diff --git "a/assignments/JeongHongjae/02_\354\240\225\355\231\215\354\236\254.ipynb" "b/assignments/JeongHongjae/02_\354\240\225\355\231\215\354\236\254.ipynb"
new file mode 100644
index 0000000..78490b6
--- /dev/null
+++ "b/assignments/JeongHongjae/02_\354\240\225\355\231\215\354\236\254.ipynb"
@@ -0,0 +1,774 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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>Team</th>\n",
+       "      <th>Tournament</th>\n",
+       "      <th>Goals</th>\n",
+       "      <th>Shots pg</th>\n",
+       "      <th>yellow_cards</th>\n",
+       "      <th>red_cards</th>\n",
+       "      <th>Possession%</th>\n",
+       "      <th>Pass%</th>\n",
+       "      <th>AerialsWon</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Manchester City</td>\n",
+       "      <td>Premier League</td>\n",
+       "      <td>83</td>\n",
+       "      <td>15.8</td>\n",
+       "      <td>46</td>\n",
+       "      <td>2</td>\n",
+       "      <td>60.8</td>\n",
+       "      <td>89.4</td>\n",
+       "      <td>12.8</td>\n",
+       "      <td>7.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bayern Munich</td>\n",
+       "      <td>Bundesliga</td>\n",
+       "      <td>99</td>\n",
+       "      <td>17.1</td>\n",
+       "      <td>44</td>\n",
+       "      <td>3</td>\n",
+       "      <td>58.1</td>\n",
+       "      <td>85.5</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>6.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Paris Saint-Germain</td>\n",
+       "      <td>Ligue 1</td>\n",
+       "      <td>86</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>73</td>\n",
+       "      <td>7</td>\n",
+       "      <td>60.1</td>\n",
+       "      <td>89.5</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>6.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Barcelona</td>\n",
+       "      <td>LaLiga</td>\n",
+       "      <td>85</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>68</td>\n",
+       "      <td>2</td>\n",
+       "      <td>62.4</td>\n",
+       "      <td>89.7</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>6.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Real Madrid</td>\n",
+       "      <td>LaLiga</td>\n",
+       "      <td>67</td>\n",
+       "      <td>14.4</td>\n",
+       "      <td>57</td>\n",
+       "      <td>2</td>\n",
+       "      <td>57.7</td>\n",
+       "      <td>87.7</td>\n",
+       "      <td>11.8</td>\n",
+       "      <td>6.86</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>Sheffield United</td>\n",
+       "      <td>Premier League</td>\n",
+       "      <td>20</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>73</td>\n",
+       "      <td>3</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>76.9</td>\n",
+       "      <td>19.1</td>\n",
+       "      <td>6.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>Crotone</td>\n",
+       "      <td>Serie A</td>\n",
+       "      <td>45</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>85</td>\n",
+       "      <td>4</td>\n",
+       "      <td>47.2</td>\n",
+       "      <td>80.4</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>6.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>Benevento</td>\n",
+       "      <td>Serie A</td>\n",
+       "      <td>40</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>90</td>\n",
+       "      <td>5</td>\n",
+       "      <td>44.2</td>\n",
+       "      <td>77.7</td>\n",
+       "      <td>13.4</td>\n",
+       "      <td>6.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>Dijon</td>\n",
+       "      <td>Ligue 1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>9.2</td>\n",
+       "      <td>75</td>\n",
+       "      <td>5</td>\n",
+       "      <td>46.9</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>14.3</td>\n",
+       "      <td>6.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>Schalke 04</td>\n",
+       "      <td>Bundesliga</td>\n",
+       "      <td>25</td>\n",
+       "      <td>8.9</td>\n",
+       "      <td>70</td>\n",
+       "      <td>2</td>\n",
+       "      <td>46.2</td>\n",
+       "      <td>76.5</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>6.41</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>98 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Team      Tournament  Goals  Shots pg  yellow_cards  \\\n",
+       "0       Manchester City  Premier League     83      15.8            46   \n",
+       "1         Bayern Munich      Bundesliga     99      17.1            44   \n",
+       "2   Paris Saint-Germain         Ligue 1     86      15.0            73   \n",
+       "3             Barcelona          LaLiga     85      15.3            68   \n",
+       "4           Real Madrid          LaLiga     67      14.4            57   \n",
+       "..                  ...             ...    ...       ...           ...   \n",
+       "93     Sheffield United  Premier League     20       8.5            73   \n",
+       "94              Crotone         Serie A     45       9.5            85   \n",
+       "95            Benevento         Serie A     40      11.0            90   \n",
+       "96                Dijon         Ligue 1     25       9.2            75   \n",
+       "97           Schalke 04      Bundesliga     25       8.9            70   \n",
+       "\n",
+       "    red_cards  Possession%  Pass%  AerialsWon  Rating  \n",
+       "0           2         60.8   89.4        12.8    7.01  \n",
+       "1           3         58.1   85.5        12.9    6.95  \n",
+       "2           7         60.1   89.5         9.5    6.88  \n",
+       "3           2         62.4   89.7        10.6    6.87  \n",
+       "4           2         57.7   87.7        11.8    6.86  \n",
+       "..        ...          ...    ...         ...     ...  \n",
+       "93          3         43.0   76.9        19.1    6.46  \n",
+       "94          4         47.2   80.4        12.7    6.43  \n",
+       "95          5         44.2   77.7        13.4    6.43  \n",
+       "96          5         46.9   80.0        14.3    6.42  \n",
+       "97          2         46.2   76.5        15.6    6.41  \n",
+       "\n",
+       "[98 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv('./Football teams.csv')\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "각 Column의 정보는 다음 링크에 있는 것과 같습니다.\n",
+    "\n",
+    "https://www.kaggle.com/varpit94/football-teams-rankings-stats\n",
+    "\n",
+    "## Assignments 1\n",
+    "패스 성공률이 높은 상위 5개의 팀을 추출 해 보세요\n",
+    "(hint: sort_values, head)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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>Team</th>\n",
+       "      <th>Tournament</th>\n",
+       "      <th>Goals</th>\n",
+       "      <th>Shots pg</th>\n",
+       "      <th>yellow_cards</th>\n",
+       "      <th>red_cards</th>\n",
+       "      <th>Possession%</th>\n",
+       "      <th>Pass%</th>\n",
+       "      <th>AerialsWon</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Barcelona</td>\n",
+       "      <td>LaLiga</td>\n",
+       "      <td>85</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>68</td>\n",
+       "      <td>2</td>\n",
+       "      <td>62.4</td>\n",
+       "      <td>89.7</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>6.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Paris Saint-Germain</td>\n",
+       "      <td>Ligue 1</td>\n",
+       "      <td>86</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>73</td>\n",
+       "      <td>7</td>\n",
+       "      <td>60.1</td>\n",
+       "      <td>89.5</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>6.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Manchester City</td>\n",
+       "      <td>Premier League</td>\n",
+       "      <td>83</td>\n",
+       "      <td>15.8</td>\n",
+       "      <td>46</td>\n",
+       "      <td>2</td>\n",
+       "      <td>60.8</td>\n",
+       "      <td>89.4</td>\n",
+       "      <td>12.8</td>\n",
+       "      <td>7.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Juventus</td>\n",
+       "      <td>Serie A</td>\n",
+       "      <td>77</td>\n",
+       "      <td>15.7</td>\n",
+       "      <td>76</td>\n",
+       "      <td>6</td>\n",
+       "      <td>55.4</td>\n",
+       "      <td>88.3</td>\n",
+       "      <td>11.4</td>\n",
+       "      <td>6.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>Sassuolo</td>\n",
+       "      <td>Serie A</td>\n",
+       "      <td>64</td>\n",
+       "      <td>13.9</td>\n",
+       "      <td>74</td>\n",
+       "      <td>4</td>\n",
+       "      <td>58.2</td>\n",
+       "      <td>87.8</td>\n",
+       "      <td>10.9</td>\n",
+       "      <td>6.67</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Team      Tournament  Goals  Shots pg  yellow_cards  \\\n",
+       "3             Barcelona          LaLiga     85      15.3            68   \n",
+       "2   Paris Saint-Germain         Ligue 1     86      15.0            73   \n",
+       "0       Manchester City  Premier League     83      15.8            46   \n",
+       "6              Juventus         Serie A     77      15.7            76   \n",
+       "35             Sassuolo         Serie A     64      13.9            74   \n",
+       "\n",
+       "    red_cards  Possession%  Pass%  AerialsWon  Rating  \n",
+       "3           2         62.4   89.7        10.6    6.87  \n",
+       "2           7         60.1   89.5         9.5    6.88  \n",
+       "0           2         60.8   89.4        12.8    7.01  \n",
+       "6           6         55.4   88.3        11.4    6.85  \n",
+       "35          4         58.2   87.8        10.9    6.67  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2 = df.sort_values(by='Pass%', ascending = False)\n",
+    "df2.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assignments 2\n",
+    "모든 팀 중에서 점유율이 60% 이상인 팀을 추출 하세요"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "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>Team</th>\n",
+       "      <th>Tournament</th>\n",
+       "      <th>Goals</th>\n",
+       "      <th>Shots pg</th>\n",
+       "      <th>yellow_cards</th>\n",
+       "      <th>red_cards</th>\n",
+       "      <th>Possession%</th>\n",
+       "      <th>Pass%</th>\n",
+       "      <th>AerialsWon</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Manchester City</td>\n",
+       "      <td>Premier League</td>\n",
+       "      <td>83</td>\n",
+       "      <td>15.8</td>\n",
+       "      <td>46</td>\n",
+       "      <td>2</td>\n",
+       "      <td>60.8</td>\n",
+       "      <td>89.4</td>\n",
+       "      <td>12.8</td>\n",
+       "      <td>7.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Paris Saint-Germain</td>\n",
+       "      <td>Ligue 1</td>\n",
+       "      <td>86</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>73</td>\n",
+       "      <td>7</td>\n",
+       "      <td>60.1</td>\n",
+       "      <td>89.5</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>6.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Barcelona</td>\n",
+       "      <td>LaLiga</td>\n",
+       "      <td>85</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>68</td>\n",
+       "      <td>2</td>\n",
+       "      <td>62.4</td>\n",
+       "      <td>89.7</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>6.87</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Team      Tournament  Goals  Shots pg  yellow_cards  \\\n",
+       "0      Manchester City  Premier League     83      15.8            46   \n",
+       "2  Paris Saint-Germain         Ligue 1     86      15.0            73   \n",
+       "3            Barcelona          LaLiga     85      15.3            68   \n",
+       "\n",
+       "   red_cards  Possession%  Pass%  AerialsWon  Rating  \n",
+       "0          2         60.8   89.4        12.8    7.01  \n",
+       "2          7         60.1   89.5         9.5    6.88  \n",
+       "3          2         62.4   89.7        10.6    6.87  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3 = df[df[\"Possession%\"]>60]\n",
+    "df3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assignments 3\n",
+    "5대 리그 중, 제일 과격 한 리그는 무엇일까요? 일단 Yellow Card 갯수 순서로 리그를 정렬 해 주고, 거기서 yellow_cards와 red_cards 수로 정렬 해 주세요!\n",
+    "(hint: groupby, sum, loc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "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>yellow_cards</th>\n",
+       "      <th>red_cards</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tournament</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>LaLiga</th>\n",
+       "      <td>1639</td>\n",
+       "      <td>75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Serie A</th>\n",
+       "      <td>1597</td>\n",
+       "      <td>69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ligue 1</th>\n",
+       "      <td>1419</td>\n",
+       "      <td>102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Premier League</th>\n",
+       "      <td>1095</td>\n",
+       "      <td>48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bundesliga</th>\n",
+       "      <td>1081</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                yellow_cards  red_cards\n",
+       "Tournament                             \n",
+       "LaLiga                  1639         75\n",
+       "Serie A                 1597         69\n",
+       "Ligue 1                 1419        102\n",
+       "Premier League          1095         48\n",
+       "Bundesliga              1081         33"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df4 = df.groupby([\"Tournament\"]).sum()\n",
+    "df4 = df4.sort_values(by=\"yellow_cards\", ascending = False)\n",
+    "df4 = df4.loc[:, \"yellow_cards\":\"red_cards\"]\n",
+    "df4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assignments 4\n",
+    "\n",
+    "생각해 보니 레드 카드도 감안을 해야 할 것 같습니다. 레드 카드 5점, 옐로우 카드 2점으로 점수를 산정 하여, 해당 점수를 바탕으로 정렬해서, 진짜 과격한 리그가 어느 리그인지 알아 보도록 해봐요!\n",
+    "\n",
+    "아 맞다, df['A'] = df['B'] * 5 + df['C'] * 2\n",
+    "\n",
+    "이런 식으로 새로운 Column을 만들면 되지 않을까요?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "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>yellow_cards</th>\n",
+       "      <th>red_cards</th>\n",
+       "      <th>score</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tournament</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>LaLiga</th>\n",
+       "      <td>1639</td>\n",
+       "      <td>75</td>\n",
+       "      <td>3653</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Serie A</th>\n",
+       "      <td>1597</td>\n",
+       "      <td>69</td>\n",
+       "      <td>3539</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ligue 1</th>\n",
+       "      <td>1419</td>\n",
+       "      <td>102</td>\n",
+       "      <td>3348</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Premier League</th>\n",
+       "      <td>1095</td>\n",
+       "      <td>48</td>\n",
+       "      <td>2430</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bundesliga</th>\n",
+       "      <td>1081</td>\n",
+       "      <td>33</td>\n",
+       "      <td>2327</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                yellow_cards  red_cards  score\n",
+       "Tournament                                    \n",
+       "LaLiga                  1639         75   3653\n",
+       "Serie A                 1597         69   3539\n",
+       "Ligue 1                 1419        102   3348\n",
+       "Premier League          1095         48   2430\n",
+       "Bundesliga              1081         33   2327"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df5 = df4\n",
+    "df5['score'] = df5['yellow_cards']*2 + df5['red_cards']*5\n",
+    "df5 = df5.sort_values(by='score', ascending = False)\n",
+    "df5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assignments 5\n",
+    "흠.. 근데 패스 성공률과 점유율의 상관관계에 대해서 알아보고 싶은데.. 선형 적인 면에서 이를 알 수 있을까요? numpy를 이용해서 covariance matrix를 통해서 알아 보면 될 것 같은데...\n",
+    "\n",
+    "hint: to_numpy, T, cov"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[21.99771618, 19.52841995],\n",
+       "       [19.52841995, 23.91276667]])"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A1 = df.sort_values('Pass%',ascending = False).head()\n",
+    "A1\n",
+    "\n",
+    "A2 = df[df['Possession%']>=60]\n",
+    "A2\n",
+    "\n",
+    "A3 = df.groupby(df['Tournament']).sum().sort_values(['yellow_cards','red_cards'],ascending= False)\n",
+    "A3 = A3.loc[:,['yellow_cards','red_cards']]\n",
+    "A3\n",
+    "\n",
+    "A4 = A3\n",
+    "A4['score'] = A4['red_cards']*5 +A4['yellow_cards']*2\n",
+    "A4"
+   ]
+  },
+  {
+   "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git "a/assignments/JeongHongjae/03_\354\240\225\355\231\215\354\236\254.ipynb" "b/assignments/JeongHongjae/03_\354\240\225\355\231\215\354\236\254.ipynb"
new file mode 100644
index 0000000..3e1f536
--- /dev/null
+++ "b/assignments/JeongHongjae/03_\354\240\225\355\231\215\354\236\254.ipynb"
@@ -0,0 +1,627 @@
+{
+ "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": 11,
+   "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[\"Ladder score\"]\n",
+    "plt.hist(df2, [i for i in range(2, 10)], density=True)  # target, int or sequence, density\n",
+    "plt.xlabel(\"score\")\n",
+    "plt.ylabel(\"count\")\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": 12,
+   "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 = df['Logged GDP per capita']\n",
+    "y = df2\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 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": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "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": [
+    "xx = ['Social', 'GDP Attribute', \"Freedom\"]\n",
+    "yy1 = [df3['Social support'], df3['Logged GDP per capita'], df3['Freedom to make life choices']]\n",
+    "yy2 = [df4['Social support'], df4['Logged GDP per capita'], df4['Freedom to make life choices']]\n",
+    "\n",
+    "\n",
+    "# 'a', 'b', 'c'...는 숫자 value가 아니기 때문에 axis를 지정해 주어야 함.\n",
+    "xx_axis = np.arange(len(xx))\n",
+    "plt.xticks(xx_axis, xx)\n",
+    "\n",
+    "plt.bar(xx_axis - 0.2, yy1, width=0.3, color='green', align='center', label='Happy')\n",
+    "plt.bar(xx_axis + 0.2, yy2, width=0.3, color='blue', align='center', label='Unhappy')  # 좌표 느낌으로 각 x_axis에 대하여 일정 간격을 두고 막대그래프 생성\n",
+    "plt.legend(loc=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "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": [
+    "df3 = df.loc[:10, \"Logged GDP per capita\" : 'Freedom to make life choices'].mean()\n",
+    "df4 = df.loc[139:, \"Logged GDP per capita\" : 'Freedom to make life choices'].mean()\n",
+    "\n",
+    "x_axis = ['Social support', ' Logged GDP per capita', 'Freedom to make life choices']\n",
+    "y1 = [df3['Social support'], df3['Logged GDP per capita'], df3['Freedom to make life choices']]\n",
+    "y2 = [df4['Social support'], df4['Logged GDP per capita'], df4['Freedom to make life choices']]\n",
+    "\n",
+    "plt.plot(x_axis, y1)\n",
+    "plt.plot(x_axis, y2)\n",
+    "plt.ylabel('Score')\n",
+    "plt.title(\"Happy country and Unhappy country\")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/assignments/JeongHongjae/test b/assignments/JeongHongjae/test
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/assignments/JeongHongjae/test
@@ -0,0 +1 @@
+