From 8cd11137011efea943d5aaba7e68dc63191f05b8 Mon Sep 17 00:00:00 2001
From: yoongja <83260809+yoongja@users.noreply.github.com>
Date: Sun, 11 Jul 2021 23:59:08 +0900
Subject: [PATCH 1/2] =?UTF-8?q?03-=EA=B9=80=EC=9C=A4=EC=A7=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../.ipynb_checkpoints/03-checkpoint.ipynb    | 548 ++++++++++++++++++
 assignments/yoongja/03.ipynb                  | 548 ++++++++++++++++++
 2 files changed, 1096 insertions(+)
 create mode 100644 assignments/yoongja/.ipynb_checkpoints/03-checkpoint.ipynb
 create mode 100644 assignments/yoongja/03.ipynb

diff --git a/assignments/yoongja/.ipynb_checkpoints/03-checkpoint.ipynb b/assignments/yoongja/.ipynb_checkpoints/03-checkpoint.ipynb
new file mode 100644
index 0000000..f70effd
--- /dev/null
+++ b/assignments/yoongja/.ipynb_checkpoints/03-checkpoint.ipynb
@@ -0,0 +1,548 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country name</th>\n",
+       "      <th>Regional indicator</th>\n",
+       "      <th>Ladder score</th>\n",
+       "      <th>Standard error of ladder score</th>\n",
+       "      <th>upperwhisker</th>\n",
+       "      <th>lowerwhisker</th>\n",
+       "      <th>Logged GDP per capita</th>\n",
+       "      <th>Social support</th>\n",
+       "      <th>Healthy life expectancy</th>\n",
+       "      <th>Freedom to make life choices</th>\n",
+       "      <th>Generosity</th>\n",
+       "      <th>Perceptions of corruption</th>\n",
+       "      <th>Ladder score in Dystopia</th>\n",
+       "      <th>Explained by: Log GDP per capita</th>\n",
+       "      <th>Explained by: Social support</th>\n",
+       "      <th>Explained by: Healthy life expectancy</th>\n",
+       "      <th>Explained by: Freedom to make life choices</th>\n",
+       "      <th>Explained by: Generosity</th>\n",
+       "      <th>Explained by: Perceptions of corruption</th>\n",
+       "      <th>Dystopia + residual</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Finland</td>\n",
+       "      <td>Western Europe</td>\n",
+       "      <td>7.842</td>\n",
+       "      <td>0.032</td>\n",
+       "      <td>7.904</td>\n",
+       "      <td>7.780</td>\n",
+       "      <td>10.775</td>\n",
+       "      <td>0.954</td>\n",
+       "      <td>72.000</td>\n",
+       "      <td>0.949</td>\n",
+       "      <td>-0.098</td>\n",
+       "      <td>0.186</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>1.446</td>\n",
+       "      <td>1.106</td>\n",
+       "      <td>0.741</td>\n",
+       "      <td>0.691</td>\n",
+       "      <td>0.124</td>\n",
+       "      <td>0.481</td>\n",
+       "      <td>3.253</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Denmark</td>\n",
+       "      <td>Western Europe</td>\n",
+       "      <td>7.620</td>\n",
+       "      <td>0.035</td>\n",
+       "      <td>7.687</td>\n",
+       "      <td>7.552</td>\n",
+       "      <td>10.933</td>\n",
+       "      <td>0.954</td>\n",
+       "      <td>72.700</td>\n",
+       "      <td>0.946</td>\n",
+       "      <td>0.030</td>\n",
+       "      <td>0.179</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>1.502</td>\n",
+       "      <td>1.108</td>\n",
+       "      <td>0.763</td>\n",
+       "      <td>0.686</td>\n",
+       "      <td>0.208</td>\n",
+       "      <td>0.485</td>\n",
+       "      <td>2.868</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Switzerland</td>\n",
+       "      <td>Western Europe</td>\n",
+       "      <td>7.571</td>\n",
+       "      <td>0.036</td>\n",
+       "      <td>7.643</td>\n",
+       "      <td>7.500</td>\n",
+       "      <td>11.117</td>\n",
+       "      <td>0.942</td>\n",
+       "      <td>74.400</td>\n",
+       "      <td>0.919</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>0.292</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>1.566</td>\n",
+       "      <td>1.079</td>\n",
+       "      <td>0.816</td>\n",
+       "      <td>0.653</td>\n",
+       "      <td>0.204</td>\n",
+       "      <td>0.413</td>\n",
+       "      <td>2.839</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Iceland</td>\n",
+       "      <td>Western Europe</td>\n",
+       "      <td>7.554</td>\n",
+       "      <td>0.059</td>\n",
+       "      <td>7.670</td>\n",
+       "      <td>7.438</td>\n",
+       "      <td>10.878</td>\n",
+       "      <td>0.983</td>\n",
+       "      <td>73.000</td>\n",
+       "      <td>0.955</td>\n",
+       "      <td>0.160</td>\n",
+       "      <td>0.673</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>1.482</td>\n",
+       "      <td>1.172</td>\n",
+       "      <td>0.772</td>\n",
+       "      <td>0.698</td>\n",
+       "      <td>0.293</td>\n",
+       "      <td>0.170</td>\n",
+       "      <td>2.967</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Netherlands</td>\n",
+       "      <td>Western Europe</td>\n",
+       "      <td>7.464</td>\n",
+       "      <td>0.027</td>\n",
+       "      <td>7.518</td>\n",
+       "      <td>7.410</td>\n",
+       "      <td>10.932</td>\n",
+       "      <td>0.942</td>\n",
+       "      <td>72.400</td>\n",
+       "      <td>0.913</td>\n",
+       "      <td>0.175</td>\n",
+       "      <td>0.338</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>1.501</td>\n",
+       "      <td>1.079</td>\n",
+       "      <td>0.753</td>\n",
+       "      <td>0.647</td>\n",
+       "      <td>0.302</td>\n",
+       "      <td>0.384</td>\n",
+       "      <td>2.798</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>Lesotho</td>\n",
+       "      <td>Sub-Saharan Africa</td>\n",
+       "      <td>3.512</td>\n",
+       "      <td>0.120</td>\n",
+       "      <td>3.748</td>\n",
+       "      <td>3.276</td>\n",
+       "      <td>7.926</td>\n",
+       "      <td>0.787</td>\n",
+       "      <td>48.700</td>\n",
+       "      <td>0.715</td>\n",
+       "      <td>-0.131</td>\n",
+       "      <td>0.915</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>0.451</td>\n",
+       "      <td>0.731</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>0.405</td>\n",
+       "      <td>0.103</td>\n",
+       "      <td>0.015</td>\n",
+       "      <td>1.800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>Botswana</td>\n",
+       "      <td>Sub-Saharan Africa</td>\n",
+       "      <td>3.467</td>\n",
+       "      <td>0.074</td>\n",
+       "      <td>3.611</td>\n",
+       "      <td>3.322</td>\n",
+       "      <td>9.782</td>\n",
+       "      <td>0.784</td>\n",
+       "      <td>59.269</td>\n",
+       "      <td>0.824</td>\n",
+       "      <td>-0.246</td>\n",
+       "      <td>0.801</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>1.099</td>\n",
+       "      <td>0.724</td>\n",
+       "      <td>0.340</td>\n",
+       "      <td>0.539</td>\n",
+       "      <td>0.027</td>\n",
+       "      <td>0.088</td>\n",
+       "      <td>0.648</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>Rwanda</td>\n",
+       "      <td>Sub-Saharan Africa</td>\n",
+       "      <td>3.415</td>\n",
+       "      <td>0.068</td>\n",
+       "      <td>3.548</td>\n",
+       "      <td>3.282</td>\n",
+       "      <td>7.676</td>\n",
+       "      <td>0.552</td>\n",
+       "      <td>61.400</td>\n",
+       "      <td>0.897</td>\n",
+       "      <td>0.061</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>0.364</td>\n",
+       "      <td>0.202</td>\n",
+       "      <td>0.407</td>\n",
+       "      <td>0.627</td>\n",
+       "      <td>0.227</td>\n",
+       "      <td>0.493</td>\n",
+       "      <td>1.095</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>Sub-Saharan Africa</td>\n",
+       "      <td>3.145</td>\n",
+       "      <td>0.058</td>\n",
+       "      <td>3.259</td>\n",
+       "      <td>3.030</td>\n",
+       "      <td>7.943</td>\n",
+       "      <td>0.750</td>\n",
+       "      <td>56.201</td>\n",
+       "      <td>0.677</td>\n",
+       "      <td>-0.047</td>\n",
+       "      <td>0.821</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>0.457</td>\n",
+       "      <td>0.649</td>\n",
+       "      <td>0.243</td>\n",
+       "      <td>0.359</td>\n",
+       "      <td>0.157</td>\n",
+       "      <td>0.075</td>\n",
+       "      <td>1.205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>South Asia</td>\n",
+       "      <td>2.523</td>\n",
+       "      <td>0.038</td>\n",
+       "      <td>2.596</td>\n",
+       "      <td>2.449</td>\n",
+       "      <td>7.695</td>\n",
+       "      <td>0.463</td>\n",
+       "      <td>52.493</td>\n",
+       "      <td>0.382</td>\n",
+       "      <td>-0.102</td>\n",
+       "      <td>0.924</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>0.370</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.126</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.122</td>\n",
+       "      <td>0.010</td>\n",
+       "      <td>1.895</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>149 rows × 20 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Country name  Regional indicator  Ladder score  \\\n",
+       "0        Finland      Western Europe         7.842   \n",
+       "1        Denmark      Western Europe         7.620   \n",
+       "2    Switzerland      Western Europe         7.571   \n",
+       "3        Iceland      Western Europe         7.554   \n",
+       "4    Netherlands      Western Europe         7.464   \n",
+       "..           ...                 ...           ...   \n",
+       "144      Lesotho  Sub-Saharan Africa         3.512   \n",
+       "145     Botswana  Sub-Saharan Africa         3.467   \n",
+       "146       Rwanda  Sub-Saharan Africa         3.415   \n",
+       "147     Zimbabwe  Sub-Saharan Africa         3.145   \n",
+       "148  Afghanistan          South Asia         2.523   \n",
+       "\n",
+       "     Standard error of ladder score  upperwhisker  lowerwhisker  \\\n",
+       "0                             0.032         7.904         7.780   \n",
+       "1                             0.035         7.687         7.552   \n",
+       "2                             0.036         7.643         7.500   \n",
+       "3                             0.059         7.670         7.438   \n",
+       "4                             0.027         7.518         7.410   \n",
+       "..                              ...           ...           ...   \n",
+       "144                           0.120         3.748         3.276   \n",
+       "145                           0.074         3.611         3.322   \n",
+       "146                           0.068         3.548         3.282   \n",
+       "147                           0.058         3.259         3.030   \n",
+       "148                           0.038         2.596         2.449   \n",
+       "\n",
+       "     Logged GDP per capita  Social support  Healthy life expectancy  \\\n",
+       "0                   10.775           0.954                   72.000   \n",
+       "1                   10.933           0.954                   72.700   \n",
+       "2                   11.117           0.942                   74.400   \n",
+       "3                   10.878           0.983                   73.000   \n",
+       "4                   10.932           0.942                   72.400   \n",
+       "..                     ...             ...                      ...   \n",
+       "144                  7.926           0.787                   48.700   \n",
+       "145                  9.782           0.784                   59.269   \n",
+       "146                  7.676           0.552                   61.400   \n",
+       "147                  7.943           0.750                   56.201   \n",
+       "148                  7.695           0.463                   52.493   \n",
+       "\n",
+       "     Freedom to make life choices  Generosity  Perceptions of corruption  \\\n",
+       "0                           0.949      -0.098                      0.186   \n",
+       "1                           0.946       0.030                      0.179   \n",
+       "2                           0.919       0.025                      0.292   \n",
+       "3                           0.955       0.160                      0.673   \n",
+       "4                           0.913       0.175                      0.338   \n",
+       "..                            ...         ...                        ...   \n",
+       "144                         0.715      -0.131                      0.915   \n",
+       "145                         0.824      -0.246                      0.801   \n",
+       "146                         0.897       0.061                      0.167   \n",
+       "147                         0.677      -0.047                      0.821   \n",
+       "148                         0.382      -0.102                      0.924   \n",
+       "\n",
+       "     Ladder score in Dystopia  Explained by: Log GDP per capita  \\\n",
+       "0                        2.43                             1.446   \n",
+       "1                        2.43                             1.502   \n",
+       "2                        2.43                             1.566   \n",
+       "3                        2.43                             1.482   \n",
+       "4                        2.43                             1.501   \n",
+       "..                        ...                               ...   \n",
+       "144                      2.43                             0.451   \n",
+       "145                      2.43                             1.099   \n",
+       "146                      2.43                             0.364   \n",
+       "147                      2.43                             0.457   \n",
+       "148                      2.43                             0.370   \n",
+       "\n",
+       "     Explained by: Social support  Explained by: Healthy life expectancy  \\\n",
+       "0                           1.106                                  0.741   \n",
+       "1                           1.108                                  0.763   \n",
+       "2                           1.079                                  0.816   \n",
+       "3                           1.172                                  0.772   \n",
+       "4                           1.079                                  0.753   \n",
+       "..                            ...                                    ...   \n",
+       "144                         0.731                                  0.007   \n",
+       "145                         0.724                                  0.340   \n",
+       "146                         0.202                                  0.407   \n",
+       "147                         0.649                                  0.243   \n",
+       "148                         0.000                                  0.126   \n",
+       "\n",
+       "     Explained by: Freedom to make life choices  Explained by: Generosity  \\\n",
+       "0                                         0.691                     0.124   \n",
+       "1                                         0.686                     0.208   \n",
+       "2                                         0.653                     0.204   \n",
+       "3                                         0.698                     0.293   \n",
+       "4                                         0.647                     0.302   \n",
+       "..                                          ...                       ...   \n",
+       "144                                       0.405                     0.103   \n",
+       "145                                       0.539                     0.027   \n",
+       "146                                       0.627                     0.227   \n",
+       "147                                       0.359                     0.157   \n",
+       "148                                       0.000                     0.122   \n",
+       "\n",
+       "     Explained by: Perceptions of corruption  Dystopia + residual  \n",
+       "0                                      0.481                3.253  \n",
+       "1                                      0.485                2.868  \n",
+       "2                                      0.413                2.839  \n",
+       "3                                      0.170                2.967  \n",
+       "4                                      0.384                2.798  \n",
+       "..                                       ...                  ...  \n",
+       "144                                    0.015                1.800  \n",
+       "145                                    0.088                0.648  \n",
+       "146                                    0.493                1.095  \n",
+       "147                                    0.075                1.205  \n",
+       "148                                    0.010                1.895  \n",
+       "\n",
+       "[149 rows x 20 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('./world-happiness-report-2021.csv')\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assignment 1\n",
+    "행복 지수를 1점 간격으로 두고 다음과 같은 **Histogram**을 작성 해 주세요.\n",
+    "행복 지수의 키 값은 \"Ladder score\" 입니다."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assignment 2\n",
+    "행복 지수를 y축으로, GDP를 x축으로 하여, scatter 한 값을 한 번 입력 해 보세요."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assignment 3\n",
+    "행복지수 상위 10개, 하위 10개를 선택 후, 각각의 Social support, Logged GDP per capita, Freedom to make life choices 의 평균을 구해서 꺾은 선 그래프로 나타내세요."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/assignments/yoongja/03.ipynb b/assignments/yoongja/03.ipynb
new file mode 100644
index 0000000..f70effd
--- /dev/null
+++ b/assignments/yoongja/03.ipynb
@@ -0,0 +1,548 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Country name</th>\n",
+       "      <th>Regional indicator</th>\n",
+       "      <th>Ladder score</th>\n",
+       "      <th>Standard error of ladder score</th>\n",
+       "      <th>upperwhisker</th>\n",
+       "      <th>lowerwhisker</th>\n",
+       "      <th>Logged GDP per capita</th>\n",
+       "      <th>Social support</th>\n",
+       "      <th>Healthy life expectancy</th>\n",
+       "      <th>Freedom to make life choices</th>\n",
+       "      <th>Generosity</th>\n",
+       "      <th>Perceptions of corruption</th>\n",
+       "      <th>Ladder score in Dystopia</th>\n",
+       "      <th>Explained by: Log GDP per capita</th>\n",
+       "      <th>Explained by: Social support</th>\n",
+       "      <th>Explained by: Healthy life expectancy</th>\n",
+       "      <th>Explained by: Freedom to make life choices</th>\n",
+       "      <th>Explained by: Generosity</th>\n",
+       "      <th>Explained by: Perceptions of corruption</th>\n",
+       "      <th>Dystopia + residual</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Finland</td>\n",
+       "      <td>Western Europe</td>\n",
+       "      <td>7.842</td>\n",
+       "      <td>0.032</td>\n",
+       "      <td>7.904</td>\n",
+       "      <td>7.780</td>\n",
+       "      <td>10.775</td>\n",
+       "      <td>0.954</td>\n",
+       "      <td>72.000</td>\n",
+       "      <td>0.949</td>\n",
+       "      <td>-0.098</td>\n",
+       "      <td>0.186</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>1.446</td>\n",
+       "      <td>1.106</td>\n",
+       "      <td>0.741</td>\n",
+       "      <td>0.691</td>\n",
+       "      <td>0.124</td>\n",
+       "      <td>0.481</td>\n",
+       "      <td>3.253</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Denmark</td>\n",
+       "      <td>Western Europe</td>\n",
+       "      <td>7.620</td>\n",
+       "      <td>0.035</td>\n",
+       "      <td>7.687</td>\n",
+       "      <td>7.552</td>\n",
+       "      <td>10.933</td>\n",
+       "      <td>0.954</td>\n",
+       "      <td>72.700</td>\n",
+       "      <td>0.946</td>\n",
+       "      <td>0.030</td>\n",
+       "      <td>0.179</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>1.502</td>\n",
+       "      <td>1.108</td>\n",
+       "      <td>0.763</td>\n",
+       "      <td>0.686</td>\n",
+       "      <td>0.208</td>\n",
+       "      <td>0.485</td>\n",
+       "      <td>2.868</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Switzerland</td>\n",
+       "      <td>Western Europe</td>\n",
+       "      <td>7.571</td>\n",
+       "      <td>0.036</td>\n",
+       "      <td>7.643</td>\n",
+       "      <td>7.500</td>\n",
+       "      <td>11.117</td>\n",
+       "      <td>0.942</td>\n",
+       "      <td>74.400</td>\n",
+       "      <td>0.919</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>0.292</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>1.566</td>\n",
+       "      <td>1.079</td>\n",
+       "      <td>0.816</td>\n",
+       "      <td>0.653</td>\n",
+       "      <td>0.204</td>\n",
+       "      <td>0.413</td>\n",
+       "      <td>2.839</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Iceland</td>\n",
+       "      <td>Western Europe</td>\n",
+       "      <td>7.554</td>\n",
+       "      <td>0.059</td>\n",
+       "      <td>7.670</td>\n",
+       "      <td>7.438</td>\n",
+       "      <td>10.878</td>\n",
+       "      <td>0.983</td>\n",
+       "      <td>73.000</td>\n",
+       "      <td>0.955</td>\n",
+       "      <td>0.160</td>\n",
+       "      <td>0.673</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>1.482</td>\n",
+       "      <td>1.172</td>\n",
+       "      <td>0.772</td>\n",
+       "      <td>0.698</td>\n",
+       "      <td>0.293</td>\n",
+       "      <td>0.170</td>\n",
+       "      <td>2.967</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Netherlands</td>\n",
+       "      <td>Western Europe</td>\n",
+       "      <td>7.464</td>\n",
+       "      <td>0.027</td>\n",
+       "      <td>7.518</td>\n",
+       "      <td>7.410</td>\n",
+       "      <td>10.932</td>\n",
+       "      <td>0.942</td>\n",
+       "      <td>72.400</td>\n",
+       "      <td>0.913</td>\n",
+       "      <td>0.175</td>\n",
+       "      <td>0.338</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>1.501</td>\n",
+       "      <td>1.079</td>\n",
+       "      <td>0.753</td>\n",
+       "      <td>0.647</td>\n",
+       "      <td>0.302</td>\n",
+       "      <td>0.384</td>\n",
+       "      <td>2.798</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>Lesotho</td>\n",
+       "      <td>Sub-Saharan Africa</td>\n",
+       "      <td>3.512</td>\n",
+       "      <td>0.120</td>\n",
+       "      <td>3.748</td>\n",
+       "      <td>3.276</td>\n",
+       "      <td>7.926</td>\n",
+       "      <td>0.787</td>\n",
+       "      <td>48.700</td>\n",
+       "      <td>0.715</td>\n",
+       "      <td>-0.131</td>\n",
+       "      <td>0.915</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>0.451</td>\n",
+       "      <td>0.731</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>0.405</td>\n",
+       "      <td>0.103</td>\n",
+       "      <td>0.015</td>\n",
+       "      <td>1.800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>Botswana</td>\n",
+       "      <td>Sub-Saharan Africa</td>\n",
+       "      <td>3.467</td>\n",
+       "      <td>0.074</td>\n",
+       "      <td>3.611</td>\n",
+       "      <td>3.322</td>\n",
+       "      <td>9.782</td>\n",
+       "      <td>0.784</td>\n",
+       "      <td>59.269</td>\n",
+       "      <td>0.824</td>\n",
+       "      <td>-0.246</td>\n",
+       "      <td>0.801</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>1.099</td>\n",
+       "      <td>0.724</td>\n",
+       "      <td>0.340</td>\n",
+       "      <td>0.539</td>\n",
+       "      <td>0.027</td>\n",
+       "      <td>0.088</td>\n",
+       "      <td>0.648</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>Rwanda</td>\n",
+       "      <td>Sub-Saharan Africa</td>\n",
+       "      <td>3.415</td>\n",
+       "      <td>0.068</td>\n",
+       "      <td>3.548</td>\n",
+       "      <td>3.282</td>\n",
+       "      <td>7.676</td>\n",
+       "      <td>0.552</td>\n",
+       "      <td>61.400</td>\n",
+       "      <td>0.897</td>\n",
+       "      <td>0.061</td>\n",
+       "      <td>0.167</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>0.364</td>\n",
+       "      <td>0.202</td>\n",
+       "      <td>0.407</td>\n",
+       "      <td>0.627</td>\n",
+       "      <td>0.227</td>\n",
+       "      <td>0.493</td>\n",
+       "      <td>1.095</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>Sub-Saharan Africa</td>\n",
+       "      <td>3.145</td>\n",
+       "      <td>0.058</td>\n",
+       "      <td>3.259</td>\n",
+       "      <td>3.030</td>\n",
+       "      <td>7.943</td>\n",
+       "      <td>0.750</td>\n",
+       "      <td>56.201</td>\n",
+       "      <td>0.677</td>\n",
+       "      <td>-0.047</td>\n",
+       "      <td>0.821</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>0.457</td>\n",
+       "      <td>0.649</td>\n",
+       "      <td>0.243</td>\n",
+       "      <td>0.359</td>\n",
+       "      <td>0.157</td>\n",
+       "      <td>0.075</td>\n",
+       "      <td>1.205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>South Asia</td>\n",
+       "      <td>2.523</td>\n",
+       "      <td>0.038</td>\n",
+       "      <td>2.596</td>\n",
+       "      <td>2.449</td>\n",
+       "      <td>7.695</td>\n",
+       "      <td>0.463</td>\n",
+       "      <td>52.493</td>\n",
+       "      <td>0.382</td>\n",
+       "      <td>-0.102</td>\n",
+       "      <td>0.924</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>0.370</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.126</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.122</td>\n",
+       "      <td>0.010</td>\n",
+       "      <td>1.895</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>149 rows × 20 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Country name  Regional indicator  Ladder score  \\\n",
+       "0        Finland      Western Europe         7.842   \n",
+       "1        Denmark      Western Europe         7.620   \n",
+       "2    Switzerland      Western Europe         7.571   \n",
+       "3        Iceland      Western Europe         7.554   \n",
+       "4    Netherlands      Western Europe         7.464   \n",
+       "..           ...                 ...           ...   \n",
+       "144      Lesotho  Sub-Saharan Africa         3.512   \n",
+       "145     Botswana  Sub-Saharan Africa         3.467   \n",
+       "146       Rwanda  Sub-Saharan Africa         3.415   \n",
+       "147     Zimbabwe  Sub-Saharan Africa         3.145   \n",
+       "148  Afghanistan          South Asia         2.523   \n",
+       "\n",
+       "     Standard error of ladder score  upperwhisker  lowerwhisker  \\\n",
+       "0                             0.032         7.904         7.780   \n",
+       "1                             0.035         7.687         7.552   \n",
+       "2                             0.036         7.643         7.500   \n",
+       "3                             0.059         7.670         7.438   \n",
+       "4                             0.027         7.518         7.410   \n",
+       "..                              ...           ...           ...   \n",
+       "144                           0.120         3.748         3.276   \n",
+       "145                           0.074         3.611         3.322   \n",
+       "146                           0.068         3.548         3.282   \n",
+       "147                           0.058         3.259         3.030   \n",
+       "148                           0.038         2.596         2.449   \n",
+       "\n",
+       "     Logged GDP per capita  Social support  Healthy life expectancy  \\\n",
+       "0                   10.775           0.954                   72.000   \n",
+       "1                   10.933           0.954                   72.700   \n",
+       "2                   11.117           0.942                   74.400   \n",
+       "3                   10.878           0.983                   73.000   \n",
+       "4                   10.932           0.942                   72.400   \n",
+       "..                     ...             ...                      ...   \n",
+       "144                  7.926           0.787                   48.700   \n",
+       "145                  9.782           0.784                   59.269   \n",
+       "146                  7.676           0.552                   61.400   \n",
+       "147                  7.943           0.750                   56.201   \n",
+       "148                  7.695           0.463                   52.493   \n",
+       "\n",
+       "     Freedom to make life choices  Generosity  Perceptions of corruption  \\\n",
+       "0                           0.949      -0.098                      0.186   \n",
+       "1                           0.946       0.030                      0.179   \n",
+       "2                           0.919       0.025                      0.292   \n",
+       "3                           0.955       0.160                      0.673   \n",
+       "4                           0.913       0.175                      0.338   \n",
+       "..                            ...         ...                        ...   \n",
+       "144                         0.715      -0.131                      0.915   \n",
+       "145                         0.824      -0.246                      0.801   \n",
+       "146                         0.897       0.061                      0.167   \n",
+       "147                         0.677      -0.047                      0.821   \n",
+       "148                         0.382      -0.102                      0.924   \n",
+       "\n",
+       "     Ladder score in Dystopia  Explained by: Log GDP per capita  \\\n",
+       "0                        2.43                             1.446   \n",
+       "1                        2.43                             1.502   \n",
+       "2                        2.43                             1.566   \n",
+       "3                        2.43                             1.482   \n",
+       "4                        2.43                             1.501   \n",
+       "..                        ...                               ...   \n",
+       "144                      2.43                             0.451   \n",
+       "145                      2.43                             1.099   \n",
+       "146                      2.43                             0.364   \n",
+       "147                      2.43                             0.457   \n",
+       "148                      2.43                             0.370   \n",
+       "\n",
+       "     Explained by: Social support  Explained by: Healthy life expectancy  \\\n",
+       "0                           1.106                                  0.741   \n",
+       "1                           1.108                                  0.763   \n",
+       "2                           1.079                                  0.816   \n",
+       "3                           1.172                                  0.772   \n",
+       "4                           1.079                                  0.753   \n",
+       "..                            ...                                    ...   \n",
+       "144                         0.731                                  0.007   \n",
+       "145                         0.724                                  0.340   \n",
+       "146                         0.202                                  0.407   \n",
+       "147                         0.649                                  0.243   \n",
+       "148                         0.000                                  0.126   \n",
+       "\n",
+       "     Explained by: Freedom to make life choices  Explained by: Generosity  \\\n",
+       "0                                         0.691                     0.124   \n",
+       "1                                         0.686                     0.208   \n",
+       "2                                         0.653                     0.204   \n",
+       "3                                         0.698                     0.293   \n",
+       "4                                         0.647                     0.302   \n",
+       "..                                          ...                       ...   \n",
+       "144                                       0.405                     0.103   \n",
+       "145                                       0.539                     0.027   \n",
+       "146                                       0.627                     0.227   \n",
+       "147                                       0.359                     0.157   \n",
+       "148                                       0.000                     0.122   \n",
+       "\n",
+       "     Explained by: Perceptions of corruption  Dystopia + residual  \n",
+       "0                                      0.481                3.253  \n",
+       "1                                      0.485                2.868  \n",
+       "2                                      0.413                2.839  \n",
+       "3                                      0.170                2.967  \n",
+       "4                                      0.384                2.798  \n",
+       "..                                       ...                  ...  \n",
+       "144                                    0.015                1.800  \n",
+       "145                                    0.088                0.648  \n",
+       "146                                    0.493                1.095  \n",
+       "147                                    0.075                1.205  \n",
+       "148                                    0.010                1.895  \n",
+       "\n",
+       "[149 rows x 20 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('./world-happiness-report-2021.csv')\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assignment 1\n",
+    "행복 지수를 1점 간격으로 두고 다음과 같은 **Histogram**을 작성 해 주세요.\n",
+    "행복 지수의 키 값은 \"Ladder score\" 입니다."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assignment 2\n",
+    "행복 지수를 y축으로, GDP를 x축으로 하여, scatter 한 값을 한 번 입력 해 보세요."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assignment 3\n",
+    "행복지수 상위 10개, 하위 10개를 선택 후, 각각의 Social support, Logged GDP per capita, Freedom to make life choices 의 평균을 구해서 꺾은 선 그래프로 나타내세요."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 67d02b6501a7645a9a41efd4260babb2a04be869 Mon Sep 17 00:00:00 2001
From: yoongja <83260809+yoongja@users.noreply.github.com>
Date: Mon, 12 Jul 2021 02:21:31 +0900
Subject: [PATCH 2/2] =?UTF-8?q?03-=EA=B9=80=EC=9C=A4=EC=A7=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../.ipynb_checkpoints/03-checkpoint.ipynb    |  63 +-
 assignments/yoongja/03.ipynb                  |  63 +-
 .../yoongja/world-happiness-report-2021.csv   | 150 +++++
 .../03-Matplotlib-checkpoint.ipynb            | 578 ++++++++++++++++++
 4 files changed, 832 insertions(+), 22 deletions(-)
 create mode 100644 assignments/yoongja/world-happiness-report-2021.csv
 create mode 100644 example/.ipynb_checkpoints/03-Matplotlib-checkpoint.ipynb

diff --git a/assignments/yoongja/.ipynb_checkpoints/03-checkpoint.ipynb b/assignments/yoongja/.ipynb_checkpoints/03-checkpoint.ipynb
index f70effd..6591405 100644
--- a/assignments/yoongja/.ipynb_checkpoints/03-checkpoint.ipynb
+++ b/assignments/yoongja/.ipynb_checkpoints/03-checkpoint.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -426,7 +426,7 @@
        "[149 rows x 20 columns]"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -447,12 +447,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -464,7 +464,12 @@
     }
    ],
    "source": [
-    "dd"
+    "s=df[\"Ladder score\"]\n",
+    "plt.hist(s,[i for i in range(2,10)])\n",
+    "plt.xlabel(\"score\")\n",
+    "plt.ylabel(\"count\")\n",
+    "plt.title(\"Happiniess Score\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -477,12 +482,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -493,7 +498,17 @@
      "output_type": "display_data"
     }
    ],
-   "source": []
+   "source": [
+    "x=df[\"Logged GDP per capita\"]\n",
+    "y=df[\"Ladder score\"]\n",
+    "plt.scatter(x,y,\n",
+    "           color='blue'\n",
+    "           )\n",
+    "plt.title(\"Happiniess Score per GDP\")\n",
+    "plt.ylabel(\"score\")\n",
+    "plt.xlabel(\"GDP\")\n",
+    "plt.show()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -505,12 +520,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -521,6 +536,32 @@
      "output_type": "display_data"
     }
    ],
+   "source": [
+    "df2=df.sort_values(by=\"Ladder score\", ascending=False)\n",
+    "df2[\"Social support\"]=df2[\"Social support\"]*10\n",
+    "df2[\"Freedom to make life choices\"]=df2[\"Freedom to make life choices\"]*10\n",
+    "\n",
+    "Happy=df2.head(10).loc[:,[\"Social support\",\"Logged GDP per capita\",\"Freedom to make life choices\"]].mean()\n",
+    "Unhappy=df2.tail(10).loc[:,[\"Social support\",\"Logged GDP per capita\",\"Freedom to make life choices\"]].mean()\n",
+    "x = ['Social','GDP Attribute','Freedom']\n",
+    "\n",
+    "x_axis = np.arange(len(x))\n",
+    "plt.xticks(x_axis,x)\n",
+    "\n",
+    "plt.ylabel(\"score\")\n",
+    "plt.title(\"Happy Country and Unhappy Country\")\n",
+    "\n",
+    "plt.bar(x_axis - 0.2,Happy,width=0.3,color='Green',align='center',label=\"Happy\")\n",
+    "plt.bar(x_axis + 0.2,Unhappy,width=0.3,color='Blue',align='center',label=\"Unhappy\")\n",
+    "#plt.legend(loc=1) 계속 TypeError 'int' object is not callable이라 떠서 삭제했습니다.\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": []
   }
  ],
diff --git a/assignments/yoongja/03.ipynb b/assignments/yoongja/03.ipynb
index f70effd..6591405 100644
--- a/assignments/yoongja/03.ipynb
+++ b/assignments/yoongja/03.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -426,7 +426,7 @@
        "[149 rows x 20 columns]"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -447,12 +447,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -464,7 +464,12 @@
     }
    ],
    "source": [
-    "dd"
+    "s=df[\"Ladder score\"]\n",
+    "plt.hist(s,[i for i in range(2,10)])\n",
+    "plt.xlabel(\"score\")\n",
+    "plt.ylabel(\"count\")\n",
+    "plt.title(\"Happiniess Score\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -477,12 +482,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -493,7 +498,17 @@
      "output_type": "display_data"
     }
    ],
-   "source": []
+   "source": [
+    "x=df[\"Logged GDP per capita\"]\n",
+    "y=df[\"Ladder score\"]\n",
+    "plt.scatter(x,y,\n",
+    "           color='blue'\n",
+    "           )\n",
+    "plt.title(\"Happiniess Score per GDP\")\n",
+    "plt.ylabel(\"score\")\n",
+    "plt.xlabel(\"GDP\")\n",
+    "plt.show()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -505,12 +520,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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N7LfffgwZMoTZs2czZ86chmXqO2cbPXo0U6dObRh//PHHs8UWWzBgwAB23XVXnn76aY477jgmT57M6tWrufrqqxu6kdgUHfaTuxExAZgAUFtb27F6bDKzTqdPnz688sorjcYtX768oe/9d7/73QB06dKFNWvWNMxTP7502vz587nkkkt4/PHH6d27N2PGjGmxK+eW/q4f7tGjBx//+Me56667uPnmmxu+I2BTVLrGv0TS9gDp90sV3r6ZWbN69uzJ9ttvzwMPPAAUoX/fffc1dKe8IV5//XXe8573sM0227BkyRLuvffeRtPrO2+rq6vjoIMOahh/yy23sHbtWv7+97/z3HPPMWjQIKDow/+cc87hgAMOYNttt93YXWxQ6Rr/3cBngR+k33dVePtmthmIKl3jX3vttZx11ll85StfAWDcuHHstttuG7yefffdlyFDhrDnnnuy6667cvDBBzeavmrVKg488EDWrl3LTTfd1DB+0KBBfPSjH2XJkiX86le/olu3bkDRvLT11lu32xe3KMp0hCXdBAwDtgOWAOOAO4GbgZ2A54HjIqLpDeB11NbWRntc3tjmSRe28UHsMutofcR3JnPnzmX33XevdjEqoqamhunTp7Pddts1Gj9mzBiOPPJIRo0atc4yixcvZtiwYTz99NNssUXzDTXNHUNJT0REbdN5y1bjj4iWvlqmen2RmpltZq699lq++c1v8qMf/ajF0N9QHfbmrplZZ1T6RFCp+kc2mzr11FM59dRT27UM7rLBzDqEcjU752BDj52D38yqrlu3bixbtszhvxEigmXLljXcCG4LN/WYWdX179+fRYsW4Q9rbpxu3brRv3//Ns/f6YPfT4SYdXxdu3Zt+KCUlZ+beszMMuPgNzPLTKdv6jGz6usITa5ubv031/jNzDLj4Dczy4yD38wsMw5+M7PMOPjNzDLj4Dczy4yD38wsMw5+M7PMOPjNzDLj4Dczy4yD38wsMw5+M7PMOPjNzDLj4Dczy4yD38wsMw5+M7PMOPjNzDLj4Dczy4yD38wsMw5+M7PMVCX4JZ0nabakpyTdJKlbNcphZpajige/pH7AOUBtROwFdAFOrHQ5zMxyVa2mni2B7pK2BHoAi6tUDjOz7FQ8+CPiBeAS4HngReC1iPh9pcthZparajT19AZGArsAOwDvkXRyM/OdLmm6pOlLly6tdDHNzDqtajT1jADmR8TSiFgN3A58uOlMETEhImojorZv374VL6SZWWdVjeB/HviQpB6SBAwH5lahHGZmWapGG/9jwK3Ak8CsVIYJlS6HmVmutqzGRiNiHDCuGts2qwSp2iUoRFS7BNYR+ZO7ZmaZcfCbmWXGwW9mlhkHv5lZZhz8ZmaZcfCbmWXGwW9mlhkHv5lZZhz8ZmaZcfCbmWXGwW9mlhkHv5lZZhz8ZmaZcfCbmWXGwW9mlpmq9MdvZtbRdebvVHCN38wsMw5+M7PMOPjNzDLj4Dczy4yD38wsMw5+M7PMOPjNzDLj4Dczy4w/wNUBdeYPjphZ9bnGb2aWmTYHv6TukgaVszBmZlZ+bQp+SZ8CZgD3peHBku4uY7nMzKxM2lrjHw8MBV4FiIgZQE05CmRmZuXV1uBfExGvtddGJfWSdKukpyXNlXRQe63bzMxa19anep6S9Bmgi6QBwDnAXzZhuz8B7ouIUZLeBfTYhHWZmdkGaGuN/2xgT2AVcCPwGnDuxmxQ0tbAR4CrACLi7Yh4dWPWZWZmG269NX5JXYC7I2IE8M122OauwFLgGkn7Ak8AX4qIFU22ezpwOsBOO+3UDps1MzNoQ40/It4B/iVpm3ba5pbAfsAvI2IIsAK4oJntToiI2oio7du3bztt2szM2trGvxKYJel+iqAGICLO2YhtLgIWRcRjafhWmgl+MzMrj7YG/2/SzyaLiH9KWihpUEQ8AwwH5rTHus3MbP3aFPwRMSk9fTMwjXomIlZvwnbPBm5I63wOOG0T1mVmZhugTcEvaRgwCVgACNhR0mcj4k8bs9H0AbDajVnWzMw2TVuben4I/EdqmkHSQOAmYP9yFczMzMqjrc/xd60PfYCImAd0LU+RzMysnNpa458u6SrgujR8EsXz92Zmtplpa/CfCZxF0VWDgD8BvyhXoczMrHzaGvxbAj+JiB9Bw6d53122UpmZWdm0tY3/AaB7yXB34A/tXxwzMyu3tgZ/t4h4s34g/e0eNc3MNkNtDf4VkvarH5BUC7xVniKZmVk5tbWN/1zgFkmLgQB2AE4oV6HMzKx8Wq3xSzpA0vsj4nHgg0AdsIbiu3fnV6B8ZmbWztbX1HM58Hb6+yDgG8DPgVeACWUsl5mZlcn6mnq6RMTy9PcJwISIuA24TdKMspbMzMzKYn01/i6S6t8chgMPlkxr6/0BMzPrQNYX3jcBD0l6meIpnocBJH2A4nt3zcxsM9Nq8EfERZIeALYHfh8RkSZtQdGnvpmZbWbW21wTEY82M25eeYpjZmbl1tYPcJmZWSfh4Dczy4yD38wsMw5+M7PMOPjNzDLj4Dczy4yD38wsMw5+M7PMOPjNzDLj4Dczy4yD38wsMw5+M7PMOPjNzDJTteCX1EXSXyVNrlYZzMxyVM0a/5eAuVXcvplZlqoS/JL6A0cAV1Zj+2ZmOatWjf9S4HxgbUszSDpd0nRJ05cuXVqxgpmZdXYVD35JRwIvRcQTrc0XERMiojYiavv27Vuh0pmZdX7VqPEfDBwlaQHwa+AwSddXoRxmZlmqePBHxNcjon9E1AAnAg9GxMmVLoeZWa78HL+ZWWa2rObGI2IKMKWaZTAzy41r/GZmmXHwm5llxsFvZpYZB7+ZWWYc/GZmmXHwm5llxsFvZpYZB7+ZWWYc/GZmmXHwm5llxsFvZpYZB7+ZWWYc/GZmmXHwm5llxsFvZpYZB7+ZWWYc/GZmmXHwm5llxsFvZpYZB7+ZWWYc/GZmmXHwm5llxsFvZpYZB7+ZWWYc/GZmmXHwm5llxsFvZpYZB7+ZWWYc/GZmmal48EvaUdIfJc2VNFvSlypdBjOznG1ZhW2uAb4SEU9K2gp4QtL9ETGnCmUxM8tOxWv8EfFiRDyZ/n4DmAv0q3Q5zMxyVdU2fkk1wBDgsWamnS5puqTpS5curXjZzMw6q6oFv6SewG3AuRHxetPpETEhImojorZv376VL6CZWSdVleCX1JUi9G+IiNurUQYzs1xV46keAVcBcyPiR5XevplZ7qpR4z8YOAU4TNKM9PPJKpTDzCxLFX+cMyIeAVTp7ZqZWcGf3DUzy4yD38wsMw5+M7PMOPjNzDLj4Dczy4yD38wsMw5+M7PMOPjNzDLj4Dczy4yD38wsMw5+M7PMOPjNzDLj4Dczy4yD38wsMw5+M7PMOPjNzDLj4Dczy4yD38wsMw5+M7PMOPjNzDLj4Dczy4yD38wsMw5+M7PMOPjNzDLj4Dczy4yD38wsMw5+M7PMOPjNzDLj4Dczy0xVgl/S4ZKekfSspAuqUQYzs1xVPPgldQF+DvwnsAcwWtIelS6HmVmuqlHjHwo8GxHPRcTbwK+BkVUoh5lZlraswjb7AQtLhhcBBzadSdLpwOlp8E1Jz1SgbGWj8Wpp0nbAyxUsSpupxSLnqYXXsMO+fuDXsNTm+D8Im/wa7tzcyGoEf3O7EeuMiJgATCh/capL0vSIqK12OWzj+PXb/OX4GlajqWcRsGPJcH9gcRXKYWaWpWoE/+PAAEm7SHoXcCJwdxXKYWaWpYo39UTEGkljgd8BXYCrI2J2pcvRgXT65qxOzq/f5i+711AR6zSvm5lZJ+ZP7pqZZcbBb2aWGQd/O5H0TUmzJc2UNEPSOp9NWM/ytZJ+up55hkmavGkltbaS9D5JN0p6TtITkqZKOia9Dq9J+mvqeuRPko4sWW68pBfSefCUpKOquR+djaR30rGt/6lp5/WPkXRZe66zo6nGc/ydjqSDgCOB/SJilaTtgHdtyDoiYjowvRzlsw0nScCdwKSI+EwatzNwFPAK8HBEHJnGDwbulPRWRDyQVvHjiLhE0u7Aw5LeGxFrK70fndRbETG4uQnpdZOPdetc428f2wMvR8QqgIh4OSIWSxqeaoWzJF0t6d0Akg6Q9BdJ/ydpmqStSmvzkoam6X9NvwdVcd9ydRjwdkT8qn5ERPwjIn7WdMaImAF8FxjbzLS5wBqKT4daGUiqkTRX0i+AJ4EdJX1V0uPpCvzCknlPTv9zMyRdnvoOQ9JpkuZJegg4uGT+nSU9kNbzgKSd0viJkn4p6Y/pivCj6X98rqSJlT0CG87B3z5+T3GyzZP0i3QSdAMmAidExN4UV1dnps8u1AFfioh9gRHAW03W9zTwkYgYAnwH+F6ldsQa7EkRIm31JPDBpiNTk99aYGk7lcuge0kzzx1p3CDg2vQ/MwgYQNEv2GBgf0kfSVdfJwAHpyuGd4CTJG0PXEgR+B+n6Dyy3mVpvfsANwClzbG9KSoI5wH3AD+mOG/2TleBHZabetpBRLwpaX/gUOBjFMH+fWB+RMxLs00CzgIeAF6MiMfTsq8DqHGHHNsAkyQNoOjOomsl9sNaJunnwCHA28BXm5ulyfB5kk4G3qB48/dz0+2nUVNPauP/R0Q8mkb9R/r5axruSfFGsA+wP/B4+n/rDrxE0VfYlIhYmtZXBwxMyx4EHJv+vg64uKQc90RESJoFLImIWWn52UANMKNd9rYMHPztJCLeAaYAU9KJ8NkWZhXN9E3UxP8Af4yIY9JJPaWdimltNxv4dP1ARJyV7t20dB9mCDC3ZPjHEXFJGctnja0o+VvA9yPi8tIZJJ1Ncc/m603GH836/yfrlc63Kv1eW/J3/XCHzlY39bQDSYNS7bzeYGAJUCPpA2ncKcBDFM04O0g6IC27laSmJ8k2wAvp7zHlKre16kGgm6QzS8b1aG5GSfsA36b4ngmrvt8Bn5PUE0BSP0nvpbjaHpX+RtK26Yb9Y8AwSX0kdQWOK1nXXyi6lQE4CXikUjtRTh36XWkz0hP4maReFDfynqXoUvom4JYU7I8Dv4qItyWdkObvTtG+P6LJ+i6maOr5MkUAWYWlS/ijgR9LOp+ijX4F8LU0y6GS/krxZvAScE7JEz1WRRHx+9SePzU16bwJnBwRcyR9C/i9pC2A1cBZEfGopPHAVOBFivs1XdLqzgGulvRVinPgtMruTXm4ywYzs8y4qcfMLDMOfjOzzDj4zcwy4+A3M8uMg9/MLDMOfsuKit41Q9IH0/BgSZ8smT5M0odbWf4oSRekvydKGrWB2//GxpbdrL04+C03oyk+hFP/oZzBwCdLpg8Dmg1+SVtGxN0R8YNN2L6D36rOz/FbNtInOZ+h6E/pboq+W56l6LPlBYoP3J1H0XnXUuBs4PPAcoouGZ4EZgG1ETE29cK4kqJjrvcBX46IyZLG1M+TtjsZuAQ4nKKfn1nA7Ig4KfXncw5FN96PAV9M3X+YlY0/uWs5ORq4LyLmSVoO7EXR+2lpSHcH3qzvZ0fS5yk67BoREe+kUC9VA3wU2A34Y0kXHeuIiAskja3vYKxJb5GrU7fCJwHXttP+mjXLwW85GQ1cmv7+dRqe3YblbmmlFn5z+tKPv0l6jma6Zm7FcJrvLdKsrBz8lgVJfSj6Tt9LUlD0xRLAuDYsvqKVaU3bSoOiv6bS+2fdWioWzfQWaVZuvrlruRhF8YUaO0dETUTsCMwHdgK2KpnvjSbD63OcpC0k7QbsSnEPYQEwOI3fkeILQeqtTj1AQsu9RZqVlYPfcjEauKPJuNuA9wN7pG9zOoHim5SOScOHtmG9z1B0t30vcEZErAT+TPGmMovipm7pN3lNAGZKuiEi5gD1vUXOBO6n+BpPs7LyUz1mZplxjd/MLDMOfjOzzDj4zcwy4+A3M8uMg9/MLDMOfjOzzDj4zcwy8/8BAjtEfi7JuVQAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -521,6 +536,32 @@
      "output_type": "display_data"
     }
    ],
+   "source": [
+    "df2=df.sort_values(by=\"Ladder score\", ascending=False)\n",
+    "df2[\"Social support\"]=df2[\"Social support\"]*10\n",
+    "df2[\"Freedom to make life choices\"]=df2[\"Freedom to make life choices\"]*10\n",
+    "\n",
+    "Happy=df2.head(10).loc[:,[\"Social support\",\"Logged GDP per capita\",\"Freedom to make life choices\"]].mean()\n",
+    "Unhappy=df2.tail(10).loc[:,[\"Social support\",\"Logged GDP per capita\",\"Freedom to make life choices\"]].mean()\n",
+    "x = ['Social','GDP Attribute','Freedom']\n",
+    "\n",
+    "x_axis = np.arange(len(x))\n",
+    "plt.xticks(x_axis,x)\n",
+    "\n",
+    "plt.ylabel(\"score\")\n",
+    "plt.title(\"Happy Country and Unhappy Country\")\n",
+    "\n",
+    "plt.bar(x_axis - 0.2,Happy,width=0.3,color='Green',align='center',label=\"Happy\")\n",
+    "plt.bar(x_axis + 0.2,Unhappy,width=0.3,color='Blue',align='center',label=\"Unhappy\")\n",
+    "#plt.legend(loc=1) 계속 TypeError 'int' object is not callable이라 떠서 삭제했습니다.\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": []
   }
  ],
diff --git a/assignments/yoongja/world-happiness-report-2021.csv b/assignments/yoongja/world-happiness-report-2021.csv
new file mode 100644
index 0000000..ce862fc
--- /dev/null
+++ b/assignments/yoongja/world-happiness-report-2021.csv
@@ -0,0 +1,150 @@
+Country name,Regional indicator,Ladder score,Standard error of ladder score,upperwhisker,lowerwhisker,Logged GDP per capita,Social support,Healthy life expectancy,Freedom to make life choices,Generosity,Perceptions of corruption,Ladder score in Dystopia,Explained by: Log GDP per capita,Explained by: Social support,Explained by: Healthy life expectancy,Explained by: Freedom to make life choices,Explained by: Generosity,Explained by: Perceptions of corruption,Dystopia + residual
+Finland,Western Europe,7.842,0.032,7.904,7.780,10.775,0.954,72.000,0.949,-0.098,0.186,2.430,1.446,1.106,0.741,0.691,0.124,0.481,3.253
+Denmark,Western Europe,7.620,0.035,7.687,7.552,10.933,0.954,72.700,0.946,0.030,0.179,2.430,1.502,1.108,0.763,0.686,0.208,0.485,2.868
+Switzerland,Western Europe,7.571,0.036,7.643,7.500,11.117,0.942,74.400,0.919,0.025,0.292,2.430,1.566,1.079,0.816,0.653,0.204,0.413,2.839
+Iceland,Western Europe,7.554,0.059,7.670,7.438,10.878,0.983,73.000,0.955,0.160,0.673,2.430,1.482,1.172,0.772,0.698,0.293,0.170,2.967
+Netherlands,Western Europe,7.464,0.027,7.518,7.410,10.932,0.942,72.400,0.913,0.175,0.338,2.430,1.501,1.079,0.753,0.647,0.302,0.384,2.798
+Norway,Western Europe,7.392,0.035,7.462,7.323,11.053,0.954,73.300,0.960,0.093,0.270,2.430,1.543,1.108,0.782,0.703,0.249,0.427,2.580
+Sweden,Western Europe,7.363,0.036,7.433,7.293,10.867,0.934,72.700,0.945,0.086,0.237,2.430,1.478,1.062,0.763,0.685,0.244,0.448,2.683
+Luxembourg,Western Europe,7.324,0.037,7.396,7.252,11.647,0.908,72.600,0.907,-0.034,0.386,2.430,1.751,1.003,0.760,0.639,0.166,0.353,2.653
+New Zealand,North America and ANZ,7.277,0.040,7.355,7.198,10.643,0.948,73.400,0.929,0.134,0.242,2.430,1.400,1.094,0.785,0.665,0.276,0.445,2.612
+Austria,Western Europe,7.268,0.036,7.337,7.198,10.906,0.934,73.300,0.908,0.042,0.481,2.430,1.492,1.062,0.782,0.640,0.215,0.292,2.784
+Australia,North America and ANZ,7.183,0.041,7.265,7.102,10.796,0.940,73.900,0.914,0.159,0.442,2.430,1.453,1.076,0.801,0.647,0.291,0.317,2.598
+Israel,Middle East and North Africa,7.157,0.034,7.224,7.090,10.575,0.939,73.503,0.800,0.031,0.753,2.430,1.376,1.074,0.788,0.509,0.208,0.119,3.083
+Germany,Western Europe,7.155,0.040,7.232,7.077,10.873,0.903,72.500,0.875,0.011,0.460,2.430,1.480,0.993,0.757,0.600,0.195,0.306,2.824
+Canada,North America and ANZ,7.103,0.042,7.185,7.021,10.776,0.926,73.800,0.915,0.089,0.415,2.430,1.447,1.044,0.798,0.648,0.246,0.335,2.585
+Ireland,Western Europe,7.085,0.040,7.164,7.006,11.342,0.947,72.400,0.879,0.077,0.363,2.430,1.644,1.092,0.753,0.606,0.238,0.367,2.384
+Costa Rica,Latin America and Caribbean,7.069,0.056,7.179,6.960,9.880,0.891,71.400,0.934,-0.126,0.809,2.430,1.134,0.966,0.722,0.673,0.105,0.083,3.387
+United Kingdom,Western Europe,7.064,0.038,7.138,6.990,10.707,0.934,72.500,0.859,0.233,0.459,2.430,1.423,1.062,0.757,0.580,0.340,0.306,2.596
+Czech Republic,Central and Eastern Europe,6.965,0.049,7.062,6.868,10.556,0.947,70.807,0.858,-0.208,0.868,2.430,1.370,1.090,0.703,0.580,0.052,0.046,3.124
+United States,North America and ANZ,6.951,0.049,7.047,6.856,11.023,0.920,68.200,0.837,0.098,0.698,2.430,1.533,1.030,0.621,0.554,0.252,0.154,2.807
+Belgium,Western Europe,6.834,0.034,6.901,6.767,10.823,0.906,72.199,0.783,-0.153,0.646,2.430,1.463,0.998,0.747,0.489,0.088,0.187,2.862
+France,Western Europe,6.690,0.037,6.762,6.618,10.704,0.942,74.000,0.822,-0.147,0.571,2.430,1.421,1.081,0.804,0.536,0.092,0.235,2.521
+Bahrain,Middle East and North Africa,6.647,0.068,6.779,6.514,10.669,0.862,69.495,0.925,0.089,0.722,2.430,1.409,0.899,0.662,0.661,0.246,0.139,2.631
+Malta,Western Europe,6.602,0.044,6.688,6.516,10.674,0.931,72.200,0.927,0.133,0.653,2.430,1.411,1.055,0.747,0.664,0.275,0.183,2.268
+Taiwan Province of China,East Asia,6.584,0.038,6.659,6.510,10.871,0.898,69.600,0.784,-0.070,0.721,2.430,1.480,0.982,0.665,0.490,0.142,0.139,2.687
+United Arab Emirates,Middle East and North Africa,6.561,0.039,6.637,6.484,11.085,0.844,67.333,0.932,0.074,0.589,2.430,1.555,0.860,0.594,0.670,0.236,0.223,2.422
+Saudi Arabia,Middle East and North Africa,6.494,0.056,6.604,6.384,10.743,0.891,66.603,0.877,-0.149,0.684,2.430,1.435,0.964,0.571,0.603,0.090,0.163,2.668
+Spain,Western Europe,6.491,0.042,6.574,6.408,10.571,0.932,74.700,0.761,-0.081,0.745,2.430,1.375,1.057,0.826,0.462,0.135,0.124,2.513
+Italy,Western Europe,6.483,0.045,6.572,6.395,10.623,0.880,73.800,0.693,-0.084,0.866,2.430,1.393,0.940,0.798,0.379,0.133,0.047,2.794
+Slovenia,Central and Eastern Europe,6.461,0.043,6.546,6.376,10.529,0.948,71.400,0.949,-0.101,0.806,2.430,1.360,1.093,0.722,0.690,0.122,0.085,2.388
+Guatemala,Latin America and Caribbean,6.435,0.073,6.577,6.292,9.053,0.813,64.958,0.906,-0.038,0.775,2.430,0.845,0.790,0.519,0.638,0.163,0.105,3.375
+Uruguay,Latin America and Caribbean,6.431,0.046,6.521,6.341,9.966,0.925,69.100,0.896,-0.092,0.590,2.430,1.164,1.042,0.649,0.625,0.128,0.223,2.600
+Singapore,Southeast Asia,6.377,0.043,6.460,6.293,11.488,0.915,76.953,0.927,-0.018,0.082,2.430,1.695,1.019,0.897,0.664,0.176,0.547,1.379
+Kosovo,Central and Eastern Europe,6.372,0.059,6.487,6.257,9.318,0.821,63.813,0.869,0.257,0.917,2.430,0.937,0.807,0.483,0.593,0.356,0.014,3.182
+Slovakia,Central and Eastern Europe,6.331,0.041,6.411,6.251,10.369,0.936,69.201,0.766,-0.124,0.911,2.430,1.304,1.066,0.653,0.468,0.107,0.018,2.714
+Brazil,Latin America and Caribbean,6.330,0.043,6.415,6.245,9.577,0.882,66.601,0.804,-0.071,0.756,2.430,1.028,0.944,0.571,0.514,0.142,0.117,3.015
+Mexico,Latin America and Caribbean,6.317,0.053,6.420,6.213,9.859,0.831,68.597,0.862,-0.147,0.799,2.430,1.126,0.830,0.634,0.585,0.092,0.089,2.961
+Jamaica,Latin America and Caribbean,6.309,0.156,6.615,6.004,9.186,0.877,67.500,0.890,-0.137,0.884,2.430,0.891,0.932,0.599,0.618,0.099,0.035,3.135
+Lithuania,Central and Eastern Europe,6.255,0.045,6.344,6.167,10.499,0.935,67.906,0.773,-0.203,0.826,2.430,1.350,1.065,0.612,0.476,0.056,0.073,2.624
+Cyprus,Western Europe,6.223,0.049,6.319,6.128,10.576,0.802,73.898,0.763,-0.015,0.844,2.430,1.377,0.765,0.801,0.464,0.178,0.061,2.578
+Estonia,Central and Eastern Europe,6.189,0.038,6.263,6.115,10.481,0.941,68.800,0.909,-0.106,0.527,2.430,1.344,1.079,0.640,0.641,0.119,0.263,2.103
+Panama,Latin America and Caribbean,6.180,0.073,6.323,6.036,10.350,0.896,69.652,0.872,-0.166,0.856,2.430,1.298,0.976,0.667,0.596,0.079,0.053,2.509
+Uzbekistan,Commonwealth of Independent States,6.179,0.068,6.312,6.045,8.836,0.918,65.255,0.970,0.311,0.515,2.430,0.769,1.027,0.528,0.716,0.391,0.271,2.477
+Chile,Latin America and Caribbean,6.172,0.046,6.262,6.081,10.071,0.882,70.000,0.742,-0.044,0.830,2.430,1.200,0.946,0.678,0.438,0.159,0.070,2.682
+Poland,Central and Eastern Europe,6.166,0.040,6.245,6.087,10.382,0.898,69.702,0.841,-0.165,0.735,2.430,1.309,0.982,0.668,0.558,0.080,0.130,2.438
+Kazakhstan,Commonwealth of Independent States,6.152,0.047,6.243,6.060,10.155,0.952,65.200,0.853,-0.069,0.733,2.430,1.230,1.103,0.527,0.573,0.143,0.132,2.446
+Romania,Central and Eastern Europe,6.140,0.057,6.253,6.027,10.284,0.832,67.355,0.845,-0.219,0.938,2.430,1.275,0.832,0.595,0.564,0.045,0.001,2.830
+Kuwait,Middle East and North Africa,6.106,0.066,6.235,5.977,10.817,0.843,66.900,0.867,-0.104,0.736,2.430,1.461,0.857,0.580,0.591,0.120,0.130,2.368
+Serbia,Central and Eastern Europe,6.078,0.053,6.181,5.974,9.787,0.873,68.600,0.778,0.002,0.835,2.430,1.101,0.924,0.634,0.482,0.189,0.066,2.682
+El Salvador,Latin America and Caribbean,6.061,0.065,6.188,5.933,9.054,0.762,66.402,0.888,-0.110,0.688,2.430,0.845,0.675,0.565,0.615,0.116,0.160,3.085
+Mauritius,Sub-Saharan Africa,6.049,0.059,6.165,5.933,10.008,0.905,66.701,0.867,-0.054,0.789,2.430,1.178,0.996,0.574,0.590,0.153,0.096,2.462
+Latvia,Central and Eastern Europe,6.032,0.036,6.103,5.961,10.315,0.927,67.100,0.715,-0.162,0.800,2.430,1.285,1.047,0.587,0.405,0.082,0.089,2.536
+Colombia,Latin America and Caribbean,6.012,0.061,6.132,5.892,9.557,0.847,68.001,0.837,-0.135,0.841,2.430,1.021,0.866,0.615,0.554,0.100,0.063,2.794
+Hungary,Central and Eastern Europe,5.992,0.047,6.085,5.899,10.358,0.943,68.000,0.755,-0.186,0.876,2.430,1.301,1.083,0.615,0.454,0.067,0.040,2.432
+Thailand,Southeast Asia,5.985,0.047,6.077,5.893,9.805,0.888,67.401,0.884,0.287,0.895,2.430,1.107,0.957,0.596,0.611,0.375,0.028,2.309
+Nicaragua,Latin America and Caribbean,5.972,0.083,6.134,5.810,8.620,0.864,67.657,0.836,0.020,0.664,2.430,0.693,0.904,0.604,0.553,0.201,0.176,2.841
+Japan,East Asia,5.940,0.040,6.020,5.861,10.611,0.884,75.100,0.796,-0.258,0.638,2.430,1.389,0.949,0.838,0.504,0.020,0.192,2.048
+Argentina,Latin America and Caribbean,5.929,0.056,6.040,5.819,9.962,0.898,69.000,0.828,-0.182,0.834,2.430,1.162,0.980,0.646,0.544,0.069,0.067,2.461
+Portugal,Western Europe,5.929,0.055,6.037,5.821,10.421,0.879,72.600,0.892,-0.244,0.887,2.430,1.323,0.939,0.760,0.621,0.029,0.033,2.225
+Honduras,Latin America and Caribbean,5.919,0.082,6.081,5.758,8.648,0.812,67.300,0.857,0.081,0.809,2.430,0.703,0.787,0.593,0.578,0.241,0.083,2.934
+Croatia,Central and Eastern Europe,5.882,0.048,5.975,5.788,10.217,0.924,70.799,0.754,-0.118,0.939,2.430,1.251,1.039,0.703,0.453,0.111,0.000,2.325
+Philippines,Southeast Asia,5.880,0.052,5.982,5.778,9.076,0.830,62.000,0.917,-0.097,0.742,2.430,0.853,0.828,0.426,0.651,0.125,0.126,2.872
+South Korea,East Asia,5.845,0.042,5.928,5.763,10.651,0.799,73.900,0.672,-0.083,0.727,2.430,1.403,0.758,0.801,0.353,0.134,0.135,2.262
+Peru,Latin America and Caribbean,5.840,0.075,5.988,5.692,9.458,0.832,68.250,0.822,-0.154,0.891,2.430,0.986,0.833,0.623,0.536,0.087,0.031,2.744
+Bosnia and Herzegovina,Central and Eastern Europe,5.813,0.050,5.911,5.715,9.590,0.870,68.098,0.706,0.113,0.931,2.430,1.032,0.919,0.618,0.395,0.261,0.005,2.583
+Moldova,Commonwealth of Independent States,5.766,0.046,5.856,5.677,9.454,0.857,65.699,0.822,-0.079,0.918,2.430,0.985,0.888,0.542,0.536,0.137,0.013,2.665
+Ecuador,Latin America and Caribbean,5.764,0.057,5.875,5.653,9.313,0.821,68.800,0.842,-0.124,0.843,2.430,0.935,0.806,0.640,0.560,0.107,0.062,2.653
+Kyrgyzstan,Commonwealth of Independent States,5.744,0.046,5.834,5.653,8.538,0.893,64.401,0.935,0.119,0.908,2.430,0.665,0.971,0.501,0.673,0.266,0.020,2.648
+Greece,Western Europe,5.723,0.046,5.813,5.632,10.279,0.823,72.600,0.582,-0.288,0.823,2.430,1.273,0.811,0.760,0.243,0.000,0.074,2.561
+Bolivia,Latin America and Caribbean,5.716,0.053,5.819,5.613,9.046,0.810,63.901,0.875,-0.077,0.839,2.430,0.842,0.782,0.486,0.600,0.138,0.064,2.805
+Mongolia,East Asia,5.677,0.042,5.760,5.595,9.400,0.935,62.500,0.708,0.116,0.856,2.430,0.966,1.065,0.442,0.397,0.263,0.053,2.492
+Paraguay,Latin America and Caribbean,5.653,0.092,5.832,5.473,9.448,0.893,65.900,0.876,0.028,0.882,2.430,0.983,0.970,0.549,0.602,0.206,0.037,2.306
+Montenegro,Central and Eastern Europe,5.581,0.054,5.686,5.475,9.940,0.858,68.699,0.708,-0.034,0.812,2.430,1.155,0.891,0.637,0.397,0.166,0.081,2.254
+Dominican Republic,Latin America and Caribbean,5.545,0.071,5.685,5.405,9.802,0.853,66.102,0.860,-0.133,0.714,2.430,1.106,0.879,0.555,0.581,0.101,0.144,2.178
+North Cyprus,Western Europe,5.536,0.051,5.636,5.435,10.576,0.820,73.898,0.795,0.012,0.626,2.430,1.377,0.806,0.801,0.503,0.196,0.200,1.653
+Belarus,Commonwealth of Independent States,5.534,0.047,5.625,5.442,9.853,0.910,66.253,0.650,-0.180,0.627,2.430,1.124,1.007,0.560,0.326,0.070,0.199,2.247
+Russia,Commonwealth of Independent States,5.477,0.033,5.541,5.413,10.189,0.903,64.703,0.718,-0.111,0.845,2.430,1.241,0.992,0.511,0.409,0.115,0.060,2.148
+Hong Kong S.A.R. of China,East Asia,5.477,0.049,5.573,5.380,11.000,0.836,76.820,0.717,0.067,0.403,2.430,1.525,0.841,0.893,0.408,0.232,0.342,1.236
+Tajikistan,Commonwealth of Independent States,5.466,0.034,5.532,5.400,8.091,0.860,64.281,0.832,-0.056,0.553,2.430,0.508,0.895,0.498,0.548,0.152,0.247,2.619
+Vietnam,Southeast Asia,5.411,0.039,5.488,5.334,8.973,0.850,68.034,0.940,-0.098,0.796,2.430,0.817,0.873,0.616,0.679,0.124,0.091,2.211
+Libya,Middle East and North Africa,5.410,0.076,5.558,5.262,9.622,0.827,62.300,0.771,-0.087,0.667,2.430,1.044,0.821,0.435,0.474,0.131,0.174,2.331
+Malaysia,Southeast Asia,5.384,0.049,5.480,5.289,10.238,0.817,67.102,0.895,0.125,0.839,2.430,1.259,0.797,0.587,0.624,0.270,0.064,1.784
+Indonesia,Southeast Asia,5.345,0.056,5.454,5.235,9.365,0.811,62.236,0.873,0.542,0.867,2.430,0.954,0.786,0.433,0.598,0.541,0.046,1.987
+Congo (Brazzaville),Sub-Saharan Africa,5.342,0.097,5.533,5.151,8.117,0.636,58.221,0.695,-0.068,0.745,2.430,0.518,0.392,0.307,0.381,0.144,0.124,3.476
+China,East Asia,5.339,0.029,5.397,5.281,9.673,0.811,69.593,0.904,-0.146,0.755,2.430,1.061,0.785,0.665,0.636,0.093,0.117,1.982
+Ivory Coast,Sub-Saharan Africa,5.306,0.078,5.460,5.152,8.551,0.644,50.114,0.741,-0.016,0.794,2.430,0.669,0.409,0.052,0.438,0.177,0.092,3.469
+Armenia,Commonwealth of Independent States,5.283,0.058,5.397,5.168,9.487,0.799,67.055,0.825,-0.168,0.629,2.430,0.996,0.758,0.585,0.540,0.079,0.198,2.127
+Nepal,South Asia,5.269,0.070,5.406,5.132,8.120,0.774,64.233,0.782,0.152,0.727,2.430,0.519,0.702,0.496,0.488,0.287,0.135,2.642
+Bulgaria,Central and Eastern Europe,5.266,0.054,5.371,5.160,10.016,0.931,67.000,0.788,-0.096,0.932,2.430,1.181,1.055,0.583,0.494,0.125,0.005,1.823
+Maldives,South Asia,5.198,0.072,5.339,5.057,9.826,0.913,70.600,0.854,0.024,0.825,2.430,1.115,1.015,0.697,0.575,0.204,0.073,1.520
+Azerbaijan,Commonwealth of Independent States,5.171,0.040,5.250,5.091,9.569,0.836,65.656,0.814,-0.223,0.506,2.430,1.025,0.841,0.541,0.526,0.043,0.276,1.919
+Cameroon,Sub-Saharan Africa,5.142,0.074,5.288,4.996,8.189,0.710,53.515,0.731,0.026,0.848,2.430,0.543,0.556,0.159,0.425,0.205,0.058,3.195
+Senegal,Sub-Saharan Africa,5.132,0.068,5.266,4.998,8.118,0.710,59.802,0.695,-0.046,0.801,2.430,0.518,0.558,0.357,0.381,0.158,0.088,3.071
+Albania,Central and Eastern Europe,5.117,0.059,5.234,5.001,9.520,0.697,68.999,0.785,-0.030,0.901,2.430,1.008,0.529,0.646,0.491,0.168,0.024,2.250
+North Macedonia,Central and Eastern Europe,5.101,0.051,5.202,5.001,9.693,0.805,65.474,0.751,0.038,0.905,2.430,1.068,0.772,0.535,0.450,0.212,0.022,2.042
+Ghana,Sub-Saharan Africa,5.088,0.067,5.219,4.958,8.580,0.727,57.586,0.807,0.123,0.848,2.430,0.680,0.595,0.287,0.517,0.268,0.058,2.684
+Niger,Sub-Saharan Africa,5.074,0.102,5.273,4.875,7.098,0.641,53.780,0.806,0.018,0.693,2.430,0.162,0.402,0.167,0.516,0.200,0.157,3.470
+Turkmenistan,Commonwealth of Independent States,5.066,0.036,5.136,4.996,9.629,0.983,62.409,0.877,0.273,0.888,2.430,1.046,1.172,0.439,0.602,0.366,0.033,1.409
+Gambia,Sub-Saharan Africa,5.051,0.089,5.225,4.877,7.686,0.690,55.160,0.697,0.424,0.746,2.430,0.367,0.511,0.210,0.384,0.465,0.123,2.990
+Benin,Sub-Saharan Africa,5.045,0.073,5.189,4.901,8.087,0.489,54.713,0.757,-0.034,0.661,2.430,0.507,0.058,0.196,0.457,0.166,0.178,3.482
+Laos,Southeast Asia,5.030,0.045,5.119,4.941,8.947,0.728,58.968,0.910,0.123,0.658,2.430,0.808,0.598,0.330,0.643,0.268,0.179,2.204
+Bangladesh,South Asia,5.025,0.046,5.115,4.934,8.454,0.693,64.800,0.877,-0.041,0.682,2.430,0.635,0.520,0.514,0.603,0.161,0.164,2.427
+Guinea,Sub-Saharan Africa,4.984,0.090,5.160,4.808,7.838,0.639,55.008,0.697,0.095,0.766,2.430,0.420,0.399,0.206,0.384,0.250,0.111,3.216
+South Africa,Sub-Saharan Africa,4.956,0.060,5.074,4.839,9.403,0.860,56.904,0.749,-0.067,0.860,2.430,0.967,0.895,0.265,0.447,0.144,0.051,2.187
+Turkey,Middle East and North Africa,4.948,0.046,5.038,4.857,10.240,0.822,67.199,0.576,-0.139,0.776,2.430,1.260,0.809,0.590,0.236,0.097,0.104,1.852
+Pakistan,South Asia,4.934,0.068,5.066,4.802,8.458,0.651,58.709,0.726,0.098,0.787,2.430,0.637,0.423,0.322,0.418,0.252,0.097,2.784
+Morocco,Middle East and North Africa,4.918,0.060,5.036,4.800,8.903,0.560,66.208,0.774,-0.236,0.801,2.430,0.792,0.219,0.558,0.477,0.034,0.088,2.749
+Venezuela,Latin America and Caribbean,4.892,0.064,5.017,4.767,9.073,0.861,66.700,0.615,-0.169,0.827,2.430,0.852,0.897,0.574,0.284,0.078,0.072,2.135
+Georgia,Commonwealth of Independent States,4.891,0.054,4.998,4.785,9.585,0.671,64.300,0.783,-0.238,0.655,2.430,1.030,0.470,0.498,0.488,0.032,0.181,2.191
+Algeria,Middle East and North Africa,4.887,0.053,4.991,4.783,9.342,0.802,66.005,0.480,-0.067,0.752,2.430,0.946,0.765,0.552,0.119,0.144,0.120,2.242
+Ukraine,Commonwealth of Independent States,4.875,0.052,4.977,4.773,9.436,0.888,64.902,0.724,-0.011,0.924,2.430,0.979,0.958,0.517,0.417,0.181,0.010,1.813
+Iraq,Middle East and North Africa,4.854,0.059,4.970,4.738,9.240,0.746,60.583,0.630,-0.053,0.875,2.430,0.910,0.638,0.381,0.302,0.153,0.041,2.429
+Gabon,Sub-Saharan Africa,4.852,0.075,4.998,4.706,9.603,0.776,59.962,0.731,-0.200,0.840,2.430,1.037,0.707,0.362,0.424,0.058,0.064,2.201
+Burkina Faso,Sub-Saharan Africa,4.834,0.081,4.993,4.675,7.678,0.672,54.151,0.695,-0.009,0.748,2.430,0.364,0.472,0.179,0.381,0.182,0.122,3.133
+Cambodia,Southeast Asia,4.830,0.067,4.963,4.698,8.360,0.765,62.000,0.959,0.034,0.843,2.430,0.603,0.680,0.426,0.702,0.210,0.061,2.148
+Mozambique,Sub-Saharan Africa,4.794,0.103,4.997,4.592,7.158,0.744,54.706,0.882,0.061,0.684,2.430,0.183,0.634,0.196,0.608,0.228,0.163,2.783
+Nigeria,Sub-Saharan Africa,4.759,0.052,4.861,4.658,8.533,0.740,50.102,0.737,0.037,0.878,2.430,0.663,0.625,0.051,0.433,0.212,0.039,2.736
+Mali,Sub-Saharan Africa,4.723,0.082,4.884,4.563,7.744,0.724,51.969,0.697,-0.036,0.827,2.430,0.387,0.590,0.110,0.384,0.164,0.072,3.016
+Iran,Middle East and North Africa,4.721,0.055,4.828,4.614,9.584,0.710,66.300,0.608,0.218,0.714,2.430,1.030,0.557,0.561,0.275,0.330,0.144,1.823
+Uganda,Sub-Saharan Africa,4.636,0.073,4.780,4.493,7.677,0.781,56.101,0.709,0.122,0.855,2.430,0.364,0.718,0.240,0.398,0.267,0.054,2.596
+Liberia,Sub-Saharan Africa,4.625,0.106,4.833,4.417,7.288,0.720,56.498,0.735,0.050,0.850,2.430,0.228,0.580,0.253,0.430,0.221,0.057,2.857
+Kenya,Sub-Saharan Africa,4.607,0.072,4.747,4.466,8.361,0.688,60.704,0.779,0.287,0.825,2.430,0.603,0.508,0.385,0.483,0.375,0.073,2.180
+Tunisia,Middle East and North Africa,4.596,0.058,4.709,4.484,9.266,0.691,67.201,0.656,-0.201,0.870,2.430,0.919,0.515,0.590,0.334,0.057,0.044,2.138
+Lebanon,Middle East and North Africa,4.584,0.055,4.691,4.477,9.626,0.848,67.355,0.525,-0.073,0.898,2.430,1.045,0.868,0.595,0.175,0.140,0.026,1.736
+Namibia,Sub-Saharan Africa,4.574,0.064,4.700,4.448,9.161,0.818,56.799,0.719,-0.149,0.847,2.430,0.882,0.801,0.262,0.411,0.091,0.059,2.068
+Palestinian Territories,Middle East and North Africa,4.517,0.067,4.649,4.384,8.485,0.826,62.250,0.653,-0.163,0.821,2.430,0.646,0.819,0.434,0.330,0.082,0.075,2.131
+Myanmar,Southeast Asia,4.426,0.052,4.527,4.324,8.541,0.779,59.302,0.876,0.509,0.660,2.430,0.666,0.713,0.341,0.601,0.520,0.178,1.407
+Jordan,Middle East and North Africa,4.395,0.062,4.516,4.273,9.182,0.767,67.000,0.755,-0.167,0.705,2.430,0.890,0.685,0.583,0.455,0.079,0.150,1.553
+Chad,Sub-Saharan Africa,4.355,0.094,4.540,4.171,7.364,0.619,48.478,0.579,0.041,0.807,2.430,0.255,0.353,0.000,0.240,0.215,0.084,3.209
+Sri Lanka,South Asia,4.325,0.066,4.454,4.196,9.470,0.827,67.299,0.841,0.079,0.863,2.430,0.990,0.820,0.593,0.559,0.239,0.049,1.075
+Swaziland,Sub-Saharan Africa,4.308,0.071,4.448,4.168,9.065,0.770,50.833,0.647,-0.185,0.708,2.430,0.849,0.693,0.074,0.323,0.067,0.147,2.155
+Comoros,Sub-Saharan Africa,4.289,0.084,4.454,4.123,8.031,0.626,57.349,0.548,0.082,0.781,2.430,0.488,0.367,0.279,0.202,0.241,0.101,2.610
+Egypt,Middle East and North Africa,4.283,0.045,4.371,4.195,9.367,0.750,61.998,0.749,-0.182,0.795,2.430,0.954,0.647,0.426,0.446,0.069,0.092,1.648
+Ethiopia,Sub-Saharan Africa,4.275,0.051,4.374,4.175,7.694,0.764,59.000,0.752,0.082,0.761,2.430,0.370,0.679,0.331,0.451,0.241,0.114,2.089
+Mauritania,Sub-Saharan Africa,4.227,0.070,4.365,4.090,8.542,0.795,57.161,0.561,-0.106,0.731,2.430,0.666,0.749,0.273,0.218,0.119,0.133,2.069
+Madagascar,Sub-Saharan Africa,4.208,0.072,4.349,4.068,7.396,0.686,59.305,0.552,-0.005,0.803,2.430,0.266,0.503,0.341,0.207,0.185,0.087,2.620
+Togo,Sub-Saharan Africa,4.107,0.077,4.258,3.956,7.362,0.569,54.914,0.619,0.032,0.772,2.430,0.254,0.239,0.203,0.289,0.209,0.107,2.806
+Zambia,Sub-Saharan Africa,4.073,0.069,4.209,3.938,8.145,0.708,55.809,0.782,0.061,0.823,2.430,0.528,0.552,0.231,0.487,0.227,0.074,1.975
+Sierra Leone,Sub-Saharan Africa,3.849,0.077,4.001,3.698,7.434,0.630,51.651,0.717,0.084,0.866,2.430,0.279,0.377,0.100,0.408,0.243,0.047,2.396
+India,South Asia,3.819,0.026,3.869,3.769,8.755,0.603,60.633,0.893,0.089,0.774,2.430,0.741,0.316,0.383,0.622,0.246,0.106,1.405
+Burundi,Sub-Saharan Africa,3.775,0.107,3.985,3.565,6.635,0.490,53.400,0.626,-0.024,0.607,2.430,0.000,0.062,0.155,0.298,0.172,0.212,2.876
+Yemen,Middle East and North Africa,3.658,0.070,3.794,3.521,7.578,0.832,57.122,0.602,-0.147,0.800,2.430,0.329,0.831,0.272,0.268,0.092,0.089,1.776
+Tanzania,Sub-Saharan Africa,3.623,0.071,3.762,3.485,7.876,0.702,57.999,0.833,0.183,0.577,2.430,0.433,0.540,0.300,0.549,0.307,0.231,1.263
+Haiti,Latin America and Caribbean,3.615,0.173,3.953,3.276,7.477,0.540,55.700,0.593,0.422,0.721,2.430,0.294,0.173,0.227,0.257,0.463,0.139,2.060
+Malawi,Sub-Saharan Africa,3.600,0.092,3.781,3.419,6.958,0.537,57.948,0.780,0.038,0.729,2.430,0.113,0.168,0.298,0.484,0.213,0.134,2.190
+Lesotho,Sub-Saharan Africa,3.512,0.120,3.748,3.276,7.926,0.787,48.700,0.715,-0.131,0.915,2.430,0.451,0.731,0.007,0.405,0.103,0.015,1.800
+Botswana,Sub-Saharan Africa,3.467,0.074,3.611,3.322,9.782,0.784,59.269,0.824,-0.246,0.801,2.430,1.099,0.724,0.340,0.539,0.027,0.088,0.648
+Rwanda,Sub-Saharan Africa,3.415,0.068,3.548,3.282,7.676,0.552,61.400,0.897,0.061,0.167,2.430,0.364,0.202,0.407,0.627,0.227,0.493,1.095
+Zimbabwe,Sub-Saharan Africa,3.145,0.058,3.259,3.030,7.943,0.750,56.201,0.677,-0.047,0.821,2.430,0.457,0.649,0.243,0.359,0.157,0.075,1.205
+Afghanistan,South Asia,2.523,0.038,2.596,2.449,7.695,0.463,52.493,0.382,-0.102,0.924,2.430,0.370,0.000,0.126,0.000,0.122,0.010,1.895
diff --git a/example/.ipynb_checkpoints/03-Matplotlib-checkpoint.ipynb b/example/.ipynb_checkpoints/03-Matplotlib-checkpoint.ipynb
new file mode 100644
index 0000000..e93c46a
--- /dev/null
+++ b/example/.ipynb_checkpoints/03-Matplotlib-checkpoint.ipynb
@@ -0,0 +1,578 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Module Import"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Line Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.arange(0, 10, 0.01)  # x축 입력\n",
+    "y = np.sin(x)  # y축 입력\n",
+    "\n",
+    "fig, ax = plt.subplots()  # subplots\n",
+    "ax.plot(x, y)  # x, y 값 삽입\n",
+    "\n",
+    "ax.set_xlim(0, 10)  # x 범위\n",
+    "ax.set_xlabel(\"time\")  # x 라벨 설정\n",
+    "ax.set_ylim(-1.5, 1.5)  # y 범위\n",
+    "ax.set_ylabel(\"value\")  # y 라벨 설정\n",
+    "ax.set_title(\"value per time\")  # 타이틀 설정\n",
+    "ax.grid()  # 점자형 그래프 설정\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# subplot 없이 그냥 plt만 써도 가능\n",
+    "\n",
+    "years = [2015, 2016, 2017, 2018, 2019]\n",
+    "rank = [5, 3, 2, 3, 4]\n",
+    "plt.plot(\n",
+    "    years,  # x축 데이터\n",
+    "    rank,  # y축 데이터\n",
+    "    color='blue',  # 선 색깔\n",
+    "    marker='o',  # 꼭짓점 설정\n",
+    "    linestyle='solid'  # 선 스타일 설정\n",
+    ")\n",
+    "plt.xlim(2014.5, 2019.5)  # x 범위 설정 (start, end)\n",
+    "plt.ylim(6, 1)  # y 범위 설정 (start, end)\n",
+    "plt.title(\"Tottenham Hotspur's EPL Rankings 2015-2019\")  # title 설정\n",
+    "plt.xlabel('year')  # x축 레이블 설정\n",
+    "plt.ylabel('rank')  # y축 레이블 설정\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multiple Line Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dele = [7.35, 7.35, 7.09, 6.95, 6.89]\n",
+    "eriksen = [7.45, 7.53, 7.4, 7.08, 6.51]\n",
+    "son = [6.53, 7.14, 6.99, 7.1, 7.23]\n",
+    "kane = [7.53, 7.68, 7.6, 7.38, 7.2]\n",
+    "xs = [2015, 2016, 2017, 2018, 2019]\n",
+    "\n",
+    "plt.plot(xs, dele, 'r-', label='Dele Alli')  # x, y, 선 색깔 및 타입 (r: 빨강, -: 실선)\n",
+    "plt.plot(xs, eriksen, 'g--', label='Christian Eriksen')  # (g: 초록, --: 파선)\n",
+    "plt.plot(xs, son, 'b-.', label='Heung-Min Son')  # (b: 파랑 -.: 1점 쇄선)\n",
+    "plt.plot(xs, kane, 'k:', label='Harry Kane')  # (k: 검정, -: 점선)\n",
+    "plt.xlim(2014.5, 2019.5)  # x 범위 설정 (start, end)\n",
+    "plt.ylim(6, 8)  # y 범위 설정 (start, end)\n",
+    "plt.legend(loc=8)  # loc=8로 하단 중앙에 범례 설정\n",
+    "plt.xlabel(\"Year\")\n",
+    "plt.ylabel(\"Rating\")\n",
+    "plt.title(\"DESK's Annual rating\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multiple Subplots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.arange(0, 10, 0.01)  # x축 입력\n",
+    "y1 = np.sin(x)  # y축 입력\n",
+    "y2 = np.sin(x ** 1.1)  # y축 입력\n",
+    "\n",
+    "fig, (ax1, ax2) = plt.subplots(2, 1)  # subplots (row, col)\n",
+    "ax1.plot(x, y1)  # x, y 값 삽입\n",
+    "ax2.plot(x, y2)\n",
+    "\n",
+    "ax1.set_xlim(0, 10)  # x 범위\n",
+    "ax2.set_xlim(0, 10)\n",
+    "ax1.set_ylim(-1.5, 1.5)\n",
+    "ax2.set_ylim(-1.5, 1.5)\n",
+    "ax2.set_xlabel(\"time\")\n",
+    "ax1.set_ylabel(\"value\")  # y 라벨 설정\n",
+    "ax2.set_ylabel(\"value\")\n",
+    "ax1.set_title(\"value per time\")  # 타이틀 설정\n",
+    "ax1.grid()  # 점자형 그래프 설정\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.arange(0, 10, 0.01)  # x축 입력\n",
+    "y1 = np.sin(x)  # y축 입력\n",
+    "y2 = np.sin(x ** 1.1)  # y축 입력\n",
+    "y3 = np.sin(x ** 1.2)  # y축 입력\n",
+    "y4 = np.sin(x ** 1.3)  # y축 입력\n",
+    "\n",
+    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)  # subplots (row, col)\n",
+    "ax1.plot(x, y1)  # x, y 값 삽입\n",
+    "ax2.plot(x, y2)\n",
+    "ax3.plot(x, y3)\n",
+    "ax4.plot(x, y4)\n",
+    "\n",
+    "for ax in [ax1, ax2, ax3, ax4]:\n",
+    "    ax.set_xlim(0, 10)\n",
+    "    ax.set_ylim(-1.5, 1.5)\n",
+    "\n",
+    "ax3.set_xlabel(\"time\")\n",
+    "ax4.set_xlabel(\"time\")\n",
+    "ax1.set_ylabel(\"value\")  # y 라벨 설정\n",
+    "ax3.set_ylabel(\"value\")\n",
+    "ax1.set_title(\"value per time\")  # 타이틀 설정\n",
+    "ax1.grid()  # 점자형 그래프 설정\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bar Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = list('abcde')\n",
+    "y = [2, 4, 5, 3, 7]\n",
+    "plt.bar(x, y, width=0.3, color='green', align='center')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multiple Bar Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = list('abcde')\n",
+    "y1 = [2, 4, 5, 3, 7]\n",
+    "y2 = [3, 6, 5, 8, 2]\n",
+    "\n",
+    "# 'a', 'b', 'c'.. 는 숫자 value가 아니기 때문에 axis를 지정 해 주어야 함.\n",
+    "x_axis = np.arange(len(x))\n",
+    "plt.xticks(x_axis, x)\n",
+    "\n",
+    "plt.bar(x_axis - 0.2, y1, width=0.3, color='red', align='center', label='y1')\n",
+    "plt.bar(x_axis + 0.2, y2, width=0.3, color='blue', align='center', label='y2')\n",
+    "plt.legend(loc=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Histogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 6]        0\n",
+       "(6, 7]        3\n",
+       "(7, 8]       21\n",
+       "(8, 9]      141\n",
+       "(9, 10]     353\n",
+       "(10, 11]    329\n",
+       "(11, 12]    134\n",
+       "(12, 13]     17\n",
+       "(13, 14]      2\n",
+       "(14, 15]      0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mu, sigma = 10, 1\n",
+    "s = np.random.normal(mu, sigma, 1000)\n",
+    "factor = pd.cut(pd.Series(s), [i for i in range(5, 16)])\n",
+    "temp = pd.value_counts(factor).sort_index()\n",
+    "temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xs = [i for i, _ in enumerate(temp.values)]\n",
+    "plt.bar(xs, temp.values)  # 해당 인덱스에 일치하는 값을 가진, 바 형태 그래프 생성\n",
+    "plt.xlabel(\"range\")\n",
+    "plt.ylabel(\"count\")\n",
+    "plt.xticks([i for i, _ in enumerate(temp.index)], temp.index)  # x 눈금 및 레이블 설정 \n",
+    "plt.title(\"Histogram\")\n",
+    "plt.ylim(0, 400)  # y 범위 설정 (start, end)\n",
+    "\n",
+    "for index, value in enumerate(temp.values):\n",
+    "    plt.text(index - 0.3, value + 10, str(value))  # x 좌표, y 좌표, text\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 대략적으로 빠르게\n",
+    "plt.hist(s, [i for i in range(5, 16)] , density=True)  # target, int or sequence, density\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Scatter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.random.randn(10)\n",
+    "y = np.random.randn(10)\n",
+    "plt.scatter(x, y,\n",
+    "            c=[np.random.choice(range(256), size=3) / 256 for i in range(10)],\n",
+    "            s=[np.random.normal(100, 20) for i in range(10)]\n",
+    "           )\n",
+    "\n",
+    "for X, Y, index in zip(x, y, [i for i in range(10)]):\n",
+    "    plt.annotate(index,  # Text\n",
+    "                 xy=(X, Y),  # 텍스트 X, Y 좌표\n",
+    "                 xytext=(5, -5),  # 텍스트 이격\n",
+    "                 textcoords=\"offset points\"  # 좌표 point 기준으로 설정함)\n",
+    "                )\n",
+    "\n",
+    "plt.title(\"x and y\")\n",
+    "plt.xlim(-3, 3)\n",
+    "plt.ylim(-3, 3)\n",
+    "plt.xlabel(\"x\")\n",
+    "plt.ylabel(\"y\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Heatmap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = list('abcde')\n",
+    "y = list('ijklm')\n",
+    "arr = np.random.randint(5, 20, (5, 5))\n",
+    "\n",
+    "plt.xticks([i for i, _ in enumerate(x)], x)\n",
+    "plt.yticks([i for i, _ in enumerate(y)], y)\n",
+    "plt.xlabel(\"abcde\")\n",
+    "plt.ylabel(\"ijklm\")\n",
+    "plt.title(\"Heatmap Example\")\n",
+    "plt.imshow(arr)  # image와 유사한 데이터 표시\n",
+    "plt.colorbar()  # 우측 컬러바 설정\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Image Show"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(256, 256, 3)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import matplotlib.image as img\n",
+    "\n",
+    "fileName = './mushroom.jpeg'\n",
+    "\n",
+    "ndarray = img.imread(fileName)\n",
+    "ndarray.shape  # 256, 256, 3(RGB)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.imshow(ndarray)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}