From d9054d378ebfa893e59295e6cf2bc613f2175b84 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sat, 30 Aug 2025 11:29:37 +0200 Subject: [PATCH 01/14] add textbooks, course materials --- docs/_quarto.yml | 2 ++ docs/textbooks.qmd | 33 +++++++++++++++++++++++++++++++++ 2 files changed, 35 insertions(+) create mode 100644 docs/textbooks.qmd diff --git a/docs/_quarto.yml b/docs/_quarto.yml index b2e48dcf7..38dbf1dc2 100644 --- a/docs/_quarto.yml +++ b/docs/_quarto.yml @@ -53,6 +53,8 @@ website: file: stata-2-pyfixest.qmd - text: "PyFixest on the GPU" file: pyfixest_gpu.ipynb + - text: "Learnings Econometrics with PyFixest" + file: textbooks.qmd - text: "Replicating 'The Effect' with PyFixest" file: replicating-the-effect.qmd diff --git a/docs/textbooks.qmd b/docs/textbooks.qmd new file mode 100644 index 000000000..e7ef09b70 --- /dev/null +++ b/docs/textbooks.qmd @@ -0,0 +1,33 @@ +--- +title: Learning Econometrics with PyFixest +format: + html: + html-table-processing: none +toc: true +toc-title: "On this page" +toc-location: left +--- + +A few classes and textbooks have adopted PyFixest for teaching. And for core textbooks in causal inference that have not (yet =) ) , we have (or are planning to) write PyFixest translations (there are also multiple current "good first issues" for this if you'd like to help us). + +## Textbooks + +- Data Analysis for Business, Economics, and Policy ("Gabor's Data Analysis"). [Data and code](https://gabors-data-analysis.com/data-and-code/) and [Jupyter Notebooks on github](https://github.com/gabors-data-analysis/da_case_studies) +- Coding for Economists: [Regression](https://aeturrell.github.io/coding-for-economists/econmt-regression.html) +- The Effect (first edition): [our PyFixest translation](https://py-econometrics.github.io/pyfixest/replicating-the-effect.html) + +Textbooks that we still want to cover: + +- The panel data chapter in the Mixtape ([github issue here](https://github.com/py-econometrics/pyfixest/issues/998)) +- All of the Python translation of Ding's textbook on causal inference ([github issue here](https://github.com/py-econometrics/pyfixest/issues/957)) +- The "Brave and True" chapters on [Dummy Regression](https://matheusfacure.github.io/python-causality-handbook/06-Grouped-and-Dummy-Regression.html), [Instrumental Variables](https://matheusfacure.github.io/python-causality-handbook/08-Instrumental-Variables.html), [Difference-in-Differences](https://matheusfacure.github.io/python-causality-handbook/13-Difference-in-Differences.html) and [Panel Data and Fixed Effects](https://matheusfacure.github.io/python-causality-handbook/14-Panel-Data-and-Fixed-Effects.html). + +## Classes + +If you are teaching with pyfixest, we'd love to hear from you! + +- Econometrics II (taught by Vladislav Morozov at UBonn): Great intro to fixed effects estimation theory. Slides on fixed effects [here](https://vladislav-morozov.github.io/econometrics-2/slides/panel/fe.html#/title-slide), full class notes [here](https://vladislav-morozov.github.io/econometrics-2/), [github repository](https://github.com/vladislav-morozov/econometrics-2) + +## Blog Posts, Notebooks + +If you've written a blog post that illustrates how to use pyfixest, please let us know, we'd love to link to it. From f67734e9e1a0e448767da0f1f2d43eea9aacf5fe Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sat, 30 Aug 2025 11:31:24 +0200 Subject: [PATCH 02/14] tidy finance --- docs/textbooks.qmd | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/textbooks.qmd b/docs/textbooks.qmd index e7ef09b70..7fcec7a08 100644 --- a/docs/textbooks.qmd +++ b/docs/textbooks.qmd @@ -14,6 +14,7 @@ A few classes and textbooks have adopted PyFixest for teaching. And for core tex - Data Analysis for Business, Economics, and Policy ("Gabor's Data Analysis"). [Data and code](https://gabors-data-analysis.com/data-and-code/) and [Jupyter Notebooks on github](https://github.com/gabors-data-analysis/da_case_studies) - Coding for Economists: [Regression](https://aeturrell.github.io/coding-for-economists/econmt-regression.html) +- Tidy Finance comes has a [chapter on fixed effects](https://www.tidy-finance.org/python/fixed-effects-and-clustered-standard-errors.html) and clustered standard errors that uses pyfixest - The Effect (first edition): [our PyFixest translation](https://py-econometrics.github.io/pyfixest/replicating-the-effect.html) Textbooks that we still want to cover: From 290eef6bd57df484ea389c6673f75de8a2d60b53 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sat, 30 Aug 2025 11:32:09 +0200 Subject: [PATCH 03/14] DiD in tidyF --- docs/textbooks.qmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/textbooks.qmd b/docs/textbooks.qmd index 7fcec7a08..4fdce37b3 100644 --- a/docs/textbooks.qmd +++ b/docs/textbooks.qmd @@ -14,7 +14,7 @@ A few classes and textbooks have adopted PyFixest for teaching. And for core tex - Data Analysis for Business, Economics, and Policy ("Gabor's Data Analysis"). [Data and code](https://gabors-data-analysis.com/data-and-code/) and [Jupyter Notebooks on github](https://github.com/gabors-data-analysis/da_case_studies) - Coding for Economists: [Regression](https://aeturrell.github.io/coding-for-economists/econmt-regression.html) -- Tidy Finance comes has a [chapter on fixed effects](https://www.tidy-finance.org/python/fixed-effects-and-clustered-standard-errors.html) and clustered standard errors that uses pyfixest +- Tidy Finance comes has a [chapter on fixed effects](https://www.tidy-finance.org/python/fixed-effects-and-clustered-standard-errors.html) and clustered standard errors and [Differene-in-Differences estimation](https://www.tidy-finance.org/python/difference-in-differences.html) that uses pyfixest - The Effect (first edition): [our PyFixest translation](https://py-econometrics.github.io/pyfixest/replicating-the-effect.html) Textbooks that we still want to cover: From 7431c0e7a14a4883383c07a781d00890b5cf97d7 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sat, 30 Aug 2025 12:17:28 +0200 Subject: [PATCH 04/14] add mixtape --- docs/mixtape.ipynb | 837 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 837 insertions(+) create mode 100644 docs/mixtape.ipynb diff --git a/docs/mixtape.ipynb b/docs/mixtape.ipynb new file mode 100644 index 000000000..d81323d7e --- /dev/null +++ b/docs/mixtape.ipynb @@ -0,0 +1,837 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ebd6371e", + "metadata": {}, + "source": [ + "# The Mixtape with PyFixest\n", + "\n", + "In this notebook, we translate some of the Python code in [Scott Cunningham's mixtape](https://mixtape.scunning.com/) to PyFixest." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "15b9d555", + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import pyfixest as pf" + ] + }, + { + "cell_type": "markdown", + "id": "aac47e73", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "## Chapter 8: Panel Data" + ] + }, + { + "cell_type": "markdown", + "id": "71cb69c1", + "metadata": {}, + "source": [ + "Instead of demeaning by hand and then fitting the model via statsmodels, we just let PyFixest do all the work for us. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7aae389f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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0243.02.027.030.05.0NaN11.0900.01. No0.0...0.00.00.0729.01.00.00.00.00.00.0
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" + ], + "text/plain": [ + " id session age age_cl appearance_cl bmi schooling asq_cl \\\n", + "0 243.0 2.0 27.0 30.0 5.0 NaN 11.0 900.0 \n", + "1 397.0 4.0 28.0 56.0 5.0 28.971931 16.0 3136.0 \n", + "2 598.0 4.0 50.0 52.0 6.0 21.453857 16.0 2704.0 \n", + "3 28.0 1.0 41.0 72.0 5.0 24.028320 12.0 5184.0 \n", + "4 28.0 4.0 41.0 46.0 8.0 24.028320 12.0 2116.0 \n", + "\n", + " provider_second asian_cl ... hispanic other white asq cohab \\\n", + "0 1. No 0.0 ... 0.0 0.0 0.0 729.0 1.0 \n", + "1 1. No 0.0 ... 0.0 0.0 1.0 784.0 0.0 \n", + "2 1. No 0.0 ... 0.0 0.0 1.0 2500.0 0.0 \n", + "3 1. No 0.0 ... 0.0 0.0 1.0 1681.0 0.0 \n", + "4 1. No 0.0 ... 0.0 0.0 1.0 1681.0 0.0 \n", + "\n", + " married divorced separated nevermarried widowed \n", + "0 0.0 0.0 0.0 0.0 0.0 \n", + "1 1.0 0.0 0.0 0.0 0.0 \n", + "2 0.0 1.0 0.0 0.0 0.0 \n", + "3 0.0 1.0 0.0 0.0 0.0 \n", + "4 0.0 1.0 0.0 0.0 0.0 \n", + "\n", + "[5 rows x 31 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# read the data from github & load into pandas\n", + "url = \"https://raw.githubusercontent.com/scunning1975/mixtape/master/sasp_panel.dta\"\n", + "sasp = pd.read_stata(url)\n", + "sasp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea4f3aa3", + "metadata": {}, + "outputs": [], + "source": [ + "# some initial data cleaning\n", + "sasp = sasp.dropna()\n", + "# order by id and session\n", + "sasp.sort_values(\"id\", inplace=True)\n", + "\n", + "# create balanced panel\n", + "times = len(sasp.session.unique())\n", + "in_all_times = (\n", + " sasp.groupby(\"id\")[\"session\"].apply(lambda x: len(x) == times).reset_index()\n", + ")\n", + "in_all_times.rename(columns={\"session\": \"in_all_times\"}, inplace=True)\n", + "balanced_sasp = pd.merge(in_all_times, sasp, how=\"left\", on=\"id\")\n", + "balanced_sasp = balanced_sasp[balanced_sasp.in_all_times]\n", + "\n", + "provider_second = np.zeros(balanced_sasp.shape[0])\n", + "provider_second[balanced_sasp.provider_second == \"2. Yes\"] = 1\n", + "balanced_sasp.provider_second = provider_second" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "62252710", + "metadata": {}, + "outputs": [], + "source": [ + "# define formulas\n", + "\n", + "covars = \"\"\"\n", + " age + asq + bmi + hispanic + black + other + asian + schooling + cohab +\n", + " married + divorced + separated + age_cl + unsafe + llength + reg + asq_cl +\n", + " appearance_cl + provider_second + asian_cl + black_cl + hispanic_cl +\n", + " othrace_cl + hot + massage_cl\n", + " \"\"\"\n", + "\n", + "# we fit on all covariates\n", + "fml_pooled = f\"lnw ~ {covars}\"\n", + "# we fit on all covariates and add one-hot encoded id fixed effects\n", + "fml_onehot = f\"lnw ~ {covars} + C(id)\"\n", + "# we fit on all covariates and swipe out the fixed effects (i.e. we apply the within transformation via pyfixest.feols)\n", + "fml_fe = f\"lnw ~ {covars} | id\"" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "23eadf65", + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "fit_pooled = pf.feols(fml=fml_pooled, data=balanced_sasp, vcov={\"CRV1\": \"id\"})\n", + "fit_fe = pf.feols(fml=fml_fe, data=balanced_sasp, vcov={\"CRV1\": \"id\"})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "304632e2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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See [here](https://py-econometrics.github.io/pyfixest/ssc.html)\n", + "for an overview of how pyfixest handles small sample adjustments. " + ] + }, + { + "cell_type": "markdown", + "id": "bb5239ba", + "metadata": {}, + "source": [ + "## Chapter 9: Difference-in-Differences" + ] + }, + { + "cell_type": "markdown", + "id": "b7a077e1", + "metadata": {}, + "source": [ + "### Code Example 1" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "4218b23c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " fip age race year sex totcase totpop rate totrate \\\n", + "19 1.0 15.0 2.0 1985.0 2 5683.0 106187 6527.500000 5351.899902 \n", + "39 1.0 15.0 2.0 1986.0 2 5344.0 106831 6351.200195 5002.299805 \n", + "71 1.0 15.0 2.0 1987.0 2 4983.0 106496 5759.100098 4679.000000 \n", + "89 1.0 15.0 2.0 1988.0 2 5276.0 105238 6139.600098 5013.399902 \n", + "106 1.0 15.0 2.0 1989.0 2 5692.0 102956 5951.500000 5528.600098 \n", + "\n", + " id ... female lnr t younger fa pi wm15 wf15 bm15 \\\n", + "19 14.0 ... 1.0 8.783779 1.0 1.0 1.0 0.0 0.0 0.0 0.0 \n", + "39 14.0 ... 1.0 8.756399 2.0 1.0 1.0 0.0 0.0 0.0 0.0 \n", + "71 14.0 ... 1.0 8.658537 3.0 1.0 1.0 1.0 0.0 0.0 0.0 \n", + "89 14.0 ... 1.0 8.722515 4.0 1.0 1.0 1.0 0.0 0.0 0.0 \n", + "106 14.0 ... 1.0 8.691399 5.0 1.0 1.0 1.0 0.0 0.0 0.0 \n", + "\n", + " bf15 \n", + "19 1.0 \n", + "39 1.0 \n", + "71 1.0 \n", + "89 1.0 \n", + "106 1.0 \n", + "\n", + "[5 rows x 39 columns]" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "abortion = pd.read_stata(\n", + " \"https://raw.githubusercontent.com/scunning1975/mixtape/master/abortion.dta\"\n", + ")\n", + "abortion = abortion[~pd.isnull(abortion.lnr)]\n", + "abortion_bf15 = abortion[abortion.bf15 == 1]\n", + "# pf throws error when weights are 0\n", + "abortion_bf15 = abortion_bf15[abortion_bf15.totpop > 0]\n", + "abortion_bf15.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "8b880c1d", + "metadata": {}, + "outputs": [], + "source": [ + "def _extract_bracket_numbers(str_list) -> dict:\n", + " \"Extract time from event study coefficients and store in dict.\"\n", + " out = {}\n", + " pattern = re.compile(r\"\\[(\\d+\\.?\\d*)\\]\") # capture numbers inside []\n", + "\n", + " for s in str_list:\n", + " match = pattern.search(str(s)) # convert np.str_ -> str\n", + " if match:\n", + " out[s] = match.group(1)\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "913e912c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\alexa\\Documents\\pyfixest\\pyfixest\\estimation\\feols_.py:2759: UserWarning: \n", + " 1 variables dropped due to multicollinearity.\n", + " The following variables are dropped: ['C(fip)[T.53.0]'].\n", + " \n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fml = \"\"\"lnr ~ i(year, repeal, ref = 1985) + C(repeal) + C(year) + C(fip)\n", + " + acc + ir + pi + alcohol + crack + poverty + income + ur\n", + "\"\"\"\n", + "\n", + "fit = pf.feols(fml=fml, data=abortion_bf15, weights=\"totpop\", vcov={\"CRV1\": \"fip\"})\n", + "\n", + "pf.iplot(\n", + " fit,\n", + " coord_flip=False,\n", + " plot_backend=\"matplotlib\",\n", + " rotate_xticks=45,\n", + " title=\"Event Study Estimate\",\n", + " labels=_extract_bracket_numbers(fit._coefnames),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "772e36e7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "docs", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From bc0bf8feb6cb9556447fbdd5c971c0c3e72a94b1 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sat, 30 Aug 2025 12:21:41 +0200 Subject: [PATCH 05/14] panel headers --- docs/_quarto.yml | 2 + docs/mixtape.ipynb | 122 +++++++++++++++++++++++++-------------------- docs/textbooks.qmd | 5 +- 3 files changed, 72 insertions(+), 57 deletions(-) diff --git a/docs/_quarto.yml b/docs/_quarto.yml index 38dbf1dc2..3d602c925 100644 --- a/docs/_quarto.yml +++ b/docs/_quarto.yml @@ -57,6 +57,8 @@ website: file: textbooks.qmd - text: "Replicating 'The Effect' with PyFixest" file: replicating-the-effect.qmd + - text: "Replicating 'The Mixtape' with PyFixest" + file: mixtape.ipynb quartodoc: diff --git a/docs/mixtape.ipynb b/docs/mixtape.ipynb index d81323d7e..40d56ae85 100644 --- a/docs/mixtape.ipynb +++ b/docs/mixtape.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 62, "id": "15b9d555", "metadata": {}, "outputs": [], @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 63, "id": "7aae389f", "metadata": {}, "outputs": [ @@ -246,7 +246,7 @@ "[5 rows x 31 columns]" ] }, - "execution_count": 4, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -260,7 +260,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 64, "id": "ea4f3aa3", "metadata": {}, "outputs": [], @@ -286,7 +286,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 65, "id": "62252710", "metadata": {}, "outputs": [], @@ -310,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 66, "id": "23eadf65", "metadata": {}, "outputs": [], @@ -322,70 +322,81 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "id": "304632e2", "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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"metadata": {}, "outputs": [], @@ -760,7 +772,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 70, "id": "913e912c", "metadata": {}, "outputs": [ @@ -782,7 +794,7 @@ "
" ] }, - "execution_count": 57, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } diff --git a/docs/textbooks.qmd b/docs/textbooks.qmd index 4fdce37b3..6ba610f8a 100644 --- a/docs/textbooks.qmd +++ b/docs/textbooks.qmd @@ -16,10 +16,11 @@ A few classes and textbooks have adopted PyFixest for teaching. And for core tex - Coding for Economists: [Regression](https://aeturrell.github.io/coding-for-economists/econmt-regression.html) - Tidy Finance comes has a [chapter on fixed effects](https://www.tidy-finance.org/python/fixed-effects-and-clustered-standard-errors.html) and clustered standard errors and [Differene-in-Differences estimation](https://www.tidy-finance.org/python/difference-in-differences.html) that uses pyfixest - The Effect (first edition): [our PyFixest translation](https://py-econometrics.github.io/pyfixest/replicating-the-effect.html) +- The Panel Data Chapter in [the Mixtape](https://mixtape.scunning.com/08-panel_data#data-exercise-survey-of-adult-service-providers). You can find a PyFixest translation [here](https://py-econometrics.github.io/pyfixest/replicating-the-mixtape.html) -Textbooks that we still want to cover: +Textbooks / textbook chapters that we still want to cover: -- The panel data chapter in the Mixtape ([github issue here](https://github.com/py-econometrics/pyfixest/issues/998)) +- The Difference-in-Differences chapter in the Mixtape ([github issue here](https://github.com/py-econometrics/pyfixest/issues/998)) - All of the Python translation of Ding's textbook on causal inference ([github issue here](https://github.com/py-econometrics/pyfixest/issues/957)) - The "Brave and True" chapters on [Dummy Regression](https://matheusfacure.github.io/python-causality-handbook/06-Grouped-and-Dummy-Regression.html), [Instrumental Variables](https://matheusfacure.github.io/python-causality-handbook/08-Instrumental-Variables.html), [Difference-in-Differences](https://matheusfacure.github.io/python-causality-handbook/13-Difference-in-Differences.html) and [Panel Data and Fixed Effects](https://matheusfacure.github.io/python-causality-handbook/14-Panel-Data-and-Fixed-Effects.html). From b7f0b3f2ec299451b3db0feb08da1710a5b8498a Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sat, 30 Aug 2025 12:25:59 +0200 Subject: [PATCH 06/14] panel headers --- docs/mixtape.ipynb | 197 +++++++++++++++++++++++++++++++-------------- docs/textbooks.qmd | 2 +- 2 files changed, 136 insertions(+), 63 deletions(-) diff --git a/docs/mixtape.ipynb b/docs/mixtape.ipynb index 40d56ae85..8e566706a 100644 --- a/docs/mixtape.ipynb +++ b/docs/mixtape.ipynb @@ -12,10 +12,91 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 1, "id": "15b9d555", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import re\n", "\n", @@ -47,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 2, "id": "7aae389f", "metadata": {}, "outputs": [ @@ -246,7 +327,7 @@ "[5 rows x 31 columns]" ] }, - "execution_count": 63, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -260,7 +341,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 3, "id": "ea4f3aa3", "metadata": {}, "outputs": [], @@ -286,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 4, "id": "62252710", "metadata": {}, "outputs": [], @@ -310,7 +391,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 5, "id": "23eadf65", "metadata": {}, "outputs": [], @@ -322,59 +403,59 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 6, "id": "304632e2", "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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" ] }, - "execution_count": 70, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -815,14 +896,6 @@ " labels=_extract_bracket_numbers(fit._coefnames),\n", ")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "772e36e7", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/docs/textbooks.qmd b/docs/textbooks.qmd index 6ba610f8a..acdfad74e 100644 --- a/docs/textbooks.qmd +++ b/docs/textbooks.qmd @@ -14,7 +14,7 @@ A few classes and textbooks have adopted PyFixest for teaching. And for core tex - Data Analysis for Business, Economics, and Policy ("Gabor's Data Analysis"). [Data and code](https://gabors-data-analysis.com/data-and-code/) and [Jupyter Notebooks on github](https://github.com/gabors-data-analysis/da_case_studies) - Coding for Economists: [Regression](https://aeturrell.github.io/coding-for-economists/econmt-regression.html) -- Tidy Finance comes has a [chapter on fixed effects](https://www.tidy-finance.org/python/fixed-effects-and-clustered-standard-errors.html) and clustered standard errors and [Differene-in-Differences estimation](https://www.tidy-finance.org/python/difference-in-differences.html) that uses pyfixest +- Tidy Finance comes with a [chapter on fixed effects](https://www.tidy-finance.org/python/fixed-effects-and-clustered-standard-errors.html) and clustered standard errors and [Differene-in-Differences estimation](https://www.tidy-finance.org/python/difference-in-differences.html) that uses pyfixest - The Effect (first edition): [our PyFixest translation](https://py-econometrics.github.io/pyfixest/replicating-the-effect.html) - The Panel Data Chapter in [the Mixtape](https://mixtape.scunning.com/08-panel_data#data-exercise-survey-of-adult-service-providers). You can find a PyFixest translation [here](https://py-econometrics.github.io/pyfixest/replicating-the-mixtape.html) From c3fe20884f91dcbcd3e26082b9bfdbfc77dfcf56 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sat, 30 Aug 2025 12:28:57 +0200 Subject: [PATCH 07/14] i operator --- docs/mixtape.ipynb | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/docs/mixtape.ipynb b/docs/mixtape.ipynb index 8e566706a..0ffe550b4 100644 --- a/docs/mixtape.ipynb +++ b/docs/mixtape.ipynb @@ -595,8 +595,7 @@ "id": "1ba7928d", "metadata": {}, "source": [ - "Note that standard errors differ slightly due to differences in small sample adjustments in Stata and Pyfixest. See [here](https://py-econometrics.github.io/pyfixest/ssc.html)\n", - "for an overview of how pyfixest handles small sample adjustments. " + "Our point estimates match the Stata results that Scott reports in the mixtape exactly. The standard errors differ slightly due to differences in small sample adjustments in Stata and Pyfixest. See [here](https://py-econometrics.github.io/pyfixest/ssc.html) for an overview of how pyfixest handles small sample adjustments (tldr - exactly like r-fixest). " ] }, { @@ -853,7 +852,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "913e912c", "metadata": {}, "outputs": [ @@ -875,12 +874,15 @@ "
" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "# we use the i() operator pyfixest provides, as it allows us to easily set the\n", + "# reference year, and works smoothly with the iplot() method\n", + "\n", "fml = \"\"\"lnr ~ i(year, repeal, ref = 1985) + C(repeal) + C(year) + C(fip)\n", " + acc + ir + pi + alcohol + crack + poverty + income + ur\n", "\"\"\"\n", @@ -896,6 +898,14 @@ " labels=_extract_bracket_numbers(fit._coefnames),\n", ")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a9c0d905", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 505f70386b036b2c6f0e0e3758144e4549f19db9 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sat, 30 Aug 2025 12:41:17 +0200 Subject: [PATCH 08/14] improved image resolution --- docs/mixtape.ipynb | 202 ++++++++++++++------------------------------- 1 file changed, 64 insertions(+), 138 deletions(-) diff --git a/docs/mixtape.ipynb b/docs/mixtape.ipynb index 0ffe550b4..d5f635e64 100644 --- a/docs/mixtape.ipynb +++ b/docs/mixtape.ipynb @@ -12,98 +12,19 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "15b9d555", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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\n", - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import re\n", "\n", "import numpy as np\n", "import pandas as pd\n", "\n", - "import pyfixest as pf" + "import pyfixest as pf\n", + "\n", + "%config InlineBackend.figure_format = \"retina\"" ] }, { @@ -128,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 12, "id": "7aae389f", "metadata": {}, "outputs": [ @@ -327,7 +248,7 @@ "[5 rows x 31 columns]" ] }, - "execution_count": 2, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -341,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 13, "id": "ea4f3aa3", "metadata": {}, "outputs": [], @@ -367,7 +288,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 14, "id": "62252710", "metadata": {}, "outputs": [], @@ -391,7 +312,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 15, "id": "23eadf65", "metadata": {}, "outputs": [], @@ -403,59 +324,59 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 16, "id": "304632e2", "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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category='source_notes', type='boolean', value=True), source_notes_sep=OptionsInfo(scss=False, category='source_notes', type='value', value=' '), row_striping_background_color=OptionsInfo(scss=True, category='row', type='value', value='rgba(128,128,128,0.05)'), row_striping_include_stub=OptionsInfo(scss=False, category='row', type='boolean', value=False), row_striping_include_table_body=OptionsInfo(scss=False, category='row', type='boolean', value=False), container_width=OptionsInfo(scss=False, category='container', type='px', value='auto'), container_height=OptionsInfo(scss=False, category='container', type='px', value='auto'), container_padding_x=OptionsInfo(scss=False, category='container', type='px', value='0px'), container_padding_y=OptionsInfo(scss=False, category='container', type='px', value='10px'), container_overflow_x=OptionsInfo(scss=False, category='container', type='overflow', value='auto'), container_overflow_y=OptionsInfo(scss=False, category='container', type='overflow', value='auto'), quarto_disable_processing=OptionsInfo(scss=False, category='quarto', type='logical', value=False), quarto_use_bootstrap=OptionsInfo(scss=False, category='quarto', type='logical', value=False)), _has_built=False)" ] }, - "execution_count": 6, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -616,7 +537,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 17, "id": "4218b23c", "metadata": {}, "outputs": [ @@ -815,7 +736,7 @@ "[5 rows x 39 columns]" ] }, - "execution_count": 7, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -833,7 +754,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 18, "id": "8b880c1d", "metadata": {}, "outputs": [], @@ -852,7 +773,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "913e912c", "metadata": {}, "outputs": [ @@ -869,13 +790,18 @@ }, { "data": { - "image/png": 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", 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Format of coefficient cell:\\nCoefficient \\n (Std. 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" ] }, - "execution_count": 12, + "execution_count": 9, "metadata": { "image/png": { "height": 588, From f37c33cea000a5ae42b19505f8d8831941af79ab Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sat, 30 Aug 2025 15:21:21 +0200 Subject: [PATCH 10/14] use cat_template for iplot, coefplot --- docs/mixtape.ipynb | 161 ++++++++++++++++------------------- pyfixest/report/utils.py | 2 +- pyfixest/report/visualize.py | 43 ++++++++-- 3 files changed, 112 insertions(+), 94 deletions(-) diff --git a/docs/mixtape.ipynb b/docs/mixtape.ipynb index 3fa8b6977..7b4dba486 100644 --- a/docs/mixtape.ipynb +++ b/docs/mixtape.ipynb @@ -20,7 +20,7 @@ "data": { "text/html": [ "\n", - "
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\n", " \n", @@ -60,7 +60,7 @@ "data": { "text/html": [ "\n", - "
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15.0 2.0 1986.0 2 5344.0 106831 6351.200195 5002.299805 \n", - "71 1.0 15.0 2.0 1987.0 2 4983.0 106496 5759.100098 4679.000000 \n", - "89 1.0 15.0 2.0 1988.0 2 5276.0 105238 6139.600098 5013.399902 \n", - "106 1.0 15.0 2.0 1989.0 2 5692.0 102956 5951.500000 5528.600098 \n", + " fip age race year sex totcase totpop rate totrate \\\n", + "19 1.0 15.0 2.0 1985 2 5683.0 106187 6527.500000 5351.899902 \n", + "39 1.0 15.0 2.0 1986 2 5344.0 106831 6351.200195 5002.299805 \n", + "71 1.0 15.0 2.0 1987 2 4983.0 106496 5759.100098 4679.000000 \n", + "89 1.0 15.0 2.0 1988 2 5276.0 105238 6139.600098 5013.399902 \n", + "106 1.0 15.0 2.0 1989 2 5692.0 102956 5951.500000 5528.600098 \n", "\n", " id ... female lnr t younger fa pi wm15 wf15 bm15 \\\n", "19 14.0 ... 1.0 8.783779 1.0 1.0 1.0 0.0 0.0 0.0 0.0 \n", @@ -817,7 +815,7 @@ "[5 rows x 39 columns]" ] }, - "execution_count": 7, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -830,35 +828,13 @@ "abortion_bf15 = abortion[abortion.bf15 == 1]\n", "# pf throws error when weights are 0\n", "abortion_bf15 = abortion_bf15[abortion_bf15.totpop > 0]\n", + "abortion_bf15[\"year\"] = abortion_bf15[\"year\"].astype(int)\n", "abortion_bf15.head()" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "8b880c1d", - "metadata": {}, - "outputs": [], - "source": [ - "def _extract_bracket_numbers(str_list) -> dict:\n", - " \"Extract time from event study coefficients and store in dict.\"\n", - " out = {}\n", - " pattern = re.compile(r\"\\[(\\d+\\.?\\d*)\\]\") # capture numbers inside []\n", - "\n", - " for s in str_list:\n", - " match = pattern.search(str(s)) # convert np.str_ -> str\n", - " if match:\n", - " num_str = match.group(1)\n", - " # Convert to int if it's whole, else keep as string\n", - " if num_str.endswith(\".0\"):\n", - " num_str = str(int(float(num_str)))\n", - " out[s] = num_str\n", - " return out" - ] - }, - { - "cell_type": "code", - "execution_count": 9, + "execution_count": null, 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" ] }, - "execution_count": 9, + "execution_count": 12, "metadata": { "image/png": { "height": 588, @@ -905,7 +881,8 @@ " coord_flip=False,\n", " plot_backend=\"matplotlib\",\n", " title=\"Event Study Estimate\",\n", - " labels=_extract_bracket_numbers(fit._coefnames),\n", + " cat_template=\"{value}\",\n", + " rotate_xticks=90,\n", ")" ] }, @@ -916,6 +893,14 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e1120d82", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/pyfixest/report/utils.py b/pyfixest/report/utils.py index 80f7260b2..91be864b7 100644 --- a/pyfixest/report/utils.py +++ b/pyfixest/report/utils.py @@ -35,7 +35,7 @@ def _relabel_expvar( str The relabeled variable """ - if labels == {} and cat_template != "" and ("C(" in varname or "[" in varname): + if not labels and cat_template != "" and ("C(" in varname or "[" in varname): v = _rename_categorical(varname, template=cat_template, labels=labels) else: v = labels.get(varname, varname) diff --git a/pyfixest/report/visualize.py b/pyfixest/report/visualize.py index 150a59ae5..cd4fbf43e 100644 --- a/pyfixest/report/visualize.py +++ b/pyfixest/report/visualize.py @@ -94,6 +94,7 @@ def iplot( exact_match: bool = False, plot_backend: str = "lets_plot" if _HAS_LETS_PLOT else "matplotlib", labels: Optional[dict] = None, + cat_template: Optional[str] = None, rename_models: Optional[dict[str, str]] = None, ax: Optional[plt.Axes] = None, joint: Optional[Union[str, bool]] = None, @@ -148,6 +149,11 @@ def iplot( A dictionary to relabel the variables. The keys in this dictionary are the original variable names, which correspond to the names stored in the `_coefnames` attribute of the model. The values in the dictionary are the new names you want to assign to these variables. Note that interaction terms will also be relabeled using the labels of the individual variables. The renaming is applied after the selection of the coefficients via `keep` and `drop`. + cat_template: str, optional + Template to relabel categorical variables. None by default, which applies no relabeling. + Other options include combinations of "{variable}" and "{value}", e.g. "{variable}::{value}" + to mimic fixest encoding. But "{variable}--{value}" or "{variable}{value}" or just "{value}" + are also possible. joint: str or bool, optional Whether to plot simultaneous confidence bands for the coefficients. If True, simultaneous confidence bands are plotted. If False, "standard" confidence intervals are plotted. If "both", both are plotted in @@ -259,6 +265,7 @@ def iplot( title=title, flip_coord=coord_flip, labels=labels, + cat_template=cat_template, ax=ax, ) @@ -514,6 +521,7 @@ def _coefplot_lets_plot( title: Optional[str] = None, flip_coord: Optional[bool] = True, labels: Optional[dict] = None, + cat_template: Optional[str] = None, ax=None, # for compatibility with matplotlib backend ): """ @@ -539,6 +547,11 @@ def _coefplot_lets_plot( Whether to flip the coordinates of the plot. Default is True. labels : dict, optional A dictionary to relabel the variables. The keys are the original variable names and the values the new names. + cat_template : str, optional + Template to relabel categorical variables. None by default, which applies no relabeling. + Other options include combinations of "{variable}" and "{value}", e.g. "{variable}::{value}" + to mimic fixest encoding. But "{variable}--{value}" or "{variable}{value}" or just "{value}" + are also possible. ax : None, optional Not used. Only for compatibility with the matplotlib backend. @@ -551,10 +564,17 @@ def _coefplot_lets_plot( df.rename(columns={"fml": "Model"}, inplace=True) ub, lb = 1 - alpha / 2, alpha / 2 - if labels is not None: - interactionSymbol = " x " + labels_dict = {} if labels is None else labels + + if not labels_dict or cat_template is not None: + interactionSymbol = ":" df["Coefficient"] = df["Coefficient"].apply( - lambda x: _relabel_expvar(x, labels, interactionSymbol) + lambda x: _relabel_expvar( + x, + labels_dict, + interactionSymbol, + cat_template if cat_template is not None else "", + ) ) plot = ( @@ -594,6 +614,7 @@ def _coefplot_matplotlib( title: Optional[str] = None, flip_coord: Optional[bool] = True, labels: Optional[dict] = None, + cat_template: Optional[str] = None, ax: Optional[plt.Axes] = None, dodge: float = 0.5, **fig_kwargs, @@ -622,6 +643,11 @@ def _coefplot_matplotlib( Whether to flip the coordinates of the plot. Default is True. labels : dict, optional A dictionary to relabel the variables. The keys are the original variable names and the values the new names. + cat_template : str, optional + Template to relabel categorical variables. None by default, which applies no relabeling. + Other options include combinations of "{variable}" and "{value}", e.g. "{variable}::{value}" + to mimic fixest encoding. But "{variable}--{value}" or "{variable}{value}" or just "{value}" + are also possible. dodge : float, optional The amount to dodge each model's points by. Default is 0.1. fig_kwargs : dict @@ -632,10 +658,17 @@ def _coefplot_matplotlib( matplotlib.figure.Figure A matplotlib Figure object. """ - if labels is not None: + labels_dict = {} if labels is None else labels + + if not labels_dict or cat_template is not None: interactionSymbol = ":" df["Coefficient"] = df["Coefficient"].apply( - lambda x: _relabel_expvar(x, labels, interactionSymbol) + lambda x: _relabel_expvar( + x, + labels_dict, + interactionSymbol, + cat_template if cat_template is not None else "", + ) ) ub, lb = (f"{round(x * 100, 1)}%" for x in [1 - alpha / 2, alpha / 2]) From 9a1626f3b12c6af000954e075bad760bf7fd5b66 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sat, 30 Aug 2025 15:22:07 +0200 Subject: [PATCH 11/14] Update nb --- docs/mixtape.ipynb | 103 ++++++++++++++++++++++----------------------- 1 file changed, 51 insertions(+), 52 deletions(-) diff --git a/docs/mixtape.ipynb b/docs/mixtape.ipynb index 7b4dba486..209741fbd 100644 --- a/docs/mixtape.ipynb +++ b/docs/mixtape.ipynb @@ -20,7 +20,7 @@ "data": { "text/html": [ "\n", - "
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source_notes_padding_horizontal=OptionsInfo(scss=True, category='source_notes', type='px', value='5px'), source_notes_background_color=OptionsInfo(scss=True, category='source_notes', type='value', value=None), source_notes_font_size=OptionsInfo(scss=True, category='source_notes', type='px', value='90%'), source_notes_border_bottom_style=OptionsInfo(scss=True, category='source_notes', type='value', value='none'), source_notes_border_bottom_width=OptionsInfo(scss=True, category='source_notes', type='px', value='2px'), source_notes_border_bottom_color=OptionsInfo(scss=True, category='source_notes', type='value', value='#D3D3D3'), source_notes_border_lr_style=OptionsInfo(scss=True, category='source_notes', type='value', value='none'), source_notes_border_lr_width=OptionsInfo(scss=True, category='source_notes', type='px', value='2px'), source_notes_border_lr_color=OptionsInfo(scss=True, category='source_notes', type='value', value='#D3D3D3'), source_notes_multiline=OptionsInfo(scss=False, category='source_notes', type='boolean', value=True), source_notes_sep=OptionsInfo(scss=False, category='source_notes', type='value', value=' '), row_striping_background_color=OptionsInfo(scss=True, category='row', type='value', value='rgba(128,128,128,0.05)'), row_striping_include_stub=OptionsInfo(scss=False, category='row', type='boolean', value=False), row_striping_include_table_body=OptionsInfo(scss=False, category='row', type='boolean', value=False), container_width=OptionsInfo(scss=False, category='container', type='px', value='auto'), container_height=OptionsInfo(scss=False, category='container', type='px', value='auto'), container_padding_x=OptionsInfo(scss=False, category='container', type='px', value='0px'), container_padding_y=OptionsInfo(scss=False, category='container', type='px', value='10px'), container_overflow_x=OptionsInfo(scss=False, category='container', type='overflow', value='auto'), container_overflow_y=OptionsInfo(scss=False, category='container', type='overflow', value='auto'), quarto_disable_processing=OptionsInfo(scss=False, category='quarto', type='logical', value=False), quarto_use_bootstrap=OptionsInfo(scss=False, category='quarto', type='logical', value=False)), _has_built=False)" ] }, "execution_count": 6, @@ -616,7 +616,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "id": "4218b23c", "metadata": {}, "outputs": [ @@ -815,7 +815,7 @@ "[5 rows x 39 columns]" ] }, - "execution_count": 11, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -834,7 +834,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "913e912c", "metadata": {}, "outputs": [ @@ -851,12 +851,12 @@ }, { "data": { - "image/png": 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53M7q1PPf//63wu+i80z5I/CGPCr/B7T8H9bKTqzFYDLro48+qlDm6quvDn369EneIPv37x8uvvji8PTTTydfLu64447Qt2/fZD1+2IxXWq48aiGeIIhTTWVHNZxzzjnhe9/7XnjrrbcqTL0RTyTsvffe4auvvlqj11+sGlK/rkqc6ib7pnniiSfmRqjS8Ps1/p7eeeedSZl4lV6/fv2SsnG/WNdFF12UXMkXjxGP9cc//jEvP4di05D6dcstt0xOBNXkatry2ytrS2OWrz7N1jN//vzw6quv1qgvPv300wpTusUTAlGnTp1W2fbOnTsnj/ELDQ27X2m8/XrppZfm1uM0dKSrX8eMGZN83o1LnB4yTtcZw9LsSKU4Ci1+1yE9/XrllVeGSZMmJX3561//uoavhobcp1G8GOWNN95IjhunY40jpP7973+HsrKy5HN29pZcNPx+zZ6UX2+99ZIALU6ZG0cylhc/+8b315122sl3m5T0a3XivtlRjfF3tvwtQ2jY/RpnSomDn+L5pvjZqHfv3uGmm25KzjnFz1DxvNKOO+6YXCQY33f//ve/1/IVNw4NqV9jIBpDzyiej6rud7mxnmeKmcgf/vCHar/jZc/r1OTcTva8TmXndlannnhhRPn9ytfjPFMe5PF+4FB0Pvjgg3ipcbIMGTJkleU/+uijTNOmTZPyHTt2zMyZM6dCmWXLlmUOOOCAXL1xue+++yqtb+HChZk///nPmY022miF8nEpKSnJnHbaaZmpU6dWuu8XX3yRK9uiRYvksUOHDpm77ror2fbNN99kxo4dm9lll11y5crKyjKNQZr7dVX23nvvXF3Tpk3LNCbF0q+xTCwb91m5nlh3PEY8VmOR9n7df//9c+XvueeeSsvE58vXe8ghh2SKWX316T/+8Y/ctgMPPDDZZ2Wx7k022WSFej7//PMVyqyzzjrJ8zvvvPMq277TTjslZVu1apUpdmnv16pkyw8YMCDTGBVrv2bF42b33XHHHTPLly/PNAbF1K+jR4+u8P4cly5dumSefPLJTGNSDP367rvv5r67Pv744ytsu+2223L7xvXGoBj6dKuttsoceuihmeuuuy4zZsyYzGuvvZb83l566aWZzp075/bt0aNH5quvvso0Bmnv1zZt2iTPN2vWLPk+FP8d+/fTTz/NLFq0KPPyyy9nDj744Nz+/fr1yyxdujRT7NLer9UZNmxYbj/vrens1//+97+Zn/70p5m11lqrwmem+F31kksuycydOzfTWKS9X88444zctvh+Wplnn302U1pamivXq1evTGNx1VVX5V73EUccUWmZK664Ildm5c+cK3vsscdyZWPd5R100EG5bd9++2219fzsZz/LlY3vleU5z5Q/Am/I4xtg9IMf/CC3T7du3TIPPvhg5uuvv07+6E2YMCEXfsQvB9lyd955Z6V1Pfzww5m+fftWehInLu3atcv8/Oc/zyxevLjSDzPly8Y/nG+//XaFcjH47t27d67cCy+8kCl2ae7X6sQ+z36YiRcyNDbF0K9x2y9/+cukbFX1xGP85z//yTQWae/XeLzsF6P45TJ+kfzwww8zS5YsSR7jv+Pz5duyzz77ZIpZffVpPNFW/v0ufhmN+8Y6Yl2xzlj3yvXEv63lZf/O9u/ff5XtjmVi2SZNmmSKXdr7tSrZ8gLv4urXaMqUKZnWrVsn+6299trJvxuLYurXBQsWZCZNmpQs8cTRAw88kBk6dGjydzde7HvLLbdkGoti6NfsxbtHH310hW0C73T26ZdfflllW+fNm5fZb7/9cvufe+65mcYg7f0a/75mt8XPxfFChpXFECeGOdly//znPzPFLu39WpVYT/Yihxj0VRbQFbNi6Nd4QeeVV16Z2XTTTXPlVl66d++eufXWWzONRdr79f333899j4kXHv34xz/OvPPOO8l5ptmzZycXIcXt5evo2rVrpjGI70nZc3Dt27dPLsaqzMUXX5z72TzzzDPV1hm3Z8vGc3hVDTxb1d/H3/zmN7my8YKE8pxnyh+BN+T5DTBe0Vr+6p7KlhienHXWWbl/xze0lcURhdkRnnvuuWfmqaeeSt78YqgST8jFK/OyXzR23333zPz58ytcEVb+mD/60Y+qbPMjjzzSqL5kprlfq3PZZZfljn3DDTdkGpu092s8YVv+Q0u88i+OHI51xLrildR77LFH7gPtH//4x0xjkPZ+jeIXx+wH7sqWGLTELyTZfx9++OGZYlaffRq/GG655ZbV1nPmmWcmI1Gy/155tJErb4uzX6uSLS/wLq5+nTVrVjICOPueGkdbNCbF2q/lPf3005nmzZsn+1900UWZxiDt/ZoNtGOwEn9HVybwTl+f1kTcp23btsn+LVu2rPVF32mU9n6N/ZTdFkfvV+Wtt95qVLMJpr1fq1J+RGq80LuxSXu/xgAuXkSW3X7qqadmXn311SRgjeegxo8fn/weZ7efc845mcYg7f2aHXUczzNUVUc8V/W3v/0t9+/tt98+U+zi+87666+fvN44Y1Cc2bYqRngXL4E35PkNMPuB4uabb07eTMpPSxyvLDr//POTP4DxzSv7/Mp/gN94443clT377rtvldM/xRAlW8eIESMqvBGXf6OLoXZVYnuyYUwM1Ipdmvu1Ottss02yTzy5F6etb2zS3q8xPM1uv/322yut47vvvsvstddeSZl4zNdffz1T7NLer1mvvPJKcqKn/Ami+Hc3frmMFzbE2TWyz59yyimZYlZffZoVvyzG8itf4R7/hmZ/9+IV7tkviStPcZyd3r5nz56rbHMsE8tuvPHGmWKX9n6tSrYegXfx9GucrjH7uxmXeMFRY1OM/VrdSaX4fr66twtKkzT362effZbZYIMNkm3XXHNNpfULvNPVp7UxfPjwXN3PPfdcptilvV/j59qavofGEcGxXKdOnTLFLu39WpXyI/UnT56caWzS3q/lL6y/8MILq2zvSSedlCv30EMPZYpd2vs1K47qPvnkkzPrrbdero7Yrnje8Pnnn8988sknuefjc8UsXkiQnQo+/swqu9igvDhQLPuzuffee6stG7dny954440rbDv22GNz2yqb6r68s88+O1d25Vl4nWfKH4E31MEb4MrTdMV7kcWr1MtPbRGnrK3qD2KciqSmX/jiPbFiuXhV9MpvgOWnRY7hTXWyX1q23nrrTLFLe79WZuLEibm6jzzyyExjlOZ+jevZkQ1x+qLqxCtws8eLxy52ae7Xqi5aiPd/eu+991a4+jNOdVXVFaPFpr76tDKxzLRp01a4J1a8uCE7KrCy+1zF+/zGbeuuu+4q689O/xevzi52ae/XqmSPK/Aujn6NbSk/WmLlKekai2Lr16rE9+9sW37/+99nil2a+zXefzI+H0/Y3nXXXcmIwpWX8ieQ43r2+aqmqiwGae7T2igfyPz73//OFLu092v599FV3Wor3motlov1Fbu092tlYliWndEsfgdqjNLerzGYjdvi9NZxYFRVyt8Ws9hnnCuGfl1ZPP7MmTOTADyO3M+K02Zn2xKnZC9WsQ+22GKLXOB/xx13rHKfePvC7M/mT3/6U7Vlr7766lzZRx99dIVtP/nJT3LbXnvttWrryc6mENtYvp8i55nyp2kA6lTr1q2Tpbxly5aF119/PVnfYostwoYbbrjC9qlTp+bW+/TpU239cfu7774bvvjii/DZZ5+FjTbaKLetZ8+eYcyYMbljVie7vWlTfxYaer9WZuTIkbn1IUOG1Oq1UP/9+umnnybPRTvssEO1dey444659bfffrvGr60xa0i/r/FvbOfOnSs8/8orr+TWd9pppxq8qsZtdfq0MrHMyuXeeuutsHjx4ir7Yptttkn66+uvvw6ffPJJ2HjjjSute/bs2WHevHnJeo8ePWrx6hqv+uxXir9fv/322zB48OAwceLE5N/nnXde+PWvf13r10PD6tfqtGvXLrf+4YcfrnY9jUl99Wv2+a+++iqceOKJq6z/xhtvTJZo9OjRoX379qvcp7FKw+9qSUnJau/bWNVnv8bzTNn3UueZivv39e677871ofNM6ezX7HmN+B22efPmVdbdqVOn5DxGPDflPFP6fl9LS0tDx44dG+V5ps8//zwMGjQovP/++8m/r7322nDyySevcr/4O5G1qv/z5bevfG5n5Xq23377VdYTzwm2bNmyQj3OM+VHaZ7qAWohfimfO3dusn7sscdW2F7+y8DSpUurreu7776rdL9ozz33zK1n//BXJv6hjG8QUWVvkDSsfq2s7D//+c/cib0DDzyw1m2nfvu1rv5v0PB+XysTvxQ98MADuQ++u+22W63rYNV9WlP33ntvbr2yevbYY4/c+tixY6usp/y23XfffbXb09gVql8p7n6Nf6ePPPLI3O/lmWeeGa644orVPi7p+H2dNWtWbr1Vq1arXU9j19D6leLr0ylTpuTWN9lkk9Wup7ErVL/W9DxT+e3OM6Xz9zU7sGKttdYKxx9//Gofm/rr1+z5iVWd0yh/XsN5puJ5f83Ws/baaycX/habGA7vv//+uc8Rf/jDH8LZZ59do30333zz3GeO6s7rROPGjcu9l3Xp0mW1zg/FEPudd96p8vyQ80x5lMfR4lB08jHFycridLe77757Uudaa62V3GNiZXGakexxH3vssSrrWrJkSaZDhw65KS9Wnko3TmOered73/telfXE+4M0pqkd096vK4vTiGXrPeecczKNVZr7NU4/lJ2SJt5zJk57XZNpd374wx9mil2a+7Wmbrrpptyxfve732WKXX31aU3Ee4jGfszeXqCy/pw9e3bu/u77779/lXXFbbFMLBv3KXZp79eqmNI8/f0apwQ86qijcq8j3qNwde8zWyyKoV9re1/gxnDP52LvV/fwLr4+zd7HNHv/9nXWWafaKXeLRdr7NU67G48Ry8RjVmXMmDG513nqqadmil3a+3Vlb775Zu71HHbYYZnGKu39GqfDzt5W4Msvv6y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" ] }, - "execution_count": 12, + "execution_count": 9, "metadata": { "image/png": { "height": 588, @@ -882,7 +882,6 @@ " plot_backend=\"matplotlib\",\n", " title=\"Event Study Estimate\",\n", " cat_template=\"{value}\",\n", - " rotate_xticks=90,\n", ")" ] }, From 5daf23d3816c2fe5a00fab84fe79f7b6d9c29980 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sat, 30 Aug 2025 15:23:20 +0200 Subject: [PATCH 12/14] link to pydata video --- docs/textbooks.qmd | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/textbooks.qmd b/docs/textbooks.qmd index acdfad74e..56865f372 100644 --- a/docs/textbooks.qmd +++ b/docs/textbooks.qmd @@ -30,6 +30,8 @@ If you are teaching with pyfixest, we'd love to hear from you! - Econometrics II (taught by Vladislav Morozov at UBonn): Great intro to fixed effects estimation theory. Slides on fixed effects [here](https://vladislav-morozov.github.io/econometrics-2/slides/panel/fe.html#/title-slide), full class notes [here](https://vladislav-morozov.github.io/econometrics-2/), [github repository](https://github.com/vladislav-morozov/econometrics-2) -## Blog Posts, Notebooks +## Blog Posts, Notebooks, Videos If you've written a blog post that illustrates how to use pyfixest, please let us know, we'd love to link to it. + +- PyData Berlin Presentation (2024) on PyFixest: [link](https://www.youtube.com/watch?v=kSQxGGA7Rr4) From 56502eb73517c2d6c3a6a2ffdaa7c936f12e8453 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sat, 30 Aug 2025 15:24:08 +0200 Subject: [PATCH 13/14] rename textbooks.qmd to resources.qmd --- docs/_quarto.yml | 4 ++-- docs/{textbooks.qmd => resources.qmd} | 0 2 files changed, 2 insertions(+), 2 deletions(-) rename docs/{textbooks.qmd => resources.qmd} (100%) diff --git a/docs/_quarto.yml b/docs/_quarto.yml index 3d602c925..b74ef48db 100644 --- a/docs/_quarto.yml +++ b/docs/_quarto.yml @@ -53,8 +53,8 @@ website: file: stata-2-pyfixest.qmd - text: "PyFixest on the GPU" file: pyfixest_gpu.ipynb - - text: "Learnings Econometrics with PyFixest" - file: textbooks.qmd + - text: "Other Resources around PyFixest" + file: resources.qmd - text: "Replicating 'The Effect' with PyFixest" file: replicating-the-effect.qmd - text: "Replicating 'The Mixtape' with PyFixest" diff --git a/docs/textbooks.qmd b/docs/resources.qmd similarity index 100% rename from docs/textbooks.qmd rename to docs/resources.qmd From 2919ffb623d6256b26a5e8cc70f2b3363787d395 Mon Sep 17 00:00:00 2001 From: Alexander Fischer Date: Sat, 30 Aug 2025 15:29:57 +0200 Subject: [PATCH 14/14] use cat_template for did plots, deprecation warning to encoding function --- docs/difference-in-differences.qmd | 6 +++--- pyfixest/report/utils.py | 5 +++++ 2 files changed, 8 insertions(+), 3 deletions(-) diff --git a/docs/difference-in-differences.qmd b/docs/difference-in-differences.qmd index 9b749e8ea..56fa3d0ac 100644 --- a/docs/difference-in-differences.qmd +++ b/docs/difference-in-differences.qmd @@ -186,7 +186,7 @@ fit_dynamic_twfe.iplot( figsize=[1200, 400], yintercept=0, xintercept=13.5, - labels=rename_event_study_coefs(fit_dynamic_twfe._coefnames), + cat_template="{variable}::{value}" ) ``` @@ -218,7 +218,7 @@ fit_dynamic_twfe.iplot( yintercept=0, xintercept=13.5, joint="both", - labels=rename_event_study_coefs(fit_dynamic_twfe._coefnames), + cat_template="{variable}::{value}" ) ``` @@ -348,7 +348,7 @@ fit_twfe.iplot( figsize=figsize, xintercept=18.5, yintercept=0, - labels=rename_event_study_coefs(fit_twfe._coefnames), # rename coefficients + cat_template="{variable}::{value}" ).show() ``` diff --git a/pyfixest/report/utils.py b/pyfixest/report/utils.py index 91be864b7..ee91e228e 100644 --- a/pyfixest/report/utils.py +++ b/pyfixest/report/utils.py @@ -166,4 +166,9 @@ def rename_event_study_coefs(coef_names_list: list): 'Intercept': 'Intercept' } """ + # add a deprecation warning + warnings.warn( + "rename_event_study_coefs is deprecated and will be removed in a future version. Please use the cat_template argument instead." + ) + return {col: _rename_event_study_coefs(col) for col in coef_names_list}