diff --git "a/assignments/ejalice/03-\354\265\234\354\235\200\354\247\200.ipynb" "b/assignments/ejalice/03-\354\265\234\354\235\200\354\247\200.ipynb"
new file mode 100644
index 0000000..2aebe23
--- /dev/null
+++ "b/assignments/ejalice/03-\354\265\234\354\235\200\354\247\200.ipynb"
@@ -0,0 +1,590 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "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": 30,
+     "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": 53,
+   "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": [
+    "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(\"Happiness Score\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "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": 56,
+   "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": [
+    "y=df[\"Ladder score\"]\n",
+    "x=df[\"Logged GDP per capita\"]\n",
+    "plt.scatter(x, y)\n",
+    "plt.title(\"Happiness Score per GDP\")\n",
+    "plt.xlabel(\"GDP\")\n",
+    "plt.ylabel(\"score\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "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 막대 그래프로 나타내세요."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "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": [
+    "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",
+    "\n",
+    "x=[\"Social\", \"GDP Attribute\", \"Freedom\"]\n",
+    "\n",
+    "y1=Happy\n",
+    "y2=Unhappy\n",
+    "\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=\"green\", align=\"center\", label=\"Happy\")\n",
+    "plt.bar(x_axis+0.2, y2, width=0.3, color=\"Blue\", align=\"center\", label=\"Unhappy\")\n",
+    "plt.legend(loc=1)\n",
+    "plt.ylabel(\"score\")\n",
+    "plt.title(\"Happy Country and Unhappy Country\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "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
+}