "rate": 2.9}, {"date": "2007-09-01", "series": "Self-employed", "rate": 2.8}, {"date": "2007-10-01", "series": "Self-employed", "rate": 3.1}, {"date": "2007-11-01", "series": "Self-employed", "rate": 3.2}, {"date": "2007-12-01", "series": "Self-employed", "rate": 3.2}, {"date": "2008-01-01", "series": "Self-employed", "rate": 3.3}, {"date": "2008-02-01", "series": "Self-employed", "rate": 3.2}, {"date": "2008-03-01", "series": "Self-employed", "rate": 3.3}, {"date": "2008-04-01", "series": "Self-employed", "rate": 3.2}, {"date": "2008-05-01", "series": "Self-employed", "rate": 3.4}, {"date": "2008-06-01", "series": "Self-employed", "rate": 3.3}, {"date": "2008-07-01", "series": "Self-employed", "rate": 3.1}, {"date": "2008-08-01", "series": "Self-employed", "rate": 3.5}, {"date": "2008-09-01", "series": "Self-employed", "rate": 3.9}, {"date": "2008-10-01", "series": "Self-employed", "rate": 3.9}, {"date": "2008-11-01", "series": "Self-employed", "rate": 4.1}, {"date": "2008-12-01", "series": "Self-employed", "rate": 5.5}, {"date": "2009-01-01", "series": "Self-employed", "rate": 6.5}, {"date": "2009-02-01", "series": "Self-employed", "rate": 5.7}, {"date": "2009-03-01", "series": "Self-employed", "rate": 5.9}, {"date": "2009-04-01", "series": "Self-employed", "rate": 4.6}, {"date": "2009-05-01", "series": "Self-employed", "rate": 5}, {"date": "2009-06-01", "series": "Self-employed", "rate": 4.4}, {"date": "2009-07-01", "series": "Self-employed", "rate": 5.2}, {"date": "2009-08-01", "series": "Self-employed", "rate": 5.3}, {"date": "2009-09-01", "series": "Self-employed", "rate": 5.9}, {"date": "2009-10-01", "series": "Self-employed", "rate": 5.9}, {"date": "2009-11-01", "series": "Self-employed", "rate": 5.7}, {"date": "2009-12-01", "series": "Self-employed", "rate": 5.9}, {"date": "2010-01-01", "series": "Self-employed", "rate": 7.2}, {"date": "2010-02-01", "series": "Self-employed", "rate": 6.5}], "metadata": {"date": {"type": "date", "semanticType": "YearMonth"}, "series": {"type": "string", "semanticType": "String"}, "rate": {"type": "number", "semanticType": "Percentage"}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime and then to string format for visualization\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date']).dt.strftime('%Y-%m-%d')\n \n # Select only the necessary columns for visualization\n transformed_df = df_unemployment[['date', 'series', 'rate']].copy()\n \n return transformed_df\n", "source": ["unemployment-across-industries"], "dialog": [{"role": "system", "content": "You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"}, {"role": "user", "content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[GOAL]\n\nCompare **unemployment trends** across all industries from 2000-2010"}, {"role": "assistant", "content": "```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to compare unemployment trends across all industries from 2000-2010 to understand how different sectors were affected over time.\",\n \"display_instruction\": \"Compare **unemployment rates** across **industries** over time\",\n \"recommendation\": \"A line chart with time on x-axis and unemployment rate on y-axis, with different colored lines for each industry series, will effectively show the trends and allow comparison of how unemployment evolved across different sectors during this period.\",\n \"output_fields\": [\"date\", \"series\", \"rate\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"rate\",\n \"color\": \"series\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime and then to string format for visualization\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date']).dt.strftime('%Y-%m-%d')\n \n # Select only the necessary columns for visualization\n transformed_df = df_unemployment[['date', 'series', 'rate']].copy()\n \n return transformed_df\n```"}], "trigger": {"tableId": "unemployment-across-industries", "resultTableId": "table-544555", "chart": {"id": "chart-1760745546913", "chartType": "Auto", "encodingMap": {}, "tableRef": "unemployment-across-industries", "saved": false, "source": "trigger", "unread": true}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "Compare **unemployment trends** across all industries from 2000-2010", "displayContent": "Compare **unemployment rates** across **industries** over time"}]}, "explanation": {"agent": "CodeExplanationAgent", "code": "- Convert the **date** field from timestamp format to a standardized string format (`YYYY-MM-DD`) for cleaner visualization\n- Select and retain only three key fields: **date**, **series** (industry name), and **rate** (unemployment rate percentage)\n- Return a simplified dataset focused on tracking unemployment rates across different industries over time", "concepts": [], "dialog": [{"content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime and then to string format for visualization\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date']).dt.strftime('%Y-%m-%d')\n \n # Select only the necessary columns for visualization\n transformed_df = df_unemployment[['date', 'series', 'rate']].copy()\n \n return transformed_df\n\n\n[EXPLANATION]\n", "role": "user"}, {"content": "[CODE EXPLANATION]\n\n- Convert the **date** field from timestamp format to a standardized string format (`YYYY-MM-DD`) for cleaner visualization\n- Select and retain only three key fields: **date**, **series** (industry name), and **rate** (unemployment rate percentage)\n- Return a simplified dataset focused on tracking unemployment rates across different industries over time\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```", "role": "assistant"}], "status": "ok"}, "status": "completed"}, "anchored": false, "attachedMetadata": ""}, {"kind": "table", "id": "table-540763", "displayId": "unemployment-rate", "names": ["avg_rate", "month", "month_name", "series"], "rows": [{"avg_rate": 13.7636363636, "month": 1, "month_name": "Jan", "series": "Agriculture"}, {"avg_rate": 13.6454545455, "month": 2, "month_name": "Feb", "series": "Agriculture"}, {"avg_rate": 13.83, "month": 3, "month_name": "Mar", "series": "Agriculture"}, {"avg_rate": 9.13, "month": 4, "month_name": "Apr", "series": "Agriculture"}, {"avg_rate": 7.1, "month": 5, "month_name": "May", "series": "Agriculture"}, {"avg_rate": 6.77, "month": 6, "month_name": "Jun", "series": "Agriculture"}, {"avg_rate": 7.01, "month": 7, "month_name": "Jul", "series": "Agriculture"}, {"avg_rate": 8.08, "month": 8, "month_name": "Aug", "series": "Agriculture"}, {"avg_rate": 7.09, "month": 9, "month_name": "Sep", "series": "Agriculture"}, {"avg_rate": 7.49, "month": 10, "month_name": "Oct", "series": "Agriculture"}, {"avg_rate": 10.47, "month": 11, "month_name": "Nov", "series": "Agriculture"}, {"avg_rate": 12.94, "month": 12, "month_name": "Dec", "series": "Agriculture"}, {"avg_rate": 7.8636363636, "month": 1, "month_name": "Jan", "series": "Business services"}, {"avg_rate": 7.6454545455, "month": 2, "month_name": "Feb", "series": "Business services"}, {"avg_rate": 7.13, "month": 3, "month_name": "Mar", "series": "Business services"}, {"avg_rate": 6.27, "month": 4, "month_name": "Apr", "series": "Business services"}, {"avg_rate": 6.6, "month": 5, "month_name": "May", "series": "Business services"}, {"avg_rate": 6.72, "month": 6, "month_name": "Jun", "series": "Business services"}, {"avg_rate": 6.74, "month": 7, "month_name": "Jul", "series": "Business services"}, {"avg_rate": 6.57, "month": 8, "month_name": "Aug", "series": "Business services"}, {"avg_rate": 6.79, "month": 9, "month_name": "Sep", "series": "Business services"}, {"avg_rate": 6.71, "month": 10, "month_name": "Oct", "series": "Business services"}, {"avg_rate": 6.75, "month": 11, "month_name": "Nov", "series": "Business services"}, {"avg_rate": 7.08, "month": 12, "month_name": "Dec", "series": "Business services"}, {"avg_rate": 12.9090909091, "month": 1, "month_name": "Jan", "series": "Construction"}, {"avg_rate": 13.6, "month": 2, "month_name": "Feb", "series": "Construction"}, {"avg_rate": 11.29, "month": 3, "month_name": "Mar", "series": "Construction"}, {"avg_rate": 9.45, "month": 4, "month_name": "Apr", "series": "Construction"}, {"avg_rate": 8.12, "month": 5, "month_name": "May", "series": "Construction"}, {"avg_rate": 7.43, "month": 6, "month_name": "Jun", "series": "Construction"}, {"avg_rate": 7.35, "month": 7, "month_name": "Jul", "series": "Construction"}, {"avg_rate": 7.3, "month": 8, "month_name": "Aug", "series": "Construction"}, {"avg_rate": 7.56, "month": 9, "month_name": "Sep", "series": "Construction"}, {"avg_rate": 7.84, "month": 10, "month_name": "Oct", "series": "Construction"}, {"avg_rate": 8.7, "month": 11, "month_name": "Nov", "series": "Construction"}, {"avg_rate": 10.8, "month": 12, "month_name": "Dec", "series": "Construction"}, {"avg_rate": 3.3636363636, "month": 1, "month_name": "Jan", "series": "Education and Health"}, {"avg_rate": 3.2909090909, "month": 2, "month_name": "Feb", "series": "Education and Health"}, {"avg_rate": 3.11, "month": 3, "month_name": "Mar", "series": "Education and Health"}, {"avg_rate": 3.04, "month": 4, "month_name": "Apr", "series": "Education and Health"}, {"avg_rate": 3.29, "month": 5, "month_name": "May", "series": "Education and Health"}, {"avg_rate": 3.82, "month": 6, "month_name": "Jun", "series": "Education and Health"}, {"avg_rate": 3.87, "month": 7, "month_name": "Jul", "series": "Education and Health"}, {"avg_rate": 3.89, "month": 8, "month_name": "Aug", "series": "Education and Health"}, {"avg_rate": 3.54, "month": 9, "month_name": "Sep", "series": "Education and Health"}, {"avg_rate": 3.33, "month": 10, "month_name": "Oct", "series": "Education and Health"}, {"avg_rate": 3.33, "month": 11, "month_name": "Nov", "series": "Education and Health"}, {"avg_rate": 3.19, "month": 12, "month_name": "Dec", "series": "Education and Health"}, {"avg_rate": 3.5727272727, "month": 1, "month_name": "Jan", "series": "Finance"}, {"avg_rate": 3.8909090909, "month": 2, "month_name": "Feb", "series": "Finance"}, {"avg_rate": 3.45, "month": 3, "month_name": "Mar", "series": "Finance"}, {"avg_rate": 3.28, "month": 4, "month_name": "Apr", "series": "Finance"}, {"avg_rate": 3.35, "month": 5, "month_name": "May", "series": "Finance"}, {"avg_rate": 3.54, "month": 6, "month_name": "Jun", "series": "Finance"}, {"avg_rate": 3.52, "month": 7, "month_name": "Jul", "series": "Finance"}, {"avg_rate": 3.61, "month": 8, "month_name": "Aug", "series": "Finance"}, {"avg_rate": 3.54, "month": 9, "month_name": "Sep", "series": "Finance"}, {"avg_rate": 3.6, "month": 10, "month_name": "Oct", "series": "Finance"}, {"avg_rate": 3.55, "month": 11, "month_name": "Nov", "series": "Finance"}, {"avg_rate": 3.54, "month": 12, "month_name": "Dec", "series": "Finance"}, {"avg_rate": 2.6, "month": 1, "month_name": "Jan", "series": "Government"}, {"avg_rate": 2.3272727273, "month": 2, "month_name": "Feb", "series": "Government"}, {"avg_rate": 2.19, "month": 3, "month_name": "Mar", "series": "Government"}, {"avg_rate": 2.02, "month": 4, "month_name": "Apr", "series": "Government"}, {"avg_rate": 2.2, "month": 5, "month_name": "May", "series": "Government"}, {"avg_rate": 3.1, "month": 6, "month_name": "Jun", "series": "Government"}, {"avg_rate": 3.49, "month": 7, "month_name": "Jul", "series": "Government"}, {"avg_rate": 3.36, "month": 8, "month_name": "Aug", "series": "Government"}, {"avg_rate": 2.61, "month": 9, "month_name": "Sep", "series": "Government"}, {"avg_rate": 2.45, "month": 10, "month_name": "Oct", "series": "Government"}, {"avg_rate": 2.37, "month": 11, "month_name": "Nov", "series": "Government"}, {"avg_rate": 2.28, "month": 12, "month_name": "Dec", "series": "Government"}, {"avg_rate": 5.7727272727, "month": 1, "month_name": "Jan", "series": "Information"}, {"avg_rate": 5.9, "month": 2, "month_name": "Feb", "series": "Information"}, {"avg_rate": 5.36, "month": 3, "month_name": "Mar", "series": "Information"}, {"avg_rate": 5.23, "month": 4, "month_name": "Apr", "series": "Information"}, {"avg_rate": 5.48, "month": 5, "month_name": "May", "series": "Information"}, {"avg_rate": 5.26, "month": 6, "month_name": "Jun", "series": "Information"}, {"avg_rate": 5.32, "month": 7, "month_name": "Jul", "series": "Information"}, {"avg_rate": 5.55, "month": 8, "month_name": "Aug", "series": "Information"}, {"avg_rate": 5.73, "month": 9, "month_name": "Sep", "series": "Information"}, {"avg_rate": 5.05, "month": 10, "month_name": "Oct", "series": "Information"}, {"avg_rate": 5.47, "month": 11, "month_name": "Nov", "series": "Information"}, {"avg_rate": 5.65, "month": 12, "month_name": "Dec", "series": "Information"}, {"avg_rate": 9.3454545455, "month": 1, "month_name": "Jan", "series": "Leisure and hospitality"}, {"avg_rate": 9.1363636364, "month": 2, "month_name": "Feb", "series": "Leisure and hospitality"}, {"avg_rate": 8.37, "month": 3, "month_name": "Mar", "series": "Leisure and hospitality"}, {"avg_rate": 7.7, "month": 4, "month_name": "Apr", "series": "Leisure and hospitality"}, {"avg_rate": 7.93, "month": 5, "month_name": "May", "series": "Leisure and hospitality"}, {"avg_rate": 8.42, "month": 6, "month_name": "Jun", "series": "Leisure and hospitality"}, {"avg_rate": 7.95, "month": 7, "month_name": "Jul", "series": "Leisure and hospitality"}, {"avg_rate": 7.92, "month": 8, "month_name": "Aug", "series": "Leisure and hospitality"}, {"avg_rate": 7.93, "month": 9, "month_name": "Sep", "series": "Leisure and hospitality"}, {"avg_rate": 8.11, "month": 10, "month_name": "Oct", "series": "Leisure and hospitality"}, {"avg_rate": 8.59, "month": 11, "month_name": "Nov", "series": "Leisure and hospitality"}, {"avg_rate": 8.2, "month": 12, "month_name": "Dec", "series": "Leisure and hospitality"}, {"avg_rate": 6.6090909091, "month": 1, "month_name": "Jan", "series": "Manufacturing"}, {"avg_rate": 6.5, "month": 2, "month_name": "Feb", "series": "Manufacturing"}, {"avg_rate": 6, "month": 3, "month_name": "Mar", "series": "Manufacturing"}, {"avg_rate": 5.89, "month": 4, "month_name": "Apr", "series": "Manufacturing"}, {"avg_rate": 5.72, "month": 5, "month_name": "May", "series": "Manufacturing"}, {"avg_rate": 5.73, "month": 6, "month_name": "Jun", "series": "Manufacturing"}, {"avg_rate": 6, "month": 7, "month_name": "Jul", "series": "Manufacturing"}, {"avg_rate": 5.66, "month": 8, "month_name": "Aug", "series": "Manufacturing"}, {"avg_rate": 5.72, "month": 9, "month_name": "Sep", "series": "Manufacturing"}, {"avg_rate": 5.78, "month": 10, "month_name": "Oct", "series": "Manufacturing"}, {"avg_rate": 6.02, "month": 11, "month_name": "Nov", "series": "Manufacturing"}, {"avg_rate": 6.05, "month": 12, "month_name": "Dec", "series": "Manufacturing"}, {"avg_rate": 5.6, "month": 1, "month_name": "Jan", "series": "Mining and Extraction"}, {"avg_rate": 5.7454545455, "month": 2, "month_name": "Feb", "series": "Mining and Extraction"}, {"avg_rate": 5.14, "month": 3, "month_name": "Mar", "series": "Mining and Extraction"}, {"avg_rate": 5.64, "month": 4, "month_name": "Apr", "series": "Mining and Extraction"}, {"avg_rate": 5.28, "month": 5, "month_name": "May", "series": "Mining and Extraction"}, {"avg_rate": 5.57, "month": 6, "month_name": "Jun", "series": "Mining and Extraction"}, {"avg_rate": 4.95, "month": 7, "month_name": "Jul", "series": "Mining and Extraction"}, {"avg_rate": 4.5, "month": 8, "month_name": "Aug", "series": "Mining and Extraction"}, {"avg_rate": 4.48, "month": 9, "month_name": "Sep", "series": "Mining and Extraction"}, {"avg_rate": 4.41, "month": 10, "month_name": "Oct", "series": "Mining and Extraction"}, {"avg_rate": 4.4, "month": 11, "month_name": "Nov", "series": "Mining and Extraction"}, {"avg_rate": 5.23, "month": 12, "month_name": "Dec", "series": "Mining and Extraction"}, {"avg_rate": 5.4363636364, "month": 1, "month_name": "Jan", "series": "Other"}, {"avg_rate": 5.6181818182, "month": 2, "month_name": "Feb", "series": "Other"}, {"avg_rate": 4.91, "month": 3, "month_name": "Mar", "series": "Other"}, {"avg_rate": 4.67, "month": 4, "month_name": "Apr", "series": "Other"}, {"avg_rate": 4.81, "month": 5, "month_name": "May", "series": "Other"}, {"avg_rate": 5.16, "month": 6, "month_name": "Jun", "series": "Other"}, {"avg_rate": 5.11, "month": 7, "month_name": "Jul", "series": "Other"}, {"avg_rate": 5.41, "month": 8, "month_name": "Aug", "series": "Other"}, {"avg_rate": 5.02, "month": 9, "month_name": "Sep", "series": "Other"}, {"avg_rate": 4.87, "month": 10, "month_name": "Oct", "series": "Other"}, {"avg_rate": 5.25, "month": 11, "month_name": "Nov", "series": "Other"}, {"avg_rate": 4.81, "month": 12, "month_name": "Dec", "series": "Other"}, {"avg_rate": 3.6, "month": 1, "month_name": "Jan", "series": "Self-employed"}, {"avg_rate": 3.3909090909, "month": 2, "month_name": "Feb", "series": "Self-employed"}, {"avg_rate": 2.88, "month": 3, "month_name": "Mar", "series": "Self-employed"}, {"avg_rate": 2.68, "month": 4, "month_name": "Apr", "series": "Self-employed"}, {"avg_rate": 2.77, "month": 5, "month_name": "May", "series": "Self-employed"}, {"avg_rate": 2.6, "month": 6, "month_name": "Jun", "series": "Self-employed"}, {"avg_rate": 2.77, "month": 7, "month_name": "Jul", "series": "Self-employed"}, {"avg_rate": 2.89, "month": 8, "month_name": "Aug", "series": "Self-employed"}, {"avg_rate": 3.07, "month": 9, "month_name": "Sep", "series": "Self-employed"}, {"avg_rate": 3.06, "month": 10, "month_name": "Oct", "series": "Self-employed"}, {"avg_rate": 3.21, "month": 11, "month_name": "Nov", "series": "Self-employed"}, {"avg_rate": 3.37, "month": 12, "month_name": "Dec", "series": "Self-employed"}, {"avg_rate": 5.7909090909, "month": 1, "month_name": "Jan", "series": "Transportation and Utilities"}, {"avg_rate": 5.6181818182, "month": 2, "month_name": "Feb", "series": "Transportation and Utilities"}, {"avg_rate": 5.1, "month": 3, "month_name": "Mar", "series": "Transportation and Utilities"}, {"avg_rate": 4.79, "month": 4, "month_name": "Apr", "series": "Transportation and Utilities"}, {"avg_rate": 4.5, "month": 5, "month_name": "May", "series": "Transportation and Utilities"}, {"avg_rate": 4.82, "month": 6, "month_name": "Jun", "series": "Transportation and Utilities"}, {"avg_rate": 5.04, "month": 7, "month_name": "Jul", "series": "Transportation and Utilities"}, {"avg_rate": 4.58, "month": 8, "month_name": "Aug", "series": "Transportation and Utilities"}, {"avg_rate": 4.65, "month": 9, "month_name": "Sep", "series": "Transportation and Utilities"}, {"avg_rate": 4.8, "month": 10, "month_name": "Oct", "series": "Transportation and Utilities"}, {"avg_rate": 4.58, "month": 11, "month_name": "Nov", "series": "Transportation and Utilities"}, {"avg_rate": 4.8, "month": 12, "month_name": "Dec", "series": "Transportation and Utilities"}, {"avg_rate": 6.4818181818, "month": 1, "month_name": "Jan", "series": "Wholesale and Retail Trade"}, {"avg_rate": 6.3727272727, "month": 2, "month_name": "Feb", "series": "Wholesale and Retail Trade"}, {"avg_rate": 5.86, "month": 3, "month_name": "Mar", "series": "Wholesale and Retail Trade"}, {"avg_rate": 5.46, "month": 4, "month_name": "Apr", "series": "Wholesale and Retail Trade"}, {"avg_rate": 5.49, "month": 5, "month_name": "May", "series": "Wholesale and Retail Trade"}, {"avg_rate": 5.84, "month": 6, "month_name": "Jun", "series": "Wholesale and Retail Trade"}, {"avg_rate": 5.75, "month": 7, "month_name": "Jul", "series": "Wholesale and Retail Trade"}, {"avg_rate": 5.61, "month": 8, "month_name": "Aug", "series": "Wholesale and Retail Trade"}, {"avg_rate": 5.63, "month": 9, "month_name": "Sep", "series": "Wholesale and Retail Trade"}, {"avg_rate": 5.56, "month": 10, "month_name": "Oct", "series": "Wholesale and Retail Trade"}, {"avg_rate": 5.52, "month": 11, "month_name": "Nov", "series": "Wholesale and Retail Trade"}, {"avg_rate": 5.49, "month": 12, "month_name": "Dec", "series": "Wholesale and Retail Trade"}], "metadata": {"avg_rate": {"type": "number", "semanticType": "Percentage"}, "month": {"type": "number", "semanticType": "Month"}, "month_name": {"type": "string", "semanticType": "Month", "levels": ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]}, "series": {"type": "string", "semanticType": "String"}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df1):\n # Calculate average unemployment rate by month and series (industry)\n transformed_df = df1.groupby(['series', 'month'], as_index=False).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Add month name for better readability\n month_names = {\n 1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr', 5: 'May', 6: 'Jun',\n 7: 'Jul', 8: 'Aug', 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'\n }\n transformed_df['month_name'] = transformed_df['month'].map(month_names)\n \n return transformed_df\n", "source": ["unemployment-across-industries"], "dialog": [{"content": "You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[GOAL]\n\nHow does **seasonal variation** in unemployment differ across **industries**?", "role": "user"}, {"content": "```json\n{\n \"mode\": \"infer\",\n \"recap\": \"Analyze how seasonal variation in unemployment differs across industries by examining the cyclical patterns in unemployment rates throughout the year for different industry sectors.\",\n \"display_instruction\": \"Compare seasonal unemployment patterns across **industries**\",\n \"recommendation\": \"To visualize seasonal variation across industries, we should calculate average unemployment rates by month for each industry series. This will reveal the typical seasonal patterns and allow comparison of how different industries experience seasonal fluctuations in unemployment throughout the year.\",\n \"output_fields\": [\"series\", \"month\", \"avg_rate\", \"month_name\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"month\",\n \"y\": \"avg_rate\",\n \"color\": \"series\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df1):\n # Calculate average unemployment rate by month and series (industry)\n transformed_df = df1.groupby(['series', 'month'], as_index=False).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Add month name for better readability\n month_names = {\n 1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr', 5: 'May', 6: 'Jun',\n 7: 'Jul', 8: 'Aug', 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'\n }\n transformed_df['month_name'] = transformed_df['month'].map(month_names)\n \n return transformed_df\n```", "role": "assistant"}], "trigger": {"tableId": "unemployment-across-industries", "resultTableId": "table-540763", "chart": {"id": "chart-1760745541760", "chartType": "Auto", "encodingMap": {}, "tableRef": "unemployment-across-industries", "saved": false, "source": "trigger", "unread": true}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "How does **seasonal variation** in unemployment differ across **industries**?", "displayContent": "Compare seasonal unemployment patterns across **industries**"}]}, "explanation": {"agent": "CodeExplanationAgent", "code": "- Group the unemployment data by **series** (industry) and **month**\n- Calculate the **mean** of the **rate** field for each industry-month combination, creating a new field **avg_rate**\n- Map numeric **month** values (1-12) to their abbreviated names (**Jan**, **Feb**, **Mar**, etc.) and store in **month_name** field for improved readability\n- Return the aggregated dataset containing average unemployment rates per industry per month across all years in the data", "concepts": [], "dialog": [{"content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df1):\n # Calculate average unemployment rate by month and series (industry)\n transformed_df = df1.groupby(['series', 'month'], as_index=False).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Add month name for better readability\n month_names = {\n 1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr', 5: 'May', 6: 'Jun',\n 7: 'Jul', 8: 'Aug', 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'\n }\n transformed_df['month_name'] = transformed_df['month'].map(month_names)\n \n return transformed_df\n\n\n[EXPLANATION]\n", "role": "user"}, {"content": "[CODE EXPLANATION]\n\n- Group the unemployment data by **series** (industry) and **month**\n- Calculate the **mean** of the **rate** field for each industry-month combination, creating a new field **avg_rate**\n- Map numeric **month** values (1-12) to their abbreviated names (**Jan**, **Feb**, **Mar**, etc.) and store in **month_name** field for improved readability\n- Return the aggregated dataset containing average unemployment rates per industry per month across all years in the data\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```", "role": "assistant"}], "status": "ok"}, "status": "completed"}, "anchored": false, "attachedMetadata": ""}, {"kind": "table", "id": "table-552276", "displayId": "unemp-by-sector", "names": ["series", "rate_2008", "rate_2009", "rate_increase"], "rows": [{"series": "Mining and Extraction", "rate_2008": 3.0833333333, "rate_2009": 11.6583333333, "rate_increase": 8.575}, {"series": "Construction", "rate_2008": 10.6, "rate_2009": 19.05, "rate_increase": 8.45}, {"series": "Manufacturing", "rate_2008": 5.7583333333, "rate_2009": 12.075, "rate_increase": 6.3166666667}, {"series": "Agriculture", "rate_2008": 9.2666666667, "rate_2009": 14.3916666667, "rate_increase": 5.125}, {"series": "Business services", "rate_2008": 6.5583333333, "rate_2009": 10.8, "rate_increase": 4.2416666667}, {"series": "Information", "rate_2008": 5.0166666667, "rate_2009": 9.225, "rate_increase": 4.2083333333}, {"series": "Transportation and Utilities", "rate_2008": 5.1333333333, "rate_2009": 8.8833333333, "rate_increase": 3.75}, {"series": "Wholesale and Retail Trade", "rate_2008": 5.8416666667, "rate_2009": 9.0333333333, "rate_increase": 3.1916666667}, {"series": "Leisure and hospitality", "rate_2008": 8.6416666667, "rate_2009": 11.6833333333, "rate_increase": 3.0416666667}, {"series": "Finance", "rate_2008": 3.95, "rate_2009": 6.4, "rate_increase": 2.45}, {"series": "Other", "rate_2008": 5.2666666667, "rate_2009": 7.5083333333, "rate_increase": 2.2416666667}, {"series": "Self-employed", "rate_2008": 3.6416666667, "rate_2009": 5.5, "rate_increase": 1.8583333333}, {"series": "Education and Health", "rate_2008": 3.4916666667, "rate_2009": 5.2666666667, "rate_increase": 1.775}, {"series": "Government", "rate_2008": 2.4416666667, "rate_2009": 3.6166666667, "rate_increase": 1.175}], "metadata": {"series": {"type": "string", "semanticType": "String"}, "rate_2008": {"type": "number", "semanticType": "Percentage"}, "rate_2009": {"type": "number", "semanticType": "Percentage"}, "rate_increase": {"type": "number", "semanticType": "Percentage"}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date'])\n \n # Filter data for years 2008 and 2009\n df_2008_2009 = df_unemployment[df_unemployment['year'].isin([2008, 2009])].copy()\n \n # Calculate average rate for each industry in 2008 and 2009\n df_2008 = df_2008_2009[df_2008_2009['year'] == 2008].groupby('series')['rate'].mean().reset_index()\n df_2008.columns = ['series', 'rate_2008']\n \n df_2009 = df_2008_2009[df_2008_2009['year'] == 2009].groupby('series')['rate'].mean().reset_index()\n df_2009.columns = ['series', 'rate_2009']\n \n # Merge and calculate the increase\n transformed_df = df_2008.merge(df_2009, on='series')\n transformed_df['rate_increase'] = transformed_df['rate_2009'] - transformed_df['rate_2008']\n \n # Sort by rate increase in descending order\n transformed_df = transformed_df.sort_values('rate_increase', ascending=False).reset_index(drop=True)\n \n return transformed_df\n", "source": ["unemployment-across-industries"], "dialog": [{"role": "system", "content": "You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"}, {"role": "user", "content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[GOAL]\n\nCompare **unemployment trends** across all industries from 2000-2010"}, {"role": "assistant", "content": "```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to compare unemployment trends across all industries from 2000-2010 to understand how different sectors were affected over time.\",\n \"display_instruction\": \"Compare **unemployment rates** across **industries** over time\",\n \"recommendation\": \"A line chart with time on x-axis and unemployment rate on y-axis, with different colored lines for each industry series, will effectively show the trends and allow comparison of how unemployment evolved across different sectors during this period.\",\n \"output_fields\": [\"date\", \"series\", \"rate\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"rate\",\n \"color\": \"series\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime and then to string format for visualization\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date']).dt.strftime('%Y-%m-%d')\n \n # Select only the necessary columns for visualization\n transformed_df = df_unemployment[['date', 'series', 'rate']].copy()\n \n return transformed_df\n```"}, {"role": "user", "content": "This is the result from the latest python code:\n\n date series rate\n0 2000-01-01 Government 2.1\n1 2000-02-01 Government 2.0\n2 2000-03-01 Government 1.5\n3 2000-04-01 Government 1.3\n4 2000-05-01 Government 1.9\n5 2000-06-01 Government 3.1\n6 2000-07-01 Government 2.9\n7 2000-08-01 Government 3.1\n8 2000-09-01 Government 2.1\n9 2000-10-01 Government 2.0\n......\n\nUpdate the code above based on the following instruction:\n\nWhich industries showed the **sharpest increases** in unemployment during **2008-2009**?"}, {"role": "assistant", "content": "```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to identify which industries experienced the sharpest increases in unemployment rates during the 2008-2009 financial crisis period by calculating the change in unemployment rate for each industry.\",\n \"display_instruction\": \"Identify industries with **sharpest unemployment rate increases** during **2008-2009**\",\n \"recommendation\": \"A bar chart showing the change in unemployment rate from 2008 to 2009 for each industry will clearly visualize which sectors were hit hardest during the financial crisis. The industries should be sorted by the magnitude of increase to make comparison easier.\",\n \"output_fields\": [\"series\", \"rate_2008\", \"rate_2009\", \"rate_increase\"],\n \"chart_type\": \"bar\",\n \"chart_encodings\": {\n \"x\": \"series\",\n \"y\": \"rate_increase\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date'])\n \n # Filter data for years 2008 and 2009\n df_2008_2009 = df_unemployment[df_unemployment['year'].isin([2008, 2009])].copy()\n \n # Calculate average rate for each industry in 2008 and 2009\n df_2008 = df_2008_2009[df_2008_2009['year'] == 2008].groupby('series')['rate'].mean().reset_index()\n df_2008.columns = ['series', 'rate_2008']\n \n df_2009 = df_2008_2009[df_2008_2009['year'] == 2009].groupby('series')['rate'].mean().reset_index()\n df_2009.columns = ['series', 'rate_2009']\n \n # Merge and calculate the increase\n transformed_df = df_2008.merge(df_2009, on='series')\n transformed_df['rate_increase'] = transformed_df['rate_2009'] - transformed_df['rate_2008']\n \n # Sort by rate increase in descending order\n transformed_df = transformed_df.sort_values('rate_increase', ascending=False).reset_index(drop=True)\n \n return transformed_df\n```"}], "trigger": {"tableId": "table-544555", "resultTableId": "table-552276", "chart": {"id": "chart-1760745556114", "chartType": "Auto", "encodingMap": {}, "tableRef": "unemployment-across-industries", "saved": false, "source": "trigger", "unread": true}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "Which industries showed the **sharpest increases** in unemployment during **2008-2009**?", "displayContent": "Identify industries with **sharpest unemployment rate increases** during **2008-2009**"}]}, "explanation": {"agent": "CodeExplanationAgent", "code": "- Convert the **date** field to datetime format for proper date handling\n- Filter the dataset to include only records from **2008** and **2009**\n- Calculate the **average unemployment rate** for each **industry (series)** in **2008**\n- Calculate the **average unemployment rate** for each **industry (series)** in **2009**\n- Merge the two yearly averages and compute the **rate increase** from 2008 to 2009\n- Sort industries by **rate_increase** in descending order to identify which industries experienced the largest growth in unemployment", "concepts": [{"explanation": "The difference in average unemployment rate between 2009 and 2008 for each industry, calculated as \\( \\text{rate\\_increase} = \\text{rate\\_2009} - \\text{rate\\_2008} \\). This metric indicates how much unemployment worsened (positive values) or improved (negative values) during the financial crisis period.", "field": "rate_increase"}], "dialog": [{"content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date'])\n \n # Filter data for years 2008 and 2009\n df_2008_2009 = df_unemployment[df_unemployment['year'].isin([2008, 2009])].copy()\n \n # Calculate average rate for each industry in 2008 and 2009\n df_2008 = df_2008_2009[df_2008_2009['year'] == 2008].groupby('series')['rate'].mean().reset_index()\n df_2008.columns = ['series', 'rate_2008']\n \n df_2009 = df_2008_2009[df_2008_2009['year'] == 2009].groupby('series')['rate'].mean().reset_index()\n df_2009.columns = ['series', 'rate_2009']\n \n # Merge and calculate the increase\n transformed_df = df_2008.merge(df_2009, on='series')\n transformed_df['rate_increase'] = transformed_df['rate_2009'] - transformed_df['rate_2008']\n \n # Sort by rate increase in descending order\n transformed_df = transformed_df.sort_values('rate_increase', ascending=False).reset_index(drop=True)\n \n return transformed_df\n\n\n[EXPLANATION]\n", "role": "user"}, {"content": "[CODE EXPLANATION]\n\n- Convert the **date** field to datetime format for proper date handling\n- Filter the dataset to include only records from **2008** and **2009**\n- Calculate the **average unemployment rate** for each **industry (series)** in **2008**\n- Calculate the **average unemployment rate** for each **industry (series)** in **2009**\n- Merge the two yearly averages and compute the **rate increase** from 2008 to 2009\n- Sort industries by **rate_increase** in descending order to identify which industries experienced the largest growth in unemployment\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"rate_increase\",\n \"explanation\": \"The difference in average unemployment rate between 2009 and 2008 for each industry, calculated as \\\\( \\\\text{rate\\\\_increase} = \\\\text{rate\\\\_2009} - \\\\text{rate\\\\_2008} \\\\). This metric indicates how much unemployment worsened (positive values) or improved (negative values) during the financial crisis period.\"\n }\n]\n```", "role": "assistant"}], "status": "ok"}, "status": "completed"}, "anchored": false, "attachedMetadata": ""}, {"kind": "table", "id": "table-570117", "displayId": "unemp-rate-ind1", "names": ["date", "series", "rate"], "rows": [{"date": "2008-01-01", "series": "Construction", "rate": 11}, {"date": "2008-02-01", "series": "Construction", "rate": 11.4}, {"date": "2008-03-01", "series": "Construction", "rate": 12}, {"date": "2008-04-01", "series": "Construction", "rate": 11.1}, {"date": "2008-05-01", "series": "Construction", "rate": 8.6}, {"date": "2008-06-01", "series": "Construction", "rate": 8.2}, {"date": "2008-07-01", "series": "Construction", "rate": 8}, {"date": "2008-08-01", "series": "Construction", "rate": 8.2}, {"date": "2008-09-01", "series": "Construction", "rate": 9.9}, {"date": "2008-10-01", "series": "Construction", "rate": 10.8}, {"date": "2008-11-01", "series": "Construction", "rate": 12.7}, {"date": "2008-12-01", "series": "Construction", "rate": 15.3}, {"date": "2009-01-01", "series": "Construction", "rate": 18.2}, {"date": "2009-02-01", "series": "Construction", "rate": 21.4}, {"date": "2009-03-01", "series": "Construction", "rate": 21.1}, {"date": "2009-04-01", "series": "Construction", "rate": 18.7}, {"date": "2009-05-01", "series": "Construction", "rate": 19.2}, {"date": "2009-06-01", "series": "Construction", "rate": 17.4}, {"date": "2009-07-01", "series": "Construction", "rate": 18.2}, {"date": "2009-08-01", "series": "Construction", "rate": 16.5}, {"date": "2009-09-01", "series": "Construction", "rate": 17.1}, {"date": "2009-10-01", "series": "Construction", "rate": 18.7}, {"date": "2009-11-01", "series": "Construction", "rate": 19.4}, {"date": "2009-12-01", "series": "Construction", "rate": 22.7}, {"date": "2010-01-01", "series": "Construction", "rate": 24.7}, {"date": "2010-02-01", "series": "Construction", "rate": 27.1}, {"date": "2008-01-01", "series": "Manufacturing", "rate": 5.1}, {"date": "2008-02-01", "series": "Manufacturing", "rate": 5}, {"date": "2008-03-01", "series": "Manufacturing", "rate": 5}, {"date": "2008-04-01", "series": "Manufacturing", "rate": 4.8}, {"date": "2008-05-01", "series": "Manufacturing", "rate": 5.3}, {"date": "2008-06-01", "series": "Manufacturing", "rate": 5.2}, {"date": "2008-07-01", "series": "Manufacturing", "rate": 5.5}, {"date": "2008-08-01", "series": "Manufacturing", "rate": 5.7}, {"date": "2008-09-01", "series": "Manufacturing", "rate": 6}, {"date": "2008-10-01", "series": "Manufacturing", "rate": 6.2}, {"date": "2008-11-01", "series": "Manufacturing", "rate": 7}, {"date": "2008-12-01", "series": "Manufacturing", "rate": 8.3}, {"date": "2009-01-01", "series": "Manufacturing", "rate": 10.9}, {"date": "2009-02-01", "series": "Manufacturing", "rate": 11.5}, {"date": "2009-03-01", "series": "Manufacturing", "rate": 12.2}, {"date": "2009-04-01", "series": "Manufacturing", "rate": 12.4}, {"date": "2009-05-01", "series": "Manufacturing", "rate": 12.6}, {"date": "2009-06-01", "series": "Manufacturing", "rate": 12.6}, {"date": "2009-07-01", "series": "Manufacturing", "rate": 12.4}, {"date": "2009-08-01", "series": "Manufacturing", "rate": 11.8}, {"date": "2009-09-01", "series": "Manufacturing", "rate": 11.9}, {"date": "2009-10-01", "series": "Manufacturing", "rate": 12.2}, {"date": "2009-11-01", "series": "Manufacturing", "rate": 12.5}, {"date": "2009-12-01", "series": "Manufacturing", "rate": 11.9}, {"date": "2010-01-01", "series": "Manufacturing", "rate": 13}, {"date": "2010-02-01", "series": "Manufacturing", "rate": 12.1}, {"date": "2008-01-01", "series": "Mining and Extraction", "rate": 4}, {"date": "2008-02-01", "series": "Mining and Extraction", "rate": 2.2}, {"date": "2008-03-01", "series": "Mining and Extraction", "rate": 3.7}, {"date": "2008-04-01", "series": "Mining and Extraction", "rate": 3.6}, {"date": "2008-05-01", "series": "Mining and Extraction", "rate": 3.4}, {"date": "2008-06-01", "series": "Mining and Extraction", "rate": 3.3}, {"date": "2008-07-01", "series": "Mining and Extraction", "rate": 1.5}, {"date": "2008-08-01", "series": "Mining and Extraction", "rate": 1.9}, {"date": "2008-09-01", "series": "Mining and Extraction", "rate": 2.8}, {"date": "2008-10-01", "series": "Mining and Extraction", "rate": 1.7}, {"date": "2008-11-01", "series": "Mining and Extraction", "rate": 3.7}, {"date": "2008-12-01", "series": "Mining and Extraction", "rate": 5.2}, {"date": "2009-01-01", "series": "Mining and Extraction", "rate": 7}, {"date": "2009-02-01", "series": "Mining and Extraction", "rate": 7.6}, {"date": "2009-03-01", "series": "Mining and Extraction", "rate": 12.6}, {"date": "2009-04-01", "series": "Mining and Extraction", "rate": 16.1}, {"date": "2009-05-01", "series": "Mining and Extraction", "rate": 13.3}, {"date": "2009-06-01", "series": "Mining and Extraction", "rate": 13.6}, {"date": "2009-07-01", "series": "Mining and Extraction", "rate": 12.6}, {"date": "2009-08-01", "series": "Mining and Extraction", "rate": 11.8}, {"date": "2009-09-01", "series": "Mining and Extraction", "rate": 10.7}, {"date": "2009-10-01", "series": "Mining and Extraction", "rate": 10.8}, {"date": "2009-11-01", "series": "Mining and Extraction", "rate": 12}, {"date": "2009-12-01", "series": "Mining and Extraction", "rate": 11.8}, {"date": "2010-01-01", "series": "Mining and Extraction", "rate": 9.1}, {"date": "2010-02-01", "series": "Mining and Extraction", "rate": 10.7}], "metadata": {"date": {"type": "date", "semanticType": "YearMonth"}, "series": {"type": "string", "semanticType": "String"}, "rate": {"type": "number", "semanticType": "Percentage"}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime and then to string format for visualization\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date'])\n \n # Filter for years 2008-2010\n df_filtered = df_unemployment[df_unemployment['year'].isin([2008, 2009, 2010])].copy()\n \n # Filter for top 3 most affected industries\n top_3_industries = ['Mining and Extraction', 'Construction', 'Manufacturing']\n df_filtered = df_filtered[df_filtered['series'].isin(top_3_industries)].copy()\n \n # Convert date to string format for visualization\n df_filtered['date'] = df_filtered['date'].dt.strftime('%Y-%m-%d')\n \n # Select only the necessary columns\n transformed_df = df_filtered[['date', 'series', 'rate']].copy()\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values(['series', 'date']).reset_index(drop=True)\n \n return transformed_df\n", "source": ["unemployment-across-industries"], "dialog": [{"role": "system", "content": "You are a data scientist to help user to recommend data that will be used for visualization.\nThe user will provide you information about what visualization they would like to create, and your job is to recommend a transformed data that can be used to create the visualization and write a python function to transform the data.\nThe recommendation and transformation function should be based on the [CONTEXT] and [GOAL] provided by the user. \nThe [CONTEXT] shows what the current dataset is, and the [GOAL] describes what the user wants the data for.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should infer the appropriate data and create in the output section a python function based off the [CONTEXT] and [GOAL] in two steps:\n\n1. First, based on users' [GOAL]. Create a json object that represents the inferred user intent. The json object should have the following format:\n\n{\n \"mode\": \"\" // string, one of \"infer\", \"overview\", \"distribution\", \"summary\", \"forecast\"\n \"recap\": \"...\" // string, a short summary of the user's goal.\n \"display_instruction\": \"...\" // string, the even shorter verb phrase describing the users' goal.\n \"recommendation\": \"...\" // string, explain why this recommendation is made\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have (i.e., the goal of transformed data), it's a good idea to preseve intermediate fields here\n \"chart_type\": \"\" // string, one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\". \"chart_type\" should either be inferred from user instruction, or recommend if the user didn't specify any.\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of output fields, appropriate visual channels for different chart types are defined below.\n}\n\nConcretely:\n - recap what the user's goal is in a short summary in \"recap\".\n - If the user's [GOAL] is clear already, simply infer what the user mean. Set \"mode\" as \"infer\" and create \"output_fields\" and \"chart_encodings\" based off user description.\n - If the user's [GOAL] is not clear, make recommendations to the user:\n - choose one of \"distribution\", \"overview\", \"summary\", \"forecast\" in \"mode\":\n * if it is \"overview\" and the data is in wide format, reshape it into long format.\n * if it is \"distribution\", select a few fields that would be interesting to visualize together.\n * if it is \"summary\", calculate some aggregated statistics to show intresting facts of the data.\n * if it is \"forecast\", concretize the x,y fields that will be used for forecasting and decide if it is about regression or forecasting.\n - describe the recommendation reason in \"recommendation\"\n - based on the recommendation, determine what is an ideal output data. Note, the output data must be in tidy format.\n - then suggest recommendations of chart encoding that should be used to create the visualization.\n - \"display_instruction\" should be a short verb phrase describing the users' goal, it should be even shorter than \"recap\". \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate based on \"recap\" and the suggested visualization, but don't need to mention the visualization details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user instruction builds up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - \"chart_type\" must be one of \"point\", \"bar\", \"line\", \"area\", \"heatmap\", \"group_bar\"\n - \"chart_encodings\" should specify which fields should be used to create the visualization\n - decide which visual channels should be used to create the visualization appropriate for the chart type.\n - point: x, y, color, size, facet\n - histogram: x, color, facet\n - bar: x, y, color, facet\n - line: x, y, color, facet\n - area: x, y, color, facet\n - heatmap: x, y, color, facet\n - group_bar: x, y, color, facet\n - note that all fields used in \"chart_encodings\" should be included in \"output_fields\".\n - all fields you need for visualizations should be transformed into the output fields!\n - \"output_fields\" should include important intermediate fields that are not used in visualization but are used for data transformation.\n - typically only 2-3 fields should be used to create the visualization (x, y, color/size), facet use be added if it's a faceted visualization (totally 4 fields used).\n - Guidelines for choosing chart type and visualization fields:\n - Consider chart types as follows:\n - (point) Scatter Plots: x,y: Quantitative/Categorical, color: Categorical (optional), size: Quantitative (optional for creating bubble chart), \n - best for: Relationships, correlations, distributions, forecasting, regression analysis\n - scatter plots are good default way to visualize data when other chart types are not applicable.\n - use color to visualize points from different categories.\n - use size to visualize data points with an additional quantitative dimension of the data points.\n - (histogram) Histograms: x: Quantitative/Categorical, color: Categorical (optional for creating grouped histogram), \n - best for: Distribution of a quantitative field\n - use x values directly if x values are categorical, and transform the data into bins if the field values are quantitative.\n - when color is specified, the histogram will be grouped automatically (items with the same x values will be grouped).\n - (bar) Bar Charts: x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical/Quantitative (for stacked bar chart / showing additional quantitative dimension), \n - best for: Comparisons across categories\n - use (bar) for simple bar chart or stacked bar chart (when it makes sense to add up Y values for each category with the same X value), \n - when color is specified, the bar will be stacked automatically (items with the same x values will be stacked).\n - note that when there are multiple rows in the data with same x values, the bar will be stacked automatically.\n - 1. consider to use an aggregated field for y values if the value is not suitable for stacking.\n - 2. consider to introduce facets so that each group is visualized in a separate bar.\n - (group_bar) for grouped bar chart, x: Categorical (nominal/ordinal), y: Quantitative, color: Categorical\n - when color is specifed, bars from different groups will be grouped automatically.\n - only use facet if the cardinality of color field is small (less than 5).\n - (line) Line Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating multiple lines), \n - best for: Trends over time, continuous data, forecasting, regression analysis\n - note that when there are multiple rows in the data belong to the same group (same x and color values) but different y values, the line will not look correct.\n - consider to use an aggregated field for y values, or introduce facets so that each group is visualized in a separate line.\n - (area) Area Charts: x: Temporal (preferred) or ordinal, y: Quantitative, color: Categorical (optional for creating stacked areas), \n - best for: Trends over time, continuous data\n - (heatmap) Heatmaps: x,y: Categorical (you need to convert quantitative to nominal), color: Quantitative intensity, \n - best for: Pattern discovery in matrix data\n - facet channel is available for all chart types, it supports a categorical field with small cardinality to visualize the data in different facets.\n - if you really need additional legend fields:\n - you can use opacity for legend (support Quantitative and Categorical).\n - visualization fields require tidy data. \n - similar to VegaLite and ggplot2 so that each field is mapped to a visualization axis or legend. \n - consider data transformations if you want to visualize multiple fields together:\n - exapmle 1: suggest reshaping the data into long format in data transformation description (if these fields are all of the same type, e.g., they are all about sales, price, two columns about min/max-values, etc. don't mix different types of fields in reshaping) so we can visualize multiple fields as categories or in different facets.\n - exapmle 2: calculate some derived fields from these fields(e.g., correlation, difference, profit etc.) in data transformation description to visualize them in one visualization.\n - example 3: create a visualization only with a subset of the fields, you don't have to visualize all of them in one chart, you can later create a visualization with the rest of the fields. With the subset of charts, you can also consider reshaping or calculate some derived value.\n - again, it does not make sense to have five fields like [item, A, B, C, D, E] in visualization fields, you should consider data transformation to reduce the number of fields.\n - when reshaping data to long format, only fields of the same semantic type should be rehaped into the same column.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - put is_predicted field in 'opacity' channel to distinguish them.\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n 2. Then, write a python function based on the inferred goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \n\"transformed_df\" should contain all \"output_fields\" from the refined user intent in the json object.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable. \nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- you can use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n \n 3. The output must only contain a json object representing inferred user intent and a python code block representing the transformation code, do not add any extra text explanation.\n"}, {"role": "user", "content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[GOAL]\n\nCompare **unemployment trends** across all industries from 2000-2010"}, {"role": "assistant", "content": "```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to compare unemployment trends across all industries from 2000-2010 to understand how different sectors were affected over time.\",\n \"display_instruction\": \"Compare **unemployment rates** across **industries** over time\",\n \"recommendation\": \"A line chart with time on x-axis and unemployment rate on y-axis, with different colored lines for each industry series, will effectively show the trends and allow comparison of how unemployment evolved across different sectors during this period.\",\n \"output_fields\": [\"date\", \"series\", \"rate\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"rate\",\n \"color\": \"series\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime and then to string format for visualization\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date']).dt.strftime('%Y-%m-%d')\n \n # Select only the necessary columns for visualization\n transformed_df = df_unemployment[['date', 'series', 'rate']].copy()\n \n return transformed_df\n```"}, {"role": "user", "content": "This is the result from the latest python code:\n\n date series rate\n0 2000-01-01 Government 2.1\n1 2000-02-01 Government 2.0\n2 2000-03-01 Government 1.5\n3 2000-04-01 Government 1.3\n4 2000-05-01 Government 1.9\n5 2000-06-01 Government 3.1\n6 2000-07-01 Government 2.9\n7 2000-08-01 Government 3.1\n8 2000-09-01 Government 2.1\n9 2000-10-01 Government 2.0\n......\n\nUpdate the code above based on the following instruction:\n\nWhich industries showed the **sharpest increases** in unemployment during **2008-2009**?"}, {"role": "assistant", "content": "```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to identify which industries experienced the sharpest increases in unemployment rates during the 2008-2009 financial crisis period by calculating the change in unemployment rate for each industry.\",\n \"display_instruction\": \"Identify industries with **sharpest unemployment rate increases** during **2008-2009**\",\n \"recommendation\": \"A bar chart showing the change in unemployment rate from 2008 to 2009 for each industry will clearly visualize which sectors were hit hardest during the financial crisis. The industries should be sorted by the magnitude of increase to make comparison easier.\",\n \"output_fields\": [\"series\", \"rate_2008\", \"rate_2009\", \"rate_increase\"],\n \"chart_type\": \"bar\",\n \"chart_encodings\": {\n \"x\": \"series\",\n \"y\": \"rate_increase\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date'])\n \n # Filter data for years 2008 and 2009\n df_2008_2009 = df_unemployment[df_unemployment['year'].isin([2008, 2009])].copy()\n \n # Calculate average rate for each industry in 2008 and 2009\n df_2008 = df_2008_2009[df_2008_2009['year'] == 2008].groupby('series')['rate'].mean().reset_index()\n df_2008.columns = ['series', 'rate_2008']\n \n df_2009 = df_2008_2009[df_2008_2009['year'] == 2009].groupby('series')['rate'].mean().reset_index()\n df_2009.columns = ['series', 'rate_2009']\n \n # Merge and calculate the increase\n transformed_df = df_2008.merge(df_2009, on='series')\n transformed_df['rate_increase'] = transformed_df['rate_2009'] - transformed_df['rate_2008']\n \n # Sort by rate increase in descending order\n transformed_df = transformed_df.sort_values('rate_increase', ascending=False).reset_index(drop=True)\n \n return transformed_df\n```"}, {"role": "user", "content": "This is the result from the latest python code:\n\n series rate_2008 rate_2009 rate_increase\n0 Mining and Extraction 3.083333 11.658333 8.575000\n1 Construction 10.600000 19.050000 8.450000\n2 Manufacturing 5.758333 12.075000 6.316667\n3 Agriculture 9.266667 14.391667 5.125000\n4 Business services 6.558333 10.800000 4.241667\n5 Information 5.016667 9.225000 4.208333\n6 Transportation and Utilities 5.133333 8.883333 3.750000\n7 Wholesale and Retail Trade 5.841667 9.033333 3.191667\n8 Leisure and hospitality 8.641667 11.683333 3.041667\n9 Finance 3.950000 6.400000 2.450000\n......\n\nUpdate the code above based on the following instruction:\n\nFor the **top 3 most affected industries** (Mining and Extraction, Construction, Manufacturing), compare their **monthly unemployment rate trends** during the **2008-2010 period** to analyze how quickly each industry recovered after the 2009 peak"}, {"role": "assistant", "content": "```json\n{\n \"mode\": \"infer\",\n \"recap\": \"User wants to analyze the monthly unemployment rate trends for the top 3 most affected industries (Mining and Extraction, Construction, Manufacturing) during 2008-2010 to understand their recovery patterns after the 2009 peak.\",\n \"display_instruction\": \"Compare **monthly unemployment trends** for **top 3 affected industries** during **2008-2010 recovery period**\",\n \"recommendation\": \"A line chart with time on x-axis and unemployment rate on y-axis, with different colored lines for the three most affected industries, will effectively show how each industry's unemployment rate evolved during and after the crisis, revealing their different recovery trajectories.\",\n \"output_fields\": [\"date\", \"series\", \"rate\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"date\",\n \"y\": \"rate\",\n \"color\": \"series\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime and then to string format for visualization\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date'])\n \n # Filter for years 2008-2010\n df_filtered = df_unemployment[df_unemployment['year'].isin([2008, 2009, 2010])].copy()\n \n # Filter for top 3 most affected industries\n top_3_industries = ['Mining and Extraction', 'Construction', 'Manufacturing']\n df_filtered = df_filtered[df_filtered['series'].isin(top_3_industries)].copy()\n \n # Convert date to string format for visualization\n df_filtered['date'] = df_filtered['date'].dt.strftime('%Y-%m-%d')\n \n # Select only the necessary columns\n transformed_df = df_filtered[['date', 'series', 'rate']].copy()\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values(['series', 'date']).reset_index(drop=True)\n \n return transformed_df\n```"}], "trigger": {"tableId": "table-552276", "resultTableId": "table-570117", "chart": {"id": "chart-1760745567484", "chartType": "Auto", "encodingMap": {}, "tableRef": "unemployment-across-industries", "saved": false, "source": "trigger", "unread": true}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "For the **top 3 most affected industries** (Mining and Extraction, Construction, Manufacturing), compare their **monthly unemployment rate trends** during the **2008-2010 period** to analyze how quickly each industry recovered after the 2009 peak", "displayContent": "Compare **monthly unemployment trends** for **top 3 affected industries** during **2008-2010 recovery period**"}]}, "explanation": {"agent": "CodeExplanationAgent", "code": "- Convert the **date** field from string to datetime format, then back to a standardized string format (`YYYY-MM-DD`)\n- **Filter** the data to include only the years **2008**, **2009**, and **2010** (the period around the financial crisis)\n- **Select** the top 3 most affected industries: **Mining and Extraction**, **Construction**, and **Manufacturing**\n- **Extract** only the essential columns: **date**, **series** (industry name), and **rate** (unemployment rate)\n- **Sort** the data by **series** and **date** to ensure proper chronological ordering for time series visualization", "concepts": [], "dialog": [{"content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df_unemployment):\n # Convert date string to datetime and then to string format for visualization\n df_unemployment['date'] = pd.to_datetime(df_unemployment['date'])\n \n # Filter for years 2008-2010\n df_filtered = df_unemployment[df_unemployment['year'].isin([2008, 2009, 2010])].copy()\n \n # Filter for top 3 most affected industries\n top_3_industries = ['Mining and Extraction', 'Construction', 'Manufacturing']\n df_filtered = df_filtered[df_filtered['series'].isin(top_3_industries)].copy()\n \n # Convert date to string format for visualization\n df_filtered['date'] = df_filtered['date'].dt.strftime('%Y-%m-%d')\n \n # Select only the necessary columns\n transformed_df = df_filtered[['date', 'series', 'rate']].copy()\n \n # Sort by date for proper line chart rendering\n transformed_df = transformed_df.sort_values(['series', 'date']).reset_index(drop=True)\n \n return transformed_df\n\n\n[EXPLANATION]\n", "role": "user"}, {"content": "[CODE EXPLANATION]\n\n- Convert the **date** field from string to datetime format, then back to a standardized string format (`YYYY-MM-DD`)\n- **Filter** the data to include only the years **2008**, **2009**, and **2010** (the period around the financial crisis)\n- **Select** the top 3 most affected industries: **Mining and Extraction**, **Construction**, and **Manufacturing**\n- **Extract** only the essential columns: **date**, **series** (industry name), and **rate** (unemployment rate)\n- **Sort** the data by **series** and **date** to ensure proper chronological ordering for time series visualization\n\n[CONCEPTS EXPLANATION]\n\n```json\n[]\n```", "role": "assistant"}], "status": "ok"}, "status": "completed"}, "anchored": false, "attachedMetadata": ""}, {"kind": "table", "id": "table-53", "displayId": "industry-rates", "names": ["avg_rate", "month", "series", "year"], "rows": [{"avg_rate": 10.3, "month": 1, "series": "Agriculture", "year": "2000"}, {"avg_rate": 11.5, "month": 2, "series": "Agriculture", "year": "2000"}, {"avg_rate": 10.4, "month": 3, "series": "Agriculture", "year": "2000"}, {"avg_rate": 8.9, "month": 4, "series": "Agriculture", "year": "2000"}, {"avg_rate": 5.1, "month": 5, "series": "Agriculture", "year": "2000"}, {"avg_rate": 6.7, "month": 6, "series": "Agriculture", "year": "2000"}, {"avg_rate": 5, "month": 7, "series": "Agriculture", "year": "2000"}, {"avg_rate": 7, "month": 8, "series": "Agriculture", "year": "2000"}, {"avg_rate": 8.2, "month": 9, "series": "Agriculture", "year": "2000"}, {"avg_rate": 8, "month": 10, "series": "Agriculture", "year": "2000"}, {"avg_rate": 13.3, "month": 11, "series": "Agriculture", "year": "2000"}, {"avg_rate": 13.9, "month": 12, "series": "Agriculture", "year": "2000"}, {"avg_rate": 13.8, "month": 1, "series": "Agriculture", "year": "2001"}, {"avg_rate": 15.1, "month": 2, "series": "Agriculture", "year": "2001"}, {"avg_rate": 19.2, "month": 3, "series": "Agriculture", "year": "2001"}, {"avg_rate": 10.4, "month": 4, "series": "Agriculture", "year": "2001"}, {"avg_rate": 7.7, "month": 5, "series": "Agriculture", "year": "2001"}, {"avg_rate": 9.7, "month": 6, "series": "Agriculture", "year": "2001"}, {"avg_rate": 7.6, "month": 7, "series": "Agriculture", "year": "2001"}, {"avg_rate": 9.3, "month": 8, "series": "Agriculture", "year": "2001"}, {"avg_rate": 7.2, "month": 9, "series": "Agriculture", "year": "2001"}, {"avg_rate": 8.7, "month": 10, "series": "Agriculture", "year": "2001"}, {"avg_rate": 11.6, "month": 11, "series": "Agriculture", "year": "2001"}, {"avg_rate": 15.1, "month": 12, "series": "Agriculture", "year": "2001"}, {"avg_rate": 14.8, "month": 1, "series": "Agriculture", "year": "2002"}, {"avg_rate": 14.8, "month": 2, "series": "Agriculture", "year": "2002"}, {"avg_rate": 19.6, "month": 3, "series": "Agriculture", "year": "2002"}, {"avg_rate": 10.8, "month": 4, "series": "Agriculture", "year": "2002"}, {"avg_rate": 6.8, "month": 5, "series": "Agriculture", "year": "2002"}, {"avg_rate": 6.3, "month": 6, "series": "Agriculture", "year": "2002"}, {"avg_rate": 7.3, "month": 7, "series": "Agriculture", "year": "2002"}, {"avg_rate": 9, "month": 8, "series": "Agriculture", "year": "2002"}, {"avg_rate": 6.3, "month": 9, "series": "Agriculture", "year": "2002"}, {"avg_rate": 6.6, "month": 10, "series": "Agriculture", "year": "2002"}, {"avg_rate": 11.1, "month": 11, "series": "Agriculture", "year": "2002"}, {"avg_rate": 9.8, "month": 12, "series": "Agriculture", "year": "2002"}, {"avg_rate": 13.2, "month": 1, "series": "Agriculture", "year": "2003"}, {"avg_rate": 14.7, "month": 2, "series": "Agriculture", "year": "2003"}, {"avg_rate": 12.9, "month": 3, "series": "Agriculture", "year": "2003"}, {"avg_rate": 12, "month": 4, "series": "Agriculture", "year": "2003"}, {"avg_rate": 10.2, "month": 5, "series": "Agriculture", "year": "2003"}, {"avg_rate": 6.9, "month": 6, "series": "Agriculture", "year": "2003"}, {"avg_rate": 8.2, "month": 7, "series": "Agriculture", "year": "2003"}, {"avg_rate": 10.7, "month": 8, "series": "Agriculture", "year": "2003"}, {"avg_rate": 6.2, "month": 9, "series": "Agriculture", "year": "2003"}, {"avg_rate": 8.5, "month": 10, "series": "Agriculture", "year": "2003"}, {"avg_rate": 10.3, "month": 11, "series": "Agriculture", "year": "2003"}, {"avg_rate": 10.9, "month": 12, "series": "Agriculture", "year": "2003"}, {"avg_rate": 15.1, "month": 1, "series": "Agriculture", "year": "2004"}, {"avg_rate": 14.2, "month": 2, "series": "Agriculture", "year": "2004"}, {"avg_rate": 12.7, "month": 3, "series": "Agriculture", "year": "2004"}, {"avg_rate": 8.3, "month": 4, "series": "Agriculture", "year": "2004"}, {"avg_rate": 7.4, "month": 5, "series": "Agriculture", "year": "2004"}, {"avg_rate": 7.6, "month": 6, "series": "Agriculture", "year": "2004"}, {"avg_rate": 10, "month": 7, "series": "Agriculture", "year": "2004"}, {"avg_rate": 7, "month": 8, "series": "Agriculture", "year": "2004"}, {"avg_rate": 6.4, "month": 9, "series": "Agriculture", "year": "2004"}, {"avg_rate": 7.7, "month": 10, "series": "Agriculture", "year": "2004"}, {"avg_rate": 10.5, "month": 11, "series": "Agriculture", "year": "2004"}, {"avg_rate": 14, "month": 12, "series": "Agriculture", "year": "2004"}, {"avg_rate": 13.2, "month": 1, "series": "Agriculture", "year": "2005"}, {"avg_rate": 9.9, "month": 2, "series": "Agriculture", "year": "2005"}, {"avg_rate": 11.8, "month": 3, "series": "Agriculture", "year": "2005"}, {"avg_rate": 6.9, "month": 4, "series": "Agriculture", "year": "2005"}, {"avg_rate": 5.3, "month": 5, "series": "Agriculture", "year": "2005"}, {"avg_rate": 5.2, "month": 6, "series": "Agriculture", "year": "2005"}, {"avg_rate": 4.7, "month": 7, "series": "Agriculture", "year": "2005"}, {"avg_rate": 7.1, "month": 8, "series": "Agriculture", "year": "2005"}, {"avg_rate": 9.5, "month": 9, "series": "Agriculture", "year": "2005"}, {"avg_rate": 6.7, "month": 10, "series": "Agriculture", "year": "2005"}, {"avg_rate": 9.6, "month": 11, "series": "Agriculture", "year": "2005"}, {"avg_rate": 11.1, "month": 12, "series": "Agriculture", "year": "2005"}, {"avg_rate": 11.5, "month": 1, "series": "Agriculture", "year": "2006"}, {"avg_rate": 11.8, "month": 2, "series": "Agriculture", "year": "2006"}, {"avg_rate": 9.8, "month": 3, "series": "Agriculture", "year": "2006"}, {"avg_rate": 6.2, "month": 4, "series": "Agriculture", "year": "2006"}, {"avg_rate": 6, "month": 5, "series": "Agriculture", "year": "2006"}, {"avg_rate": 2.4, "month": 6, "series": "Agriculture", "year": "2006"}, {"avg_rate": 3.6, "month": 7, "series": "Agriculture", "year": "2006"}, {"avg_rate": 5.3, "month": 8, "series": "Agriculture", "year": "2006"}, {"avg_rate": 5.9, "month": 9, "series": "Agriculture", "year": "2006"}, {"avg_rate": 5.8, "month": 10, "series": "Agriculture", "year": "2006"}, {"avg_rate": 9.6, "month": 11, "series": "Agriculture", "year": "2006"}, {"avg_rate": 10.4, "month": 12, "series": "Agriculture", "year": "2006"}, {"avg_rate": 10, "month": 1, "series": "Agriculture", "year": "2007"}, {"avg_rate": 9.6, "month": 2, "series": "Agriculture", "year": "2007"}, {"avg_rate": 9.7, "month": 3, "series": "Agriculture", "year": "2007"}, {"avg_rate": 5.7, "month": 4, "series": "Agriculture", "year": "2007"}, {"avg_rate": 5.1, "month": 5, "series": "Agriculture", "year": "2007"}, {"avg_rate": 4.5, "month": 6, "series": "Agriculture", "year": "2007"}, {"avg_rate": 3.1, "month": 7, "series": "Agriculture", "year": "2007"}, {"avg_rate": 4.7, "month": 8, "series": "Agriculture", "year": "2007"}, {"avg_rate": 4.3, "month": 9, "series": "Agriculture", "year": "2007"}, {"avg_rate": 4, "month": 10, "series": "Agriculture", "year": "2007"}, {"avg_rate": 6.6, "month": 11, "series": "Agriculture", "year": "2007"}, {"avg_rate": 7.5, "month": 12, "series": "Agriculture", "year": "2007"}, {"avg_rate": 9.5, "month": 1, "series": "Agriculture", "year": "2008"}, {"avg_rate": 10.9, "month": 2, "series": "Agriculture", "year": "2008"}, {"avg_rate": 13.2, "month": 3, "series": "Agriculture", "year": "2008"}, {"avg_rate": 8.6, "month": 4, "series": "Agriculture", "year": "2008"}, {"avg_rate": 7.4, "month": 5, "series": "Agriculture", "year": "2008"}, {"avg_rate": 6.1, "month": 6, "series": "Agriculture", "year": "2008"}, {"avg_rate": 8.5, "month": 7, "series": "Agriculture", "year": "2008"}, {"avg_rate": 7.6, "month": 8, "series": "Agriculture", "year": "2008"}, {"avg_rate": 5.8, "month": 9, "series": "Agriculture", "year": "2008"}, {"avg_rate": 7.1, "month": 10, "series": "Agriculture", "year": "2008"}, {"avg_rate": 9.5, "month": 11, "series": "Agriculture", "year": "2008"}, {"avg_rate": 17, "month": 12, "series": "Agriculture", "year": "2008"}, {"avg_rate": 18.7, "month": 1, "series": "Agriculture", "year": "2009"}, {"avg_rate": 18.8, "month": 2, "series": "Agriculture", "year": "2009"}, {"avg_rate": 19, "month": 3, "series": "Agriculture", "year": "2009"}, {"avg_rate": 13.5, "month": 4, "series": "Agriculture", "year": "2009"}, {"avg_rate": 10, "month": 5, "series": "Agriculture", "year": "2009"}, {"avg_rate": 12.3, "month": 6, "series": "Agriculture", "year": "2009"}, {"avg_rate": 12.1, "month": 7, "series": "Agriculture", "year": "2009"}, {"avg_rate": 13.1, "month": 8, "series": "Agriculture", "year": "2009"}, {"avg_rate": 11.1, "month": 9, "series": "Agriculture", "year": "2009"}, {"avg_rate": 11.8, "month": 10, "series": "Agriculture", "year": "2009"}, {"avg_rate": 12.6, "month": 11, "series": "Agriculture", "year": "2009"}, {"avg_rate": 19.7, "month": 12, "series": "Agriculture", "year": "2009"}, {"avg_rate": 21.3, "month": 1, "series": "Agriculture", "year": "2010"}, {"avg_rate": 18.8, "month": 2, "series": "Agriculture", "year": "2010"}, {"avg_rate": 9.7, "month": 1, "series": "Construction", "year": "2000"}, {"avg_rate": 10.6, "month": 2, "series": "Construction", "year": "2000"}, {"avg_rate": 8.7, "month": 3, "series": "Construction", "year": "2000"}, {"avg_rate": 5.8, "month": 4, "series": "Construction", "year": "2000"}, {"avg_rate": 5, "month": 5, "series": "Construction", "year": "2000"}, {"avg_rate": 4.6, "month": 6, "series": "Construction", "year": "2000"}, {"avg_rate": 4.4, "month": 7, "series": "Construction", "year": "2000"}, {"avg_rate": 5.1, "month": 8, "series": "Construction", "year": "2000"}, {"avg_rate": 4.6, "month": 9, "series": "Construction", "year": "2000"}, {"avg_rate": 4.9, "month": 10, "series": "Construction", "year": "2000"}, {"avg_rate": 5.7, "month": 11, "series": "Construction", "year": "2000"}, {"avg_rate": 6.8, "month": 12, "series": "Construction", "year": "2000"}, {"avg_rate": 9.8, "month": 1, "series": "Construction", "year": "2001"}, {"avg_rate": 9.9, "month": 2, "series": "Construction", "year": "2001"}, {"avg_rate": 8.4, "month": 3, "series": "Construction", "year": "2001"}, {"avg_rate": 7.1, "month": 4, "series": "Construction", "year": "2001"}, {"avg_rate": 5.6, "month": 5, "series": "Construction", "year": "2001"}, {"avg_rate": 5.1, "month": 6, "series": "Construction", "year": "2001"}, {"avg_rate": 4.9, "month": 7, "series": "Construction", "year": "2001"}, {"avg_rate": 5.8, "month": 8, "series": "Construction", "year": "2001"}, {"avg_rate": 5.5, "month": 9, "series": "Construction", "year": "2001"}, {"avg_rate": 6.1, "month": 10, "series": "Construction", "year": "2001"}, {"avg_rate": 7.6, "month": 11, "series": "Construction", "year": "2001"}, {"avg_rate": 9, "month": 12, "series": "Construction", "year": "2001"}, {"avg_rate": 13.6, "month": 1, "series": "Construction", "year": "2002"}, {"avg_rate": 12.2, "month": 2, "series": "Construction", "year": "2002"}, {"avg_rate": 11.8, "month": 3, "series": "Construction", "year": "2002"}, {"avg_rate": 10.1, "month": 4, "series": "Construction", "year": "2002"}, {"avg_rate": 7.4, "month": 5, "series": "Construction", "year": "2002"}, {"avg_rate": 6.9, "month": 6, "series": "Construction", "year": "2002"}, {"avg_rate": 6.9, "month": 7, "series": "Construction", "year": "2002"}, {"avg_rate": 7.4, "month": 8, "series": "Construction", "year": "2002"}, {"avg_rate": 7, "month": 9, "series": "Construction", "year": "2002"}, {"avg_rate": 7.7, "month": 10, "series": "Construction", "year": "2002"}, {"avg_rate": 8.5, "month": 11, "series": "Construction", "year": "2002"}, {"avg_rate": 10.9, "month": 12, "series": "Construction", "year": "2002"}, {"avg_rate": 14, "month": 1, "series": "Construction", "year": "2003"}, {"avg_rate": 14, "month": 2, "series": "Construction", "year": "2003"}, {"avg_rate": 11.8, "month": 3, "series": "Construction", "year": "2003"}, {"avg_rate": 9.3, "month": 4, "series": "Construction", "year": "2003"}, {"avg_rate": 8.4, "month": 5, "series": "Construction", "year": "2003"}, {"avg_rate": 7.9, "month": 6, "series": "Construction", "year": "2003"}, {"avg_rate": 7.5, "month": 7, "series": "Construction", "year": "2003"}, {"avg_rate": 7.1, "month": 8, "series": "Construction", "year": "2003"}, {"avg_rate": 7.6, "month": 9, "series": "Construction", "year": "2003"}, {"avg_rate": 7.4, "month": 10, "series": "Construction", "year": "2003"}, {"avg_rate": 7.8, "month": 11, "series": "Construction", "year": "2003"}, {"avg_rate": 9.3, "month": 12, "series": "Construction", "year": "2003"}, {"avg_rate": 11.3, "month": 1, "series": "Construction", "year": "2004"}, {"avg_rate": 11.6, "month": 2, "series": "Construction", "year": "2004"}, {"avg_rate": 11.3, "month": 3, "series": "Construction", "year": "2004"}, {"avg_rate": 9.5, "month": 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"Mining and Extraction", "year": "2006"}, {"avg_rate": 3.4, "month": 12, "series": "Mining and Extraction", "year": "2006"}, {"avg_rate": 4.7, "month": 1, "series": "Mining and Extraction", "year": "2007"}, {"avg_rate": 4.5, "month": 2, "series": "Mining and Extraction", "year": "2007"}, {"avg_rate": 3.2, "month": 3, "series": "Mining and Extraction", "year": "2007"}, {"avg_rate": 2.3, "month": 4, "series": "Mining and Extraction", "year": "2007"}, {"avg_rate": 3, "month": 5, "series": "Mining and Extraction", "year": "2007"}, {"avg_rate": 4.3, "month": 6, "series": "Mining and Extraction", "year": "2007"}, {"avg_rate": 4.3, "month": 7, "series": "Mining and Extraction", "year": "2007"}, {"avg_rate": 4.6, "month": 8, "series": "Mining and Extraction", "year": "2007"}, {"avg_rate": 3.2, "month": 9, "series": "Mining and Extraction", "year": "2007"}, {"avg_rate": 1.3, "month": 10, "series": "Mining and Extraction", "year": "2007"}, {"avg_rate": 2.3, "month": 11, "series": "Mining and Extraction", "year": "2007"}, {"avg_rate": 3.4, "month": 12, "series": "Mining and Extraction", "year": "2007"}, {"avg_rate": 4, "month": 1, "series": "Mining and Extraction", "year": "2008"}, {"avg_rate": 2.2, "month": 2, "series": "Mining and Extraction", "year": "2008"}, {"avg_rate": 3.7, "month": 3, "series": "Mining and Extraction", "year": "2008"}, {"avg_rate": 3.6, "month": 4, "series": "Mining and Extraction", "year": "2008"}, {"avg_rate": 3.4, "month": 5, "series": "Mining and Extraction", "year": "2008"}, {"avg_rate": 3.3, "month": 6, "series": "Mining and Extraction", "year": "2008"}, {"avg_rate": 1.5, "month": 7, "series": "Mining and Extraction", "year": "2008"}, {"avg_rate": 1.9, "month": 8, "series": "Mining and Extraction", "year": "2008"}, {"avg_rate": 2.8, "month": 9, "series": "Mining and Extraction", "year": "2008"}, {"avg_rate": 1.7, "month": 10, "series": "Mining and Extraction", "year": "2008"}, {"avg_rate": 3.7, "month": 11, "series": "Mining and Extraction", "year": "2008"}, {"avg_rate": 5.2, "month": 12, "series": "Mining and Extraction", "year": "2008"}, {"avg_rate": 7, "month": 1, "series": "Mining and Extraction", "year": "2009"}, {"avg_rate": 7.6, "month": 2, "series": "Mining and Extraction", "year": "2009"}, {"avg_rate": 12.6, "month": 3, "series": "Mining and Extraction", "year": "2009"}, {"avg_rate": 16.1, "month": 4, "series": "Mining and Extraction", "year": "2009"}, {"avg_rate": 13.3, "month": 5, "series": "Mining and Extraction", "year": "2009"}, {"avg_rate": 13.6, "month": 6, "series": "Mining and Extraction", "year": "2009"}, {"avg_rate": 12.6, "month": 7, "series": "Mining and Extraction", "year": "2009"}, {"avg_rate": 11.8, "month": 8, "series": "Mining and Extraction", "year": "2009"}, {"avg_rate": 10.7, "month": 9, "series": "Mining and Extraction", "year": "2009"}, {"avg_rate": 10.8, "month": 10, "series": "Mining and Extraction", "year": "2009"}, {"avg_rate": 12, "month": 11, "series": "Mining and Extraction", "year": "2009"}, {"avg_rate": 11.8, "month": 12, "series": "Mining and Extraction", "year": "2009"}, {"avg_rate": 9.1, "month": 1, "series": "Mining and Extraction", "year": "2010"}, {"avg_rate": 10.7, "month": 2, "series": "Mining and Extraction", "year": "2010"}], "metadata": {"avg_rate": {"type": "number", "semanticType": "Number"}, "month": {"type": "number", "semanticType": "Month"}, "series": {"type": "string", "semanticType": "String"}, "year": {"type": "number", "semanticType": "Year"}}, "derive": {"code": "\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df1):\n # Calculate average unemployment rate by month and series\n monthly_avg = df1.groupby(['series', 'month'], as_index=False).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Calculate seasonal variation (standard deviation across months) for each series\n seasonal_variation = monthly_avg.groupby('series')['avg_rate'].std().reset_index()\n seasonal_variation.columns = ['series', 'seasonal_std']\n \n # Get top 3 most seasonal industries\n top_3_seasonal = seasonal_variation.nlargest(3, 'seasonal_std')['series'].tolist()\n \n # Filter original data for top 3 industries and aggregate by year, month, series\n transformed_df = df1[df1['series'].isin(top_3_seasonal)].groupby(\n ['series', 'year', 'month'], as_index=False\n ).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Convert year to string for color encoding\n transformed_df['year'] = transformed_df['year'].astype(str)\n \n return transformed_df\n", "source": ["unemployment-across-industries"], "dialog": [{"content": "You are a data scientist to help user to transform data that will be used for visualization.\nThe user will provide you information about what data would be needed, and your job is to create a python function based on the input data summary, transformation instruction and expected fields.\nThe users' instruction includes \"chart_type\" and \"chart_encodings\" that describe the visualization they want, and natural language instructions \"goal\" that describe what data is needed.\n\n**Important:**\n- NEVER make assumptions or judgments about a person's gender, biological sex, sexuality, religion, race, nationality, ethnicity, political stance, socioeconomic status, mental health, invisible disabilities, medical conditions, personality type, social impressions, emotional state, and cognitive state.\n- NEVER create formulas that could be used to discriminate based on age. Ageism of any form (explicit and implicit) is strictly prohibited.\n- If above issue occurs, generate columns with np.nan.\n\nConcretely, you should first refine users' goal and then create a python function in the output section based off the [CONTEXT] and [GOAL]:\n\n 1. First, refine users' [GOAL]. The main objective in this step is to check if \"chart_type\" and \"chart_encodings\" provided by the user are sufficient to achieve their \"goal\". Concretely:\n - based on the user's \"goal\" and \"chart_type\" and \"chart_encodings\", elaborate the goal into a \"detailed_instruction\".\n - \"display_instruction\" is a short verb phrase describing the users' goal. \n - it would be a short verbal description of user intent as a verb phrase (<12 words).\n - generate it based on detailed_instruction and the suggested chart_type and chart_encodings, but don't need to mention the chart details.\n - should capture key computation ideas: by reading the display, the user can understand the purpose and what's derived from the data.\n - if the user specification follows up the previous instruction, the 'display_instruction' should only describe how it builds up the previous instruction without repeating information from previous steps.\n - the phrase can be presented in different styles, e.g., question (what's xxx), instruction (show xxx), description, etc.\n - if you mention column names from the input or the output data, highlight the text in **bold**.\n * the column can either be a column in the input data, or a new column that will be computed in the output data.\n * the mention don't have to be exact match, it can be semantically matching, e.g., if you mentioned \"average score\" in the text while the column to be computed is \"Avg_Score\", you should still highlight \"**average score**\" in the text.\n - determine \"output_fields\", the desired fields that the output data should have to achieve the user's goal, it's a good idea to include intermediate fields here.\n - then decide \"chart_encodings\", which maps visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized, \n - the \"chart_encodings\" should be created to support the user's \"chart_type\".\n - first, determine whether the user has provided sufficient fields in \"chart_encodings\" that are needed to achieve their goal:\n - if the user's \"chart_encodings\" are sufficient, simply copy it.\n - if the user didn't provide sufficient fields in \"chart_encodings\", add missing fields in \"chart_encodings\" (ordered them based on whether the field will be used in x,y axes or legends);\n - \"chart_encodings\" should only include fields that will be visualized (do not include other intermediate fields from \"output_fields\") \n - when adding new fields to \"chart_encodings\", be efficient and add only a minimal number of fields that are needed to achive the user's goal. \n - generally, the total number of fields in \"chart_encodings\" should be no more than 3 for x,y,legend.\n - if the user's \"chart_encodings\" is sufficient but can be optimized, you can reorder encodings to visualize the data more effectively.\n - sometimes, user may provide instruction to update visualizations fields they provided. You should leverage the user's goal to resolve the conflict and decide the final \"chart_encodings\"\n - e.g., they may mention \"use B metric instead\" while A metric is in provided fields, in this case, you should update \"chart_encodings\" to update A metric with B metric.\n - guide on statistical analysis:\n - when the user asks for forecasting or regression analysis, you should consider the following:\n - the output should be a long format table where actual x, y pairs and predicted x, y pairs are included in the X, Y columns, they are differentiated with a third column \"is_predicted\" that is a boolean field.\n - i.e., if the user ask for forecasting based on two columns T and Y, the output should be three columns: T, Y, is_predicted, where\n - T, Y columns contain BOTH original values from the data and predicted values from the data.\n - is_predicted is a boolean field to indicate whether the x, y pairs are original values from the data or predicted / regression values from the data.\n - the recommended chart should be line chart (time series) or scatter plot (quantitative x, y)\n - if the user asks for forecasting, it's good to include predicted x, y pairs for both x in the original data and future x values (i.e., combine regression and forecasting results)\n - in this case, is_predicted should be of three values 'original', 'regression', 'forecasting'\n - when the user asks for clustering:\n - the output should be a long format table where actual x, y pairs with a third column \"cluster_id\" that indicates the cluster id of the data point.\n - the recommended chart should be scatter plot (quantitative x, y)\n \n Prepare the result in the following json format:\n\n```\n{\n \"detailed_instruction\": \"...\" // string, elaborate user instruction with details if the user\n \"display_instruction\": \"...\" // string, the short verb phrase describing the users' goal.\n \"output_fields\": [...] // string[], describe the desired output fields that the output data should have based on the user's goal, it's a good idea to preserve intermediate fields here (i.e., the goal of transformed data)\n \"chart_encodings\": {\n \"x\": \"\",\n \"y\": \"\",\n \"color\": \"\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\",\n ... // other visualization channels user used\n } // object: map visualization channels (x, y, color, size, opacity, facet, etc.) to a subset of \"output_fields\" that will be visualized.\n \"reason\": \"...\" // string, explain why this refinement is made\n}\n```\n\n 2. Then, write a python function based on the refined goal, the function input is a dataframe \"df\" (or multiple dataframes based on tables presented in the [CONTEXT] section) and the output is the transformed dataframe \"transformed_df\". \"transformed_df\" should contain all \"output_fields\" from the refined goal.\nThe python function must follow the template provided in [TEMPLATE], do not import any other libraries or modify function name. The function should be as simple as possible and easily readable.\nIf there is no data transformation needed based on \"output_fields\", the transformation function can simply \"return df\".\n\n[TEMPLATE]\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\nfrom sklearn import ... # import necessary libraries from sklearn if needed\n\ndef transform_data(df1, df2, ...): \n # complete the template here\n return transformed_df\n```\n\nnote: \n- if the user provided one table, then it should be `def transform_data(df1)`, if the user provided multiple tables, then it should be `def transform_data(df1, df2, ...)` and you should consider the join between tables to derive the output.\n- **VERY IMPORTANT** the number of arguments in the function must match the number of tables provided, and the order of arguments must match the order of tables provided.\n- try to use intuitive table names to refer to the input dataframes, for example, if the user provided two tables city and weather, you can use `transform_data(df_city, df_weather)` to refer to the two dataframes, as long as the number and order of the arguments match the number and order of the tables provided.\n- datetime objects handling:\n - if the output field is year, convert it to number, if it is year-month / year-month-day, convert it to string object (e.g., \"2020-01\" / \"2020-01-01\").\n - if the output is time only: convert hour to number if it's just the hour (e.g., 10), but convert hour:min or h:m:s to string object (e.g., \"10:30\", \"10:30:45\")\n - never return datetime object directly, convert it to either number (if it only contains year) or string so it's readable.\n\n 3. The output must only contain a json object representing the refined goal and a python code block representing the transformation code, do not add any extra text explanation.\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[GOAL]\n\nHow does **seasonal variation** in unemployment differ across **industries**?", "role": "user"}, {"content": "```json\n{\n \"mode\": \"infer\",\n \"recap\": \"Analyze how seasonal variation in unemployment differs across industries by examining the cyclical patterns in unemployment rates throughout the year for different industry sectors.\",\n \"display_instruction\": \"Compare seasonal unemployment patterns across **industries**\",\n \"recommendation\": \"To visualize seasonal variation across industries, we should calculate average unemployment rates by month for each industry series. This will reveal the typical seasonal patterns and allow comparison of how different industries experience seasonal fluctuations in unemployment throughout the year.\",\n \"output_fields\": [\"series\", \"month\", \"avg_rate\", \"month_name\"],\n \"chart_type\": \"line\",\n \"chart_encodings\": {\n \"x\": \"month\",\n \"y\": \"avg_rate\",\n \"color\": \"series\",\n \"size\": \"\",\n \"opacity\": \"\",\n \"facet\": \"\"\n }\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df1):\n # Calculate average unemployment rate by month and series (industry)\n transformed_df = df1.groupby(['series', 'month'], as_index=False).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Add month name for better readability\n month_names = {\n 1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr', 5: 'May', 6: 'Jun',\n 7: 'Jul', 8: 'Aug', 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'\n }\n transformed_df['month_name'] = transformed_df['month'].map(month_names)\n \n return transformed_df\n```", "role": "assistant"}, {"content": "This is the result from the latest python code:\n\n avg_rate month month_name series\n0 13.763636 1 Jan Agriculture\n1 13.645455 2 Feb Agriculture\n2 13.830000 3 Mar Agriculture\n3 9.130000 4 Apr Agriculture\n4 7.100000 5 May Agriculture\n5 6.770000 6 Jun Agriculture\n6 7.010000 7 Jul Agriculture\n7 8.080000 8 Aug Agriculture\n8 7.090000 9 Sep Agriculture\n9 7.490000 10 Oct Agriculture\n......\n\nUpdate the code above based on the following instruction:\n\n{\n \"followup_instruction\": \"show the seasonal trends for top 3 industries that are most seaonal\",\n \"chart_type\": \"Custom Area\",\n \"chart_encodings\": {\n \"x\": \"month\",\n \"y\": \"avg_rate\",\n \"color\": \"year\",\n \"column\": \"series\"\n }\n}", "role": "user"}, {"content": "```json\n{\n \"detailed_instruction\": \"Identify the top 3 industries with the highest seasonal variation in unemployment rates, then show their seasonal trends over time with monthly unemployment rates for each year, faceted by industry.\",\n \"display_instruction\": \"Show seasonal trends for top 3 most seasonal **industries** over **years**\",\n \"output_fields\": [\"series\", \"year\", \"month\", \"avg_rate\"],\n \"chart_encodings\": {\n \"x\": \"month\",\n \"y\": \"avg_rate\",\n \"color\": \"year\",\n \"facet\": \"series\"\n },\n \"reason\": \"To identify the most seasonal industries, we need to calculate the seasonal variation (e.g., standard deviation or range of monthly rates) for each industry. Then select the top 3 industries with highest variation. The output should include year-month-series level data to show trends over time, with faceting by the top 3 most seasonal industries.\"\n}\n```\n\n```python\nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df1):\n # Calculate average unemployment rate by month and series\n monthly_avg = df1.groupby(['series', 'month'], as_index=False).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Calculate seasonal variation (standard deviation across months) for each series\n seasonal_variation = monthly_avg.groupby('series')['avg_rate'].std().reset_index()\n seasonal_variation.columns = ['series', 'seasonal_std']\n \n # Get top 3 most seasonal industries\n top_3_seasonal = seasonal_variation.nlargest(3, 'seasonal_std')['series'].tolist()\n \n # Filter original data for top 3 industries and aggregate by year, month, series\n transformed_df = df1[df1['series'].isin(top_3_seasonal)].groupby(\n ['series', 'year', 'month'], as_index=False\n ).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Convert year to string for color encoding\n transformed_df['year'] = transformed_df['year'].astype(str)\n \n return transformed_df\n```", "role": "assistant"}], "trigger": {"tableId": "table-540763", "resultTableId": "table-53", "chart": {"id": "chart-1760745840104", "chartType": "Custom Area", "encodingMap": {"x": {"fieldID": "original--unemployment-across-industries--month"}, "y": {"fieldID": "concept-avg_rate-1760745549715"}, "x2": {}, "y2": {}, "color": {"fieldID": "original--unemployment-across-industries--year"}, "column": {"fieldID": "original--unemployment-across-industries--series"}, "row": {"channel": "row", "bin": false}}, "tableRef": "table-540763", "saved": false, "source": "trigger", "unread": false}, "interaction": [{"from": "user", "to": "datatransform-agent", "role": "instruction", "content": "show the seasonal trends for top 3 industries that are most seaonal", "displayContent": "Show seasonal trends for top 3 most seasonal **industries** over **years**"}]}, "explanation": {"agent": "CodeExplanationAgent", "code": "- Calculate the **average unemployment rate** (`avg_rate`) for each combination of **series** (industry) and **month** across all years\n- Compute the **seasonal variation** for each **series** by calculating the standard deviation of `avg_rate` across the 12 months\n- Identify the **top 3 industries** with the highest `seasonal_std` (most seasonal unemployment patterns)\n- Filter the original data to include only these **top 3 most seasonal industries**\n- Aggregate the filtered data by **series**, **year**, and **month**, computing the mean unemployment **rate** as `avg_rate`\n- Convert **year** to string format for categorical visualization purposes", "concepts": [{"explanation": "Standard deviation of average monthly unemployment rates across the 12 months for each industry. This metric quantifies the degree of seasonal variation in unemployment - higher values indicate stronger seasonal patterns (e.g., construction might have higher unemployment in winter months), while lower values indicate more stable year-round employment patterns. Mathematically: \\( \\text{seasonal\\_std} = \\sigma(\\text{avg\\_rate}_{\\text{month}=1..12}) \\)", "field": "seasonal_std"}], "dialog": [{"content": "You are a data scientist to help user explain code, \nso that a non-code can clearly understand what the code is doing, you are provided with a summary of the input data, and the transformation code.\n\nYour goal:\n1. You should generate a good itemized explanation of the code so that the reader can understand high-level steps of what the data transformation is doing.\n - Be very concise, and stay at a high-level. The reader doesn't understand code and does not want to learn exactly what the code is doing. They just want to learn what have been done from a logical level.\n - The explanation should be a markdown string that is a list of bullet points (with new lines), highlight constants, data fields, and important verbs.\n2. Generate a list of explanations for new fields (fields not from the input data) that introduce metrics/concepts that are not obvious from the code.\n - provide a declarative definition that explains the new field, use a mathematical notation if applicable.\n - only include new fields explanation of new metrics that are involved in computation (e.g., ROI, commerical_success_score)\n - *DO NOT* explain trivial new fields like \"Decade\" or \"Avg_Rating\", \"US_Sales\" that are self-explanatory.\n - Avoid explaining fields that are simple aggregate of fields in the original data (min_score, avg_value, count, etc.)\n - When a field involves mathematical computation, you can use LaTeX math notation in the explanation. Format mathematical expressions using:\n - Inline math: `\\( ... \\)` for formulas within text\n - Block math: `\\[ ... \\]` for standalone formulas\n - Examples: `\\( \\frac{\\text{Revenue}}{\\text{Cost}} \\)` for ratios, `\\[ \\text{Score} = \\text{Rating} \\times \\text{Worldwide\\_Gross} \\]` for formulas\n - note: when using underscores as part of the text, you need to escape them with a backslash, e.g., `\\_`\n - Note: don't use math notation for fields whose computation is trivial (use plain english), it will likely be confusing to the reader. \n Only use math notation for fields that can not be easilyexplained in plain english. Use it sparingly.\n3. If there are multiple fields that have the similar computation, you can explain them together in one explanation.\n - in \"field\", you can provide a list of fields in format of \"field1, field2, ...\"\n - in \"explanation\", you can provide a single explanation for the computation of the fields.\n - for example, if you have fields like \"Norm_Rating\", \"Norm_Gross\", \"Critical_Commercial_Score\", you can explain Norm_Rating, Norm_Gross together in one explanation and explain Critical_Commercial_Score in another explanation.\n4. If the code is about statistical analysis, you should explain the statistical analysis in the explanation as a concept named \"Statistical Analysis\" in the [CONCEPTS EXPLANATION] section.\n - explain how you model the data, which fields are used, how data processing is done, and what models are used.\n - suggest some other modeling approaches that can be used to analyze the data in the explanation as well.\n \nThe focus is to explain how new fields are computed, don't generate explanation for low-level actions like \"return\", \"load data\" etc. \n\nProvide the result in the following two sections:\n - first section is the code explanation that should be a markdown block explaining the code, in the [CODE EXPLANATION] section.\n - remember to highlight constants, data fields, and important verbs in the code explanation.\n - second section is the concepts explanation that should be a json block (start with ```json) in the [CONCEPTS EXPLANATION] section.\n\n[CODE EXPLANATION]\n\n...(explanation of the code)\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"...\",\n \"explanation\": \"...\"\n }\n]\n\n```\n", "role": "system"}, {"content": "[CONTEXT]\n\nHere are our datasets, here are their summaries and samples:\n\n# table1 (unemployment_across_industries)\n\n## fields\n\t*series -- type: object, values: Agriculture, Business services, Construction, ..., Other, Self-employed, Transportation and Utilities, Wholesale and Retail Trade\n\t*year -- type: int64, values: 2000, 2001, 2002, ..., 2007, 2008, 2009, 2010\n\t*month -- type: int64, values: 1, 2, 3, ..., 9, 10, 11, 12\n\t*count -- type: int64, values: 2, 8, 9, ..., 2071, 2154, 2194, 2440\n\t*rate -- type: float64, values: 0.3, 1.3, 1.5, ..., 21.4, 22.7, 24.7, 27.1\n\t*date -- type: object, values: 2000-01-01T08:00:00.000Z, 2000-02-01T08:00:00.000Z, 2000-03-01T08:00:00.000Z, ..., 2009-11-01T07:00:00.000Z, 2009-12-01T08:00:00.000Z, 2010-01-01T08:00:00.000Z, 2010-02-01T08:00:00.000Z\n\n## sample\n series year month count rate date\n0 Government 2000 1 430 2.1 2000-01-01T08:00:00.000Z\n1 Government 2000 2 409 2.0 2000-02-01T08:00:00.000Z\n2 Government 2000 3 311 1.5 2000-03-01T08:00:00.000Z\n3 Government 2000 4 269 1.3 2000-04-01T08:00:00.000Z\n4 Government 2000 5 370 1.9 2000-05-01T07:00:00.000Z\n......\n\n\n\n\n[CODE]\n\nhere is the transformation code: \nimport pandas as pd\nimport collections\nimport numpy as np\n\ndef transform_data(df1):\n # Calculate average unemployment rate by month and series\n monthly_avg = df1.groupby(['series', 'month'], as_index=False).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Calculate seasonal variation (standard deviation across months) for each series\n seasonal_variation = monthly_avg.groupby('series')['avg_rate'].std().reset_index()\n seasonal_variation.columns = ['series', 'seasonal_std']\n \n # Get top 3 most seasonal industries\n top_3_seasonal = seasonal_variation.nlargest(3, 'seasonal_std')['series'].tolist()\n \n # Filter original data for top 3 industries and aggregate by year, month, series\n transformed_df = df1[df1['series'].isin(top_3_seasonal)].groupby(\n ['series', 'year', 'month'], as_index=False\n ).agg({\n 'rate': 'mean'\n }).rename(columns={'rate': 'avg_rate'})\n \n # Convert year to string for color encoding\n transformed_df['year'] = transformed_df['year'].astype(str)\n \n return transformed_df\n\n\n[EXPLANATION]\n", "role": "user"}, {"content": "[CODE EXPLANATION]\n\n- Calculate the **average unemployment rate** (`avg_rate`) for each combination of **series** (industry) and **month** across all years\n- Compute the **seasonal variation** for each **series** by calculating the standard deviation of `avg_rate` across the 12 months\n- Identify the **top 3 industries** with the highest `seasonal_std` (most seasonal unemployment patterns)\n- Filter the original data to include only these **top 3 most seasonal industries**\n- Aggregate the filtered data by **series**, **year**, and **month**, computing the mean unemployment **rate** as `avg_rate`\n- Convert **year** to string format for categorical visualization purposes\n\n[CONCEPTS EXPLANATION]\n\n```json\n[\n {\n \"field\": \"seasonal_std\",\n \"explanation\": \"Standard deviation of average monthly unemployment rates across the 12 months for each industry. This metric quantifies the degree of seasonal variation in unemployment - higher values indicate stronger seasonal patterns (e.g., construction might have higher unemployment in winter months), while lower values indicate more stable year-round employment patterns. Mathematically: \\\\( \\\\text{seasonal\\\\_std} = \\\\sigma(\\\\text{avg\\\\_rate}_{\\\\text{month}=1..12}) \\\\)\"\n }\n]\n```", "role": "assistant"}], "status": "ok"}, "status": "completed"}, "anchored": false, "attachedMetadata": ""}], "charts": [{"id": "chart-1760745839180", "chartType": "Line Chart", "encodingMap": {"x": {"fieldID": "original--unemployment-across-industries--month"}, "y": {"fieldID": "concept-avg_rate-1760745549715"}, "color": {"fieldID": "original--unemployment-across-industries--year"}, "opacity": {"channel": "opacity", "bin": false}, "column": {"fieldID": "original--unemployment-across-industries--series"}, "row": {"channel": "row", "bin": false}}, "tableRef": "table-53", "saved": false, "source": "user", "unread": false}, {"id": "chart-1760745566172", "chartType": "Line Chart", "encodingMap": {"x": {"fieldID": "original--unemployment-across-industries--date"}, "y": {"fieldID": "original--unemployment-across-industries--rate"}, "color": {"fieldID": 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{"formulateTimeoutSeconds": 60, "maxRepairAttempts": 1, "defaultChartWidth": 300, "defaultChartHeight": 300}, "dataCleanBlocks": [], "cleanInProgress": false, "generatedReports": [{"id": "report-1760831469874-4137", "content": "# The 2008-2010 Recession Hit Construction Hardest\n\nDuring the 2008-2010 financial crisis, unemployment surged dramatically across key industries. Construction workers bore the brunt, with unemployment rates skyrocketing from 11% to a staggering 27% by early 2010—nearly tripling in just two years.\n\n[IMAGE(chart-1760745566172)]\n\nManufacturing and Mining sectors also suffered significant job losses, though less severe than Construction. Manufacturing unemployment doubled from 5% to 13%, while Mining spiked from under 4% to 16% before showing signs of recovery by 2010.\n\n**In summary**, the recession's impact was far from uniform: Construction faced catastrophic job losses, suggesting the housing market collapse disproportionately affected building trades. What factors enabled Mining's earlier recovery? How can vulnerable sectors build resilience against future economic shocks?", "style": "short note", "selectedChartIds": ["chart-1760745566172"], "createdAt": 1760831476855, "status": "completed", "title": "The 2008-2010 Recession Hit Construction Hardest", "anchorChartId": "chart-1760745566172"}, {"id": "report-1760831405296-2186", "content": "# Seasonal Employment Patterns Reveal Industry Vulnerabilities Across Economic Cycles\n\nThe unemployment landscape across U.S. industries from 2000-2010 reveals striking seasonal patterns and differential impacts during economic downturns. Analysis of 14 industry sectors demonstrates that certain industries face predictable cyclical challenges while others maintain relative stability throughout the year.\n\n[IMAGE(chart-1760745546664)]\n\n**Cross-industry seasonal dynamics** show distinct unemployment patterns throughout the calendar year. Key findings include:\n\n- **Agriculture** experiences the most dramatic seasonal swings, with unemployment rates peaking above 13% in winter months (January-March) and dropping to approximately 7% during summer harvest seasons\n- **Construction** exhibits similar seasonality, with rates near 13% in January declining to 7-8% during peak building months (May-September)\n- **Government and Finance** sectors demonstrate remarkable stability, maintaining unemployment rates between 2-4% year-round, suggesting these sectors are largely insulated from seasonal fluctuations\n\n[IMAGE(chart-1760745839180)]\n\n**Year-over-year trends for the most seasonal industries** reveal how economic conditions compound seasonal effects. The 2008-2009 recession created unprecedented spikes:\n\n- Construction unemployment surged from typical winter peaks of 10-13% to over 25% by early 2010\n- Agriculture showed dramatic year-to-year variation, with 2009-2010 winter unemployment reaching 19-21%, nearly double the levels seen in 2000\n- Mining and Extraction maintained lower overall rates (2-7%) but still exhibited seasonal patterns, with late-year increases across most periods\n\n**In summary**, industries reliant on weather-dependent operations face significant seasonal unemployment challenges, which are dramatically amplified during economic recessions. While sectors like Government and Finance maintain stable employment year-round, Agriculture and Construction workers experience predictable winter unemployment that can more than double during economic downturns. These patterns suggest the need for targeted workforce development and social safety net programs that account for both seasonal and cyclical employment disruptions. Further investigation should examine whether these patterns have evolved post-2010 and assess the effectiveness of countercyclical policies in stabilizing employment in vulnerable sectors.", "style": "executive summary", "selectedChartIds": ["chart-1760745546664", "chart-1760745839180"], "createdAt": 1760831421331, "status": "completed", "title": "Seasonal Employment Patterns Reveal Industry Vulnerabilities Across Economic Cycles", "anchorChartId": "chart-1760745546664"}, {"id": "report-1760831364867-9457", "content": "# Unemployment Surge Across Industries: The 2008-2009 Economic Crisis Impact\n\nThe decade from 2000 to 2010 witnessed significant volatility in unemployment rates across major industry sectors, with a dramatic escalation during the 2008-2009 financial crisis that fundamentally reshaped the American labor market.\n\n[IMAGE(chart-1760745539559)]\n\nThe longitudinal analysis reveals that while most industries maintained relatively stable unemployment rates between 2000 and 2007—typically fluctuating between 2% and 8%—the landscape changed dramatically beginning in late 2008. **Construction** emerged as the most severely impacted sector, with unemployment rates soaring to approximately **27% by early 2010**. Similarly, **Agriculture** experienced unprecedented spikes reaching above **22%**, demonstrating the crisis's far-reaching effects beyond traditional white-collar sectors. Even traditionally stable sectors like Government and Finance, which historically maintained rates below 3%, saw significant increases, reaching 5-7% during the peak crisis period.\n\n[IMAGE(chart-1760745556213)]\n\nExamining the year-over-year impact between 2008 and 2009 reveals the crisis's differential industry effects:\n\n- **Mining and Extraction** and **Construction** led all sectors with unemployment rate increases exceeding **8.5 percentage points**\n- **Manufacturing** and **Agriculture** followed with increases above **5 percentage points**\n- **Business services** and **Information** sectors experienced moderate increases of approximately **4 percentage points**\n- **Government** showed the smallest increase at just **1.2 percentage points**, demonstrating relative stability during economic turbulence\n\n**In summary**, the 2008-2009 financial crisis created an unprecedented unemployment shock across American industries, with resource extraction, construction, and manufacturing bearing the heaviest burden. The data reveals that sectors tied to physical production and cyclical economic activity experienced the most severe dislocations, while government employment remained comparatively insulated. Key follow-up questions include: What were the long-term recovery trajectories for these industries post-2010? Did the workers displaced from heavily impacted sectors successfully transition to more stable industries? How did policy interventions differentially affect recovery across these sectors?", "style": "executive summary", "selectedChartIds": ["chart-1760745556213", "chart-1760745539559"], "createdAt": 1760831379076, "status": "completed", "title": "Unemployment Surge Across Industries: The 2008-2009 Economic Crisis Impact", "anchorChartId": "chart-1760745556213"}], "currentReport": {"id": "report-1760750575650-2619", "content": "# Hollywood's Billion-Dollar Hitmakers\n\n*Avatar* stands alone—earning over $2.5B in profit, dwarfing all competition. Action and Adventure films dominate the most profitable titles, with franchises like *Jurassic Park*, *The Dark Knight*, and *Lord of the Rings* proving blockbuster formulas work.\n\n\"Chart\"\n\nSteven Spielberg leads all directors with $7.2B in total profit across his career, showcasing remarkable consistency with hits spanning decades—from *Jurassic Park* to *E.T.* His nearest competitors trail by billions, underlining his unmatched commercial impact.\n\n\"Chart\"\n\n**In summary**, mega-budget Action and Adventure films generate extraordinary returns when they succeed, and a handful of elite directors—led by Spielberg—have mastered the formula for sustained box office dominance.", "style": "short note", "selectedChartIds": ["chart-1760743347871", "chart-1760743768741"], "chartImages": {}, "createdAt": 1760750584189, "title": "Report - 10/17/2025"}, "activeChallenges": [], "_persist": {"version": -1, "rehydrated": true}, "draftNodes": [], "focusedId": {"type": "report", "reportId": "report-1760831469874-4137"}} \ No newline at end of file diff --git a/py-src/data_formulator/agent_routes.py b/py-src/data_formulator/agent_routes.py index a05882c6..6a675e39 100644 --- a/py-src/data_formulator/agent_routes.py +++ b/py-src/data_formulator/agent_routes.py @@ -7,7 +7,6 @@ import os import mimetypes import re -import traceback mimetypes.add_type('application/javascript', '.js') mimetypes.add_type('application/javascript', '.mjs') @@ -23,9 +22,9 @@ from data_formulator.agents.agent_data_rec import DataRecAgent from data_formulator.agents.agent_sort_data import SortDataAgent -from data_formulator.auth import get_identity_id -from data_formulator.code_signing import sign_result, verify_code, MAX_CODE_SIZE -from data_formulator.datalake.workspace import Workspace, WorkspaceWithTempData +from data_formulator.security.auth import get_identity_id +from data_formulator.security.code_signing import sign_result, verify_code, MAX_CODE_SIZE +from data_formulator.datalake.workspace import Workspace from data_formulator.workspace_factory import get_workspace from data_formulator.agents.agent_data_load import DataLoadAgent from data_formulator.agents.agent_data_clean_stream import DataCleanAgentStream @@ -34,135 +33,157 @@ from data_formulator.agents.agent_interactive_explore import InteractiveExploreAgent from data_formulator.agents.agent_report_gen import ReportGenAgent from data_formulator.agents.client_utils import Client +from data_formulator.model_registry import model_registry from data_formulator.agents.data_agent import DataAgent +from data_formulator.agents.agent_language import build_language_instruction +from data_formulator.security.sanitize import classify_llm_error # Get logger for this module (logging config done in app.py) logger = logging.getLogger(__name__) -def get_temp_tables(workspace, input_tables: list[dict]) -> list[dict]: - """ - Determine which input tables are temp tables (not persisted in the workspace datalake). - - Args: - workspace: The user's workspace instance - input_tables: List of table dicts with 'name' and 'rows' keys - - Returns: - List of table dicts that don't exist in the workspace (temp tables) +def get_language_instruction(*, mode: str = "full") -> str: + """Read the UI language from the Accept-Language header and build the prompt instruction. + + mode: "full" for text-heavy agents, "compact" for code-generation agents. """ - existing_tables = set(workspace.list_tables()) - return [table for table in input_tables if table.get('name') not in existing_tables] + lang = request.headers.get('Accept-Language', 'en').split(',')[0].split('-')[0].strip().lower() + return build_language_instruction(lang, mode=mode) + agent_bp = Blueprint('agent', __name__, url_prefix='/api/agent') + +@agent_bp.after_request +def _set_cors(response): + """Set CORS headers from server configuration. + + By default no ``Access-Control-Allow-Origin`` header is emitted + (same-origin only). To allow cross-origin requests set the + ``CORS_ORIGIN`` env-var (e.g. ``CORS_ORIGIN=https://my-embed-host``). + Use ``CORS_ORIGIN=*`` only for development / fully trusted networks. + """ + origin = os.environ.get('CORS_ORIGIN', '') + if origin: + response.headers['Access-Control-Allow-Origin'] = origin + response.headers['Access-Control-Allow-Methods'] = 'GET, POST, OPTIONS' + response.headers['Access-Control-Allow-Headers'] = 'Content-Type' + return response + @agent_bp.errorhandler(Exception) def handle_agent_error(e): """Catch-all error handler to ensure JSON responses instead of HTML error pages.""" - logger.error(f"Unhandled error in agent route: {e}") - logger.error(traceback.format_exc()) + logger.error("Unhandled error in agent route", exc_info=e) response = flask.jsonify({ "status": "error", - "error_message": sanitize_model_error(str(e)), + "error_message": "An unexpected error occurred", "results": [], "result": [] }) - response.headers.add('Access-Control-Allow-Origin', '*') return response, 500 def get_client(model_config): + # For global models, resolve real credentials from the server-side registry. + # The frontend only knows the model id; the api_key never leaves the server. + if model_config.get("is_global"): + real_config = model_registry.get_config(model_config["id"]) + if real_config: + model_config = real_config + for key in model_config: - model_config[key] = model_config[key].strip() + if isinstance(model_config[key], str): + model_config[key] = model_config[key].strip() + + # Validate user-provided api_base against the allowlist (SSRF protection). + # Global models are trusted (their api_base comes from server env vars). + if not model_config.get("is_global"): + from data_formulator.security.url_allowlist import validate_api_base + validate_api_base(model_config.get("api_base")) client = Client( model_config["endpoint"], model_config["model"], - model_config["api_key"] if "api_key" in model_config else None, - html.escape(model_config["api_base"]) if "api_base" in model_config else None, - model_config["api_version"] if "api_version" in model_config else None) + model_config.get("api_key") or None, + html.escape(model_config["api_base"]) if model_config.get("api_base") else None, + model_config.get("api_version") or None, + ) return client +@agent_bp.route('/list-global-models', methods=['GET', 'POST']) +def list_global_models(): + """Return all globally configured models instantly, without connectivity checks. + + The frontend calls this first to render the model list immediately (with a + 'checking' status), then calls /check-available-models to get real statuses. + """ + public_models = model_registry.list_public() + return jsonify(public_models) + + @agent_bp.route('/check-available-models', methods=['GET', 'POST']) def check_available_models(): - results = [] - - # Define configurations for different providers - providers = ['openai', 'azure', 'anthropic', 'gemini', 'ollama'] + """ + Return all globally configured models with their connectivity status. - for provider in providers: - # Skip if provider is not enabled - if not os.getenv(f"{provider.upper()}_ENABLED", "").lower() == "true": - continue - - api_key = os.getenv(f"{provider.upper()}_API_KEY", "") - api_base = os.getenv(f"{provider.upper()}_API_BASE", "") - api_version = os.getenv(f"{provider.upper()}_API_VERSION", "") - models = os.getenv(f"{provider.upper()}_MODELS", "") - - if not (api_key or api_base): - continue - - if not models: - continue - - # Build config for each model - for model in models.split(","): - model = model.strip() - if not model: - continue - - model_config = { - "id": f"{provider}-{model}-{api_key}-{api_base}-{api_version}", - "endpoint": provider, - "model": model, - "api_key": api_key, - "api_base": api_base, - "api_version": api_version - } - - # Retry with backoff — DefaultAzureCredential and other providers - # may need a moment to initialize on cold start. - max_retries = 3 - for attempt in range(max_retries): + Connectivity checks run in parallel (ThreadPoolExecutor) so the total + wall-clock time equals the slowest single model, not the sum of all. + Sensitive credentials (api_key) are never sent to the client. + """ + import time + from concurrent.futures import ThreadPoolExecutor, as_completed + + all_public = model_registry.list_public() + logger.info("=" * 60) + logger.info(f"[check-available-models] Checking {len(all_public)} global models") + for p in all_public: + logger.info(f" -> {p['id']} (endpoint={p['endpoint']}, model={p['model']}, api_base={p['api_base']})") + overall_start = time.time() + + def _check_one(public_info: dict) -> dict: + model_id = public_info["id"] + t0 = time.time() + full_config = model_registry.get_config(model_id) + status = "disconnected" + error = None + + try: + client = get_client(full_config) + logger.info(f" [{model_id}] Sending connectivity ping (max_tokens=3)...") + client.ping(timeout=10) + status = "connected" + logger.info(f" [{model_id}] Connected ({time.time() - t0:.1f}s)") + except Exception as e: + elapsed = time.time() - t0 + logger.warning(f" [{model_id}] Failed ({elapsed:.1f}s): {type(e).__name__}: {e}") + error = classify_llm_error(e) + + return {**public_info, "status": status, "error": error} + + results = [] + if all_public: + with ThreadPoolExecutor(max_workers=min(len(all_public), 8)) as executor: + futures = {executor.submit(_check_one, p): p["id"] for p in all_public} + for future in as_completed(futures): try: - client = get_client(model_config) - response = client.get_completion( - messages=[ - {"role": "system", "content": "You are a helpful assistant."}, - {"role": "user", "content": "Respond 'I can hear you.' if you can hear me."}, - ] - ) - - if "I can hear you." in response.choices[0].message.content: - results.append(model_config) - break # success or non-matching response — don't retry + results.append(future.result()) except Exception as e: - if attempt < max_retries - 1: - import time - wait = 2 ** attempt # 1s, 2s - logger.warning(f"Retrying {provider}/{model} in {wait}s (attempt {attempt+1}/{max_retries}): {e}") - time.sleep(wait) - else: - logger.error(f"Error testing {provider} model {model} after {max_retries} attempts: {e}") - - return json.dumps(results) - -def sanitize_model_error(error_message: str) -> str: - """Sanitize model API error messages before sending to client.""" - # HTML escape the message - message = html.escape(error_message) - - # Remove any potential API keys that might be in the error - message = re.sub(r'(api[-_]?key|api[-_]?token)[=:]\s*[^\s&]+', r'\1=', message, flags=re.IGNORECASE) - - # Keep only the essential error info - if len(message) > 500: # Truncate very long messages - message = message[:500] + "..." + model_id = futures[future] + logger.error(f" [{model_id}] Thread exception: {e}") + pub = next(p for p in all_public if p["id"] == model_id) + results.append({**pub, "status": "disconnected", "error": "Check thread exception"}) + + id_order = [p["id"] for p in all_public] + results.sort(key=lambda r: id_order.index(r["id"])) + + total_elapsed = time.time() - overall_start + connected = sum(1 for r in results if r["status"] == "connected") + logger.info(f"[check-available-models] Done: {connected}/{len(results)} connected, total {total_elapsed:.1f}s") + logger.info("=" * 60) - return message + return jsonify(results) @agent_bp.route('/test-model', methods=['GET', 'POST']) def test_model(): @@ -194,17 +215,16 @@ def test_model(): "message": "" } except Exception as e: - print(f"Error: {e}") - logger.info(f"Error: {e}") + logger.exception(f"Error testing model {content['model'].get('id', '')}") result = { "model": content['model'], "status": 'error', - "message": sanitize_model_error(str(e)), + "message": classify_llm_error(e), } else: result = {'status': 'error'} - return json.dumps(result) + return jsonify(result) @agent_bp.route('/process-data-on-load', methods=['GET', 'POST']) def process_data_on_load_request(): @@ -226,12 +246,11 @@ def process_data_on_load_request(): # Check if input table is in workspace, if not add as temp data input_tables = [{"name": input_data.get("name"), "rows": input_data.get("rows", [])}] - temp_data = get_temp_tables(workspace, input_tables) - with WorkspaceWithTempData(workspace, temp_data) as workspace: - agent = DataLoadAgent(client=client, workspace=workspace) - candidates = agent.run(content["input_data"]) - candidates = [c['content'] for c in candidates if c['status'] == 'ok'] + language_instruction = get_language_instruction(mode="compact") + agent = DataLoadAgent(client=client, workspace=workspace, language_instruction=language_instruction) + candidates = agent.run(content["input_data"]) + candidates = [c['content'] for c in candidates if c['status'] == 'ok'] response = flask.jsonify({ "status": "ok", "token": token, "result": candidates }) except Exception as e: @@ -240,7 +259,6 @@ def process_data_on_load_request(): else: response = flask.jsonify({ "token": -1, "status": "error", "result": [] }) - response.headers.add('Access-Control-Allow-Origin', '*') return response @@ -256,7 +274,8 @@ def generate(): logger.debug(f" model: {content['model']}") - agent = DataCleanAgentStream(client=client) + language_instruction = get_language_instruction() + agent = DataCleanAgentStream(client=client, language_instruction=language_instruction) try: for chunk in agent.stream(content.get('prompt', ''), content.get('artifacts', []), content.get('dialog', [])): @@ -275,23 +294,18 @@ def generate(): "status": "error", "result": 'unable to process data clean request' } - yield '\n' + json.dumps(error_data) + '\n' + yield '\n' + json.dumps(error_data, ensure_ascii=False) + '\n' else: error_data = { "token": -1, "status": "error", "result": "Invalid request format" } - yield '\n' + json.dumps(error_data) + '\n' + yield '\n' + json.dumps(error_data, ensure_ascii=False) + '\n' response = Response( stream_with_context(generate()), mimetype='application/json', - headers={ - 'Access-Control-Allow-Origin': '*', - 'Access-Control-Allow-Methods': 'GET, POST, OPTIONS', - 'Access-Control-Allow-Headers': 'Content-Type' - } ) return response @@ -313,13 +327,11 @@ def sort_data_request(): candidates = candidates if candidates != None else [] response = flask.jsonify({ "status": "ok", "token": token, "result": candidates }) except Exception as e: - logger.error(f"Error in sort-data: {e}") - logger.error(traceback.format_exc()) - response = flask.jsonify({ "token": token, "status": "error", "result": [], "error_message": sanitize_model_error(str(e)) }) + logger.error("Error in sort-data", exc_info=e) + response = flask.jsonify({ "token": token, "status": "error", "result": [], "error_message": classify_llm_error(e) }) else: response = flask.jsonify({ "token": -1, "status": "error", "result": [] }) - response.headers.add('Access-Control-Allow-Origin', '*') return response @agent_bp.route('/derive-data', methods=['GET', 'POST']) @@ -361,38 +373,57 @@ def derive_data(): try: identity_id = get_identity_id() workspace = get_workspace(identity_id) - temp_data = get_temp_tables(workspace, input_tables) max_display_rows = current_app.config['CLI_ARGS']['max_display_rows'] - with WorkspaceWithTempData(workspace, temp_data) as workspace: - if mode == "recommendation": - # Use unified Python agent for recommendations - agent = DataRecAgent(client=client, workspace=workspace, agent_coding_rules=agent_coding_rules, max_display_rows=max_display_rows) - results = agent.run(input_tables, instruction, n=1, prev_messages=prev_messages) - else: - # Use unified Python agent that generates Python scripts with DuckDB + pandas - agent = DataTransformationAgent(client=client, workspace=workspace, agent_coding_rules=agent_coding_rules, max_display_rows=max_display_rows) - results = agent.run(input_tables, instruction, prev_messages, - current_visualization=current_visualization, expected_visualization=expected_visualization) + language_instruction = get_language_instruction(mode="compact") - repair_attempts = 0 - while results[0]['status'] == 'error' and repair_attempts < max_repair_attempts: - error_message = results[0]['content'] - logger.warning(f"[derive-data] Code generation failed (attempt {repair_attempts + 1}/{max_repair_attempts}), mode={mode}. Error: {error_message}") - new_instruction = f"We run into the following problem executing the code, please fix it:\n\n{error_message}\n\nPlease think step by step, reflect why the error happens and fix the code so that no more errors would occur." + model_info = { + "model": content['model'].get("model", ""), + "endpoint": content['model'].get("endpoint", ""), + "api_base": content['model'].get("api_base", ""), + } - prev_dialog = results[0]['dialog'] + if mode == "recommendation": + agent = DataRecAgent(client=client, workspace=workspace, agent_coding_rules=agent_coding_rules, language_instruction=language_instruction, max_display_rows=max_display_rows, model_info=model_info) + results = agent.run(input_tables, instruction, n=1, prev_messages=prev_messages) + else: + agent = DataTransformationAgent(client=client, workspace=workspace, agent_coding_rules=agent_coding_rules, language_instruction=language_instruction, max_display_rows=max_display_rows, model_info=model_info) + results = agent.run(input_tables, instruction, prev_messages, + current_visualization=current_visualization, expected_visualization=expected_visualization) + + repair_attempts = 0 + while ( + isinstance(results, list) + and len(results) > 0 + and results[0].get('status') in ('error', 'other error') + and repair_attempts < max_repair_attempts + ): + error_message = results[0].get('content', 'Unknown error') + logger.warning(f"[derive-data] Code generation failed (attempt {repair_attempts + 1}/{max_repair_attempts}), mode={mode}. Error: {error_message}") + new_instruction = f"We run into the following problem executing the code, please fix it:\n\n{error_message}\n\nPlease think step by step, reflect why the error happens and fix the code so that no more errors would occur." + + prev_dialog = results[0].get('dialog', []) + try: if mode == "transform": results = agent.followup(input_tables, prev_dialog, [], new_instruction, n=1) if mode == "recommendation": results = agent.followup(input_tables, prev_dialog, [], new_instruction, n=1) - - repair_attempts += 1 - logger.warning(f"[derive-data] Repair attempt {repair_attempts}/{max_repair_attempts} result: {results[0]['status']}") - - if repair_attempts > 0: - logger.warning(f"[derive-data] Finished repair loop after {repair_attempts} attempt(s). Final status: {results[0]['status']}") + except Exception as followup_exc: + logger.exception("derive_data followup failed") + results = [{ + "status": "error", + "content": classify_llm_error(followup_exc), + "code": "", + "dialog": [], + }] + break + + repair_attempts += 1 + logger.warning(f"[derive-data] Repair attempt {repair_attempts}/{max_repair_attempts} result: {results[0].get('status', 'unknown')}") + + if repair_attempts > 0: + logger.warning(f"[derive-data] Finished repair loop after {repair_attempts} attempt(s). Final status: {results[0].get('status', 'unknown')}") # Sign code in each result so the frontend can send it back # for re-execution during data refresh with proof of authenticity. @@ -401,13 +432,11 @@ def derive_data(): response = flask.jsonify({ "token": token, "status": "ok", "results": results }) except Exception as e: - logger.error(f"Error in derive-data: {e}") - logger.error(traceback.format_exc()) - response = flask.jsonify({ "token": token, "status": "error", "results": [], "error_message": sanitize_model_error(str(e)) }) + logger.error("Error in derive-data", exc_info=e) + response = flask.jsonify({ "token": token, "status": "error", "results": [], "error_message": classify_llm_error(e) }) else: response = flask.jsonify({ "token": "", "status": "error", "results": [] }) - response.headers.add('Access-Control-Allow-Origin', '*') return response @agent_bp.route('/data-agent-streaming', methods=['GET', 'POST']) @@ -458,60 +487,62 @@ def generate(): "token": token, "status": "error", "result": {"type": "error", "error_message": "Identity ID required"}, - }) + '\n' + }, ensure_ascii=False) + '\n' return workspace = get_workspace(identity_id) - temp_data = get_temp_tables(workspace, input_tables) if input_tables else None + + language_instruction = get_language_instruction(mode="full") + rec_language_instruction = get_language_instruction(mode="compact") try: - with WorkspaceWithTempData(workspace, temp_data) as ws: - agent = DataAgent( - client=client, - workspace=ws, - agent_exploration_rules=agent_exploration_rules, - agent_coding_rules=agent_coding_rules, - max_iterations=max_iterations, - max_repair_attempts=max_repair_attempts, - ) - - # Build trajectory for resume or fresh start - trajectory = None - if resume_trajectory and clarification_response: - # Append the user's clarification to the saved trajectory - trajectory = list(resume_trajectory) - trajectory.append({ - "role": "user", - "content": f"[USER CLARIFICATION]\n\n{clarification_response}", - }) - logger.debug(f"== resuming with clarification ===> {clarification_response}") - - for event in agent.run( - input_tables=input_tables, - user_question=user_question, - conversation_history=conversation_history, - trajectory=trajectory, - completed_step_count=completed_step_count, - ): - yield json.dumps({ - "token": token, - "status": "ok", - "result": event, - }) + '\n' - - # Stop streaming after terminal events - if event.get("type") in ("completion", "clarify"): - break + agent = DataAgent( + client=client, + workspace=workspace, + agent_exploration_rules=agent_exploration_rules, + agent_coding_rules=agent_coding_rules, + language_instruction=language_instruction, + rec_language_instruction=rec_language_instruction, + max_iterations=max_iterations, + max_repair_attempts=max_repair_attempts, + ) + + # Build trajectory for resume or fresh start + trajectory = None + if resume_trajectory and clarification_response: + # Append the user's clarification to the saved trajectory + trajectory = list(resume_trajectory) + trajectory.append({ + "role": "user", + "content": f"[USER CLARIFICATION]\n\n{clarification_response}", + }) + logger.debug(f"== resuming with clarification ===> {clarification_response}") + + for event in agent.run( + input_tables=input_tables, + user_question=user_question, + conversation_history=conversation_history, + trajectory=trajectory, + completed_step_count=completed_step_count, + ): + yield json.dumps({ + "token": token, + "status": "ok", + "result": event, + }, ensure_ascii=False) + '\n' + + # Stop streaming after terminal events + if event.get("type") in ("completion", "clarify"): + break except Exception as e: - logger.error(f"Error in data-agent-streaming: {e}") - logger.error(traceback.format_exc()) + logger.error("Error in data-agent-streaming", exc_info=e) yield json.dumps({ "token": token, "status": "error", "result": None, - "error_message": sanitize_model_error(str(e)), - }) + '\n' + "error_message": classify_llm_error(e), + }, ensure_ascii=False) + '\n' logger.setLevel(logging.WARNING) @@ -521,16 +552,11 @@ def generate(): "status": "error", "result": None, "error_message": "Invalid request format", - }) + '\n' + }, ensure_ascii=False) + '\n' response = Response( stream_with_context(generate()), mimetype='application/json', - headers={ - 'Access-Control-Allow-Origin': '*', - 'Access-Control-Allow-Methods': 'GET, POST, OPTIONS', - 'Access-Control-Allow-Headers': 'Content-Type', - } ) return response @@ -568,28 +594,49 @@ def refine_data(): try: identity_id = get_identity_id() workspace = get_workspace(identity_id) - temp_data = get_temp_tables(workspace, input_tables) max_display_rows = current_app.config['CLI_ARGS']['max_display_rows'] - with WorkspaceWithTempData(workspace, temp_data) as workspace: - # Use unified Python agent for followup transformations - agent = DataTransformationAgent(client=client, workspace=workspace, agent_coding_rules=agent_coding_rules, max_display_rows=max_display_rows) - results = agent.followup(input_tables, dialog, latest_data_sample, new_instruction, n=1, - current_visualization=current_visualization, expected_visualization=expected_visualization) + language_instruction = get_language_instruction(mode="compact") - repair_attempts = 0 - while results[0]['status'] == 'error' and repair_attempts < max_repair_attempts: - error_message = results[0]['content'] - logger.info(f"[refine-data] Code generation failed (attempt {repair_attempts + 1}/{max_repair_attempts}). Error: {error_message}") - new_instruction = f"We run into the following problem executing the code, please fix it:\n\n{error_message}\n\nPlease think step by step, reflect why the error happens and fix the code so that no more errors would occur." - prev_dialog = results[0]['dialog'] + model_info = { + "model": content['model'].get("model", ""), + "endpoint": content['model'].get("endpoint", ""), + "api_base": content['model'].get("api_base", ""), + } - results = agent.followup(input_tables, prev_dialog, [], new_instruction, n=1) - repair_attempts += 1 - logger.info(f"[refine-data] Repair attempt {repair_attempts}/{max_repair_attempts} result: {results[0]['status']}") + agent = DataTransformationAgent(client=client, workspace=workspace, agent_coding_rules=agent_coding_rules, language_instruction=language_instruction, max_display_rows=max_display_rows, model_info=model_info) + results = agent.followup(input_tables, dialog, latest_data_sample, new_instruction, n=1, + current_visualization=current_visualization, expected_visualization=expected_visualization) + + repair_attempts = 0 + while ( + isinstance(results, list) + and len(results) > 0 + and results[0].get('status') in ('error', 'other error') + and repair_attempts < max_repair_attempts + ): + error_message = results[0].get('content', 'Unknown error') + logger.info(f"[refine-data] Code generation failed (attempt {repair_attempts + 1}/{max_repair_attempts}). Error: {error_message}") + new_instruction = f"We run into the following problem executing the code, please fix it:\n\n{error_message}\n\nPlease think step by step, reflect why the error happens and fix the code so that no more errors would occur." + prev_dialog = results[0].get('dialog', []) - if repair_attempts > 0: - logger.info(f"[refine-data] Finished repair loop after {repair_attempts} attempt(s). Final status: {results[0]['status']}") + try: + results = agent.followup(input_tables, prev_dialog, [], new_instruction, n=1) + except Exception as followup_exc: + logger.exception("refine_data followup failed") + results = [{ + "status": "error", + "content": classify_llm_error(followup_exc), + "code": "", + "dialog": [], + }] + break + + repair_attempts += 1 + logger.info(f"[refine-data] Repair attempt {repair_attempts}/{max_repair_attempts} result: {results[0].get('status', 'unknown')}") + + if repair_attempts > 0: + logger.info(f"[refine-data] Finished repair loop after {repair_attempts} attempt(s). Final status: {results[0].get('status', 'unknown')}") # Sign code in each result for secure refresh later. for r in results: @@ -597,13 +644,11 @@ def refine_data(): response = flask.jsonify({ "token": token, "status": "ok", "results": results}) except Exception as e: - logger.error(f"Error in refine-data: {e}") - logger.error(traceback.format_exc()) - response = flask.jsonify({ "token": token, "status": "error", "results": [], "error_message": sanitize_model_error(str(e)) }) + logger.error("Error in refine-data", exc_info=e) + response = flask.jsonify({ "token": token, "status": "error", "results": [], "error_message": classify_llm_error(e) }) else: response = flask.jsonify({ "token": "", "status": "error", "results": []}) - response.headers.add('Access-Control-Allow-Origin', '*') return response @agent_bp.route('/code-expl', methods=['GET', 'POST']) @@ -620,26 +665,24 @@ def request_code_expl(): # Get workspace and mount temp data identity_id = get_identity_id() workspace = get_workspace(identity_id) - temp_data = get_temp_tables(workspace, input_tables) - with WorkspaceWithTempData(workspace, temp_data) as workspace: - try: - code_expl_agent = CodeExplanationAgent(client=client, workspace=workspace) - candidates = code_expl_agent.run(input_tables, code) - - # Return the first candidate's content as JSON - if candidates and len(candidates) > 0: - result = candidates[0] - if result['status'] == 'ok': - return jsonify(result) - else: - return jsonify(result), 400 + language_instruction = get_language_instruction() + + try: + code_expl_agent = CodeExplanationAgent(client=client, workspace=workspace, language_instruction=language_instruction) + candidates = code_expl_agent.run(input_tables, code) + + if candidates and len(candidates) > 0: + result = candidates[0] + if result['status'] == 'ok': + return jsonify(result) else: - return jsonify({'error': 'No explanation generated'}), 400 - except Exception as e: - logger.error(f"Error in code-expl: {e}") - logger.error(traceback.format_exc()) - return jsonify({'error': sanitize_model_error(str(e))}), 400 + return jsonify(result), 400 + else: + return jsonify({'error': 'No explanation generated'}), 400 + except Exception as e: + logger.error("Error in code-expl", exc_info=e) + return jsonify({'error': classify_llm_error(e)}), 400 else: return jsonify({'error': 'Invalid request format'}), 400 @@ -658,25 +701,23 @@ def request_chart_insight(): # Get workspace identity_id = get_identity_id() workspace = get_workspace(identity_id) - temp_data = get_temp_tables(workspace, input_tables) - with WorkspaceWithTempData(workspace, temp_data) as workspace: - try: - agent = ChartInsightAgent(client=client, workspace=workspace) - candidates = agent.run(chart_image, chart_type, field_names, input_tables) - - if candidates and len(candidates) > 0: - result = candidates[0] - if result['status'] == 'ok': - return jsonify(result) - else: - return jsonify(result), 400 + try: + agent = ChartInsightAgent(client=client, workspace=workspace, + language_instruction=get_language_instruction()) + candidates = agent.run(chart_image, chart_type, field_names, input_tables) + + if candidates and len(candidates) > 0: + result = candidates[0] + if result['status'] == 'ok': + return jsonify(result) else: - return jsonify({'error': 'No insight generated'}), 400 - except Exception as e: - logger.error(f"Error in chart-insight: {e}") - logger.error(traceback.format_exc()) - return jsonify({'error': sanitize_model_error(str(e))}), 400 + return jsonify(result), 400 + else: + return jsonify({'error': 'No insight generated'}), 400 + except Exception as e: + logger.error("Error in chart-insight", exc_info=e) + return jsonify({'error': classify_llm_error(e)}), 400 else: return jsonify({'error': 'Invalid request format'}), 400 @@ -706,29 +747,28 @@ def generate(): all_tables = list(input_tables) if exploration_thread: all_tables.extend(exploration_thread) - temp_data = get_temp_tables(workspace, all_tables) if all_tables else None - with WorkspaceWithTempData(workspace, temp_data) as workspace: - agent = InteractiveExploreAgent(client=client, workspace=workspace, agent_exploration_rules=agent_exploration_rules) - try: - for chunk in agent.run(input_tables, start_question, exploration_thread, current_data_sample, current_chart, mode): - yield chunk - except Exception as e: - logger.error(e) - error_data = { - "content": "unable to process recommendation questions request" - } - yield 'error: ' + json.dumps(error_data) + '\n' + agent = InteractiveExploreAgent(client=client, workspace=workspace, + agent_exploration_rules=agent_exploration_rules, + language_instruction=get_language_instruction()) + try: + for chunk in agent.run(input_tables, start_question, exploration_thread, current_data_sample, current_chart, mode): + yield chunk + except Exception as e: + logger.exception("get-recommendation-questions failed") + error_data = { + "content": classify_llm_error(e) + } + yield 'error: ' + json.dumps(error_data, ensure_ascii=False) + '\n' else: error_data = { "content": "Invalid request format" } - yield 'error: ' + json.dumps(error_data) + '\n' + yield 'error: ' + json.dumps(error_data, ensure_ascii=False) + '\n' response = Response( stream_with_context(generate()), mimetype='application/json', - headers={ 'Access-Control-Allow-Origin': '*', } ) return response @@ -747,29 +787,34 @@ def generate(): style = content.get("style", "blog post") identity_id = get_identity_id() workspace = get_workspace(identity_id) - temp_data = get_temp_tables(workspace, input_tables) if input_tables else None + # Include both input tables and chart data tables as temp data + # so derived tables referenced by charts are also available + all_tables = list(input_tables) + for chart in charts: + chart_data = chart.get("chart_data") + if chart_data and chart_data.get("name") and chart_data.get("rows"): + all_tables.append(chart_data) - with WorkspaceWithTempData(workspace, temp_data) as workspace: - agent = ReportGenAgent(client=client, workspace=workspace) - try: - for chunk in agent.stream(input_tables, charts, style): - yield chunk - except Exception as e: - logger.error(e) - error_data = { - "content": "unable to process report generation request" - } - yield 'error: ' + json.dumps(error_data) + '\n' + agent = ReportGenAgent(client=client, workspace=workspace, + language_instruction=get_language_instruction()) + try: + for chunk in agent.stream(input_tables, charts, style): + yield chunk + except Exception as e: + logger.exception("generate-report-stream failed") + error_data = { + "content": classify_llm_error(e) + } + yield 'error: ' + json.dumps(error_data, ensure_ascii=False) + '\n' else: error_data = { "content": "Invalid request format" } - yield 'error: ' + json.dumps(error_data) + '\n' + yield 'error: ' + json.dumps(error_data, ensure_ascii=False) + '\n' response = Response( stream_with_context(generate()), mimetype='application/json', - headers={ 'Access-Control-Allow-Origin': '*', } ) return response @@ -884,7 +929,6 @@ def refresh_derived_data(): # Get workspace and mount temp data for tables not in workspace identity_id = get_identity_id() workspace = get_workspace(identity_id) - temp_data = get_temp_tables(workspace, input_tables) # Get settings from app config cli_args = current_app.config.get('CLI_ARGS', {}) @@ -892,56 +936,110 @@ def refresh_derived_data(): sandbox = create_sandbox(cli_args.get('sandbox', 'local')) - with WorkspaceWithTempData(workspace, temp_data) as workspace: - # Run the transformation code in the sandbox - result = sandbox.run_python_code( - code=code, - workspace=workspace, - output_variable=output_variable, - ) - - if result['status'] == 'ok': - result_df = result['content'] - row_count = len(result_df) - - response_data = { - "status": "ok", - "message": "Successfully refreshed derived data", + # Run the transformation code in the sandbox + result = sandbox.run_python_code( + code=code, + workspace=workspace, + output_variable=output_variable, + ) + + if result['status'] == 'ok': + result_df = result['content'] + row_count = len(result_df) + + response_data = { + "status": "ok", + "message": "Successfully refreshed derived data", + "row_count": row_count + } + + if virtual: + # Virtual table: update workspace and return limited rows for display + workspace.write_parquet(result_df, output_table_name) + response_data["virtual"] = { + "table_name": output_table_name, "row_count": row_count } - - if virtual: - # Virtual table: update workspace and return limited rows for display - workspace.write_parquet(result_df, output_table_name) - response_data["virtual"] = { - "table_name": output_table_name, - "row_count": row_count - } - # Limit rows for response payload since full data is in workspace - if row_count > max_display_rows: - display_df = result_df.head(max_display_rows) - else: - display_df = result_df - # Remove duplicate columns to avoid orient='records' error - display_df = display_df.loc[:, ~display_df.columns.duplicated()] - response_data["rows"] = json.loads(display_df.to_json(orient='records', date_format='iso')) + # Limit rows for response payload since full data is in workspace + if row_count > max_display_rows: + display_df = result_df.head(max_display_rows) else: - # Temp table: return full data since there's no workspace storage - # Remove duplicate columns to avoid orient='records' error - result_df = result_df.loc[:, ~result_df.columns.duplicated()] - response_data["rows"] = json.loads(result_df.to_json(orient='records', date_format='iso')) - - return jsonify(response_data) + display_df = result_df + # Remove duplicate columns to avoid orient='records' error + display_df = display_df.loc[:, ~display_df.columns.duplicated()] + response_data["rows"] = json.loads(display_df.to_json(orient='records', date_format='iso')) else: - return jsonify({ - "status": "error", - "message": result.get('content', 'Unknown error during transformation') - }), 400 - + # Temp table: return full data since there's no workspace storage + # Remove duplicate columns to avoid orient='records' error + result_df = result_df.loc[:, ~result_df.columns.duplicated()] + response_data["rows"] = json.loads(result_df.to_json(orient='records', date_format='iso')) + + return jsonify(response_data) + else: + return jsonify({ + "status": "error", + "message": result.get('content', 'Unknown error during transformation') + }), 400 + except Exception as e: - logger.error(f"Error refreshing derived data: {str(e)}") - logger.error(traceback.format_exc()) + logger.error("Error refreshing derived data", exc_info=e) return jsonify({ "status": "error", - "message": str(e) + "message": classify_llm_error(e) }), 400 + + +@agent_bp.route('/workspace-summary', methods=['POST']) +def workspace_summary(): + """Generate a short name/summary for the current workspace. + + Called after the first agent interaction to auto-name the workspace. + Expects: { model: , context: { tables: [...], userQuery: "..." } } + Returns: { status: "ok", summary: "3-5 word name" } + """ + if not request.is_json: + return jsonify(status="error", summary=""), 400 + + content = request.get_json() + + try: + client = get_client(content['model']) + + ctx = content.get('context', {}) + table_names = ctx.get('tables', []) + user_query = ctx.get('userQuery', '') + + prompt_parts = [] + if table_names: + prompt_parts.append(f"Data tables: {', '.join(table_names)}") + if user_query: + prompt_parts.append(f"User's first request: {user_query}") + + context_str = '. '.join(prompt_parts) if prompt_parts else 'A data analysis session' + + messages = [ + { + "role": "system", + "content": ( + "You are a helpful assistant. Generate a very short name (3-5 words) " + "for a data analysis workspace based on the context below. " + "Return ONLY the name, no quotes, no explanation." + ), + }, + { + "role": "user", + "content": context_str, + }, + ] + + response = client.get_completion(messages) + summary = response.choices[0].message.content.strip().strip('"\'') + # Truncate if too long + if len(summary) > 60: + summary = summary[:57] + "..." + + return jsonify(status="ok", summary=summary) + + except Exception as e: + logger.warning(f"Failed to generate workspace summary: {e}") + return jsonify(status="error", summary=""), 500 diff --git a/py-src/data_formulator/agents/agent_chart_insight.py b/py-src/data_formulator/agents/agent_chart_insight.py index b8bdf171..3e55775d 100644 --- a/py-src/data_formulator/agents/agent_chart_insight.py +++ b/py-src/data_formulator/agents/agent_chart_insight.py @@ -25,9 +25,10 @@ class ChartInsightAgent(object): - def __init__(self, client, workspace=None): + def __init__(self, client, workspace=None, language_instruction=""): self.client = client self.workspace = workspace + self.language_instruction = language_instruction def run(self, chart_image_base64, chart_type, field_names, input_tables=None, n=1): """ @@ -64,13 +65,17 @@ def run(self, chart_image_base64, chart_type, field_names, input_tables=None, n= "type": "image_url", "image_url": { "url": f"data:image/png;base64,{chart_image_base64}", - "detail": "low" + "detail": "high" } } ] + system_prompt = SYSTEM_PROMPT + if self.language_instruction: + system_prompt = system_prompt + "\n\n" + self.language_instruction + messages = [ - {"role": "system", "content": SYSTEM_PROMPT}, + {"role": "system", "content": system_prompt}, {"role": "user", "content": user_content} ] diff --git a/py-src/data_formulator/agents/agent_code_explanation.py b/py-src/data_formulator/agents/agent_code_explanation.py index 03916be9..87275f7b 100644 --- a/py-src/data_formulator/agents/agent_code_explanation.py +++ b/py-src/data_formulator/agents/agent_code_explanation.py @@ -140,9 +140,10 @@ def extract_decade(date_str): class CodeExplanationAgent(object): - def __init__(self, client, workspace): + def __init__(self, client, workspace, language_instruction=""): self.client = client self.workspace = workspace + self.language_instruction = language_instruction def run(self, input_tables, code, n=1): @@ -153,7 +154,11 @@ def run(self, input_tables, code, n=1): logger.debug(user_query) logger.info(f"[CodeExplanationAgent] run start") - messages = [{"role":"system", "content": SYSTEM_PROMPT}, + system_prompt = SYSTEM_PROMPT + if self.language_instruction: + system_prompt = system_prompt + "\n\n" + self.language_instruction + + messages = [{"role":"system", "content": system_prompt}, {"role":"user","content": user_query}] response = self.client.get_completion(messages = messages) diff --git a/py-src/data_formulator/agents/agent_data_clean_stream.py b/py-src/data_formulator/agents/agent_data_clean_stream.py index 473b3c0f..0f2539f4 100644 --- a/py-src/data_formulator/agents/agent_data_clean_stream.py +++ b/py-src/data_formulator/agents/agent_data_clean_stream.py @@ -139,8 +139,9 @@ def parse_table_sections(text): class DataCleanAgentStream(object): - def __init__(self, client): + def __init__(self, client, language_instruction=""): self.client = client + self.language_instruction = language_instruction def stream(self, prompt, artifacts=[], dialog=[]): """derive a new concept based on the raw input data @@ -186,9 +187,13 @@ def stream(self, prompt, artifacts=[], dialog=[]): logger.debug(user_prompt) logger.info(f"[DataCleanAgent] run start (streaming)") + prompt_text = SYSTEM_PROMPT + if self.language_instruction: + prompt_text = prompt_text + "\n\n" + self.language_instruction + system_message = { 'role': 'system', - 'content': [ {'type': 'text', 'text': SYSTEM_PROMPT}] + 'content': [ {'type': 'text', 'text': prompt_text}] } messages = [ @@ -232,4 +237,4 @@ def stream(self, prompt, artifacts=[], dialog=[]): logger.info(f"[DataCleanAgent] run done | status={result.get('status', '?')}") # add a newline to the beginning of the result to separate it from the previous result - yield '\n' + json.dumps(result) + '\n' \ No newline at end of file + yield '\n' + json.dumps(result, ensure_ascii=False) + '\n' \ No newline at end of file diff --git a/py-src/data_formulator/agents/agent_data_load.py b/py-src/data_formulator/agents/agent_data_load.py index c23361bb..7d94752f 100644 --- a/py-src/data_formulator/agents/agent_data_load.py +++ b/py-src/data_formulator/agents/agent_data_load.py @@ -4,6 +4,7 @@ import json from data_formulator.agents.agent_utils import extract_json_objects, generate_data_summary +from data_formulator.agents.agent_diagnostics import AgentDiagnostics from data_formulator.agents.semantic_types import ( generate_semantic_types_prompt, ) @@ -33,7 +34,7 @@ - Infer from data values and context: e.g., if a "rating" column has values 1-10, the domain is [1, 10]; if it's clearly a 5-star system, use [1, 5]. - For Percentage: [0, 100] if values are whole-number percentages, [0, 1] if fractional. - For Correlation: always [-1, 1]. - - Do NOT provide for open-ended measures like Revenue, Count, Quantity, Temperature, etc. + - Do NOT provide for open-ended measures like Amount, Count, Quantity, Temperature, etc. - Only provide when the scale bounds are clear from the data or domain knowledge. - "unit": a short unit string for physical/monetary quantities. - Temperature: "°C", "°F", "K" @@ -158,9 +159,22 @@ class DataLoadAgent(object): - def __init__(self, client, workspace): + def __init__(self, client, workspace, language_instruction="", model_info=None): self.client = client self.workspace = workspace + self.language_instruction = language_instruction + + self.system_prompt = SYSTEM_PROMPT + if language_instruction: + self.system_prompt = self.system_prompt + "\n\n" + language_instruction + + self._diag = AgentDiagnostics( + agent_name="DataLoadAgent", + model_info=model_info or {}, + base_system_prompt=SYSTEM_PROMPT, + language_instruction=language_instruction, + assembled_system_prompt=self.system_prompt, + ) def run(self, input_data, n=1): @@ -178,7 +192,7 @@ def run(self, input_data, n=1): logger.debug(user_query) logger.info(f"[DataLoadAgent] run start") - messages = [{"role":"system", "content": SYSTEM_PROMPT}, + messages = [{"role":"system", "content": self.system_prompt}, {"role":"user","content": user_query}] response = self.client.get_completion(messages = messages) @@ -204,6 +218,11 @@ def run(self, input_data, n=1): # individual dialog for the agent result['dialog'] = [*messages, {"role": choice.message.role, "content": choice.message.content}] result['agent'] = 'DataLoadAgent' + result['diagnostics'] = self._diag.for_json_only( + messages, + raw_content=choice.message.content, + finish_reason=getattr(choice, 'finish_reason', None), + ) candidates.append(result) diff --git a/py-src/data_formulator/agents/agent_data_rec.py b/py-src/data_formulator/agents/agent_data_rec.py index c8c05934..984231cd 100644 --- a/py-src/data_formulator/agents/agent_data_rec.py +++ b/py-src/data_formulator/agents/agent_data_rec.py @@ -4,7 +4,8 @@ import json import time -from data_formulator.agents.agent_utils import extract_json_objects, extract_code_from_gpt_response, generate_data_summary +from data_formulator.agents.agent_utils import extract_json_objects, extract_code_from_gpt_response, generate_data_summary, supplement_missing_block, ensure_output_variable_in_code +from data_formulator.agents.agent_diagnostics import AgentDiagnostics import traceback import pandas as pd @@ -36,7 +37,7 @@ - Only use DuckDB when the dataset is very large and you need efficient SQL aggregations, filtering, joins, or window functions. - You can combine both: DuckDB for initial loading/filtering on large files, then pandas for complex operations. -**Code structure:** standalone script (no function wrapper), imports at top, assign final result to a variable (specified in JSON).''' +**Code structure:** standalone script (no function wrapper), imports at top. **CRITICAL:** The final result DataFrame MUST be assigned to the exact variable name you specified in `"output_variable"` in the JSON spec — the system uses this name to extract the result. For example, if your output_variable is `sales_by_region`, the script must contain `sales_by_region = ...`.''' SHARED_SEMANTIC_TYPE_REFERENCE = '''**[SEMANTIC TYPE REFERENCE]** @@ -46,21 +47,21 @@ | Category | Types | |---|---| | Temporal | DateTime, Date, Time, Timestamp, Year, Quarter, Month, Week, Day, Hour, YearMonth, YearQuarter, YearWeek, Decade, Duration | -| Monetary measures | Amount, Price, Revenue, Cost | +| Monetary measures | Amount, Price | | Physical measures | Quantity, Temperature | | Proportion | Percentage | | Signed/diverging | Profit, PercentageChange, Sentiment, Correlation | | Generic measures | Count, Number | -| Discrete numeric | Rank, Score, Rating, Index | +| Discrete numeric | Rank, Score | | Identifier | ID | | Geographic | Latitude, Longitude, Country, State, City, Region, Address, ZipCode | -| Entity names | PersonName, Company, Product, Category, Name | -| Coded categorical | Status, Type, Boolean, Direction | -| Binned ranges | Range, AgeGroup | -| Fallback | String, Unknown | +| Entity names | Category, Name | +| Coded categorical | Status, Boolean, Direction | +| Binned ranges | Range | +| Fallback | Unknown | Key guidelines: -- Use **Revenue/Cost** for summed monetary totals, **Price** for per-unit prices, **Profit** for values that can be negative. +- Use **Amount** for summed monetary totals, **Price** for per-unit prices, **Profit** for values that can be negative. - Use **Temperature** (not Quantity) for temperature — it has special diverging behavior. - Use **Year** (not Number) for columns like "year" with values 2020, 2021.''' @@ -109,7 +110,11 @@ - Escape single quotes with '' (not \\') - No Unicode escapes (\\u0400); use character ranges directly: [а-яА-Я] - Cast date columns explicitly: `CAST(col AS DATE)`, `CAST(col AS TIMESTAMP)` -- For complex datetime operations, load data first then use pandas datetime functions''' +- For complex datetime operations, load data first then use pandas datetime functions +- Critical identifier quoting rule: + * If a table/column name contains non-ASCII characters (e.g., Chinese, Japanese, Korean, Cyrillic, etc.), spaces, or punctuation, + you MUST wrap it in double quotes, e.g. SELECT "金额" FROM "客户表". + * Never output placeholder identifiers like your_table_name, your_column, your_condition.''' # ============================================================================= @@ -136,7 +141,7 @@ "config": {{{{}}}} // optional styling }}}}, "field_metadata": {{{{ // semantic type for each encoding field - "": "Type" // from [SEMANTIC TYPE REFERENCE] + "": "Category" // from [SEMANTIC TYPE REFERENCE] }}}}, "output_variable": "" // descriptive snake_case name (e.g. "sales_by_region"), not "result_df" }}}} @@ -154,7 +159,7 @@ {SHARED_STATISTICAL_ANALYSIS} -**Step 2: Python script** — transform input data to produce a DataFrame with all "output_fields". Keep it simple and readable. +**Step 2: Python script** — transform input data to produce a DataFrame with all "output_fields". Keep it simple and readable. The script MUST assign the final result to the variable named in `"output_variable"` from Step 1. **Datetime handling:** - Year → number. Year-month / year-month-day → string ("2020-01" / "2020-01-01"). @@ -165,28 +170,57 @@ class DataRecAgent(object): - def __init__(self, client, workspace, system_prompt=None, agent_coding_rules="", max_display_rows=10000): + def __init__(self, client, workspace, system_prompt=None, agent_coding_rules="", language_instruction="", max_display_rows=10000, model_info=None): self.client = client self.workspace = workspace self.max_display_rows = max_display_rows + self._model_info = model_info or {} + self._agent_coding_rules = agent_coding_rules + self._language_instruction = language_instruction - # Incorporate agent coding rules into system prompt if provided if system_prompt is not None: + self._base_prompt = system_prompt self.system_prompt = system_prompt else: + self._base_prompt = SYSTEM_PROMPT base_prompt = SYSTEM_PROMPT if agent_coding_rules and agent_coding_rules.strip(): self.system_prompt = base_prompt + "\n\n[AGENT CODING RULES]\nPlease follow these rules when generating code. Note: if the user instruction conflicts with these rules, you should prioritize user instructions.\n\n" + agent_coding_rules.strip() else: self.system_prompt = base_prompt + if language_instruction: + # Insert early (after role definition, before technical sections) + # so the LLM's "last impression" remains chart/code rules, + # reducing recency-bias interference on chart-type selection. + marker = "**About the execution environment:**" + idx = self.system_prompt.find(marker) + if idx > 0: + self.system_prompt = ( + self.system_prompt[:idx] + + language_instruction + "\n\n" + + self.system_prompt[idx:] + ) + else: + self.system_prompt = self.system_prompt + "\n\n" + language_instruction + + self._diag = AgentDiagnostics( + agent_name="DataRecAgent", + model_info=self._model_info, + base_system_prompt=self._base_prompt, + agent_coding_rules=self._agent_coding_rules, + language_instruction=self._language_instruction, + assembled_system_prompt=self.system_prompt, + ) + def process_gpt_response(self, input_tables, messages, response, t_llm=None): """Process GPT response to handle Python code execution""" t_start = time.time() t_exec_total = 0.0 if isinstance(response, Exception): - result = {'status': 'other error', 'content': str(response.body)} + result = {'status': 'other error', 'content': str(response.body), + 'diagnostics': self._diag.for_error(messages, error=str(response.body))} return [result] candidates = [] @@ -195,33 +229,70 @@ def process_gpt_response(self, input_tables, messages, response, t_llm=None): logger.debug("\n=== Data recommendation result ===>\n") logger.debug(choice.message.content + "\n") + # --- Parse JSON spec and Python code --- json_blocks = extract_json_objects(choice.message.content + "\n") - # Find the first JSON dict (skip any arrays the model may have emitted) refined_goal = None for jb in json_blocks: if isinstance(jb, dict): refined_goal = jb break + code_blocks = extract_code_from_gpt_response(choice.message.content + "\n", "python") + + # If only one block was produced, request the missing one + refined_goal, code_blocks, _supplement_content, t_supplement = supplement_missing_block( + self.client, messages, choice.message.content, + refined_goal, code_blocks, prefix="[DataRecAgent]" + ) + + # Apply fallbacks for missing JSON + json_fallback_used = refined_goal is None if refined_goal is None: refined_goal = {'output_fields': [], 'chart': {'chart_type': "", 'encodings': {}, 'config': {}}, 'output_variable': 'result_df'} - output_variable = refined_goal.get('output_variable', 'result_df') - - code_blocks = extract_code_from_gpt_response(choice.message.content + "\n", "python") + logger.warning( + "[DataRecAgent] JSON spec parsing failed — using fallback defaults. " + f"Response snippet: {choice.message.content[:300]!r}" + ) + output_variable = refined_goal.get('output_variable', 'result_df') or 'result_df' + logger.info(f"[DataRecAgent] extracted output_variable={output_variable!r}") + + # Diagnostics tracking + import re as _re + _diag_code = code_blocks[-1] if code_blocks else None + _diag_output_var_in_code = bool( + _diag_code and output_variable + and _re.search(rf'(?:^|\n)\s*{_re.escape(output_variable)}\s*=(?!=)', _diag_code) + ) + _diag_sandbox_mode = None + _diag_exec = {"status": None} + _diag_code_patched = False if len(code_blocks) > 0: code = code_blocks[-1] + if output_variable and not _diag_output_var_in_code: + code, was_patched, detected_var = ensure_output_variable_in_code(code, output_variable) + _diag_code_patched = was_patched + if was_patched: + logger.info( + f"[DataRecAgent] output_variable {output_variable!r} not in code — " + f"patched: appended `{output_variable} = {detected_var}`" + ) + else: + logger.warning( + f"[DataRecAgent] output_variable {output_variable!r} not in code " + f"and auto-patch found no candidate variable." + ) + try: from data_formulator.sandbox import create_sandbox - # Get sandbox setting (with fallback for non-Flask contexts like MCP server) try: from flask import current_app sandbox_mode = current_app.config.get('CLI_ARGS', {}).get('sandbox', 'local') except (ImportError, RuntimeError): sandbox_mode = 'local' + _diag_sandbox_mode = sandbox_mode - # Execute the Python script in the appropriate sandbox t_exec_start = time.time() sandbox = create_sandbox(sandbox_mode) execution_result = sandbox.run_python_code( @@ -231,23 +302,23 @@ def process_gpt_response(self, input_tables, messages, response, t_llm=None): ) t_exec_total += time.time() - t_exec_start + _diag_exec = { + "status": execution_result['status'], + "error_message": execution_result.get('content') if execution_result['status'] != 'ok' else None, + "available_dataframes": execution_result.get('df_names', []), + } + if execution_result['status'] == 'ok': full_df = execution_result['content'] row_count = len(full_df) - # Generate unique table name for workspace storage output_table_name = self.workspace.get_fresh_name(f"d-{output_variable}") - - # Write full result to workspace as parquet self.workspace.write_parquet(full_df, output_table_name) - # Limit rows for response payload if row_count > self.max_display_rows: query_output = full_df.head(self.max_display_rows) else: query_output = full_df - - # Remove duplicate columns to avoid orient='records' error query_output = query_output.loc[:, ~query_output.columns.duplicated()] result = { @@ -262,7 +333,6 @@ def process_gpt_response(self, input_tables, messages, response, t_llm=None): }, } else: - # Execution error error_message = execution_result.get('content', execution_result.get('error_message', 'Unknown error')) result = { 'status': 'error', @@ -275,29 +345,62 @@ def process_gpt_response(self, input_tables, messages, response, t_llm=None): error_message = traceback.format_exc() logger.warning(error_message) result = {'status': 'other error', 'code': code, 'content': f"Unexpected error: {error_message}"} + _diag_exec = {"status": "exception", "error_message": str(e)} else: result = {'status': 'error', 'code': "", 'content': "No code block found in the response. The model is unable to generate code to complete the task."} - result['dialog'] = [*messages, {"role": choice.message.role, "content": choice.message.content}] + _effective_content = choice.message.content + if _supplement_content: + _effective_content += "\n\n" + _supplement_content + result['dialog'] = [*messages, {"role": choice.message.role, "content": _effective_content}] result['agent'] = 'DataRecAgent' result['refined_goal'] = refined_goal + + # --- Build diagnostics --- + usage = getattr(response, 'usage', None) + result['diagnostics'] = self._diag.for_response( + messages, + raw_content=choice.message.content, + finish_reason=getattr(choice, 'finish_reason', None), + json_spec=refined_goal, + json_fallback_used=json_fallback_used, + code_found=len(code_blocks) > 0, + code=_diag_code, + output_variable=output_variable, + output_variable_in_code=_diag_output_var_in_code, + code_patched=_diag_code_patched, + supplemented=_supplement_content is not None, + sandbox_mode=_diag_sandbox_mode, + exec_status=_diag_exec.get("status"), + exec_error=_diag_exec.get("error_message"), + exec_df_names=_diag_exec.get("available_dataframes"), + t_llm=t_llm or 0, + t_supplement=t_supplement, + t_exec=t_exec_total, + prompt_tokens=getattr(usage, 'prompt_tokens', None) if usage else None, + completion_tokens=getattr(usage, 'completion_tokens', None) if usage else None, + ) + candidates.append(result) + t_total = time.time() - t_start + t_llm_val = t_llm or 0.0 + logger.debug("=== Recommendation Candidates ===>") for candidate in candidates: for key, value in candidate.items(): - if key in ['dialog', 'content']: + if key in ['dialog', 'content', 'diagnostics']: logger.debug(f"##{key}:\n{str(value)[:1000]}...") else: logger.debug(f"## {key}:\n{value}") - t_total = time.time() - t_start - t_llm_val = t_llm or 0.0 - t_misc = t_total - t_exec_total - logger.info(f"[DataRecAgent] timing: llm={t_llm_val:.3f}s, exec={t_exec_total:.3f}s, misc={t_misc:.3f}s, total={t_total + t_llm_val:.3f}s") + usage = getattr(response, 'usage', None) + usage_str = "" + if usage: + usage_str = f" | tokens: in={getattr(usage, 'prompt_tokens', None)}, out={getattr(usage, 'completion_tokens', None)}" + logger.info(f"[DataRecAgent] timing: llm={t_llm_val:.3f}s, supplement={t_supplement:.3f}s, exec={t_exec_total:.3f}s, total={t_total + t_llm_val:.3f}s{usage_str}") return candidates - def run(self, input_tables, description, n=1, prev_messages: list[dict] = []): """ Args: diff --git a/py-src/data_formulator/agents/agent_data_transform.py b/py-src/data_formulator/agents/agent_data_transform.py index c8056a2d..f73c6f6d 100644 --- a/py-src/data_formulator/agents/agent_data_transform.py +++ b/py-src/data_formulator/agents/agent_data_transform.py @@ -4,7 +4,8 @@ import json import time -from data_formulator.agents.agent_utils import extract_json_objects, extract_code_from_gpt_response +from data_formulator.agents.agent_utils import extract_json_objects, extract_code_from_gpt_response, supplement_missing_block, ensure_output_variable_in_code +from data_formulator.agents.agent_diagnostics import AgentDiagnostics from data_formulator.agents.agent_data_rec import ( SHARED_ENVIRONMENT, SHARED_SEMANTIC_TYPE_REFERENCE, @@ -50,7 +51,7 @@ "config": {{{{}}}} // optional styling }}}}, "field_metadata": {{{{ // semantic type for each encoding field - "": "Type" // from [SEMANTIC TYPE REFERENCE] + "": "Category" // from [SEMANTIC TYPE REFERENCE] }}}}, "output_variable": "", // descriptive snake_case name (e.g. "sales_by_region"), not "result_df" "reason": "" // why this refinement is made @@ -63,7 +64,7 @@ {SHARED_STATISTICAL_ANALYSIS} -**Step 2: Python script** — transform input data to produce a DataFrame with all "output_fields". Keep it simple and readable. +**Step 2: Python script** — transform input data to produce a DataFrame with all "output_fields". Keep it simple and readable. The script MUST assign the final result to the variable named in `"output_variable"` from Step 1. **Datetime handling:** - Year → number. Year-month / year-month-day → string ("2020-01" / "2020-01-01"). @@ -74,21 +75,45 @@ class DataTransformationAgent(object): - def __init__(self, client, workspace, system_prompt=None, agent_coding_rules="", max_display_rows=10000): + def __init__(self, client, workspace, system_prompt=None, agent_coding_rules="", language_instruction="", max_display_rows=10000, model_info=None): self.client = client self.workspace = workspace self.max_display_rows = max_display_rows + self._model_info = model_info or {} + self._agent_coding_rules = agent_coding_rules + self._language_instruction = language_instruction - # Incorporate agent coding rules into system prompt if provided if system_prompt is not None: + self._base_prompt = system_prompt self.system_prompt = system_prompt else: + self._base_prompt = SYSTEM_PROMPT base_prompt = SYSTEM_PROMPT if agent_coding_rules and agent_coding_rules.strip(): self.system_prompt = base_prompt + "\n\n[AGENT CODING RULES]\nPlease follow these rules when generating code. Note: if the user instruction conflicts with these rules, you should prioritize user instructions.\n\n" + agent_coding_rules.strip() else: self.system_prompt = base_prompt + if language_instruction: + marker = "**About the execution environment:**" + idx = self.system_prompt.find(marker) + if idx > 0: + self.system_prompt = ( + self.system_prompt[:idx] + + language_instruction + "\n\n" + + self.system_prompt[idx:] + ) + else: + self.system_prompt = self.system_prompt + "\n\n" + language_instruction + + self._diag = AgentDiagnostics( + agent_name="DataTransformationAgent", + model_info=self._model_info, + base_system_prompt=self._base_prompt, + agent_coding_rules=self._agent_coding_rules, + language_instruction=self._language_instruction, + assembled_system_prompt=self.system_prompt, + ) def process_gpt_response(self, response, messages, t_llm=None): """Process GPT response to handle Python code execution""" @@ -96,7 +121,8 @@ def process_gpt_response(self, response, messages, t_llm=None): t_exec_total = 0.0 if isinstance(response, Exception): - result = {'status': 'other error', 'content': str(response.body)} + result = {'status': 'other error', 'content': str(response.body), + 'diagnostics': self._diag.for_error(messages, error=str(response.body))} return [result] candidates = [] @@ -104,33 +130,69 @@ def process_gpt_response(self, response, messages, t_llm=None): logger.debug("=== Python script result ===>") logger.debug(choice.message.content + "\n") + # --- Parse JSON spec and Python code --- json_blocks = extract_json_objects(choice.message.content + "\n") - # Find the first JSON dict (skip any arrays the model may have emitted) refined_goal = None for jb in json_blocks: if isinstance(jb, dict): refined_goal = jb break + code_blocks = extract_code_from_gpt_response(choice.message.content + "\n", "python") + + # If only one block was produced, request the missing one + refined_goal, code_blocks, _supplement_content, t_supplement = supplement_missing_block( + self.client, messages, choice.message.content, + refined_goal, code_blocks, prefix="[DataTransformAgent]" + ) + + # Apply fallbacks for missing JSON + json_fallback_used = refined_goal is None if refined_goal is None: refined_goal = {'chart': {'chart_type': '', 'encodings': {}, 'config': {}}, 'instruction': '', 'reason': '', 'output_variable': 'result_df'} - output_variable = refined_goal.get('output_variable', 'result_df') - - code_blocks = extract_code_from_gpt_response(choice.message.content + "\n", "python") + logger.warning( + "[DataTransformAgent] JSON spec parsing failed — using fallback defaults. " + f"Response snippet: {choice.message.content[:300]!r}" + ) + output_variable = refined_goal.get('output_variable', 'result_df') or 'result_df' + logger.info(f"[DataTransformAgent] extracted output_variable={output_variable!r}") + + import re as _re + _diag_code = code_blocks[-1] if code_blocks else None + _diag_output_var_in_code = bool( + _diag_code and output_variable + and _re.search(rf'(?:^|\n)\s*{_re.escape(output_variable)}\s*=(?!=)', _diag_code) + ) + _diag_sandbox_mode = None + _diag_exec = {"status": None} + _diag_code_patched = False if len(code_blocks) > 0: code = code_blocks[-1] + if output_variable and not _diag_output_var_in_code: + code, was_patched, detected_var = ensure_output_variable_in_code(code, output_variable) + _diag_code_patched = was_patched + if was_patched: + logger.info( + f"[DataTransformAgent] output_variable {output_variable!r} not in code — " + f"patched: appended `{output_variable} = {detected_var}`" + ) + else: + logger.warning( + f"[DataTransformAgent] output_variable {output_variable!r} not in code " + f"and auto-patch found no candidate variable." + ) + try: from data_formulator.sandbox import create_sandbox - # Get sandbox setting (with fallback for non-Flask contexts like MCP server) try: from flask import current_app sandbox_mode = current_app.config.get('CLI_ARGS', {}).get('sandbox', 'local') except (ImportError, RuntimeError): sandbox_mode = 'local' + _diag_sandbox_mode = sandbox_mode - # Execute the Python script in the appropriate sandbox t_exec_start = time.time() sandbox = create_sandbox(sandbox_mode) execution_result = sandbox.run_python_code( @@ -140,23 +202,23 @@ def process_gpt_response(self, response, messages, t_llm=None): ) t_exec_total += time.time() - t_exec_start + _diag_exec = { + "status": execution_result['status'], + "error_message": execution_result.get('content') if execution_result['status'] != 'ok' else None, + "available_dataframes": execution_result.get('df_names', []), + } + if execution_result['status'] == 'ok': full_df = execution_result['content'] row_count = len(full_df) - # Generate unique table name for workspace storage output_table_name = self.workspace.get_fresh_name(f"d-{output_variable}") - - # Write full result to workspace as parquet self.workspace.write_parquet(full_df, output_table_name) - # Limit rows for response payload if row_count > self.max_display_rows: query_output = full_df.head(self.max_display_rows) else: query_output = full_df - - # Remove duplicate columns to avoid orient='records' error query_output = query_output.loc[:, ~query_output.columns.duplicated()] result = { @@ -171,7 +233,6 @@ def process_gpt_response(self, response, messages, t_llm=None): }, } else: - # Execution error result = { 'status': 'error', 'code': code, @@ -183,27 +244,61 @@ def process_gpt_response(self, response, messages, t_llm=None): logger.warning(f"Error type: {type(e).__name__}, message: {str(e)}") error_message = f"An error occurred during code execution. Error type: {type(e).__name__}, message: {str(e)}" result = {'status': 'error', 'code': code, 'content': error_message} + _diag_exec = {"status": "exception", "error_message": str(e)} else: result = {'status': 'error', 'code': "", 'content': "No code block found in the response. The model is unable to generate code to complete the task."} - result['dialog'] = [*messages, {"role": choice.message.role, "content": choice.message.content}] + _effective_content = choice.message.content + if _supplement_content: + _effective_content += "\n\n" + _supplement_content + result['dialog'] = [*messages, {"role": choice.message.role, "content": _effective_content}] result['agent'] = 'DataTransformationAgent' result['refined_goal'] = refined_goal + + # --- Build diagnostics --- + usage = getattr(response, 'usage', None) + result['diagnostics'] = self._diag.for_response( + messages, + raw_content=choice.message.content, + finish_reason=getattr(choice, 'finish_reason', None), + json_spec=refined_goal, + json_fallback_used=json_fallback_used, + code_found=len(code_blocks) > 0, + code=_diag_code, + output_variable=output_variable, + output_variable_in_code=_diag_output_var_in_code, + code_patched=_diag_code_patched, + supplemented=_supplement_content is not None, + sandbox_mode=_diag_sandbox_mode, + exec_status=_diag_exec.get("status"), + exec_error=_diag_exec.get("error_message"), + exec_df_names=_diag_exec.get("available_dataframes"), + t_llm=t_llm or 0, + t_supplement=t_supplement, + t_exec=t_exec_total, + prompt_tokens=getattr(usage, 'prompt_tokens', None) if usage else None, + completion_tokens=getattr(usage, 'completion_tokens', None) if usage else None, + ) + candidates.append(result) + t_total = time.time() - t_start + t_llm_val = t_llm or 0.0 + logger.debug("=== Transform Candidates ===>") for candidate in candidates: for key, value in candidate.items(): - if key in ['dialog', 'content']: + if key in ['dialog', 'content', 'diagnostics']: logger.debug(f"##{key}:\n{str(value)[:1000]}...") else: logger.debug(f"## {key}:\n{value}") - t_total = time.time() - t_start - t_llm_val = t_llm or 0.0 - t_misc = t_total - t_exec_total - logger.info(f"[DataTransformAgent] timing: llm={t_llm_val:.3f}s, exec={t_exec_total:.3f}s, misc={t_misc:.3f}s, total={t_total + t_llm_val:.3f}s") + usage = getattr(response, 'usage', None) + usage_str = "" + if usage: + usage_str = f" | tokens: in={getattr(usage, 'prompt_tokens', None)}, out={getattr(usage, 'completion_tokens', None)}" + logger.info(f"[DataTransformAgent] timing: llm={t_llm_val:.3f}s, supplement={t_supplement:.3f}s, exec={t_exec_total:.3f}s, total={t_total + t_llm_val:.3f}s{usage_str}") return candidates @@ -227,9 +322,9 @@ def run(self, input_tables, description, prev_messages: list[dict] = [], n=1, # Build visualization context section vis_section = "" if current_visualization: - vis_section = f"\n\n[CURRENT VISUALIZATION] This is the current visualization the user has:\n\n{json.dumps(current_visualization.get('chart_spec', {}), indent=4)}" + vis_section = f"\n\n[CURRENT VISUALIZATION] This is the current visualization the user has:\n\n{json.dumps(current_visualization.get('chart_spec', {}), indent=4, ensure_ascii=False)}" elif expected_visualization: - vis_section = f"\n\n[EXPECTED VISUALIZATION] This is the visualization expected by the user:\n\n{json.dumps(expected_visualization.get('chart_spec', {}), indent=4)}" + vis_section = f"\n\n[EXPECTED VISUALIZATION] This is the visualization expected by the user:\n\n{json.dumps(expected_visualization.get('chart_spec', {}), indent=4, ensure_ascii=False)}" # Order: context → visualization → goal if len(prev_messages) > 0: @@ -244,11 +339,13 @@ def run(self, input_tables, description, prev_messages: list[dict] = [], n=1, # Build user message content: include chart image if available chart_image = current_visualization.get('chart_image') if current_visualization else None + has_image = bool(chart_image) + logger.info(f"[DataTransformAgent] run LLM call | messages={1 + len(filtered_prev_messages) + 1}, has_image={has_image}") try: if chart_image: user_content = [ {"type": "text", "text": user_query}, - {"type": "image_url", "image_url": {"url": chart_image, "detail": "high"}} + {"type": "image_url", "image_url": {"url": chart_image, "detail": "low"}} ] else: user_content = user_query @@ -304,9 +401,9 @@ def followup(self, input_tables, dialog, latest_data_sample, new_instruction: st # Build visualization context section vis_section = "" if current_visualization: - vis_section = f"\n\n[CURRENT VISUALIZATION] This is the current visualization the user has:\n\n{json.dumps(current_visualization.get('chart_spec', {}), indent=4)}" + vis_section = f"\n\n[CURRENT VISUALIZATION] This is the current visualization the user has:\n\n{json.dumps(current_visualization.get('chart_spec', {}), indent=4, ensure_ascii=False)}" elif expected_visualization: - vis_section = f"\n\n[EXPECTED VISUALIZATION] This is the visualization expected by the user:\n\n{json.dumps(expected_visualization.get('chart_spec', {}), indent=4)}" + vis_section = f"\n\n[EXPECTED VISUALIZATION] This is the visualization expected by the user:\n\n{json.dumps(expected_visualization.get('chart_spec', {}), indent=4, ensure_ascii=False)}" # Order: data sample → visualization → instruction followup_text = f"This is the result from the latest transformation:\n\n{sample_data_str}{vis_section}\n\nUpdate the Python script above based on the following instruction:\n\n{new_instruction}" @@ -315,11 +412,13 @@ def followup(self, input_tables, dialog, latest_data_sample, new_instruction: st # Build user message content: include chart image if available chart_image = current_visualization.get('chart_image') if current_visualization else None + has_image = bool(chart_image) + logger.info(f"[DataTransformAgent] followup LLM call | messages={len(updated_dialog) + 1}, has_image={has_image}") try: if chart_image: user_content = [ {"type": "text", "text": followup_text}, - {"type": "image_url", "image_url": {"url": chart_image, "detail": "high"}} + {"type": "image_url", "image_url": {"url": chart_image, "detail": "low"}} ] else: user_content = followup_text diff --git a/py-src/data_formulator/agents/agent_diagnostics.py b/py-src/data_formulator/agents/agent_diagnostics.py new file mode 100644 index 00000000..aef7b491 --- /dev/null +++ b/py-src/data_formulator/agents/agent_diagnostics.py @@ -0,0 +1,146 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +"""Unified diagnostics builder for all agent pipelines. + +Centralises the JSON structure returned as ``result['diagnostics']``, +ensuring a single schema definition for both back-end construction +and front-end consumption (DiagnosticsViewer in MessageSnackbar.tsx). +""" + +from __future__ import annotations + +import time +from typing import Any + + +class AgentDiagnostics: + """Captures prompt context once at agent init, then builds diagnostics per request.""" + + def __init__( + self, + agent_name: str, + model_info: dict, + base_system_prompt: str, + agent_coding_rules: str = "", + language_instruction: str = "", + assembled_system_prompt: str = "", + ): + self._agent_name = agent_name + self._model_info = model_info + self._prompt_ctx = { + "base_system_prompt": base_system_prompt, + "agent_coding_rules": agent_coding_rules, + "language_instruction": language_instruction, + "assembled_system_prompt": assembled_system_prompt, + } + + # -- helpers ---------------------------------------------------------- + + def _base(self, messages: list[dict]) -> dict[str, Any]: + return { + "agent": self._agent_name, + "timestamp": _now(), + "model": self._model_info, + "prompt_components": self._prompt_ctx, + "llm_request": { + "message_count": len(messages), + "messages": messages, + }, + } + + # -- 1. LLM connection failure / early exception ---------------------- + + def for_error(self, messages: list[dict], error: str = "") -> dict[str, Any]: + return {**self._base(messages), "error": error} + + # -- 2. Full diagnostics (code-execution agents) ---------------------- + + def for_response( + self, + messages: list[dict], + *, + raw_content: str, + finish_reason: str | None, + json_spec: dict | None, + json_fallback_used: bool, + code_found: bool, + code: str | None, + output_variable: str, + output_variable_in_code: bool, + code_patched: bool = False, + supplemented: bool, + sandbox_mode: str | None, + exec_status: str | None, + exec_error: str | None = None, + exec_df_names: list[str] | None = None, + t_llm: float = 0.0, + t_supplement: float = 0.0, + t_exec: float = 0.0, + prompt_tokens: int | None = None, + completion_tokens: int | None = None, + ) -> dict[str, Any]: + exec_dict: dict[str, Any] = { + "sandbox_mode": sandbox_mode, + "status": exec_status, + } + if exec_error is not None: + exec_dict["error_message"] = exec_error + if exec_df_names is not None: + exec_dict["available_dataframes"] = exec_df_names + + return { + **self._base(messages), + "llm_response": { + "raw_content": raw_content, + "finish_reason": finish_reason, + }, + "parsing": { + "json_spec_found": not json_fallback_used, + "json_spec": json_spec, + "json_fallback_used": json_fallback_used, + "code_found": code_found, + "code": code, + "output_variable": output_variable, + "output_variable_in_code": output_variable_in_code, + "code_patched": code_patched, + "supplemented": supplemented, + }, + "execution": exec_dict, + "performance": { + "llm_seconds": round(t_llm, 3), + "supplement_seconds": round(t_supplement, 3), + "exec_seconds": round(t_exec, 3), + "prompt_tokens": prompt_tokens, + "completion_tokens": completion_tokens, + }, + } + + # -- 3. JSON-only agents (DataLoadAgent — no code/execution) ---------- + + def for_json_only( + self, + messages: list[dict], + *, + raw_content: str = "", + finish_reason: str | None = None, + t_llm: float = 0.0, + prompt_tokens: int | None = None, + completion_tokens: int | None = None, + ) -> dict[str, Any]: + return { + **self._base(messages), + "llm_response": { + "raw_content": raw_content, + "finish_reason": finish_reason, + }, + "performance": { + "llm_seconds": round(t_llm, 3), + "prompt_tokens": prompt_tokens, + "completion_tokens": completion_tokens, + }, + } + + +def _now() -> str: + return time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()) diff --git a/py-src/data_formulator/agents/agent_interactive_explore.py b/py-src/data_formulator/agents/agent_interactive_explore.py index 603bc18d..1e3b1ac2 100644 --- a/py-src/data_formulator/agents/agent_interactive_explore.py +++ b/py-src/data_formulator/agents/agent_interactive_explore.py @@ -114,10 +114,11 @@ class InteractiveExploreAgent(object): - def __init__(self, client, workspace, agent_exploration_rules=""): + def __init__(self, client, workspace, agent_exploration_rules="", language_instruction=""): self.client = client self.agent_exploration_rules = agent_exploration_rules self.workspace = workspace # when set (SQL/datalake mode), use parquet tables for summary + self.language_instruction = language_instruction def run(self, input_tables, start_question=None, exploration_thread=None, current_data_sample=None, current_chart=None, mode='interactive'): @@ -167,6 +168,9 @@ def run(self, input_tables, start_question=None, exploration_thread=None, else: system_prompt = base_system_prompt + if self.language_instruction: + system_prompt = system_prompt + "\n\n" + self.language_instruction + logger.debug(f"Interactive explore agent input: {context}") logger.info(f"[InteractiveExploreAgent] run start") @@ -176,7 +180,7 @@ def run(self, input_tables, start_question=None, exploration_thread=None, {"role": "system", "content": system_prompt}, {"role": "user", "content": [ {"type": "text", "text": context}, - {"type": "image_url", "image_url": {"url": current_chart, "detail": "high"}} + {"type": "image_url", "image_url": {"url": current_chart, "detail": "low"}} ]} ] else: diff --git a/py-src/data_formulator/agents/agent_language.py b/py-src/data_formulator/agents/agent_language.py new file mode 100644 index 00000000..358c64a0 --- /dev/null +++ b/py-src/data_formulator/agents/agent_language.py @@ -0,0 +1,150 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +""" +Language instruction builder for Agent prompts. + +Generates a prompt fragment that constrains LLM output language for +user-visible fields while keeping all internal / programmatic fields +stable in English. + +Two modes are provided: + +- **"full"** — detailed field-by-field rules for text-heavy agents + (ChartInsight, InteractiveExplore, ReportGen, CodeExplanation, + DataClean, DataAgent). +- **"compact"** — a short 3-sentence instruction for code-generation + agents (DataRec, DataTransformation, DataLoad) so that the extra + text does not distract the model from writing correct code. + +Usage: + from data_formulator.agents.agent_language import build_language_instruction + + instruction = build_language_instruction("zh") # full (default) + instruction = build_language_instruction("zh", mode="compact") # compact + instruction = build_language_instruction("en") # returns "" +""" + +# ── Language registry ─────────────────────────────────────── + +LANGUAGE_DISPLAY_NAMES: dict[str, str] = { + "en": "English", + "zh": "Simplified Chinese (简体中文)", + "ja": "Japanese (日本語)", + "ko": "Korean (한국어)", + "fr": "French (Français)", + "de": "German (Deutsch)", + "es": "Spanish (Español)", + "pt": "Portuguese (Português)", + "ru": "Russian (Русский)", + "ar": "Arabic (العربية)", + "hi": "Hindi (हिन्दी)", + "th": "Thai (ไทย)", + "vi": "Vietnamese (Tiếng Việt)", + "it": "Italian (Italiano)", + "nl": "Dutch (Nederlands)", + "pl": "Polish (Polski)", + "tr": "Turkish (Türkçe)", + "id": "Indonesian (Bahasa Indonesia)", + "ms": "Malay (Bahasa Melayu)", + "sv": "Swedish (Svenska)", +} + +# ── Per-language extra rules ──────────────────────────────── + +LANGUAGE_EXTRA_RULES: dict[str, str] = { + "zh": ( + "\n\nAdditional rules for Chinese:\n" + "- Use Simplified Chinese (简体中文), NOT Traditional Chinese (繁體中文).\n" + "- Keep technical terms natural: prefer widely-used Chinese equivalents " + "(e.g., 销售额, 利润率) over awkward literal translations." + ), + "ja": ( + "\n\nAdditional rules for Japanese:\n" + "- Use polite form (です/ます体) in all user-facing text." + ), +} + +DEFAULT_LANGUAGE = "en" + + +def build_language_instruction(language: str, *, mode: str = "full") -> str: + """Return a prompt instruction block for the given language code. + + Parameters + ---------- + language : str + BCP-47 primary subtag, e.g. ``"zh"``, ``"en"``, ``"ja"``. + mode : ``"full"`` | ``"compact"`` + ``"full"`` – detailed field-level rules (for text-heavy agents). + ``"compact"`` – minimal instruction (for code-generation agents). + + Returns ``""`` when *language* is ``"en"`` (or empty / unrecognised). + """ + lang = (language or DEFAULT_LANGUAGE).strip().lower() + + if lang == "en": + return "" + + display_name = LANGUAGE_DISPLAY_NAMES.get(lang, lang) + extra = LANGUAGE_EXTRA_RULES.get(lang, "") + + if mode == "compact": + return _build_compact(display_name, extra) + return _build_full(display_name, extra) + + +# ── Compact instruction (code-generation agents) ────────── + +def _build_compact(display_name: str, extra: str) -> str: + return ( + "[LANGUAGE INSTRUCTION]\n" + f"Write `display_instruction` and `suggested_table_name` in {display_name}. " + "All other JSON fields, Python code, variable names, column references, " + "and comments MUST stay in English. " + f"Keep original dataset column names exactly as-is — do NOT translate them." + f"{extra}" + ) + + +# ── Full instruction (text-heavy agents) ────────────────── + +def _build_full(display_name: str, extra: str) -> str: + return ( + "[LANGUAGE INSTRUCTION]\n" + "\n" + f"The user's interface language is **{display_name}**.\n" + "\n" + + f"**User-visible fields** — MUST be written in {display_name}:\n" + "\n" + f"Write these in {display_name}:\n" + '- `title` (chart title)\n' + '- `takeaways` (chart insight bullet points)\n' + '- `text`, `goal`, `tag` (exploration question cards)\n' + '- `display_instruction` (short description shown on data thread)\n' + '- `message` (clarification questions to the user)\n' + '- `summary` (final exploration summary)\n' + '- `explanation` (concept / code explanations)\n' + '- `data_summary` (dataset description)\n' + '- `suggested_table_name` (human-readable table name)\n' + '- Report markdown output (entire content)\n' + '- Any other free-text that the user will read\n' + "\n" + + "**Internal / programmatic fields** — MUST remain in English:\n" + '`output_variable`, `output_fields`, `chart_type`, `encodings`, ' + '`config`, `semantic_type`, `field_metadata`, `reason`, ' + '`detailed_instruction`, `thought`, `difficulty`, ' + "all JSON keys, all Python code (variables, column names, comments).\n" + "\n" + + "**Original dataset column names** — DO NOT translate or rename. " + "Keep them exactly as-is.\n" + "\n" + + "**New derived columns** — use English snake_case in code; " + f"describe them in {display_name} in user-visible text.\n" + + f"{extra}" + ) diff --git a/py-src/data_formulator/agents/agent_report_gen.py b/py-src/data_formulator/agents/agent_report_gen.py index bcee8be3..0059ad87 100644 --- a/py-src/data_formulator/agents/agent_report_gen.py +++ b/py-src/data_formulator/agents/agent_report_gen.py @@ -52,9 +52,10 @@ class ReportGenAgent(object): - def __init__(self, client, workspace): + def __init__(self, client, workspace, language_instruction=""): self.client = client self.workspace = workspace + self.language_instruction = language_instruction def get_data_summary(self, input_tables): return generate_data_summary(input_tables, self.workspace) @@ -109,9 +110,13 @@ def stream(self, input_tables, charts=[], style="blog post"): 'content': content + [{'type': 'text', 'text': 'Now based off the data and visualizations provided by the user, generate a report in markdown. The style of the report should be ' + style + '.'}] } + prompt_text = SYSTEM_PROMPT + if self.language_instruction: + prompt_text = prompt_text + "\n\n" + self.language_instruction + system_message = { 'role': 'system', - 'content': [ {'type': 'text', 'text': SYSTEM_PROMPT}] + 'content': [ {'type': 'text', 'text': prompt_text}] } messages = [ diff --git a/py-src/data_formulator/agents/agent_sort_data.py b/py-src/data_formulator/agents/agent_sort_data.py index 89bdc9df..f251cfb0 100644 --- a/py-src/data_formulator/agents/agent_sort_data.py +++ b/py-src/data_formulator/agents/agent_sort_data.py @@ -75,7 +75,7 @@ def run(self, name, values, n=1): 'value': values } - user_query = f"[INPUT]\n\n{json.dumps(input_obj)}\n\n[OUTPUT]" + user_query = f"[INPUT]\n\n{json.dumps(input_obj, ensure_ascii=False)}\n\n[OUTPUT]" logger.debug(user_query) logger.info(f"[SortDataAgent] run start") diff --git a/py-src/data_formulator/agents/agent_utils.py b/py-src/data_formulator/agents/agent_utils.py index e58f9d20..b2b3067b 100644 --- a/py-src/data_formulator/agents/agent_utils.py +++ b/py-src/data_formulator/agents/agent_utils.py @@ -3,9 +3,14 @@ import json import keyword +import logging +import time + import numpy as np import re +_logger = logging.getLogger(__name__) + def string_to_py_varname(var_str): var_name = re.sub(r'\W|^(?=\d)', '_', var_str) if keyword.iskeyword(var_name): @@ -106,6 +111,57 @@ def find_matching_bracket(text, start_index, bracket_type='curly'): return index return -1 +def _strip_json_comments(s: str) -> str: + """Remove single-line ``//`` comments from a JSON-like string. + + Correctly skips ``//`` that appears inside quoted strings. + """ + result: list[str] = [] + in_string = False + escape_next = False + i = 0 + while i < len(s): + ch = s[i] + if escape_next: + result.append(ch) + escape_next = False + i += 1 + continue + if ch == '\\' and in_string: + escape_next = True + result.append(ch) + i += 1 + continue + if ch == '"': + in_string = not in_string + result.append(ch) + i += 1 + continue + if not in_string and s[i:i + 2] == '//': + while i < len(s) and s[i] != '\n': + i += 1 + continue + result.append(ch) + i += 1 + return ''.join(result) + + +def _fix_json_trailing_commas(s: str) -> str: + """Remove trailing commas before ``}`` or ``]``.""" + return re.sub(r',\s*([}\]])', r'\1', s) + + +def _lenient_json_loads(json_str: str): + """Try ``json.loads`` first; on failure, strip comments / trailing commas + and retry. Returns the parsed object or raises ``ValueError``. + """ + try: + return json.loads(json_str) + except ValueError: + cleaned = _fix_json_trailing_commas(_strip_json_comments(json_str)) + return json.loads(cleaned) + + def extract_json_objects(text): """Extracts JSON objects and arrays from a text string. Returns a list of parsed JSON objects and arrays. @@ -137,7 +193,7 @@ def extract_json_objects(text): json_str = text[start_index:end_index + 1] try: - json_obj = json.loads(json_str) + json_obj = _lenient_json_loads(json_str) json_objects.append(json_obj) except ValueError: pass @@ -147,6 +203,69 @@ def extract_json_objects(text): return json_objects +def supplement_missing_block(client, messages, assistant_content, + parsed_json, code_blocks, prefix="[Agent]"): + """When model produces only JSON or only code, request the missing block. + + Smaller models often fail to produce both JSON + code in a single + response. Rather than retrying the full prompt (which tends to + reproduce the same partial output), we ask for *just* the missing + piece in a focused single-task follow-up — much higher success rate. + + Returns (parsed_json, code_blocks, supplement_content, elapsed_seconds). + supplement_content is None if no supplement was needed or it failed. + """ + has_json = parsed_json is not None + has_code = len(code_blocks) > 0 + + if has_json == has_code: + return parsed_json, code_blocks, None, 0.0 + + if has_json: + output_var = parsed_json.get('output_variable', 'result_df') or 'result_df' + _logger.info(f"{prefix} JSON found but no Python code — requesting supplement") + prompt = ( + "You produced the JSON spec but no Python code block. " + "Now write ONLY the ```python``` code block. " + f"The final DataFrame must be assigned to `{output_var}`." + ) + else: + _logger.info(f"{prefix} Python code found but no JSON spec — requesting supplement") + prompt = ( + "You produced the Python code but no JSON spec. " + "Now write ONLY the ```json``` spec block. " + "Make sure to include `output_variable` matching the " + "variable name used in your code." + ) + + try: + t0 = time.time() + supp_resp = client.get_completion(messages=[ + *messages, + {"role": "assistant", "content": assistant_content}, + {"role": "user", "content": prompt}, + ]) + elapsed = time.time() - t0 + supp_text = supp_resp.choices[0].message.content + + if has_json: + supp_codes = extract_code_from_gpt_response(supp_text + "\n", "python") + if supp_codes: + _logger.info(f"{prefix} Supplement succeeded — got Python code") + return parsed_json, supp_codes, supp_text, elapsed + _logger.warning(f"{prefix} Supplement did not produce Python code") + else: + for jb in extract_json_objects(supp_text + "\n"): + if isinstance(jb, dict): + _logger.info(f"{prefix} Supplement succeeded — got JSON spec") + return jb, code_blocks, supp_text, elapsed + _logger.warning(f"{prefix} Supplement did not produce JSON spec") + except Exception as e: + _logger.warning(f"{prefix} Supplement call failed: {e}") + + return parsed_json, code_blocks, None, 0.0 + + def get_field_summary(field_name, df, field_sample_size, max_val_chars=100): # Convert lists to strings to make them hashable def make_hashable(val): @@ -271,3 +390,47 @@ def assemble_table_summary(table, idx): return separator.join(table_summaries) +def ensure_output_variable_in_code(code: str, output_variable: str) -> tuple[str, bool, str]: + """Zero-cost regex patch: align code's actual output with the JSON-declared variable. + + This is a deterministic local fix (<1ms, 0 tokens) that runs *before* + sandbox execution, avoiding an expensive LLM repair round-trip. + It scans all top-level assignments (not just the last line, which may + be ``print(...)``), picks the last non-library one, and appends an + alias ``output_variable = ``. + + Returns + ------- + (patched_code, was_patched, detected_variable_name) + """ + if not output_variable or not code: + return code, False, "" + + # Check if output_variable appears as an assignment target (= but not ==, !=, <=, >=) + pattern = rf'(?:^|\n)\s*{re.escape(output_variable)}\s*=(?!=)' + if re.search(pattern, code): + return code, False, "" + + # output_variable not assigned — find the likely actual output variable. + all_assignments = re.findall(r'^([a-zA-Z_]\w*)\s*=(?!=)', code, re.MULTILINE) + if not all_assignments: + return code, False, "" + + LIBRARY_NAMES = frozenset({ + 'pd', 'np', 'duckdb', 'conn', 'cursor', 'engine', 'warnings', + 'math', 'json', 're', 'datetime', 'os', 'sys', 'random', 'time', + 'itertools', 'functools', 'operator', 'collections', 'statistics', + }) + + candidates = [v for v in all_assignments + if v not in LIBRARY_NAMES and not v.startswith('_')] + + if candidates: + best = candidates[-1] + else: + best = all_assignments[-1] + + patched_code = code.rstrip() + f"\n{output_variable} = {best}\n" + return patched_code, True, best + + diff --git a/py-src/data_formulator/agents/agent_utils_sql.py b/py-src/data_formulator/agents/agent_utils_sql.py index d9946951..1a81f96f 100644 --- a/py-src/data_formulator/agents/agent_utils_sql.py +++ b/py-src/data_formulator/agents/agent_utils_sql.py @@ -6,17 +6,12 @@ These functions are used across multiple agents for DuckDB operations and SQL data summaries. """ -import re +from data_formulator.datalake.table_names import sanitize_duckdb_sql_table_name def sanitize_table_name(table_name: str) -> str: - """Sanitize table name to be used in SQL queries""" - # Replace spaces with underscores - sanitized_name = table_name.replace(" ", "_") - sanitized_name = sanitized_name.replace("-", "_") - # Allow alphanumeric, underscore, dot, dash, and dollar sign - sanitized_name = re.sub(r'[^a-zA-Z0-9_\.$]', '', sanitized_name) - return sanitized_name + """Sanitize table name for DuckDB views; see :func:`sanitize_duckdb_sql_table_name`.""" + return sanitize_duckdb_sql_table_name(table_name) def create_duckdb_conn_with_parquet_views(workspace, input_tables: list[dict]): diff --git a/py-src/data_formulator/agents/client_utils.py b/py-src/data_formulator/agents/client_utils.py index 43cf0ee3..7f0555ab 100644 --- a/py-src/data_formulator/agents/client_utils.py +++ b/py-src/data_formulator/agents/client_utils.py @@ -48,6 +48,38 @@ def __init__(self, endpoint, model, api_key=None, api_base=None, api_version=No else: self.model = f"ollama/{model}" + def _strip_image_blocks(self, content): + """Remove image_url blocks from multimodal content arrays.""" + if isinstance(content, list): + sanitized = [] + for item in content: + if isinstance(item, dict): + if item.get("type") == "image_url": + continue + sanitized.append(item) + else: + sanitized.append(item) + return sanitized + return content + + def _strip_images_from_messages(self, messages): + """Create a copy of messages with image_url blocks removed.""" + sanitized_messages = [] + for msg in messages: + if isinstance(msg, dict): + new_msg = dict(msg) + if "content" in new_msg: + new_msg["content"] = self._strip_image_blocks(new_msg["content"]) + sanitized_messages.append(new_msg) + else: + sanitized_messages.append(msg) + return sanitized_messages + + def _is_image_deserialize_error(self, error_text: str) -> bool: + """Detect provider errors caused by image blocks on text-only models.""" + lowered = error_text.lower() + return ("image_url" in lowered and "expected `text`" in lowered) or "unknown variant `image_url`" in lowered + @classmethod def from_config(cls, model_config: dict[str, str]): """ @@ -72,6 +104,28 @@ def from_config(cls, model_config: dict[str, str]): model_config.get("api_version") ) + def ping(self, timeout: int = 10): + """Lightweight connectivity check: send a minimal completion with + max_tokens=3 and a short timeout. Raises on any failure.""" + messages = [{"role": "user", "content": "Reply only 'ok'."}] + + if self.endpoint == "openai": + client = openai.OpenAI( + base_url=self.params.get("api_base", None), + api_key=self.params.get("api_key", ""), + timeout=timeout, + ) + client.chat.completions.create( + model=self.model, messages=messages, max_tokens=3, + ) + else: + params = self.params.copy() + params["timeout"] = timeout + litellm.completion( + model=self.model, messages=messages, + max_tokens=3, drop_params=True, **params, + ) + def get_completion(self, messages, stream=False): """ Returns a LiteLLM client configured for the specified endpoint and model. @@ -93,8 +147,16 @@ def get_completion(self, messages, stream=False): if self.model.startswith("gpt-5") or self.model.startswith("o1") or self.model.startswith("o3"): completion_params["reasoning_effort"] = "low" - - return client.chat.completions.create(**completion_params, stream=stream) + + try: + return client.chat.completions.create(**completion_params, stream=stream) + except Exception as e: + error_text = str(e) + if self._is_image_deserialize_error(error_text): + sanitized_messages = self._strip_images_from_messages(messages) + completion_params["messages"] = sanitized_messages + return client.chat.completions.create(**completion_params, stream=stream) + raise else: params = self.params.copy() @@ -103,13 +165,26 @@ def get_completion(self, messages, stream=False): or self.model.startswith("claude-sonnet-4-5") or self.model.startswith("claude-opus-4")): params["reasoning_effort"] = "low" - return litellm.completion( - model=self.model, - messages=messages, - drop_params=True, - stream=stream, - **params - ) + try: + return litellm.completion( + model=self.model, + messages=messages, + drop_params=True, + stream=stream, + **params + ) + except Exception as e: + error_text = str(e) + if self._is_image_deserialize_error(error_text): + sanitized_messages = self._strip_images_from_messages(messages) + return litellm.completion( + model=self.model, + messages=sanitized_messages, + drop_params=True, + stream=stream, + **params + ) + raise def get_response(self, messages: list[dict], tools: list | None = None): diff --git a/py-src/data_formulator/agents/data_agent.py b/py-src/data_formulator/agents/data_agent.py index cc5f0eff..557e0bd2 100644 --- a/py-src/data_formulator/agents/data_agent.py +++ b/py-src/data_formulator/agents/data_agent.py @@ -24,7 +24,7 @@ from data_formulator.agents.agent_data_rec import DataRecAgent from data_formulator.agents.agent_utils import extract_json_objects, generate_data_summary from data_formulator.agents.client_utils import Client -from data_formulator.code_signing import sign_result +from data_formulator.security.code_signing import sign_result from data_formulator.workflows.create_vl_plots import ( assemble_vegailte_chart, coerce_field_type, @@ -59,7 +59,8 @@ {{ "action": "visualize", "thought": "", - "question": "" + "question": "", + "display_instruction": "" }} ``` @@ -78,7 +79,8 @@ {{ "action": "clarify", "thought": "", - "message": "" + "message": "", + "options": ["See supported providers. - Use openai provider for OpenAI-compatible APIs. + • {t('model.litellmNote').split('.')[0]}. {t('model.seeDocs')}. + {t('model.openaiProviderTip')} @@ -575,18 +599,18 @@ export const ModelSelectionButton: React.FC<{}> = ({ }) => { {!serverConfig.DISABLE_DISPLAY_KEYS && ( )} + setModelDialogOpen(false);}}>{t('model.useModel', { modelName: tempModelName })} + }}>{t('model.cancel')} ; diff --git a/src/views/MultiTablePreview.tsx b/src/views/MultiTablePreview.tsx index 7d1d9a55..c812d020 100644 --- a/src/views/MultiTablePreview.tsx +++ b/src/views/MultiTablePreview.tsx @@ -3,6 +3,7 @@ import * as React from 'react'; import { useState, useEffect } from 'react'; +import { useTranslation } from 'react-i18next'; import { Box, Chip, @@ -53,7 +54,7 @@ export const MultiTablePreview: React.FC = ({ error = null, table, tables, - emptyLabel = 'No tables to preview.', + emptyLabel, meta, onRemoveTable, activeIndex: controlledActiveIndex, @@ -63,10 +64,12 @@ export const MultiTablePreview: React.FC = ({ compact = true, hideRowCount = false, }) => { + const { t } = useTranslation(); const previewTables = tables ?? (table ? [table] : null); const [internalActiveIndex, setInternalActiveIndex] = useState(0); const activeIndex = controlledActiveIndex !== undefined ? controlledActiveIndex : internalActiveIndex; const setActiveIndex = onActiveIndexChange || setInternalActiveIndex; + const effectiveEmptyLabel = emptyLabel ?? t('preview.noTablesToPreview'); useEffect(() => { if (!previewTables || previewTables.length === 0) { @@ -109,7 +112,7 @@ export const MultiTablePreview: React.FC = ({ {!loading && !error && (!previewTables || previewTables.length === 0) && ( - {emptyLabel} + {effectiveEmptyLabel} )} @@ -127,7 +130,7 @@ export const MultiTablePreview: React.FC = ({ }} > - Preview + {t('preview.preview')} {previewTables.map((t, idx) => { const label = t.displayId || t.id; @@ -179,13 +182,13 @@ export const MultiTablePreview: React.FC = ({ ); })} {onRemoveTable && ( - + onRemoveTable(activeIndex)} sx={{ ml: 'auto', flexShrink: 0 }} - aria-label="Remove table" + aria-label={t('preview.removeTable')} > @@ -211,7 +214,10 @@ export const MultiTablePreview: React.FC = ({ {!hideRowCount && ( - {activeTable.rows.length} rows × {activeTable.names.length} columns + {t('preview.rowsColumns', { + rows: activeTable.rows.length, + columns: activeTable.names.length, + })} )} diff --git a/src/views/ReactTable.tsx b/src/views/ReactTable.tsx index a885811f..df110350 100644 --- a/src/views/ReactTable.tsx +++ b/src/views/ReactTable.tsx @@ -100,7 +100,9 @@ export const CustomReactTable: React.FC = ({ sx={{ backgroundColor }}> {column.format ? column.format(value) - : (typeof value === "boolean" ? `${value}` : value)} + : (value != null && typeof value === 'object' + ? String(value) + : (typeof value === "boolean" ? `${value}` : value))} ); })} diff --git a/src/views/RefreshDataDialog.tsx b/src/views/RefreshDataDialog.tsx index 063f7323..307e3d13 100644 --- a/src/views/RefreshDataDialog.tsx +++ b/src/views/RefreshDataDialog.tsx @@ -26,7 +26,8 @@ import UploadFileIcon from '@mui/icons-material/UploadFile'; import { useSelector } from 'react-redux'; import { DataFormulatorState } from '../app/dfSlice'; import { DictTable } from '../components/ComponentType'; -import { createTableFromText, loadTextDataWrapper, loadBinaryDataWrapper } from '../data/utils'; +import { createTableFromText, loadTextDataWrapper, loadBinaryDataWrapper, readFileText } from '../data/utils'; +import { useTranslation } from 'react-i18next'; interface TabPanelProps { children?: React.ReactNode; @@ -63,6 +64,7 @@ export const RefreshDataDialog: React.FC = ({ onRefreshComplete, }) => { const theme = useTheme(); + const { t } = useTranslation(); const [tabValue, setTabValue] = useState(0); const [pasteContent, setPasteContent] = useState(''); const [urlContent, setUrlContent] = useState(''); @@ -74,16 +76,13 @@ export const RefreshDataDialog: React.FC = ({ // Constants for content size limits const MAX_DISPLAY_LINES = 20; const LARGE_CONTENT_THRESHOLD = 50000; - const MAX_CONTENT_SIZE = 2 * 1024 * 1024; // 2MB - const [displayContent, setDisplayContent] = useState(''); const [isLargeContent, setIsLargeContent] = useState(false); const [showFullContent, setShowFullContent] = useState(false); - const [isOverSizeLimit, setIsOverSizeLimit] = useState(false); const validateColumns = (newRows: any[]): { valid: boolean; message: string } => { if (!newRows || newRows.length === 0) { - return { valid: false, message: 'No data found in the uploaded content.' }; + return { valid: false, message: t('refresh.errorNoData') }; } const newColumns = Object.keys(newRows[0]).sort(); @@ -92,7 +91,7 @@ export const RefreshDataDialog: React.FC = ({ if (newColumns.length !== existingColumns.length) { return { valid: false, - message: `Column count mismatch. Expected ${existingColumns.length} columns (${existingColumns.join(', ')}), but got ${newColumns.length} columns (${newColumns.join(', ')}).`, + message: t('refresh.errorColumnCountMismatch', { expected: existingColumns.length, expectedNames: existingColumns.join(', '), actual: newColumns.length, actualNames: newColumns.join(', ') }), }; } @@ -100,12 +99,12 @@ export const RefreshDataDialog: React.FC = ({ const extraColumns = newColumns.filter(col => !existingColumns.includes(col)); if (missingColumns.length > 0 || extraColumns.length > 0) { - let message = 'Column names do not match.'; + let message = t('refresh.errorColumnNamesMismatch'); if (missingColumns.length > 0) { - message += ` Missing: ${missingColumns.join(', ')}.`; + message += ` ${t('refresh.errorMissingColumns', { columns: missingColumns.join(', ') })}`; } if (extraColumns.length > 0) { - message += ` Unexpected: ${extraColumns.join(', ')}.`; + message += ` ${t('refresh.errorUnexpectedColumns', { columns: extraColumns.join(', ') })}`; } return { valid: false, message }; } @@ -133,7 +132,6 @@ export const RefreshDataDialog: React.FC = ({ setIsLoading(false); setIsLargeContent(false); setShowFullContent(false); - setIsOverSizeLimit(false); onClose(); }; @@ -147,9 +145,7 @@ export const RefreshDataDialog: React.FC = ({ const newContent = event.target.value; setPasteContent(newContent); - const contentSizeBytes = new Blob([newContent]).size; - const isOverLimit = contentSizeBytes > MAX_CONTENT_SIZE; - setIsOverSizeLimit(isOverLimit); + const isLarge = newContent.length > LARGE_CONTENT_THRESHOLD; setIsLargeContent(isLarge); @@ -157,12 +153,14 @@ export const RefreshDataDialog: React.FC = ({ if (isLarge && !showFullContent) { const lines = newContent.split('\n'); const previewLines = lines.slice(0, MAX_DISPLAY_LINES); - const preview = previewLines.join('\n') + (lines.length > MAX_DISPLAY_LINES ? '\n... (truncated for performance)' : ''); + const preview = + previewLines.join('\n') + + (lines.length > MAX_DISPLAY_LINES ? `\n${t('upload.pastePreviewTruncatedSuffix')}` : ''); setDisplayContent(preview); } else { setDisplayContent(newContent); } - }, [showFullContent]); + }, [showFullContent, t]); const toggleFullContent = useCallback(() => { setShowFullContent(!showFullContent); @@ -171,15 +169,17 @@ export const RefreshDataDialog: React.FC = ({ } else { const lines = pasteContent.split('\n'); const previewLines = lines.slice(0, MAX_DISPLAY_LINES); - const preview = previewLines.join('\n') + (lines.length > MAX_DISPLAY_LINES ? '\n... (truncated for performance)' : ''); + const preview = + previewLines.join('\n') + + (lines.length > MAX_DISPLAY_LINES ? `\n${t('upload.pastePreviewTruncatedSuffix')}` : ''); setDisplayContent(preview); } - }, [showFullContent, pasteContent]); + }, [showFullContent, pasteContent, t]); // Handle paste submit const handlePasteSubmit = () => { if (!pasteContent.trim()) { - setError('Please paste some data.'); + setError(t('refresh.errorPleaseAddData')); return; } @@ -191,7 +191,7 @@ export const RefreshDataDialog: React.FC = ({ if (Array.isArray(jsonContent)) { newRows = jsonContent; } else { - setError('JSON content must be an array of objects.'); + setError(t('refresh.errorJsonArray')); setIsLoading(false); return; } @@ -201,14 +201,14 @@ export const RefreshDataDialog: React.FC = ({ if (tempTable) { newRows = tempTable.rows; } else { - setError('Could not parse the pasted content as JSON or CSV/TSV.'); + setError(t('refresh.errorParsePaste')); setIsLoading(false); return; } } processAndValidateData(newRows); } catch (err) { - setError('Failed to parse the pasted content.'); + setError(t('refresh.errorParseContent')); } finally { setIsLoading(false); } @@ -217,13 +217,13 @@ export const RefreshDataDialog: React.FC = ({ // Handle URL submit const handleUrlSubmit = () => { if (!urlContent.trim()) { - setError('Please enter a URL.'); + setError(t('refresh.errorPleaseEnterUrl')); return; } const hasValidSuffix = urlContent.endsWith('.csv') || urlContent.endsWith('.tsv') || urlContent.endsWith('.json'); if (!hasValidSuffix) { - setError('URL must point to a .csv, .tsv, or .json file.'); + setError(t('refresh.errorUrlSuffix')); return; } @@ -237,7 +237,7 @@ export const RefreshDataDialog: React.FC = ({ if (Array.isArray(jsonContent)) { newRows = jsonContent; } else { - setError('JSON content must be an array of objects.'); + setError(t('refresh.errorJsonArray')); setIsLoading(false); return; } @@ -246,7 +246,7 @@ export const RefreshDataDialog: React.FC = ({ if (tempTable) { newRows = tempTable.rows; } else { - setError('Could not parse the URL content as JSON or CSV/TSV.'); + setError(t('refresh.errorParseUrl')); setIsLoading(false); return; } @@ -254,7 +254,7 @@ export const RefreshDataDialog: React.FC = ({ processAndValidateData(newRows); }) .catch(err => { - setError(`Failed to fetch data from URL: ${err.message}`); + setError(t('refresh.errorFetchUrl', { message: err.message })); }) .finally(() => { setIsLoading(false); @@ -276,21 +276,14 @@ export const RefreshDataDialog: React.FC = ({ file.name.endsWith('.tsv') || file.name.endsWith('.json')) { - const MAX_FILE_SIZE = 5 * 1024 * 1024; - if (file.size > MAX_FILE_SIZE) { - setError(`File is too large (${(file.size / (1024 * 1024)).toFixed(2)}MB). Maximum size is 5MB.`); - setIsLoading(false); - return; - } - - file.text().then((text) => { + readFileText(file).then((text) => { let newRows: any[] = []; try { const jsonContent = JSON.parse(text); if (Array.isArray(jsonContent)) { newRows = jsonContent; } else { - setError('JSON content must be an array of objects.'); + setError(t('refresh.errorJsonArray')); setIsLoading(false); return; } @@ -299,14 +292,14 @@ export const RefreshDataDialog: React.FC = ({ if (tempTable) { newRows = tempTable.rows; } else { - setError('Could not parse the file content.'); + setError(t('refresh.errorParseFile')); setIsLoading(false); return; } } processAndValidateData(newRows); }).catch(err => { - setError(`Failed to read file: ${err.message}`); + setError(t('refresh.errorReadFile', { message: err.message })); }).finally(() => { setIsLoading(false); }); @@ -324,17 +317,17 @@ export const RefreshDataDialog: React.FC = ({ if (tables.length > 0) { processAndValidateData(tables[0].rows); } else { - setError('Failed to parse Excel file.'); + setError(t('refresh.errorParseExcel')); } } catch (err) { - setError('Failed to parse Excel file.'); + setError(t('refresh.errorParseExcel')); } } setIsLoading(false); }; reader.readAsArrayBuffer(file); } else { - setError('Unsupported file format. Please use CSV, TSV, JSON, or Excel files.'); + setError(t('refresh.errorUnsupportedFormat')); setIsLoading(false); } @@ -356,7 +349,7 @@ export const RefreshDataDialog: React.FC = ({ > - Refresh Data for "{table.displayId || table.id}" + {t('refresh.titleForTable', { table: table.displayId || table.id })} = ({ lineHeight: 1.5 }} > - Upload new data to replace the current table content. Required columns: {table.names.join(', ')} + {t('refresh.description')} {table.names.join(', ')} {error && ( @@ -401,28 +394,14 @@ export const RefreshDataDialog: React.FC = ({ - - - + + + - {isOverSizeLimit && ( - - - Content exceeds {(MAX_CONTENT_SIZE / (1024 * 1024)).toFixed(0)}MB limit ({(new Blob([pasteContent]).size / (1024 * 1024)).toFixed(2)}MB) - - - )} - {isLargeContent && !isOverSizeLimit && ( + {isLargeContent && ( = ({ border: `1px solid ${borderColor.divider}` }}> - Large content ({Math.round(pasteContent.length / 1000)}KB) • {showFullContent ? 'Full view' : 'Preview'} + {t('upload.largeContentDetected', { size: Math.round(pasteContent.length / 1000) })}{' '} + •{' '} + {showFullContent ? t('upload.showingFullContent') : t('upload.showingPreview')} )} @@ -459,7 +440,7 @@ export const RefreshDataDialog: React.FC = ({ fullWidth value={displayContent} onChange={handlePasteContentChange} - placeholder="Paste your data here (CSV, TSV, or JSON format)" + placeholder={t('upload.placeholder.paste')} disabled={isLoading} sx={{ '& .MuiInputBase-root': { @@ -480,10 +461,10 @@ export const RefreshDataDialog: React.FC = ({ {serverConfig.DISABLE_FILE_UPLOAD ? ( - File upload is disabled in this environment. + {t('upload.fileUploadDisabled')} - Install Data Formulator locally to enable file upload.
+ {t('refresh.installLocallyForUpload')}
= ({ > - Drag & drop file here + {t('upload.dragDrop')} - or Browse + {t('upload.or')}{' '} + + {t('upload.browse')} + - Supported: CSV, TSV, JSON, Excel (xlsx, xls) + {t('upload.supportedFormats')} @@ -537,12 +521,12 @@ export const RefreshDataDialog: React.FC = ({ setUrlContent(e.target.value.trim())} disabled={isLoading} error={urlContent !== '' && !hasValidUrlSuffix} - helperText={urlContent !== '' && !hasValidUrlSuffix ? 'URL should link to a .csv, .tsv, or .json file' : ''} + helperText={urlContent !== '' && !hasValidUrlSuffix ? t('refresh.urlSuffixHelper') : ''} size="small" sx={{ '& .MuiInputBase-input': { @@ -564,16 +548,16 @@ export const RefreshDataDialog: React.FC = ({ disabled={isLoading} sx={{ textTransform: 'none' }} > - Cancel + {t('app.cancel')} {tabValue === 0 && ( )} {tabValue === 2 && ( @@ -583,7 +567,7 @@ export const RefreshDataDialog: React.FC = ({ disabled={isLoading || !urlContent.trim() || !hasValidUrlSuffix} sx={{ textTransform: 'none' }} > - Refresh Data + {t('refresh.refreshData')} )} diff --git a/src/views/ReportView.tsx b/src/views/ReportView.tsx index f0d82125..4908e102 100644 --- a/src/views/ReportView.tsx +++ b/src/views/ReportView.tsx @@ -1,8 +1,8 @@ // Copyright (c) Microsoft Corporation. // Licensed under the MIT License. -import React, { FC, useState, useRef, useEffect, memo, useMemo } from 'react'; -import { borderColor, shadow, transition, radius } from '../app/tokens'; +import React, { FC, useState, useRef, useEffect, useMemo } from 'react'; +import { borderColor, shadow, transition } from '../app/tokens'; import { Box, Button, @@ -12,15 +12,8 @@ import { Card, CardContent, CircularProgress, - Alert, Link, - Divider, Paper, - TableBody, - TableCell, - TableContainer, - TableHead, - TableRow, ToggleButton, ToggleButtonGroup, Tooltip, @@ -30,188 +23,24 @@ import { import Masonry from '@mui/lab/Masonry'; import ArrowForwardIcon from '@mui/icons-material/ArrowForward'; import ArrowBackIcon from '@mui/icons-material/ArrowBack'; -import CreateChartifact from '@mui/icons-material/Description'; import EditIcon from '@mui/icons-material/Edit'; -import HelpOutlineIcon from '@mui/icons-material/HelpOutline'; -import HistoryIcon from '@mui/icons-material/History'; import DeleteIcon from '@mui/icons-material/Delete'; -import ShareIcon from '@mui/icons-material/Share'; +import ContentCopyIcon from '@mui/icons-material/ContentCopy'; import CheckCircleIcon from '@mui/icons-material/CheckCircle'; import html2canvas from 'html2canvas'; import { useDispatch, useSelector } from 'react-redux'; import { DataFormulatorState, dfActions, dfSelectors, GeneratedReport } from '../app/dfSlice'; import { Message } from './MessageSnackbar'; import { getUrls, assembleVegaChart, getTriggers, prepVisTable, fetchWithIdentity } from '../app/utils'; -import { MuiMarkdown, getOverrides } from 'mui-markdown'; import embed from 'vega-embed'; -import { getDataTable } from './VisualizationView'; +import { getDataTable } from './ChartUtils'; import { DictTable } from '../components/ComponentType'; import { AppDispatch } from '../app/store'; -import { Collapse } from '@mui/material'; -import ExpandMoreIcon from '@mui/icons-material/ExpandMore'; -import ExpandLessIcon from '@mui/icons-material/ExpandLess'; -import { convertToChartifact, openChartifactViewer } from './ChartifactDialog'; -import { StreamIcon } from '../icons'; - -// Typography constants -const FONT_FAMILY_SYSTEM = '-apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, "Apple Color Emoji", Arial, sans-serif, "Segoe UI Emoji", "Segoe UI Symbol"'; -const FONT_FAMILY_SERIF = 'Georgia, Cambria, "Times New Roman", Times, serif'; -const FONT_FAMILY_MONO = '"SFMono-Regular", Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace'; - -// Color constants -const COLOR_HEADING = 'rgb(37, 37, 37)'; -const COLOR_BODY = 'rgb(55, 53, 47)'; -const COLOR_MUTED = 'rgb(73, 73, 73)'; -const COLOR_BG_LIGHT = 'rgba(247, 246, 243, 1)'; - -// Social post style constants (Twitter/X style) -const COLOR_SOCIAL_TEXT = 'rgb(15, 20, 25)'; -const COLOR_SOCIAL_BORDER = 'rgb(207, 217, 222)'; -const COLOR_SOCIAL_ACCENT = 'rgb(29, 155, 240)'; - -// Executive summary style constants (professional/business look) -const COLOR_EXEC_TEXT = 'rgb(33, 37, 41)'; -const COLOR_EXEC_HEADING = 'rgb(20, 24, 28)'; -const COLOR_EXEC_BORDER = 'rgb(108, 117, 125)'; -const COLOR_EXEC_ACCENT = 'rgb(0, 123, 255)'; -const COLOR_EXEC_BG = 'rgb(248, 249, 250)'; - - -const HEADING_BASE = { - fontFamily: FONT_FAMILY_SYSTEM, - color: COLOR_HEADING, - fontWeight: 700, - letterSpacing: '-0.01em', -}; - -const BODY_TEXT_BASE = { - fontFamily: FONT_FAMILY_SYSTEM, - fontSize: '0.9375rem', - lineHeight: 1.75, - fontWeight: 400, - letterSpacing: '0.003em', - color: COLOR_BODY, -}; +import { TiptapReportEditor } from './TiptapReportEditor'; +import { getCachedChart } from '../app/chartCache'; -const TABLE_CELL_BASE = { - fontFamily: FONT_FAMILY_SYSTEM, - fontSize: '0.875rem', - py: 1.5, - px: 2, -}; - -// Notion-style markdown overrides with MUI components -const notionStyleMarkdownOverrides = { - ...getOverrides(), - h1: { component: Typography, props: { variant: 'h4', gutterBottom: true, - sx: { ...HEADING_BASE, fontSize: '1.75rem', lineHeight: 1.25, letterSpacing: '-0.02em', pb: 0.5, mb: 3, mt: 4 } } }, - h2: { component: Typography, props: { variant: 'h5', gutterBottom: true, - sx: { ...HEADING_BASE, fontSize: '1.5rem', lineHeight: 1.3, pb: 0.5, mb: 2.5, mt: 3.5 } } }, - h3: { component: Typography, props: { variant: 'h6', gutterBottom: true, - sx: { ...HEADING_BASE, fontWeight: 600, fontSize: '1.25rem', lineHeight: 1.4, letterSpacing: '-0.005em', mb: 2, mt: 3 } } }, - h4: { component: Typography, props: { variant: 'h6', gutterBottom: true, - sx: { ...HEADING_BASE, fontWeight: 600, fontSize: '1.125rem', lineHeight: 1.4, mb: 1.5, mt: 2.5 } } }, - h5: { component: Typography, props: { variant: 'subtitle1', gutterBottom: true, - sx: { ...HEADING_BASE, fontWeight: 600, fontSize: '1rem', lineHeight: 1.5, mb: 1.5, mt: 2 } } }, - h6: { component: Typography, props: { variant: 'subtitle2', gutterBottom: true, - sx: { ...HEADING_BASE, fontWeight: 600, fontSize: '0.9375rem', lineHeight: 1.5, mb: 1.5, mt: 2 } } }, - p: { component: Typography, props: { variant: 'body2', paragraph: true, - sx: { ...BODY_TEXT_BASE, mb: 1.75 } } }, - a: { component: Link, props: { underline: 'hover' as const, color: 'primary' as const, - sx: { fontSize: 'inherit', fontWeight: 500 } } }, - ul: { component: 'ul', props: { style: { - paddingLeft: '1.8em', marginTop: '0.75em', marginBottom: '1.5em', fontFamily: FONT_FAMILY_SYSTEM - } } }, - ol: { component: 'ol', props: { style: { - paddingLeft: '1.8em', marginTop: '0.75em', marginBottom: '1.5em', fontFamily: FONT_FAMILY_SYSTEM - } } }, - li: { component: Typography, props: { component: 'li', variant: 'body1', - sx: { ...BODY_TEXT_BASE, mb: 0.5 } } }, - blockquote: { component: Box, props: { sx: { - borderLeft: '3px solid', borderColor: borderColor.component, pl: 2.5, py: 1, my: 2.5, - fontFamily: FONT_FAMILY_SERIF, fontStyle: 'italic', color: COLOR_MUTED, fontSize: '1rem', lineHeight: 1.7 - } } }, - pre: { component: Paper, props: { elevation: 0, sx: { - backgroundColor: COLOR_BG_LIGHT, p: 2, borderRadius: '4px', overflow: 'auto', my: 2, - border: `1px solid ${borderColor.divider}`, - '& code': { - backgroundColor: 'transparent !important', padding: '0 !important', fontSize: '0.8125rem', - fontFamily: FONT_FAMILY_MONO, lineHeight: 1.7, color: COLOR_BODY - } - } } }, - table: { component: TableContainer, props: { component: Paper, elevation: 0, - sx: { my: 2, border: `1px solid ${borderColor.divider}` } } }, - thead: { component: TableHead, props: { sx: { backgroundColor: COLOR_BG_LIGHT } } }, - tbody: { component: TableBody }, - tr: { component: TableRow }, - th: { component: TableCell, props: { sx: { - ...TABLE_CELL_BASE, fontWeight: 600, borderBottom: `2px solid ${borderColor.divider}` - } } }, - td: { component: TableCell, props: { sx: { - ...TABLE_CELL_BASE, borderBottom: `1px solid ${borderColor.divider}`, lineHeight: 1.6 - } } }, - hr: { component: Divider, props: { sx: { my: 3 } } } -} as any; - -// Social post style markdown overrides (X/Twitter style) -const socialStyleMarkdownOverrides = { - ...notionStyleMarkdownOverrides, - h1: { component: Typography, props: { variant: 'h6', gutterBottom: true, - sx: { fontFamily: FONT_FAMILY_SYSTEM, fontWeight: 700, fontSize: '1.125rem', - lineHeight: 1.25, color: COLOR_SOCIAL_TEXT, mb: 1.5, mt: 1.5 } } }, - h2: { component: Typography, props: { variant: 'h6', gutterBottom: true, - sx: { fontFamily: FONT_FAMILY_SYSTEM, fontWeight: 700, fontSize: '1rem', - lineHeight: 1.25, color: COLOR_SOCIAL_TEXT, mb: 1.25, mt: 1.5 } } }, - h3: { component: Typography, props: { variant: 'subtitle1', gutterBottom: true, - sx: { fontFamily: FONT_FAMILY_SYSTEM, fontWeight: 600, fontSize: '0.9375rem', - lineHeight: 1.3, color: COLOR_SOCIAL_TEXT, mb: 1, mt: 1.25 } } }, - p: { component: Typography, props: { variant: 'body2', paragraph: true, - sx: { fontFamily: FONT_FAMILY_SYSTEM, fontSize: '0.875rem', lineHeight: 1.4, - fontWeight: 400, mb: 0.75, color: COLOR_SOCIAL_TEXT } } }, - li: { component: Typography, props: { component: 'li', variant: 'body2', - sx: { fontFamily: FONT_FAMILY_SYSTEM, fontSize: '0.875rem', lineHeight: 1.4, - fontWeight: 400, mb: 0.25, color: COLOR_SOCIAL_TEXT } } } -} as any; - -// Executive summary style markdown overrides (compact serif styling) -const executiveSummaryMarkdownOverrides = { - ...getOverrides(), - h1: { component: Typography, props: { variant: 'h5', gutterBottom: true, - sx: { fontFamily: FONT_FAMILY_SERIF, fontWeight: 700, fontSize: '1.25rem', lineHeight: 1.3, color: COLOR_EXEC_HEADING, mb: 2, mt: 2.5 } } }, - h2: { component: Typography, props: { variant: 'h6', gutterBottom: true, - sx: { fontFamily: FONT_FAMILY_SERIF, fontWeight: 600, fontSize: '1.125rem', lineHeight: 1.3, color: COLOR_EXEC_HEADING, mb: 1.5, mt: 2 } } }, - h3: { component: Typography, props: { variant: 'h6', gutterBottom: true, - sx: { fontFamily: FONT_FAMILY_SERIF, fontWeight: 600, fontSize: '1rem', lineHeight: 1.4, color: COLOR_EXEC_HEADING, mb: 1.25, mt: 1.5 } } }, - h4: { component: Typography, props: { variant: 'subtitle1', gutterBottom: true, - sx: { fontFamily: FONT_FAMILY_SERIF, fontWeight: 600, fontSize: '0.9375rem', lineHeight: 1.4, color: COLOR_EXEC_HEADING, mb: 1, mt: 1.5 } } }, - p: { component: Typography, props: { variant: 'body2', paragraph: true, - sx: { fontFamily: FONT_FAMILY_SERIF, fontSize: '0.875rem', lineHeight: 1.5, fontWeight: 400, color: COLOR_EXEC_TEXT, mb: 1.25, textAlign: 'justify' } } }, - a: { component: Link, props: { underline: 'hover' as const, color: 'primary' as const, - sx: { fontSize: 'inherit', fontWeight: 500, color: COLOR_EXEC_ACCENT, '&:hover': { color: 'rgb(0, 86, 179)' } } } }, - ul: { component: 'ul', props: { style: { paddingLeft: '1.5em', marginTop: '0.5em', marginBottom: '1em', fontFamily: FONT_FAMILY_SERIF } } }, - ol: { component: 'ol', props: { style: { paddingLeft: '1.5em', marginTop: '0.5em', marginBottom: '1em', fontFamily: FONT_FAMILY_SERIF } } }, - li: { component: Typography, props: { component: 'li', variant: 'body2', - sx: { fontFamily: FONT_FAMILY_SERIF, fontSize: '0.875rem', lineHeight: 1.5, fontWeight: 400, color: COLOR_EXEC_TEXT, mb: 0.25 } } }, - blockquote: { component: Box, props: { sx: { - borderLeft: '2px solid', borderLeftColor: COLOR_EXEC_ACCENT, pl: 2, py: 1, my: 1.5, - backgroundColor: COLOR_EXEC_BG, fontFamily: FONT_FAMILY_SERIF, fontStyle: 'italic', color: COLOR_EXEC_TEXT, fontSize: '0.875rem', lineHeight: 1.6 - } } }, - pre: { component: Paper, props: { elevation: 0, sx: { - backgroundColor: COLOR_EXEC_BG, p: 1.5, borderRadius: '4px', overflow: 'auto', my: 1.5, - '& code': { backgroundColor: 'transparent !important', padding: '0 !important', fontSize: '0.75rem', fontFamily: FONT_FAMILY_MONO, lineHeight: 1.5, color: COLOR_EXEC_TEXT } - } } }, - table: { component: TableContainer, props: { component: Paper, elevation: 0, sx: { my: 1.5, borderRadius: '4px' } } }, - thead: { component: TableHead, props: { sx: { backgroundColor: COLOR_EXEC_BG } } }, - tbody: { component: TableBody }, - tr: { component: TableRow }, - th: { component: TableCell, props: { sx: { - fontFamily: FONT_FAMILY_SERIF, fontSize: '0.8125rem', py: 1, px: 1.5, fontWeight: 600, borderBottom: '1px solid', borderColor: COLOR_EXEC_BORDER, color: COLOR_EXEC_HEADING - } } }, - td: { component: TableCell, props: { sx: { - fontFamily: FONT_FAMILY_SERIF, fontSize: '0.8125rem', py: 1, px: 1.5, borderBottom: '1px solid', borderColor: COLOR_EXEC_BORDER, lineHeight: 1.5, color: COLOR_EXEC_TEXT - } } }, - hr: { component: Divider, props: { sx: { my: 2, borderColor: COLOR_EXEC_BORDER } } } -} as any; +import { StreamIcon } from '../icons'; +import { useTranslation } from 'react-i18next'; export const ReportView: FC = () => { // Get all generated reports from Redux state @@ -219,8 +48,7 @@ export const ReportView: FC = () => { const charts = useSelector((state: DataFormulatorState) => state.charts); const tables = useSelector((state: DataFormulatorState) => state.tables); - const selectedModelId = useSelector((state: DataFormulatorState) => state.selectedModelId); - const models = useSelector((state: DataFormulatorState) => state.models); + const activeModel = useSelector(dfSelectors.getActiveModel); const conceptShelfItems = useSelector((state: DataFormulatorState) => state.conceptShelfItems); const config = useSelector((state: DataFormulatorState) => state.config); const allGeneratedReports = useSelector(dfSelectors.getAllGeneratedReports); @@ -228,12 +56,25 @@ export const ReportView: FC = () => { const focusedId = useSelector((state: DataFormulatorState) => state.focusedId); const focusedChartId = focusedId?.type === 'chart' ? focusedId.chartId : undefined; const theme = useTheme(); + const { t } = useTranslation(); + + const reportStyleDisplayLabel = (styleKey: string) => { + const map: Record = { + 'live report': 'report.styleLiveReport', + 'blog post': 'report.styleBlogPost', + 'social post': 'report.styleSocialPost', + 'executive summary': 'report.styleExecutiveSummary', + 'short note': 'report.styleShortNote', + }; + const labelKey = map[styleKey]; + return labelKey ? t(labelKey) : styleKey; + }; const [selectedChartIds, setSelectedChartIds] = useState>(new Set(focusedChartId ? [focusedChartId] : [])); const [previewImages, setPreviewImages] = useState>(new Map()); const [isLoadingPreviews, setIsLoadingPreviews] = useState(false); const [isGenerating, setIsGenerating] = useState(false); - const [error, setError] = useState(''); + const [style, setStyle] = useState('social post'); const [mode, setMode] = useState<'compose' | 'post'>(allGeneratedReports.length > 0 ? 'post' : 'compose'); @@ -243,7 +84,7 @@ export const ReportView: FC = () => { const [generatedStyle, setGeneratedStyle] = useState('social post'); const [cachedReportImages, setCachedReportImages] = useState>({}); const [shareButtonSuccess, setShareButtonSuccess] = useState(false); - const [hideTableOfContents, setHideTableOfContents] = useState(false); + const [copyButtonSuccess, setCopyButtonSuccess] = useState(false); const updateCachedReportImages = (chartId: string, blobUrl: string, width: number, height: number) => { setCachedReportImages(prev => ({ @@ -256,7 +97,7 @@ export const ReportView: FC = () => { const showMessage = (message: string, type: 'success' | 'error' | 'info' | 'warning' = 'success') => { const msg: Message = { type, - component: 'ReportView', + component: t('messages.report.component'), timestamp: Date.now(), value: message }; @@ -271,7 +112,7 @@ export const ReportView: FC = () => { // Find the report content element const reportElement = document.querySelector('[data-report-content]') as HTMLElement; if (!reportElement) { - showMessage('Could not find report content to capture', 'error'); + showMessage(t('report.couldNotFindContent'), 'error'); return; } @@ -292,7 +133,7 @@ export const ReportView: FC = () => { // Convert canvas to blob canvas.toBlob((blob: Blob | null) => { if (!blob) { - showMessage('Failed to generate image', 'error'); + showMessage(t('report.failedToGenerateImage'), 'error'); return; } @@ -303,37 +144,61 @@ export const ReportView: FC = () => { 'image/png': blob }) ]).then(() => { - showMessage('Report image copied to clipboard! You can now paste it anywhere to share.'); + showMessage(t('report.imageCopied')); setShareButtonSuccess(true); setTimeout(() => setShareButtonSuccess(false), 2000); }).catch(() => { - showMessage('Failed to copy to clipboard. Your browser may not support this feature.', 'error'); + showMessage(t('report.failedToCopyClipboard'), 'error'); }); } else { - showMessage('Clipboard API not supported in your browser. Please use a modern browser.', 'error'); + showMessage(t('report.clipboardNotSupported'), 'error'); } }, 'image/png', 0.95); } catch (error) { console.error('Error generating report image:', error); - showMessage('Failed to generate report image. Please try again.', 'error'); + showMessage(t('report.failedToGenerateReportImage'), 'error'); } }; - // Update like this: const processReport = (rawReport: string): string => { const markdownMatch = rawReport.match(/```markdown\n([\s\S]*?)(?:\n```)?$/); let processed = markdownMatch ? markdownMatch[1] : rawReport; - + + const makeImg = (chartId: string, url: string, width: number, height: number) => { + return `${t('report.chartAlt')}`; + }; + + const usedKeys = new Set(); Object.entries(cachedReportImages).forEach(([chartId, { url, width, height }]) => { - processed = processed.replace( - new RegExp(`\\[IMAGE\\(${chartId}\\)\\]`, 'g'), - `Chart` - ); + const escaped = chartId.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); + const regex = new RegExp(`\\[IMAGE\\(${escaped}\\)\\]`, 'g'); + if (regex.test(processed)) { + usedKeys.add(chartId); + processed = processed.replace(regex, makeImg(chartId, url, width, height)); + } }); - + + const unusedEntries = Object.entries(cachedReportImages) + .filter(([key]) => !usedKeys.has(key)); + let unusedIdx = 0; + processed = processed.replace(/\[IMAGE\([^\)]+\)\]/g, () => { + if (unusedIdx < unusedEntries.length) { + const [chartId, { url, width, height }] = unusedEntries[unusedIdx++]; + return makeImg(chartId, url, width, height); + } + return ''; + }); + + // Refresh stale tags that have data-chart-id with updated blob URLs + Object.entries(cachedReportImages).forEach(([chartId, { url, width, height }]) => { + const escaped = chartId.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); + const imgRegex = new RegExp(`]*?)data-chart-id="${escaped}"([^>]*?)>`, 'g'); + processed = processed.replace(imgRegex, makeImg(chartId, url, width, height)); + }); + return processed; }; @@ -344,20 +209,25 @@ export const ReportView: FC = () => { setGeneratedReport(report.content); setGeneratedStyle(report.style); - // load / assemble chart images for the report report.selectedChartIds.forEach((chartId) => { const chart = charts.find(c => c.id === chartId); - if (!chart) return null; + if (!chart) return; + if (chart.chartType === 'Table' || chart.chartType === '?') return; + + // Try SVG cache first (instant, high quality) + const cached = getCachedChart(chartId); + if (cached?.svg) { + const blob = new Blob([cached.svg], { type: 'image/svg+xml;charset=utf-8' }); + const blobUrl = URL.createObjectURL(blob); + updateCachedReportImages(chartId, blobUrl, config.defaultChartWidth, config.defaultChartHeight); + return; + } + // Fall back to full Vega render const chartTable = tables.find(t => t.id === chart.tableRef); - if (!chartTable) return null; - - if (chart.chartType === 'Table' || chart.chartType === '?') { - return null; - } + if (!chartTable) return; getChartImageFromVega(chart, chartTable).then(({ blobUrl, width, height }) => { if (blobUrl) { - // Use blob URL for local display and caching updateCachedReportImages(chart.id, blobUrl, width, height); } }); @@ -371,6 +241,25 @@ export const ReportView: FC = () => { } }, [currentReportId]); + // Derive focused report ID from Redux state + const focusedReportId = focusedId?.type === 'report' ? focusedId.reportId : undefined; + + // When focused report is cleared (e.g. user clicked compose button), switch to compose mode + useEffect(() => { + if (!focusedReportId && !isGenerating) { + setMode('compose'); + } + }, [focusedReportId]); + + // When a report is focused via the thread, load it automatically + // Re-runs when charts/tables load so images render on initial page load + useEffect(() => { + if (focusedReportId) { + loadReport(focusedReportId); + if (mode !== 'post') setMode('post'); + } + }, [focusedReportId, charts, tables]); + // Auto-refresh chart images when underlying table data changes // This enables real-time chart updates in reports when data is streaming const tableRowSignaturesRef = useRef>(new Map()); @@ -409,9 +298,7 @@ export const ReportView: FC = () => { } }); - // If data changed, regenerate chart images for the report if (hasChanges) { - reportChartIds.forEach(chartId => { const chart = charts.find(c => c.id === chartId); if (!chart) return; @@ -422,7 +309,7 @@ export const ReportView: FC = () => { if (chart.chartType === 'Table' || chart.chartType === '?') { return; } - + getChartImageFromVega(chart, chartTable).then(({ blobUrl, width, height }) => { if (blobUrl) { updateCachedReportImages(chart.id, blobUrl, width, height); @@ -483,7 +370,7 @@ export const ReportView: FC = () => { // eslint-disable-next-line react-hooks/exhaustive-deps }, []); // Only cleanup on unmount, not when images change - // Use existing thumbnails from ChartRenderService instead of re-rendering + // Use cached SVG (high-res) for chart previews, falling back to thumbnail PNG useEffect(() => { const newPreviewImages = new Map(); @@ -496,8 +383,17 @@ export const ReportView: FC = () => { for (const chart of sortedCharts) { if (chart.chartType === 'Table' || chart.chartType === '?' || chart.chartType === 'Auto') continue; - if (chart.thumbnail) { - // Use the pre-rendered thumbnail from ChartRenderService + const cached = getCachedChart(chart.id); + if (cached?.svg) { + // Use SVG blob URL for crisp rendering at any size + const blob = new Blob([cached.svg], { type: 'image/svg+xml;charset=utf-8' }); + newPreviewImages.set(chart.id, { + url: URL.createObjectURL(blob), + width: config.defaultChartWidth, + height: config.defaultChartHeight, + }); + } else if (chart.thumbnail) { + // Fallback to low-res thumbnail newPreviewImages.set(chart.id, { url: chart.thumbnail, width: config.defaultChartWidth, @@ -567,70 +463,32 @@ export const ReportView: FC = () => { document.body.appendChild(tempDiv); try { - // Embed the chart + // Use canvas renderer for reliable PNG generation + // (SVG → Image → Canvas pipeline can fail to render text and complex features) + const scale = 2; const result = await embed(`#${tempId}`, assembledChart, { actions: false, - renderer: 'svg' + renderer: 'canvas', + scaleFactor: scale, }); - // Export to SVG with high resolution - const svgString = await result.view.toSVG(4); - - // Parse SVG to get original dimensions - const parser = new DOMParser(); - const svgDoc = parser.parseFromString(svgString, 'image/svg+xml'); - const svgElement = svgDoc.querySelector('svg'); - - if (!svgElement) { - throw new Error('Could not parse SVG'); - } - - // Get original dimensions - const originalWidth = parseFloat(svgElement.getAttribute('width') || '0'); - const originalHeight = parseFloat(svgElement.getAttribute('height') || '0'); - - // Convert SVG to PNG using canvas - const { dataUrl, blobUrl } = await new Promise<{ dataUrl: string; blobUrl: string }>((resolve, reject) => { - const canvas = document.createElement('canvas'); - const ctx = canvas.getContext('2d'); - if (!ctx) { - reject(new Error('Could not get canvas context')); - return; - } - - const img = new Image(); - const svgBlob = new Blob([svgString], { type: 'image/svg+xml;charset=utf-8' }); - const svgUrl = URL.createObjectURL(svgBlob); - - img.onload = () => { - canvas.width = img.width; - canvas.height = img.height; - ctx.drawImage(img, 0, 0); - URL.revokeObjectURL(svgUrl); - - const dataUrl = canvas.toDataURL('image/png'); - - canvas.toBlob((blob) => { - if (blob) { - const blobUrl = URL.createObjectURL(blob); - resolve({ dataUrl, blobUrl }); - } else { - resolve({ dataUrl, blobUrl: '' }); - } - }, 'image/png'); - }; + // Get high-resolution canvas directly from Vega + const canvas = await result.view.toCanvas(scale); + const displayWidth = Math.round(canvas.width / scale); + const displayHeight = Math.round(canvas.height / scale); - img.onerror = (err) => { - URL.revokeObjectURL(svgUrl); - reject(err); - }; + const dataUrl = canvas.toDataURL('image/png'); - img.src = svgUrl; + // Create blob URL for display in the report + const blob = await new Promise((resolve) => { + canvas.toBlob(resolve, 'image/png'); }); + const blobUrl = blob ? URL.createObjectURL(blob) : ''; + result.view.finalize(); document.body.removeChild(tempDiv); - return { dataUrl, blobUrl, width: originalWidth, height: originalHeight }; + return { dataUrl, blobUrl, width: displayWidth, height: displayHeight }; } catch (error) { if (document.body.contains(tempDiv)) { document.body.removeChild(tempDiv); @@ -645,23 +503,41 @@ export const ReportView: FC = () => { const generateReport = async () => { if (selectedChartIds.size === 0) { - setError('Please select at least one chart'); + showMessage(t('report.pleaseSelectChart'), 'error'); return; } setIsGenerating(true); - setError(''); setGeneratedReport(''); setGeneratedStyle(style); // Create a new report ID const reportId = `report-${Date.now()}-${Math.floor(Math.random() * 10000)}`; + // Save an in-progress report to Redux immediately so it appears in the thread + const orderedChartIds = sortedCharts + .filter(c => selectedChartIds.has(c.id)) + .map(c => c.id); + const inProgressReport: GeneratedReport = { + id: reportId, + content: '', + style: style, + selectedChartIds: orderedChartIds, + createdAt: Date.now(), + status: 'generating', + }; + dispatch(dfActions.saveGeneratedReport(inProgressReport)); + dispatch(dfActions.setFocused({ type: 'report', reportId })); + setCurrentReportId(reportId); + if (mode === 'compose') { + setMode('post'); + } + try { - let model = models.find(m => m.id == selectedModelId); + let model = activeModel; if (!model) { - throw new Error('No model selected'); + throw new Error(t('report.noModelSelected')); } const maxRows = serverConfig.MAX_DISPLAY_ROWS; @@ -680,12 +556,20 @@ export const ReportView: FC = () => { const totalRows = t.virtual?.rowCount || t.rows.length; return totalRows > maxRows; }); + const truncationList = truncatedTables.map((tbl) => + t('report.truncationTableEntry', { + name: tbl.displayId || tbl.id, + totalRows: (tbl.virtual?.rowCount || tbl.rows.length).toLocaleString(), + }) + ).join(', '); const truncationNote = truncatedTables.length > 0 - ? `\n\nNote: Some tables were truncated to ${maxRows.toLocaleString()} rows for this report. ` + - `Tables affected: ${truncatedTables.map(t => `"${t.displayId || t.id}" (${(t.virtual?.rowCount || t.rows.length).toLocaleString()} total rows)`).join(', ')}.` + ? `\n\n${t('report.truncationNote', { maxRows: maxRows.toLocaleString(), list: truncationList })}` : ''; + let chartSeqIndex = 0; + const seqToActualId: Record = {}; + const capturedImages: Record = {}; const selectedCharts = await Promise.all( sortedCharts .filter(chart => selectedChartIds.has(chart.id)) @@ -698,21 +582,23 @@ export const ReportView: FC = () => { return null; } + const seqKey = `chart${++chartSeqIndex}`; + seqToActualId[seqKey] = chart.id; const { dataUrl, blobUrl, width, height } = await getChartImageFromVega(chart, chartTable); if (blobUrl) { - // Use blob URL for local display and caching + capturedImages[chart.id] = { blobUrl, width, height }; updateCachedReportImages(chart.id, blobUrl, width, height); } return { - chart_id: chart.id, + chart_id: seqKey, code: chartTable.derive?.code || '', chart_data: { name: chartTable.id, rows: chartTable.rows.length > maxRows ? chartTable.rows.slice(0, maxRows) : chartTable.rows }, - chart_url: dataUrl // use data_url to send to the agent + chart_url: dataUrl }; }) ); @@ -733,12 +619,12 @@ export const ReportView: FC = () => { }); if (!response.ok) { - throw new Error('Failed to generate report'); + throw new Error(t('report.failedToGenerateReport')); } const reader = response.body?.getReader(); if (!reader) { - throw new Error('No response body'); + throw new Error(t('report.noResponseBody')); } const decoder = new TextDecoder(); @@ -747,16 +633,33 @@ export const ReportView: FC = () => { while (true) { const { done, value } = await reader.read(); if (done) { - // Create the report object for saving to Redux + let finalContent = accumulatedReport; + for (const [seqKey, actualId] of Object.entries(seqToActualId)) { + finalContent = finalContent.replace( + new RegExp(`\\[IMAGE\\(${seqKey}\\)\\]`, 'g'), + `[IMAGE(${actualId})]` + ); + } + // Extract title from first markdown # heading + const finalTitleMatch = finalContent.match(/^#\s+(.+)$/m); + const finalTitle = finalTitleMatch ? finalTitleMatch[1].trim() : undefined; + const report: GeneratedReport = { id: reportId, - content: accumulatedReport, + content: finalContent, style: style, - selectedChartIds: Array.from(selectedChartIds), - createdAt: Date.now(), + selectedChartIds: orderedChartIds, + createdAt: inProgressReport.createdAt, + status: 'completed', + title: finalTitle, }; - // Save to Redux state + // Re-apply captured images so React 18 batches them + // with the final content update in a single render + for (const [chartId, { blobUrl, width, height }] of Object.entries(capturedImages)) { + updateCachedReportImages(chartId, blobUrl, width, height); + } dispatch(dfActions.saveGeneratedReport(report)); + setGeneratedReport(finalContent); break; }; @@ -764,22 +667,24 @@ export const ReportView: FC = () => { if (chunk.startsWith('error:')) { const errorData = JSON.parse(chunk.substring(6)); - throw new Error(errorData.content || 'Error generating report'); + throw new Error(errorData.content || t('report.errorGeneratingReport')); } accumulatedReport += chunk; - // Update local state + // Extract title from first markdown # heading + const titleMatch = accumulatedReport.match(/^#\s+(.+)$/m); + const extractedTitle = titleMatch ? titleMatch[1].trim() : undefined; + + // Update both local state and Redux for thread card preview setGeneratedReport(accumulatedReport); - setCurrentReportId(reportId); - - if (mode === 'compose') { - setMode('post'); - } + dispatch(dfActions.updateGeneratedReportContent({ id: reportId, content: accumulatedReport, title: extractedTitle })); } } catch (err) { - setError((err as Error).message || 'Failed to generate report'); + showMessage((err as Error).message || t('report.failedToGenerateReport'), 'error'); + // Mark the in-progress report as errored (if it was created) + dispatch(dfActions.updateGeneratedReportContent({ id: reportId, content: '', status: 'error' })); } finally { setIsGenerating(false); } @@ -806,8 +711,7 @@ export const ReportView: FC = () => { } }; - let displayedReport = isGenerating ? - `${generatedReport} ✏️` : generatedReport; + let displayedReport = generatedReport; displayedReport = processReport(displayedReport); return ( @@ -815,17 +719,6 @@ export const ReportView: FC = () => { {mode === 'compose' ? ( - - {/* Centered Top Bar */} @@ -848,8 +741,9 @@ export const ReportView: FC = () => { sx={{ display: 'flex', alignItems: 'center', - gap: 1, - p: 1, + gap: 1.25, + px: 2, + py: 1, borderRadius: 2, backgroundColor: 'rgba(255, 255, 255, 0.9)', backdropFilter: 'blur(12px)', @@ -863,14 +757,13 @@ export const ReportView: FC = () => { transition: transition.normal }, '.MuiTypography-root': { - fontSize: '1rem', + fontSize: '0.8125rem', } - }} > {/* Natural Flow */} - - Create a + + {t('report.createA')} { }} > {[ - { value: 'live report', label: 'live report' }, - { value: 'blog post', label: 'blog post' }, - { value: 'social post', label: 'social post' }, - { value: 'executive summary', label: 'executive summary' }, + { value: 'live report', labelKey: 'report.styleLiveReport' }, + { value: 'blog post', labelKey: 'report.styleBlogPost' }, + { value: 'social post', labelKey: 'report.styleSocialPost' }, + { value: 'executive summary', labelKey: 'report.styleExecutiveSummary' }, ].map((option) => ( - {option.value === 'live report' ? : <>} {option.label} + {option.value === 'live report' ? : <>} {t(option.labelKey)} ))} - - from + + {t('report.from')} { {selectedChartIds.size} - - {selectedChartIds.size <= 1 ? 'chart' : 'charts'} + + {selectedChartIds.size <= 1 ? t('report.chart') : t('report.charts')} {/* Generate Button */} @@ -940,37 +835,33 @@ export const ReportView: FC = () => { size="small" sx={{ textTransform: 'none', - ml: 2, - px: 2, - py: 0.75, - borderRadius: 1.5, + ml: 1.5, + pl: 1.75, + pr: 2.5, + py: 0.625, + borderRadius: '4px', fontWeight: 500, - fontSize: '1rem', + fontSize: '0.875rem', + lineHeight: 1.5, minWidth: 'auto' }} - startIcon={isGenerating ? : } + startIcon={isGenerating ? : } > - {isGenerating ? 'composing...' : 'compose'} + {isGenerating ? t('report.composing') : t('report.compose')} - {error && ( - setError('')}> - {error} - - )} - {sortedCharts.length === 0 ? ( - No charts available. Create some visualizations first. + {t('report.noChartsAvailable')} ) : isLoadingPreviews ? ( - loading chart previews... + {t('report.loadingChartPreviews')} ) : (() => { @@ -986,7 +877,7 @@ export const ReportView: FC = () => { if (availableCharts.length === 0) { return ( - No available charts to display. Charts may still be loading or unavailable. + {t('report.noAvailableCharts')} ); } @@ -1027,7 +918,7 @@ export const ReportView: FC = () => { @@ -1057,270 +948,175 @@ export const ReportView: FC = () => { ) : mode === 'post' ? ( - - - - - AI generated the post from the selected charts, and it could be inaccurate! - - - - {/* Table of Contents Sidebar */} - {allGeneratedReports.length > 0 && ( - - - {allGeneratedReports.map((report) => ( - - - - deleteReport(report.id, e)} - sx={{ - position: 'absolute', - right: 4, - top: '50%', - transform: 'translateY(-50%)', - width: 20, - height: 20, - '&:hover': { - transform: 'translateY(-50%) scale(1.2)', transition: 'all 0.2s ease-in-out' + backgroundColor: 'background.paper', + boxShadow: '0 1px 4px rgba(0,0,0,0.12)', + '&:hover': { backgroundColor: 'action.hover' }, + }} + > + + + + {currentReportId && ( + <> + + { + const reportEl = document.querySelector('[data-report-content]') as HTMLElement; + if (!reportEl) return; + try { + // Clone the report and sanitize for external paste targets + const clone = reportEl.cloneNode(true) as HTMLElement; + + // Inline all images as base64 data URLs so they survive + // clipboard paste into Word, Google Docs, etc. + const imgs = clone.querySelectorAll('img'); + await Promise.all(Array.from(imgs).map(async (img) => { + try { + const src = (reportEl.querySelector(`img[src="${CSS.escape(img.getAttribute('src') || '')}"]`) as HTMLImageElement) + || (reportEl.querySelector(`img[data-chart-id="${CSS.escape(img.getAttribute('data-chart-id') || '')}"]`) as HTMLImageElement); + if (!src || !src.complete || src.naturalWidth === 0) return; + const canvas = document.createElement('canvas'); + canvas.width = src.naturalWidth; + canvas.height = src.naturalHeight; + const ctx = canvas.getContext('2d'); + if (!ctx) return; + ctx.drawImage(src, 0, 0); + img.setAttribute('src', canvas.toDataURL('image/png')); + } catch { + // Cross-origin or tainted canvas — keep original src } - }} - > - - - - - ))} - - - )} - - {/* Main Content Area */} - - {/* Action Buttons */} - {currentReportId && ( - - - - - - - - - )} + }} + sx={{ + backgroundColor: 'background.paper', + boxShadow: '0 1px 4px rgba(0,0,0,0.12)', + '&:hover': { backgroundColor: 'action.hover' }, + }} + > + {copyButtonSuccess ? : } + + + + deleteReport(currentReportId, e)} + sx={{ + backgroundColor: 'background.paper', + boxShadow: '0 1px 4px rgba(0,0,0,0.12)', + color: 'error.main', + '&:hover': { backgroundColor: 'error.50' }, + }} + > + + + + + )} + + {/* Continuous canvas — content flows cleanly */} + + + { + if (currentReportId) { + setGeneratedReport(html); + dispatch(dfActions.updateGeneratedReportContent({ id: currentReportId, content: html })); + } + }} + /> - - + {t('report.createdWithAI')}{' '} + - {displayedReport} - - {/* Attribution */} - - created with AI using{' '} - - https://github.com/microsoft/data-formulator - - - - + https://github.com/microsoft/data-formulator + + diff --git a/src/views/SelectableDataGrid.tsx b/src/views/SelectableDataGrid.tsx index 252e11dd..b6a35e4c 100644 --- a/src/views/SelectableDataGrid.tsx +++ b/src/views/SelectableDataGrid.tsx @@ -2,6 +2,7 @@ // Licensed under the MIT License. import * as React from 'react'; +import { useTranslation } from 'react-i18next'; import { shadow, transition } from '../app/tokens'; import { TableVirtuoso } from 'react-virtuoso'; import Table from '@mui/material/Table'; @@ -104,6 +105,7 @@ interface DraggableHeaderProps { const DraggableHeader: React.FC = ({ columnDef, orderBy, order, onSortClick, tableId }) => { + const { t } = useTranslation(); const theme = useTheme(); const conceptShelfItems = useSelector((state: DataFormulatorState) => state.conceptShelfItems); const tables = useSelector((state: DataFormulatorState) => state.tables); @@ -229,7 +231,7 @@ const DraggableHeader: React.FC = ({ {/* Separate sort handler button */} - Sort by {columnDef.label}}> + {t('dataGrid.sortBy', { label: columnDef.label })}}> { @@ -257,6 +259,7 @@ const DraggableHeader: React.FC = ({ export const SelectableDataGrid: React.FC = ({ tableId, rows, tableName, columnDefs, rowCount, virtual }) => { + const { t } = useTranslation(); const [orderBy, setOrderBy] = React.useState(undefined); const [order, setOrder] = React.useState<'asc' | 'desc'>('asc'); @@ -284,13 +287,22 @@ export const SelectableDataGrid: React.FC = ({ Scroller: React.forwardRef((props, ref) => ( )), - Table: (props: any) => , - TableHead: React.forwardRef((props, ref) => ( - + Table: ({ children, style, ...rest }: any) => ( +
+ + {columnDefs.map(col => ( + + ))} + + {children} +
+ ), + TableHead: React.forwardRef>((props, ref) => ( + )), TableRow: (props: any) => { const index = props['data-index']; - return + return }, TableBody: React.forwardRef((props, ref) => ( @@ -344,7 +356,8 @@ export const SelectableDataGrid: React.FC = ({ } : { table: tableId, size: 1000, - method: 'random' + method: 'head', + order_by_fields: ['#rowId'] } // Use the SAMPLE_TABLE endpoint with appropriate ordering @@ -358,7 +371,7 @@ export const SelectableDataGrid: React.FC = ({ .then(response => response.json()) .then(data => { if (data.status === 'success') { - setRowsToDisplay(data.rows); + setRowsToDisplay(data.rows || []); } // Set loading to false when done setIsLoading(false); @@ -399,7 +412,7 @@ export const SelectableDataGrid: React.FC = ({ borderTopRightRadius: '4px' }}> - Loading ... + {t('dataGrid.loading')} )} @@ -417,33 +430,63 @@ export const SelectableDataGrid: React.FC = ({ className='data-view-header-cell' key={columnDef.id} align={columnDef.align} - sx={{p: 0, minWidth: columnDef.minWidth, width: columnDef.width,}} + sx={{ + p: columnDef.id === '#rowId' ? '0 2px' : 0, + minWidth: columnDef.minWidth, + width: columnDef.width, + }} > - { - let newOrder: 'asc' | 'desc' = 'asc'; - let newOrderBy : string | undefined = columnDef.id; - if (orderBy === columnDef.id && order === 'asc') { - newOrder = 'desc'; - } else if (orderBy === columnDef.id && order === 'desc') { - newOrder = 'asc'; - newOrderBy = undefined; - } else { - newOrder = 'asc'; - } + {columnDef.id === '#rowId' ? ( + + + {columnDef.label} + + + ) : ( + { + let newOrder: 'asc' | 'desc' = 'asc'; + let newOrderBy : string | undefined = columnDef.id; + if (orderBy === columnDef.id && order === 'asc') { + newOrder = 'desc'; + } else if (orderBy === columnDef.id && order === 'desc') { + newOrder = 'asc'; + newOrderBy = undefined; + } else { + newOrder = 'asc'; + } - setOrder(newOrder); - setOrderBy(newOrderBy); - - if (virtual) { - fetchVirtualData(newOrderBy ? [newOrderBy] : [], newOrder); - } - }} - /> + setOrder(newOrder); + setOrderBy(newOrderBy); + + if (virtual) { + fetchVirtualData(newOrderBy ? [newOrderBy] : [], newOrder); + } + }} + /> + )} ); })} @@ -467,7 +510,11 @@ export const SelectableDataGrid: React.FC = ({ sx={{backgroundColor}} align={column.align || 'left'} > - {column.format ? column.format(data[column.id]) : data[column.id]} + {column.format + ? column.format(data[column.id]) + : (data[column.id] != null && typeof data[column.id] === 'object' + ? String(data[column.id]) + : data[column.id])} ) })} @@ -478,14 +525,14 @@ export const SelectableDataGrid: React.FC = ({ + sx={{ display: 'flex', flexDirection: 'row', position: 'absolute', bottom: 6, right: 25 }}> {virtual && } - {`${rowCount} rows`} + {t('dataGrid.rowCount', { count: rowCount })} {virtual && rowCount > 10000 && ( - + = ({ )} - + void }> = ({ relevantAgentActions, theme, onCancel }) => { - const runningAction = relevantAgentActions.find(a => a.status === 'running'); - const latestMessage = runningAction?.description || 'thinking...'; +const AgentWorkingOverlay: FC<{ message?: string; theme: Theme; onCancel?: () => void }> = ({ message, theme, onCancel }) => { + const { t } = useTranslation(); + const latestMessage = message || t('dataThread.thinking'); return ( - - ✏️ - + - Agent is working... + {t('chartRec.agentWorking')} {onCancel && ( @@ -110,9 +102,10 @@ export const SimpleChartRecBox: FC = function () { const config = useSelector((state: DataFormulatorState) => state.config); const agentRules = useSelector((state: DataFormulatorState) => state.agentRules); const activeModel = useSelector(dfSelectors.getActiveModel); - const agentActions = useSelector((state: DataFormulatorState) => state.agentActions); + const draftNodes = useSelector((state: DataFormulatorState) => state.draftNodes); const theme = useTheme(); + const { t } = useTranslation(); const dispatch = useDispatch(); const [chatPrompt, setChatPrompt] = useState(""); @@ -127,34 +120,18 @@ export const SimpleChartRecBox: FC = function () { // pendingClarification is now derived from Redux (stored on the agentAction itself) // so it persists when user clicks away and comes back. - // On mount, clean up any stale "running" agent actions left over from a page refresh. - // The streaming connection is lost on refresh, so these will never complete. - // Only mark actions whose lastUpdate predates the current page load as stale. - useEffect(() => { - const pageLoadTime = performance.timeOrigin; // ms timestamp of when the page was loaded - const staleRunning = agentActions.filter(a => a.status === 'running' && a.lastUpdate < pageLoadTime); - if (staleRunning.length === 0) return; - // The last stale action gets the visible message; others are silently marked warning - const lastStale = staleRunning[staleRunning.length - 1]; - for (const action of staleRunning) { - dispatch(dfActions.updateAgentWorkInProgress({ - actionId: action.actionId, - description: action === lastStale ? 'Interrupted by page refresh' : action.description, - status: 'warning', - hidden: false, - ...(action === lastStale ? { message: { content: 'Interrupted by page refresh', role: 'clarify' } } : {}), - })); - } - }, []); // eslint-disable-line react-hooks/exhaustive-deps + // Stale draft detection is handled by loadState in dfSlice (marks running/clarifying drafts as interrupted) const inputCardRef = useRef(null); const focusedTableId = useCallback(() => { if (!focusedId) return undefined; if (focusedId.type === 'table') return focusedId.tableId; - const chartId = focusedId.chartId; - const chart = charts.find(c => c.id === chartId); - return chart?.tableRef; + if (focusedId.type === 'chart') { + const chart = charts.find(c => c.id === focusedId.chartId); + return chart?.tableRef; + } + return undefined; }, [focusedId, charts])(); // Clear ideas when focused table changes — ideas are scoped per table @@ -198,43 +175,78 @@ export const SimpleChartRecBox: FC = function () { // Agent actions relevant to all tables in this thread, sorted by creation time // Agent actions relevant to the focused table's thread. // An action is relevant if: - // - its originTableId is in the ancestor chain, AND - // - it produced a table in the ancestor chain (resultTableId ∈ threadTableIds), - // OR is still running, OR the focused table is the originTableId itself. - const relevantAgentActions = React.useMemo(() => { - if (threadTableIds.size === 0) return []; - return agentActions - .filter(a => { - if (a.hidden) return false; - if (!threadTableIds.has(a.originTableId)) return false; - // Include if still running or waiting for clarification (live progress) - if (a.status === 'running' || a.status === 'warning') return true; - // Include if any message produced a table in the ancestor chain - if (a.messages?.some(m => m.resultTableId && threadTableIds.has(m.resultTableId))) return true; - // Include if the focused table IS the origin (user is at the starting table) - if (focusedTableId === a.originTableId) return true; - return false; - }) - .sort((a, b) => (a.messages?.[0]?.timestamp || a.lastUpdate) - (b.messages?.[0]?.timestamp || b.lastUpdate)); - }, [agentActions, threadTableIds, focusedTableId]); - - const hasRunningAgent = relevantAgentActions.some(a => a.status === 'running'); - - // Derive pending clarification from the current thread's relevant actions (stored in Redux) + const hasRunningAgent = draftNodes.some(d => d.derive?.status === 'running' && threadTableIds.has(d.derive.trigger.tableId)); + + // Derive pending clarification from DraftNodes const pendingClarification = React.useMemo(() => { - const action = relevantAgentActions.find(a => a.pendingClarification); - if (!action || !action.pendingClarification) return null; - return { ...action.pendingClarification, actionId: action.actionId }; - }, [relevantAgentActions]); + const clarifyingDraft = draftNodes.find(d => + d.derive?.status === 'clarifying' && d.derive?.pendingClarification && + threadTableIds.has(d.derive.trigger.tableId) + ); + if (clarifyingDraft?.derive?.pendingClarification) { + return { ...clarifyingDraft.derive.pendingClarification, actionId: clarifyingDraft.actionId || '', draftId: clarifyingDraft.id }; + } + return null; + }, [draftNodes, threadTableIds]); - // Extract the clarification question text from the last 'clarify' message + // Extract the clarification question text and options from DraftNode interaction log const clarificationQuestion = React.useMemo(() => { - if (!pendingClarification) return null; - const action = agentActions.find(a => a.actionId === pendingClarification.actionId); - if (!action?.messages) return null; - const clarifyMsgs = action.messages.filter(m => m.role === 'clarify'); - return clarifyMsgs.length > 0 ? clarifyMsgs[clarifyMsgs.length - 1].content : null; - }, [pendingClarification, agentActions]); + if (!pendingClarification?.draftId) return null; + const draft = draftNodes.find(d => d.id === pendingClarification.draftId); + const clarifyEntries = draft?.derive?.trigger?.interaction?.filter(e => e.role === 'clarify'); + return clarifyEntries && clarifyEntries.length > 0 ? clarifyEntries[clarifyEntries.length - 1].content : null; + }, [pendingClarification, draftNodes]); + + const clarificationOptions = React.useMemo(() => { + if (!pendingClarification?.draftId) return []; + const draft = draftNodes.find(d => d.id === pendingClarification.draftId); + const clarifyEntries = draft?.derive?.trigger?.interaction?.filter(e => e.role === 'clarify'); + const lastEntry = clarifyEntries && clarifyEntries.length > 0 ? clarifyEntries[clarifyEntries.length - 1] : null; + return lastEntry?.options || []; + }, [pendingClarification, draftNodes]); + + // Clarification auto-select countdown (60s) + const CLARIFY_TIMEOUT_MS = 60_000; + const [clarifyDeadline, setClarifyDeadline] = useState(null); + const [clarifyProgress, setClarifyProgress] = useState(1); // 1 → 0 + const clarifyTimerRef = useRef(null); + + // Start / reset deadline whenever a new clarification appears + useEffect(() => { + if (pendingClarification && clarificationOptions.length > 0 && !isChatFormulating) { + setClarifyDeadline(Date.now() + CLARIFY_TIMEOUT_MS); + setClarifyProgress(1); + } else { + setClarifyDeadline(null); + setClarifyProgress(1); + } + }, [pendingClarification?.draftId, clarificationOptions.length, isChatFormulating]); + + // Animate the countdown bar & auto-select on expiry + useEffect(() => { + if (clarifyDeadline == null) { + if (clarifyTimerRef.current) cancelAnimationFrame(clarifyTimerRef.current); + return; + } + const tick = () => { + const remaining = clarifyDeadline - Date.now(); + if (remaining <= 0) { + setClarifyProgress(0); + setClarifyDeadline(null); + // Auto-select first option + if (clarificationOptions.length > 0 && pendingClarification) { + const first = clarificationOptions[0]; + setChatPrompt(first); + exploreFromChat(first, pendingClarification); + } + return; + } + setClarifyProgress(remaining / CLARIFY_TIMEOUT_MS); + clarifyTimerRef.current = requestAnimationFrame(tick); + }; + clarifyTimerRef.current = requestAnimationFrame(tick); + return () => { if (clarifyTimerRef.current) cancelAnimationFrame(clarifyTimerRef.current); }; + }, [clarifyDeadline]); const getIdeasFromAgent = useCallback(async () => { if (!currentTable || isLoadingIdeas) return; @@ -248,11 +260,17 @@ export const SimpleChartRecBox: FC = function () { if (currentTable.derive && !currentTable.anchored) { const triggers = getTriggers(currentTable, tables); - explorationThread = triggers.map(trigger => ({ - name: trigger.resultTableId, - rows: tables.find(t2 => t2.id === trigger.resultTableId)?.rows, - description: `Derive from ${tables.find(t2 => t2.id === trigger.resultTableId)?.derive?.source} with instruction: ${trigger.instruction}`, - })); + explorationThread = triggers.map((trigger) => { + const tt = tables.find(t2 => t2.id === trigger.resultTableId); + return { + name: trigger.resultTableId, + rows: tt?.rows, + description: t('chartRec.explorationThreadDeriveDescription', { + source: String(tt?.derive?.source ?? ''), + instruction: trigger.interaction?.find((e: any) => e.role === 'instruction')?.content || '', + }), + }; + }); } const messageBody = JSON.stringify({ @@ -265,7 +283,7 @@ export const SimpleChartRecBox: FC = function () { attached_metadata: t.attachedMetadata })), exploration_thread: explorationThread, - agent_exploration_rules: agentRules.exploration + agent_exploration_rules: agentRules.exploration, }); const controller = new AbortController(); @@ -282,7 +300,7 @@ export const SimpleChartRecBox: FC = function () { if (!response.ok) throw new Error(`HTTP error! status: ${response.status}`); const reader = response.body?.getReader(); - if (!reader) throw new Error('No response body reader available'); + if (!reader) throw new Error(t('chartRec.noResponseReader')); const decoder = new TextDecoder(); let buffer = ''; @@ -321,7 +339,7 @@ export const SimpleChartRecBox: FC = function () { setThinkingBuffer(''); ideasAbortRef.current = null; } - }, [currentTable, isLoadingIdeas, selectedTableIds, tables, activeModel, agentRules, config, dispatch]); + }, [currentTable, isLoadingIdeas, selectedTableIds, tables, activeModel, agentRules, config, dispatch, t]); const exploreFromChat = useCallback((prompt: string, clarificationContext?: { trajectory: any[]; @@ -348,30 +366,39 @@ export const SimpleChartRecBox: FC = function () { setIsChatFormulating(true); - if (isResume) { - // Show user's clarification reply in the thread; clear pendingClarification in Redux - dispatch(dfActions.updateAgentWorkInProgress({ actionId, description: prompt, status: 'running', hidden: false, - message: { content: prompt, role: 'user' }, pendingClarification: null })); - } else { - // User instruction with source table context - dispatch(dfActions.updateAgentWorkInProgress({ actionId, originTableId: focusedTableId, description: prompt, status: 'running', hidden: false, - message: { content: prompt, role: 'user' } })); + // DraftNode handles status + // If resuming from clarification, reuse the old draft (append reply, clear clarification) + if (isResume && pendingClarification?.draftId) { + dispatch(dfActions.appendDraftInteraction({ draftId: pendingClarification.draftId, entry: { + from: 'user', to: 'data-agent', role: 'prompt', content: prompt, timestamp: Date.now() + }})); + dispatch(dfActions.updateDraftClarification({ draftId: pendingClarification.draftId, pendingClarification: null })); + dispatch(dfActions.updateDeriveStatus({ nodeId: pendingClarification.draftId, status: 'running' })); } - // Collect previous conversation messages for context (only for fresh starts) + // Collect previous conversation from trigger interaction chains let conversationHistory: { role: string; content: string }[] | undefined = undefined; if (!isResume) { const history: { role: string; content: string }[] = []; - for (const action of relevantAgentActions) { - if (action.messages) { - for (const m of action.messages) { - if (m.role === 'user') { - history.push({ role: 'user', content: m.content }); - } else if (m.role === 'thinking' || m.role === 'completion') { - history.push({ role: 'assistant', content: m.content }); + // Walk the ancestor chain from the focused table, collecting interaction entries + let walkTable = tables.find(t => t.id === focusedTableId); + const visited = new Set(); + while (walkTable?.derive?.trigger) { + if (visited.has(walkTable.id)) break; + visited.add(walkTable.id); + const interaction = walkTable.derive.trigger.interaction; + if (interaction && interaction.length > 0) { + for (const entry of interaction) { + if (entry.role === 'prompt') { + history.unshift({ role: 'user', content: entry.content }); + } else if (entry.role === 'summary') { + history.unshift({ role: 'assistant', content: entry.content }); + } else if (entry.plan) { + history.unshift({ role: 'assistant', content: entry.plan }); } } } + walkTable = tables.find(t => t.id === walkTable!.derive!.trigger.tableId); } if (history.length > 0) conversationHistory = history; } @@ -413,6 +440,44 @@ export const SimpleChartRecBox: FC = function () { let isCompleted = false; let lastCreatedTableId: string | null = isResume ? clarificationContext!.lastCreatedTableId : null; + // ── DraftNode tracking ── + // Local accumulator mirrors the DraftNode's interaction (avoids stale closure reads) + let currentDraftInteraction: InteractionEntry[] = []; + let currentDraftId: string | null = null; + const createNextDraft = (parentTableId: string, initialInteraction: InteractionEntry[]) => { + const draftId = `draft-${actionId}-${Date.now()}`; + dispatch(dfActions.createDraftNode({ + id: draftId, + displayId: draftId, + parentTableId, + source: selectedTableIds, + interaction: initialInteraction, + actionId, + })); + currentDraftId = draftId; + currentDraftInteraction = [...initialInteraction]; + return draftId; + }; + + // Create the initial draft (or reuse existing for clarification resume) + if (isResume && pendingClarification?.draftId) { + currentDraftId = pendingClarification.draftId; + // Seed local accumulator from the existing draft's interaction (fresh at this point) + const existingDraft = draftNodes.find(d => d.id === pendingClarification.draftId); + currentDraftInteraction = [...(existingDraft?.derive?.trigger?.interaction || [])]; + // The user reply was already appended above, add to local accumulator too + currentDraftInteraction.push({ from: 'user', to: 'data-agent', role: 'prompt', content: prompt, timestamp: Date.now() }); + } else { + const initialEntries: InteractionEntry[] = [ + { from: 'user', to: 'data-agent', role: 'prompt', content: prompt, timestamp: Date.now() } + ]; + createNextDraft(lastCreatedTableId || focusedTableId!, initialEntries); + } + + // Track the last agent thought and display_instruction (from "action" events) + let lastAgentThought: string | null = null; + let lastAgentDisplayInstruction: string | null = null; + const genTableId = () => { let tableSuffix = Number.parseInt((Date.now() - Math.floor(Math.random() * 10000)).toString().slice(-6)); let tId = `table-${tableSuffix}`; @@ -424,12 +489,15 @@ export const SimpleChartRecBox: FC = function () { }; const processStreamingResult = (result: any) => { - // Agent thinking / choosing next action + // Agent planning / choosing next action if (result.type === "action" && result.action === "visualize") { - const thinkingMsg = result.thought || "Thinking..."; - const currentObserveId = lastCreatedTableId || focusedTableId; - dispatch(dfActions.updateAgentWorkInProgress({ actionId, description: thinkingMsg, status: 'running', hidden: false, - message: { content: thinkingMsg, role: 'thinking', observeTableId: currentObserveId } })); + lastAgentThought = result.thought || null; + lastAgentDisplayInstruction = result.display_instruction || null; + // Plan is stored as a field on the upcoming instruction entry — not as a separate entry. + // Show the plan text on the running draft so the user sees live reasoning. + if (currentDraftId) { + dispatch(dfActions.updateDraftRunningPlan({ draftId: currentDraftId, plan: lastAgentThought || t('dataThread.thinking') })); + } } // Visualization result (same shape as old data_transformation) if (result.type === "result" && result.status === "success") { @@ -445,26 +513,40 @@ export const SimpleChartRecBox: FC = function () { const rows = transformedData.rows; const candidateTableId = transformedData.virtual?.table_name || genTableId(); - const displayInstruction = refinedGoal?.display_instruction || `Exploration step ${createdTables.length + 1}: ${question}`; + const displayInstruction = lastAgentDisplayInstruction || refinedGoal?.display_instruction || t('chartRec.explorationStep', { step: createdTables.length + 1, question }); // Chain from last created table, or focused table if first const triggerTableId = lastCreatedTableId || focusedTableId!; const candidateTable = createDictTable(candidateTableId, rows, undefined); candidateTable.derive = { - code: code || `# Exploration step ${createdTables.length + 1}`, + code: code || t('chartRec.explorationStepCodeComment', { step: createdTables.length + 1 }), codeSignature: result.content?.result?.code_signature, outputVariable: refinedGoal?.output_variable || 'result_df', source: selectedTableIds, dialog: dialog || [], trigger: { tableId: triggerTableId, - instruction: question, - displayInstruction, + resultTableId: candidateTableId, chart: undefined, - resultTableId: candidateTableId + // Use the full interaction log accumulated in the DraftNode, + // plus the instruction entry for this step + interaction: [ + // Use the local accumulator (avoids stale closure) + ...currentDraftInteraction, + // The instruction to the sub-agent (plan folded in) + { + from: 'data-agent' as const, to: 'datarec-agent' as const, role: 'instruction' as const, + plan: lastAgentThought || undefined, + content: question || displayInstruction, + displayContent: displayInstruction, + timestamp: Date.now(), + }, + ], } }; + lastAgentThought = null; // consumed + lastAgentDisplayInstruction = null; // consumed if (transformedData.virtual) { candidateTable.virtual = { tableId: transformedData.virtual.table_name, rowCount: transformedData.virtual.row_count }; } @@ -494,12 +576,8 @@ export const SimpleChartRecBox: FC = function () { } createdTables.push(candidateTable); - const observedTableId = lastCreatedTableId || focusedTableId; // table the agent was looking at before this step lastCreatedTableId = candidateTableId; - dispatch(dfActions.updateAgentWorkInProgress({ actionId, description: displayInstruction, status: 'running', hidden: false, - message: { content: displayInstruction, role: 'action', observeTableId: observedTableId, resultTableId: candidateTableId } })); - const names = candidateTable.names; const missingNames = names.filter(name => !conceptShelfItems.some(field => field.name === name) && @@ -534,6 +612,22 @@ export const SimpleChartRecBox: FC = function () { dispatch(fetchFieldSemanticType(candidateTable)); dispatch(fetchCodeExpl(candidateTable)); + // ── DraftNode: append instruction, remove draft, create next ── + if (currentDraftId) { + dispatch(dfActions.appendDraftInteraction({ draftId: currentDraftId, entry: { + from: 'data-agent', to: 'datarec-agent', role: 'instruction', + plan: lastAgentThought || undefined, + content: question || displayInstruction, + displayContent: displayInstruction, + timestamp: Date.now(), + }})); + // Remove the draft — the table was already inserted via insertDerivedTables + dispatch(dfActions.removeDraftNode(currentDraftId)); + currentDraftId = null; + } + // Create a new draft for the next potential step, chained from this table + createNextDraft(candidateTableId, []); + if (createdCharts.length > 0) { const lastChart = createdCharts[createdCharts.length - 1]; setTimeout(() => { @@ -543,21 +637,50 @@ export const SimpleChartRecBox: FC = function () { } // Agent asks for clarification — pause and let user respond if (result.type === "clarify") { - const clarifyMsg = result.message || "Could you clarify?"; - dispatch(dfActions.updateAgentWorkInProgress({ actionId, description: clarifyMsg, status: 'warning', hidden: false, - message: { content: clarifyMsg, role: 'clarify' }, - pendingClarification: { + const clarifyMsg = result.message || t('chartRec.couldYouClarify'); + const clarifyOptions: string[] = Array.isArray(result.options) ? result.options : []; + // Append clarify entry (with plan folded in) to draft + if (currentDraftId) { + const clarifyEntry: InteractionEntry = { + from: 'data-agent', to: 'user', role: 'clarify', + plan: result.thought || undefined, + content: clarifyMsg, + options: clarifyOptions.length > 0 ? clarifyOptions : undefined, + timestamp: Date.now(), + }; + dispatch(dfActions.appendDraftInteraction({ draftId: currentDraftId, entry: clarifyEntry })); + currentDraftInteraction.push(clarifyEntry); + dispatch(dfActions.updateDeriveStatus({ nodeId: currentDraftId, status: 'clarifying' })); + dispatch(dfActions.updateDraftClarification({ draftId: currentDraftId, pendingClarification: { trajectory: result.trajectory || [], completedStepCount: result.completed_step_count || 0, lastCreatedTableId, - } - })); + }})); + } setIsChatFormulating(false); agentAbortRef.current = null; clearTimeout(timeoutId); setChatPrompt(""); isCompleted = true; // prevent handleCompletion from firing } + + // ── Capture completion summary (with plan folded in) on the last created table's trigger ── + if (result.type === "completion") { + if (lastCreatedTableId) { + const summary = result.status === "max_iterations" + ? t('chartRec.maxIterationsReached') + : (result.content?.summary || result.content?.message || ""); + if (summary) { + const entry: InteractionEntry = { + from: 'data-agent', to: 'user', role: 'summary', + plan: result.content?.thought || undefined, + content: summary, + timestamp: Date.now(), + }; + dispatch(dfActions.appendTriggerInteraction({ tableId: lastCreatedTableId, entries: [entry] })); + } + } + } }; const handleCompletion = () => { @@ -567,16 +690,15 @@ export const SimpleChartRecBox: FC = function () { agentAbortRef.current = null; clearTimeout(timeoutId); + // Clean up any remaining draft (the last step created a new draft that was never filled) + if (currentDraftId) { + dispatch(dfActions.removeDraftNode(currentDraftId)); + currentDraftId = null; + } + const completionResult = allResults.find((r: any) => r.type === "completion"); if (completionResult) { - const summary = completionResult.content.summary || completionResult.content.message || ""; - const status: "completed" | "warning" = completionResult.status === "success" ? "completed" : "warning"; - dispatch(dfActions.updateAgentWorkInProgress({ actionId, description: summary, status, hidden: false, - message: { content: summary, role: 'completion' } })); setChatPrompt(""); - } else { - dispatch(dfActions.updateAgentWorkInProgress({ actionId, description: "The agent got lost in the data.", status: 'warning', hidden: false, - message: { content: "The agent got lost in the data.", role: 'clarify' } })); } }; @@ -589,7 +711,7 @@ export const SimpleChartRecBox: FC = function () { .then(async (response) => { if (!response.ok) throw new Error(`HTTP error! status: ${response.status}`); const reader = response.body?.getReader(); - if (!reader) throw new Error('No response body reader available'); + if (!reader) throw new Error(t('chartRec.noResponseReader')); const decoder = new TextDecoder(); let buffer = ''; @@ -614,8 +736,16 @@ export const SimpleChartRecBox: FC = function () { } else if (data.status === "error") { setIsChatFormulating(false); clearTimeout(timeoutId); - dispatch(dfActions.updateAgentWorkInProgress({ actionId, description: data.error_message || "Error during exploration", status: 'failed', hidden: false, - message: { content: data.error_message || "Error during exploration", role: 'error' } })); + // Mark draft as error + if (currentDraftId) { + dispatch(dfActions.appendDraftInteraction({ draftId: currentDraftId, entry: { + from: 'data-agent', to: 'user', role: 'error', + content: data.error_message || t('chartRec.errorDuringExploration'), + timestamp: Date.now(), + }})); + dispatch(dfActions.updateDeriveStatus({ nodeId: currentDraftId, status: 'error' })); + currentDraftId = null; + } return; } } @@ -634,29 +764,32 @@ export const SimpleChartRecBox: FC = function () { agentAbortRef.current = null; clearTimeout(timeoutId); const isCancelled = error.name === 'AbortError' && !isCompleted; - const errorMessage = isCancelled ? "Exploration cancelled" : error.name === 'AbortError' ? "Exploration timed out" : `Exploration failed: ${error.message}`; - dispatch(dfActions.updateAgentWorkInProgress({ actionId, description: errorMessage, status: isCancelled ? 'warning' : 'failed', hidden: false, - message: { content: errorMessage, role: isCancelled ? 'clarify' : 'error' } })); + const errorMessage = isCancelled ? t('chartRec.explorationCancelled') : error.name === 'AbortError' ? t('chartRec.explorationTimedOut') : t('chartRec.explorationFailed', { message: error.message }); + // Clean up draft on error/cancel + if (currentDraftId) { + if (isCancelled) { + dispatch(dfActions.removeDraftNode(currentDraftId)); + } else { + dispatch(dfActions.appendDraftInteraction({ draftId: currentDraftId, entry: { + from: 'data-agent', to: 'user', role: 'error', content: errorMessage, timestamp: Date.now(), + }})); + dispatch(dfActions.updateDeriveStatus({ nodeId: currentDraftId, status: 'error' })); + } + currentDraftId = null; + } }); - }, [focusedTableId, tables, activeModel, agentRules, config, conceptShelfItems, dispatch, relevantAgentActions]); + }, [focusedTableId, tables, draftNodes, activeModel, agentRules, config, conceptShelfItems, dispatch, t]); const cancelAgent = useCallback(() => { if (agentAbortRef.current) { agentAbortRef.current.abort(); agentAbortRef.current = null; } - // Also dismiss any pending clarification - if (pendingClarification) { - dispatch(dfActions.updateAgentWorkInProgress({ - actionId: pendingClarification.actionId, - description: "Conversation ended by user.", - status: 'completed', - hidden: false, - message: { content: "Conversation ended by user.", role: 'completion' }, - pendingClarification: null, - })); + // Also dismiss any pending clarification draft + if (pendingClarification?.draftId) { + dispatch(dfActions.removeDraftNode(pendingClarification.draftId)); } - }, [pendingClarification, dispatch]); + }, [pendingClarification, dispatch, t]); const gradientBorder = `linear-gradient(135deg, ${alpha(theme.palette.primary.main, 0.6)}, ${alpha(theme.palette.secondary.main, 0.55)})`; const workingBorder = `linear-gradient(135deg, ${alpha(theme.palette.primary.main, 0.3)}, ${alpha(theme.palette.secondary.main, 0.25)})`; @@ -672,7 +805,10 @@ export const SimpleChartRecBox: FC = function () { position: 'relative', overflow: isChatFormulating ? 'hidden' : 'visible', flexShrink: 0, - transition: 'box-shadow 0.2s ease, background-color 0.2s ease', + transition: 'box-shadow 0.25s ease, background-color 0.2s ease', + '&:focus-within': { + boxShadow: `0 0 0 3px ${alpha(theme.palette.primary.main, 0.10)}`, + }, ...(isChatFormulating ? { backgroundColor: alpha(theme.palette.action.disabledBackground, 0.06) } : {}), // Gradient border via pseudo-element (works with border-radius) '&::before': { @@ -695,17 +831,59 @@ export const SimpleChartRecBox: FC = function () { {/* Show clarification question above input when agent is asking */} {clarificationQuestion && pendingClarification && !isChatFormulating && ( - - - {clarificationQuestion} - + + + + {renderFieldHighlights(clarificationQuestion, alpha(theme.palette.warning.main, 0.12))} + + + {clarificationOptions.length > 0 && ( + + {clarificationOptions.map((option, idx) => ( + + {/* Countdown fill behind the first (default) option */} + {idx === 0 && clarifyDeadline != null && ( + + )} + { setClarifyDeadline(null); setChatPrompt(option); exploreFromChat(option, pendingClarification); }} sx={{ + position: 'relative', zIndex: 1, + px: '8px', py: '4px', + borderRadius: '6px', + border: `1px solid ${idx === 0 ? alpha(theme.palette.primary.main, 0.25) : alpha(theme.palette.text.primary, 0.12)}`, + backgroundColor: idx === 0 ? 'transparent' : 'white', + cursor: 'pointer', + fontSize: 11, + width: 'fit-content', + lineHeight: 1.4, + color: theme.palette.text.primary, + transition: 'all 0.1s linear', + '&:hover': { + backgroundColor: alpha(theme.palette.primary.main, 0.06), + borderColor: alpha(theme.palette.primary.main, 0.3), + }, + }}> + {renderFieldHighlights(option, alpha(theme.palette.primary.main, 0.08))} + + + ))} + + )} )} {/* Idea chips inline */} @@ -719,14 +897,14 @@ export const SimpleChartRecBox: FC = function () { }}> - Ideas - + {t('chartRec.ideas')} + getIdeasFromAgent()} sx={{ p: '2px', color: theme.palette.text.secondary, '&:hover': { color: theme.palette.primary.main } }}> - + setIdeas([])} sx={{ p: '2px', color: theme.palette.text.secondary, '&:hover': { color: theme.palette.error.main } }}> @@ -778,7 +956,7 @@ export const SimpleChartRecBox: FC = function () { onKeyDown={(event: any) => { if (event.key === 'Tab' && !event.shiftKey && chatPrompt.trim() === '' && !isChatFormulating) { event.preventDefault(); - setChatPrompt('help me suggest some exploration directions from this thread'); + setChatPrompt(t('chartRec.threadExplorePrompt')); } if (event.key === 'Enter' && !event.shiftKey) { event.preventDefault(); @@ -819,7 +997,7 @@ export const SimpleChartRecBox: FC = function () { input: { readOnly: isChatFormulating }, }} value={chatPrompt} - placeholder={pendingClarification ? "Reply to agent's question..." : "Ask agent to explore a new direction"} + placeholder={pendingClarification ? t('chartRec.replyPlaceholder') : t('chartRec.explorePlaceholder')} fullWidth multiline minRows={2} @@ -828,7 +1006,7 @@ export const SimpleChartRecBox: FC = function () { {/* Action buttons */} - + { e.stopPropagation(); setUploadDialogOpen(true); }} @@ -845,7 +1023,7 @@ export const SimpleChartRecBox: FC = function () { ) : ( <> - + {pendingClarification && !isChatFormulating && ( - + )} - + + + tables.some(t => t.id === c.tableRef)).length === 0} + onClick={() => { + dispatch(dfActions.setFocused(undefined)); + dispatch(dfActions.setViewMode('report')); + }} + > + + + + + d.derive?.status === 'running' && threadTableIds.has(d.derive.trigger.tableId)) + ?.derive?.runningPlan} theme={theme} onCancel={isLoadingIdeas && !isChatFormulating ? () => { ideasAbortRef.current?.abort(); ideasAbortRef.current = null; setIsLoadingIdeas(false); setIdeas([]); } : cancelAgent} /> @@ -920,6 +1114,7 @@ export const SimpleChartRecBox: FC = function () { open={uploadDialogOpen} onClose={() => setUploadDialogOpen(false)} initialTab="menu" + hideSampleDatasets /> ); diff --git a/src/views/TiptapReportEditor.tsx b/src/views/TiptapReportEditor.tsx new file mode 100644 index 00000000..ba614357 --- /dev/null +++ b/src/views/TiptapReportEditor.tsx @@ -0,0 +1,438 @@ +// Copyright (c) Microsoft Corporation. +// Licensed under the MIT License. + +import React, { FC, useEffect, useCallback, useRef, useState as useStateReact } from 'react'; +import { useEditor, EditorContent, NodeViewWrapper, NodeViewProps, ReactNodeViewRenderer } from '@tiptap/react'; +import StarterKit from '@tiptap/starter-kit'; +import Image from '@tiptap/extension-image'; +import { Markdown } from 'tiptap-markdown'; +import { Box, IconButton, Tooltip, Divider, useTheme, Typography } from '@mui/material'; +import { alpha } from '@mui/material/styles'; +import { WritingPencil, ShimmerText, WritingIndicator } from '../components/FunComponents'; +import FormatBoldIcon from '@mui/icons-material/FormatBold'; +import FormatItalicIcon from '@mui/icons-material/FormatItalic'; +import FormatListBulletedIcon from '@mui/icons-material/FormatListBulleted'; +import FormatListNumberedIcon from '@mui/icons-material/FormatListNumbered'; +import FormatQuoteIcon from '@mui/icons-material/FormatQuote'; +import TitleIcon from '@mui/icons-material/Title'; + +export interface TiptapReportEditorProps { + content: string; // HTML content (from processReport) + editable?: boolean; + reportId?: string; // triggers re-focus when switching reports + onUpdate?: (html: string) => void; +} + +/** Resizable image node view — drag bottom-right corner to resize */ +const ResizableImageView: FC = ({ node, updateAttributes, selected }) => { + const { src, alt, width, height } = node.attrs; + const containerRef = useRef(null); + const [isResizing, setIsResizing] = useStateReact(false); + + const handleMouseDown = useCallback((e: React.MouseEvent) => { + e.preventDefault(); + e.stopPropagation(); + const startX = e.clientX; + const startWidth = containerRef.current?.offsetWidth || width || 300; + const aspectRatio = (height && width) ? height / width : undefined; + + const onMouseMove = (moveEvent: MouseEvent) => { + const newWidth = Math.max(100, startWidth + (moveEvent.clientX - startX)); + const attrs: Record = { width: Math.round(newWidth) }; + if (aspectRatio) { + attrs.height = Math.round(newWidth * aspectRatio); + } + updateAttributes(attrs); + }; + + const onMouseUp = () => { + setIsResizing(false); + document.removeEventListener('mousemove', onMouseMove); + document.removeEventListener('mouseup', onMouseUp); + }; + + setIsResizing(true); + document.addEventListener('mousemove', onMouseMove); + document.addEventListener('mouseup', onMouseUp); + }, [width, height, updateAttributes]); + + return ( + + + {alt + + + + ); +}; + +/** Custom Image extension with resizable node view */ +const ResizableImage = Image.extend({ + addAttributes() { + return { + ...this.parent?.(), + width: { default: null, parseHTML: el => el.getAttribute('width') }, + height: { default: null, parseHTML: el => el.getAttribute('height') }, + 'data-chart-id': { + default: null, + parseHTML: el => el.getAttribute('data-chart-id'), + renderHTML: (attributes) => { + if (!attributes['data-chart-id']) return {}; + return { 'data-chart-id': attributes['data-chart-id'] }; + }, + }, + }; + }, + addNodeView() { + return ReactNodeViewRenderer(ResizableImageView); + }, +}); + +const ToolbarButton: FC<{ + onClick: () => void; + isActive?: boolean; + title: string; + children: React.ReactNode; +}> = ({ onClick, isActive, title, children }) => { + const theme = useTheme(); + return ( + + + {children} + + + ); +}; + +export const TiptapReportEditor: FC = ({ content, editable = true, reportId, onUpdate }) => { + const theme = useTheme(); + const isFocused = useRef(false); + + const editor = useEditor({ + extensions: [ + StarterKit.configure({ + heading: { levels: [1, 2, 3] }, + }), + ResizableImage.configure({ + inline: false, + }), + Markdown.configure({ + html: true, + transformPastedText: true, + }), + ], + content, + editable, + onUpdate: ({ editor }) => { + if (isFocused.current) { + onUpdate?.(editor.getHTML()); + } + }, + onFocus: () => { + isFocused.current = true; + }, + onBlur: () => { + isFocused.current = false; + }, + }); + + // Sync editable prop + useEffect(() => { + if (editor) { + editor.setEditable(editable); + } + }, [editor, editable]); + + // Auto-focus the editor when it becomes editable or when the viewed report changes + useEffect(() => { + if (editor && editable) { + // Small delay to let the DOM settle after content streaming finishes + const timer = setTimeout(() => { + editor.commands.focus('start'); + }, 100); + return () => clearTimeout(timer); + } + }, [editor, editable, reportId]); + + // Sync content when it changes externally (streaming or loading a different report) + // Always sync if the content contains new images (img tags) that aren't in the editor yet + useEffect(() => { + if (!editor) return; + if (!isFocused.current) { + editor.commands.setContent(content, { emitUpdate: false }); + } else { + // Even when focused, sync if new images arrived (user isn't typing image tags) + const currentHtml = editor.getHTML(); + const newImgCount = (content.match(/ currentImgCount) { + editor.commands.setContent(content, { emitUpdate: false }); + } + } + }, [editor, content]); + + const copyAsRichText = useCallback(async () => { + if (!editor) return; + const html = editor.getHTML(); + try { + await navigator.clipboard.write([ + new ClipboardItem({ + 'text/html': new Blob([html], { type: 'text/html' }), + 'text/plain': new Blob([editor.getText()], { type: 'text/plain' }), + }), + ]); + } catch (e) { + console.warn('Failed to copy as rich text:', e); + } + }, [editor]); + + if (!editor) return null; + + const iconSx = { fontSize: 16 }; + + return ( + + {/* Toolbar — always visible, disabled during generation */} + + editor.chain().focus().toggleBold().run()} + isActive={editor.isActive('bold')} + title="Bold (⌘B)" + > + + + editor.chain().focus().toggleItalic().run()} + isActive={editor.isActive('italic')} + title="Italic (⌘I)" + > + + + + editor.chain().focus().toggleHeading({ level: 1 }).run()} + isActive={editor.isActive('heading', { level: 1 })} + title="Heading 1" + > + + + editor.chain().focus().toggleHeading({ level: 2 }).run()} + isActive={editor.isActive('heading', { level: 2 })} + title="Heading 2" + > + + + + editor.chain().focus().toggleBulletList().run()} + isActive={editor.isActive('bulletList')} + title="Bullet List" + > + + + editor.chain().focus().toggleOrderedList().run()} + isActive={editor.isActive('orderedList')} + title="Numbered List" + > + + + editor.chain().focus().toggleBlockquote().run()} + isActive={editor.isActive('blockquote')} + title="Quote" + > + + + {!editable && ( + + + Generating… + + )} + + {/* Editor */} + + + {/* Shimmer overlay while generating */} + {!editable && ( + + + + )} + + + ); +}; diff --git a/src/views/UnifiedDataUploadDialog.tsx b/src/views/UnifiedDataUploadDialog.tsx index 4adc4a4c..668f64f8 100644 --- a/src/views/UnifiedDataUploadDialog.tsx +++ b/src/views/UnifiedDataUploadDialog.tsx @@ -38,9 +38,9 @@ import Backdrop from '@mui/material/Backdrop'; import { useDispatch, useSelector } from 'react-redux'; import { DataFormulatorState, dfActions, fetchFieldSemanticType } from '../app/dfSlice'; import { AppDispatch } from '../app/store'; -import { loadTable } from '../app/tableThunks'; +import { loadTable, loadPluginTable } from '../app/tableThunks'; import { DataSourceConfig, DictTable } from '../components/ComponentType'; -import { createTableFromFromObjectArray, createTableFromText, loadTextDataWrapper, loadBinaryDataWrapper } from '../data/utils'; +import { createTableFromFromObjectArray, createTableFromText, loadTextDataWrapper, loadBinaryDataWrapper, readFileText } from '../data/utils'; import { DataLoadingChat } from './DataLoadingChat'; import { DatasetSelectionView, DatasetMetadata } from './TableSelectionView'; import { getUrls, fetchWithIdentity } from '../app/utils'; @@ -56,8 +56,10 @@ import FolderOpenIcon from '@mui/icons-material/FolderOpen'; import CloudIcon from '@mui/icons-material/Cloud'; import OpenInNewIcon from '@mui/icons-material/OpenInNew'; import LanguageIcon from '@mui/icons-material/Language'; +import { useTranslation } from 'react-i18next'; +import { getEnabledPlugins, PluginHost } from '../plugins'; -export type UploadTabType = 'menu' | 'upload' | 'paste' | 'url' | 'database' | 'extract' | 'explore'; +export type UploadTabType = 'menu' | 'upload' | 'paste' | 'url' | 'database' | 'extract' | 'explore' | `plugin:${string}`; interface TabPanelProps { children?: React.ReactNode; @@ -110,13 +112,12 @@ const DataSourceCard: React.FC = ({ border: `1px solid ${borderColor.divider}`, borderRadius: radius.sm, opacity: disabled ? 0.5 : 1, - transition: transition.fast, display: 'flex', alignItems: 'center', gap: 1.5, '&:hover': disabled ? {} : { - borderColor: 'primary.main', - backgroundColor: alpha(theme.palette.primary.main, 0.04), + transform: 'translateY(-2px)', + backgroundColor: 'action.hover', } }} > @@ -178,63 +179,75 @@ const getUniqueTableName = (baseName: string, existingNames: Set): strin // Reusable Data Load Menu Component export interface DataLoadMenuProps { onSelectTab: (tab: UploadTabType) => void; - serverConfig?: { DISABLE_DATABASE?: boolean }; + serverConfig?: { WORKSPACE_BACKEND?: string }; variant?: 'dialog' | 'page'; // 'dialog' uses smaller cards, 'page' uses larger cards + hideSampleDatasets?: boolean; } export const DataLoadMenu: React.FC = ({ onSelectTab, - serverConfig = { DISABLE_DATABASE: false }, - variant = 'dialog' + serverConfig = { WORKSPACE_BACKEND: 'local' }, + variant = 'dialog', + hideSampleDatasets = false }) => { const theme = useTheme(); + const { t } = useTranslation(); // Data source configurations const regularDataSources = [ { value: 'explore' as UploadTabType, - title: 'Sample Datasets', - description: 'Explore and load curated example datasets', + title: t('upload.sampleDatasets'), + description: t('upload.sampleDatasetsDesc'), icon: , disabled: false }, { value: 'upload' as UploadTabType, - title: 'Upload File', - description: 'Upload local files (CSV, TSV, JSON, Excel)', + title: t('upload.uploadFile'), + description: t('upload.uploadFileDesc'), icon: , disabled: false }, { value: 'paste' as UploadTabType, - title: 'Paste Data', - description: 'Paste tabular data directly from clipboard', + title: t('upload.pasteData'), + description: t('upload.pasteDataDesc'), icon: , disabled: false }, { value: 'extract' as UploadTabType, - title: 'Extract Unstructured Data', - description: 'Extract tables from images or text using AI', + title: t('upload.extractData'), + description: t('upload.extractDataDesc'), icon: , disabled: false }, - ]; + ].filter(source => !(hideSampleDatasets && source.value === 'explore')); + + const enabledPlugins = getEnabledPlugins((serverConfig as any)?.PLUGINS); - const liveDataSources = [ + const liveDataSources: Array<{ value: UploadTabType; title: string; description: string; icon: React.ReactNode; disabled: boolean }> = [ { value: 'url' as UploadTabType, - title: 'Load from URL', - description: 'Load data from a URL with optional auto-refresh', + title: t('upload.loadFromUrl'), + description: t('upload.loadFromUrlDesc'), icon: , disabled: false }, { value: 'database' as UploadTabType, - title: 'Database', - description: 'Connect to databases or data services', + title: t('upload.database'), + description: t('upload.databaseDesc'), icon: , disabled: false }, + ...enabledPlugins.map(({ module, config }) => ({ + value: `plugin:${module.id}` as UploadTabType, + title: config.name, + description: t(`plugin.${module.id}.description`, { defaultValue: config.description || '' }), + icon: , + disabled: false, + })), ]; if (variant === 'page') { @@ -271,7 +284,7 @@ export const DataLoadMenu: React.FC = ({ '0%': { opacity: 1, color: 'primary.main' }, '50%': { opacity: 0.5, color: 'primary.light' }, '100%': { opacity: 1, color: 'primary.main' }, - }, }} /> Connect to live data sources + }, }} /> {t('upload.connectToLiveData')} = ({ letterSpacing: '0.02em' }} > - Load local data + {t('upload.loadLocalData')} {/* Background for Live Data Column */} @@ -375,7 +388,7 @@ export const DataLoadMenu: React.FC = ({ letterSpacing: '0.02em' }} > - Local data + {t('upload.localData')} = ({ '0%': { opacity: 1, color: 'primary.main' }, '50%': { opacity: 0.5, color: 'primary.light' }, '100%': { opacity: 1, color: 'primary.main' }, - }, }} /> Or connect to a data source (with optional auto-refresh) + }, }} /> {t('upload.orConnectToDataSource')} void; initialTab?: UploadTabType; + hideSampleDatasets?: boolean; } export const UnifiedDataUploadDialog: React.FC = ({ open, onClose, initialTab = 'menu', + hideSampleDatasets = false, }) => { const theme = useTheme(); + const { t } = useTranslation(); const dispatch = useDispatch(); const existingTables = useSelector((state: DataFormulatorState) => state.tables); const serverConfig = useSelector((state: DataFormulatorState) => state.serverConfig); @@ -461,22 +477,14 @@ export const UnifiedDataUploadDialog: React.FC = ( const fileInputRef = useRef(null); const urlInputRef = useRef(null); - // Store on server toggle (forced off when DISABLE_DATABASE) - const diskPersistenceDisabled = serverConfig.DISABLE_DATABASE; - const [storeOnServer, setStoreOnServer] = useState(!diskPersistenceDisabled); - - // When serverConfig loads and database is enabled, default to store on server - useEffect(() => { - if (!diskPersistenceDisabled) { - setStoreOnServer(true); - } - }, [diskPersistenceDisabled]); + // Storage is determined by backend config — no user toggle + const isEphemeral = serverConfig.WORKSPACE_BACKEND === 'ephemeral'; + const storeOnServer = !isEphemeral; // used to decide file upload behavior // Paste tab state const [pasteContent, setPasteContent] = useState(""); const [isLargeContent, setIsLargeContent] = useState(false); const [showFullContent, setShowFullContent] = useState(false); - const [isOverSizeLimit, setIsOverSizeLimit] = useState(false); // File preview state const [filePreviewTables, setFilePreviewTables] = useState(null); @@ -484,6 +492,7 @@ export const UnifiedDataUploadDialog: React.FC = ( const [filePreviewError, setFilePreviewError] = useState(null); const [filePreviewFiles, setFilePreviewFiles] = useState([]); const [filePreviewActiveIndex, setFilePreviewActiveIndex] = useState(0); + const [isDragOver, setIsDragOver] = useState(false); // URL tab state (separate from file upload) const [tableURL, setTableURL] = useState(""); @@ -500,6 +509,9 @@ export const UnifiedDataUploadDialog: React.FC = ( // Sample datasets state const [datasetPreviews, setDatasetPreviews] = useState([]); + // Loading state for table loading (file/URL/paste) + const [tableLoading, setTableLoading] = useState(false); + // Loading state for dataset loading const [datasetLoading, setDatasetLoading] = useState(false); const [datasetLoadingLabel, setDatasetLoadingLabel] = useState(''); @@ -507,7 +519,6 @@ export const UnifiedDataUploadDialog: React.FC = ( // Constants const MAX_DISPLAY_LINES = 20; const LARGE_CONTENT_THRESHOLD = 50000; - const MAX_CONTENT_SIZE = 2 * 1024 * 1024; // Update active tab when initialTab changes useEffect(() => { @@ -601,7 +612,6 @@ export const UnifiedDataUploadDialog: React.FC = ( // Reset state when closing setPasteContent(""); setIsLargeContent(false); - setIsOverSizeLimit(false); setShowFullContent(false); setFilePreviewTables(null); setFilePreviewLoading(false); @@ -619,85 +629,138 @@ export const UnifiedDataUploadDialog: React.FC = ( onClose(); }, [onClose]); - // File upload handler - const handleFileUpload = (event: React.ChangeEvent): void => { - const files = event.target.files; - - if (files && files.length > 0) { - const selectedFiles = Array.from(files); - setFilePreviewFiles(selectedFiles); - setFilePreviewError(null); - setFilePreviewTables(null); - setFilePreviewLoading(true); - - const MAX_FILE_SIZE = 5 * 1024 * 1024; - const previewTables: DictTable[] = []; - const errors: string[] = []; - - const processFiles = async () => { - for (const file of selectedFiles) { - const uniqueName = getUniqueTableName(file.name, existingNames); - const isTextFile = file.type === 'text/csv' || - file.type === 'text/tab-separated-values' || - file.type === 'application/json' || - file.name.endsWith('.csv') || - file.name.endsWith('.tsv') || - file.name.endsWith('.json'); - const isExcelFile = file.type === 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet' || - file.type === 'application/vnd.ms-excel' || - file.name.endsWith('.xlsx') || - file.name.endsWith('.xls'); - - if (file.size > MAX_FILE_SIZE && isTextFile) { - errors.push(`File ${file.name} is too large (${(file.size / (1024 * 1024)).toFixed(2)}MB). Use Database for large files.`); - continue; + // Shared file processing logic (used by both file input and drag-and-drop) + const processUploadedFiles = useCallback((selectedFiles: File[]): void => { + setFilePreviewFiles(selectedFiles); + setFilePreviewError(null); + setFilePreviewTables(null); + setFilePreviewLoading(true); + + const previewTables: DictTable[] = []; + const errors: string[] = []; + + const processFiles = async () => { + for (const file of selectedFiles) { + const uniqueName = getUniqueTableName(file.name, existingNames); + const isTextFile = file.type === 'text/csv' || + file.type === 'text/tab-separated-values' || + file.type === 'application/json' || + file.name.endsWith('.csv') || + file.name.endsWith('.tsv') || + file.name.endsWith('.json'); + const isExcelFile = file.type === 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet' || + file.type === 'application/vnd.ms-excel' || + file.name.endsWith('.xlsx') || + file.name.endsWith('.xls'); + + if (isTextFile) { + try { + const text = await readFileText(file); + const table = loadTextDataWrapper(uniqueName, text, file.type); + if (table) { + previewTables.push(table); + } else { + errors.push(t('upload.errors.failedToParse', { name: file.name })); + } + } catch { + errors.push(t('upload.errors.failedToRead', { name: file.name })); } + continue; + } - if (isTextFile) { + if (isExcelFile) { + const isLegacyXls = file.name.toLowerCase().endsWith('.xls') && !file.name.toLowerCase().endsWith('.xlsx'); + if (isLegacyXls) { try { - const text = await file.text(); - const table = loadTextDataWrapper(uniqueName, text, file.type); - if (table) { - previewTables.push(table); + const formData = new FormData(); + formData.append('file', file); + const resp = await fetchWithIdentity(getUrls().PARSE_FILE, { + method: 'POST', + body: formData, + }); + const result = await resp.json(); + if (result.status === 'success' && result.sheets?.length > 0) { + for (const sheet of result.sheets) { + const sheetTitle = result.sheets.length > 1 + ? `${uniqueName}-${sheet.sheet_name}` + : uniqueName; + const table = createTableFromFromObjectArray(sheetTitle, sheet.data, true); + previewTables.push(table); + } } else { - errors.push(`Failed to parse ${file.name}.`); + errors.push(t('upload.errors.failedToParseExcel', { name: file.name })); } } catch { - errors.push(`Failed to read ${file.name}.`); + errors.push(t('upload.errors.failedToParseExcel', { name: file.name })); } - continue; - } - - if (isExcelFile) { + } else { try { const arrayBuffer = await file.arrayBuffer(); const tables = await loadBinaryDataWrapper(uniqueName, arrayBuffer); if (tables.length > 0) { previewTables.push(...tables); } else { - errors.push(`Failed to parse Excel file ${file.name}.`); + errors.push(t('upload.errors.failedToParseExcel', { name: file.name })); } } catch { - errors.push(`Failed to parse Excel file ${file.name}.`); + errors.push(t('upload.errors.failedToParseExcel', { name: file.name })); } - continue; } - - errors.push(`Unsupported file format: ${file.name}.`); + continue; } - setFilePreviewTables(previewTables.length > 0 ? previewTables : null); - setFilePreviewError(errors.length > 0 ? errors.join(' ') : null); - setFilePreviewLoading(false); - }; + errors.push(t('upload.errors.unsupportedFormat', { name: file.name })); + } + + setFilePreviewTables(previewTables.length > 0 ? previewTables : null); + setFilePreviewError(errors.length > 0 ? errors.join(' ') : null); + setFilePreviewLoading(false); + }; - processFiles(); + processFiles(); + // eslint-disable-next-line react-hooks/exhaustive-deps + }, [existingNames, t]); + + // File input change handler + const handleFileInputChange = (event: React.ChangeEvent): void => { + const files = event.target.files; + if (files && files.length > 0) { + processUploadedFiles(Array.from(files)); } if (fileInputRef.current) { fileInputRef.current.value = ''; } }; + // Drag-and-drop handlers + const handleDragOver = useCallback((event: React.DragEvent): void => { + event.preventDefault(); + event.stopPropagation(); + }, []); + + const handleDragEnter = useCallback((event: React.DragEvent): void => { + event.preventDefault(); + event.stopPropagation(); + setIsDragOver(true); + }, []); + + const handleDragLeave = useCallback((event: React.DragEvent): void => { + event.preventDefault(); + event.stopPropagation(); + if (event.currentTarget.contains(event.relatedTarget as Node)) return; + setIsDragOver(false); + }, []); + + const handleFileDrop = useCallback((event: React.DragEvent): void => { + event.preventDefault(); + event.stopPropagation(); + setIsDragOver(false); + const files = event.dataTransfer.files; + if (files && files.length > 0) { + processUploadedFiles(Array.from(files)); + } + }, [processUploadedFiles]); + // Reset activeIndex when tables change useEffect(() => { if (filePreviewTables && filePreviewTables.length > 0) { @@ -709,7 +772,7 @@ export const UnifiedDataUploadDialog: React.FC = ( } }, [filePreviewTables, filePreviewActiveIndex]); - const handleFileLoadSingleTable = (): void => { + const handleFileLoadSingleTable = async (): Promise => { if (!filePreviewTables || filePreviewTables.length === 0) { return; } @@ -717,28 +780,60 @@ export const UnifiedDataUploadDialog: React.FC = ( if (table) { const sourceConfig: DataSourceConfig = { type: 'file', fileName: filePreviewFiles[0]?.name }; const tableWithSource = { ...table, source: sourceConfig }; - dispatch(loadTable({ - table: tableWithSource, - storeOnServer, - file: storeOnServer ? filePreviewFiles[filePreviewActiveIndex] || filePreviewFiles[0] : undefined, - })); + setTableLoading(true); + try { + await dispatch(loadTable({ + table: tableWithSource, + file: storeOnServer ? filePreviewFiles[filePreviewActiveIndex] || filePreviewFiles[0] : undefined, + })); + } finally { + setTableLoading(false); + } handleClose(); } }; - const handleFileLoadAllTables = (): void => { + const handleFileLoadAllTables = async (): Promise => { if (!filePreviewTables || filePreviewTables.length === 0) { return; } - for (let i = 0; i < filePreviewTables.length; i++) { - const table = filePreviewTables[i]; - const sourceConfig: DataSourceConfig = { type: 'file', fileName: filePreviewFiles[i]?.name || filePreviewFiles[0]?.name }; - const tableWithSource = { ...table, source: sourceConfig }; - dispatch(loadTable({ - table: tableWithSource, - storeOnServer, - file: storeOnServer ? filePreviewFiles[i] || filePreviewFiles[0] : undefined, - })); + + setTableLoading(true); + try { + // When storing on server, remove frontend-only orphans from the same + // source files (sheets that existed before but are absent in the new batch). + const seenSourceFiles = new Set(); + if (storeOnServer) { + const newTableIds = new Set(filePreviewTables.map(t => t.id)); + const sourceFileNames = new Set(); + for (let i = 0; i < filePreviewTables.length; i++) { + const fn = filePreviewFiles[i]?.name || filePreviewFiles[0]?.name; + if (fn) sourceFileNames.add(fn); + } + for (const t of existingTables) { + if (t.source?.type === 'file' && t.source.fileName && sourceFileNames.has(t.source.fileName) && !newTableIds.has(t.id)) { + dispatch(dfActions.removeTableLocally(t.id)); + } + } + } + + for (let i = 0; i < filePreviewTables.length; i++) { + const table = filePreviewTables[i]; + const fileName = filePreviewFiles[i]?.name || filePreviewFiles[0]?.name; + const sourceConfig: DataSourceConfig = { type: 'file', fileName }; + const tableWithSource = { ...table, source: sourceConfig }; + + const isFirstForFile = fileName ? !seenSourceFiles.has(fileName) : false; + if (fileName) seenSourceFiles.add(fileName); + + await dispatch(loadTable({ + table: tableWithSource, + file: storeOnServer ? filePreviewFiles[i] || filePreviewFiles[0] : undefined, + replaceSource: storeOnServer && isFirstForFile, + })); + } + } finally { + setTableLoading(false); } handleClose(); }; @@ -756,10 +851,6 @@ export const UnifiedDataUploadDialog: React.FC = ( const newContent = event.target.value; setPasteContent(newContent); - const contentSizeBytes = new Blob([newContent]).size; - const isOverLimit = contentSizeBytes > MAX_CONTENT_SIZE; - setIsOverSizeLimit(isOverLimit); - const isLarge = newContent.length > LARGE_CONTENT_THRESHOLD; setIsLargeContent(isLarge); @@ -773,7 +864,7 @@ export const UnifiedDataUploadDialog: React.FC = ( setShowFullContent(!showFullContent); }, [showFullContent]); - const handlePasteSubmit = (): void => { + const handlePasteSubmit = async (): Promise => { let table: undefined | DictTable = undefined; const defaultName = (() => { @@ -796,7 +887,12 @@ export const UnifiedDataUploadDialog: React.FC = ( if (table) { // Add source info for paste data const tableWithSource = { ...table, source: { type: 'paste' as const } }; - dispatch(loadTable({ table: tableWithSource, storeOnServer })); + setTableLoading(true); + try { + await dispatch(loadTable({ table: tableWithSource })); + } finally { + setTableLoading(false); + } handleClose(); } }; @@ -862,11 +958,11 @@ export const UnifiedDataUploadDialog: React.FC = ( if (table) { setUrlPreviewTables([table]); } else { - setUrlPreviewError('Unable to parse data from the provided URL. Please ensure the URL points to CSV, JSON, or JSONL data.'); + setUrlPreviewError(t('upload.errors.unableToParseUrl')); } }) .catch((err) => { - setUrlPreviewError(`Failed to fetch data: ${err.message}. Please ensure the URL points to CSV, JSON, or JSONL data.`); + setUrlPreviewError(t('upload.errors.failedToFetch', { message: err.message })); }) .finally(() => { setUrlPreviewLoading(false); @@ -875,7 +971,7 @@ export const UnifiedDataUploadDialog: React.FC = ( // URL tab load handlers - const handleURLLoadSingleTable = (): void => { + const handleURLLoadSingleTable = async (): Promise => { if (!urlPreviewTables || urlPreviewTables.length === 0) { return; } @@ -894,31 +990,41 @@ export const UnifiedDataUploadDialog: React.FC = ( sourceConfig = { type: 'url', url: tableURL }; } const tableWithSource = { ...table, source: sourceConfig }; - dispatch(loadTable({ table: tableWithSource, storeOnServer })); + setTableLoading(true); + try { + await dispatch(loadTable({ table: tableWithSource })); + } finally { + setTableLoading(false); + } handleClose(); } }; - const handleURLLoadAllTables = (): void => { + const handleURLLoadAllTables = async (): Promise => { if (!urlPreviewTables || urlPreviewTables.length === 0) { return; } - for (let i = 0; i < urlPreviewTables.length; i++) { - const table = urlPreviewTables[i]; - let sourceConfig: DataSourceConfig; - if (urlAutoRefresh) { - sourceConfig = { - type: 'stream', - url: tableURL, - autoRefresh: true, - refreshIntervalSeconds: urlRefreshInterval, - lastRefreshed: Date.now() - }; - } else { - sourceConfig = { type: 'url', url: tableURL }; + setTableLoading(true); + try { + for (let i = 0; i < urlPreviewTables.length; i++) { + const table = urlPreviewTables[i]; + let sourceConfig: DataSourceConfig; + if (urlAutoRefresh) { + sourceConfig = { + type: 'stream', + url: tableURL, + autoRefresh: true, + refreshIntervalSeconds: urlRefreshInterval, + lastRefreshed: Date.now() + }; + } else { + sourceConfig = { type: 'url', url: tableURL }; + } + const tableWithSource = { ...table, source: sourceConfig }; + await dispatch(loadTable({ table: tableWithSource })); } - const tableWithSource = { ...table, source: sourceConfig }; - dispatch(loadTable({ table: tableWithSource, storeOnServer })); + } finally { + setTableLoading(false); } handleClose(); }; @@ -941,18 +1047,25 @@ export const UnifiedDataUploadDialog: React.FC = ( const showUrlPreview = urlPreviewLoading || !!urlPreviewError || (urlPreviewTables && urlPreviewTables.length > 0); const hasPasteContent = (pasteContent || '').trim() !== ''; + const enabledPluginsForDialog = getEnabledPlugins(serverConfig?.PLUGINS as any); + // Get current tab title for header const getCurrentTabTitle = () => { - const tabTitles: Record = { - 'menu': 'Load Data', - 'explore': 'Sample Datasets', - 'upload': 'Upload File', - 'paste': 'Paste Data', - 'extract': 'Extract from Documents', - 'url': 'Load from URL', - 'database': 'Database', + if (activeTab.startsWith('plugin:')) { + const pluginId = activeTab.slice(7); + const found = enabledPluginsForDialog.find(p => p.module.id === pluginId); + return found?.config.name || pluginId; + } + const tabTitles: Record = { + 'menu': t('upload.title'), + 'explore': t('upload.sampleDatasets'), + 'upload': t('upload.uploadFile'), + 'paste': t('upload.pasteData'), + 'extract': t('upload.extractFromDocuments'), + 'url': t('upload.loadFromUrl'), + 'database': t('upload.database'), }; - return tabTitles[activeTab] || 'Add Data'; + return tabTitles[activeTab] || t('upload.addData'); }; return ( @@ -983,10 +1096,10 @@ export const UnifiedDataUploadDialog: React.FC = ( )} - {activeTab === 'menu' ? 'Load Data' : getCurrentTabTitle()} + {activeTab === 'menu' ? t('upload.title') : getCurrentTabTitle()} {activeTab === 'extract' && dataCleanBlocks.length > 0 && ( - + = ( )} {activeTab !== 'menu' && ( - - - Load data in - - { if (val) setStoreOnServer(val === 'disk'); }} - size="small" - sx={{ height: 26, '& .MuiToggleButton-root': { textTransform: 'none', fontSize: '0.7rem', px: 1, py: 0 } }} - > - - - - Browser - - - - - + + {isEphemeral + ? + : serverConfig.WORKSPACE_BACKEND === 'azure_blob' + ? + : } + + {isEphemeral + ? t('upload.browserLabel', 'Browser') : serverConfig.WORKSPACE_BACKEND === 'azure_blob' - ? 'Data stored in Azure Blob Storage (supports large tables)' - : 'Data stored in workspace on disk (supports large tables)'} placement="bottom"> - - {serverConfig.WORKSPACE_BACKEND === 'azure_blob' - ? <> Azure - : <> Disk} - - - - - {storeOnServer && !diskPersistenceDisabled && serverConfig.DATA_FORMULATOR_HOME - && serverConfig.WORKSPACE_BACKEND !== 'azure_blob' && ( - - { - fetchWithIdentity(getUrls().OPEN_WORKSPACE, { method: 'POST' }).catch(() => {}); - }} - sx={{ p: 0.5 }} - > - - - - )} - + ? t('upload.azureLabel', 'Azure') + : t('upload.diskLabel', 'Disk')} + + + )} = ( onSelectTab={(tab) => setActiveTab(tab)} serverConfig={serverConfig} variant="dialog" + hideSampleDatasets={hideSampleDatasets} /> @@ -1094,7 +1183,7 @@ export const UnifiedDataUploadDialog: React.FC = ( type="file" sx={{ display: 'none' }} inputRef={fileInputRef} - onChange={handleFileUpload} + onChange={handleFileInputChange} /> {/* File Upload Section - only show drop zone when file upload is enabled */} @@ -1102,39 +1191,44 @@ export const UnifiedDataUploadDialog: React.FC = ( fileInputRef.current?.click()} + onDrop={handleFileDrop} + onDragOver={handleDragOver} + onDragEnter={handleDragEnter} + onDragLeave={handleDragLeave} > - Drag & drop file here + {t('upload.dragDrop')} - or Browse + {t('upload.or')} {t('upload.browse')} {!showFilePreview && ( - Supported: CSV, TSV, JSON, Excel (xlsx, xls) + {t('upload.supportedFormats')} )} ) : ( - File upload is disabled in this environment. + {t('upload.fileUploadDisabled')} - Use "Load from URL" to load data from a remote source. + {t('upload.useLoadFromUrl')} )} @@ -1146,7 +1240,7 @@ export const UnifiedDataUploadDialog: React.FC = ( loading={filePreviewLoading} error={filePreviewError} tables={filePreviewTables} - emptyLabel="Select a file to preview." + emptyLabel={t('upload.selectFileToPreview')} onRemoveTable={handleRemoveFilePreviewTable} activeIndex={filePreviewActiveIndex} onActiveIndexChange={setFilePreviewActiveIndex} @@ -1159,19 +1253,21 @@ export const UnifiedDataUploadDialog: React.FC = ( {hasMultipleFileTables && ( )} @@ -1196,12 +1292,12 @@ export const UnifiedDataUploadDialog: React.FC = ( setTableURL((e.target.value || '').trim())} inputRef={urlInputRef} error={tableURL !== "" && !hasValidUrl} - helperText={tableURL !== "" && !hasValidUrl ? "Enter a valid URL starting with http://, https://, or /" : undefined} + helperText={tableURL !== "" && !hasValidUrl ? t('upload.helperText.urlInvalid') : undefined} size="small" sx={{ flex: 1, @@ -1220,11 +1316,11 @@ export const UnifiedDataUploadDialog: React.FC = ( disabled={!hasValidUrl || urlPreviewLoading} sx={{ textTransform: 'none', whiteSpace: 'nowrap' }} > - Preview + {t('upload.preview')} - The URL must point to data in CSV, JSON, or JSONL format + {t('upload.urlFormatHint')} @@ -1241,14 +1337,14 @@ export const UnifiedDataUploadDialog: React.FC = ( } label={ - Watch Mode + {t('upload.watchMode')} } /> {urlAutoRefresh ? ( - check data updates every + {t('upload.checkUpdatesEvery')} {[ { seconds: 5, label: '5s' }, @@ -1277,7 +1373,7 @@ export const UnifiedDataUploadDialog: React.FC = ( ))} ) : - automatically check and refresh data from the URL at regular intervals + {t('upload.watchHint')} } @@ -1287,7 +1383,7 @@ export const UnifiedDataUploadDialog: React.FC = ( {(!urlPreviewTables || urlPreviewTables.length === 0) && !urlPreviewLoading && ( - Try examples: + {t('upload.tryExamples')} = ( }} > - reset + {t('upload.resetLabel')} )} @@ -1374,7 +1470,7 @@ export const UnifiedDataUploadDialog: React.FC = ( loading={urlPreviewLoading} error={urlPreviewError} tables={urlPreviewTables} - emptyLabel="Enter a URL and click Preview to see data." + emptyLabel={t('upload.enterUrlToPreview')} onRemoveTable={handleRemoveUrlPreviewTable} activeIndex={urlPreviewActiveIndex} onActiveIndexChange={setUrlPreviewActiveIndex} @@ -1387,26 +1483,28 @@ export const UnifiedDataUploadDialog: React.FC = ( {urlAutoRefresh && ( - Watch mode: {urlRefreshInterval < 60 ? `${urlRefreshInterval}s` : `${Math.floor(urlRefreshInterval / 60)}m`} + {t('upload.watchModeStatus')} {urlRefreshInterval < 60 ? `${urlRefreshInterval}s` : `${Math.floor(urlRefreshInterval / 60)}m`} )} {hasMultipleUrlTables && ( )} @@ -1425,25 +1523,7 @@ export const UnifiedDataUploadDialog: React.FC = ( justifyContent: hasPasteContent ? 'flex-start' : 'center', alignItems: hasPasteContent ? 'stretch' : 'center', }}> - {isOverSizeLimit && ( - - - ⚠️ Content exceeds {(MAX_CONTENT_SIZE / (1024 * 1024)).toFixed(0)}MB size limit. - Current size: {(new Blob([pasteContent]).size / (1024 * 1024)).toFixed(2)}MB. - Please use the DATABASE tab for large datasets. - - - )} - - {isLargeContent && !isOverSizeLimit && ( + {isLargeContent && ( = ( borderRadius: 1 }}> - Large content detected ({Math.round(pasteContent.length / 1000)}KB). - {showFullContent ? 'Showing full content (may be slow)' : 'Showing preview for performance'} + {t('upload.largeContentDetected', { size: Math.round(pasteContent.length / 1000) })}{' '} + {showFullContent ? t('upload.showingFullContent') : t('upload.showingPreview')} )} @@ -1474,7 +1554,7 @@ export const UnifiedDataUploadDialog: React.FC = ( fullWidth value={pasteContent} onChange={handleContentChange} - placeholder="Paste your data here (CSV, TSV, or JSON format)" + placeholder={t('upload.placeholder.paste')} InputProps={{ readOnly: isLargeContent && !showFullContent, }} @@ -1506,7 +1586,7 @@ export const UnifiedDataUploadDialog: React.FC = ( border: `1px solid ${alpha(theme.palette.info.main, 0.2)}` }}> - Preview mode: Editing disabled. Click "Show Full" to enable editing. + {t('upload.previewMode')} )} @@ -1516,10 +1596,11 @@ export const UnifiedDataUploadDialog: React.FC = ( @@ -1527,12 +1608,27 @@ export const UnifiedDataUploadDialog: React.FC = ( {/* Database Tab */} - + + {/* Plugin Tabs */} + {enabledPluginsForDialog.map(({ module, config }) => ( + + { + dispatch(loadPluginTable({ tableName: info.tableName, pluginId: info.source })); + handleClose(); + }} + onClose={handleClose} + /> + + ))} + {/* Extract Data Tab */} - + {/* Explore Sample Datasets Tab */} @@ -1546,7 +1642,7 @@ export const UnifiedDataUploadDialog: React.FC = ( const isLiveDataset = dataset.live === true; setDatasetLoading(true); - setDatasetLoadingLabel(`Loading ${dataset.name}...`); + setDatasetLoadingLabel(t('upload.loadingDataset', { name: dataset.name })); try { const loadPromises = dataset.tables.map(async (table) => { @@ -1579,7 +1675,7 @@ export const UnifiedDataUploadDialog: React.FC = ( // Regular example data dictTable.source = { type: 'example', url: table.url }; } - await dispatch(loadTable({ table: dictTable, storeOnServer })); + await dispatch(loadTable({ table: dictTable })); } }); await Promise.all(loadPromises); @@ -1612,7 +1708,7 @@ export const UnifiedDataUploadDialog: React.FC = ( > - {datasetLoadingLabel || 'Loading data...'} + {datasetLoadingLabel || t('upload.loadingData')} diff --git a/src/views/VisualizationView.tsx b/src/views/VisualizationView.tsx index 6522259b..6ea109eb 100644 --- a/src/views/VisualizationView.tsx +++ b/src/views/VisualizationView.tsx @@ -28,22 +28,25 @@ import { Popover, Snackbar, Alert, - Collapse, Fade, Grow, + alpha, } from '@mui/material'; import _ from 'lodash'; -import { borderColor } from '../app/tokens'; +import { borderColor, transition } from '../app/tokens'; +import { WritingIndicator } from '../components/FunComponents'; import ButtonGroup from '@mui/material/ButtonGroup'; import '../scss/VisualizationView.scss'; +import '../scss/DataView.scss'; import { useDispatch, useSelector } from 'react-redux'; import { DataFormulatorState, dfActions, fetchChartInsight } from '../app/dfSlice'; import { assembleVegaChart, extractFieldsFromEncodingMap, getUrls, prepVisTable, fetchWithIdentity } from '../app/utils'; +import embed from 'vega-embed'; import { Chart, EncodingItem, EncodingMap, FieldItem, computeInsightKey } from '../components/ComponentType'; import { DictTable } from "../components/ComponentType"; @@ -53,11 +56,10 @@ import StarIcon from '@mui/icons-material/Star'; import TerminalIcon from '@mui/icons-material/Terminal'; import StarBorderIcon from '@mui/icons-material/StarBorder'; import QuestionAnswerIcon from '@mui/icons-material/QuestionAnswer'; -import CloseIcon from '@mui/icons-material/Close'; import ContentCopyIcon from '@mui/icons-material/ContentCopy'; import ZoomInIcon from '@mui/icons-material/ZoomIn'; import ZoomOutIcon from '@mui/icons-material/ZoomOut'; -import InfoIcon from '@mui/icons-material/Info'; +import AutoStoriesIcon from '@mui/icons-material/AutoStories'; import CasinoIcon from '@mui/icons-material/Casino'; import SaveAltIcon from '@mui/icons-material/SaveAlt'; import OpenInNewIcon from '@mui/icons-material/OpenInNew'; @@ -71,10 +73,15 @@ import 'prismjs/components/prism-markdown' // Language import 'prismjs/components/prism-typescript' // Language import 'prismjs/themes/prism.css'; //Example style, you can use another +import { useTranslation } from 'react-i18next'; + import { ChatDialog } from './ChatDialog'; import { EncodingShelfThread } from './EncodingShelfThread'; import { CustomReactTable } from './ReactTable'; import { InsightIcon } from '../icons'; +import TableChartOutlinedIcon from '@mui/icons-material/TableChartOutlined'; +import { FreeDataViewFC } from './DataView'; + import { dfSelectors } from '../app/dfSlice'; import { ChartRecBox } from './ChartRecBox'; @@ -89,27 +96,9 @@ export interface VisPanelState { viewMode: "gallery" | "carousel"; } -export let generateChartSkeleton = (icon: any, width: number = 160, height: number = 160, opacity: number = 0.5) => ( - - {icon == undefined ? - : - typeof icon == 'string' ? - - - : - - {React.cloneElement(icon, { - style: { - maxHeight: Math.min(height, 32), - maxWidth: Math.min(width, 32), - margin: "auto" - } - })} - } - -) +// Re-export shared utilities from ChartUtils (canonical location) +import { generateChartSkeleton, getDataTable, checkChartAvailability } from './ChartUtils'; +export { generateChartSkeleton, getDataTable, checkChartAvailability }; export let renderTableChart = ( chart: Chart, conceptShelfItems: FieldItem[], extTable: any[], @@ -138,33 +127,6 @@ export let renderTableChart = ( } -export let getDataTable = (chart: Chart, tables: DictTable[], charts: Chart[], - conceptShelfItems: FieldItem[], ignoreTableRef = false) => { - // given a chart, determine which table would be used to visualize the chart - - // return the table directly - if (chart.tableRef && !ignoreTableRef) { - return tables.find(t => t.id == chart.tableRef) as DictTable; - } - - let activeFields = conceptShelfItems.filter((field) => Array.from(Object.values(chart.encodingMap)).map((enc: EncodingItem) => enc.fieldID).includes(field.id)); - - let workingTableCandidates = tables.filter(t => { - return activeFields.every(f => t.names.includes(f.name)); - }); - - let confirmedTableCandidates = workingTableCandidates.filter(t => !charts.some(c => c.saved && c.tableRef == t.id)); - if(confirmedTableCandidates.length > 0) { - return confirmedTableCandidates[0]; - } else if (workingTableCandidates.length > 0) { - return workingTableCandidates[0]; - } else { - // sort base tables based on how many active fields are covered by existing tables - return tables.filter(t => t.derive == undefined).sort((a, b) => activeFields.filter(f => a.names.includes(f.name)).length - - activeFields.filter(f => b.names.includes(f.name)).length).reverse()[0]; - } -} - export let CodeBox : FC<{code: string, language: string, fontSize?: number}> = function CodeBox({ code, language, fontSize = 10 }) { useEffect(() => { Prism.highlightAll(); @@ -196,20 +158,13 @@ export let checkChartAvailabilityOnPreparedData = (chart: Chart, conceptShelfIte return visFieldsFinalNames.length > 0 && visTableRows.length > 0 && visFieldsFinalNames.every(name => Object.keys(visTableRows[0]).includes(name)); } -export let checkChartAvailability = (chart: Chart, conceptShelfItems: FieldItem[], visTableRows: any[]) => { - let visFieldIds = Object.keys(chart.encodingMap) - .filter(key => chart.encodingMap[key as keyof EncodingMap].fieldID != undefined) - .map(key => chart.encodingMap[key as keyof EncodingMap].fieldID); - let visFields = conceptShelfItems.filter(f => visFieldIds.includes(f.id)); - return visFields.length > 0 && visTableRows.length > 0 && visFields.every(f => Object.keys(visTableRows[0]).includes(f.name)); -} - export let SampleSizeEditor: FC<{ initialSize: number; totalSize: number; onSampleSizeChange: (newSize: number) => void; }> = function SampleSizeEditor({ initialSize, totalSize, onSampleSizeChange }) { + const { t } = useTranslation(); const [localSampleSize, setLocalSampleSize] = useState(initialSize); const [anchorEl, setAnchorEl] = useState(null); const open = Boolean(anchorEl); @@ -248,9 +203,9 @@ export let SampleSizeEditor: FC<{ horizontal: 'left', }} > - + - Adjust sample size: {localSampleSize} / {totalSize} rows + {t('chart.adjustSampleSize', { sampleSize: localSampleSize, totalSize })} 100 @@ -262,14 +217,14 @@ export let SampleSizeEditor: FC<{ value={localSampleSize} onChange={(_, value) => setLocalSampleSize(value as number)} valueLabelDisplay="auto" - aria-label="Sample size" + aria-label={t('chart.sampleSizeAria')} /> {maxSliderSize} @@ -278,14 +233,11 @@ export let SampleSizeEditor: FC<{ } /** - * Module-level caches that persist across component remounts. - * - displayRowsCache: avoids re-fetching server data when switching back to a chart - * - displaySvgCache: avoids re-running toSVG when chart+data haven't changed + * Module-level cache: avoids re-fetching server data when switching back to a chart. */ const displayRowsCache = new Map(); -const displaySvgCache = new Map(); -// Simple component that only handles Vega chart rendering — now uses headless toSVG() +/** Main chart uses vega-embed (interactive tooltips). Static toSVG() removes hover behavior. */ const VegaChartRenderer: FC<{ chart: Chart; conceptShelfItems: FieldItem[]; @@ -298,32 +250,27 @@ const VegaChartRenderer: FC<{ chartUnavailable: boolean; onSpecReady?: (spec: any | null) => void; }> = React.memo(({ chart, conceptShelfItems, visTableRows, tableMetadata, chartWidth, chartHeight, scaleFactor, maxStretchFactor, chartUnavailable, onSpecReady }) => { - - // Initialize from display SVG cache for instant display on chart switch - const svgCached = displaySvgCache.get(chart.id); - const [svgContent, setSvgContent] = useState(svgCached?.svg ?? null); - const [assembledSpec, setAssembledSpec] = useState(svgCached?.spec ?? null); + + const elementId = `focused-chart-element-${chart.id}`; useEffect(() => { - + if (chart.chartType === "Auto" || chart.chartType === "Table" || chartUnavailable) { - setSvgContent(null); - setAssembledSpec(null); + onSpecReady?.(null); return; } - // Skip rendering when we have no data yet (data is being fetched) if (visTableRows.length === 0) { return; } const spec = assembleVegaChart( - chart.chartType, - chart.encodingMap, - conceptShelfItems, - visTableRows, - tableMetadata, - chartWidth, + chart.chartType, + chart.encodingMap, + conceptShelfItems, + visTableRows, + tableMetadata, + chartWidth, chartHeight, true, chart.config, @@ -332,111 +279,42 @@ const VegaChartRenderer: FC<{ ); if (!spec || spec === "Table") { - setSvgContent(null); - setAssembledSpec(null); onSpecReady?.(null); return; } spec['background'] = 'white'; - - // Check display SVG cache — skip toSVG entirely if spec matches - const specKey = JSON.stringify(spec); - const cached = displaySvgCache.get(chart.id); - if (cached && cached.specKey === specKey) { - setSvgContent(cached.svg); - setAssembledSpec(cached.spec); - onSpecReady?.(cached.spec); - return; - } - - setAssembledSpec(spec); onSpecReady?.(spec); - // Headless render via Vega: compile VL → parse → View → toSVG() + const el = document.getElementById(elementId); + if (!el) return; + let cancelled = false; - (async () => { - try { - const { compile: vlCompile } = await import('vega-lite'); - const vega = await import('vega'); - const vgSpec = vlCompile(spec as any).spec; - const runtime = vega.parse(vgSpec); - const view = new vega.View(runtime, { renderer: 'none' }); - await view.runAsync(); - const svg = await view.toSVG(); - view.finalize(); - if (!cancelled) { - setSvgContent(svg); - // Cache the rendered SVG for instant reuse on revisit - displaySvgCache.set(chart.id, { specKey, svg, spec }); + const embedResult: { current?: Awaited> } = {}; + + el.innerHTML = ''; + embed(el, { ...spec }, { actions: true, renderer: 'canvas' }) + .then((result) => { + if (cancelled) { + result.finalize(); + return; } - } catch (err) { - console.warn('VegaChartRenderer: SVG render failed', err); + embedResult.current = result; + }) + .catch((err) => { if (!cancelled) { - setSvgContent(null); + console.warn('VegaChartRenderer: embed failed', err); } - } - })(); - - return () => { cancelled = true; }; - - }, [chart.id, chart.chartType, chart.encodingMap, chart.config, conceptShelfItems, visTableRows, tableMetadata, chartWidth, chartHeight, scaleFactor, maxStretchFactor, chartUnavailable]); - - const handleSavePng = useCallback(async () => { - if (!assembledSpec) return; - try { - const { compile: vlCompile } = await import('vega-lite'); - const vega = await import('vega'); - const vgSpec = vlCompile(assembledSpec as any).spec; - const runtime = vega.parse(vgSpec); - const view = new vega.View(runtime, { renderer: 'none' }); - await view.runAsync(); - const pngUrl = await view.toImageURL('png', 2); - view.finalize(); - - // Trigger download - const link = document.createElement('a'); - link.download = `${chart.chartType}-${chart.id}.png`; - link.href = pngUrl; - document.body.appendChild(link); - link.click(); - document.body.removeChild(link); - } catch (err) { - console.error('Save PNG failed:', err); - } - }, [assembledSpec, chart.chartType, chart.id]); - - const handleOpenInVegaEditor = useCallback(() => { - if (!assembledSpec) return; - // Use postMessage to pass spec to Vega Editor (same approach as vega-embed) - const editorUrl = 'https://vega.github.io/editor/'; - const editor = window.open(editorUrl); - if (!editor) return; - - const wait = 10_000; - const step = 250; - const { origin } = new URL(editorUrl); - let count = Math.floor(wait / step); + }); - function listen(evt: MessageEvent) { - if (evt.source === editor) { - count = 0; - window.removeEventListener('message', listen, false); - } - } - window.addEventListener('message', listen, false); + return () => { + cancelled = true; + embedResult.current?.finalize(); + embedResult.current = undefined; + el.innerHTML = ''; + }; - function send() { - if (count <= 0) return; - editor!.postMessage({ - spec: JSON.stringify(assembledSpec, null, 2), - mode: 'vega-lite', - }, origin); - setTimeout(send, step); - count -= 1; - } - setTimeout(send, step); - }, [assembledSpec]); + }, [chart.id, chart.chartType, chart.encodingMap, chart.config, conceptShelfItems, visTableRows, tableMetadata, chartWidth, chartHeight, scaleFactor, maxStretchFactor, chartUnavailable, onSpecReady, elementId]); if (chart.chartType === "Auto") { return @@ -459,23 +337,13 @@ const VegaChartRenderer: FC<{ return ( - {svgContent ? ( - - ) : ( - - {generateChartSkeleton(chartTemplate?.icon, 48, 48, 0.3)} - - )} - + ); }); @@ -483,12 +351,13 @@ const VegaChartRenderer: FC<{ export const ChartEditorFC: FC<{}> = function ChartEditorFC({}) { + const { t } = useTranslation(); const config = useSelector((state: DataFormulatorState) => state.config); const serverConfig = useSelector((state: DataFormulatorState) => state.serverConfig); const componentRef = useRef(null); // Add ref for the container box that holds all exploration components - const explanationComponentsRef = useRef(null); + let tables = useSelector((state: DataFormulatorState) => state.tables); @@ -536,33 +405,18 @@ export const ChartEditorFC: FC<{}> = function ChartEditorFC({}) { const conceptShelfItems = useSelector((state: DataFormulatorState) => state.conceptShelfItems); - const [codeViewOpen, setCodeViewOpen] = useState(false); - const [conceptExplanationsOpen, setConceptExplanationsOpen] = useState(false); - const [insightViewOpen, setInsightViewOpen] = useState(false); - - const [chatDialogOpen, setChatDialogOpen] = useState(false); + const [bottomTab, setBottomTab] = useState('data'); const [localScaleFactor, setLocalScaleFactor] = useState(1); + const [chatDialogOpen, setChatDialogOpen] = useState(false); // Reset local UI state when focused chart changes useEffect(() => { - setCodeViewOpen(false); - setConceptExplanationsOpen(false); - setInsightViewOpen(false); - setChatDialogOpen(false); + setBottomTab('data'); setLocalScaleFactor(1); + setChatDialogOpen(false); }, [focusedChartId]); - // Combined useEffect to scroll to exploration components when any of them open - useEffect(() => { - if ((conceptExplanationsOpen || codeViewOpen || insightViewOpen) && explanationComponentsRef.current) { - setTimeout(() => { - explanationComponentsRef.current?.scrollIntoView({ - behavior: 'smooth', - block: 'start' - }); - }, 200); // Small delay to ensure the component is rendered - } - }, [conceptExplanationsOpen, codeViewOpen, insightViewOpen]); + let table = getDataTable(focusedChart, tables, charts, conceptShelfItems); @@ -584,7 +438,7 @@ export const ChartEditorFC: FC<{}> = function ChartEditorFC({}) { let setSystemMessage = (content: string, severity: "error" | "warning" | "info" | "success") => { dispatch(dfActions.addMessages({ "timestamp": Date.now(), - "component": "Chart Builder", + "component": t('chart.chartBuilder'), "type": severity, "value": content })); @@ -748,7 +602,7 @@ export const ChartEditorFC: FC<{}> = function ChartEditorFC({}) { return !(dataFieldsAllAvailable && table.rows.length > 0); }, [focusedChart.chartType, dataFieldsAllAvailable, table.rows.length]); - let resultTable = tables.find(t => t.id == trigger?.resultTableId); + let triggerTable = tables.find(t => t.derive?.trigger?.chart?.id == focusedChart?.id); // Chart insight const chartInsightInProgress = useSelector((state: DataFormulatorState) => state.chartInsightInProgress) || []; @@ -771,7 +625,7 @@ export const ChartEditorFC: FC<{}> = function ChartEditorFC({}) { }; let saveButton = ( - + { @@ -786,7 +640,7 @@ export const ChartEditorFC: FC<{}> = function ChartEditorFC({}) { ); let deleteButton = ( - + = function ChartEditorFC({}) { // Check if concepts are available const availableConcepts = extractConceptExplanations(table); const hasConcepts = availableConcepts.length > 0; + const hasDerived = !!(triggerTable?.derive || table.derive); - let derivedTableItems = (resultTable?.derive || table.derive) ? [ - - - - - {hasConcepts && ( - - )} - - , - { setChatDialogOpen(false) }} - code={transformCode} - dialog={resultTable?.derive?.dialog || table.derive?.dialog as any[]} /> - ] : []; - - let insightButton = !chartUnavailable && focusedChart.chartType !== "Table" ? ( - - - - ) : null; - let vegaEditorButton = ( - + = function ChartEditorFC({}) { ); + // Toggle buttons for bottom-panel content (icon + text label) + const toggleBtnSx = (active: boolean) => ({ + textTransform: 'none' as const, + fontSize: '0.7rem', + padding: '2px 8px', + borderRadius: '6px', + color: active ? 'primary.main' : 'text.secondary', + backgroundColor: active ? 'rgba(25, 118, 210, 0.08)' : 'transparent', + transition: 'all 0.15s ease', + minWidth: 'auto', + '&:hover': { + backgroundColor: 'rgba(25, 118, 210, 0.08)', + color: 'primary.main', + }, + }); + + let dataButton = ( + + ); + + let derivedTableItems = hasDerived ? [ + , + ...(hasConcepts ? [ + + ] : []), + ] : []; + + let logButton = hasDerived ? ( + + + setChatDialogOpen(true)}> + + + + + ) : null; + + let insightButton = (!chartUnavailable && focusedChart.chartType !== "Table") ? ( + + ) : null; + let chartActionButtons = [ - ...derivedTableItems, + dataButton, insightButton, + ...derivedTableItems, + , + logButton, saveButton, - vegaEditorButton, + // vegaEditorButton, deleteButton, ] let chartMessage = ""; if (focusedChart.chartType == "Table") { - chartMessage = "Tell me what you want to visualize!"; + chartMessage = t('chart.msgTable'); } else if (focusedChart.chartType == "Auto") { - chartMessage = "Say something to get chart recommendations!"; + chartMessage = t('chart.msgAuto'); } else if (encodingShelfEmpty) { - chartMessage = "Put data fields to chart builder or describe what you want!"; + chartMessage = t('chart.msgEncodingEmpty'); } else if (chartUnavailable) { - chartMessage = "Formulate data to create the visualization!"; + chartMessage = t('chart.msgUnavailable'); } else if (chartSynthesisInProgress.includes(focusedChart.id)) { - chartMessage = "Synthesis in progress..."; + chartMessage = t('chart.msgSynthesizing'); } else if (table.derive) { - chartMessage = "AI generated results can be inaccurate, inspect it!"; + chartMessage = t('chart.msgWarning'); } let chartActionItems = isDataStale ? [] : ( @@ -992,7 +775,7 @@ export const ChartEditorFC: FC<{}> = function ChartEditorFC({}) { {(table.virtual ? activeVisTableTotalRowCount > serverConfig.MAX_DISPLAY_ROWS : table.rows.length > serverConfig.MAX_DISPLAY_ROWS) && !(chartUnavailable || encodingShelfEmpty) ? ( - visualizing + {t('chart.visualizing')} = function ChartEditorFC({}) { }} /> - sample rows + {t('chart.sampleRows')} - + { fetchDisplayRows(activeVisTableRows.length); }}> @@ -1024,7 +807,7 @@ export const ChartEditorFC: FC<{}> = function ChartEditorFC({}) { let focusedElement = - + {insightFresh && focusedChart.insight?.title && ( = function ChartEditorFC({}) { {focusedChart.insight.title} )} - + = function ChartEditorFC({}) { ; focusedComponent = [ - - {focusedElement} - - - - - - - - - - { - setCodeViewOpen(false); - }} color='primary' aria-label="delete"> - - - - {/* {table.derive?.source} → {table.id} */} - } - > - + + + {focusedElement} + + + {chartActionButtons} + + , + + {(() => { + const panelBoxSx = { + margin: '8px auto 24px auto', padding: '8px', borderRadius: '8px', + border: `1px solid ${borderColor.divider}`, + transition: 'box-shadow 0.2s ease', + '&:hover': { boxShadow: '0 0 8px rgba(25, 118, 210, 0.25)' }, + }; + return + {bottomTab === 'data' && (() => { + const ROW_HEIGHT = 25; + const HEADER_HEIGHT = 32; + const FOOTER_HEIGHT = 32; + const MIN_TABLE_HEIGHT = 150; + const MAX_TABLE_HEIGHT = 400; + const MIN_TABLE_WIDTH = 300; + const MAX_TABLE_WIDTH = 900; + const rowCount = table.virtual?.rowCount || table.rows?.length || 0; + const contentHeight = HEADER_HEIGHT + rowCount * ROW_HEIGHT + FOOTER_HEIGHT; + const adaptiveHeight = Math.max(MIN_TABLE_HEIGHT, Math.min(MAX_TABLE_HEIGHT, contentHeight)); + + // Estimate total width from columns (generous: account for type icons, sort arrows, padding) + const ROW_ID_COL_WIDTH = 56; + const sampleSize = Math.min(29, table.rows.length); + const step = table.rows.length > sampleSize ? table.rows.length / sampleSize : 1; + const sampledRows = Array.from({ length: sampleSize }, (_, i) => table.rows[Math.floor(i * step)]); + const totalColWidth = table.names.reduce((sum, name) => { + const values = sampledRows.map(row => String(row[name] || '')); + const avgLen = values.length > 0 + ? values.reduce((s, v) => s + v.length, 0) / values.length + : 0; + const nameSegs = name.split(/[\s-]+/); + const maxNameSegLen = nameSegs.reduce((m, seg) => Math.max(m, seg.length), 0); + const contentLen = Math.max(maxNameSegLen, avgLen); + return sum + Math.max(80, Math.min(280, contentLen * 10)) + 60; + }, ROW_ID_COL_WIDTH); + const SCROLLBAR_WIDTH = 17; + const adaptiveWidth = Math.max(MIN_TABLE_WIDTH, Math.min(MAX_TABLE_WIDTH, totalColWidth + SCROLLBAR_WIDTH + 16)); + + return ( + + + + ); + })()} + {bottomTab === 'code' && hasDerived && ( + + - - - - - - - { - setInsightViewOpen(false); - }} color='primary' aria-label="close"> - - - - } - > + + )} + {bottomTab === 'concepts' && hasConcepts && ( + + + + )} + {bottomTab === 'insight' && ( + {insightLoading ? ( - - - Analyzing chart... + + ) : insightFresh && focusedChart.insight ? ( - - - {focusedChart.insight.title} - - {(focusedChart.insight.takeaways || []).map((t, i) => ( - - •{t} - - ))} - ) : ( - + - No insight available. + {t('chart.noInsightAvailable')} - )} - - - - - - {chartActionButtons} - - + + )} + ; + })()} + , + , + hasDerived ? setChatDialogOpen(false)} + code={transformCode} + dialog={triggerTable?.derive?.dialog || table.derive?.dialog as any[]} /> : null, ] - const ENCODING_SHELF_WIDTH = 200; + const ENCODING_SHELF_WIDTH = 240; let content = [ - + {focusedComponent} , /* Floating encoding shelf panel */ @@ -1187,7 +1006,7 @@ export const ChartEditorFC: FC<{}> = function ChartEditorFC({}) { backgroundColor: 'rgba(255, 255, 255, 0.9)', borderRadius: '4px', }} alignItems="center"> - + { setLocalScaleFactor(s => Math.max(scaleMin, Math.round((s - 0.1) * 10) / 10)); @@ -1196,11 +1015,11 @@ export const ChartEditorFC: FC<{}> = function ChartEditorFC({}) { - { setLocalScaleFactor(newValue as number); }} /> - + = scaleMax} onClick={() => { setLocalScaleFactor(s => Math.min(scaleMax, Math.round((s + 0.1) * 10) / 10)); @@ -1209,7 +1028,7 @@ export const ChartEditorFC: FC<{}> = function ChartEditorFC({}) { - , [localScaleFactor]); + , [localScaleFactor, t]); return {synthesisRunning ? = function ChartEditorFC({}) { export const VisualizationViewFC: FC = function VisualizationView({ }) { + const { t } = useTranslation(); let allCharts = useSelector(dfSelectors.getAllCharts); let focusedId = useSelector((state: DataFormulatorState) => state.focusedId); let focusedChartId = focusedId?.type === 'chart' ? focusedId.chartId : undefined; let focusedTableId = React.useMemo(() => { if (!focusedId) return undefined; if (focusedId.type === 'table') return focusedId.tableId; - const chartId = focusedId.chartId; + const chartId = (focusedId as { type: 'chart'; chartId: string }).chartId; const chart = allCharts.find(c => c.id === chartId); return chart?.tableRef; }, [focusedId, allCharts]); @@ -1239,6 +1059,8 @@ export const VisualizationViewFC: FC = function VisualizationView const dispatch = useDispatch(); + let tables = useSelector((state: DataFormulatorState) => state.tables); + let focusedChart = allCharts.find(c => c.id == focusedChartId) as Chart; let synthesisRunning = focusedChartId ? chartSynthesisInProgress.includes(focusedChartId) : false; @@ -1278,14 +1100,63 @@ export const VisualizationViewFC: FC = function VisualizationView } return ( - - {focusedTableId ? : null} - - - or, start with a chart type - - - {chartSelectionBox} + + + + + + {focusedTableId ? : null} + + + {t('chart.orStartWithChartType')} + + + {chartSelectionBox} + + + {focusedId?.type === 'table' && focusedTableId && (() => { + const focusedTable = tables.find(t => t.id === focusedTableId); + if (!focusedTable) return null; + const ROW_HEIGHT = 25; + const HEADER_HEIGHT = 32; + const FOOTER_HEIGHT = 32; + const MIN_TABLE_HEIGHT = 150; + const MAX_TABLE_HEIGHT = 400; + const MIN_TABLE_WIDTH = 300; + const MAX_TABLE_WIDTH = 900; + const rowCount = focusedTable.virtual?.rowCount || focusedTable.rows?.length || 0; + const contentHeight = HEADER_HEIGHT + rowCount * ROW_HEIGHT + FOOTER_HEIGHT; + const adaptiveHeight = Math.max(MIN_TABLE_HEIGHT, Math.min(MAX_TABLE_HEIGHT, contentHeight)); + const ROW_ID_COL_WIDTH = 56; + const sampleSize = Math.min(29, focusedTable.rows.length); + const step = focusedTable.rows.length > sampleSize ? focusedTable.rows.length / sampleSize : 1; + const sampledRows = Array.from({ length: sampleSize }, (_, i) => focusedTable.rows[Math.floor(i * step)]); + const totalColWidth = focusedTable.names.reduce((sum, name) => { + const values = sampledRows.map(row => String(row[name] || '')); + const avgLen = values.length > 0 ? values.reduce((s, v) => s + v.length, 0) / values.length : 0; + const nameSegs = name.split(/[\s-]+/); + const maxNameSegLen = nameSegs.reduce((m, seg) => Math.max(m, seg.length), 0); + const contentLen = Math.max(maxNameSegLen, avgLen); + return sum + Math.max(80, Math.min(280, contentLen * 10)) + 60; + }, ROW_ID_COL_WIDTH); + const SCROLLBAR_WIDTH = 17; + const adaptiveWidth = Math.max(MIN_TABLE_WIDTH, Math.min(MAX_TABLE_WIDTH, totalColWidth + SCROLLBAR_WIDTH + 16)); + return ( + + + + ); + })()} + + ) } diff --git a/src/views/useFormulateData.ts b/src/views/useFormulateData.ts index 3d0e5980..05403d35 100644 --- a/src/views/useFormulateData.ts +++ b/src/views/useFormulateData.ts @@ -91,7 +91,7 @@ export function useFormulateData() { return triggers.map(trigger => ({ name: trigger.resultTableId, rows: tables.find(t2 => t2.id === trigger.resultTableId)?.rows, - description: `Derive from ${tables.find(t2 => t2.id === trigger.resultTableId)?.derive?.source} with instruction: ${trigger.instruction}`, + description: `Derive from ${tables.find(t2 => t2.id === trigger.resultTableId)?.derive?.source}`, })); } @@ -361,10 +361,16 @@ export function useFormulateData() { // Create trigger const trigger: Trigger = { tableId: currentTable.id, - instruction, - displayInstruction, - chart: triggerChart, resultTableId: candidateTableId, + chart: triggerChart, + interaction: [{ + from: 'user' as const, + to: 'datarec-agent' as const, + role: 'instruction' as const, + content: instruction, + displayContent: displayInstruction, + timestamp: Date.now(), + }], }; // Create candidate table with derive info @@ -435,7 +441,6 @@ export function useFormulateData() { // Delegate chart creation to the caller const focusedChartId = createChart({ candidateTable, refinedGoal, currentConcepts }); - // Auto-generate chart insight after rendering if (focusedChartId) { const chartIdForInsight = focusedChartId; setTimeout(() => { diff --git a/tests/README.md b/tests/README.md new file mode 100644 index 00000000..4f30304f --- /dev/null +++ b/tests/README.md @@ -0,0 +1,32 @@ +# Tests + +The test tree is organized with a clear backend/frontend split: + +- `tests/backend` + - backend unit tests, integration tests, and contract tests + - driven by `pytest` + - regression tests directly related to `py-src/data_formulator/**` +- `tests/frontend` + - frontend unit tests powered by **Vitest** + **@testing-library/react** (jsdom) + - covers pure functions, Redux selectors, and React component rendering + - see `tests/frontend/README.md` for directory layout details + +## Suggested Commands + +Run backend tests (pytest): + +```bash +pytest tests/backend +``` + +Run frontend tests (Vitest): + +```bash +npm test +``` + +Run frontend tests in watch mode: + +```bash +npm run test:watch +``` diff --git a/tests/backend/README.md b/tests/backend/README.md new file mode 100644 index 00000000..e83be085 --- /dev/null +++ b/tests/backend/README.md @@ -0,0 +1,59 @@ +# Backend Tests + +This directory contains backend Python tests, organized by responsibility. +Try to keep future additions within the appropriate layer instead of mixing concerns. + +## Directory Layout + +```text +tests/backend/ + README.md + unit/ + README.md + test_unicode_table_name_sanitization.py + integration/ + README.md + contract/ + README.md + fixtures/ + README.md +``` + +## Directory Responsibilities + +- `tests/backend/unit` + - pure function tests + - name sanitization + - utility helpers + - no Flask app or external service dependency + +- `tests/backend/integration` + - Flask routes + - workspace / datalake behavior + - table import, refresh, and metadata read/write flows + - may use temp directories, temp files, and monkeypatching + +- `tests/backend/contract` + - API boundary contract tests + - focused on stable input/output fields and compatibility guarantees + - for example, a Chinese table name should not degrade into an empty placeholder + +- `tests/backend/fixtures` + - test data files + - sample JSON / CSV / parquet files + - shared fixture documentation + +## Recommended Expansion Order + +1. Use `unit` tests to lock down sanitization behavior first. +2. Add `contract` tests for route-level input/output guarantees next. +3. Add `integration` tests for full import flows last. + +## Current Scope + +This first round is focused on: + +- Chinese table names +- Chinese column names +- non-ASCII identifiers +- fallback behavior when sanitization would otherwise produce an empty name diff --git a/tests/backend/benchmarks/benchmark_sandbox.py b/tests/backend/benchmarks/benchmark_sandbox.py new file mode 100644 index 00000000..6bcd1a2a --- /dev/null +++ b/tests/backend/benchmarks/benchmark_sandbox.py @@ -0,0 +1,231 @@ +#!/usr/bin/env python3 +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +"""Benchmark sandbox execution speed across local and docker modes. + +Usage: + uv run python tests/backend/benchmarks/benchmark_sandbox.py +""" + +import statistics +import subprocess +import tempfile +import time + +from data_formulator.sandbox import LocalSandbox, DockerSandbox +from data_formulator.sandbox.not_a_sandbox import NotASandbox + + +class _MinimalWorkspace: + """Lightweight workspace stand-in for benchmarks.""" + def __init__(self, path: str): + self._path = path + +# --------------------------------------------------------------------------- +# Realistic Data Formulator code snippets (typical AI-generated transforms) +# --------------------------------------------------------------------------- + +# 1. Simple column rename + filter (small, fast) +CODE_SIMPLE = """\ +import pandas as pd +df = pd.DataFrame({ + "Name": ["Alice", "Bob", "Charlie", "Diana", "Eve"], + "Age": [25, 30, 35, 28, 22], + "Salary": [50000, 60000, 70000, 55000, 45000], + "Department": ["Eng", "Sales", "Eng", "Sales", "Eng"] +}) +result_df = df.rename(columns={"Name": "Employee"}).query("Age > 24") +""" + +# 2. Groupby + aggregation (medium complexity, typical chart prep) +CODE_GROUPBY = """\ +import pandas as pd +import numpy as np +np.random.seed(42) +n = 1000 +df = pd.DataFrame({ + "date": pd.date_range("2023-01-01", periods=n, freq="D"), + "category": np.random.choice(["A", "B", "C", "D"], n), + "value": np.random.normal(100, 25, n), + "quantity": np.random.randint(1, 50, n), +}) +result_df = ( + df.groupby([pd.Grouper(key="date", freq="M"), "category"]) + .agg(total_value=("value", "sum"), avg_quantity=("quantity", "mean")) + .reset_index() +) +""" + +# 3. Pivot + melt (reshape for visualization) +CODE_PIVOT = """\ +import pandas as pd +import numpy as np +np.random.seed(42) +df = pd.DataFrame({ + "year": [2020, 2020, 2021, 2021, 2022, 2022] * 3, + "region": (["North", "South"] * 3) * 3, + "metric": ["revenue"] * 6 + ["profit"] * 6 + ["cost"] * 6, + "value": np.random.randint(100, 1000, 18), +}) +pivot = df.pivot_table(index=["year", "region"], columns="metric", values="value", aggfunc="sum").reset_index() +result_df = pivot.melt(id_vars=["year", "region"], var_name="metric", value_name="amount") +""" + +# 4. Multi-table join + derived columns (common Data Formulator pattern) +CODE_JOIN = """\ +import pandas as pd +import numpy as np +np.random.seed(42) +orders = pd.DataFrame({ + "order_id": range(1, 501), + "customer_id": np.random.randint(1, 51, 500), + "product_id": np.random.randint(1, 21, 500), + "amount": np.random.uniform(10, 500, 500).round(2), + "date": pd.date_range("2023-01-01", periods=500, freq="6h"), +}) +customers = pd.DataFrame({ + "customer_id": range(1, 51), + "name": [f"Customer_{i}" for i in range(1, 51)], + "segment": np.random.choice(["Enterprise", "SMB", "Consumer"], 50), +}) +products = pd.DataFrame({ + "product_id": range(1, 21), + "product_name": [f"Product_{i}" for i in range(1, 21)], + "category": np.random.choice(["Electronics", "Clothing", "Food"], 20), +}) +merged = orders.merge(customers, on="customer_id").merge(products, on="product_id") +merged["month"] = merged["date"].dt.to_period("M").astype(str) +result_df = ( + merged.groupby(["month", "segment", "category"]) + .agg(total_sales=("amount", "sum"), order_count=("order_id", "count")) + .reset_index() + .sort_values("total_sales", ascending=False) +) +""" + +# 5. DuckDB SQL query (Python+SQL unified execution) +CODE_DUCKDB = """\ +import pandas as pd +import numpy as np +import duckdb +np.random.seed(42) +df = pd.DataFrame({ + "city": np.random.choice(["NYC", "LA", "Chicago", "Houston", "Phoenix"], 200), + "temperature": np.random.normal(70, 15, 200).round(1), + "humidity": np.random.uniform(20, 90, 200).round(1), + "date": pd.date_range("2023-01-01", periods=200, freq="D"), +}) +result_df = duckdb.sql(\"\"\" + SELECT city, + COUNT(*) as days, + ROUND(AVG(temperature), 1) as avg_temp, + ROUND(AVG(humidity), 1) as avg_humidity, + ROUND(MIN(temperature), 1) as min_temp, + ROUND(MAX(temperature), 1) as max_temp + FROM df + GROUP BY city + ORDER BY avg_temp DESC +\"\"\").df() +""" + +BENCHMARKS = [ + ("simple_rename_filter", CODE_SIMPLE), + ("groupby_aggregation", CODE_GROUPBY), + ("pivot_melt_reshape", CODE_PIVOT), + ("multi_table_join", CODE_JOIN), + ("duckdb_sql_query", CODE_DUCKDB), +] + + +def _docker_available() -> bool: + try: + proc = subprocess.run(["docker", "info"], capture_output=True, timeout=10) + return proc.returncode == 0 + except Exception: + return False + + +def bench(sandbox, workspace, name: str, code: str, warmup: int = 1, runs: int = 5) -> dict: + """Run a benchmark and return timing stats.""" + # Warmup + for _ in range(warmup): + r = sandbox.run_python_code(code, workspace, "result_df") + if r["status"] != "ok": + return {"name": name, "error": r.get("content", r.get("error_message", "unknown"))} + + times = [] + for _ in range(runs): + t0 = time.perf_counter() + r = sandbox.run_python_code(code, workspace, "result_df") + t1 = time.perf_counter() + if r["status"] != "ok": + return {"name": name, "error": r.get("content", r.get("error_message", "unknown"))} + times.append(t1 - t0) + + return { + "name": name, + "mean_ms": statistics.mean(times) * 1000, + "median_ms": statistics.median(times) * 1000, + "stdev_ms": statistics.stdev(times) * 1000 if len(times) > 1 else 0, + "min_ms": min(times) * 1000, + "max_ms": max(times) * 1000, + "runs": runs, + } + + +def print_results(mode: str, results: list[dict]): + print(f"\n{'=' * 72}") + print(f" {mode}") + print(f"{'=' * 72}") + print(f" {'Benchmark':<25} {'Mean':>9} {'Median':>9} {'StDev':>9} {'Min':>9} {'Max':>9}") + print(f" {'-' * 25} {'-' * 9} {'-' * 9} {'-' * 9} {'-' * 9} {'-' * 9}") + for r in results: + if "error" in r: + print(f" {r['name']:<25} ERROR: {r['error'][:40]}") + else: + print(f" {r['name']:<25} {r['mean_ms']:>8.1f}ms {r['median_ms']:>8.1f}ms " + f"{r['stdev_ms']:>8.1f}ms {r['min_ms']:>8.1f}ms {r['max_ms']:>8.1f}ms") + + +def main(): + print("Data Formulator Sandbox Benchmark") + print(f"Running each benchmark: 1 warmup + 5 timed runs\n") + + # Create a temporary workspace directory + tmpdir = tempfile.mkdtemp(prefix="df_bench_") + workspace = _MinimalWorkspace(tmpdir) + + # --- Baseline (main-process, no isolation) --- + sandbox = NotASandbox() + results_baseline = [bench(sandbox, workspace, name, code) for name, code in BENCHMARKS] + print_results("baseline (main-process, no isolation)", results_baseline) + + # --- Local (warm subprocess, audit hooks) --- + sandbox = LocalSandbox() + results_local = [bench(sandbox, workspace, name, code) for name, code in BENCHMARKS] + print_results("local (warm subprocess, audit hooks)", results_local) + + # --- Docker --- + if _docker_available(): + sandbox = DockerSandbox() + results_docker = [bench(sandbox, workspace, name, code, warmup=1, runs=3) for name, code in BENCHMARKS] + print_results("docker (container isolation)", results_docker) + else: + print("\n [Docker not available -- skipping docker benchmark]") + + # --- Summary --- + print(f"\n{'=' * 72}") + print(" Overhead vs baseline (warm subprocess)") + print(f"{'=' * 72}") + for rb, rl in zip(results_baseline, results_local): + if "error" in rb or "error" in rl: + status = "N/A (error)" + else: + overhead = rl["mean_ms"] - rb["mean_ms"] + status = f"+{overhead:.1f}ms overhead" + print(f" {rb['name']:<25} {status}") + + +if __name__ == "__main__": + main() diff --git a/tests/backend/benchmarks/benchmark_workspace.py b/tests/backend/benchmarks/benchmark_workspace.py new file mode 100644 index 00000000..ed271deb --- /dev/null +++ b/tests/backend/benchmarks/benchmark_workspace.py @@ -0,0 +1,529 @@ +#!/usr/bin/env python3 +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +""" +Benchmark workspace read performance: Local vs Azure Blob vs Azure+Cache. + +Runs all backends in one shot and prints a side-by-side comparison. +When real Azure credentials are unavailable, a *simulated* Azure backend +wraps the local workspace with configurable per-call latency so the +performance gap is still visible. + +Measured operations (typical derive-data request hot path): + 1. get_metadata() -- parsed from workspace.yaml / blob + 2. read_data_as_df() -- used by generate_data_summary per table + 3. local_dir() -- sandbox materialises all files locally + 4. WorkspaceWithTempData -- mount temp tables -> read -> cleanup + 5. run_parquet_sql() -- DuckDB query against a parquet table + 6. full_derive_data_reads -- (2) x N tables + (3) combined + +Usage: + python tests/backend/benchmarks/benchmark_workspace.py + python tests/backend/benchmarks/benchmark_workspace.py --rows 10000 --tables 3 + python tests/backend/benchmarks/benchmark_workspace.py --azure # use real Azure + python tests/backend/benchmarks/benchmark_workspace.py --latency 0.05 # 50ms/call +""" + +from __future__ import annotations + +import argparse +import io +import os +import statistics +import sys +import tempfile +import time +from contextlib import contextmanager +from pathlib import Path + +import numpy as np +import pandas as pd + +_project_root = Path(__file__).resolve().parent.parent.parent +sys.path.insert(0, str(_project_root / "py-src")) + +from data_formulator.datalake.workspace import Workspace, WorkspaceWithTempData +from data_formulator.datalake.parquet_utils import sanitize_table_name + + +# == Simulated-latency workspace ============================================ + +class SimulatedBlobWorkspace(Workspace): + """Local workspace + artificial latency to mimic Azure Blob round-trips.""" + + def __init__(self, identity_id, root_dir, latency_s=0.03, + *, use_blob_cache=False): + super().__init__(identity_id, root_dir=root_dir) + self._latency_s = latency_s + self._use_blob_cache = use_blob_cache + self._blob_cache: dict[str, bytes] = {} + self._metadata_cached = False + + def _sim_latency(self): + time.sleep(self._latency_s) + + # -- metadata ----------------------------------------------------------- + + def get_metadata(self): + if self._use_blob_cache and self._metadata_cached: + return super().get_metadata() + self._sim_latency() + result = super().get_metadata() + self._metadata_cached = True + return result + + def save_metadata(self, metadata): + self._sim_latency() + self._metadata_cached = False + return super().save_metadata(metadata) + + # -- read --------------------------------------------------------------- + + def read_data_as_df(self, table_name): + meta = self.get_table_metadata(table_name) + if meta is None: + raise FileNotFoundError(f"Table not found: {table_name}") + filename = meta.filename + + if self._use_blob_cache and filename in self._blob_cache: + buf = io.BytesIO(self._blob_cache[filename]) + return pd.read_parquet(buf) + + self._sim_latency() + df = super().read_data_as_df(table_name) + + if self._use_blob_cache: + path = self._path / filename + if path.exists(): + self._blob_cache[filename] = path.read_bytes() + return df + + # -- write -------------------------------------------------------------- + + def write_parquet(self, df, table_name, **kwargs): + self._sim_latency() + result = super().write_parquet(df, table_name, **kwargs) + safe = sanitize_table_name(table_name) + self._blob_cache.pop(f"{safe}.parquet", None) + return result + + # -- local_dir ---------------------------------------------------------- + + @contextmanager + def local_dir(self): + """Simulate downloading ALL workspace files from blob storage.""" + data_files = [f for f in self._path.glob("*") + if f.is_file() and f.name != "workspace.yaml"] + for _ in data_files: + self._sim_latency() + yield self._path + + # -- delete ------------------------------------------------------------- + + def delete_table(self, table_name): + self._sim_latency() + safe = sanitize_table_name(table_name) + self._blob_cache.pop(f"{safe}.parquet", None) + return super().delete_table(table_name) + + def invalidate_all_caches(self): + self._blob_cache.clear() + self._metadata_cached = False + + +# == Test helpers =========================================================== + +def generate_test_df(num_rows, seed=42): + rng = np.random.default_rng(seed) + categories = ["Electronics", "Clothing", "Food", "Books", "Sports", + "Home", "Garden", "Automotive", "Health", "Toys"] + regions = ["North", "South", "East", "West", "Central"] + return pd.DataFrame({ + "order_id": range(1, num_rows + 1), + "date": pd.date_range("2023-01-01", periods=num_rows, freq="h"), + "category": rng.choice(categories, num_rows), + "region": rng.choice(regions, num_rows), + "quantity": rng.integers(1, 100, num_rows), + "unit_price": rng.uniform(5.0, 500.0, num_rows).round(2), + "discount": rng.uniform(0.0, 0.3, num_rows).round(3), + "customer_name": [f"Customer_{i}" for i in rng.integers(1, 200, num_rows)], + "is_returned": rng.choice([True, False], num_rows, p=[0.05, 0.95]), + "notes": rng.choice( + ["", "Rush order", "Gift wrap", "Fragile", "Bulk discount applied"], + num_rows, p=[0.6, 0.1, 0.1, 0.1, 0.1]), + }) + + +@contextmanager +def timer(label, results): + start = time.perf_counter() + yield + results.setdefault(label, []).append(time.perf_counter() - start) + + +def fmt_ms(seconds): + return f"{seconds * 1000:8.1f} ms" + + +# == Benchmark runner ======================================================= + +def run_benchmark(workspace, num_rows, num_tables, iterations, label): + results: dict[str, list[float]] = {} + table_names: list[str] = [] + + print(f"\n{'=' * 60}") + print(f" Backend: {label}") + print(f" Tables : {num_tables} x {num_rows:,} rows") + print(f" Iters : {iterations}") + print(f"{'=' * 60}") + + dfs = [] + for i in range(num_tables): + df = generate_test_df(num_rows, seed=42 + i) + tname = f"bench_table_{i}" + with timer("setup_write_parquet", results): + workspace.write_parquet(df, tname) + table_names.append(sanitize_table_name(tname)) + dfs.append(df) + + setup_time = sum(results.get("setup_write_parquet", [])) + print(f" Setup (write {num_tables} tables): {fmt_ms(setup_time)}") + + for it in range(iterations): + if iterations > 1: + print(f" -- iteration {it + 1}/{iterations} ", end="", flush=True) + + for _ in range(5): + with timer("get_metadata", results): + workspace.get_metadata() + + for tname in table_names: + with timer("read_data_as_df", results): + df_read = workspace.read_data_as_df(tname) + assert len(df_read) == num_rows + + with timer("local_dir", results): + with workspace.local_dir() as wd: + list(Path(wd).glob("*.parquet")) + + temp_data = [ + {"name": f"temp_{i}", "rows": dfs[i].head(100).to_dict("records")} + for i in range(num_tables) + ] + with timer("workspace_with_temp_data", results): + with WorkspaceWithTempData(workspace, temp_data) as ws: + for i in range(num_tables): + ws.read_data_as_df(f"temp_{i}") + + with timer("full_derive_data_reads", results): + for tname in table_names: + workspace.read_data_as_df(tname) + with workspace.local_dir() as _: + pass + + for tname in table_names: + with timer("run_parquet_sql", results): + try: + workspace.run_parquet_sql( + tname, + "SELECT category, SUM(quantity) as total " + "FROM {parquet} GROUP BY category", + ) + except Exception: + pass + + if iterations > 1: + last = results["full_derive_data_reads"][-1] + print(f" full_derive={fmt_ms(last).strip()}") + + for tname in table_names: + try: + workspace.delete_table(tname) + except Exception: + pass + + return results + + +# == Report ================================================================= + +def print_report(all_results, latency_ms=None): + key_ops = [ + "get_metadata", "read_data_as_df", "local_dir", + "workspace_with_temp_data", "run_parquet_sql", + "full_derive_data_reads", + ] + all_ops = set() + for r in all_results.values(): + all_ops.update(r.keys()) + all_ops.discard("setup_write_parquet") + ops = [op for op in key_ops if op in all_ops] + backends = list(all_results.keys()) + + col_w = max(22, max(len(b) for b in backends) + 4) + + print(f"\n{'=' * (30 + col_w * len(backends) + 4)}") + print(" RESULTS COMPARISON") + if latency_ms is not None: + print(f" (simulated blob latency = {latency_ms:.0f} ms per call)") + print(f"{'=' * (30 + col_w * len(backends) + 4)}") + + header = f"{'Operation':<30}" + for b in backends: + header += f"{b:>{col_w}}" + print(header) + print("-" * (30 + col_w * len(backends))) + + local_medians: dict[str, float] = {} + for op in ops: + row = f"{op:<30}" + values: list = [] + for b in backends: + times = all_results[b].get(op, []) + if times: + med = statistics.median(times) + row += f"{fmt_ms(med):>{col_w}}" + values.append(med) + if b == backends[0]: + local_medians[op] = med + else: + row += f"{'N/A':>{col_w}}" + values.append(None) + base = local_medians.get(op) + if base and base > 0 and len(values) >= 2: + parts = [] + for v in values[1:]: + if v is not None: + parts.append(f"{v / base:.0f}x") + else: + parts.append("-") + row += f" ({', '.join(parts)})" + print(row) + + print(f"\n{'-' * (30 + col_w * len(backends))}") + print(" Key observations:") + print(" * read_data_as_df -- once per table (generate_data_summary)") + print(" * local_dir -- re-downloads ALL blobs (sandbox hot path)") + print(" * full_derive_data -- the two above combined (dominates latency)") + print(" * warm cache -- blob_data_cache avoids re-downloads for reads") + print(" * local_dir always bypasses the blob_data_cache") + print(" * CachedAzureBlobWorkspace keeps a local mirror => local_dir is free") + print() + + +# == Main =================================================================== + +def main(): + parser = argparse.ArgumentParser( + description="Benchmark workspace read performance across backends", + ) + parser.add_argument("--rows", type=int, default=2000) + parser.add_argument("--tables", type=int, default=2) + parser.add_argument("--iterations", type=int, default=3) + parser.add_argument("--latency", type=float, default=0.03, + help="Simulated per-call latency in seconds (default: 0.03)") + parser.add_argument("--azure", action="store_true", + help="Use real Azure Blob Storage instead of simulation") + args = parser.parse_args() + + all_results: dict[str, dict[str, list[float]]] = {} + + n_backends = 5 # local + 3 simulated azure + 1 cached + + # -- 1. Local filesystem ------------------------------------------------ + print(f"\n[1/{n_backends}] LOCAL filesystem") + with tempfile.TemporaryDirectory(prefix="df_bench_") as tmpdir: + ws_local = Workspace("bench_local", root_dir=tmpdir) + all_results["Local"] = run_benchmark( + ws_local, args.rows, args.tables, args.iterations, "Local FS", + ) + + if args.azure: + _run_real_azure(args, all_results) + else: + _run_simulated(args, all_results) + + print_report( + all_results, + latency_ms=None if args.azure else args.latency * 1000, + ) + + +def _run_simulated(args, all_results): + lat = args.latency + + # -- 2. Simulated Azure (cold) ------------------------------------------ + print(f"\n[2/5] SIMULATED Azure Blob (latency={lat * 1000:.0f}ms/call, cold)") + with tempfile.TemporaryDirectory(prefix="df_bench_sim_") as tmpdir: + ws_sim = SimulatedBlobWorkspace( + "bench_sim", root_dir=tmpdir, latency_s=lat, use_blob_cache=False, + ) + all_results["Sim Azure (cold)"] = run_benchmark( + ws_sim, args.rows, args.tables, args.iterations, + f"Simulated Azure (cold, {lat * 1000:.0f}ms)", + ) + + # -- 3. Simulated Azure + cache (cold start) ---------------------------- + print(f"\n[3/5] SIMULATED Azure + blob_data_cache (cold start)") + with tempfile.TemporaryDirectory(prefix="df_bench_cache_") as tmpdir: + ws_cache = SimulatedBlobWorkspace( + "bench_cache", root_dir=tmpdir, latency_s=lat, use_blob_cache=True, + ) + all_results["Sim Azure (cache)"] = run_benchmark( + ws_cache, args.rows, args.tables, args.iterations, + f"Simulated Azure (cache, {lat * 1000:.0f}ms)", + ) + + # -- 4. Simulated Azure warm cache (pre-populated) ---------------------- + print(f"\n[4/5] SIMULATED Azure warm cache (pre-populated)") + with tempfile.TemporaryDirectory(prefix="df_bench_warm_") as tmpdir: + ws_warm = SimulatedBlobWorkspace( + "bench_warm", root_dir=tmpdir, latency_s=lat, use_blob_cache=True, + ) + table_names = [] + for i in range(args.tables): + df = generate_test_df(args.rows, seed=42 + i) + tname = f"bench_table_{i}" + ws_warm.write_parquet(df, tname) + safe = sanitize_table_name(tname) + table_names.append(safe) + ws_warm.read_data_as_df(safe) # warm the cache + + warm_results: dict[str, list[float]] = {} + print(f"\n{'=' * 60}") + print(f" Backend: Warm cache (reads only)") + print(f"{'=' * 60}") + for _ in range(args.iterations): + for tname in table_names: + with timer("read_data_as_df", warm_results): + ws_warm.read_data_as_df(tname) + for _ in range(5): + with timer("get_metadata", warm_results): + ws_warm.get_metadata() + with timer("local_dir", warm_results): + with ws_warm.local_dir() as _: + pass + with timer("full_derive_data_reads", warm_results): + for tname in table_names: + ws_warm.read_data_as_df(tname) + with ws_warm.local_dir() as _: + pass + + all_results["Sim Azure (warm)"] = warm_results + + for tname in table_names: + try: + ws_warm.delete_table(tname) + except Exception: + pass + + # -- 5. CachedAzureBlobWorkspace simulation ------------------------------ + # The CachedAzureBlobWorkspace uses a LOCAL file mirror so reads + # are at filesystem speed. We simulate it here by wrapping the + # SimulatedBlobWorkspace with the same write-through-to-cache pattern. + print(f"\n[5/5] CachedAzureBlobWorkspace pattern (local mirror)") + with tempfile.TemporaryDirectory(prefix="df_bench_cached_") as tmpdir: + ws_cached = Workspace("bench_cached", root_dir=tmpdir) + all_results["Cached Azure"] = run_benchmark( + ws_cached, args.rows, args.tables, args.iterations, + "Cached Azure (local mirror)", + ) + + +def _run_real_azure(args, all_results): + try: + from azure.storage.blob import ContainerClient + from data_formulator.datalake.azure_blob_workspace import AzureBlobWorkspace + except ImportError as e: + print(f" [SKIP] Azure packages not installed: {e}") + return + + conn_str = os.getenv("AZURE_BLOB_CONNECTION_STRING") + account_url = os.getenv("AZURE_BLOB_ACCOUNT_URL") + container_name = os.getenv("AZURE_BLOB_CONTAINER", "data-formulator") + + if conn_str: + container = ContainerClient.from_connection_string(conn_str, container_name) + elif account_url: + from azure.identity import DefaultAzureCredential + container = ContainerClient(account_url, container_name, + credential=DefaultAzureCredential()) + else: + print(" [SKIP] Set AZURE_BLOB_CONNECTION_STRING or AZURE_BLOB_ACCOUNT_URL") + return + + # -- Azure cold --------------------------------------------------------- + print("\n[2/4] REAL Azure Blob (cold)") + ws_azure = AzureBlobWorkspace("bench_azure", container, + datalake_root="benchmark_test") + try: + all_results["Azure (cold)"] = run_benchmark( + ws_azure, args.rows, args.tables, args.iterations, + "Azure Blob (cold)", + ) + finally: + try: + ws_azure.cleanup() + except Exception: + pass + + # -- Azure warm cache --------------------------------------------------- + print("\n[3/4] REAL Azure Blob (warm cache)") + ws_warm = AzureBlobWorkspace("bench_warm", container, + datalake_root="benchmark_test") + try: + table_names = [] + for i in range(args.tables): + df = generate_test_df(args.rows, seed=42 + i) + tname = f"bench_table_{i}" + ws_warm.write_parquet(df, tname) + safe = sanitize_table_name(tname) + table_names.append(safe) + ws_warm.read_data_as_df(safe) + + warm_results: dict[str, list[float]] = {} + print(f"\n{'=' * 60}") + print(f" Backend: Azure (warm cache, reads only)") + print(f"{'=' * 60}") + for _ in range(args.iterations): + for tname in table_names: + with timer("read_data_as_df", warm_results): + ws_warm.read_data_as_df(tname) + for _ in range(5): + with timer("get_metadata", warm_results): + ws_warm.get_metadata() + with timer("local_dir", warm_results): + with ws_warm.local_dir() as _: + pass + with timer("full_derive_data_reads", warm_results): + for tname in table_names: + ws_warm.read_data_as_df(tname) + with ws_warm.local_dir() as _: + pass + + all_results["Azure (warm)"] = warm_results + finally: + try: + ws_warm.cleanup() + except Exception: + pass + + # -- Azure full benchmark ----------------------------------------------- + print("\n[4/4] REAL Azure Blob (full benchmark)") + ws_full = AzureBlobWorkspace("bench_full", container, + datalake_root="benchmark_test") + try: + all_results["Azure (full)"] = run_benchmark( + ws_full, args.rows, args.tables, args.iterations, + "Azure Blob (full)", + ) + finally: + try: + ws_full.cleanup() + except Exception: + pass + + +if __name__ == "__main__": + main() diff --git a/tests/backend/contract/README.md b/tests/backend/contract/README.md new file mode 100644 index 00000000..251f397c --- /dev/null +++ b/tests/backend/contract/README.md @@ -0,0 +1,16 @@ +# Backend Contract Tests + +This directory contains API contract tests. + +Contract tests focus on guarantees rather than implementation details: + +- what is accepted as input +- what is returned as output +- which fields must remain stable +- which compatibility guarantees must not regress + +For the current issue, the main guarantees to lock down are: + +- a Chinese table name must not be sanitized into an empty string +- Chinese column names must not disappear at the boundary layer +- the `table_name` returned to the frontend must remain traceable to the actual stored name diff --git a/tests/backend/contract/test_same_basename_upload.py b/tests/backend/contract/test_same_basename_upload.py new file mode 100644 index 00000000..5e7f88c7 --- /dev/null +++ b/tests/backend/contract/test_same_basename_upload.py @@ -0,0 +1,269 @@ +"""Contract / integration tests for uploading files with the same base name +but different extensions (e.g. data.csv + data.xlsx). + +Covers two known issues: + +1. **Orphan-cleanup ID mismatch** (frontend contract): + The frontend preview creates table IDs from the raw filename (e.g. "数据.csv"), + but after upload the workspace returns a sanitized name (e.g. "数据_csv"). + The orphan-cleanup in handleFileLoadAllTables compares these two sets, + and a mismatch causes it to wrongly delete the first table on re-upload. + +2. **Backend coexistence**: + Two files with the same basename but different extensions must produce + distinct workspace tables and not overwrite each other. +""" +from __future__ import annotations + +import io +import shutil +from unittest.mock import patch + +import pandas as pd +import pytest +from flask import Flask + +from data_formulator.datalake.workspace import Workspace +from data_formulator.datalake.parquet_utils import ( + sanitize_table_name as parquet_sanitize_table_name, +) +from data_formulator.tables_routes import tables_bp + +pytestmark = [pytest.mark.backend, pytest.mark.contract] + + +# ── helpers ────────────────────────────────────────────────────────── + +@pytest.fixture() +def tmp_workspace(tmp_path): + ws = Workspace("test-user", root_dir=tmp_path) + yield ws + shutil.rmtree(tmp_path, ignore_errors=True) + + +@pytest.fixture() +def client(tmp_workspace): + app = Flask(__name__) + app.config["TESTING"] = True + app.register_blueprint(tables_bp) + with patch("data_formulator.tables_routes._get_workspace", return_value=tmp_workspace): + with app.test_client() as c: + yield c + + +def _upload(client, file_bytes: bytes, filename: str, table_name: str, + replace_source: bool = False): + data = { + "file": (io.BytesIO(file_bytes), filename), + "table_name": table_name, + } + if replace_source: + data["replace_source"] = "true" + return client.post( + "/api/tables/create-table", + data=data, + content_type="multipart/form-data", + ) + + +CSV_CONTENT = "姓名,年龄\n张三,25\n李四,30\n" + + +def _make_excel_bytes(rows: list[dict]) -> bytes: + buf = io.BytesIO() + pd.DataFrame(rows).to_excel(buf, index=False, engine="openpyxl") + return buf.getvalue() + + +EXCEL_CONTENT = _make_excel_bytes([{"姓名": "王五", "年龄": 28}]) + + +# ═══════════════════════════════════════════════════════════════════════ +# 1. Contract: preview ID ≠ workspace ID — the root cause of orphan bug +# ═══════════════════════════════════════════════════════════════════════ + +class TestPreviewIdVsWorkspaceIdMismatch: + """Demonstrate that the frontend preview ID (raw filename) does NOT match + the sanitized workspace table name returned by the backend. + + This mismatch is harmless on first upload but causes the orphan-cleanup + to wrongly remove tables on re-upload. + """ + + @pytest.mark.parametrize("preview_id, expected_ws_name", [ + ("数据.csv", "数据_csv"), + ("数据.xlsx", "数据_xlsx"), + ("数据.xlsx-Sheet1", "数据_xlsx_sheet1"), + ("sales report.csv", "sales_report_csv"), + ]) + def test_preview_id_differs_from_workspace_name( + self, preview_id: str, expected_ws_name: str, + ) -> None: + """The frontend sends preview_id as table_name. + parquet_sanitize_table_name converts it to a different string. + Any comparison between the two will fail.""" + ws_name = parquet_sanitize_table_name(preview_id) + assert ws_name == expected_ws_name + assert ws_name != preview_id, ( + "If these are equal the orphan bug wouldn't trigger, " + "but currently they always differ." + ) + + def test_orphan_cleanup_would_wrongly_remove_on_reupload( + self, client, tmp_workspace, + ) -> None: + """Simulate the re-upload scenario that triggers the orphan bug. + + 1. First upload: 数据.csv → workspace table name "数据_csv" + 2. Re-upload: frontend orphan-cleanup checks + newTableIds = {"数据.csv"} (preview IDs) + existing table id = "数据_csv" (workspace name) + "数据_csv" NOT in {"数据.csv"} → WRONGLY marked as orphan + """ + csv_bytes = CSV_CONTENT.encode("utf-8") + + # -- first upload -- + resp = _upload(client, csv_bytes, "数据.csv", "数据.csv") + ws_name = resp.get_json()["table_name"] + assert ws_name == "数据_csv" + + # -- simulate the frontend orphan-cleanup check -- + # On re-upload, the preview still uses raw filename as ID + new_preview_ids = {"数据.csv"} + existing_ws_id = ws_name # "数据_csv" + + would_be_removed = existing_ws_id not in new_preview_ids + assert would_be_removed is True, ( + "BUG: the existing table would be wrongly removed because " + f"'{existing_ws_id}' is not in preview IDs {new_preview_ids}" + ) + + +# ═══════════════════════════════════════════════════════════════════════ +# 2. Backend: same basename, different extensions → both must coexist +# ═══════════════════════════════════════════════════════════════════════ + +class TestSameBasenameDifferentExtension: + """Upload 数据.csv and 数据.xlsx — both must exist as separate tables.""" + + def test_both_tables_exist_after_upload( + self, client, tmp_workspace, + ) -> None: + """Upload two CSV files with names that mimic CSV + Excel scenario. + Both must produce distinct workspace tables.""" + csv_bytes = CSV_CONTENT.encode("utf-8") + csv2_bytes = "姓名,年龄\n王五,28\n".encode("utf-8") + + resp1 = _upload(client, csv_bytes, "数据.csv", "数据.csv") + assert resp1.get_json()["status"] == "success" + name1 = resp1.get_json()["table_name"] + + # Simulate the Excel sheet upload: different table_name, different file + resp2 = _upload(client, csv2_bytes, "数据_sheet1.csv", "数据.xlsx-Sheet1") + assert resp2.get_json()["status"] == "success" + name2 = resp2.get_json()["table_name"] + + assert name1 != name2, ( + f"Table names must differ: got {name1} and {name2}" + ) + + tables = tmp_workspace.list_tables() + assert name1 in tables + assert name2 in tables + + def test_both_readable_after_upload( + self, client, tmp_workspace, + ) -> None: + csv_bytes = CSV_CONTENT.encode("utf-8") + csv2_bytes = "姓名,年龄\n王五,28\n".encode("utf-8") + + resp1 = _upload(client, csv_bytes, "数据.csv", "数据.csv") + name1 = resp1.get_json()["table_name"] + + resp2 = _upload(client, csv2_bytes, "数据_sheet1.csv", "数据.xlsx-Sheet1") + name2 = resp2.get_json()["table_name"] + + df1 = tmp_workspace.read_data_as_df(name1) + df2 = tmp_workspace.read_data_as_df(name2) + assert list(df1.columns) == ["姓名", "年龄"] + assert list(df2.columns) == ["姓名", "年龄"] + assert df1.iloc[0]["姓名"] == "张三" + assert df2.iloc[0]["姓名"] == "王五" + + def test_reupload_csv_does_not_destroy_other_table( + self, client, tmp_workspace, + ) -> None: + """Re-uploading one file should not affect another file's table.""" + csv_v1 = CSV_CONTENT.encode("utf-8") + csv_v2 = "姓名,年龄\n赵六,35\n".encode("utf-8") + other_csv = "姓名,年龄\n王五,28\n".encode("utf-8") + + _upload(client, csv_v1, "数据.csv", "数据.csv") + _upload(client, other_csv, "数据_sheet1.csv", "数据.xlsx-Sheet1") + + # Re-upload first CSV with new data + resp = _upload(client, csv_v2, "数据.csv", "数据.csv") + assert resp.get_json()["status"] == "success" + + tables = tmp_workspace.list_tables() + csv_name = resp.get_json()["table_name"] + assert csv_name in tables + assert "数据_xlsx_sheet1" in tables, ( + "Other table must survive the first file's re-upload" + ) + + def test_replace_source_only_affects_same_source_file( + self, client, tmp_workspace, + ) -> None: + """replace_source on one file should only remove tables from that + file, not tables from a different source file.""" + csv_bytes = CSV_CONTENT.encode("utf-8") + other_csv = "姓名,年龄\n王五,28\n".encode("utf-8") + + _upload(client, csv_bytes, "数据.csv", "数据.csv") + _upload(client, other_csv, "数据_sheet1.csv", "数据.xlsx-Sheet1") + + # Re-upload with replace_source=true — should only touch 数据.csv tables + _upload(client, csv_bytes, "数据.csv", "数据.csv", replace_source=True) + + tables = tmp_workspace.list_tables() + assert "数据_csv" in tables + assert "数据_xlsx_sheet1" in tables, ( + "replace_source for 数据.csv must not remove tables from other source files" + ) + + +# ═══════════════════════════════════════════════════════════════════════ +# 3. filePreviewFiles / filePreviewTables array mismatch (contract test) +# ═══════════════════════════════════════════════════════════════════════ + +class TestFileArrayMismatch: + """Document the array-index mismatch that happens when an Excel file + has multiple sheets while being co-uploaded with other files. + + filePreviewFiles: [csv_file, xlsx_file] — length 2 + filePreviewTables: [csv_table, sheet1, sheet2] — length 3 + + The fallback `filePreviewFiles[i] || filePreviewFiles[0]` at i=2 + sends the WRONG file (csv instead of xlsx) for sheet2. + """ + + def test_index_fallback_sends_wrong_file(self) -> None: + """Pure logic test: demonstrate the fallback picks the wrong file.""" + # Simulated arrays matching the frontend structure + files = ["数据.csv", "数据.xlsx"] # 2 files + tables = ["数据.csv", "数据.xlsx-Sheet1", "数据.xlsx-Sheet2"] # 3 tables + + for i, table_id in enumerate(tables): + # This mirrors: filePreviewFiles[i]?.name || filePreviewFiles[0]?.name + file_name = files[i] if i < len(files) else files[0] + + if i == 2: + assert file_name == "数据.csv", ( + "BUG: table '数据.xlsx-Sheet2' gets the CSV file " + "because filePreviewFiles[2] is out of bounds and " + "falls back to filePreviewFiles[0]" + ) + assert file_name != "数据.xlsx", ( + "The correct file should be 数据.xlsx but we got 数据.csv" + ) diff --git a/tests/backend/contract/test_table_name_contracts.py b/tests/backend/contract/test_table_name_contracts.py new file mode 100644 index 00000000..ad8eab09 --- /dev/null +++ b/tests/backend/contract/test_table_name_contracts.py @@ -0,0 +1,23 @@ +from __future__ import annotations + +import pytest + +from data_formulator.datalake.parquet_utils import sanitize_table_name as parquet_sanitize +from data_formulator.tables_routes import sanitize_table_name as route_sanitize + + +pytestmark = [pytest.mark.backend, pytest.mark.contract] + + +def test_route_sanitize_should_not_turn_pure_chinese_name_into_placeholder() -> None: + sanitized = route_sanitize("订单明细") + assert sanitized not in {"", "_unnamed", "unnamed", "table"} + assert "订单" in sanitized + + +def test_route_sanitize_should_keep_unicode_and_apply_safe_prefix_if_needed() -> None: + assert route_sanitize("2024销售订单") == "table_2024销售订单" + + +def test_route_sanitize_should_delegate_to_parquet_sanitizer_for_ascii_name() -> None: + assert route_sanitize("Sales_Report") == parquet_sanitize("Sales_Report") diff --git a/tests/backend/fixtures/README.md b/tests/backend/fixtures/README.md new file mode 100644 index 00000000..968bcf67 --- /dev/null +++ b/tests/backend/fixtures/README.md @@ -0,0 +1,17 @@ +# Backend Fixtures + +This directory is for backend test sample data. + +Suggested future additions: + +- `excel/` + - hand-crafted Excel samples for upload and parsing regression tests +- `json/` + - samples with Chinese table names + - samples with Chinese column names +- `csv/` + - CSV files with Chinese headers +- `expected/` + - expected output notes or golden files + +Current contents may include hand-crafted regression samples such as `excel/test_cn.xls`. diff --git a/tests/backend/fixtures/excel/test_cn.xls b/tests/backend/fixtures/excel/test_cn.xls new file mode 100644 index 00000000..331753b6 Binary files /dev/null and b/tests/backend/fixtures/excel/test_cn.xls differ diff --git a/tests/backend/integration/README.md b/tests/backend/integration/README.md new file mode 100644 index 00000000..f1d66552 --- /dev/null +++ b/tests/backend/integration/README.md @@ -0,0 +1,30 @@ +# Backend Integration Tests + +This directory contains backend integration tests. + +Good candidates for this layer: + +- Flask route tests +- table create / ingest / refresh flows +- real workspace and datalake interactions +- sandbox execution (local and Docker) + +Data loader tests (MySQL, MongoDB, PostgreSQL, BigQuery) live in +`tests/plugin/` — see that directory's README for setup instructions. + +## Running + +```bash +# All integration tests +pytest tests/backend/integration/ -v + +# Sandbox tests only +pytest tests/backend/integration/test_sandbox.py -v +``` + +# Start + run all loader tests in one shot +./tests/run_test_dbs.sh test + +# Tear down +./tests/run_test_dbs.sh stop +``` diff --git a/tests/backend/integration/test_auth_info_endpoint.py b/tests/backend/integration/test_auth_info_endpoint.py new file mode 100644 index 00000000..47dee8ef --- /dev/null +++ b/tests/backend/integration/test_auth_info_endpoint.py @@ -0,0 +1,90 @@ +"""Integration tests for the ``/api/auth/info`` endpoint. + +Background +---------- +The ``/api/auth/info`` endpoint delegates to the active provider's +``get_auth_info()`` method, letting the frontend discover how to +initiate the login flow without hard-coding provider details. +""" +from __future__ import annotations + +import flask +import pytest + +import data_formulator.security.auth as auth_module +from data_formulator.auth_providers.azure_easyauth import AzureEasyAuthProvider +from data_formulator.auth_providers.github_oauth import GitHubOAuthProvider +from data_formulator.auth_providers.oidc import OIDCProvider + +pytestmark = [pytest.mark.backend, pytest.mark.auth] + + +@pytest.fixture +def app(): + """Minimal Flask app with the /api/auth/info route registered.""" + _app = flask.Flask(__name__) + _app.config["TESTING"] = True + + @_app.route("/api/auth/info") + def auth_info(): + provider = auth_module.get_active_provider() + if provider: + return flask.jsonify(provider.get_auth_info()) + return flask.jsonify({"action": "none"}) + + return _app + + +@pytest.fixture +def client(app): + return app.test_client() + + +@pytest.fixture(autouse=True) +def _reset_auth(monkeypatch): + monkeypatch.setattr(auth_module, "_provider", None) + monkeypatch.setattr(auth_module, "_allow_anonymous", True) + + +# ------------------------------------------------------------------ +# Tests +# ------------------------------------------------------------------ + +class TestAuthInfoEndpoint: + + def test_anonymous_mode_returns_none_action(self, client): + resp = client.get("/api/auth/info") + assert resp.status_code == 200 + data = resp.get_json() + assert data["action"] == "none" + + def test_oidc_provider_returns_frontend_action(self, client, monkeypatch): + monkeypatch.setenv("OIDC_ISSUER_URL", "https://idp.example.com") + monkeypatch.setenv("OIDC_CLIENT_ID", "my-client") + provider = OIDCProvider() + monkeypatch.setattr(auth_module, "_provider", provider) + + resp = client.get("/api/auth/info") + data = resp.get_json() + assert data["action"] == "frontend" + assert data["oidc"]["authority"] == "https://idp.example.com" + assert data["oidc"]["clientId"] == "my-client" + + def test_github_provider_returns_redirect_action(self, client, monkeypatch): + monkeypatch.setenv("GITHUB_CLIENT_ID", "gh-id") + monkeypatch.setenv("GITHUB_CLIENT_SECRET", "gh-secret") + provider = GitHubOAuthProvider() + monkeypatch.setattr(auth_module, "_provider", provider) + + resp = client.get("/api/auth/info") + data = resp.get_json() + assert data["action"] == "redirect" + assert data["url"] == "/api/auth/github/login" + + def test_azure_provider_returns_transparent_action(self, client, monkeypatch): + provider = AzureEasyAuthProvider() + monkeypatch.setattr(auth_module, "_provider", provider) + + resp = client.get("/api/auth/info") + data = resp.get_json() + assert data["action"] == "transparent" diff --git a/tests/backend/integration/test_create_table_replace_source.py b/tests/backend/integration/test_create_table_replace_source.py new file mode 100644 index 00000000..30587f10 --- /dev/null +++ b/tests/backend/integration/test_create_table_replace_source.py @@ -0,0 +1,112 @@ +"""Integration tests for create-table with replace_source (P3+P4). + +P3: re-uploading the same file should overwrite, not append _1 / _2. +P4: "Load All" with replace_source=true should remove orphaned sheets. +""" +from __future__ import annotations + +import io +import shutil +from pathlib import Path +from unittest.mock import patch + +import pandas as pd +import pytest +from flask import Flask + +from data_formulator.datalake.workspace import Workspace +from data_formulator.tables_routes import tables_bp + +pytestmark = [pytest.mark.backend] + +FIXTURE_DIR = Path(__file__).resolve().parents[1] / "fixtures" / "excel" + + +@pytest.fixture() +def tmp_workspace(tmp_path): + ws = Workspace("test-user", root_dir=tmp_path) + yield ws + shutil.rmtree(tmp_path, ignore_errors=True) + + +@pytest.fixture() +def client(tmp_workspace): + app = Flask(__name__) + app.config["TESTING"] = True + app.register_blueprint(tables_bp) + with patch("data_formulator.tables_routes._get_workspace", return_value=tmp_workspace): + with app.test_client() as c: + yield c + + +def _upload(client, file_bytes: bytes, filename: str, table_name: str, + replace_source: bool = False): + data = { + "file": (io.BytesIO(file_bytes), filename), + "table_name": table_name, + } + if replace_source: + data["replace_source"] = "true" + return client.post( + "/api/tables/create-table", + data=data, + content_type="multipart/form-data", + ) + + +def _make_csv(rows: list[dict]) -> bytes: + return pd.DataFrame(rows).to_csv(index=False).encode() + + +# ── P3: overwrite semantics ────────────────────────────────────────── + +def test_overwrite_same_table_name(client, tmp_workspace) -> None: + """Uploading the same table_name twice should overwrite, not create _1.""" + csv_v1 = _make_csv([{"a": 1}]) + csv_v2 = _make_csv([{"a": 1}, {"a": 2}]) + + resp1 = _upload(client, csv_v1, "data.csv", "my_table") + assert resp1.get_json()["status"] == "success" + + resp2 = _upload(client, csv_v2, "data.csv", "my_table") + assert resp2.get_json()["status"] == "success" + assert resp2.get_json()["table_name"] == "my_table" + + tables = tmp_workspace.list_tables() + assert "my_table" in tables + assert "my_table_1" not in tables + + +# ── P3: replace_source cleans old tables ───────────────────────────── + +def test_replace_source_removes_old_tables(client, tmp_workspace) -> None: + """replace_source=true should remove all tables from the same source file.""" + csv = _make_csv([{"x": 1}]) + + _upload(client, csv, "report.csv", "report_sheet1") + _upload(client, csv, "report.csv", "report_sheet2") + assert "report_sheet1" in tmp_workspace.list_tables() + assert "report_sheet2" in tmp_workspace.list_tables() + + _upload(client, csv, "report.csv", "report_new", replace_source=True) + tables = tmp_workspace.list_tables() + assert "report_new" in tables + assert "report_sheet1" not in tables + assert "report_sheet2" not in tables + + +# ── P4: sheet count changes ────────────────────────────────────────── + +def test_fewer_sheets_after_replace_no_orphans(client, tmp_workspace) -> None: + """First upload 2 sheets, then replace_source with 1 → old sheet2 gone.""" + csv = _make_csv([{"v": 42}]) + + _upload(client, csv, "业绩.xlsx", "业绩_xlsx_sheet1") + _upload(client, csv, "业绩.xlsx", "业绩_xlsx_sheet2") + assert len(tmp_workspace.list_tables()) == 2 + + _upload(client, csv, "业绩.xlsx", "业绩_xlsx_sheet1", replace_source=True) + tables = tmp_workspace.list_tables() + assert "业绩_xlsx_sheet1" in tables + assert "业绩_xlsx_sheet2" not in tables + assert len(tables) == 1 diff --git a/tests/backend/integration/test_create_table_xls_upload.py b/tests/backend/integration/test_create_table_xls_upload.py new file mode 100644 index 00000000..3f4bfc48 --- /dev/null +++ b/tests/backend/integration/test_create_table_xls_upload.py @@ -0,0 +1,166 @@ +"""Integration test for the full .xls upload flow via /api/tables/create-table. + +Exercises the real chain: Flask request -> create_table route -> save_uploaded_file +-> workspace parquet storage, using the test_cn.xls fixture. +""" +from __future__ import annotations + +import shutil +import tempfile +from pathlib import Path +from unittest.mock import patch + +import pandas as pd +import pytest +from flask import Flask + +from data_formulator.datalake.workspace import Workspace +from data_formulator.tables_routes import tables_bp + +pytestmark = [pytest.mark.backend] + +FIXTURE_DIR = Path(__file__).resolve().parents[1] / "fixtures" / "excel" + + +@pytest.fixture() +def tmp_workspace(tmp_path): + ws = Workspace("test-user", root_dir=tmp_path) + yield ws + shutil.rmtree(tmp_path, ignore_errors=True) + + +@pytest.fixture() +def client(tmp_workspace): + app = Flask(__name__) + app.config["TESTING"] = True + app.register_blueprint(tables_bp) + + with patch("data_formulator.tables_routes._get_workspace", return_value=tmp_workspace): + with app.test_client() as c: + yield c + + +def test_upload_xls_creates_table_and_returns_columns(client, tmp_workspace): + """POST a real .xls file -> create-table should succeed and store it.""" + xls_path = FIXTURE_DIR / "test_cn.xls" + if not xls_path.exists(): + pytest.skip("test_cn.xls fixture not found") + + with open(xls_path, "rb") as f: + resp = client.post( + "/api/tables/create-table", + data={ + "file": (f, "test_cn.xls"), + "table_name": "测试中文表", + }, + content_type="multipart/form-data", + ) + + assert resp.status_code == 200, resp.get_json() + data = resp.get_json() + assert data["status"] == "success" + assert data["row_count"] > 0 + assert len(data["columns"]) > 0 + assert data["table_name"] + + df = tmp_workspace.read_data_as_df(data["table_name"]) + assert len(df) == data["row_count"] + + +def test_upload_xls_preserves_chinese_column_names(client, tmp_workspace): + """Chinese column headers in .xls should survive the full round-trip.""" + xls_path = FIXTURE_DIR / "test_cn.xls" + if not xls_path.exists(): + pytest.skip("test_cn.xls fixture not found") + + with open(xls_path, "rb") as f: + resp = client.post( + "/api/tables/create-table", + data={ + "file": (f, "test_cn.xls"), + "table_name": "列名保留测试", + }, + content_type="multipart/form-data", + ) + + data = resp.get_json() + assert data["status"] == "success" + + original_df = pd.read_excel(xls_path) + original_columns = set(original_df.columns) + returned_columns = set(data["columns"]) + assert original_columns == returned_columns, ( + f"Column mismatch: expected {original_columns}, got {returned_columns}" + ) + + +def test_upload_xls_table_name_sanitized_for_unicode(client, tmp_workspace): + """A pure-Chinese table_name should not become empty after sanitization.""" + xls_path = FIXTURE_DIR / "test_cn.xls" + if not xls_path.exists(): + pytest.skip("test_cn.xls fixture not found") + + with open(xls_path, "rb") as f: + resp = client.post( + "/api/tables/create-table", + data={ + "file": (f, "test_cn.xls"), + "table_name": "订单明细", + }, + content_type="multipart/form-data", + ) + + data = resp.get_json() + assert data["status"] == "success" + assert len(data["table_name"]) > 0 + assert "订单" in data["table_name"] or "table" not in data["table_name"] + + +def test_upload_xls_rejects_missing_table_name(client): + """Omitting table_name should return 400.""" + xls_path = FIXTURE_DIR / "test_cn.xls" + if not xls_path.exists(): + pytest.skip("test_cn.xls fixture not found") + + with open(xls_path, "rb") as f: + resp = client.post( + "/api/tables/create-table", + data={"file": (f, "test_cn.xls")}, + content_type="multipart/form-data", + ) + + assert resp.status_code == 400 + assert resp.get_json()["status"] == "error" + + +def test_list_tables_returns_sample_rows_for_xls(client, tmp_workspace): + """After uploading .xls, list-tables must return non-empty sample_rows and correct row_count.""" + xls_path = FIXTURE_DIR / "test_cn.xls" + if not xls_path.exists(): + pytest.skip("test_cn.xls fixture not found") + + with open(xls_path, "rb") as f: + create_resp = client.post( + "/api/tables/create-table", + data={ + "file": (f, "test_cn.xls"), + "table_name": "list_tables_test", + }, + content_type="multipart/form-data", + ) + create_data = create_resp.get_json() + assert create_data["status"] == "success" + + list_resp = client.get("/api/tables/list-tables") + assert list_resp.status_code == 200 + list_data = list_resp.get_json() + assert list_data["status"] == "success" + + table_entry = next( + (t for t in list_data["tables"] if t["name"] == create_data["table_name"]), + None, + ) + assert table_entry is not None, f"Table {create_data['table_name']} not found in list-tables" + assert table_entry["row_count"] > 0, "row_count should be > 0 for uploaded .xls" + assert len(table_entry["sample_rows"]) > 0, "sample_rows should not be empty for uploaded .xls" + assert len(table_entry["columns"]) > 0, "columns should not be empty for uploaded .xls" diff --git a/tests/backend/integration/test_credential_routes.py b/tests/backend/integration/test_credential_routes.py new file mode 100644 index 00000000..9e3b1f39 --- /dev/null +++ b/tests/backend/integration/test_credential_routes.py @@ -0,0 +1,165 @@ +"""Integration tests for the credential management API endpoints. + +Verifies: +- POST /api/credentials/store → stores credential in Vault +- GET /api/credentials/list → returns source_key list (no secrets) +- POST /api/credentials/delete → removes credential +- Vault not configured → /store and /delete return 503, /list returns empty +- User isolation (different X-Identity-Id headers) +""" +from __future__ import annotations + +import flask +import pytest +from cryptography.fernet import Fernet +from unittest.mock import patch + +pytestmark = [pytest.mark.backend, pytest.mark.vault] + + +@pytest.fixture +def vault(tmp_path): + from data_formulator.credential_vault.local_vault import LocalCredentialVault + + key = Fernet.generate_key().decode() + return LocalCredentialVault(tmp_path / "test_creds.db", key) + + +@pytest.fixture +def app_with_vault(vault): + """Flask app with credential routes and a real Vault.""" + _app = flask.Flask(__name__) + _app.config["TESTING"] = True + _app.secret_key = "test-secret" + + from data_formulator.credential_routes import credential_bp + _app.register_blueprint(credential_bp) + + with patch("data_formulator.credential_routes.get_credential_vault", return_value=vault), \ + patch("data_formulator.credential_routes.get_identity_id") as mock_id: + mock_id.return_value = "user:alice" + yield _app, mock_id + + +@pytest.fixture +def app_no_vault(): + """Flask app with credential routes but no Vault configured.""" + _app = flask.Flask(__name__) + _app.config["TESTING"] = True + _app.secret_key = "test-secret" + + from data_formulator.credential_routes import credential_bp + _app.register_blueprint(credential_bp) + + with patch("data_formulator.credential_routes.get_credential_vault", return_value=None), \ + patch("data_formulator.credential_routes.get_identity_id", return_value="user:alice"): + yield _app + + +class TestStoreEndpoint: + + def test_store_success(self, app_with_vault): + app, _ = app_with_vault + with app.test_client() as c: + resp = c.post("/api/credentials/store", json={ + "source_key": "superset", + "credentials": {"username": "alice", "password": "pw"}, + }) + assert resp.status_code == 200 + data = resp.get_json() + assert data["status"] == "stored" + assert data["source_key"] == "superset" + + def test_store_missing_fields(self, app_with_vault): + app, _ = app_with_vault + with app.test_client() as c: + resp = c.post("/api/credentials/store", json={"source_key": "superset"}) + assert resp.status_code == 400 + + def test_store_no_vault_returns_503(self, app_no_vault): + with app_no_vault.test_client() as c: + resp = c.post("/api/credentials/store", json={ + "source_key": "superset", + "credentials": {"username": "alice", "password": "pw"}, + }) + assert resp.status_code == 503 + + +class TestListEndpoint: + + def test_list_empty(self, app_with_vault): + app, _ = app_with_vault + with app.test_client() as c: + resp = c.get("/api/credentials/list") + assert resp.status_code == 200 + assert resp.get_json()["sources"] == [] + + def test_list_after_store(self, app_with_vault, vault): + app, _ = app_with_vault + vault.store("user:alice", "superset", {"pw": "x"}) + vault.store("user:alice", "metabase", {"pw": "y"}) + with app.test_client() as c: + resp = c.get("/api/credentials/list") + sources = resp.get_json()["sources"] + assert set(sources) == {"superset", "metabase"} + + def test_list_no_vault_returns_empty(self, app_no_vault): + with app_no_vault.test_client() as c: + resp = c.get("/api/credentials/list") + assert resp.status_code == 200 + assert resp.get_json()["sources"] == [] + + +class TestDeleteEndpoint: + + def test_delete_success(self, app_with_vault, vault): + app, _ = app_with_vault + vault.store("user:alice", "superset", {"pw": "x"}) + with app.test_client() as c: + resp = c.post("/api/credentials/delete", json={"source_key": "superset"}) + assert resp.status_code == 200 + assert resp.get_json()["status"] == "deleted" + assert vault.retrieve("user:alice", "superset") is None + + def test_delete_missing_key(self, app_with_vault): + app, _ = app_with_vault + with app.test_client() as c: + resp = c.post("/api/credentials/delete", json={}) + assert resp.status_code == 400 + + def test_delete_no_vault_returns_503(self, app_no_vault): + with app_no_vault.test_client() as c: + resp = c.post("/api/credentials/delete", json={"source_key": "superset"}) + assert resp.status_code == 503 + + +class TestUserIsolation: + + def test_different_users_see_own_credentials(self, app_with_vault, vault): + app, mock_id = app_with_vault + + vault.store("user:alice", "superset", {"pw": "alice"}) + vault.store("user:bob", "superset", {"pw": "bob"}) + + with app.test_client() as c: + mock_id.return_value = "user:alice" + resp = c.get("/api/credentials/list") + assert "superset" in resp.get_json()["sources"] + + with app.test_client() as c: + mock_id.return_value = "user:bob" + resp = c.get("/api/credentials/list") + assert "superset" in resp.get_json()["sources"] + + def test_delete_only_affects_own(self, app_with_vault, vault): + app, mock_id = app_with_vault + + vault.store("user:alice", "superset", {"pw": "alice"}) + vault.store("user:bob", "superset", {"pw": "bob"}) + + with app.test_client() as c: + mock_id.return_value = "user:alice" + c.post("/api/credentials/delete", json={"source_key": "superset"}) + + assert vault.retrieve("user:alice", "superset") is None + assert vault.retrieve("user:bob", "superset") == {"pw": "bob"} diff --git a/tests/backend/integration/test_csv_encoding_roundtrip.py b/tests/backend/integration/test_csv_encoding_roundtrip.py new file mode 100644 index 00000000..ecfddb6d --- /dev/null +++ b/tests/backend/integration/test_csv_encoding_roundtrip.py @@ -0,0 +1,151 @@ +"""Integration tests: uploading a non-UTF-8 CSV should not produce garbled data. + +These tests exercise the real user flow — upload a CSV file (possibly GBK), +have it saved to the workspace, and verify the data read back through +pd.read_csv / the API contains the correct Chinese characters. +""" +from __future__ import annotations + +import io +import shutil +from unittest.mock import patch + +import pandas as pd +import pytest +from flask import Flask + +from data_formulator.datalake.workspace import Workspace +from data_formulator.tables_routes import tables_bp + +pytestmark = [pytest.mark.backend] + +CHINESE_CSV_TEXT = "姓名,年龄,部门\n张三,25,技术部\n李四,30,市场部\n王五,28,财务部\n" + + +@pytest.fixture() +def tmp_workspace(tmp_path): + ws = Workspace("test-user", root_dir=tmp_path) + yield ws + shutil.rmtree(tmp_path, ignore_errors=True) + + +@pytest.fixture() +def client(tmp_workspace): + app = Flask(__name__) + app.config["TESTING"] = True + app.register_blueprint(tables_bp) + with patch("data_formulator.tables_routes._get_workspace", return_value=tmp_workspace): + with app.test_client() as c: + yield c + + +def _upload(client, file_bytes: bytes, filename: str, table_name: str): + return client.post( + "/api/tables/create-table", + data={ + "file": (io.BytesIO(file_bytes), filename), + "table_name": table_name, + }, + content_type="multipart/form-data", + ) + + +# ── Round-trip: save_uploaded_file → read_data_as_df ───────────────── + +class TestWorkspaceRoundTrip: + """Upload a GBK CSV to workspace, then read it back as a DataFrame.""" + + def test_gbk_csv_columns_are_chinese(self, tmp_workspace) -> None: + from data_formulator.datalake.file_manager import save_uploaded_file + + gbk_bytes = CHINESE_CSV_TEXT.encode("gbk") + save_uploaded_file(tmp_workspace, gbk_bytes, "销售数据.csv") + + df = tmp_workspace.read_data_as_df("销售数据") + assert list(df.columns) == ["姓名", "年龄", "部门"] + + def test_gbk_csv_cell_values_are_correct(self, tmp_workspace) -> None: + from data_formulator.datalake.file_manager import save_uploaded_file + + gbk_bytes = CHINESE_CSV_TEXT.encode("gbk") + save_uploaded_file(tmp_workspace, gbk_bytes, "销售数据.csv") + + df = tmp_workspace.read_data_as_df("销售数据") + assert df.iloc[0]["姓名"] == "张三" + assert df.iloc[1]["部门"] == "市场部" + assert df.iloc[2]["年龄"] == 28 + + def test_utf8_csv_still_works(self, tmp_workspace) -> None: + from data_formulator.datalake.file_manager import save_uploaded_file + + utf8_bytes = CHINESE_CSV_TEXT.encode("utf-8") + save_uploaded_file(tmp_workspace, utf8_bytes, "utf8数据.csv") + + df = tmp_workspace.read_data_as_df("utf8数据") + assert list(df.columns) == ["姓名", "年龄", "部门"] + assert df.iloc[0]["姓名"] == "张三" + + def test_utf8_bom_csv_columns_correct(self, tmp_workspace) -> None: + from data_formulator.datalake.file_manager import save_uploaded_file + + bom_bytes = b"\xef\xbb\xbf" + CHINESE_CSV_TEXT.encode("utf-8") + save_uploaded_file(tmp_workspace, bom_bytes, "bom数据.csv") + + df = tmp_workspace.read_data_as_df("bom数据") + assert df.columns[0] == "姓名", ( + f"BOM should be stripped; first column is {df.columns[0]!r}" + ) + + +# ── API: parse-file endpoint with GBK CSV ──────────────────────────── + +class TestParseFileEndpoint: + """POST a GBK-encoded CSV to /api/tables/parse-file and check the response.""" + + def test_gbk_csv_parse_returns_chinese_columns(self, client) -> None: + gbk_bytes = CHINESE_CSV_TEXT.encode("gbk") + resp = client.post( + "/api/tables/parse-file", + data={"file": (io.BytesIO(gbk_bytes), "report.csv")}, + content_type="multipart/form-data", + ) + assert resp.status_code == 200 + data = resp.get_json() + assert data["status"] == "success" + sheet = data["sheets"][0] + assert sheet["columns"] == ["姓名", "年龄", "部门"] + + def test_gbk_csv_parse_returns_correct_rows(self, client) -> None: + gbk_bytes = CHINESE_CSV_TEXT.encode("gbk") + resp = client.post( + "/api/tables/parse-file", + data={"file": (io.BytesIO(gbk_bytes), "report.csv")}, + content_type="multipart/form-data", + ) + rows = resp.get_json()["sheets"][0]["data"] + assert rows[0]["姓名"] == "张三" + assert rows[1]["部门"] == "市场部" + assert rows[2]["年龄"] == 28 + + +# ── API: create-table + get-table full round-trip ──────────────────── + +class TestCreateAndGetTable: + """Upload a GBK CSV via create-table, then fetch via get-table.""" + + def test_gbk_upload_then_get_returns_chinese(self, client, tmp_workspace) -> None: + gbk_bytes = CHINESE_CSV_TEXT.encode("gbk") + resp = _upload(client, gbk_bytes, "员工.csv", "员工") + assert resp.get_json()["status"] == "success" + + get_resp = client.get("/api/tables/get-table?table_name=员工") + assert get_resp.status_code == 200 + body = get_resp.get_json() + assert body["status"] == "success" + + columns = body["columns"] + assert "姓名" in columns + assert "部门" in columns + + rows = body["rows"] + assert any(row.get("姓名") == "张三" for row in rows) diff --git a/tests/backend/integration/test_derive_data_repair_loop.py b/tests/backend/integration/test_derive_data_repair_loop.py new file mode 100644 index 00000000..e771abe6 --- /dev/null +++ b/tests/backend/integration/test_derive_data_repair_loop.py @@ -0,0 +1,384 @@ +"""Integration tests for the derive-data and refine-data repair loop improvements. + +Covers: +- Repair loop triggers on both 'error' and 'other error' statuses +- Empty results list does not crash (IndexError guard) +- Followup exceptions are caught gracefully with safe generic messages +- get-recommendation-questions never leaks exception details to the client +""" +from __future__ import annotations + +import json +import shutil +from contextlib import contextmanager +from unittest.mock import MagicMock, patch + +import pytest +from flask import Flask + +from data_formulator.agent_routes import agent_bp + +pytestmark = [pytest.mark.backend] + +MODULE = "data_formulator.agent_routes" + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +def _make_ok_result(code: str = "x = 1") -> dict: + return { + "status": "ok", + "code": code, + "content": {"rows": [], "virtual": {"table_name": "t", "row_count": 0}}, + "dialog": [{"role": "system", "content": "..."}], + "agent": "DataRecAgent", + "refined_goal": {}, + } + + +def _make_error_result(status: str = "error", content: str = "some error") -> dict: + return { + "status": status, + "code": "bad_code()", + "content": content, + "dialog": [{"role": "system", "content": "..."}], + "agent": "DataRecAgent", + "refined_goal": {}, + } + + +@contextmanager +def _mock_workspace(): + """Yield a (mock_workspace, tmp_workspace_cm) that stubs out workspace deps.""" + ws = MagicMock() + ws.list_tables.return_value = set() + + @contextmanager + def fake_temp_data(ws_inner, temp_data): + yield ws_inner + + yield ws, fake_temp_data + + +def _build_app(): + app = Flask(__name__) + app.config["TESTING"] = True + app.config["CLI_ARGS"] = {"max_display_rows": 100} + app.register_blueprint(agent_bp) + return app + + +def _derive_data_payload(**overrides) -> dict: + base = { + "token": "test-token", + "model": {"endpoint": "openai", "model": "gpt-4", "api_key": "k", "api_base": "http://x"}, + "input_tables": [{"name": "t1", "rows": [{"a": 1}]}], + "extra_prompt": "do something", + "max_repair_attempts": 1, + } + base.update(overrides) + return base + + +def _refine_data_payload(**overrides) -> dict: + base = { + "token": "test-token", + "model": {"endpoint": "openai", "model": "gpt-4", "api_key": "k", "api_base": "http://x"}, + "input_tables": [{"name": "t1", "rows": [{"a": 1}]}], + "dialog": [{"role": "system", "content": "..."}], + "new_instruction": "fix it", + "latest_data_sample": [{"a": 1}], + "max_repair_attempts": 1, + } + base.update(overrides) + return base + + +# --------------------------------------------------------------------------- +# derive-data: repair loop status matching +# --------------------------------------------------------------------------- + +class TestDeriveDataRepairLoop: + + def _post_derive(self, client, payload): + return client.post( + "/api/agent/derive-data", + data=json.dumps(payload), + content_type="application/json", + ) + + def test_repair_loop_triggers_on_other_error(self) -> None: + """'other error' status should enter the repair loop (not just 'error').""" + app = _build_app() + + mock_agent = MagicMock() + mock_agent.run.return_value = [_make_error_result(status="other error")] + mock_agent.followup.return_value = [_make_ok_result()] + + with _mock_workspace() as (ws, fake_ctx): + with ( + patch(f"{MODULE}.get_client", return_value=MagicMock()), + patch(f"{MODULE}.get_identity_id", return_value="test-user"), + patch(f"{MODULE}.get_workspace", return_value=ws), + patch(f"{MODULE}.get_language_instruction", return_value=""), + patch(f"{MODULE}.DataRecAgent", return_value=mock_agent), + patch(f"{MODULE}.sign_result"), + ): + with app.test_client() as client: + resp = self._post_derive(client, _derive_data_payload()) + + data = resp.get_json() + assert data["status"] == "ok" + assert data["results"][0]["status"] == "ok" + mock_agent.followup.assert_called_once() + + def test_repair_loop_skips_when_status_is_ok(self) -> None: + """When initial result is 'ok', repair loop should not execute.""" + app = _build_app() + + mock_agent = MagicMock() + mock_agent.run.return_value = [_make_ok_result()] + + with _mock_workspace() as (ws, fake_ctx): + with ( + patch(f"{MODULE}.get_client", return_value=MagicMock()), + patch(f"{MODULE}.get_identity_id", return_value="test-user"), + patch(f"{MODULE}.get_workspace", return_value=ws), + patch(f"{MODULE}.get_language_instruction", return_value=""), + patch(f"{MODULE}.DataRecAgent", return_value=mock_agent), + patch(f"{MODULE}.sign_result"), + ): + with app.test_client() as client: + resp = self._post_derive(client, _derive_data_payload()) + + data = resp.get_json() + assert data["results"][0]["status"] == "ok" + mock_agent.followup.assert_not_called() + + def test_empty_results_does_not_crash(self) -> None: + """If agent.run() returns an empty list, no IndexError should occur.""" + app = _build_app() + + mock_agent = MagicMock() + mock_agent.run.return_value = [] + + with _mock_workspace() as (ws, fake_ctx): + with ( + patch(f"{MODULE}.get_client", return_value=MagicMock()), + patch(f"{MODULE}.get_identity_id", return_value="test-user"), + patch(f"{MODULE}.get_workspace", return_value=ws), + patch(f"{MODULE}.get_language_instruction", return_value=""), + patch(f"{MODULE}.DataRecAgent", return_value=mock_agent), + patch(f"{MODULE}.sign_result"), + ): + with app.test_client() as client: + resp = self._post_derive(client, _derive_data_payload()) + + data = resp.get_json() + assert data["status"] == "ok" + assert data["results"] == [] + + def test_followup_exception_is_caught(self) -> None: + """If agent.followup() raises, the error should be caught and a safe + classified message returned (no raw exception text).""" + app = _build_app() + + mock_agent = MagicMock() + mock_agent.run.return_value = [_make_error_result(status="error")] + mock_agent.followup.side_effect = RuntimeError("LLM connection timeout") + + with _mock_workspace() as (ws, fake_ctx): + with ( + patch(f"{MODULE}.get_client", return_value=MagicMock()), + patch(f"{MODULE}.get_identity_id", return_value="test-user"), + patch(f"{MODULE}.get_workspace", return_value=ws), + patch(f"{MODULE}.get_language_instruction", return_value=""), + patch(f"{MODULE}.DataRecAgent", return_value=mock_agent), + patch(f"{MODULE}.sign_result"), + ): + with app.test_client() as client: + resp = self._post_derive(client, _derive_data_payload()) + + data = resp.get_json() + assert data["status"] == "ok" + result = data["results"][0] + assert result["status"] == "error" + # classify_llm_error maps "timeout" → safe timeout message + assert "timed out" in result["content"].lower() or "timeout" in result["content"].lower() + # Raw exception text must not leak + assert "LLM connection timeout" not in result["content"] + + +# --------------------------------------------------------------------------- +# refine-data: same repair loop tests +# --------------------------------------------------------------------------- + +class TestRefineDataRepairLoop: + + def _post_refine(self, client, payload): + return client.post( + "/api/agent/refine-data", + data=json.dumps(payload), + content_type="application/json", + ) + + def test_repair_loop_triggers_on_other_error(self) -> None: + app = _build_app() + + mock_agent = MagicMock() + mock_agent.followup.side_effect = [ + [_make_error_result(status="other error")], + [_make_ok_result()], + ] + + with _mock_workspace() as (ws, fake_ctx): + with ( + patch(f"{MODULE}.get_client", return_value=MagicMock()), + patch(f"{MODULE}.get_identity_id", return_value="test-user"), + patch(f"{MODULE}.get_workspace", return_value=ws), + patch(f"{MODULE}.get_language_instruction", return_value=""), + patch(f"{MODULE}.DataTransformationAgent", return_value=mock_agent), + patch(f"{MODULE}.sign_result"), + ): + with app.test_client() as client: + resp = self._post_refine(client, _refine_data_payload()) + + data = resp.get_json() + assert data["results"][0]["status"] == "ok" + assert mock_agent.followup.call_count == 2 + + def test_empty_results_does_not_crash(self) -> None: + app = _build_app() + + mock_agent = MagicMock() + mock_agent.followup.return_value = [] + + with _mock_workspace() as (ws, fake_ctx): + with ( + patch(f"{MODULE}.get_client", return_value=MagicMock()), + patch(f"{MODULE}.get_identity_id", return_value="test-user"), + patch(f"{MODULE}.get_workspace", return_value=ws), + patch(f"{MODULE}.get_language_instruction", return_value=""), + patch(f"{MODULE}.DataTransformationAgent", return_value=mock_agent), + patch(f"{MODULE}.sign_result"), + ): + with app.test_client() as client: + resp = self._post_refine(client, _refine_data_payload()) + + data = resp.get_json() + assert data["status"] == "ok" + assert data["results"] == [] + + def test_followup_exception_in_repair_is_caught(self) -> None: + """Followup exception returns a safe classified message, not raw exception text.""" + app = _build_app() + + mock_agent = MagicMock() + mock_agent.followup.side_effect = [ + [_make_error_result(status="error")], + RuntimeError("API key expired"), + ] + + with _mock_workspace() as (ws, fake_ctx): + with ( + patch(f"{MODULE}.get_client", return_value=MagicMock()), + patch(f"{MODULE}.get_identity_id", return_value="test-user"), + patch(f"{MODULE}.get_workspace", return_value=ws), + patch(f"{MODULE}.get_language_instruction", return_value=""), + patch(f"{MODULE}.DataTransformationAgent", return_value=mock_agent), + patch(f"{MODULE}.sign_result"), + ): + with app.test_client() as client: + resp = self._post_refine(client, _refine_data_payload()) + + data = resp.get_json() + result = data["results"][0] + assert result["status"] == "error" + # Raw exception text must not appear + assert "API key expired" not in result["content"] + # Should be classified as a model request failure (generic fallback) + assert result["content"] in ( + "Model request failed", + "Authentication failed — please check your API key", + ) + + +# --------------------------------------------------------------------------- +# get-recommendation-questions: error message uses classify_llm_error +# --------------------------------------------------------------------------- + +class TestGetRecommendationQuestionsError: + + def test_error_message_is_classified_not_raw(self) -> None: + """Error response uses classify_llm_error — safe pre-defined message, + not the raw exception text.""" + app = _build_app() + + mock_agent = MagicMock() + mock_agent.run.side_effect = ValueError("column 'x' not found in table") + + with _mock_workspace() as (ws, fake_ctx): + with ( + patch(f"{MODULE}.get_client", return_value=MagicMock()), + patch(f"{MODULE}.get_identity_id", return_value="test-user"), + patch(f"{MODULE}.get_workspace", return_value=ws), + patch(f"{MODULE}.get_language_instruction", return_value=""), + patch(f"{MODULE}.InteractiveExploreAgent", return_value=mock_agent), + ): + with app.test_client() as client: + resp = client.post( + "/api/agent/get-recommendation-questions", + data=json.dumps({ + "model": {"endpoint": "openai", "model": "gpt-4", + "api_key": "k", "api_base": "http://x"}, + "input_tables": [{"name": "t", "rows": []}], + }), + content_type="application/json", + ) + + lines = resp.data.decode("utf-8").strip().split("\n") + assert len(lines) >= 1 + error_line = [l for l in lines if l.startswith("error:")] + assert len(error_line) == 1 + + error_payload = json.loads(error_line[0].removeprefix("error: ")) + # Raw exception must not leak + assert "column 'x' not found" not in error_payload["content"] + # classify_llm_error returns a pre-defined safe message + assert error_payload["content"] == "Model request failed" + + def test_error_message_never_leaks_api_keys(self) -> None: + """Even when exception contains API keys, classify_llm_error returns + a safe pre-defined message without any raw exception text.""" + app = _build_app() + + mock_agent = MagicMock() + mock_agent.run.side_effect = RuntimeError("auth failed api_key=sk-secret123 for model") + + with _mock_workspace() as (ws, fake_ctx): + with ( + patch(f"{MODULE}.get_client", return_value=MagicMock()), + patch(f"{MODULE}.get_identity_id", return_value="test-user"), + patch(f"{MODULE}.get_workspace", return_value=ws), + patch(f"{MODULE}.get_language_instruction", return_value=""), + patch(f"{MODULE}.InteractiveExploreAgent", return_value=mock_agent), + ): + with app.test_client() as client: + resp = client.post( + "/api/agent/get-recommendation-questions", + data=json.dumps({ + "model": {"endpoint": "openai", "model": "gpt-4", + "api_key": "k", "api_base": "http://x"}, + "input_tables": [{"name": "t", "rows": []}], + }), + content_type="application/json", + ) + + lines = resp.data.decode("utf-8").strip().split("\n") + error_line = [l for l in lines if l.startswith("error:")] + error_payload = json.loads(error_line[0].removeprefix("error: ")) + assert "sk-secret123" not in error_payload["content"] + # "auth failed" matches the authentication pattern + assert "Authentication failed" in error_payload["content"] diff --git a/tests/backend/integration/test_excel_fixture_parsing.py b/tests/backend/integration/test_excel_fixture_parsing.py new file mode 100644 index 00000000..55627703 --- /dev/null +++ b/tests/backend/integration/test_excel_fixture_parsing.py @@ -0,0 +1,23 @@ +from __future__ import annotations + +from pathlib import Path + +import pandas as pd +import pytest + + +pytestmark = [pytest.mark.backend] + + +def test_manual_xls_fixture_can_be_parsed() -> None: + fixture_path = ( + Path(__file__).resolve().parents[1] + / "fixtures" + / "excel" + / "test_cn.xls" + ) + assert fixture_path.exists() + + df = pd.read_excel(fixture_path) + + assert not df.empty diff --git a/tests/backend/integration/test_parse_file_endpoint.py b/tests/backend/integration/test_parse_file_endpoint.py new file mode 100644 index 00000000..393db11e --- /dev/null +++ b/tests/backend/integration/test_parse_file_endpoint.py @@ -0,0 +1,78 @@ +"""Integration tests for the /api/tables/parse-file endpoint.""" +from __future__ import annotations + +import io +from pathlib import Path + +import pandas as pd +import pytest +from flask import Flask + +from data_formulator.tables_routes import tables_bp + +pytestmark = [pytest.mark.backend] + +FIXTURE_DIR = Path(__file__).resolve().parents[1] / "fixtures" / "excel" + + +@pytest.fixture() +def client(): + app = Flask(__name__) + app.config["TESTING"] = True + app.register_blueprint(tables_bp) + with app.test_client() as c: + yield c + + +def test_parse_xls_returns_sheet_data(client): + xls_path = FIXTURE_DIR / "test_cn.xls" + if not xls_path.exists(): + pytest.skip("test_cn.xls fixture not found") + + with open(xls_path, "rb") as f: + resp = client.post( + "/api/tables/parse-file", + data={"file": (f, "test_cn.xls")}, + content_type="multipart/form-data", + ) + + assert resp.status_code == 200 + data = resp.get_json() + assert data["status"] == "success" + assert len(data["sheets"]) >= 1 + + sheet = data["sheets"][0] + assert sheet["row_count"] > 0 + assert len(sheet["columns"]) > 0 + assert len(sheet["data"]) == sheet["row_count"] + + +def test_parse_file_rejects_missing_file(client): + resp = client.post("/api/tables/parse-file") + assert resp.status_code == 400 + assert resp.get_json()["status"] == "error" + + +def test_parse_file_rejects_unsupported_format(client): + fake = io.BytesIO(b"hello world") + resp = client.post( + "/api/tables/parse-file", + data={"file": (fake, "data.txt")}, + content_type="multipart/form-data", + ) + assert resp.status_code == 400 + + +def test_parse_csv_via_endpoint(client): + csv_content = "name,age\nAlice,30\nBob,25\n" + buf = io.BytesIO(csv_content.encode("utf-8")) + resp = client.post( + "/api/tables/parse-file", + data={"file": (buf, "people.csv")}, + content_type="multipart/form-data", + ) + assert resp.status_code == 200 + data = resp.get_json() + assert data["status"] == "success" + assert data["sheets"][0]["row_count"] == 2 + assert "name" in data["sheets"][0]["columns"] diff --git a/tests/backend/integration/test_plugin_app_config.py b/tests/backend/integration/test_plugin_app_config.py new file mode 100644 index 00000000..a0bad17a --- /dev/null +++ b/tests/backend/integration/test_plugin_app_config.py @@ -0,0 +1,184 @@ +"""Integration tests for plugin information in ``/api/app-config``. + +Verifies that enabled plugins appear under the ``PLUGINS`` key in the +``/api/app-config`` response, with their manifest + frontend config merged. +""" +from __future__ import annotations + +import types +from unittest.mock import patch + +import flask +import pytest + +import data_formulator.plugins as plugins_module +from data_formulator.plugins import ( + DISABLED_PLUGINS, + ENABLED_PLUGINS, + discover_and_register, +) +from data_formulator.plugins.base import DataSourcePlugin +import data_formulator.security.auth as auth_module + +pytestmark = [pytest.mark.backend, pytest.mark.plugin] + + +# ------------------------------------------------------------------ +# Stub plugin +# ------------------------------------------------------------------ + +class _DemoPlugin(DataSourcePlugin): + + @staticmethod + def manifest(): + return { + "id": "demo", + "name": "Demo Plugin", + "env_prefix": "PLG_DEMO", + "required_env": [], + "capabilities": ["datasets"], + "auth_modes": ["password"], + "icon": "demo-icon", + "description": "A demo plugin for testing", + } + + def create_blueprint(self): + return flask.Blueprint("plugin_demo", __name__, url_prefix="/api/plugins/demo/") + + def get_frontend_config(self): + return {"base_url": "http://demo.local", "login_url": "/api/plugins/demo/login"} + + +# ------------------------------------------------------------------ +# Fixtures +# ------------------------------------------------------------------ + +@pytest.fixture +def app(): + """Minimal Flask app with /api/app-config and plugin discovery.""" + _app = flask.Flask(__name__) + _app.config["TESTING"] = True + _app.config["CLI_ARGS"] = { + "sandbox": "local", + "disable_display_keys": False, + "disable_file_upload": False, + "project_front_page": False, + "max_display_rows": 10000, + "dev": False, + "workspace_backend": "ephemeral", + "available_languages": ["en"], + } + + @_app.route("/api/app-config") + def get_app_config(): + args = _app.config["CLI_ARGS"] + config = { + "SANDBOX": args["sandbox"], + "DISABLE_DISPLAY_KEYS": args["disable_display_keys"], + "DISABLE_FILE_UPLOAD": args["disable_file_upload"], + "PROJECT_FRONT_PAGE": args["project_front_page"], + "MAX_DISPLAY_ROWS": args["max_display_rows"], + "DEV_MODE": args.get("dev", False), + "WORKSPACE_BACKEND": args.get("workspace_backend", "local"), + "AVAILABLE_LANGUAGES": args.get("available_languages", ["en"]), + } + + from data_formulator.plugins import ENABLED_PLUGINS as ep + if ep: + plugins_info: dict[str, dict] = {} + for pid, plugin in ep.items(): + manifest = plugin.manifest() + frontend_cfg = plugin.get_frontend_config() + plugins_info[pid] = { + "id": manifest.get("id", pid), + "name": manifest.get("name", pid), + "icon": manifest.get("icon"), + "description": manifest.get("description"), + "capabilities": manifest.get("capabilities", []), + "auth_modes": manifest.get("auth_modes", []), + **frontend_cfg, + } + config["PLUGINS"] = plugins_info + + return flask.jsonify(config) + + return _app + + +@pytest.fixture +def client(app): + return app.test_client() + + +@pytest.fixture(autouse=True) +def _clean_plugin_state(): + ENABLED_PLUGINS.clear() + DISABLED_PLUGINS.clear() + yield + ENABLED_PLUGINS.clear() + DISABLED_PLUGINS.clear() + + +@pytest.fixture(autouse=True) +def _reset_auth(monkeypatch): + monkeypatch.setattr(auth_module, "_provider", None) + monkeypatch.setattr(auth_module, "_allow_anonymous", True) + + +# ------------------------------------------------------------------ +# Tests +# ------------------------------------------------------------------ + +class TestAppConfigPlugins: + + def test_no_plugins_key_when_none_enabled(self, client): + resp = client.get("/api/app-config") + data = resp.get_json() + assert resp.status_code == 200 + assert "PLUGINS" not in data + + def test_plugin_info_exposed_when_enabled(self, app, client): + plugin = _DemoPlugin() + ENABLED_PLUGINS["demo"] = plugin + + resp = client.get("/api/app-config") + data = resp.get_json() + + assert "PLUGINS" in data + demo = data["PLUGINS"]["demo"] + assert demo["id"] == "demo" + assert demo["name"] == "Demo Plugin" + assert demo["icon"] == "demo-icon" + assert demo["capabilities"] == ["datasets"] + assert demo["auth_modes"] == ["password"] + assert demo["base_url"] == "http://demo.local" + assert demo["login_url"] == "/api/plugins/demo/login" + + def test_multiple_plugins(self, app, client): + + class _SecondPlugin(DataSourcePlugin): + @staticmethod + def manifest(): + return { + "id": "second", + "name": "Second", + "env_prefix": "PLG_SECOND", + "required_env": [], + } + + def create_blueprint(self): + return flask.Blueprint("plugin_second", __name__, url_prefix="/api/plugins/second/") + + def get_frontend_config(self): + return {"mode": "read-only"} + + ENABLED_PLUGINS["demo"] = _DemoPlugin() + ENABLED_PLUGINS["second"] = _SecondPlugin() + + resp = client.get("/api/app-config") + data = resp.get_json() + + assert len(data["PLUGINS"]) == 2 + assert "demo" in data["PLUGINS"] + assert "second" in data["PLUGINS"] + assert data["PLUGINS"]["second"]["mode"] == "read-only" diff --git a/tests/backend/integration/test_plugin_auth_with_vault.py b/tests/backend/integration/test_plugin_auth_with_vault.py new file mode 100644 index 00000000..f3e0cf38 --- /dev/null +++ b/tests/backend/integration/test_plugin_auth_with_vault.py @@ -0,0 +1,170 @@ +"""Integration tests for plugin authentication + CredentialVault interplay. + +Verifies: +- Vault has valid credentials → plugin auto-login succeeds (mode=vault) +- Vault has stale credentials (password changed) → returns vault_stale +- User manually logs in with remember=true → credentials stored in Vault +- User manually logs in with remember=false → old Vault credentials deleted +- Vault not configured → status endpoint still works (skips Vault step) +""" +from __future__ import annotations + +from unittest.mock import MagicMock, patch + +import flask +import pytest +from cryptography.fernet import Fernet + +pytestmark = [pytest.mark.backend, pytest.mark.vault, pytest.mark.plugin] + + +@pytest.fixture +def vault(tmp_path): + from data_formulator.credential_vault.local_vault import LocalCredentialVault + + key = Fernet.generate_key().decode() + return LocalCredentialVault(tmp_path / "test_creds.db", key) + + +@pytest.fixture +def superset_app(vault): + """Flask app with Superset plugin auth routes and a mocked bridge.""" + _app = flask.Flask(__name__) + _app.config["TESTING"] = True + _app.secret_key = "test-secret" + + mock_bridge = MagicMock() + _app.extensions = {"plugin_superset_bridge": mock_bridge} + + from data_formulator.plugins.superset.routes.auth import auth_bp + _app.register_blueprint(auth_bp) + + yield _app, mock_bridge, vault + + +class TestVaultAutoLogin: + + def test_vault_credentials_valid_auto_login(self, superset_app): + """Vault has valid credentials → auth/status returns authenticated + mode=vault.""" + app, mock_bridge, vault = superset_app + + mock_bridge.login.return_value = { + "access_token": "tok_valid", + "refresh_token": "ref_valid", + } + mock_bridge.get_user_info.return_value = { + "id": 1, "username": "alice", "first_name": "Alice", "last_name": "W", + } + + vault.store("user:alice", "superset", {"username": "alice", "password": "correct_pw"}) + + with app.test_client() as c, \ + patch("data_formulator.credential_vault.get_credential_vault", return_value=vault), \ + patch("data_formulator.security.auth.get_identity_id", return_value="user:alice"): + resp = c.get("/api/plugins/superset/auth/status") + data = resp.get_json() + + assert data["authenticated"] is True + assert data.get("mode") == "vault" + + def test_vault_credentials_stale(self, superset_app): + """Vault credentials are stale (password changed) → vault_stale=true.""" + app, mock_bridge, vault = superset_app + + mock_bridge.login.side_effect = Exception("Invalid credentials") + + vault.store("user:alice", "superset", {"username": "alice", "password": "old_pw"}) + + with app.test_client() as c, \ + patch("data_formulator.credential_vault.get_credential_vault", return_value=vault), \ + patch("data_formulator.security.auth.get_identity_id", return_value="user:alice"): + resp = c.get("/api/plugins/superset/auth/status") + data = resp.get_json() + + assert data["authenticated"] is False + assert data["vault_stale"] is True + + +class TestLoginWithRemember: + + def test_remember_true_stores_in_vault(self, superset_app): + """Login with remember=true → credentials written to Vault.""" + app, mock_bridge, vault = superset_app + + mock_bridge.login.return_value = {"access_token": "tok", "refresh_token": "ref"} + mock_bridge.get_user_info.return_value = { + "id": 1, "username": "alice", "first_name": "Alice", "last_name": "W", + } + + with app.test_client() as c, \ + patch("data_formulator.credential_vault.get_credential_vault", return_value=vault), \ + patch("data_formulator.security.auth.get_identity_id", return_value="user:alice"): + resp = c.post("/api/plugins/superset/auth/login", json={ + "username": "alice", + "password": "new_pw", + "remember": True, + }) + assert resp.status_code == 200 + + stored = vault.retrieve("user:alice", "superset") + assert stored is not None + assert stored["username"] == "alice" + assert stored["password"] == "new_pw" + + def test_remember_false_deletes_vault_credential(self, superset_app): + """Login with remember=false → old Vault credential removed.""" + app, mock_bridge, vault = superset_app + + vault.store("user:alice", "superset", {"username": "alice", "password": "old"}) + + mock_bridge.login.return_value = {"access_token": "tok", "refresh_token": "ref"} + mock_bridge.get_user_info.return_value = { + "id": 1, "username": "alice", "first_name": "Alice", "last_name": "W", + } + + with app.test_client() as c, \ + patch("data_formulator.credential_vault.get_credential_vault", return_value=vault), \ + patch("data_formulator.security.auth.get_identity_id", return_value="user:alice"): + resp = c.post("/api/plugins/superset/auth/login", json={ + "username": "alice", + "password": "new_pw", + "remember": False, + }) + assert resp.status_code == 200 + + assert vault.retrieve("user:alice", "superset") is None + + +class TestVaultNotConfigured: + + def test_status_works_without_vault(self, superset_app): + """No Vault configured → status still works, no crash.""" + app, mock_bridge, _ = superset_app + + with app.test_client() as c, \ + patch("data_formulator.credential_vault.get_credential_vault", return_value=None), \ + patch("data_formulator.security.auth.get_identity_id", return_value="user:alice"): + resp = c.get("/api/plugins/superset/auth/status") + data = resp.get_json() + + assert data["authenticated"] is False + assert "vault_stale" not in data + + def test_login_remember_without_vault_still_succeeds(self, superset_app): + """Login with remember=true but no Vault → login works, no crash.""" + app, mock_bridge, _ = superset_app + + mock_bridge.login.return_value = {"access_token": "tok", "refresh_token": "ref"} + mock_bridge.get_user_info.return_value = { + "id": 1, "username": "alice", "first_name": "Alice", "last_name": "W", + } + + with app.test_client() as c, \ + patch("data_formulator.credential_vault.get_credential_vault", return_value=None), \ + patch("data_formulator.security.auth.get_identity_id", return_value="user:alice"): + resp = c.post("/api/plugins/superset/auth/login", json={ + "username": "alice", + "password": "pw", + "remember": True, + }) + assert resp.status_code == 200 diff --git a/tests/backend/integration/test_sandbox.py b/tests/backend/integration/test_sandbox.py new file mode 100644 index 00000000..b6c277b0 --- /dev/null +++ b/tests/backend/integration/test_sandbox.py @@ -0,0 +1,267 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +"""Tests for sandbox execution backends (local and Docker).""" + +import os +import shutil +import subprocess +import tempfile +from contextlib import contextmanager + +import pandas as pd +import pytest + +from data_formulator.sandbox import LocalSandbox, DockerSandbox, create_sandbox + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +class _MinimalWorkspace: + """Lightweight workspace stand-in for sandbox tests (no metadata/yaml).""" + def __init__(self, path: str): + self._path = path + + @contextmanager + def local_dir(self): + yield self._path + +SIMPLE_TRANSFORM = """\ +import pandas as pd +output_df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) +""" + +TRANSFORM_WITH_INPUT = """\ +import pandas as pd +df = pd.DataFrame({"x": [10, 20], "y": [30, 40]}) +output_df = df.assign(z=df["x"] + df["y"]) +""" + +SYNTAX_ERROR_CODE = """\ +import pandas as pd +output_df = pd.DataFrame({"a" [1, 2]}) +""" + +RUNTIME_ERROR_CODE = """\ +import pandas as pd +1 / 0 +""" + +NON_DF_OUTPUT = """\ +output_df = "I am not a DataFrame" +""" + +MULTI_TABLE_TRANSFORM = """\ +import pandas as pd, json +data = json.loads('[{"a":1},{"a":2}]') +df = pd.DataFrame.from_records(data) +output_df = df.assign(doubled=df["a"] * 2) +""" + + +@pytest.fixture +def workspace(tmp_path): + """Create a temporary workspace directory with a small CSV file.""" + csv_path = tmp_path / "sample.csv" + csv_path.write_text("name,value\nAlice,10\nBob,20\n") + return _MinimalWorkspace(str(tmp_path)) + + +def _docker_available() -> bool: + """Return True if Docker CLI is present and the daemon is reachable.""" + try: + proc = subprocess.run( + ["docker", "info"], + capture_output=True, + timeout=10, + ) + return proc.returncode == 0 + except Exception: + return False + + +skip_no_docker = pytest.mark.skipif( + not _docker_available(), + reason="Docker is not available", +) + + +# =================================================================== +# create_sandbox factory +# =================================================================== + +class TestCreateSandbox: + def test_local(self): + sb = create_sandbox("local") + assert isinstance(sb, LocalSandbox) + + def test_docker(self): + sb = create_sandbox("docker") + assert isinstance(sb, DockerSandbox) + + def test_default_is_local(self): + sb = create_sandbox() + assert isinstance(sb, LocalSandbox) + + +# =================================================================== +# LocalSandbox +# =================================================================== + +class TestLocalSandbox: + """Tests for LocalSandbox (warm subprocess with audit hooks).""" + + @pytest.fixture + def sandbox(self): + return LocalSandbox() + + def test_simple_transform(self, sandbox, workspace): + result = sandbox.run_python_code(SIMPLE_TRANSFORM, workspace, "output_df") + assert result["status"] == "ok" + df = result["content"] + assert isinstance(df, pd.DataFrame) + assert len(df) == 3 + + def test_derived_columns(self, sandbox, workspace): + result = sandbox.run_python_code(TRANSFORM_WITH_INPUT, workspace, "output_df") + assert result["status"] == "ok" + assert list(result["content"]["z"]) == [40, 60] + + def test_read_csv_from_workspace(self, sandbox, workspace): + code = """\ +import pandas as pd +output_df = pd.read_csv("sample.csv") +""" + result = sandbox.run_python_code(code, workspace, "output_df") + assert result["status"] == "ok" + assert len(result["content"]) == 2 + + def test_write_to_workdir_blocked(self, sandbox, workspace): + """Subprocess audit hook blocks all file writes, even in workdir. + (Detailed security tests in tests/backend/security/test_sandbox_security.py) + """ + code = """\ +import pandas as pd +df = pd.DataFrame({"x": [1]}) +df.to_csv("temp_output.csv", index=False) +output_df = pd.read_csv("temp_output.csv") +""" + result = sandbox.run_python_code(code, workspace, "output_df") + assert result["status"] == "error" + + def test_syntax_error(self, sandbox, workspace): + result = sandbox.run_python_code(SYNTAX_ERROR_CODE, workspace, "output_df") + assert result["status"] == "error" + + def test_runtime_error(self, sandbox, workspace): + result = sandbox.run_python_code(RUNTIME_ERROR_CODE, workspace, "output_df") + assert result["status"] == "error" + + def test_non_dataframe_output(self, sandbox, workspace): + result = sandbox.run_python_code(NON_DF_OUTPUT, workspace, "output_df") + assert result["status"] == "error" + assert "not a DataFrame" in result["content"] + + def test_duckdb_sql(self, sandbox, workspace): + """duckdb.sql() works in subprocess mode (unrestricted globals).""" + code = """\ +import pandas as pd +import duckdb +df = pd.DataFrame({"x": [1, 2, 3]}) +output_df = duckdb.sql("SELECT x, x*10 AS x10 FROM df").df() +""" + result = sandbox.run_python_code(code, workspace, "output_df") + assert result["status"] == "ok" + assert list(result["content"]["x10"]) == [10, 20, 30] + + +# =================================================================== +# DockerSandbox +# =================================================================== + +@skip_no_docker +class TestDockerSandbox: + """Tests for DockerSandbox — requires a running Docker daemon.""" + + @pytest.fixture + def sandbox(self): + return DockerSandbox(timeout=120) + + def test_simple_transform(self, sandbox, workspace): + result = sandbox.run_python_code(SIMPLE_TRANSFORM, workspace, "output_df") + assert result["status"] == "ok" + df = result["content"] + assert isinstance(df, pd.DataFrame) + assert list(df.columns) == ["a", "b"] + assert len(df) == 3 + + def test_derived_columns(self, sandbox, workspace): + result = sandbox.run_python_code(TRANSFORM_WITH_INPUT, workspace, "output_df") + assert result["status"] == "ok" + df = result["content"] + assert "z" in df.columns + assert list(df["z"]) == [40, 60] + + def test_read_csv_from_workspace(self, sandbox, workspace): + code = """\ +import pandas as pd +output_df = pd.read_csv("sample.csv") +""" + result = sandbox.run_python_code(code, workspace, "output_df") + assert result["status"] == "ok" + assert list(result["content"].columns) == ["name", "value"] + + def test_syntax_error(self, sandbox, workspace): + result = sandbox.run_python_code(SYNTAX_ERROR_CODE, workspace, "output_df") + assert result["status"] == "error" + + def test_runtime_error(self, sandbox, workspace): + result = sandbox.run_python_code(RUNTIME_ERROR_CODE, workspace, "output_df") + assert result["status"] == "error" + + def test_non_dataframe_output(self, sandbox, workspace): + result = sandbox.run_python_code(NON_DF_OUTPUT, workspace, "output_df") + assert result["status"] == "error" + assert "not a DataFrame" in result["content"] + + def test_json_usage(self, sandbox, workspace): + result = sandbox.run_python_code(MULTI_TABLE_TRANSFORM, workspace, "output_df") + assert result["status"] == "ok" + assert list(result["content"]["doubled"]) == [2, 4] + + def test_duckdb_sql(self, sandbox, workspace): + code = """\ +import pandas as pd +import duckdb +df = pd.DataFrame({"x": [1, 2, 3]}) +output_df = duckdb.sql("SELECT x, x*10 AS x10 FROM df").df() +""" + result = sandbox.run_python_code(code, workspace, "output_df") + assert result["status"] == "ok" + assert list(result["content"]["x10"]) == [10, 20, 30] + + +# =================================================================== +# DockerSandbox — missing Docker +# =================================================================== + +class TestDockerSandboxNoDocker: + """Verify graceful error when Docker is unavailable.""" + + def test_missing_docker_returns_error(self, workspace, monkeypatch): + """If 'docker' binary doesn't exist, run_python_code should return + an error dict, not raise.""" + import subprocess as sp + + original_run = sp.run + + def fake_run(*args, **kwargs): + raise FileNotFoundError("docker not found") + + monkeypatch.setattr(sp, "run", fake_run) + + sb = DockerSandbox() + result = sb.run_python_code(SIMPLE_TRANSFORM, workspace, "output_df") + assert result["status"] == "error" + assert "not installed" in result["content"].lower() or "docker" in result["content"].lower() diff --git a/tests/backend/security/test_auth.py b/tests/backend/security/test_auth.py new file mode 100644 index 00000000..0592887c --- /dev/null +++ b/tests/backend/security/test_auth.py @@ -0,0 +1,138 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +"""Tests for authentication and identity management (auth.py). + +Verifies that identity extraction, validation, and namespace isolation +work correctly — especially that client-provided headers cannot spoof +an authenticated user identity. +""" + +from unittest.mock import patch + +import flask +import pytest + +import data_formulator.security.auth as auth_module +from data_formulator.security.auth import get_identity_id, _validate_identity_value +from data_formulator.auth_providers.azure_easyauth import AzureEasyAuthProvider + +pytestmark = [pytest.mark.backend] + + +@pytest.fixture +def app(): + """Minimal Flask app for request-context tests.""" + app = flask.Flask(__name__) + app.config["TESTING"] = True + return app + + +@pytest.fixture +def azure_provider(monkeypatch): + """Activate the Azure EasyAuth provider for the duration of a test.""" + monkeypatch.setattr(auth_module, "_provider", AzureEasyAuthProvider()) + + +# =================================================================== +# _validate_identity_value +# =================================================================== + +class TestValidateIdentityValue: + + def test_valid_uuid(self): + val = _validate_identity_value("550e8400-e29b-41d4-a716-446655440000", "test") + assert val == "550e8400-e29b-41d4-a716-446655440000" + + def test_valid_email(self): + val = _validate_identity_value("alice@example.com", "test") + assert val == "alice@example.com" + + def test_strips_whitespace(self): + val = _validate_identity_value(" alice@example.com ", "test") + assert val == "alice@example.com" + + def test_empty_raises(self): + with pytest.raises(ValueError, match="Empty"): + _validate_identity_value("", "test") + + def test_whitespace_only_raises(self): + with pytest.raises(ValueError, match="Empty"): + _validate_identity_value(" ", "test") + + def test_too_long_raises(self): + with pytest.raises(ValueError, match="exceeds"): + _validate_identity_value("a" * 300, "test") + + def test_path_separator_rejected(self): + with pytest.raises(ValueError, match="disallowed"): + _validate_identity_value("../../etc/passwd", "test") + + def test_shell_metachar_rejected(self): + with pytest.raises(ValueError, match="disallowed"): + _validate_identity_value("user; rm -rf /", "test") + + def test_control_chars_rejected(self): + with pytest.raises(ValueError, match="disallowed"): + _validate_identity_value("user\x00name", "test") + + +# =================================================================== +# get_identity_id — namespace isolation +# =================================================================== + +class TestGetIdentityId: + + def test_azure_principal_returns_user_prefix(self, app, azure_provider): + with app.test_request_context( + headers={"X-MS-CLIENT-PRINCIPAL-ID": "azure-user-123"} + ): + identity = get_identity_id() + assert identity == "user:azure-user-123" + + def test_browser_identity_returns_browser_prefix(self, app): + with app.test_request_context( + headers={"X-Identity-Id": "550e8400-e29b-41d4-a716-446655440000"} + ): + identity = get_identity_id() + assert identity == "browser:550e8400-e29b-41d4-a716-446655440000" + + def test_client_cannot_spoof_user_prefix(self, app): + """Even if X-Identity-Id sends 'user:alice', the result is browser:alice.""" + with app.test_request_context( + headers={"X-Identity-Id": "user:alice@example.com"} + ): + identity = get_identity_id() + assert identity.startswith("browser:") + assert "alice@example.com" in identity + + def test_azure_header_takes_priority_over_browser(self, app, azure_provider): + """When both headers are present, Azure provider wins.""" + with app.test_request_context( + headers={ + "X-MS-CLIENT-PRINCIPAL-ID": "azure-user-456", + "X-Identity-Id": "browser-uuid-789", + } + ): + identity = get_identity_id() + assert identity == "user:azure-user-456" + + def test_missing_all_headers_raises(self, app): + with app.test_request_context(): + with pytest.raises(ValueError, match="X-Identity-Id"): + get_identity_id() + + def test_malformed_azure_header_rejected(self, app, azure_provider): + with app.test_request_context( + headers={"X-MS-CLIENT-PRINCIPAL-ID": "../../etc/passwd"} + ): + with pytest.raises(ValueError, match="disallowed"): + get_identity_id() + + def test_browser_identity_strips_prefix(self, app): + """If client sends 'browser:abc', the 'browser:' prefix is stripped and re-added.""" + with app.test_request_context( + headers={"X-Identity-Id": "browser:my-uuid-123"} + ): + identity = get_identity_id() + assert identity == "browser:my-uuid-123" diff --git a/tests/backend/security/test_auth_provider_chain.py b/tests/backend/security/test_auth_provider_chain.py new file mode 100644 index 00000000..a4f49a3f --- /dev/null +++ b/tests/backend/security/test_auth_provider_chain.py @@ -0,0 +1,300 @@ +"""Tests for the AuthProvider chain initialisation and runtime dispatch. + +Background +---------- +The refactored ``auth.py`` delegates identity extraction to a pluggable +AuthProvider selected via the ``AUTH_PROVIDER`` environment variable. +These tests verify the chain's initialisation logic, provider dispatch, +anonymous fallback behaviour, and the ``get_sso_token()`` helper. +""" +from __future__ import annotations + +import flask +import pytest + +import data_formulator.security.auth as auth_module +from data_formulator.security.auth import ( + get_auth_result, + get_identity_id, + get_sso_token, + init_auth, +) +from data_formulator.auth_providers.base import AuthProvider, AuthResult, AuthenticationError +from data_formulator.auth_providers import get_provider_class, list_available_providers +from data_formulator.security.auth import _validate_identity_value + +pytestmark = [pytest.mark.backend, pytest.mark.auth] + + +# ------------------------------------------------------------------ +# Fixtures +# ------------------------------------------------------------------ + +@pytest.fixture +def app(): + """Minimal Flask app for request-context tests.""" + _app = flask.Flask(__name__) + _app.config["TESTING"] = True + return _app + + +@pytest.fixture(autouse=True) +def _reset_auth_state(monkeypatch): + """Ensure each test starts with a clean auth module state.""" + monkeypatch.setattr(auth_module, "_provider", None) + monkeypatch.setattr(auth_module, "_allow_anonymous", True) + + +# ------------------------------------------------------------------ +# Provider discovery +# ------------------------------------------------------------------ + +class TestProviderDiscovery: + + def test_azure_easyauth_is_discovered(self): + assert "azure_easyauth" in list_available_providers() + + def test_get_provider_class_returns_class(self): + cls = get_provider_class("azure_easyauth") + assert cls is not None + assert issubclass(cls, AuthProvider) + + def test_unknown_provider_returns_none(self): + assert get_provider_class("nonexistent") is None + + +# ------------------------------------------------------------------ +# init_auth +# ------------------------------------------------------------------ + +class TestInitAuth: + + def test_no_env_var_stays_anonymous(self, app, monkeypatch): + monkeypatch.delenv("AUTH_PROVIDER", raising=False) + init_auth(app) + assert auth_module._provider is None + assert auth_module._allow_anonymous is True + + def test_explicit_anonymous_stays_anonymous(self, app, monkeypatch): + monkeypatch.setenv("AUTH_PROVIDER", "anonymous") + init_auth(app) + assert auth_module._provider is None + + def test_azure_easyauth_activates(self, app, monkeypatch): + monkeypatch.setenv("AUTH_PROVIDER", "azure_easyauth") + init_auth(app) + assert auth_module._provider is not None + assert auth_module._provider.name == "azure_easyauth" + + def test_unknown_provider_logs_error_stays_none(self, app, monkeypatch): + monkeypatch.setenv("AUTH_PROVIDER", "totally_bogus") + init_auth(app) + assert auth_module._provider is None + + def test_allow_anonymous_false(self, app, monkeypatch): + monkeypatch.setenv("ALLOW_ANONYMOUS", "false") + monkeypatch.delenv("AUTH_PROVIDER", raising=False) + init_auth(app) + assert auth_module._allow_anonymous is False + + +# ------------------------------------------------------------------ +# get_identity_id — provider dispatch +# ------------------------------------------------------------------ + +class TestProviderDispatch: + + def test_provider_authenticated_returns_user_prefix(self, app, monkeypatch): + monkeypatch.setenv("AUTH_PROVIDER", "azure_easyauth") + init_auth(app) + + with app.test_request_context( + headers={"X-MS-CLIENT-PRINCIPAL-ID": "azure-user-123"} + ): + identity = get_identity_id() + assert identity == "user:azure-user-123" + + def test_provider_miss_falls_back_to_anonymous(self, app, monkeypatch): + monkeypatch.setenv("AUTH_PROVIDER", "azure_easyauth") + init_auth(app) + + with app.test_request_context( + headers={"X-Identity-Id": "550e8400-e29b-41d4-a716-446655440000"} + ): + identity = get_identity_id() + assert identity == "browser:550e8400-e29b-41d4-a716-446655440000" + + def test_provider_miss_no_anonymous_raises(self, app, monkeypatch): + monkeypatch.setenv("AUTH_PROVIDER", "azure_easyauth") + monkeypatch.setenv("ALLOW_ANONYMOUS", "false") + init_auth(app) + + with app.test_request_context( + headers={"X-Identity-Id": "some-uuid"} + ): + with pytest.raises(ValueError, match="Authentication required"): + get_identity_id() + + +# ------------------------------------------------------------------ +# get_identity_id — anonymous-only mode (no AUTH_PROVIDER) +# ------------------------------------------------------------------ + +class TestAnonymousMode: + + def test_browser_identity_works(self, app): + with app.test_request_context( + headers={"X-Identity-Id": "my-browser-uuid"} + ): + assert get_identity_id() == "browser:my-browser-uuid" + + def test_prefixed_identity_stripped(self, app): + with app.test_request_context( + headers={"X-Identity-Id": "browser:my-uuid"} + ): + assert get_identity_id() == "browser:my-uuid" + + def test_missing_header_raises(self, app): + with app.test_request_context(): + with pytest.raises(ValueError, match="X-Identity-Id"): + get_identity_id() + + def test_spoofed_user_prefix_forced_to_browser(self, app): + with app.test_request_context( + headers={"X-Identity-Id": "user:alice@corp.com"} + ): + identity = get_identity_id() + assert identity.startswith("browser:") + assert "alice@corp.com" in identity + + +# ------------------------------------------------------------------ +# get_sso_token / get_auth_result +# ------------------------------------------------------------------ + +class TestSSOToken: + + def test_anonymous_mode_returns_none(self, app): + with app.test_request_context( + headers={"X-Identity-Id": "browser-uuid"} + ): + get_identity_id() + assert get_sso_token() is None + assert get_auth_result() is None + + def test_provider_without_token_returns_none(self, app, monkeypatch): + """Azure EasyAuth does not supply raw_token.""" + monkeypatch.setenv("AUTH_PROVIDER", "azure_easyauth") + init_auth(app) + + with app.test_request_context( + headers={"X-MS-CLIENT-PRINCIPAL-ID": "azure-user"} + ): + get_identity_id() + result = get_auth_result() + assert result is not None + assert result.user_id == "azure-user" + assert get_sso_token() is None + + +# ------------------------------------------------------------------ +# AuthenticationError propagation +# ------------------------------------------------------------------ + +class TestAuthenticationErrorPropagation: + + def test_authentication_error_becomes_value_error(self, app, monkeypatch): + """When a provider raises AuthenticationError it should surface as ValueError.""" + + class _FailingProvider(AuthProvider): + @property + def name(self): return "failing" + def authenticate(self, request): + raise AuthenticationError("token expired", provider="failing") + + monkeypatch.setattr(auth_module, "_provider", _FailingProvider()) + + with app.test_request_context( + headers={"Authorization": "Bearer bad-token"} + ): + with pytest.raises(ValueError, match="Authentication failed"): + get_identity_id() + + +# ------------------------------------------------------------------ +# Provider discovery — all Phase 1 providers +# ------------------------------------------------------------------ + +class TestAllPhase1ProvidersDiscovered: + + def test_oidc_discovered(self): + assert "oidc" in list_available_providers() + + def test_github_discovered(self): + assert "github" in list_available_providers() + + def test_azure_easyauth_discovered(self): + assert "azure_easyauth" in list_available_providers() + + +# ------------------------------------------------------------------ +# OIDC provider via init_auth (end-to-end without real IdP) +# ------------------------------------------------------------------ + +class TestOIDCViaInitAuth: + """Verify that AUTH_PROVIDER=oidc activates OIDCProvider. + + These tests don't verify JWT validation (see test_oidc_provider.py); + they only confirm init_auth wiring and the chain's behaviour when + the OIDC provider is active but the request has no Bearer token. + """ + + def test_oidc_activates_when_configured(self, app, monkeypatch): + monkeypatch.setenv("AUTH_PROVIDER", "oidc") + monkeypatch.setenv("OIDC_ISSUER_URL", "https://idp.example.com") + monkeypatch.setenv("OIDC_CLIENT_ID", "test-client") + init_auth(app) + assert auth_module._provider is not None + assert auth_module._provider.name == "oidc" + + def test_oidc_disabled_without_env_vars(self, app, monkeypatch): + monkeypatch.setenv("AUTH_PROVIDER", "oidc") + monkeypatch.delenv("OIDC_ISSUER_URL", raising=False) + monkeypatch.delenv("OIDC_CLIENT_ID", raising=False) + init_auth(app) + assert auth_module._provider is None + + def test_oidc_no_bearer_falls_back_to_anonymous(self, app, monkeypatch): + monkeypatch.setenv("AUTH_PROVIDER", "oidc") + monkeypatch.setenv("OIDC_ISSUER_URL", "https://idp.example.com") + monkeypatch.setenv("OIDC_CLIENT_ID", "test-client") + init_auth(app) + + with app.test_request_context( + headers={"X-Identity-Id": "browser-uuid-123"} + ): + identity = get_identity_id() + assert identity == "browser:browser-uuid-123" + + +# ------------------------------------------------------------------ +# Identity regex — OIDC sub claim characters +# ------------------------------------------------------------------ + +class TestIdentityRegexExpansion: + + @pytest.mark.parametrize("value", [ + "auth0|5f1234abc", + "github:12345", + "user+tag@example.com", + ]) + def test_oidc_style_sub_claims_accepted(self, value): + assert _validate_identity_value(value, "test") == value + + def test_path_separator_still_rejected(self): + with pytest.raises(ValueError, match="disallowed"): + _validate_identity_value("../../etc/passwd", "test") + + def test_space_still_rejected(self): + with pytest.raises(ValueError, match="disallowed"): + _validate_identity_value("user name", "test") diff --git a/tests/backend/security/test_code_signing.py b/tests/backend/security/test_code_signing.py new file mode 100644 index 00000000..29bc6011 --- /dev/null +++ b/tests/backend/security/test_code_signing.py @@ -0,0 +1,93 @@ +# Copyright (c) Microsoft Corporation. +# Licensed under the MIT License. + +"""Tests for HMAC-based code signing (code_signing.py). + +Verifies that transformation code is signed before being returned to the +frontend, and that tampered or unsigned code is rejected before sandbox +execution. +""" + +import pytest + +from data_formulator.security.code_signing import sign_code, verify_code, sign_result + +pytestmark = [pytest.mark.backend] + + +# =================================================================== +# sign_code / verify_code round-trip +# =================================================================== + +class TestSignVerifyRoundTrip: + + def test_valid_signature_accepted(self): + code = "output_df = pd.DataFrame({'a': [1, 2, 3]})" + sig = sign_code(code) + assert verify_code(code, sig) + + def test_tampered_code_rejected(self): + code = "output_df = pd.DataFrame({'a': [1, 2, 3]})" + sig = sign_code(code) + tampered = code.replace("1, 2, 3", "1, 2, 3, 4") + assert not verify_code(tampered, sig) + + def test_tampered_signature_rejected(self): + code = "output_df = pd.DataFrame({'a': [1]})" + sig = sign_code(code) + bad_sig = sig[:-4] + "dead" + assert not verify_code(code, bad_sig) + + def test_empty_code_returns_empty_sig(self): + assert sign_code("") == "" + + def test_empty_code_verify_returns_false(self): + assert not verify_code("", "anything") + + def test_empty_signature_verify_returns_false(self): + assert not verify_code("some code", "") + + def test_whitespace_matters(self): + """Trailing whitespace changes the signature.""" + code = "output_df = pd.DataFrame()" + sig = sign_code(code) + assert not verify_code(code + " ", sig) + + def test_unicode_code(self): + """Non-ASCII code signs and verifies correctly.""" + code = 'output_df = pd.DataFrame({"名前": ["太郎"]})' + sig = sign_code(code) + assert verify_code(code, sig) + + def test_signature_is_hex_string(self): + sig = sign_code("x = 1") + assert isinstance(sig, str) + assert len(sig) == 64 # SHA-256 hex + int(sig, 16) # should not raise + + +# =================================================================== +# sign_result helper +# =================================================================== + +class TestSignResult: + + def test_adds_signature_when_code_present(self): + result = {"code": "output_df = pd.DataFrame()", "data": [1, 2, 3]} + sign_result(result) + assert "code_signature" in result + assert verify_code(result["code"], result["code_signature"]) + + def test_no_signature_when_code_empty(self): + result = {"code": "", "data": [1]} + sign_result(result) + assert result.get("code_signature") is None or result.get("code_signature") == "" + + def test_no_signature_when_code_missing(self): + result = {"data": [1]} + sign_result(result) + assert "code_signature" not in result or result.get("code_signature") is None + + def test_returns_result_for_chaining(self): + result = {"code": "x = 1"} + assert sign_result(result) is result diff --git a/tests/backend/security/test_global_model_security.py b/tests/backend/security/test_global_model_security.py new file mode 100644 index 00000000..e9330a32 --- /dev/null +++ b/tests/backend/security/test_global_model_security.py @@ -0,0 +1,186 @@ +"""Tests for global model security: credential resolution and error sanitization. + +These verify two critical security properties: +1. get_client() resolves real credentials from the registry for global models. +2. test_model error messages never leak API keys for global models. +""" +from __future__ import annotations + +import os +from unittest.mock import patch, MagicMock + +import pytest + +from data_formulator.model_registry import ModelRegistry + +pytestmark = [pytest.mark.backend] + + +SAMPLE_ENV = { + "OPENAI_ENABLED": "true", + "OPENAI_API_KEY": "sk-secret-key-12345", + "OPENAI_MODELS": "gpt-4o", +} + + +# --------------------------------------------------------------------------- +# get_client: global model credential resolution +# --------------------------------------------------------------------------- + +class TestGetClientGlobalResolution: + """get_client() must resolve real credentials from model_registry + when the model config has is_global=True.""" + + @patch.dict(os.environ, SAMPLE_ENV, clear=True) + def test_global_model_gets_real_api_key(self): + """A global model config (no api_key from frontend) should be + resolved to the full config with the real api_key.""" + registry = ModelRegistry() + + with patch("data_formulator.agent_routes.model_registry", registry): + from data_formulator.agent_routes import get_client + + client = get_client({ + "id": "global-openai-gpt-4o", + "endpoint": "openai", + "model": "gpt-4o", + "is_global": True, + }) + + assert client.params.get("api_key") == "sk-secret-key-12345" + + @patch.dict(os.environ, SAMPLE_ENV, clear=True) + def test_user_model_keeps_own_credentials(self): + """A non-global (user-added) model should use its own api_key, + not touch the registry.""" + registry = ModelRegistry() + + with patch("data_formulator.agent_routes.model_registry", registry): + from data_formulator.agent_routes import get_client + + client = get_client({ + "id": "user-custom-model", + "endpoint": "openai", + "model": "gpt-4o", + "api_key": "sk-user-own-key", + "api_base": "", + "api_version": "", + }) + + assert client.params.get("api_key") == "sk-user-own-key" + + @patch.dict(os.environ, SAMPLE_ENV, clear=True) + def test_global_model_without_registry_match_falls_through(self): + """If a global model id is not in the registry, get_client should + still work (using whatever config was passed).""" + registry = ModelRegistry() + + with patch("data_formulator.agent_routes.model_registry", registry): + from data_formulator.agent_routes import get_client + + client = get_client({ + "id": "global-nonexistent-model", + "endpoint": "openai", + "model": "nonexistent", + "api_key": "sk-fallback", + "api_base": "", + "api_version": "", + "is_global": True, + }) + + assert client.params.get("api_key") == "sk-fallback" + + +# --------------------------------------------------------------------------- +# Error sanitization (shared sanitize module) +# --------------------------------------------------------------------------- + +class TestSharedErrorSanitization: + """The shared sanitize_error_message function must strip sensitive data.""" + + def test_sanitize_redacts_api_key_patterns(self): + from data_formulator.security.sanitize import sanitize_error_message + + raw = "Connection failed: api_key=sk-secret-key-12345 is invalid" + sanitized = sanitize_error_message(raw) + + assert "sk-secret-key-12345" not in sanitized + assert "" in sanitized + + def test_sanitize_truncates_long_messages(self): + from data_formulator.security.sanitize import sanitize_error_message + + raw = "x" * 1000 + sanitized = sanitize_error_message(raw) + + assert len(sanitized) <= 503 # 500 + "..." + assert sanitized.endswith("...") + + def test_sanitize_escapes_html(self): + from data_formulator.security.sanitize import sanitize_error_message + + raw = '' + sanitized = sanitize_error_message(raw) + + assert "' + result = sanitize_error_message(msg) + assert "