Exploratory_Data_Analysis.ipynb

{
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  {
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   "id": "d6fc22d6-c653-48e3-b1f8-8b8821331adf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "pd.options.display.float_format = \"{:.2f}\".format\n",
    "pd.options.display.max_columns = 40"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96a23b6d-f169-4cc4-a9b4-e5c204e2e043",
   "metadata": {},
   "source": [
    "### Game Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6d8db5cf-0c2a-40b4-a1ac-7b268a77ab1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1872-11-30</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1873-03-08</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1874-03-07</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1875-03-06</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1876-03-04</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date home_team away_team  home_score  away_score tournament\n",
       "0 1872-11-30  Scotland   England           0           0   Friendly\n",
       "1 1873-03-08   England  Scotland           4           2   Friendly\n",
       "2 1874-03-07  Scotland   England           2           1   Friendly\n",
       "3 1875-03-06   England  Scotland           2           2   Friendly\n",
       "4 1876-03-04  Scotland   England           3           0   Friendly"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_game = pd.read_csv('E:\\COM480-DataVis\\datavis-project-2022-y3\\Data\\match_results.csv', \n",
    "                      parse_dates=['date'],\n",
    "                      usecols=['date', 'home_team', 'away_team', 'home_score', 'away_score', 'tournament'])\n",
    "df_game.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e4fef02-54b3-4360-a27e-40065ad50416",
   "metadata": {},
   "source": [
    "First thing first, we are going to filter the dataframe by year of which matches were held, as well as tournament that are methods of qualifaction toward FIFA world cup, or just world cup games. To be specific, we only keep games took place after 1990 that were part of the following tournaments:\n",
    "- AFC Asian Cup\n",
    "- AFC Asian Cup qualification\n",
    "- African Cup of Nations\n",
    "- African Cup of Nations qualification\n",
    "- Copa América\n",
    "- Copa América qualification\n",
    "- FIFA World Cup\n",
    "- FIFA World Cup qualification\n",
    "- Oceania Nations Cup\n",
    "- UEFA Euro\n",
    "- UEFA Euro qualification\n",
    "- UEFA Nations League\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "70953116-19a0-4a30-9c42-ec86c15781d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16061</th>\n",
       "      <td>1990-03-02</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>African Cup of Nations</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16062</th>\n",
       "      <td>1990-03-03</td>\n",
       "      <td>Cameroon</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>African Cup of Nations</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16063</th>\n",
       "      <td>1990-03-03</td>\n",
       "      <td>Ivory Coast</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>African Cup of Nations</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16064</th>\n",
       "      <td>1990-03-03</td>\n",
       "      <td>Senegal</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>African Cup of Nations</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16066</th>\n",
       "      <td>1990-03-05</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>Ivory Coast</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>African Cup of Nations</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "            date    home_team    away_team  home_score  away_score  \\\n",
       "16061 1990-03-02      Algeria      Nigeria           5           1   \n",
       "16062 1990-03-03     Cameroon       Zambia           0           1   \n",
       "16063 1990-03-03  Ivory Coast        Egypt           3           1   \n",
       "16064 1990-03-03      Senegal        Kenya           0           0   \n",
       "16066 1990-03-05      Algeria  Ivory Coast           3           0   \n",
       "\n",
       "                   tournament  \n",
       "16061  African Cup of Nations  \n",
       "16062  African Cup of Nations  \n",
       "16063  African Cup of Nations  \n",
       "16064  African Cup of Nations  \n",
       "16066  African Cup of Nations  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter data by year and tournament\n",
    "\n",
    "# Only keep games from 1990\n",
    "df_game = df_game[df_game.date.dt.year >= 1990]\n",
    "\n",
    "# Only keep tournament that are methods of qualifaction for world cup, that is:\n",
    "tournaments=['AFC Asian Cup', 'AFC Asian Cup qualification', 'African Cup of Nations', 'African Cup of Nations qualification', 'Copa América', 'Copa América qualification'\n",
    "             'FIFA World Cup', 'FIFA World Cup qualification', 'Oceania Nations Cup', 'UEFA Euro', 'UEFA Euro qualification', 'UEFA Nations League']\n",
    "\n",
    "df_game = df_game[df_game.tournament.isin(tournaments)]\n",
    "df_game.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a69a7140-0fb8-40d5-8c8b-d92b9ed721c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>result_away_win</th>\n",
       "      <th>result_home_win</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16061</th>\n",
       "      <td>1990-03-02</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>African Cup of Nations</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16062</th>\n",
       "      <td>1990-03-03</td>\n",
       "      <td>Cameroon</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>African Cup of Nations</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16063</th>\n",
       "      <td>1990-03-03</td>\n",
       "      <td>Ivory Coast</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>African Cup of Nations</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>16064</th>\n",
       "      <td>1990-03-03</td>\n",
       "      <td>Senegal</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>African Cup of Nations</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16066</th>\n",
       "      <td>1990-03-05</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>Ivory Coast</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>African Cup of Nations</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "            date    home_team    away_team  home_score  away_score  \\\n",
       "16061 1990-03-02      Algeria      Nigeria           5           1   \n",
       "16062 1990-03-03     Cameroon       Zambia           0           1   \n",
       "16063 1990-03-03  Ivory Coast        Egypt           3           1   \n",
       "16064 1990-03-03      Senegal        Kenya           0           0   \n",
       "16066 1990-03-05      Algeria  Ivory Coast           3           0   \n",
       "\n",
       "                   tournament  result_away_win  result_home_win  \n",
       "16061  African Cup of Nations                0                1  \n",
       "16062  African Cup of Nations                1                0  \n",
       "16063  African Cup of Nations                0                1  \n",
       "16064  African Cup of Nations                1                0  \n",
       "16066  African Cup of Nations                0                1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One-hot Encoding on result of the match\n",
    "\n",
    "# In football matches on a home-and-away basis, draw is equivalent to away team win\n",
    "df_game['result'] = df_game.apply(lambda Row: 'home_win' if Row['home_score'] > Row['away_score'] else 'away_win', axis=1)\n",
    "\n",
    "# One-hot Encoding\n",
    "df_game = pd.get_dummies(df_game, columns=['result'])\n",
    "df_game.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9e9535da-86ee-4f37-8f49-86c4a953a2d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate goal difference\n",
    "\n",
    "df_game[\"goal_diff\"] = df_game.home_score - df_game.away_score\n",
    "df_game[\"home_goal_diff\"] = df_game.apply(lambda Row: Row['goal_diff'] if Row['goal_diff'] > 0 else 0, axis=1)\n",
    "df_game[\"away_goal_diff\"] = df_game.apply(lambda Row: -Row['goal_diff'] if Row['goal_diff'] <= 0 else 0, axis=1)\n",
    "df_game.drop(['goal_diff'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0a62f77b-a843-434d-ac08-9cfe4631a451",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>result_away_win</th>\n",
       "      <th>result_home_win</th>\n",
       "      <th>home_goal_diff</th>\n",
       "      <th>away_goal_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16061</th>\n",
       "      <td>1990-03-02</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>African Cup of Nations</td>\n",
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       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16062</th>\n",
       "      <td>1990-03-03</td>\n",
       "      <td>Cameroon</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>0</td>\n",
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       "      <td>African Cup of Nations</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>16063</th>\n",
       "      <td>1990-03-03</td>\n",
       "      <td>Ivory Coast</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>African Cup of Nations</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16064</th>\n",
       "      <td>1990-03-03</td>\n",
       "      <td>Senegal</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>African Cup of Nations</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16066</th>\n",
       "      <td>1990-03-05</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>Ivory Coast</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>African Cup of Nations</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</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>43178</th>\n",
       "      <td>2022-01-30</td>\n",
       "      <td>Senegal</td>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>African Cup of Nations</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43184</th>\n",
       "      <td>2022-02-02</td>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>Senegal</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>African Cup of Nations</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43185</th>\n",
       "      <td>2022-02-03</td>\n",
       "      <td>Cameroon</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>African Cup of Nations</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43186</th>\n",
       "      <td>2022-02-05</td>\n",
       "      <td>Cameroon</td>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>African Cup of Nations</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43187</th>\n",
       "      <td>2022-02-06</td>\n",
       "      <td>Senegal</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>African Cup of Nations</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11463 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date     home_team          away_team  home_score  away_score  \\\n",
       "16061 1990-03-02       Algeria            Nigeria           5           1   \n",
       "16062 1990-03-03      Cameroon             Zambia           0           1   \n",
       "16063 1990-03-03   Ivory Coast              Egypt           3           1   \n",
       "16064 1990-03-03       Senegal              Kenya           0           0   \n",
       "16066 1990-03-05       Algeria        Ivory Coast           3           0   \n",
       "...          ...           ...                ...         ...         ...   \n",
       "43178 2022-01-30       Senegal  Equatorial Guinea           3           1   \n",
       "43184 2022-02-02  Burkina Faso            Senegal           1           3   \n",
       "43185 2022-02-03      Cameroon              Egypt           0           0   \n",
       "43186 2022-02-05      Cameroon       Burkina Faso           3           3   \n",
       "43187 2022-02-06       Senegal              Egypt           0           0   \n",
       "\n",
       "                   tournament  result_away_win  result_home_win  \\\n",
       "16061  African Cup of Nations                0                1   \n",
       "16062  African Cup of Nations                1                0   \n",
       "16063  African Cup of Nations                0                1   \n",
       "16064  African Cup of Nations                1                0   \n",
       "16066  African Cup of Nations                0                1   \n",
       "...                       ...              ...              ...   \n",
       "43178  African Cup of Nations                0                1   \n",
       "43184  African Cup of Nations                1                0   \n",
       "43185  African Cup of Nations                1                0   \n",
       "43186  African Cup of Nations                1                0   \n",
       "43187  African Cup of Nations                1                0   \n",
       "\n",
       "       home_goal_diff  away_goal_diff  \n",
       "16061               4               0  \n",
       "16062               0               1  \n",
       "16063               2               0  \n",
       "16064               0               0  \n",
       "16066               3               0  \n",
       "...               ...             ...  \n",
       "43178               2               0  \n",
       "43184               0               2  \n",
       "43185               0               0  \n",
       "43186               0               0  \n",
       "43187               0               0  \n",
       "\n",
       "[11463 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "65ff5f0f-2dad-49d1-828d-18a9950ddf2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\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>home_win</th>\n",
       "      <th>home_count</th>\n",
       "      <th>home_score</th>\n",
       "      <th>home_goal_diff</th>\n",
       "      <th>away_win</th>\n",
       "      <th>away_count</th>\n",
       "      <th>away_score</th>\n",
       "      <th>away_goal_diff</th>\n",
       "      <th>total_count</th>\n",
       "      <th>total_win</th>\n",
       "      <th>total_score</th>\n",
       "      <th>home_lose</th>\n",
       "      <th>away_lose</th>\n",
       "      <th>total_lose</th>\n",
       "      <th>home_win_rate</th>\n",
       "      <th>away_win_rate</th>\n",
       "      <th>total_win_rate</th>\n",
       "      <th>avg_home_goal_diff</th>\n",
       "      <th>avg_away_goal_diff</th>\n",
       "      <th>total_goal_diff</th>\n",
       "      <th>avg_total_goal_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.33</td>\n",
       "      <td>8</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>28</td>\n",
       "      <td>84</td>\n",
       "      <td>90</td>\n",
       "      <td>45</td>\n",
       "      <td>31</td>\n",
       "      <td>86</td>\n",
       "      <td>62</td>\n",
       "      <td>32</td>\n",
       "      <td>170</td>\n",
       "      <td>59</td>\n",
       "      <td>152</td>\n",
       "      <td>56</td>\n",
       "      <td>55</td>\n",
       "      <td>111</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.61</td>\n",
       "      <td>1.03</td>\n",
       "      <td>77</td>\n",
       "      <td>1.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>59</td>\n",
       "      <td>89</td>\n",
       "      <td>193</td>\n",
       "      <td>146</td>\n",
       "      <td>57</td>\n",
       "      <td>98</td>\n",
       "      <td>104</td>\n",
       "      <td>46</td>\n",
       "      <td>187</td>\n",
       "      <td>116</td>\n",
       "      <td>297</td>\n",
       "      <td>30</td>\n",
       "      <td>41</td>\n",
       "      <td>71</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.62</td>\n",
       "      <td>2.47</td>\n",
       "      <td>0.81</td>\n",
       "      <td>192</td>\n",
       "      <td>1.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>American Samoa</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>4</td>\n",
       "      <td>67</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>67</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>134</td>\n",
       "      <td>9</td>\n",
       "      <td>39</td>\n",
       "      <td>63</td>\n",
       "      <td>62</td>\n",
       "      <td>125</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.60</td>\n",
       "      <td>8</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             team  home_win  home_count  home_score  home_goal_diff  away_win  \\\n",
       "0     Afghanistan         4          14          15               6         6   \n",
       "1         Albania        28          84          90              45        31   \n",
       "2         Algeria        59          89         193             146        57   \n",
       "3  American Samoa         0           4           1               0         0   \n",
       "4         Andorra         4          67          22               5         5   \n",
       "\n",
       "   away_count  away_score  away_goal_diff  total_count  total_win  \\\n",
       "0          16           9               2           30         10   \n",
       "1          86          62              32          170         59   \n",
       "2          98         104              46          187        116   \n",
       "3           4           0               0            8          0   \n",
       "4          67          17               3          134          9   \n",
       "\n",
       "   total_score  home_lose  away_lose  total_lose  home_win_rate  \\\n",
       "0           24         10         10          20           0.29   \n",
       "1          152         56         55         111           0.33   \n",
       "2          297         30         41          71           0.66   \n",
       "3            1          4          4           8           0.00   \n",
       "4           39         63         62         125           0.06   \n",
       "\n",
       "   away_win_rate  total_win_rate  avg_home_goal_diff  avg_away_goal_diff  \\\n",
       "0           0.38            0.33                1.50                0.33   \n",
       "1           0.36            0.35                1.61                1.03   \n",
       "2           0.58            0.62                2.47                0.81   \n",
       "3           0.00            0.00                0.00                0.00   \n",
       "4           0.07            0.07                1.25                0.60   \n",
       "\n",
       "   total_goal_diff  avg_total_goal_diff  \n",
       "0                8                 0.80  \n",
       "1               77                 1.31  \n",
       "2              192                 1.66  \n",
       "3                0                 0.00  \n",
       "4                8                 0.89  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gather statistics: number of win and lose matches as home team and away team respectively, as well as total\n",
    "\n",
    "df_home_count = df_game.groupby('home_team').agg({'result_home_win': 'sum', 'away_team': 'count', 'home_score': 'sum', 'home_goal_diff': 'sum'})\\\n",
    "                                            .rename(columns={'result_home_win':'home_win', 'away_team': 'home_count'}).reset_index()\n",
    "\n",
    "df_away_count = df_game.groupby('away_team').agg({'result_away_win': 'sum', 'home_team': 'count', 'away_score': 'sum', 'away_goal_diff': 'sum'})\\\n",
    "                                            .rename(columns={'result_away_win':'away_win', 'home_team': 'away_count'}).reset_index()\n",
    "\n",
    "df_game_stat = df_home_count.merge(df_away_count, left_on='home_team', right_on='away_team', how='inner').drop(['away_team'], axis=1)\n",
    "df_game_stat.rename(columns={'home_team':'team'}, inplace=True)\n",
    "\n",
    "df_game_stat['total_count'] = df_game_stat.home_count + df_game_stat.away_count\n",
    "df_game_stat['total_win']   = df_game_stat.home_win + df_game_stat.away_win\n",
    "df_game_stat['total_score'] = df_game_stat.home_score + df_game_stat.away_score\n",
    "\n",
    "df_game_stat['home_lose']  = df_game_stat.home_count - df_game_stat.home_win\n",
    "df_game_stat['away_lose']  = df_game_stat.away_count - df_game_stat.away_win\n",
    "df_game_stat['total_lose'] = df_game_stat.home_lose + df_game_stat.away_lose\n",
    "\n",
    "# Win rate statistics\n",
    "df_game_stat['home_win_rate']   = df_game_stat.home_win / df_game_stat.home_count\n",
    "df_game_stat['away_win_rate']   = df_game_stat.away_win / df_game_stat.away_count\n",
    "df_game_stat['total_win_rate']  = df_game_stat.total_win / df_game_stat.total_count\n",
    "\n",
    "# Goal difference statistics\n",
    "df_game_stat['avg_home_goal_diff']  = df_game_stat.home_goal_diff / df_game_stat.home_win\n",
    "df_game_stat['avg_away_goal_diff']  = df_game_stat.away_goal_diff / df_game_stat.away_win\n",
    "df_game_stat['total_goal_diff']     = df_game_stat.home_goal_diff + df_game_stat.away_goal_diff\n",
    "df_game_stat['avg_total_goal_diff'] = df_game_stat.total_goal_diff / df_game_stat.total_win\n",
    "\n",
    "# Fill 0 for teams with 0 home_win and/or away_win count \n",
    "df_game_stat = df_game_stat.fillna(0)\n",
    "df_game_stat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "778b14cf-20cd-4c62-ab9c-9440c76ae75a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team</th>\n",
       "      <th>home_win</th>\n",
       "      <th>home_count</th>\n",
       "      <th>home_score</th>\n",
       "      <th>home_goal_diff</th>\n",
       "      <th>away_win</th>\n",
       "      <th>away_count</th>\n",
       "      <th>away_score</th>\n",
       "      <th>away_goal_diff</th>\n",
       "      <th>total_count</th>\n",
       "      <th>total_win</th>\n",
       "      <th>total_score</th>\n",
       "      <th>home_lose</th>\n",
       "      <th>away_lose</th>\n",
       "      <th>total_lose</th>\n",
       "      <th>home_win_rate</th>\n",
       "      <th>away_win_rate</th>\n",
       "      <th>total_win_rate</th>\n",
       "      <th>avg_home_goal_diff</th>\n",
       "      <th>avg_away_goal_diff</th>\n",
       "      <th>total_goal_diff</th>\n",
       "      <th>avg_total_goal_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Italy</td>\n",
       "      <td>72</td>\n",
       "      <td>101</td>\n",
       "      <td>208</td>\n",
       "      <td>158</td>\n",
       "      <td>87</td>\n",
       "      <td>99</td>\n",
       "      <td>143</td>\n",
       "      <td>95</td>\n",
       "      <td>200</td>\n",
       "      <td>159</td>\n",
       "      <td>351</td>\n",
       "      <td>29</td>\n",
       "      <td>12</td>\n",
       "      <td>41</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.80</td>\n",
       "      <td>2.19</td>\n",
       "      <td>1.09</td>\n",
       "      <td>253</td>\n",
       "      <td>1.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>Spain</td>\n",
       "      <td>80</td>\n",
       "      <td>98</td>\n",
       "      <td>290</td>\n",
       "      <td>257</td>\n",
       "      <td>86</td>\n",
       "      <td>100</td>\n",
       "      <td>193</td>\n",
       "      <td>128</td>\n",
       "      <td>198</td>\n",
       "      <td>166</td>\n",
       "      <td>483</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>32</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.84</td>\n",
       "      <td>3.21</td>\n",
       "      <td>1.49</td>\n",
       "      <td>385</td>\n",
       "      <td>2.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>71</td>\n",
       "      <td>100</td>\n",
       "      <td>240</td>\n",
       "      <td>192</td>\n",
       "      <td>81</td>\n",
       "      <td>98</td>\n",
       "      <td>178</td>\n",
       "      <td>118</td>\n",
       "      <td>198</td>\n",
       "      <td>152</td>\n",
       "      <td>418</td>\n",
       "      <td>29</td>\n",
       "      <td>17</td>\n",
       "      <td>46</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1.46</td>\n",
       "      <td>310</td>\n",
       "      <td>2.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Ivory Coast</td>\n",
       "      <td>68</td>\n",
       "      <td>99</td>\n",
       "      <td>228</td>\n",
       "      <td>169</td>\n",
       "      <td>75</td>\n",
       "      <td>98</td>\n",
       "      <td>122</td>\n",
       "      <td>72</td>\n",
       "      <td>197</td>\n",
       "      <td>143</td>\n",
       "      <td>350</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>54</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.73</td>\n",
       "      <td>2.49</td>\n",
       "      <td>0.96</td>\n",
       "      <td>241</td>\n",
       "      <td>1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>76</td>\n",
       "      <td>115</td>\n",
       "      <td>196</td>\n",
       "      <td>145</td>\n",
       "      <td>63</td>\n",
       "      <td>82</td>\n",
       "      <td>111</td>\n",
       "      <td>65</td>\n",
       "      <td>197</td>\n",
       "      <td>139</td>\n",
       "      <td>307</td>\n",
       "      <td>39</td>\n",
       "      <td>19</td>\n",
       "      <td>58</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1.91</td>\n",
       "      <td>1.03</td>\n",
       "      <td>210</td>\n",
       "      <td>1.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>54</td>\n",
       "      <td>101</td>\n",
       "      <td>168</td>\n",
       "      <td>109</td>\n",
       "      <td>50</td>\n",
       "      <td>96</td>\n",
       "      <td>91</td>\n",
       "      <td>37</td>\n",
       "      <td>197</td>\n",
       "      <td>104</td>\n",
       "      <td>259</td>\n",
       "      <td>47</td>\n",
       "      <td>46</td>\n",
       "      <td>93</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2.02</td>\n",
       "      <td>0.74</td>\n",
       "      <td>146</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>56</td>\n",
       "      <td>95</td>\n",
       "      <td>183</td>\n",
       "      <td>138</td>\n",
       "      <td>77</td>\n",
       "      <td>97</td>\n",
       "      <td>163</td>\n",
       "      <td>109</td>\n",
       "      <td>192</td>\n",
       "      <td>133</td>\n",
       "      <td>346</td>\n",
       "      <td>39</td>\n",
       "      <td>20</td>\n",
       "      <td>59</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.69</td>\n",
       "      <td>2.46</td>\n",
       "      <td>1.42</td>\n",
       "      <td>247</td>\n",
       "      <td>1.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>72</td>\n",
       "      <td>112</td>\n",
       "      <td>216</td>\n",
       "      <td>154</td>\n",
       "      <td>58</td>\n",
       "      <td>79</td>\n",
       "      <td>108</td>\n",
       "      <td>60</td>\n",
       "      <td>191</td>\n",
       "      <td>130</td>\n",
       "      <td>324</td>\n",
       "      <td>40</td>\n",
       "      <td>21</td>\n",
       "      <td>61</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.68</td>\n",
       "      <td>2.14</td>\n",
       "      <td>1.03</td>\n",
       "      <td>214</td>\n",
       "      <td>1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>53</td>\n",
       "      <td>97</td>\n",
       "      <td>140</td>\n",
       "      <td>104</td>\n",
       "      <td>62</td>\n",
       "      <td>94</td>\n",
       "      <td>93</td>\n",
       "      <td>47</td>\n",
       "      <td>191</td>\n",
       "      <td>115</td>\n",
       "      <td>233</td>\n",
       "      <td>44</td>\n",
       "      <td>32</td>\n",
       "      <td>76</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.96</td>\n",
       "      <td>0.76</td>\n",
       "      <td>151</td>\n",
       "      <td>1.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>79</td>\n",
       "      <td>103</td>\n",
       "      <td>247</td>\n",
       "      <td>196</td>\n",
       "      <td>60</td>\n",
       "      <td>87</td>\n",
       "      <td>96</td>\n",
       "      <td>53</td>\n",
       "      <td>190</td>\n",
       "      <td>139</td>\n",
       "      <td>343</td>\n",
       "      <td>24</td>\n",
       "      <td>27</td>\n",
       "      <td>51</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.73</td>\n",
       "      <td>2.48</td>\n",
       "      <td>0.88</td>\n",
       "      <td>249</td>\n",
       "      <td>1.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Ghana</td>\n",
       "      <td>69</td>\n",
       "      <td>99</td>\n",
       "      <td>191</td>\n",
       "      <td>144</td>\n",
       "      <td>61</td>\n",
       "      <td>91</td>\n",
       "      <td>103</td>\n",
       "      <td>68</td>\n",
       "      <td>190</td>\n",
       "      <td>130</td>\n",
       "      <td>294</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>60</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.68</td>\n",
       "      <td>2.09</td>\n",
       "      <td>1.11</td>\n",
       "      <td>212</td>\n",
       "      <td>1.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>48</td>\n",
       "      <td>93</td>\n",
       "      <td>174</td>\n",
       "      <td>114</td>\n",
       "      <td>66</td>\n",
       "      <td>97</td>\n",
       "      <td>125</td>\n",
       "      <td>78</td>\n",
       "      <td>190</td>\n",
       "      <td>114</td>\n",
       "      <td>299</td>\n",
       "      <td>45</td>\n",
       "      <td>31</td>\n",
       "      <td>76</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.60</td>\n",
       "      <td>2.38</td>\n",
       "      <td>1.18</td>\n",
       "      <td>192</td>\n",
       "      <td>1.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>60</td>\n",
       "      <td>88</td>\n",
       "      <td>186</td>\n",
       "      <td>141</td>\n",
       "      <td>75</td>\n",
       "      <td>101</td>\n",
       "      <td>127</td>\n",
       "      <td>66</td>\n",
       "      <td>189</td>\n",
       "      <td>135</td>\n",
       "      <td>313</td>\n",
       "      <td>28</td>\n",
       "      <td>26</td>\n",
       "      <td>54</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.71</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0.88</td>\n",
       "      <td>207</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Iran</td>\n",
       "      <td>64</td>\n",
       "      <td>95</td>\n",
       "      <td>237</td>\n",
       "      <td>199</td>\n",
       "      <td>79</td>\n",
       "      <td>93</td>\n",
       "      <td>202</td>\n",
       "      <td>151</td>\n",
       "      <td>188</td>\n",
       "      <td>143</td>\n",
       "      <td>439</td>\n",
       "      <td>31</td>\n",
       "      <td>14</td>\n",
       "      <td>45</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.76</td>\n",
       "      <td>3.11</td>\n",
       "      <td>1.91</td>\n",
       "      <td>350</td>\n",
       "      <td>2.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>59</td>\n",
       "      <td>90</td>\n",
       "      <td>192</td>\n",
       "      <td>143</td>\n",
       "      <td>69</td>\n",
       "      <td>98</td>\n",
       "      <td>150</td>\n",
       "      <td>101</td>\n",
       "      <td>188</td>\n",
       "      <td>128</td>\n",
       "      <td>342</td>\n",
       "      <td>31</td>\n",
       "      <td>29</td>\n",
       "      <td>60</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.68</td>\n",
       "      <td>2.42</td>\n",
       "      <td>1.46</td>\n",
       "      <td>244</td>\n",
       "      <td>1.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Romania</td>\n",
       "      <td>50</td>\n",
       "      <td>93</td>\n",
       "      <td>171</td>\n",
       "      <td>125</td>\n",
       "      <td>69</td>\n",
       "      <td>95</td>\n",
       "      <td>154</td>\n",
       "      <td>104</td>\n",
       "      <td>188</td>\n",
       "      <td>119</td>\n",
       "      <td>325</td>\n",
       "      <td>43</td>\n",
       "      <td>26</td>\n",
       "      <td>69</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.63</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.51</td>\n",
       "      <td>229</td>\n",
       "      <td>1.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Peru</td>\n",
       "      <td>38</td>\n",
       "      <td>87</td>\n",
       "      <td>124</td>\n",
       "      <td>59</td>\n",
       "      <td>40</td>\n",
       "      <td>101</td>\n",
       "      <td>85</td>\n",
       "      <td>34</td>\n",
       "      <td>188</td>\n",
       "      <td>78</td>\n",
       "      <td>209</td>\n",
       "      <td>49</td>\n",
       "      <td>61</td>\n",
       "      <td>110</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.41</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.85</td>\n",
       "      <td>93</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>78</td>\n",
       "      <td>103</td>\n",
       "      <td>206</td>\n",
       "      <td>162</td>\n",
       "      <td>66</td>\n",
       "      <td>84</td>\n",
       "      <td>97</td>\n",
       "      <td>58</td>\n",
       "      <td>187</td>\n",
       "      <td>144</td>\n",
       "      <td>303</td>\n",
       "      <td>25</td>\n",
       "      <td>18</td>\n",
       "      <td>43</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2.08</td>\n",
       "      <td>0.88</td>\n",
       "      <td>220</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>75</td>\n",
       "      <td>99</td>\n",
       "      <td>266</td>\n",
       "      <td>225</td>\n",
       "      <td>66</td>\n",
       "      <td>88</td>\n",
       "      <td>177</td>\n",
       "      <td>127</td>\n",
       "      <td>187</td>\n",
       "      <td>141</td>\n",
       "      <td>443</td>\n",
       "      <td>24</td>\n",
       "      <td>22</td>\n",
       "      <td>46</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.92</td>\n",
       "      <td>352</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>59</td>\n",
       "      <td>89</td>\n",
       "      <td>193</td>\n",
       "      <td>146</td>\n",
       "      <td>57</td>\n",
       "      <td>98</td>\n",
       "      <td>104</td>\n",
       "      <td>46</td>\n",
       "      <td>187</td>\n",
       "      <td>116</td>\n",
       "      <td>297</td>\n",
       "      <td>30</td>\n",
       "      <td>41</td>\n",
       "      <td>71</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.62</td>\n",
       "      <td>2.47</td>\n",
       "      <td>0.81</td>\n",
       "      <td>192</td>\n",
       "      <td>1.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               team  home_win  home_count  home_score  home_goal_diff  \\\n",
       "94            Italy        72         101         208             158   \n",
       "177           Spain        80          98         290             257   \n",
       "151        Portugal        71         100         240             192   \n",
       "95      Ivory Coast        68          99         228             169   \n",
       "34         Cameroon        76         115         196             145   \n",
       "203         Uruguay        54         101         168             109   \n",
       "54          Denmark        56          95         183             138   \n",
       "8         Argentina        72         112         216             154   \n",
       "42         Colombia        53          97         140             104   \n",
       "59            Egypt        79         103         247             196   \n",
       "75            Ghana        69          99         191             144   \n",
       "195          Turkey        48          93         174             114   \n",
       "194         Tunisia        60          88         186             141   \n",
       "91             Iran        64          95         237             199   \n",
       "51   Czech Republic        59          90         192             143   \n",
       "155         Romania        50          93         171             125   \n",
       "148            Peru        38          87         124              59   \n",
       "137         Nigeria        78         103         206             162   \n",
       "132     Netherlands        75          99         266             225   \n",
       "2           Algeria        59          89         193             146   \n",
       "\n",
       "     away_win  away_count  away_score  away_goal_diff  total_count  total_win  \\\n",
       "94         87          99         143              95          200        159   \n",
       "177        86         100         193             128          198        166   \n",
       "151        81          98         178             118          198        152   \n",
       "95         75          98         122              72          197        143   \n",
       "34         63          82         111              65          197        139   \n",
       "203        50          96          91              37          197        104   \n",
       "54         77          97         163             109          192        133   \n",
       "8          58          79         108              60          191        130   \n",
       "42         62          94          93              47          191        115   \n",
       "59         60          87          96              53          190        139   \n",
       "75         61          91         103              68          190        130   \n",
       "195        66          97         125              78          190        114   \n",
       "194        75         101         127              66          189        135   \n",
       "91         79          93         202             151          188        143   \n",
       "51         69          98         150             101          188        128   \n",
       "155        69          95         154             104          188        119   \n",
       "148        40         101          85              34          188         78   \n",
       "137        66          84          97              58          187        144   \n",
       "132        66          88         177             127          187        141   \n",
       "2          57          98         104              46          187        116   \n",
       "\n",
       "     total_score  home_lose  away_lose  total_lose  home_win_rate  \\\n",
       "94           351         29         12          41           0.71   \n",
       "177          483         18         14          32           0.82   \n",
       "151          418         29         17          46           0.71   \n",
       "95           350         31         23          54           0.69   \n",
       "34           307         39         19          58           0.66   \n",
       "203          259         47         46          93           0.53   \n",
       "54           346         39         20          59           0.59   \n",
       "8            324         40         21          61           0.64   \n",
       "42           233         44         32          76           0.55   \n",
       "59           343         24         27          51           0.77   \n",
       "75           294         30         30          60           0.70   \n",
       "195          299         45         31          76           0.52   \n",
       "194          313         28         26          54           0.68   \n",
       "91           439         31         14          45           0.67   \n",
       "51           342         31         29          60           0.66   \n",
       "155          325         43         26          69           0.54   \n",
       "148          209         49         61         110           0.44   \n",
       "137          303         25         18          43           0.76   \n",
       "132          443         24         22          46           0.76   \n",
       "2            297         30         41          71           0.66   \n",
       "\n",
       "     away_win_rate  total_win_rate  avg_home_goal_diff  avg_away_goal_diff  \\\n",
       "94            0.88            0.80                2.19                1.09   \n",
       "177           0.86            0.84                3.21                1.49   \n",
       "151           0.83            0.77                2.70                1.46   \n",
       "95            0.77            0.73                2.49                0.96   \n",
       "34            0.77            0.71                1.91                1.03   \n",
       "203           0.52            0.53                2.02                0.74   \n",
       "54            0.79            0.69                2.46                1.42   \n",
       "8             0.73            0.68                2.14                1.03   \n",
       "42            0.66            0.60                1.96                0.76   \n",
       "59            0.69            0.73                2.48                0.88   \n",
       "75            0.67            0.68                2.09                1.11   \n",
       "195           0.68            0.60                2.38                1.18   \n",
       "194           0.74            0.71                2.35                0.88   \n",
       "91            0.85            0.76                3.11                1.91   \n",
       "51            0.70            0.68                2.42                1.46   \n",
       "155           0.73            0.63                2.50                1.51   \n",
       "148           0.40            0.41                1.55                0.85   \n",
       "137           0.79            0.77                2.08                0.88   \n",
       "132           0.75            0.75                3.00                1.92   \n",
       "2             0.58            0.62                2.47                0.81   \n",
       "\n",
       "     total_goal_diff  avg_total_goal_diff  \n",
       "94               253                 1.59  \n",
       "177              385                 2.32  \n",
       "151              310                 2.04  \n",
       "95               241                 1.69  \n",
       "34               210                 1.51  \n",
       "203              146                 1.40  \n",
       "54               247                 1.86  \n",
       "8                214                 1.65  \n",
       "42               151                 1.31  \n",
       "59               249                 1.79  \n",
       "75               212                 1.63  \n",
       "195              192                 1.68  \n",
       "194              207                 1.53  \n",
       "91               350                 2.45  \n",
       "51               244                 1.91  \n",
       "155              229                 1.92  \n",
       "148               93                 1.19  \n",
       "137              220                 1.53  \n",
       "132              352                 2.50  \n",
       "2                192                 1.66  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show top 20 teams ordered by total matches, total number of winned matches, and the average goal difference\n",
    "df_game_stat.sort_values(by=['total_count', 'total_win', 'avg_total_goal_diff'], ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "face0b76-b30f-4b73-abbb-026b28c53985",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>home_win</th>\n",
       "      <th>home_count</th>\n",
       "      <th>home_score</th>\n",
       "      <th>home_goal_diff</th>\n",
       "      <th>away_win</th>\n",
       "      <th>away_count</th>\n",
       "      <th>away_score</th>\n",
       "      <th>away_goal_diff</th>\n",
       "      <th>total_count</th>\n",
       "      <th>total_win</th>\n",
       "      <th>total_score</th>\n",
       "      <th>home_lose</th>\n",
       "      <th>away_lose</th>\n",
       "      <th>total_lose</th>\n",
       "      <th>home_win_rate</th>\n",
       "      <th>away_win_rate</th>\n",
       "      <th>total_win_rate</th>\n",
       "      <th>avg_home_goal_diff</th>\n",
       "      <th>avg_away_goal_diff</th>\n",
       "      <th>total_goal_diff</th>\n",
       "      <th>avg_total_goal_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>count</th>\n",
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       "      <td>213.00</td>\n",
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       "      <td>213.00</td>\n",
       "      <td>213.00</td>\n",
       "      <td>213.00</td>\n",
       "      <td>213.00</td>\n",
       "      <td>213.00</td>\n",
       "      <td>213.00</td>\n",
       "      <td>213.00</td>\n",
       "      <td>213.00</td>\n",
       "      <td>213.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>26.71</td>\n",
       "      <td>53.82</td>\n",
       "      <td>90.62</td>\n",
       "      <td>63.76</td>\n",
       "      <td>27.10</td>\n",
       "      <td>53.82</td>\n",
       "      <td>58.35</td>\n",
       "      <td>31.49</td>\n",
       "      <td>107.63</td>\n",
       "      <td>53.82</td>\n",
       "      <td>148.96</td>\n",
       "      <td>27.10</td>\n",
       "      <td>26.71</td>\n",
       "      <td>53.82</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1.11</td>\n",
       "      <td>95.25</td>\n",
       "      <td>1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>22.06</td>\n",
       "      <td>31.75</td>\n",
       "      <td>71.81</td>\n",
       "      <td>58.69</td>\n",
       "      <td>23.17</td>\n",
       "      <td>30.27</td>\n",
       "      <td>47.50</td>\n",
       "      <td>32.84</td>\n",
       "      <td>61.52</td>\n",
       "      <td>44.62</td>\n",
       "      <td>117.17</td>\n",
       "      <td>16.32</td>\n",
       "      <td>15.68</td>\n",
       "      <td>30.97</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.87</td>\n",
       "      <td>89.14</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>28.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.81</td>\n",
       "      <td>0.67</td>\n",
       "      <td>22.00</td>\n",
       "      <td>1.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>21.00</td>\n",
       "      <td>56.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>47.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>58.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>19.00</td>\n",
       "      <td>115.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>115.00</td>\n",
       "      <td>26.00</td>\n",
       "      <td>26.00</td>\n",
       "      <td>52.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.44</td>\n",
       "      <td>2.14</td>\n",
       "      <td>1.00</td>\n",
       "      <td>67.00</td>\n",
       "      <td>1.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.00</td>\n",
       "      <td>83.00</td>\n",
       "      <td>140.00</td>\n",
       "      <td>97.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>82.00</td>\n",
       "      <td>93.00</td>\n",
       "      <td>47.00</td>\n",
       "      <td>164.00</td>\n",
       "      <td>87.00</td>\n",
       "      <td>236.00</td>\n",
       "      <td>36.00</td>\n",
       "      <td>36.00</td>\n",
       "      <td>69.00</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.59</td>\n",
       "      <td>2.67</td>\n",
       "      <td>1.40</td>\n",
       "      <td>148.00</td>\n",
       "      <td>2.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.00</td>\n",
       "      <td>115.00</td>\n",
       "      <td>334.00</td>\n",
       "      <td>298.00</td>\n",
       "      <td>87.00</td>\n",
       "      <td>101.00</td>\n",
       "      <td>202.00</td>\n",
       "      <td>151.00</td>\n",
       "      <td>200.00</td>\n",
       "      <td>166.00</td>\n",
       "      <td>483.00</td>\n",
       "      <td>81.00</td>\n",
       "      <td>79.00</td>\n",
       "      <td>160.00</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.88</td>\n",
       "      <td>5.11</td>\n",
       "      <td>8.00</td>\n",
       "      <td>385.00</td>\n",
       "      <td>4.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       home_win  home_count  home_score  home_goal_diff  away_win  away_count  \\\n",
       "count    213.00      213.00      213.00          213.00    213.00      213.00   \n",
       "mean      26.71       53.82       90.62           63.76     27.10       53.82   \n",
       "std       22.06       31.75       71.81           58.69     23.17       30.27   \n",
       "min        0.00        2.00        1.00            0.00      0.00        4.00   \n",
       "25%        6.00       23.00       28.00           15.00      7.00       25.00   \n",
       "50%       21.00       56.00       75.00           47.00     20.00       58.00   \n",
       "75%       43.00       83.00      140.00           97.00     45.00       82.00   \n",
       "max       80.00      115.00      334.00          298.00     87.00      101.00   \n",
       "\n",
       "       away_score  away_goal_diff  total_count  total_win  total_score  \\\n",
       "count      213.00          213.00       213.00     213.00       213.00   \n",
       "mean        58.35           31.49       107.63      53.82       148.96   \n",
       "std         47.50           32.84        61.52      44.62       117.17   \n",
       "min          0.00            0.00         8.00       0.00         1.00   \n",
       "25%         18.00            7.00        45.00      14.00        45.00   \n",
       "50%         43.00           19.00       115.00      43.00       115.00   \n",
       "75%         93.00           47.00       164.00      87.00       236.00   \n",
       "max        202.00          151.00       200.00     166.00       483.00   \n",
       "\n",
       "       home_lose  away_lose  total_lose  home_win_rate  away_win_rate  \\\n",
       "count     213.00     213.00      213.00         213.00         213.00   \n",
       "mean       27.10      26.71       53.82           0.43           0.43   \n",
       "std        16.32      15.68       30.97           0.19           0.22   \n",
       "min         1.00       0.00        1.00           0.00           0.00   \n",
       "25%        13.00      14.00       30.00           0.29           0.28   \n",
       "50%        26.00      26.00       52.00           0.45           0.41   \n",
       "75%        36.00      36.00       69.00           0.58           0.58   \n",
       "max        81.00      79.00      160.00           0.82           1.00   \n",
       "\n",
       "       total_win_rate  avg_home_goal_diff  avg_away_goal_diff  \\\n",
       "count          213.00              213.00              213.00   \n",
       "mean             0.43                2.25                1.11   \n",
       "std              0.20                0.87                0.87   \n",
       "min              0.00                0.00                0.00   \n",
       "25%              0.31                1.81                0.67   \n",
       "50%              0.44                2.14                1.00   \n",
       "75%              0.59                2.67                1.40   \n",
       "max              0.88                5.11                8.00   \n",
       "\n",
       "       total_goal_diff  avg_total_goal_diff  \n",
       "count           213.00               213.00  \n",
       "mean             95.25                 1.69  \n",
       "std              89.14                 0.70  \n",
       "min               0.00                 0.00  \n",
       "25%              22.00                 1.27  \n",
       "50%              67.00                 1.57  \n",
       "75%             148.00                 2.03  \n",
       "max             385.00                 4.33  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_game_stat.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2c05741-f65f-4e31-af22-cf103bb51e01",
   "metadata": {},
   "source": [
    "**Discussion**: The table above shows the match statistics derived from **data transformation** in the past ~30 years, we have generated a bunch of attributes from `home_score` and `away_score`, including some important measurement which reflects the strength of the team, e.g. `win_rate` and `goal_difference`. Furthermore, we have gain the following insights from the statistics: \n",
    "- 213 national and regional teams involved in the FIFA qualification tournaments in the past 30 years\n",
    "- The upper quartile shows: a home team needs to win **43** out of **83** matches (winning rate **58%**) and achieved goal difference of **2.67 goals per match** to be considered better than 75% of the national teams, whereas an away team needs to win **45** out of **82** matches (winning rate **58%**) and achieved goal difference of **1.4 goals per match**. However, considering the World Cup only features 24 teams, a team needs to have a way more prettier statistics than this to get an entry to the world cup.\n",
    "- The best football team in the past 30 years has wonned *80* games as home team and **87** games as away team, with a total winning rate of **82%**. What is interesting is that, this team seems to have a better performance in the opponent's stadium: wonned 87 games out of 101 with 8 goals per game.\n",
    "\n",
    "In the remaining part of the section, we are going to dive more deep into the dataset to see the performance of each specific team."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dfa7b50e-88e8-4b80-93f3-f66c9131851b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team</th>\n",
       "      <th>home_win</th>\n",
       "      <th>home_count</th>\n",
       "      <th>home_score</th>\n",
       "      <th>home_goal_diff</th>\n",
       "      <th>away_win</th>\n",
       "      <th>away_count</th>\n",
       "      <th>away_score</th>\n",
       "      <th>away_goal_diff</th>\n",
       "      <th>total_count</th>\n",
       "      <th>total_win</th>\n",
       "      <th>total_score</th>\n",
       "      <th>home_lose</th>\n",
       "      <th>away_lose</th>\n",
       "      <th>total_lose</th>\n",
       "      <th>home_win_rate</th>\n",
       "      <th>away_win_rate</th>\n",
       "      <th>total_win_rate</th>\n",
       "      <th>avg_home_goal_diff</th>\n",
       "      <th>avg_away_goal_diff</th>\n",
       "      <th>total_goal_diff</th>\n",
       "      <th>avg_total_goal_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Spain</td>\n",
       "      <td>80</td>\n",
       "      <td>98</td>\n",
       "      <td>290</td>\n",
       "      <td>257</td>\n",
       "      <td>86</td>\n",
       "      <td>100</td>\n",
       "      <td>193</td>\n",
       "      <td>128</td>\n",
       "      <td>198</td>\n",
       "      <td>166</td>\n",
       "      <td>483</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>32</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.84</td>\n",
       "      <td>3.21</td>\n",
       "      <td>1.49</td>\n",
       "      <td>385</td>\n",
       "      <td>2.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Australia</td>\n",
       "      <td>72</td>\n",
       "      <td>88</td>\n",
       "      <td>334</td>\n",
       "      <td>298</td>\n",
       "      <td>47</td>\n",
       "      <td>61</td>\n",
       "      <td>107</td>\n",
       "      <td>81</td>\n",
       "      <td>149</td>\n",
       "      <td>119</td>\n",
       "      <td>441</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>30</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.80</td>\n",
       "      <td>4.14</td>\n",
       "      <td>1.72</td>\n",
       "      <td>379</td>\n",
       "      <td>3.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>75</td>\n",
       "      <td>99</td>\n",
       "      <td>266</td>\n",
       "      <td>225</td>\n",
       "      <td>66</td>\n",
       "      <td>88</td>\n",
       "      <td>177</td>\n",
       "      <td>127</td>\n",
       "      <td>187</td>\n",
       "      <td>141</td>\n",
       "      <td>443</td>\n",
       "      <td>24</td>\n",
       "      <td>22</td>\n",
       "      <td>46</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.92</td>\n",
       "      <td>352</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iran</td>\n",
       "      <td>64</td>\n",
       "      <td>95</td>\n",
       "      <td>237</td>\n",
       "      <td>199</td>\n",
       "      <td>79</td>\n",
       "      <td>93</td>\n",
       "      <td>202</td>\n",
       "      <td>151</td>\n",
       "      <td>188</td>\n",
       "      <td>143</td>\n",
       "      <td>439</td>\n",
       "      <td>31</td>\n",
       "      <td>14</td>\n",
       "      <td>45</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.76</td>\n",
       "      <td>3.11</td>\n",
       "      <td>1.91</td>\n",
       "      <td>350</td>\n",
       "      <td>2.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Germany</td>\n",
       "      <td>62</td>\n",
       "      <td>89</td>\n",
       "      <td>245</td>\n",
       "      <td>184</td>\n",
       "      <td>76</td>\n",
       "      <td>91</td>\n",
       "      <td>200</td>\n",
       "      <td>146</td>\n",
       "      <td>180</td>\n",
       "      <td>138</td>\n",
       "      <td>445</td>\n",
       "      <td>27</td>\n",
       "      <td>15</td>\n",
       "      <td>42</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2.97</td>\n",
       "      <td>1.92</td>\n",
       "      <td>330</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>England</td>\n",
       "      <td>65</td>\n",
       "      <td>93</td>\n",
       "      <td>221</td>\n",
       "      <td>178</td>\n",
       "      <td>75</td>\n",
       "      <td>87</td>\n",
       "      <td>179</td>\n",
       "      <td>138</td>\n",
       "      <td>180</td>\n",
       "      <td>140</td>\n",
       "      <td>400</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>40</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.78</td>\n",
       "      <td>2.74</td>\n",
       "      <td>1.84</td>\n",
       "      <td>316</td>\n",
       "      <td>2.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Japan</td>\n",
       "      <td>59</td>\n",
       "      <td>84</td>\n",
       "      <td>228</td>\n",
       "      <td>184</td>\n",
       "      <td>68</td>\n",
       "      <td>80</td>\n",
       "      <td>166</td>\n",
       "      <td>127</td>\n",
       "      <td>164</td>\n",
       "      <td>127</td>\n",
       "      <td>394</td>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "      <td>37</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.77</td>\n",
       "      <td>3.12</td>\n",
       "      <td>1.87</td>\n",
       "      <td>311</td>\n",
       "      <td>2.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>71</td>\n",
       "      <td>100</td>\n",
       "      <td>240</td>\n",
       "      <td>192</td>\n",
       "      <td>81</td>\n",
       "      <td>98</td>\n",
       "      <td>178</td>\n",
       "      <td>118</td>\n",
       "      <td>198</td>\n",
       "      <td>152</td>\n",
       "      <td>418</td>\n",
       "      <td>29</td>\n",
       "      <td>17</td>\n",
       "      <td>46</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1.46</td>\n",
       "      <td>310</td>\n",
       "      <td>2.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>72</td>\n",
       "      <td>94</td>\n",
       "      <td>258</td>\n",
       "      <td>218</td>\n",
       "      <td>53</td>\n",
       "      <td>70</td>\n",
       "      <td>108</td>\n",
       "      <td>72</td>\n",
       "      <td>164</td>\n",
       "      <td>125</td>\n",
       "      <td>366</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>39</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.76</td>\n",
       "      <td>3.03</td>\n",
       "      <td>1.36</td>\n",
       "      <td>290</td>\n",
       "      <td>2.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>56</td>\n",
       "      <td>86</td>\n",
       "      <td>215</td>\n",
       "      <td>168</td>\n",
       "      <td>60</td>\n",
       "      <td>85</td>\n",
       "      <td>148</td>\n",
       "      <td>103</td>\n",
       "      <td>171</td>\n",
       "      <td>116</td>\n",
       "      <td>363</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>55</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.68</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.72</td>\n",
       "      <td>271</td>\n",
       "      <td>2.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>South Korea</td>\n",
       "      <td>58</td>\n",
       "      <td>80</td>\n",
       "      <td>222</td>\n",
       "      <td>189</td>\n",
       "      <td>57</td>\n",
       "      <td>71</td>\n",
       "      <td>110</td>\n",
       "      <td>76</td>\n",
       "      <td>151</td>\n",
       "      <td>115</td>\n",
       "      <td>332</td>\n",
       "      <td>22</td>\n",
       "      <td>14</td>\n",
       "      <td>36</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.76</td>\n",
       "      <td>3.26</td>\n",
       "      <td>1.33</td>\n",
       "      <td>265</td>\n",
       "      <td>2.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Russia</td>\n",
       "      <td>61</td>\n",
       "      <td>92</td>\n",
       "      <td>192</td>\n",
       "      <td>152</td>\n",
       "      <td>64</td>\n",
       "      <td>93</td>\n",
       "      <td>158</td>\n",
       "      <td>111</td>\n",
       "      <td>185</td>\n",
       "      <td>125</td>\n",
       "      <td>350</td>\n",
       "      <td>31</td>\n",
       "      <td>29</td>\n",
       "      <td>60</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.68</td>\n",
       "      <td>2.49</td>\n",
       "      <td>1.73</td>\n",
       "      <td>263</td>\n",
       "      <td>2.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>China PR</td>\n",
       "      <td>53</td>\n",
       "      <td>82</td>\n",
       "      <td>213</td>\n",
       "      <td>175</td>\n",
       "      <td>45</td>\n",
       "      <td>70</td>\n",
       "      <td>119</td>\n",
       "      <td>84</td>\n",
       "      <td>152</td>\n",
       "      <td>98</td>\n",
       "      <td>332</td>\n",
       "      <td>29</td>\n",
       "      <td>25</td>\n",
       "      <td>54</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.64</td>\n",
       "      <td>3.30</td>\n",
       "      <td>1.87</td>\n",
       "      <td>259</td>\n",
       "      <td>2.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Italy</td>\n",
       "      <td>72</td>\n",
       "      <td>101</td>\n",
       "      <td>208</td>\n",
       "      <td>158</td>\n",
       "      <td>87</td>\n",
       "      <td>99</td>\n",
       "      <td>143</td>\n",
       "      <td>95</td>\n",
       "      <td>200</td>\n",
       "      <td>159</td>\n",
       "      <td>351</td>\n",
       "      <td>29</td>\n",
       "      <td>12</td>\n",
       "      <td>41</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.80</td>\n",
       "      <td>2.19</td>\n",
       "      <td>1.09</td>\n",
       "      <td>253</td>\n",
       "      <td>1.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>73</td>\n",
       "      <td>103</td>\n",
       "      <td>230</td>\n",
       "      <td>185</td>\n",
       "      <td>46</td>\n",
       "      <td>59</td>\n",
       "      <td>100</td>\n",
       "      <td>64</td>\n",
       "      <td>162</td>\n",
       "      <td>119</td>\n",
       "      <td>330</td>\n",
       "      <td>30</td>\n",
       "      <td>13</td>\n",
       "      <td>43</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.73</td>\n",
       "      <td>2.53</td>\n",
       "      <td>1.39</td>\n",
       "      <td>249</td>\n",
       "      <td>2.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>79</td>\n",
       "      <td>103</td>\n",
       "      <td>247</td>\n",
       "      <td>196</td>\n",
       "      <td>60</td>\n",
       "      <td>87</td>\n",
       "      <td>96</td>\n",
       "      <td>53</td>\n",
       "      <td>190</td>\n",
       "      <td>139</td>\n",
       "      <td>343</td>\n",
       "      <td>24</td>\n",
       "      <td>27</td>\n",
       "      <td>51</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.73</td>\n",
       "      <td>2.48</td>\n",
       "      <td>0.88</td>\n",
       "      <td>249</td>\n",
       "      <td>1.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>56</td>\n",
       "      <td>95</td>\n",
       "      <td>183</td>\n",
       "      <td>138</td>\n",
       "      <td>77</td>\n",
       "      <td>97</td>\n",
       "      <td>163</td>\n",
       "      <td>109</td>\n",
       "      <td>192</td>\n",
       "      <td>133</td>\n",
       "      <td>346</td>\n",
       "      <td>39</td>\n",
       "      <td>20</td>\n",
       "      <td>59</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.69</td>\n",
       "      <td>2.46</td>\n",
       "      <td>1.42</td>\n",
       "      <td>247</td>\n",
       "      <td>1.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>59</td>\n",
       "      <td>90</td>\n",
       "      <td>192</td>\n",
       "      <td>143</td>\n",
       "      <td>69</td>\n",
       "      <td>98</td>\n",
       "      <td>150</td>\n",
       "      <td>101</td>\n",
       "      <td>188</td>\n",
       "      <td>128</td>\n",
       "      <td>342</td>\n",
       "      <td>31</td>\n",
       "      <td>29</td>\n",
       "      <td>60</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.68</td>\n",
       "      <td>2.42</td>\n",
       "      <td>1.46</td>\n",
       "      <td>244</td>\n",
       "      <td>1.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Ivory Coast</td>\n",
       "      <td>68</td>\n",
       "      <td>99</td>\n",
       "      <td>228</td>\n",
       "      <td>169</td>\n",
       "      <td>75</td>\n",
       "      <td>98</td>\n",
       "      <td>122</td>\n",
       "      <td>72</td>\n",
       "      <td>197</td>\n",
       "      <td>143</td>\n",
       "      <td>350</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>54</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.73</td>\n",
       "      <td>2.49</td>\n",
       "      <td>0.96</td>\n",
       "      <td>241</td>\n",
       "      <td>1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>France</td>\n",
       "      <td>59</td>\n",
       "      <td>90</td>\n",
       "      <td>201</td>\n",
       "      <td>152</td>\n",
       "      <td>74</td>\n",
       "      <td>85</td>\n",
       "      <td>127</td>\n",
       "      <td>82</td>\n",
       "      <td>175</td>\n",
       "      <td>133</td>\n",
       "      <td>328</td>\n",
       "      <td>31</td>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.76</td>\n",
       "      <td>2.58</td>\n",
       "      <td>1.11</td>\n",
       "      <td>234</td>\n",
       "      <td>1.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              team  home_win  home_count  home_score  home_goal_diff  \\\n",
       "0            Spain        80          98         290             257   \n",
       "1        Australia        72          88         334             298   \n",
       "2      Netherlands        75          99         266             225   \n",
       "3             Iran        64          95         237             199   \n",
       "4          Germany        62          89         245             184   \n",
       "5          England        65          93         221             178   \n",
       "6            Japan        59          84         228             184   \n",
       "7         Portugal        71         100         240             192   \n",
       "8     Saudi Arabia        72          94         258             218   \n",
       "9          Belgium        56          86         215             168   \n",
       "10     South Korea        58          80         222             189   \n",
       "11          Russia        61          92         192             152   \n",
       "12        China PR        53          82         213             175   \n",
       "13           Italy        72         101         208             158   \n",
       "14          Brazil        73         103         230             185   \n",
       "15           Egypt        79         103         247             196   \n",
       "16         Denmark        56          95         183             138   \n",
       "17  Czech Republic        59          90         192             143   \n",
       "18     Ivory Coast        68          99         228             169   \n",
       "19          France        59          90         201             152   \n",
       "\n",
       "    away_win  away_count  away_score  away_goal_diff  total_count  total_win  \\\n",
       "0         86         100         193             128          198        166   \n",
       "1         47          61         107              81          149        119   \n",
       "2         66          88         177             127          187        141   \n",
       "3         79          93         202             151          188        143   \n",
       "4         76          91         200             146          180        138   \n",
       "5         75          87         179             138          180        140   \n",
       "6         68          80         166             127          164        127   \n",
       "7         81          98         178             118          198        152   \n",
       "8         53          70         108              72          164        125   \n",
       "9         60          85         148             103          171        116   \n",
       "10        57          71         110              76          151        115   \n",
       "11        64          93         158             111          185        125   \n",
       "12        45          70         119              84          152         98   \n",
       "13        87          99         143              95          200        159   \n",
       "14        46          59         100              64          162        119   \n",
       "15        60          87          96              53          190        139   \n",
       "16        77          97         163             109          192        133   \n",
       "17        69          98         150             101          188        128   \n",
       "18        75          98         122              72          197        143   \n",
       "19        74          85         127              82          175        133   \n",
       "\n",
       "    total_score  home_lose  away_lose  total_lose  home_win_rate  \\\n",
       "0           483         18         14          32           0.82   \n",
       "1           441         16         14          30           0.82   \n",
       "2           443         24         22          46           0.76   \n",
       "3           439         31         14          45           0.67   \n",
       "4           445         27         15          42           0.70   \n",
       "5           400         28         12          40           0.70   \n",
       "6           394         25         12          37           0.70   \n",
       "7           418         29         17          46           0.71   \n",
       "8           366         22         17          39           0.77   \n",
       "9           363         30         25          55           0.65   \n",
       "10          332         22         14          36           0.72   \n",
       "11          350         31         29          60           0.66   \n",
       "12          332         29         25          54           0.65   \n",
       "13          351         29         12          41           0.71   \n",
       "14          330         30         13          43           0.71   \n",
       "15          343         24         27          51           0.77   \n",
       "16          346         39         20          59           0.59   \n",
       "17          342         31         29          60           0.66   \n",
       "18          350         31         23          54           0.69   \n",
       "19          328         31         11          42           0.66   \n",
       "\n",
       "    away_win_rate  total_win_rate  avg_home_goal_diff  avg_away_goal_diff  \\\n",
       "0            0.86            0.84                3.21                1.49   \n",
       "1            0.77            0.80                4.14                1.72   \n",
       "2            0.75            0.75                3.00                1.92   \n",
       "3            0.85            0.76                3.11                1.91   \n",
       "4            0.84            0.77                2.97                1.92   \n",
       "5            0.86            0.78                2.74                1.84   \n",
       "6            0.85            0.77                3.12                1.87   \n",
       "7            0.83            0.77                2.70                1.46   \n",
       "8            0.76            0.76                3.03                1.36   \n",
       "9            0.71            0.68                3.00                1.72   \n",
       "10           0.80            0.76                3.26                1.33   \n",
       "11           0.69            0.68                2.49                1.73   \n",
       "12           0.64            0.64                3.30                1.87   \n",
       "13           0.88            0.80                2.19                1.09   \n",
       "14           0.78            0.73                2.53                1.39   \n",
       "15           0.69            0.73                2.48                0.88   \n",
       "16           0.79            0.69                2.46                1.42   \n",
       "17           0.70            0.68                2.42                1.46   \n",
       "18           0.77            0.73                2.49                0.96   \n",
       "19           0.87            0.76                2.58                1.11   \n",
       "\n",
       "    total_goal_diff  avg_total_goal_diff  \n",
       "0               385                 2.32  \n",
       "1               379                 3.18  \n",
       "2               352                 2.50  \n",
       "3               350                 2.45  \n",
       "4               330                 2.39  \n",
       "5               316                 2.26  \n",
       "6               311                 2.45  \n",
       "7               310                 2.04  \n",
       "8               290                 2.32  \n",
       "9               271                 2.34  \n",
       "10              265                 2.30  \n",
       "11              263                 2.10  \n",
       "12              259                 2.64  \n",
       "13              253                 1.59  \n",
       "14              249                 2.09  \n",
       "15              249                 1.79  \n",
       "16              247                 1.86  \n",
       "17              244                 1.91  \n",
       "18              241                 1.69  \n",
       "19              234                 1.76  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show top 20 teams with highest avg_total_goal_diff\n",
    "df_game_stat.sort_values(by=['total_goal_diff'], ascending=False).head(20).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4f7a7bfe-a849-42b7-a7ed-3659298d750e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualisation\n",
    "\n",
    "# Process df for visualisation\n",
    "df_vis = df_game_stat.sort_values(by=['total_goal_diff'], ascending=False).head(20)\n",
    "df_vis = df_vis[['team', 'home_goal_diff', 'away_goal_diff']]\n",
    "df_vis = df_vis.set_index('team').stack().reset_index()\n",
    "df_vis.rename({'level_1': 'place', 0: 'count'}, axis=1, inplace=True)\n",
    "\n",
    "f, ax = plt.subplots(figsize = (20,8))\n",
    "\n",
    "sns.set_context('paper')\n",
    "\n",
    "sns.set_color_codes('muted')\n",
    "sns.barplot(x = 'count', y = 'team', hue = 'place',\n",
    "           data = df_vis, color = 'b', edgecolor = 'black')\n",
    "\n",
    "ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.1), shadow=True, ncol=2)\n",
    "sns.despine(left = True, bottom = True)\n",
    "plt.title(\"Top 20 national football teams with highest goal difference\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "57230fd3-6222-4660-b744-a6177a4c61b0",
   "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>home_win</th>\n",
       "      <th>home_count</th>\n",
       "      <th>home_score</th>\n",
       "      <th>home_goal_diff</th>\n",
       "      <th>away_win</th>\n",
       "      <th>away_count</th>\n",
       "      <th>away_score</th>\n",
       "      <th>away_goal_diff</th>\n",
       "      <th>total_count</th>\n",
       "      <th>total_win</th>\n",
       "      <th>total_score</th>\n",
       "      <th>home_lose</th>\n",
       "      <th>away_lose</th>\n",
       "      <th>total_lose</th>\n",
       "      <th>home_win_rate</th>\n",
       "      <th>away_win_rate</th>\n",
       "      <th>total_win_rate</th>\n",
       "      <th>avg_home_goal_diff</th>\n",
       "      <th>avg_away_goal_diff</th>\n",
       "      <th>total_goal_diff</th>\n",
       "      <th>avg_total_goal_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Spain</td>\n",
       "      <td>80</td>\n",
       "      <td>98</td>\n",
       "      <td>290</td>\n",
       "      <td>257</td>\n",
       "      <td>86</td>\n",
       "      <td>100</td>\n",
       "      <td>193</td>\n",
       "      <td>128</td>\n",
       "      <td>198</td>\n",
       "      <td>166</td>\n",
       "      <td>483</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>32</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.84</td>\n",
       "      <td>3.21</td>\n",
       "      <td>1.49</td>\n",
       "      <td>385</td>\n",
       "      <td>2.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Italy</td>\n",
       "      <td>72</td>\n",
       "      <td>101</td>\n",
       "      <td>208</td>\n",
       "      <td>158</td>\n",
       "      <td>87</td>\n",
       "      <td>99</td>\n",
       "      <td>143</td>\n",
       "      <td>95</td>\n",
       "      <td>200</td>\n",
       "      <td>159</td>\n",
       "      <td>351</td>\n",
       "      <td>29</td>\n",
       "      <td>12</td>\n",
       "      <td>41</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.80</td>\n",
       "      <td>2.19</td>\n",
       "      <td>1.09</td>\n",
       "      <td>253</td>\n",
       "      <td>1.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>71</td>\n",
       "      <td>100</td>\n",
       "      <td>240</td>\n",
       "      <td>192</td>\n",
       "      <td>81</td>\n",
       "      <td>98</td>\n",
       "      <td>178</td>\n",
       "      <td>118</td>\n",
       "      <td>198</td>\n",
       "      <td>152</td>\n",
       "      <td>418</td>\n",
       "      <td>29</td>\n",
       "      <td>17</td>\n",
       "      <td>46</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1.46</td>\n",
       "      <td>310</td>\n",
       "      <td>2.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>78</td>\n",
       "      <td>103</td>\n",
       "      <td>206</td>\n",
       "      <td>162</td>\n",
       "      <td>66</td>\n",
       "      <td>84</td>\n",
       "      <td>97</td>\n",
       "      <td>58</td>\n",
       "      <td>187</td>\n",
       "      <td>144</td>\n",
       "      <td>303</td>\n",
       "      <td>25</td>\n",
       "      <td>18</td>\n",
       "      <td>43</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2.08</td>\n",
       "      <td>0.88</td>\n",
       "      <td>220</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Iran</td>\n",
       "      <td>64</td>\n",
       "      <td>95</td>\n",
       "      <td>237</td>\n",
       "      <td>199</td>\n",
       "      <td>79</td>\n",
       "      <td>93</td>\n",
       "      <td>202</td>\n",
       "      <td>151</td>\n",
       "      <td>188</td>\n",
       "      <td>143</td>\n",
       "      <td>439</td>\n",
       "      <td>31</td>\n",
       "      <td>14</td>\n",
       "      <td>45</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.76</td>\n",
       "      <td>3.11</td>\n",
       "      <td>1.91</td>\n",
       "      <td>350</td>\n",
       "      <td>2.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ivory Coast</td>\n",
       "      <td>68</td>\n",
       "      <td>99</td>\n",
       "      <td>228</td>\n",
       "      <td>169</td>\n",
       "      <td>75</td>\n",
       "      <td>98</td>\n",
       "      <td>122</td>\n",
       "      <td>72</td>\n",
       "      <td>197</td>\n",
       "      <td>143</td>\n",
       "      <td>350</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>54</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.73</td>\n",
       "      <td>2.49</td>\n",
       "      <td>0.96</td>\n",
       "      <td>241</td>\n",
       "      <td>1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>75</td>\n",
       "      <td>99</td>\n",
       "      <td>266</td>\n",
       "      <td>225</td>\n",
       "      <td>66</td>\n",
       "      <td>88</td>\n",
       "      <td>177</td>\n",
       "      <td>127</td>\n",
       "      <td>187</td>\n",
       "      <td>141</td>\n",
       "      <td>443</td>\n",
       "      <td>24</td>\n",
       "      <td>22</td>\n",
       "      <td>46</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.92</td>\n",
       "      <td>352</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>England</td>\n",
       "      <td>65</td>\n",
       "      <td>93</td>\n",
       "      <td>221</td>\n",
       "      <td>178</td>\n",
       "      <td>75</td>\n",
       "      <td>87</td>\n",
       "      <td>179</td>\n",
       "      <td>138</td>\n",
       "      <td>180</td>\n",
       "      <td>140</td>\n",
       "      <td>400</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>40</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.78</td>\n",
       "      <td>2.74</td>\n",
       "      <td>1.84</td>\n",
       "      <td>316</td>\n",
       "      <td>2.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>76</td>\n",
       "      <td>115</td>\n",
       "      <td>196</td>\n",
       "      <td>145</td>\n",
       "      <td>63</td>\n",
       "      <td>82</td>\n",
       "      <td>111</td>\n",
       "      <td>65</td>\n",
       "      <td>197</td>\n",
       "      <td>139</td>\n",
       "      <td>307</td>\n",
       "      <td>39</td>\n",
       "      <td>19</td>\n",
       "      <td>58</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1.91</td>\n",
       "      <td>1.03</td>\n",
       "      <td>210</td>\n",
       "      <td>1.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>79</td>\n",
       "      <td>103</td>\n",
       "      <td>247</td>\n",
       "      <td>196</td>\n",
       "      <td>60</td>\n",
       "      <td>87</td>\n",
       "      <td>96</td>\n",
       "      <td>53</td>\n",
       "      <td>190</td>\n",
       "      <td>139</td>\n",
       "      <td>343</td>\n",
       "      <td>24</td>\n",
       "      <td>27</td>\n",
       "      <td>51</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.73</td>\n",
       "      <td>2.48</td>\n",
       "      <td>0.88</td>\n",
       "      <td>249</td>\n",
       "      <td>1.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Germany</td>\n",
       "      <td>62</td>\n",
       "      <td>89</td>\n",
       "      <td>245</td>\n",
       "      <td>184</td>\n",
       "      <td>76</td>\n",
       "      <td>91</td>\n",
       "      <td>200</td>\n",
       "      <td>146</td>\n",
       "      <td>180</td>\n",
       "      <td>138</td>\n",
       "      <td>445</td>\n",
       "      <td>27</td>\n",
       "      <td>15</td>\n",
       "      <td>42</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2.97</td>\n",
       "      <td>1.92</td>\n",
       "      <td>330</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>60</td>\n",
       "      <td>88</td>\n",
       "      <td>186</td>\n",
       "      <td>141</td>\n",
       "      <td>75</td>\n",
       "      <td>101</td>\n",
       "      <td>127</td>\n",
       "      <td>66</td>\n",
       "      <td>189</td>\n",
       "      <td>135</td>\n",
       "      <td>313</td>\n",
       "      <td>28</td>\n",
       "      <td>26</td>\n",
       "      <td>54</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.71</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0.88</td>\n",
       "      <td>207</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>56</td>\n",
       "      <td>95</td>\n",
       "      <td>183</td>\n",
       "      <td>138</td>\n",
       "      <td>77</td>\n",
       "      <td>97</td>\n",
       "      <td>163</td>\n",
       "      <td>109</td>\n",
       "      <td>192</td>\n",
       "      <td>133</td>\n",
       "      <td>346</td>\n",
       "      <td>39</td>\n",
       "      <td>20</td>\n",
       "      <td>59</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.69</td>\n",
       "      <td>2.46</td>\n",
       "      <td>1.42</td>\n",
       "      <td>247</td>\n",
       "      <td>1.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>France</td>\n",
       "      <td>59</td>\n",
       "      <td>90</td>\n",
       "      <td>201</td>\n",
       "      <td>152</td>\n",
       "      <td>74</td>\n",
       "      <td>85</td>\n",
       "      <td>127</td>\n",
       "      <td>82</td>\n",
       "      <td>175</td>\n",
       "      <td>133</td>\n",
       "      <td>328</td>\n",
       "      <td>31</td>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.76</td>\n",
       "      <td>2.58</td>\n",
       "      <td>1.11</td>\n",
       "      <td>234</td>\n",
       "      <td>1.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>72</td>\n",
       "      <td>112</td>\n",
       "      <td>216</td>\n",
       "      <td>154</td>\n",
       "      <td>58</td>\n",
       "      <td>79</td>\n",
       "      <td>108</td>\n",
       "      <td>60</td>\n",
       "      <td>191</td>\n",
       "      <td>130</td>\n",
       "      <td>324</td>\n",
       "      <td>40</td>\n",
       "      <td>21</td>\n",
       "      <td>61</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.68</td>\n",
       "      <td>2.14</td>\n",
       "      <td>1.03</td>\n",
       "      <td>214</td>\n",
       "      <td>1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ghana</td>\n",
       "      <td>69</td>\n",
       "      <td>99</td>\n",
       "      <td>191</td>\n",
       "      <td>144</td>\n",
       "      <td>61</td>\n",
       "      <td>91</td>\n",
       "      <td>103</td>\n",
       "      <td>68</td>\n",
       "      <td>190</td>\n",
       "      <td>130</td>\n",
       "      <td>294</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>60</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.68</td>\n",
       "      <td>2.09</td>\n",
       "      <td>1.11</td>\n",
       "      <td>212</td>\n",
       "      <td>1.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>59</td>\n",
       "      <td>90</td>\n",
       "      <td>192</td>\n",
       "      <td>143</td>\n",
       "      <td>69</td>\n",
       "      <td>98</td>\n",
       "      <td>150</td>\n",
       "      <td>101</td>\n",
       "      <td>188</td>\n",
       "      <td>128</td>\n",
       "      <td>342</td>\n",
       "      <td>31</td>\n",
       "      <td>29</td>\n",
       "      <td>60</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.68</td>\n",
       "      <td>2.42</td>\n",
       "      <td>1.46</td>\n",
       "      <td>244</td>\n",
       "      <td>1.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Japan</td>\n",
       "      <td>59</td>\n",
       "      <td>84</td>\n",
       "      <td>228</td>\n",
       "      <td>184</td>\n",
       "      <td>68</td>\n",
       "      <td>80</td>\n",
       "      <td>166</td>\n",
       "      <td>127</td>\n",
       "      <td>164</td>\n",
       "      <td>127</td>\n",
       "      <td>394</td>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "      <td>37</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.77</td>\n",
       "      <td>3.12</td>\n",
       "      <td>1.87</td>\n",
       "      <td>311</td>\n",
       "      <td>2.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>72</td>\n",
       "      <td>94</td>\n",
       "      <td>258</td>\n",
       "      <td>218</td>\n",
       "      <td>53</td>\n",
       "      <td>70</td>\n",
       "      <td>108</td>\n",
       "      <td>72</td>\n",
       "      <td>164</td>\n",
       "      <td>125</td>\n",
       "      <td>366</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>39</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.76</td>\n",
       "      <td>3.03</td>\n",
       "      <td>1.36</td>\n",
       "      <td>290</td>\n",
       "      <td>2.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Russia</td>\n",
       "      <td>61</td>\n",
       "      <td>92</td>\n",
       "      <td>192</td>\n",
       "      <td>152</td>\n",
       "      <td>64</td>\n",
       "      <td>93</td>\n",
       "      <td>158</td>\n",
       "      <td>111</td>\n",
       "      <td>185</td>\n",
       "      <td>125</td>\n",
       "      <td>350</td>\n",
       "      <td>31</td>\n",
       "      <td>29</td>\n",
       "      <td>60</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.68</td>\n",
       "      <td>2.49</td>\n",
       "      <td>1.73</td>\n",
       "      <td>263</td>\n",
       "      <td>2.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              team  home_win  home_count  home_score  home_goal_diff  \\\n",
       "0            Spain        80          98         290             257   \n",
       "1            Italy        72         101         208             158   \n",
       "2         Portugal        71         100         240             192   \n",
       "3          Nigeria        78         103         206             162   \n",
       "4             Iran        64          95         237             199   \n",
       "5      Ivory Coast        68          99         228             169   \n",
       "6      Netherlands        75          99         266             225   \n",
       "7          England        65          93         221             178   \n",
       "8         Cameroon        76         115         196             145   \n",
       "9            Egypt        79         103         247             196   \n",
       "10         Germany        62          89         245             184   \n",
       "11         Tunisia        60          88         186             141   \n",
       "12         Denmark        56          95         183             138   \n",
       "13          France        59          90         201             152   \n",
       "14       Argentina        72         112         216             154   \n",
       "15           Ghana        69          99         191             144   \n",
       "16  Czech Republic        59          90         192             143   \n",
       "17           Japan        59          84         228             184   \n",
       "18    Saudi Arabia        72          94         258             218   \n",
       "19          Russia        61          92         192             152   \n",
       "\n",
       "    away_win  away_count  away_score  away_goal_diff  total_count  total_win  \\\n",
       "0         86         100         193             128          198        166   \n",
       "1         87          99         143              95          200        159   \n",
       "2         81          98         178             118          198        152   \n",
       "3         66          84          97              58          187        144   \n",
       "4         79          93         202             151          188        143   \n",
       "5         75          98         122              72          197        143   \n",
       "6         66          88         177             127          187        141   \n",
       "7         75          87         179             138          180        140   \n",
       "8         63          82         111              65          197        139   \n",
       "9         60          87          96              53          190        139   \n",
       "10        76          91         200             146          180        138   \n",
       "11        75         101         127              66          189        135   \n",
       "12        77          97         163             109          192        133   \n",
       "13        74          85         127              82          175        133   \n",
       "14        58          79         108              60          191        130   \n",
       "15        61          91         103              68          190        130   \n",
       "16        69          98         150             101          188        128   \n",
       "17        68          80         166             127          164        127   \n",
       "18        53          70         108              72          164        125   \n",
       "19        64          93         158             111          185        125   \n",
       "\n",
       "    total_score  home_lose  away_lose  total_lose  home_win_rate  \\\n",
       "0           483         18         14          32           0.82   \n",
       "1           351         29         12          41           0.71   \n",
       "2           418         29         17          46           0.71   \n",
       "3           303         25         18          43           0.76   \n",
       "4           439         31         14          45           0.67   \n",
       "5           350         31         23          54           0.69   \n",
       "6           443         24         22          46           0.76   \n",
       "7           400         28         12          40           0.70   \n",
       "8           307         39         19          58           0.66   \n",
       "9           343         24         27          51           0.77   \n",
       "10          445         27         15          42           0.70   \n",
       "11          313         28         26          54           0.68   \n",
       "12          346         39         20          59           0.59   \n",
       "13          328         31         11          42           0.66   \n",
       "14          324         40         21          61           0.64   \n",
       "15          294         30         30          60           0.70   \n",
       "16          342         31         29          60           0.66   \n",
       "17          394         25         12          37           0.70   \n",
       "18          366         22         17          39           0.77   \n",
       "19          350         31         29          60           0.66   \n",
       "\n",
       "    away_win_rate  total_win_rate  avg_home_goal_diff  avg_away_goal_diff  \\\n",
       "0            0.86            0.84                3.21                1.49   \n",
       "1            0.88            0.80                2.19                1.09   \n",
       "2            0.83            0.77                2.70                1.46   \n",
       "3            0.79            0.77                2.08                0.88   \n",
       "4            0.85            0.76                3.11                1.91   \n",
       "5            0.77            0.73                2.49                0.96   \n",
       "6            0.75            0.75                3.00                1.92   \n",
       "7            0.86            0.78                2.74                1.84   \n",
       "8            0.77            0.71                1.91                1.03   \n",
       "9            0.69            0.73                2.48                0.88   \n",
       "10           0.84            0.77                2.97                1.92   \n",
       "11           0.74            0.71                2.35                0.88   \n",
       "12           0.79            0.69                2.46                1.42   \n",
       "13           0.87            0.76                2.58                1.11   \n",
       "14           0.73            0.68                2.14                1.03   \n",
       "15           0.67            0.68                2.09                1.11   \n",
       "16           0.70            0.68                2.42                1.46   \n",
       "17           0.85            0.77                3.12                1.87   \n",
       "18           0.76            0.76                3.03                1.36   \n",
       "19           0.69            0.68                2.49                1.73   \n",
       "\n",
       "    total_goal_diff  avg_total_goal_diff  \n",
       "0               385                 2.32  \n",
       "1               253                 1.59  \n",
       "2               310                 2.04  \n",
       "3               220                 1.53  \n",
       "4               350                 2.45  \n",
       "5               241                 1.69  \n",
       "6               352                 2.50  \n",
       "7               316                 2.26  \n",
       "8               210                 1.51  \n",
       "9               249                 1.79  \n",
       "10              330                 2.39  \n",
       "11              207                 1.53  \n",
       "12              247                 1.86  \n",
       "13              234                 1.76  \n",
       "14              214                 1.65  \n",
       "15              212                 1.63  \n",
       "16              244                 1.91  \n",
       "17              311                 2.45  \n",
       "18              290                 2.32  \n",
       "19              263                 2.10  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show top 20 teams with most wins\n",
    "df_game_stat.sort_values(by=['total_win'], ascending=False).head(20).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b2141ad7-fb37-42e8-8666-aba77a8f1d3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1DTPzeeAx4FQajArLzEmZGfXbkPU26tHgJUmSJEmS9IqBK29Seb8ALoqImcAvgbMj4gPA88BTDdqfB/wMGN9rEUqSJEmSJKlLTIZ1IjMfAcYWL79TV7Vjg7bD614uBi7KzAU9F50kSZIkSZJWh8mwblSMGPsIcFTZsUiSJEmSJGl5rhnWjTLzh5m5Z2b+uexYJEmSJEmStDyTYZIkSZIkSaoMk2GSJEmSJEmqjMjMsmOotKbmlmxb2F52GJIkSZLUJ0UkLVtvVXYYUr/WvHkTs2bOKDuM7hadVpgMK51vgCRJkiRJUvfqNBnmNElJkiRJkiRVhskwSZIkSZIkVYbJMEmSJEmSJFXGwLIDqLqm5hZcQF+SJElSX+Bi9VL/0U8Xxe8WJsNK1rawnbETppYdhiRJkiQx58ITmDxletlhSOoGE8ePLjuEPstpkpIkSZIkSaoMk2GSJEmSJEmqDJNhkiRJkiRJqox+lwyLiGER8XREzI6I2yNipZNki2MO6cYYWiPiW93VnyRJkiRJkrpHv0uGFW7OzFbgcOCrK2oYEQOAYUC3JcMkSZIkSZLUN/XrT5PMzMciYoOIOA/YDlgMfCgz/xAR84CrgV2B+cAeETEc+DDw08wcDhARVwITgGeAH1O7Z/OAbTNzbEScBIwFNgG+l5nf781rlCRJkiRJUtf115FhAETEzsAi4MXM3Bf4NHB6Ub0BcFFmHgicDfw8M1sz84FOujsemJ6ZBwF315Wfk5kHAHsBJ0ZEp/c0IiZFRNZvC16av2YXKUmSJEmSpC7rr8mwvSPiWmpTJKcAtwBk5m3Aa4s2L2XmPV3oK4qv2wG3F/u31tW/KyKuA34DbAVs2llHmTkpM6N+G7LeRl29JkmSJEmSJK2h/poMuzkz98/Mg4EHqY3aIiL2AP5UtGmva9/GslNGX46IoRExGNixKHsIGFHsv6mu7WeBA4GDged4JXkmSZIkSZKkPqZfrxlW+AUwLiKuBxL4UIM2c4GdImIatamUZwGzgXuAx4s25wKXRcTbgD9QS6ABTAduLPp4tkeuQJIkSZIkSd2i3yXDMvMRagvaL3m9GDi2Qbvhdfv/BN5cV/0n4JL69hERwKGZuSgi3k8xYiwzT2gQxuxikyRJkiRJUh/S75JhPWggMCsiktoUy6NLjkeSJEmSJEmryGRYF2XmQpYdPSZJkiRJkqS1TH9dQF+SJEmSJElajskwSZIkSZIkVUZkZtkxVFpTc0u2LWwvOwxJkiRJIiJp2XqrssOQ1A2aN29i1swZZYdRpui0wmRY6XwDJEmSJEmSulenyTCnSUqSJEmSJKkyTIZJkiRJkiSpMgaWHUDVNTW34JphkiRJUv/mWlxS3+FaWjIZVrK2he2MnTC17DAkSZIk9aA5F57A5CnTyw5DEjBx/OiyQ1DJnCYpSZIkSZKkyjAZJkmSJEmSpMowGSZJkiRJkqTKMBkmSZIkSZKkyqhEMiwihkVERsTBda+vjIiTI2L7Nez72IjYr3silSRJkiRJUk+q0qdJ/g44GVj6+amZedqadBgRAzLz/DWMS5IkSZIkSb2kSsmwPwHPRsS+wKMAEXE+8C3gHuAiYAvgRuDtmTk8Iv4N+C6wLvA08D5gL+BTwMvAnRExGJibmdMiYgYwuNjen5kP9t7lSZIkSZIkaWUqMU2yzqnA/zQofzvwRGa2UjdyDDgDODkzDwCuA95flG8BHJWZp3bo5/Cij8nASR1PEhGTiumaS7cFL81fowuSJEmSJElS11VpZBiZ+buImA/s0aFqO+D2Yv/WuvIdgW9EBNRGh10FPADclpnt9R1ExPrAtyPitcAg4MkG558ETKov23jolrmalyNJkiRJkqRVVLWRYVAbHfa5DmUPASOK/TfVlf8e+HBmtmbmXsDpRfkyibDCGOC5zHwztYRXdFvEkiRJkiRJ6haVS4Zl5j3AnzsU/wLYOiJmU5sy2VaUfxI4MyJmRcQsYNQKur4FGBUR04GDuzNmSZIkSZIkdY9KTJPMzEeAsXWvx3VsExFHZ+bCiNgPeHXR7s/A2xp0Obuur0l15SOWaylJkiRJkqQ+oxLJsC6aFhGbUPskyONKjkWSJEmSJEk9wGRYITPfXnYMkiRJkiRJ6lmVWzNMkiRJkiRJ1WUyTJIkSZIkSZURmVl2DJXW1NySbQvbyw5DkiRJUg+KSFq23qrsMCQBzZs3MWvmjLLDUM+LTitMhpXON0CSJEmSJKl7dZoMc5qkJEmSJEmSKsNkmCRJkiRJkipjYNkBVF1TcwuuGSZJkiT1D64NJq2dXEesWkyGlaxtYTtjJ0wtOwxJkiRJ3WDOhScwecr0ssOQtIomjh9ddgjqRU6TlCRJkiRJUmWYDJMkSZIkSVJlmAyTJEmSJElSZZgMkyRJkiRJUmWYDFuJiBgWEVeWHYckSZIkSZLWnMmwVRSFsuOQJEmSJEnSqhtYdgBri4iYDdwB7Ar8R0RcDAwutvdn5oNFm7uKNs9m5jtLCVaSJEmSJEkNOTJs1dyQmQdm5pPA4ZnZCkwGTqpr8+vMPAAYFBE71x8cEZMiIuu3BS/N773oJUmSJEmSKs6RYavmZoCIWB/4dkS8FhgEPFnX5u7i66PA0PqDM3MSMKm+bOOhW2YPxSpJkiRJkqQOHBm2atqLr2OA5zLzzdSSW/VriNUnt1xbTJIkSZIkqQ8xGbZ6bgFGRcR04OCyg5EkSZIkSVLXOE1yJTLzEWBsh7LHgREN2rbW7U/o6dgkSZIkSZK0ahwZJkmSJEmSpMowGSZJkiRJkqTKMBkmSZIkSZKkyjAZJkmSJEmSpMqIzCw7hkpram7JtoXtZYchSZIkqRtEJC1bb1V2GJJWUfPmTcyaOaPsMNS9otMKk2Gl8w2QJEmSJEnqXp0mw5wmKUmSJEmSpMowGSZJkiRJkqTKGFh2AFXX1NyCa4ZJkiRJ/YNrhkkqg2uerRqTYSVrW9jO2AlTyw5DkiRJUjeYc+EJTJ4yvewwJFXMxPGjyw5hreI0SUmSJEmSJFWGyTBJkiRJkiRVhskwSZIkSZIkVUafToZFxLCIuLKHzzEoIk6LiOsi4vqIuCQiNlzDPodFxCHdFaMkSZIkSZK6R79eQD8iBmTmyj6q8WRgUWbuWxyzOzB4DU89DDgE+NUa9iNJkiRJkqRu1KdHhi0REZtFxLV1r8+JiH0jYoeImFOM6Do3aloj4qqI+CnwuYi4IyIGFMd9PiKO7tD9e4D/XfIiM+/MzGc66bspIq6JiNkRMSsiXlX0OyUiboiIayNiJPBx4PCi3fY9foMkSZIkSZLUJWtFMiwznwb+GRGviYhBwJuA64HTgZMy883AQmBcccgWwFGZeQowEzioKB8H/KxD90My8+UGp23U9/PAmMxsBX4MfLCI543Avpm5P3AzcDbw88xszcwH1vwOSJIkSZIkqTusFcmwwsXA0cBbgV9nZgKvycw7i/qbgCWjsG6rmx75A+D9xYitOzPzpQ79LoiIdRucr1HfQ4FLI+I64KNAS2YupJY4+1FEfJ9aIq6hiJgUEVm/LXhp/ircAkmSJEmSJK2JtSkZdgUwllpC7KKi7JFijS+AfYA/FPtL1wnLzD8AmwEnAj9s0O8lwOeXvIiI3SJiaCd9Hw1cX6wv9r1a8xhAbRTY+4DrgAlAGw3WY8vMSZkZ9duQ9TZaxdsgSZIkSZKk1bXWJMOKqYz3UxuxdX9RfDLw9Yi4HhhCLWHWyMXA6zPztgZ1pwEDi7XBrgc+TS2Z1ajvmdSmRl4J7FYcvxEwMyJmUxst9hNgLrBTREyLiH9fk+uWJEmSJElS9+nTnyaZmY9QGw225PUHO9TfD7y5w2Gzi63eYuC8Ts6xkFriq6NGfd8L7NKg7b4NyjoeK0mSJEmSpJL16WRYd4iIk4FDi02SJEmSJEkVttZMk1xdmXlaZr45M/9ZdiySJEmSJEkqV79PhkmSJEmSJElLmAyTJEmSJElSZURmlh1DpTU1t2Tbwvayw5AkSZLUDSKSlq23KjsMSRXTvHkTs2bOKDuMviY6rTAZVjrfAEmSJEmSpO7VaTLMaZKSJEmSJEmqDJNhkiRJkiRJqgyTYZIkSZIkSaqMgWUHUHVNzS24gL4kSZLUt7kwvtR/uNi8TIaVrG1hO2MnTC07DEmSJEkrMOfCE5g8ZXrZYUjqBhPHjy47BJXMaZKSJEmSJEmqDJNhkiRJkiRJqgyTYZIkSZIkSaqMPpUMi4hhEZERcXDd6ys7abtrROxT93ruap7z2Ij45OpFvGbnliRJkiRJUu/qU8mwwu+Ak7vQbldgn5U1WpGIGLAmx0uSJEmSJGnt0hc/TfJPwLMRsS/wKEBEvAE4CxgAPAB8BPg4sHFEjAXeDqwbEVOoJckuzcwzI2JT4FxgKPAy8H5gA+AS4BHgH8A9S04cERcA2xRt/jMzb4mI84EFRfl6wJjMfDki/g8YCfweGFQc/zbgi8C/gBsy8wvdf3skSZIkSZK0uvriyDCAU4H/qXv9deDozGyllmg6GDgb+G5mtmbm80Az8Glqo8U+VBx3MnBBZh4AfBc4qSj/N2B8Zp7Y4bwfLc7xXmBiXfmdmflW4F7goIjYDdghM/cBvlycG+BI4MOZuX+H4wGIiEnFNNCl24KX5q/CbZEkSZIkSdKa6Isjw8jM30XEfGCPomg4cGlEAGwIzAWyw2GPZuYzABHRVnfcqIg4idq1zivK783MF+sPLqZM/m9EvAlYBGxWV333knNQG2W2PnBLEesfI+Kpov4U4FMRsSEwFVhmvbPMnARMqi/beOiWHa9DkiRJkiRJPaRPJsMKpwI/Av4C3AcclZlPAUTEIGqjsOrjb5RUuh+YmZnTi+MGA1sB7Q3avgEYlpmjImIHYFonfQfwIPCeos/XAE1F3eOZ+eGIGEItYddw8X9JkiRJkiSVo88mwzLznoj4M7WpnP8NTI2IgcBi4L+Am4ATImIE8IFOujkVOCciPl30c25xXCMPAEMjYhZw40piuysiHoqIm6gl6p4oqr5YfMLlIGBKFy9VkiRJkiRJvSQynaVXpo2HbpljJ0wtOwxJkiRJKzDnwhM4+8ezyw5DUjeYOH408+69q+ww1POis4q+uoC+JEmSJEmS1O1MhkmSJEmSJKkyTIZJkiRJkiSpMkyGSZIkSZIkqTJcQL9kTc0t2bawvewwJEmSJK1ARNKy9VZlhyGpGzRv3sSsmTPKDkM9r9MF9E2Glc83QJIkSZIkqXv5aZKSJEmSJEmSyTBJkiRJkiRVxsCyA6i6puYWXDNMkiRJ6htcG0zqXq7Ppb7IZFjJ2ha2M3bC1LLDkCRJkgTMufAEJk+ZXnYYUr8xcfzoskOQluM0SUmSJEmSJFWGyTBJkiRJkiRVhskwSZIkSZIkVYbJMEmSJEmSJFVGv0yGRcSwiHg6ImYX26WrePykiDhiDWOYuybHS5IkSZIkqfv150+TvDkzx5YdhCRJkiRJkvqO/pwMW0ZEzAbuAnYFns3Md0bEQOAiYAvgRuDtmTm8w3EzgMHF9v7MfLCTvtYBLgBagDt745okSZIkSZK0avrlNMnC3nXTJL9alP06Mw8ABkXEzsDbgScysxWY0Uk/hxf1k4GT6sob9fVs0faKRh0V0y+zflvw0vw1vU5JkiRJkiR1UX8eGbbMNMliNNfdxctHgaHAdsDtRdmtHTuIiPWBb0fEa4FBwJN11R37eh1wS2d9AWTmJGBSfdnGQ7fMLl6PJEmSJEmS1lB/HhnWSH3iKYCHgBHF6zc1aD8GeC4z30wtiRUr6OtBYI/i9R5IkiRJkiSpz+nPI8P2LkaDAbzUSZtfAEcV7e4A2jrU3wJ8NiKmA/NWcr7LgSOKvm5ZSVtJkiRJkiSVoF8mwzLzEWCzFdRPWLIfEUdn5sKI2A94dVE/qa75iA6HU6wLtlxfwHvq9k9e1bglSZIkSZLUs/plMmwVTYuITah9WuRxJcciSZIkSZKkHlT5ZFhmvr3sGCRJkiRJktQ7qraAviRJkiRJkirMZJgkSZIkSZIqIzKz7Bgqram5JdsWtpcdhiRJkiQgImnZequyw5D6jebNm5g1c0bZYaiaotMKk2Gl8w2QJEmSJEnqXp0mw5wmKUmSJEmSpMowGSZJkiRJkqTKGFh2AFXX1NyCa4ZJkiRJvc/1wST1F67NtmpMhpWsbWE7YydMLTsMSZIkqXLmXHgCk6dMLzsMSVpjE8ePLjuEtYrTJCVJkiRJklQZJsMkSZIkSZJUGSbDJEmSJEmSVBkmwyRJkiRJklQZa2UyLCJGRMRvImJORNwQEYeXHZMkSZIkSZL6vrXu0yQjYhPgB8DYzHw0IgYDe/fQuQZkZntP9C1JkiRJkqTet9Ylw4BDgcsz81GAzGwD5kTEDGBwsb0/Mx+MiNnAPcDuwO+AR4G3Ag9k5geLRNo5wL8VfX8oMx+KiIeAa4ENIuKUos06wO+BDwEBnAtsBywujvtDRMwDpgN7Abdl5ok9eickSZIkSZK0StbGaZItwGMNyg/PzFZgMnBSXfmvMvPNwAhgXmaOBLaNiC2B44C7M/MA4OPAl4pjtga+mJnvAU4HTir6WAiMA94OvJiZ+wKfLtoArA98LzP3AfaPiI3rA4yISRGR9duCl+av2d2QJEmSJElSl62NI8MeA15fXxAR6wPfjojXAoOAJ+uq7y6+Pk5tlNiS/aHAcGCviHhHUf5C8fWRzPxbsf+azLyz2L8J2J7ayLBbADLztuK8UEuQ/aHY/wuwKfDPJYFk5iRgUn3sGw/dMrty0ZIkSZIkSVpza+PIsKuAcRGxDUBEDAI+CTxXjN6aRC1ZtUR2sh/A/cD3M7O1GFW2ZCH++nXCHomI3Yv9fYA/AA9SmwpJROwB/KlB/0vOIUmSJEmSpD5irRsZlpnPRcQHgR8UibABwHepJcimA/NWobtzge9ExLuoJa6uAL7Woc3JwDkRAbVE2BVF+biIuJ5aAuxDq3s9kiRJkiRJ6j1rXTIMIDPvAA7sUHxxg3atdftj6/bfXddsfIPjhtft3w+8uUEYx67kuLEd6yVJkiRJklSutXGapCRJkiRJkrRaTIZJkiRJkiSpMkyGSZIkSZIkqTJMhkmSJEmSJKkyIjPLjqHSmppbsm1he9lhSJIkSZUTkbRsvVXZYUjSGmvevIlZM2eUHUZfE51WmAwrnW+AJEmSJElS9+o0GeY0SUmSJEmSJFWGyTBJkiRJkiRVxsCyA6i6puYWXDNMkiRJ6n2uGSZpdbg+19rPZFjJ2ha2M3bC1LLDkCRJkipnzoUnMHnK9LLDkLSWmTh+dNkhaA05TVKSJEmSJEmVYTJMkiRJkiRJlWEyTJIkSZIkSZVR+TXDImIYcCdwX1H0RGa+ew37bAX+kZn3r1FwkiRJkiRJ6laVT4YVbs7Msd3YXyswFzAZJkmSJEmS1IeYDGsgIg4Avgr8BVgITAWagfbM/F5ErA9cl5kjIuIh4GZge+Ay4FzgWOCFiDguM99axjVIkiRJkiRpeSbDavaOiNnF/p3AvsAhwBPAzKL8YuDnwPeAw4GfFuUtwKeAJ4GbgAuA84G5mTmtF2KXJEmSJElSF3UpGVaMhNoSGLCkLDP/0FNBlWCZaZIRcV9m/q3Yvx0gM5+LiMcjYgfgvcBxRfO/ZOYTRdt7gdd0dpKImARMrC8bvO4G3XkdkiRJkiRJWoGVJsOKBM4HgIeBLIoTOKDnwirdgohoBv4BjADuKMrPA74ALMrMx4uylqLtk8DOwCNAGw3ubWZOAibVl208dMvs2E6SJEmSJEk9oysjw94NbJeZbT0dTInqp0m+BJwM/Bp4HHiRWnIL4FpgCvDpumMfA04DdgKmZebfI+Ia4KsRcWhmvq8X4pckSZIkSVIXdCUZdie1KZJ/7uFYSpGZjwCb1ZdFxKDM3C0iAvgN8GBd9RPAFXWvF2TmBzr0eSswqmciliRJkiRJ0urqSjLsHOCuiHiY2gipADIz9+nRyMp1SEScBGwAzMzM30XEMGoL41/Uz0fJSZIkSZIk9VtdSYadR23NsPuAxT0bTt+QmZcDl3coewRobdB2eO9EJUmSJEmSpDXVlWTYPzLzipU3kyRJkiRJkvq2riTDfh8RVwFX88pC8mTm93ssKkmSJEmSJKkHRGauuEHExEblmTm5RyKqmKbmlmxb2F52GJIkSVLlRCQtW29VdhiS1jLNmzcxa+aMssPQykWnFStLhqnH+QZIkiRJkiR1r06TYSudJhkRzcBngNcDQ5aUZ+YB3RKaJEmSJEmS1EvW6UKbi4GbgW2ATwH3A7f0ZFCSJEmSJElST+jKmmF3ZubuEXFvZu5SlN2RmSN6JcL+z2mSkiRJkiRJ3Wv1p0kCCyIigIcj4hjgb8BG3RVZ1TU1t+AC+pIkSVLPcrF8qRwuNq++qCvJsJOADYD/BE6hlgj7QE8GVSVtC9sZO2Fq2WFIkiRJ/dqcC09g8pTpZYchVc7E8aPLDkFazkqTYZl5S0QMqO3msT0fkiRJkiRJktQzVrqAfkQcCvwWuK54/YaImNbTgUmSJEmSJEndrSufJvklYG/gOYDM/C2wfQ/GJEmSJEmSJPWIrqwZtigz59fW0IdiMf0+9QmIEbE78BVgMLVrmpOZnys3KkmSJEmSJPU1XUmG3RQR/wUMjoi9gI8Bv+7ZsLouIjYBfgi8LTP/XJQd0IXjBmSmH+MoSZIkSZJUIV1Jhj0GLAB+B3wKmAls2JNBraJDgcuXJMIAMnNWRGwKnAsMBV4G3k/tUzEvAR4B/lEk0hYC/1Z8/SXwrqL9ocWxPwYGAIuBd2XmkxExD5gO7AXclpknRsRlwBcz8/cRMQp4f2Ye39MXL0mSJEmSpK7rypph78nMczLziMx8Z2Z+Fzi6pwNbBS3UEnZExLYRMTsifg+cBlyQmQcA3wVOKtr/GzA+M08sXt+emQcDbcB6mbk/8BSwJ/A8MCYzW6klxT5YHLM+8L3M3AfYPyI2Bn4AHFvUv794vYyImBQRWb8teGl+d90HSZIkSZIkrUSnybCIOC4ibgZeFxE31W33Aff3Xogr9RjwaoDMfKhIXD1BLUn2mYiYDXyG2igvgHsz88W64+8uvv4VuKduf2ixXRoR1wEfLfoEeDEz/1Ds/wXYFJgBtEbEhsDOmXlzx0Azc1JmRv02ZL2N1ujiJUmSJEmS1HUrmiY5DbgG+F+gfjH6+Zn5TI9GtWquopb0mpKZjxRlA4F/AN/IzOkAETEY2ArouE5YdrIf1EbAXZ+ZX4+IjwDDG7QDiMxcHBEzqI1C+9kaXpMkSZIkSZJ6QKfJsMx8nto0wff2XjirLjOfi4gPAOdGxCBq0x1vBM4Evh0Rn6Y2Au5c4KZV7H4mcFFEHEhttNjClbT/IfAAtbXVJEmSJEmS1Md0ZQH9Pi8z7wQOalB1VIOysXXHHVu3P6Fu/+S69rs0ON/wuv2xdVWLgSsz84kuBS5JkiRJkqRe1ZUF9NUFEXEocBnwlbJjkSRJkiRJUmP9YmRYX5CZV1Fbv0ySJEmSJEl9lCPDJEmSJEmSVBkmwyRJkiRJklQZkZllx1BpTc0t2bawvewwJEmSpH4tImnZequyw5Aqp3nzJmbNnFF2GKqm6LTCZFjpfAMkSZIkSZK6V6fJMKdJSpIkSZIkqTJMhkmSJEmSJKkyBpYdQNU1NbfgmmGSJEnSirnml6QyuOZZ/2QyrGRtC9sZO2Fq2WFIkiRJfdqcC09g8pTpZYchqWImjh9ddgjqAU6TlCRJkiRJUmWYDJMkSZIkSVJlmAyTJEmSJElSZZgMkyRJkiRJUmVUJhkWEQMiYnaxPRcRNxb7B3Th2C0i4isrqP9h90YrSZIkSZKknlCZT5PMzHagFSAiZgNHZOZTXTz2CeB/VlD/gW4IUZIkSZIkST2sMiPDGmiNiG8BRERTkSCjGC12VkTMioifFmXDIuLKYn9yRNxS1L+zKJtbfH1PRFwbEbdFxCmlXJUkSZIkSZI6VeVk2Ir8OjMPAAZFxM4d6sYB+xb1P+9Qd3lm7g/sCbwlIraor4yISRGR9duCl+b32EVIkiRJkiRpWVVOhj1Ztx8d6u4uvj4KDO1Q92ng3Ig4H9i+Q90BEXEtMBt4LbB1fWVmTsrMqN+GrLfRGlyCJEmSJEmSVkVl1gxrIIGWYn+3BnVLdEyU3ZiZv4mIUcAXgPfU1X0JOAB4FrixwbGSJEmSJEkqUZWTYb8DFkfELOCWVTju5xExBBgCTOxQdylwLXA/8GK3RClJkiRJkqRuU8lkWGa2FrvvWEEdmTmhrmpsUTa6wTHDi6+nAad1Y6iSJEmSJEnqRlVeM0ySJEmSJEkVYzJMkiRJkiRJlWEyTJIkSZIkSZVhMkySJEmSJEmVEZlZdgyV1tTckm0L28sOQ5IkSerTIpKWrbcqOwxJFdO8eROzZs4oOwytnui0wmRY6XwDJEmSJEmSulenyTCnSUqSJEmSJKkyTIZJkiRJkiSpMgaWHUDVNTW34JphkiRJqiLXAZPU37nmWN9kMqxkbQvbGTthatlhSJIkSb1uzoUnMHnK9LLDkKQeM3H86LJDUANOk5QkSZIkSVJlmAyTJEmSJElSZZgMkyRJkiRJUmX0i2RYRAyLiKcjYlax/SgitiwhjtaI+FZvn1eSJEmSJEld0y+SYYWbM/OAzDwA+ClwaW+ePCIG9Ob5JEmSJEmStOr6UzJsqcy8AhgQEa+OiGnFaLFfRcSrilFkt0TEJRFxb0S8GyAizo+IcyNiRkRcFREfiYhrI+LqiFgnIpoi4pqImF3096riuHkRcSaw9GNwImJARJwTEePLuQOSJEmSJElqpF8mwwqPARcAFxSjxb4LnFTUNQPHAgcAn6w75vbMPBhoA9bLzP2Bp4A9geeBMZnZCvwY+GBxzAbARZl5YPF6CHARMCMzp/TMpUmSJEmSJGl19OdkWAuwGPhMRMwGPgMMLeruz8y2zHwKWLfumLuLr38F7qnbH1psl0bEdcBHi/4BXsrMe+r6eCswIDN/2jGgiJgUEVm/LXhp/hpepiRJkiRJkrqqXybDIuJQaomwu4BTMrM1M0cB/1k0yU4OzU72AzgauD4z9wW+V5QBtHfo4xfAvRFx+nKdZ07KzKjfhqy30apcmiRJkiRJktZAf0qG7V2s8TULeBfwH8CpwHHFWl/XAkesQf8zgQ9GxJXAbitqmJn/C8yPiNPW4HySJEmSJEnqZgPLDqA7ZOYjwGadVB/VoGxs3bHDi6/H1pVNqNs/ue64XRqce3jd/mxgdrH/v10IXZIkSZIkSb2oP40MkyRJkiRJklbIZJgkSZIkSZIqw2SYJEmSJEmSKsNkmCRJkiRJkiojMrPsGCqtqbkl2xa2lx2GJEmS1Osikpattyo7DEnqMc2bNzFr5oyyw6iq6LTCZFjpfAMkSZIkSZK6V6fJMKdJSpIkSZIkqTJMhkmSJEmSJKkyTIZJkiRJkiSpMgaWHUDVNTW34AL6kiRJqiIX0JfK4aLuqjqTYSVrW9jO2AlTyw5DkiRJ6nVzLjyByVOmlx2GVDkTx48uOwSpVE6TlCRJkiRJUmWYDJMkSZIkSVJlmAyTJEmSJElSZVQuGRYRwyLi6YiYXWzHlh2TJEmSJEmSekdVF9C/OTPH1hdExIDM9GMdJUmSJEmS+rGqJsOWioh5wNXArhHxbuDHwABgMfCuzHyyaDMd2Au4LTNPjIghwLnAMKAdeC/wYlE2FHgZeH9mPtnLlyRJkiRJkqROVG6aZGHvJdMkgS2AizLzQOB5YExmtlJLin2waL8+8L3M3AfYPyI2Bo4HHsrMfTNzf+BvwMnABZl5APBd4KTevChJkiRJkiStWFVHhi2dJhkRv8vMe4ryocB3IuJVwMbA9UX5i5n5h2L/L8CmwI7A1CUdZubiiBgOjIqIk6jd23n1J42IScDE+rLB627QjZclSZIkSZKkFanqyLB69euEHQ1cn5n7At8DoijPDscEtUTXfksLItYB7gdOyczWzBwF/Gf9QZk5KTOjfhuy3kbdfDmSJEmSJEnqjMmwZc0EPhgRVwK7raTtFGD7iLguImYBWwKnAsdFxDURcS1wRM+GK0mSJEmSpFVRuWmSmfkIMLbu9fC6/XuBXRocU9+m/lMo39fgFEd1S6CSJEmSJEnqdo4MkyRJkiRJUmWYDJMkSZIkSVJlmAyTJEmSJElSZZgMkyRJkiRJUmVEZpYdQ6U1Nbdk28L2ssOQJEmSel1E0rL1VmWHIVVO8+ZNzJo5o+wwpJ4WnVaYDCudb4AkSZIkSVL36jQZ5jRJSZIkSZIkVYbJMEmSJEmSJFWGyTBJkiRJkiRVxsCyA6i6puYWXEBfkiRJayMXwJekGj+UYO1iMqxkbQvbGTthatlhSJIkSatszoUnMHnK9LLDkKTSTRw/uuwQtAqcJilJkiRJkqTKMBkmSZIkSZKkyjAZJkmSJEmSpMpYq5NhETEtIi7tob6HRcQhda+/HhH/ryfOJUmSJEmSpN6x1ibDImJjYBNgq4jYoEPdgG44xTBgaTIsM0/MzOe7oV9JkiRJkiSVZG3+NMnDgZ8Cg4G3R8TjwKeAl4E7I+IW4KvAX4CFwNTMnBYR/w28g9q1fzkzr4yI2cBdwK7As5n5TuDjwB4RMRz4MHAOcAQwFngbMAB4DfAfmXl/RJwBvAnYGJicmVf0wj2QJEmSJEnSKlhrR4YB7wQuAy6hlqQC2AI4KjNPBU6nNrLr7dRGkBEROwIjM/PNwP7ApLr+fp2ZBwCDImJn4Gzg55nZmpkPdDj3gsw8DJgMHFeUTc7M/YEDgYmNAo6ISRGR9duCl+av9g2QJEmSJEnSqlkrR4ZFxKuAEcDFRdFwYChwW2a2F2XrZubfiva3F2U7AbsVI8EANoyIjYr9u4uvjxZ9rUh927cV+/8VEYcCi4BtGh2UmZNYNgHHxkO3zJWcS5IkSZIkSd1krUyGAUcCX8jM8wAiYjywGdBe12ZBRDQD/6CWOLsD+D1wS2YeXRw3ODPbIgKgPikVQBud359l2kbE0CKmEdRGof1uja5OkiRJkiRJPWJtTYb9B3B03evfAA8D36krOxn4NfA48CLQlpn3RcRtEXEdtcTZo8D7OznHXGCniJgGfHol8TwL/Bm4jtqosedW6WokSZIkSZLUK9bKZFix5lf96z+z/PpnczJzt6gN+/oN8GDR9mxq64HVH99atz+hrqr+PEvanF/X9g7g2OLl4at2FZIkSZIkSepta/MC+itzSETMAW4H7shMpy5KkiRJkiRV3Fo5MqwrMvNy4PKy45AkSZIkSVLf0Z9HhkmSJEmSJEnLMBkmSZIkSZKkyojMLDuGSmtqbsm2he1lhyFJkiStsoikZeutyg5DkkrXvHkTs2bOKDsMLSs6rTAZVjrfAEmSJEmSpO7VaTLMaZKSJEmSJEmqDJNhkiRJkiRJqoyBZQdQdU3NLbhmmCRJktZGrhkmqYpcH2ztZzKsZG0L2xk7YWrZYUiSJEmrbM6FJzB5yvSyw5CkXjVx/OiyQ9AacpqkJEmSJEmSKsNkmCRJkiRJkirDZJgkSZIkSZIqw2SYJEmSJEmSKqOSC+hHxO7AV4DB1O7BHGAhMDczp5UZmyRJkiRJknpO5ZJhEbEJ8EPgbZn556LsAGDfMuOSJEmSJElSz6viNMlDgcuXJMIAMnNWsXtYRPwyIm6LiGaAiLggImZHxO0RsVdRdn5EnBMRVxd16xblM4rXN0XEdr19YZIkSZIkSVqxKibDWoDHACJi2yJ59XtgU+DRzHwbcClwVNH+o5nZCrwXmFjXz52Z+VbgXuCgouzwou1k4KSOJ46ISRGR9duCl+Z3/xVKkiRJkiSpocpNk6SWCHs9QGY+BLRGxGxgAHB30eZRYKeIGAD8b0S8CVgEbFbXT33boRGxPvDtiHgtMAh4suOJM3MSMKm+bOOhW2a3XJUkSZIkSZJWqoojw64CxkXEsLqyJUnB+sRUAG8AhmXmKOAjRRmdtB0DPJeZb6aW8KpvK0mSJEmSpD6gciPDMvO5iPgAcG5EDALagBupjfzq6AFqo75mFW1W5BbgsxExHZjXnTFLkiRJkiSpe1QuGQaQmXfyyjpfjeqnAdOKl/vVVX2hqD+2ru2ZdfUjui9KSZIkSZIkdbcqTpOUJEmSJElSRZkMkyRJkiRJUmWYDJMkSZIkSVJlmAyTJEmSJElSZURmlh1DpTU1t2Tbwvayw5AkSZJWWUTSsvVWZYchSb2qefMmZs2cUXYYWrnotMJkWOl8AyRJkiRJkrpXp8kwp0lKkiRJkiSpMkyGSZIkSZIkqTIGlh1A1TU1t+CaYZIkSeorXAdMUhW5Dli1mAwrWdvCdsZOmFp2GJIkSRIAcy48gclTppcdhiT1qonjR5cdgnqR0yQlSZIkSZJUGSbDJEmSJEmSVBkmwyRJkiRJklQZPZYMi4gREfGbiJgTETdExOFr2F9rRHxrJW3+GBGzI+KmiPjampyvQ79zVxRPRHw9Iv5fd51PkiRJkiRJPaNHFtCPiE2AHwBjM/PRiBgM7N0T5+rgpcxsLWK4JiJen5m/6+mTZuaJPX0OSZIkSZIkrbmeGhl2KHB5Zj4KkJltmTknIg4tRm7NjognI+JtEbFpREyLiFkR8auIeBVARHwiIm6JiGsj4pCi3+2KtnMjYr/OTh4RA4ENl1xfRPxfcc4bImKPomxeRHyrKPtaUXZsRHyy2B8REee/0uWybTucb3ZENEXEkIi4ICKuK+LeujtupiRJkiRJkrpHTyXDWoDHOhZm5lXFyK3PA7cAVwMnAxdk5gHAd4GTImI4MBoYmZn7A0s+23kD4EjgGOC/Gpx3vYiYDfwF+G1mzouItwIDivMeDpxe19d5mTkKeE1EvHEF19PVtscDD2XmvkXcf1tBn5IkSZIkSeplPTJNkloi7PWNKiJiO+BLwNszc1GR+BoVEScV8cwDdgSuz8x2gMxsjwioJbgyIh4Fhjbo/qXMbI2ILYCfF9MzhwOjiyQZwEbF14WZeXexfzuwHZD1odbtN2r79wbn3xGYuuRFZi7ucO2TgIn1ZYPX3aBBN5IkSZIkSeoJPTUy7CpgXERsAxARgyJi34jYDDgPeH9m/rNoez9wSma2FiOv/rMoGxURS6Y5Lomzs2TVMjLzCeBK4LiiryuK/lt5Ze2yQRHxhmJ/BPAQ8Cy1UW0Au9V12ahtI/OApdM36+JeEtekzIz6bch6Gy3XiSRJkiRJknpGjyTDMvM54IPADyJiDnAtsBnwMWrJpguKdbZGAacCxxUL3l8LHJGZc4HfADdFxCzgrasRxjnACcCvqa35Nbvo/0tF/QvAsRFxI/CXzLyzOOduEXE1tRFlrKBtI1OA7Ys1w2YBW65G3JIkSZIkSeohkZkrb9UPRcTczBy+8pY9a+OhW+bYCVNX3lCSJEnqBXMuPIGzfzy77DAkqVdNHD+aeffeVXYY6l6dzijsqWmSkiRJkiRJUp9T2WRYXxgVJkmSJEmSpN5V2WSYJEmSJEmSqsdkmCRJkiRJkiqjsgvo9xVNzS3ZtrC97DAkSZIkACKSlq23KjsMSepVzZs3MWvmjLLDUPfqdAF9k2Hl8w2QJEmSJEnqXn6apCRJkiRJkmQyTJIkSZIkSZVhMkySJEmSJEmVMbDsAKquqbkFF9CXJElSGVwsX1qWi6hL1WAyrGRtC9sZO2Fq2WFIkiSpguZceAKTp0wvOwypz5g4fnTZIUjqBU6TlCRJkiRJUmWYDJMkSZIkSVJlmAyTJEmSJElSZVQ+GRYRwyLiyrLjkCRJkiRJUs+rfDJMkiRJkiRJ1WEyrBARJ0XErIi4KyI+VJQdGxGXR8RVEXF7RAwvys+IiGsj4s6IGFeUTYqIiyLilxFxW0Q0l3k9kiRJkiRJWp7JsFeck5kHAHsBJ0bEknuTmXko8CHg1KJscmbuDxwITKzr49HMfBtwKXBUL8UtSZIkSZKkLjIZ9op3RcR1wG+ArYBNi/LbATLzbmBYUfZfEXED8HNgm7o+7i6+PgoM7XiCYvRY1m8LXprf/VciSZIkSZKkhkyGveKz1EZ6HQw8B0RRvjtARLwB+HNEDAWOBPYF3gEsrusj6/aDDjJzUmZG/TZkvY26/UIkSZIkSZLU2MCyA+gDAmgHpgM3AnOBZ+vq2yPiV8DmwAeLuj8D11EbCfZcbwYrSZIkSZKk1WcyrDbN8fHMPKFjRUQA3JqZZ3aoOrxj28ycVLc/DZjWvWFKkiRJkiRpTVU6GRYRRwCfoTbiS5IkSZIkSf1cpZNhKxvBlZnn9140kiRJkiRJ6mkuoC9JkiRJkqTKMBkmSZIkSZKkyojMLDuGSmtqbsm2he1lhyFJkqQKikhatt6q7DCkPqN58yZmzZxRdhiSukd0WmEyrHS+AZIkSZIkSd2r02SY0yQlSZIkSZJUGSbDJEmSJEmSVBkmwyRJkiRJklQZA8sOoOqamltwAX1JkiR1JxfGl1aPC+hL1WAyrGRtC9sZO2Fq2WFIkiSpH5lz4QlMnjK97DCktc7E8aPLDkFSL3CapCRJkiRJkirDZJgkSZIkSZIqw2SYJEmSJEmSKqP0ZFhEDIiI8yLihmI7fQ37mxQRRxT7P+ykzdsi4qWI+H+r2PfcBmW7RsTHVy9aSZIkSZIk9aa+sID+aODlzBwFEBFDu6vjzPxAJ1X/AZwLvANYJmEWEQMys8sf75iZ9wD3rGaIkiRJkiRJ6kWljwwDXgR2iIjtADLzGYCIeE9EXBsRt0XEKUVZa0R8q9hviojZxf6+EXF3RPwSGLGk405Gcq0PbAGcAhxRVz4vIs4Ephd9XxMRsyNiVkS86pVm8a1iBNvXGsR0RhHznRExrntvkyRJkiRJktZU6cmwzJwNXAr8ICIejIijiqrLM3N/YE/gLRGxxQq6+T9gLDAOWH8lpxwH/CIznwJejojmonwD4KLMPBB4HhiTma3Aj4EP1rU5rxjF9pqIeGOHvicXMR8ITOx44mIKZ9ZvC16av5JwJUmSJEmS1F1KT4YBZOa5mflmYCRwakQMBA6IiGuB2cBrga2BrDss6vbXz8y/ZmYCt63kdEcB74iIXwP/DhxZlL9UTHkEGApcGhHXAR8FWoryhZl5d7F/O7Bdh77/KyJuAH4ObNPgOidlZtRvQ9bbaCXhSpIkSZIkqbuUngyLiC0jYklG6Dmgrdj/EvBOoBX4E7Xk17O8kpjara6bl4p+grppkg3OtQmwcWa2ZuYYYBS1dcMA6tcJOxq4PjP3Bb7HK4m3QRHxhmJ/BPBQXd9DqSXW9i36XLyya5ckSZIkSVLv6gsL6L8a+HpELAIGAadn5qKIuBS4Frif2rpiAPcBiyNiFnBLXR+fBn4FPA78cwXneidw3ZIXmflCMV1xWId2M4GLIuJA4K/AwqL8BeDYiNgDuDMz74yI1qLuWeDPRf93U0vsSZIkSZIkqQ8pPRmWmbcB+zQoPw04rcEh72jQdjbLjhRbUj68w+vzGrR5S7E7vK7sXmCXBm136uTcs4uXhzeIV5IkSZIkSX1E6dMkJUmSJEmSpN5iMkySJEmSJEmVYTJMkiRJkiRJlWEyTJIkSZIkSZURmVl2DJXW1NySbQvbyw5DkiRJ/UhE0rL1VmWHIa11mjdvYtbMGWWHIal7RKcVJsNK5xsgSZIkSZLUvTpNhjlNUpIkSZIkSZVhMkySJEmSJEmVMbDsAKquqbkF1wyTJElSd3LNMElV43pvWhUmw0rWtrCdsROmlh2GJEmS+pE5F57A5CnTyw5DknrNxPGjyw5BaxGnSUqSJEmSJKkyTIZJkiRJkiSpMkyGSZIkSZIkqTJMhkmSJEmSJKkyKpcMi4hhEfF0RMyOiNsi4j1r2N/JEbF9d8UnSZIkSZKknlPVT5O8OTPHRsT6wL3AJavbUWae1n1hSZIkSZIkqSdVbmRYBxsDgyNiUkQcARARRxSvB0XE5RExJyKujYjXRsSexWiyayPi3KL9+RExIiKaIuKaYsTZrIh4ValXJkmSJEmSpOVUNRm2d0RcBzwIfLaTNq8GIjP3y8z9gYeBQ4HTitcf7tD+eWBMZrYCPwY+2LHDIsmW9duCl+Z30yVJkiRJkiRpZaqaDLs5M/cFDgNGA1lXFwCZ+SdgekRcHBFnAxsC3wb2i4hLgGM69DkUuLRIsn0UaOl40syclJlRvw1Zb6PuvjZJkiRJkiR1oqrJMAAy8xpgc+A5Xkle7QYQEUOA72bm0cCTwLuB+Zn5ceBo4H+KNkscDVxfJNm+R5FUkyRJkiRJUt9R1QX0630T+E9gaEQcCDwBPAb8G3BeRCyiljR8H/ChiHgntUTX9MxcELE05zUTuKjo46/Awt69DEmSJEmSJK1M5ZJhmfkIMLbu9ZXAlZ00f3OH118vtvr+jq17ucuaxidJkiRJkqSeU+lpkpIkSZIkSaoWk2GSJEmSJEmqDJNhkiRJkiRJqgyTYZIkSZIkSaqMyMyyY6i0puaWbFvYXnYYkiRJ6kcikpattyo7DEnqNc2bNzFr5oyyw1DfEp1WmAwrnW+AJEmSJElS9+o0GeY0yZJFxGRqb5CbWwDhM+HWcfOZcOu4+Uy4ddx8Jtw6bj4TbvWbz4Nbx81nwq3j1k+fiU45MqxkEZGZucI3SdXiM6GOfCbUkc+EOvKZUEc+E6rn86COfCbUUdWeCUeGSZIkSZIkqTJMhkmSJEmSJKkyTIZJkiRJkiSpMkyGlW9y2QGoz/GZUEc+E+rIZ0Id+UyoI58J1fN5UEc+E+qoUs+EC+hLkiRJkiSpMhwZJkmSJEmSpMowGVaiiDg+Im6KiNkR8e9lx6PeFxF7R8TNETEnIq6KiE0iYlJEzCuei0vLjlG9LyJeKN7/2RExJiLWi4gfR8T1EfHdiPB7d4VExI51z8MdEXFXRBwbEX9cUl52jOp5ETEoIm6MiOci4oiirCkiro6IGyJiUl3bscXPlpsi4k2lBa0e1ckzcW7x3t8aEe+ra7vMz5XyolZP6uSZaPh7pd8nqqGTZ+KMuu8HT0XEuKL8j3Xlx5YauHpEJ397VvZ3CadJliQihgJXAyOB3YBPZeZR5Ual3hYRWwHPZeaLEfERYDNgEDA3M6eVG53KEhFzM3N43esTgPUz8/8i4tvAVZn5q/IiVFmKZ2ET4K9AU2aeWW5E6i0REcAWwIcpfkZExP8Bt2XmTyLiKuBTwAPAHcC+wMbAjzNzVFlxq+d08kxsl5kPRsQQ4LfA8Mxc1PHnivqnTp6JSXT4vTIiBuD3iUpo9EzU1Q0Afgfskpkv+32i/+vkb89NqOjvEo4uKM8ewOzMXJSZtwPblx2Qel9mPp6ZLxYv24BFxf7ni1FA/1FSaCrXqyPiuoi4OCI2A94MXFnUXUntB5Oq6T+AqcX++OJ/8U4sMR71kqz5W4fiUSz/vWE74A+ZOT8z/woMioh1ezFU9ZJGz0RmPljstgHtwJL/9e74c0X9UCffJ2D53yv9PlERK3gmAA4Abs7Ml4vXg4tRYZdHxGt6KUT1ok7+9qzs7xImw8ozFHi27rXvRYUVv5h+DDgP+GZm7gqMBT4ZEa8uMzaV4rWZuS9wDfBllv1+8VzxWhUTEcOAAZn5J+AXwE7AW4BDImKvEkNTeTbIzJeK/eeofW/o+PvFknJVyyeAyzKzvXjd8eeKqqPR75V+nxDAe3jlP9gA9snMVuAbwHdKiUi9osPfnpX9XcIETHmepTYkcYn2Ttqpn4uI9YGfAP+VmU9l5tMAmfk8tV9aHa5cMZn5VLF7KbVp1PXfL/4f8EwJYal876b2TJCZz2Vme2YuoJYY263MwFSaF+v+p3bJ94aOv1/4PaNiinWB9gK+tKSswc8VVUQnv1f6faLiiqnUo4CZS8qWfJ/IzGsA/zO+n+r4tycV/l3CZFh5bgX2i4gBEfFG4MGVHaD+JyIGUvvF9JuZeVNR9v/q6vYG/lhehOptEbFBsYYDwH7UvjdcBxxSlB0CXF9GbCrdu4AfwzLfJ4LacHZ/hlTT9bzyveGtxesHgdcV30u2ABbVTYFRPxcRrdT+t/99mbm4KGv0c0UV0cnvlX6f0KHAjMxcBLXk2JKESEQMB54uMzj1jEZ/e1Lh3yUGlh1AVWXmMxHxI2oP20LguJJDUjn+g2Jhwoj4OHAVsH1E7AgMAC7JzD+UGaB63Q7AlIj4J7AAGA88BZwfEddRW+j06hLjUwkiYifg6cx8oij6REQcTG09oOsyc2bnR6u/iIjLgBHAvyJiD+AM4IKIOAmYlZnzinaTqP1vfwL/XVK46gUNnol3AvOBq2u5co4EtmH5nyvqpxo8E0Mb/V7p94nq6PhMZOanqf0NcnZds82AqyJifvF6Qi+Hqd7R6G/Pyv4u4adJSpIkSZIkqTKcJilJkiRJkqTKMBkmSZIkSZKkyjAZJkmSJEmSpMowGSZJkiRJkqTKMBkmSZIkSZKkyjAZJkmSJEmSpMowGSZJktQPRcQmETF+JW1aI2JEF/qaHRE7rGYcIyLitNU5VpIkqSeYDJMkSeqfNgFWmAwDWoGVJsPWRGbekZkn9+Q5JEmSVsXAsgOQJElSj/gysHNE3AP8HNgBGA78E/gA8BLwEWBhRHwEeBewP3AcMAS4EzguMxev6CQR8Tng8cz8YURcDjySmR+PiMnAA8DjwEcy890RMQnYGng9sCXw0cyc0b2XLUmStGKODJMkSeqfPgfcl5m7Ukt8PZmZO1NLkn0nM/8CfA/4cmbumpkPAJdl5psyc5fimLd14Tw3AvsU+03ALsX+yKKuo38D9gOOAiau1pVJkiStAZNhkiRJ/d8+wEUAmfkraiOzGnlDRNwYEfdRS4Tt2IW+bwP2iIjtgfuBlyNiY6AlM//coP1VmdkO3A0MW7XLkCRJWnNOk5QkSdIS5wJvzcwHI+KTwIYrOyAzX4yIl4FxwA3Aa4APAfd2csiC4rjFETGge8KWJEnqOkeGSZIk9U/zgY2K/ZuorQlGRIyhNoKrYxuADYAnI2LdJe276Gbg49SmRd4InEjjKZKSJEmlMxkmSZLUD2Xm08C9EXEvsB6wVbH/BeCEotkvgfdFxN3FNMcvA3cB11KbxthVNwKDMvMh4BZgC2oJOEmSpD4nMrPsGCRJkiRJkqRe4cgwSZIkSZIkVYYL6EuSJKlLIuJWYEiH4j0ys62MeCRJklaH0yQlSZIkSZJUGU6TlCRJkiRJUmWYDJMkSZIkSVJlmAyTJEmSJElSZZgMkyRJkiRJUmWYDJMkSZIkSVJlmAyTJEmSJElSZZgMkyRJkiRJUmWYDJMkSZIkSVJlmAyTJEmSJElSZZgMkyRJkiRJUmWYDJMkSZIkSVJlmAyTJEmSJElSZZgMkyRJkiRJUmWYDJMkSZIkSVJlDCw7AEmStGJ33XXXWwcMGHBeRJQdiiT1BZmZDyxatOjtu++++/yyg5EkrX1MhkmS1McNHDjwrB122GHLwYMHlx2KJPUJTz31VPPvfve7m8eNGzfyiiuueL7seCRJaxenSUqS1PdtbCJMkl7R1NQ0YMiQIcOAT40bN87/4JckrRKTYZIk9X29Oj+yvb2d1tZWWltb2WSTTRg5ciStra3MmjVrmXbf+c53VtjPI488wtixY5crHzZsGJ/97GeXvh4+fPgaxXvsscdyxx13rFEfnbngggvYY489OPvss5cpjwi++tWvLn19/PHHr/A6HnnkEX71q1+t8Fxreh/WBj5br2j0bJ1++umce+65ADz44IMMGDCA55+vDXqaOHEiF1xwAaeddhoPPPBAt8fT6FrPP/98Bg0axD/+8Q8AXnrpJTbeeGPOPPPMTvv5xS9+weOPP95p/fnnn7/C41dFRCSwKbBJt3QoSaoM/xdFkqS1yAEHHszf//HUGvfTvHkTs2bOaFg3YMAAZs+eDUBrayvTpk2jqalpuXbf+c53+NjHPrbK515//fWZPn06n/70p9lkk01W+fjukJlALam1IhdffDGXX345W2655TLlr3vd6/jNb37DJz7xCdra2lb4xz+8kgw75JBD1izwHjRqv7fwtyf+scb9bLnF5tww55qGdT5br2j0bO2zzz784Ac/4Pjjj+fGG29kv/3245ZbbmH06NHcfPPNvO9972Pbbbft0fg72n333Zk2bRof+9jHuPLKK9lpp51W2P4Xv/gFLS0tbLXVVr0UIYn/wS9JWkUmwyRJWov8/R9PMXnK9DXuZ+L40V1u+8ILL/DhD3+Yp556ivXWW48f/ehHXHrppTz88MO0trby0Y9+lPb2ds4991xeeOEFxowZwymnnNJpf+ussw4f+9jH+MY3vsEXv/jFpeWTJk1i+PDhHHHEEUybNo25c+cyadIktt12W/bdd1/uuOMOPvrRj3LzzTdz7733csIJJ3D88ccDteTJY489xoABA7jsssvYaKON+NrXvsbPfvYzFi1axOc+9znGjh1La2srI0aM4J577mHq1Km86lWvWnqNxxxzzDLXePXVV3PrrbdyxBFH8IUvfIExY8YsjXXQoEFss802PPzww9x3332MHj2a73//+wBccskly92Ls88+m9tuu425c+dyzjnn8Je//IUvfvGLDBkyhIMOOojPfvazvPzyy4wfP5577rmHd7/73Xzyk5/k2Wef5fjjj+eZZ55h3XXX5Uc/+hEDBw7kHe94x9JYrrzySjbYYIMuv5+d+dsT/2DPd31zjfu59cf/2eW2PlvLPltvetObliYBb775Zk466SRuuukmDjroIB5++GG23XZbjj32WCZMmMC//vUvvvKVr7DRRhvx+9//nm9/+9vst99+tLa28sY3vpF77rmHTTfdlJ/+9KcAfOpTn+L2229n0aJFnHXWWeyxxx785Cc/4dRTT2XYsGE8++yzDe/poYceyq9+9Ss+9rGPMW3aNI444oilCb9jjjmGRx99lBdeeIFvfvObbLrppvz617/mvvvuY5ddduGHP/whn/jEJ7jlllsYPHjw0tGUN998M4cddhgPP/wwU6dOZccdd2TWrFlMmjSJzOTAAw9k4sSJ/PKXv+SUU05hww03ZNSoUXzpS1/q8rMlSdKKmAyTJEkrdNlll7HPPvvwiU98gssuu4zTTjuNr33ta5x77rlLR/m88MILvOc97yEzGTVq1EpH9RxzzDHsueeenHTSSSs9/9///nfOPPNM1llnHbbccksefvhhNt54Y/bee++lCYudd96ZH/zgB0vjGjNmDDfeeCPXX389L7/8MqNGjVo6rW7UqFHLTdP6/ve/3/Aazz///E5HL73rXe/i0ksvZd68eZx55plLk2Fvf/vbl7sXH//4x5k2bRrf+ta3WLx4Me94xzu47rrr2GyzzWhvb196nWeccQYbbrghw4cP55Of/CSnnXYaxxxzDOPGjeOXv/wlZ511FgcddBA777wz3/jGN5YmJdZWPlvLPlvrrrsuG264IU8//TR//etfGTNmDFOmTGHevHkNp3y+8MIL/PrXv+buu+/my1/+Mvvttx8AY8aM4ayzzmLcuHHcd999PPbYY7S3tzN79myefPJJjjrqKGbOnMnEiRO5/fbbGTRoEDvvvHPDe7TeeuvR1NTE/fffz8KFC9lss8146qna6NTvfve7bLDBBjzwwAOceOKJXH311YwZM4YJEyYwYsQIrrzySp599lluvPFGoDZN9t5772XIkCFccskl/OxnP+O8887jzDPP5DOf+QyzZ89mgw024IgjjuCBBx7gJz/5Ceeccw5vfOMbWbx48UrfT0mSusohxZIkaYX+9Kc/sddeewG1aVyN1iuaNWsW+++/P62trfzxj3/kr3/96wr7HDRoEMcdd9wya0PVTyurT/Jss802DB06lE022YR///d/Z4sttmD99ddfmkQC2GOPPZZ+ffDBB5k3bx533303ra2tjBkzhn/961/Mnz8fgL333nu5eP7whz+s9Bo7am1tZcaMGTz//PNsscUWXb4XTz31FFtssQWbbbYZUJs6WH+dgwcPZskHJsydO5fTTz+d1tZWTj/9dJ555hn2228/Nt98c9773vfyuc99jkWLFq001r7KZ2t5e++9N1dffTWbbLIJAwcOZNGiRdxwww3ss88+y7V9wxveQESwzTbb8Mwzzywt32233ZZe3zPPPMPcuXOZPn06ra2tHHnkkfzzn//kqaeeYuutt2aDDTZg8ODBS49p5Mgjj+SYY47hsMMOW1rW3t7O5z//eUaNGsWHP/xhHnvsseWOu//++9l///2Xvl7yrHeM76mnnuJPf/oThx56KK2trTz00EM8+uijfPGLX+Scc87h6KOPXumae5IkrQpHhkmSpBV6zWtewy233MLIkSO56aabeN3rXgcsm2D4whe+wKxZs9h0000ZOXJkl0YsHXfccey11168/PLLAGy66aZL/6C+++67lyaE6s/T2TpMd9xxByNHjuSOO+5g2223ZYcddmCvvfbi4osvBqCtrW1pf0v+IK+33XbbNbzGFRkwYABHHXUUr371q5cpb3QvBg8evDRp1dTUxN///neeffZZNt10UxYvXsw666zT8Np23HFHDjzwQEaPHr30Otra2vj85z8PwPjx47n22ms5+OCDVxpvX+Sztbx99tmHU089lQ984AMA7LTTTnz/+9/nW9/61nJtO0vydSzfcccdGTduHF/5yleWxjxgwAD++te/8uKLLzJo0CDuueeeTmM6+OCDufTSSznssMP42c9+BsBvf/tbHnnkEW644QZ+//vfc8QRRwAs86zvtNNO/PznP+d973sfwNLRXR3ja2pq4nWvex1XX3016623HosXLyYzWbBgAeeccw4LFixg+PDhDT80QZKk1WEyTJIkrdB73vMeJkyYwOWXX866667LBRdcAMCuu+7K4Ycfzgc/+EHe/e53s//++7Pjjjuy/vrrd6nfddddl2OOOWbp2k5HHnkk48aNY+bMmWyxxRa0tLR0Ocb777+fgw46iHXWWYef/OQnbLzxxuyxxx7su+++DBgwgG222YYf/ehHnR7/oQ99iPe+973LXePKfPSjH12urNG9GD58OPPmzeOII47gjDPO4Gtf+xqHHHII66677tI1wxr57Gc/y4c//GHOOOMMFi9ezPHHH09LSwuf/exnGThwIOuttx577rlnl2Lti3y2ljdy5EjuvvvupdNu9957b77+9a8zYsSILsfc0aGHHsr1119Pa2srEcEee+zB6aefzhe/+EVGjhzJsGHDlkvq1hs0aBAXXnjhMmXbb789zzzzDAcccAAjR45cWn7IIYfwmc98ht13352zzjqLa665hn322Yd1112X//u//2vYf0Tw5S9/mbe+9a1EBAMHDuTiiy/mrLPO4qabbmLhwoWMHz9+ta9fkqSOYm1fa0KSpP7u3nvvfXyXXXbZEnrn0yRVTb3xaZJSd7r99tv/9aUvfelHwClXXHHFmj+8kqTKcGSYJElrERNY6ikmsCRJUlW4gL4kSZIkSZIqw2SYJEmSJEmSKsNkmCRJfZ8LfErS8vzeKElaLSbDJEnq+/7Z1tZWdgyS1Gc89dRTi+fPn/902XFIktZOLqAvSVIft2jRopPuvffeSxYvXjwoIhwJIanqcv78+U9PmTJlKjAUeLnsgCRJa5fI9HdqSZL6unHjxm0GfAZYH4iSw5GkvmAAcPUVV1xxedmBSJLWLibDJElaS4wbN24wtVEQLnMgSfAS8NwVV1zhHzSSpFViMkySJEmSJEmV4f8sS5IkSZIkqTJMhkmSJEmSJKkyTIZJkiRJkiSpMv4/DQYTjaT2ylwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualisation\n",
    "\n",
    "df_vis = df_game_stat.sort_values(by=['total_win'], ascending=False).head(20)\n",
    "\n",
    "sns.set_context('paper')\n",
    "\n",
    "f, ax = plt.subplots(figsize = (20,8))\n",
    "\n",
    "sns.set_color_codes('pastel')\n",
    "sns.barplot(x = 'total_count', y = 'team', data = df_vis,\n",
    "            label = 'Total Number of Matches', color = 'b', edgecolor = 'black')\n",
    "\n",
    "sns.set_color_codes('muted')\n",
    "sns.barplot(x = 'total_win', y = 'team', data = df_vis,\n",
    "            label = 'Total Number of Winned Matches', color = 'b', edgecolor = 'black')\n",
    "\n",
    "ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.1), shadow=True, ncol=2)\n",
    "sns.despine(left = True, bottom = True)\n",
    "plt.title(\"Top 20 national football teams with highest winning matches\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5867bbd4-9948-4748-b694-9eb910ae8375",
   "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>home_win</th>\n",
       "      <th>home_count</th>\n",
       "      <th>home_score</th>\n",
       "      <th>home_goal_diff</th>\n",
       "      <th>away_win</th>\n",
       "      <th>away_count</th>\n",
       "      <th>away_score</th>\n",
       "      <th>away_goal_diff</th>\n",
       "      <th>total_count</th>\n",
       "      <th>total_win</th>\n",
       "      <th>total_score</th>\n",
       "      <th>home_lose</th>\n",
       "      <th>away_lose</th>\n",
       "      <th>total_lose</th>\n",
       "      <th>home_win_rate</th>\n",
       "      <th>away_win_rate</th>\n",
       "      <th>total_win_rate</th>\n",
       "      <th>avg_home_goal_diff</th>\n",
       "      <th>avg_away_goal_diff</th>\n",
       "      <th>total_goal_diff</th>\n",
       "      <th>avg_total_goal_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yugoslavia</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.88</td>\n",
       "      <td>4.33</td>\n",
       "      <td>2.00</td>\n",
       "      <td>21</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spain</td>\n",
       "      <td>80</td>\n",
       "      <td>98</td>\n",
       "      <td>290</td>\n",
       "      <td>257</td>\n",
       "      <td>86</td>\n",
       "      <td>100</td>\n",
       "      <td>193</td>\n",
       "      <td>128</td>\n",
       "      <td>198</td>\n",
       "      <td>166</td>\n",
       "      <td>483</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>32</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.84</td>\n",
       "      <td>3.21</td>\n",
       "      <td>1.49</td>\n",
       "      <td>385</td>\n",
       "      <td>2.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Australia</td>\n",
       "      <td>72</td>\n",
       "      <td>88</td>\n",
       "      <td>334</td>\n",
       "      <td>298</td>\n",
       "      <td>47</td>\n",
       "      <td>61</td>\n",
       "      <td>107</td>\n",
       "      <td>81</td>\n",
       "      <td>149</td>\n",
       "      <td>119</td>\n",
       "      <td>441</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>30</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.80</td>\n",
       "      <td>4.14</td>\n",
       "      <td>1.72</td>\n",
       "      <td>379</td>\n",
       "      <td>3.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Italy</td>\n",
       "      <td>72</td>\n",
       "      <td>101</td>\n",
       "      <td>208</td>\n",
       "      <td>158</td>\n",
       "      <td>87</td>\n",
       "      <td>99</td>\n",
       "      <td>143</td>\n",
       "      <td>95</td>\n",
       "      <td>200</td>\n",
       "      <td>159</td>\n",
       "      <td>351</td>\n",
       "      <td>29</td>\n",
       "      <td>12</td>\n",
       "      <td>41</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.80</td>\n",
       "      <td>2.19</td>\n",
       "      <td>1.09</td>\n",
       "      <td>253</td>\n",
       "      <td>1.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>England</td>\n",
       "      <td>65</td>\n",
       "      <td>93</td>\n",
       "      <td>221</td>\n",
       "      <td>178</td>\n",
       "      <td>75</td>\n",
       "      <td>87</td>\n",
       "      <td>179</td>\n",
       "      <td>138</td>\n",
       "      <td>180</td>\n",
       "      <td>140</td>\n",
       "      <td>400</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>40</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.78</td>\n",
       "      <td>2.74</td>\n",
       "      <td>1.84</td>\n",
       "      <td>316</td>\n",
       "      <td>2.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Japan</td>\n",
       "      <td>59</td>\n",
       "      <td>84</td>\n",
       "      <td>228</td>\n",
       "      <td>184</td>\n",
       "      <td>68</td>\n",
       "      <td>80</td>\n",
       "      <td>166</td>\n",
       "      <td>127</td>\n",
       "      <td>164</td>\n",
       "      <td>127</td>\n",
       "      <td>394</td>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "      <td>37</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.77</td>\n",
       "      <td>3.12</td>\n",
       "      <td>1.87</td>\n",
       "      <td>311</td>\n",
       "      <td>2.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>78</td>\n",
       "      <td>103</td>\n",
       "      <td>206</td>\n",
       "      <td>162</td>\n",
       "      <td>66</td>\n",
       "      <td>84</td>\n",
       "      <td>97</td>\n",
       "      <td>58</td>\n",
       "      <td>187</td>\n",
       "      <td>144</td>\n",
       "      <td>303</td>\n",
       "      <td>25</td>\n",
       "      <td>18</td>\n",
       "      <td>43</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2.08</td>\n",
       "      <td>0.88</td>\n",
       "      <td>220</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>71</td>\n",
       "      <td>100</td>\n",
       "      <td>240</td>\n",
       "      <td>192</td>\n",
       "      <td>81</td>\n",
       "      <td>98</td>\n",
       "      <td>178</td>\n",
       "      <td>118</td>\n",
       "      <td>198</td>\n",
       "      <td>152</td>\n",
       "      <td>418</td>\n",
       "      <td>29</td>\n",
       "      <td>17</td>\n",
       "      <td>46</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1.46</td>\n",
       "      <td>310</td>\n",
       "      <td>2.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Germany</td>\n",
       "      <td>62</td>\n",
       "      <td>89</td>\n",
       "      <td>245</td>\n",
       "      <td>184</td>\n",
       "      <td>76</td>\n",
       "      <td>91</td>\n",
       "      <td>200</td>\n",
       "      <td>146</td>\n",
       "      <td>180</td>\n",
       "      <td>138</td>\n",
       "      <td>445</td>\n",
       "      <td>27</td>\n",
       "      <td>15</td>\n",
       "      <td>42</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2.97</td>\n",
       "      <td>1.92</td>\n",
       "      <td>330</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>72</td>\n",
       "      <td>94</td>\n",
       "      <td>258</td>\n",
       "      <td>218</td>\n",
       "      <td>53</td>\n",
       "      <td>70</td>\n",
       "      <td>108</td>\n",
       "      <td>72</td>\n",
       "      <td>164</td>\n",
       "      <td>125</td>\n",
       "      <td>366</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>39</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.76</td>\n",
       "      <td>3.03</td>\n",
       "      <td>1.36</td>\n",
       "      <td>290</td>\n",
       "      <td>2.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>South Korea</td>\n",
       "      <td>58</td>\n",
       "      <td>80</td>\n",
       "      <td>222</td>\n",
       "      <td>189</td>\n",
       "      <td>57</td>\n",
       "      <td>71</td>\n",
       "      <td>110</td>\n",
       "      <td>76</td>\n",
       "      <td>151</td>\n",
       "      <td>115</td>\n",
       "      <td>332</td>\n",
       "      <td>22</td>\n",
       "      <td>14</td>\n",
       "      <td>36</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.76</td>\n",
       "      <td>3.26</td>\n",
       "      <td>1.33</td>\n",
       "      <td>265</td>\n",
       "      <td>2.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Iran</td>\n",
       "      <td>64</td>\n",
       "      <td>95</td>\n",
       "      <td>237</td>\n",
       "      <td>199</td>\n",
       "      <td>79</td>\n",
       "      <td>93</td>\n",
       "      <td>202</td>\n",
       "      <td>151</td>\n",
       "      <td>188</td>\n",
       "      <td>143</td>\n",
       "      <td>439</td>\n",
       "      <td>31</td>\n",
       "      <td>14</td>\n",
       "      <td>45</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.76</td>\n",
       "      <td>3.11</td>\n",
       "      <td>1.91</td>\n",
       "      <td>350</td>\n",
       "      <td>2.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>France</td>\n",
       "      <td>59</td>\n",
       "      <td>90</td>\n",
       "      <td>201</td>\n",
       "      <td>152</td>\n",
       "      <td>74</td>\n",
       "      <td>85</td>\n",
       "      <td>127</td>\n",
       "      <td>82</td>\n",
       "      <td>175</td>\n",
       "      <td>133</td>\n",
       "      <td>328</td>\n",
       "      <td>31</td>\n",
       "      <td>11</td>\n",
       "      <td>42</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.76</td>\n",
       "      <td>2.58</td>\n",
       "      <td>1.11</td>\n",
       "      <td>234</td>\n",
       "      <td>1.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>131</td>\n",
       "      <td>117</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>41</td>\n",
       "      <td>30</td>\n",
       "      <td>70</td>\n",
       "      <td>53</td>\n",
       "      <td>172</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.76</td>\n",
       "      <td>3.66</td>\n",
       "      <td>1.43</td>\n",
       "      <td>147</td>\n",
       "      <td>2.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>75</td>\n",
       "      <td>99</td>\n",
       "      <td>266</td>\n",
       "      <td>225</td>\n",
       "      <td>66</td>\n",
       "      <td>88</td>\n",
       "      <td>177</td>\n",
       "      <td>127</td>\n",
       "      <td>187</td>\n",
       "      <td>141</td>\n",
       "      <td>443</td>\n",
       "      <td>24</td>\n",
       "      <td>22</td>\n",
       "      <td>46</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.92</td>\n",
       "      <td>352</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>73</td>\n",
       "      <td>103</td>\n",
       "      <td>230</td>\n",
       "      <td>185</td>\n",
       "      <td>46</td>\n",
       "      <td>59</td>\n",
       "      <td>100</td>\n",
       "      <td>64</td>\n",
       "      <td>162</td>\n",
       "      <td>119</td>\n",
       "      <td>330</td>\n",
       "      <td>30</td>\n",
       "      <td>13</td>\n",
       "      <td>43</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.73</td>\n",
       "      <td>2.53</td>\n",
       "      <td>1.39</td>\n",
       "      <td>249</td>\n",
       "      <td>2.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>79</td>\n",
       "      <td>103</td>\n",
       "      <td>247</td>\n",
       "      <td>196</td>\n",
       "      <td>60</td>\n",
       "      <td>87</td>\n",
       "      <td>96</td>\n",
       "      <td>53</td>\n",
       "      <td>190</td>\n",
       "      <td>139</td>\n",
       "      <td>343</td>\n",
       "      <td>24</td>\n",
       "      <td>27</td>\n",
       "      <td>51</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.73</td>\n",
       "      <td>2.48</td>\n",
       "      <td>0.88</td>\n",
       "      <td>249</td>\n",
       "      <td>1.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ivory Coast</td>\n",
       "      <td>68</td>\n",
       "      <td>99</td>\n",
       "      <td>228</td>\n",
       "      <td>169</td>\n",
       "      <td>75</td>\n",
       "      <td>98</td>\n",
       "      <td>122</td>\n",
       "      <td>72</td>\n",
       "      <td>197</td>\n",
       "      <td>143</td>\n",
       "      <td>350</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>54</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.73</td>\n",
       "      <td>2.49</td>\n",
       "      <td>0.96</td>\n",
       "      <td>241</td>\n",
       "      <td>1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Morocco</td>\n",
       "      <td>63</td>\n",
       "      <td>85</td>\n",
       "      <td>167</td>\n",
       "      <td>146</td>\n",
       "      <td>56</td>\n",
       "      <td>81</td>\n",
       "      <td>84</td>\n",
       "      <td>44</td>\n",
       "      <td>166</td>\n",
       "      <td>119</td>\n",
       "      <td>251</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>47</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.72</td>\n",
       "      <td>2.32</td>\n",
       "      <td>0.79</td>\n",
       "      <td>190</td>\n",
       "      <td>1.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>60</td>\n",
       "      <td>88</td>\n",
       "      <td>186</td>\n",
       "      <td>141</td>\n",
       "      <td>75</td>\n",
       "      <td>101</td>\n",
       "      <td>127</td>\n",
       "      <td>66</td>\n",
       "      <td>189</td>\n",
       "      <td>135</td>\n",
       "      <td>313</td>\n",
       "      <td>28</td>\n",
       "      <td>26</td>\n",
       "      <td>54</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.71</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0.88</td>\n",
       "      <td>207</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            team  home_win  home_count  home_score  home_goal_diff  away_win  \\\n",
       "0     Yugoslavia         3           4          16              13         4   \n",
       "1          Spain        80          98         290             257        86   \n",
       "2      Australia        72          88         334             298        47   \n",
       "3          Italy        72         101         208             158        87   \n",
       "4        England        65          93         221             178        75   \n",
       "5          Japan        59          84         228             184        68   \n",
       "6        Nigeria        78         103         206             162        66   \n",
       "7       Portugal        71         100         240             192        81   \n",
       "8        Germany        62          89         245             184        76   \n",
       "9   Saudi Arabia        72          94         258             218        53   \n",
       "10   South Korea        58          80         222             189        57   \n",
       "11          Iran        64          95         237             199        79   \n",
       "12        France        59          90         201             152        74   \n",
       "13   New Zealand        32          40         131             117        21   \n",
       "14   Netherlands        75          99         266             225        66   \n",
       "15        Brazil        73         103         230             185        46   \n",
       "16         Egypt        79         103         247             196        60   \n",
       "17   Ivory Coast        68          99         228             169        75   \n",
       "18       Morocco        63          85         167             146        56   \n",
       "19       Tunisia        60          88         186             141        75   \n",
       "\n",
       "    away_count  away_score  away_goal_diff  total_count  total_win  \\\n",
       "0            4           8               8            8          7   \n",
       "1          100         193             128          198        166   \n",
       "2           61         107              81          149        119   \n",
       "3           99         143              95          200        159   \n",
       "4           87         179             138          180        140   \n",
       "5           80         166             127          164        127   \n",
       "6           84          97              58          187        144   \n",
       "7           98         178             118          198        152   \n",
       "8           91         200             146          180        138   \n",
       "9           70         108              72          164        125   \n",
       "10          71         110              76          151        115   \n",
       "11          93         202             151          188        143   \n",
       "12          85         127              82          175        133   \n",
       "13          30          41              30           70         53   \n",
       "14          88         177             127          187        141   \n",
       "15          59         100              64          162        119   \n",
       "16          87          96              53          190        139   \n",
       "17          98         122              72          197        143   \n",
       "18          81          84              44          166        119   \n",
       "19         101         127              66          189        135   \n",
       "\n",
       "    total_score  home_lose  away_lose  total_lose  home_win_rate  \\\n",
       "0            24          1          0           1           0.75   \n",
       "1           483         18         14          32           0.82   \n",
       "2           441         16         14          30           0.82   \n",
       "3           351         29         12          41           0.71   \n",
       "4           400         28         12          40           0.70   \n",
       "5           394         25         12          37           0.70   \n",
       "6           303         25         18          43           0.76   \n",
       "7           418         29         17          46           0.71   \n",
       "8           445         27         15          42           0.70   \n",
       "9           366         22         17          39           0.77   \n",
       "10          332         22         14          36           0.72   \n",
       "11          439         31         14          45           0.67   \n",
       "12          328         31         11          42           0.66   \n",
       "13          172          8          9          17           0.80   \n",
       "14          443         24         22          46           0.76   \n",
       "15          330         30         13          43           0.71   \n",
       "16          343         24         27          51           0.77   \n",
       "17          350         31         23          54           0.69   \n",
       "18          251         22         25          47           0.74   \n",
       "19          313         28         26          54           0.68   \n",
       "\n",
       "    away_win_rate  total_win_rate  avg_home_goal_diff  avg_away_goal_diff  \\\n",
       "0            1.00            0.88                4.33                2.00   \n",
       "1            0.86            0.84                3.21                1.49   \n",
       "2            0.77            0.80                4.14                1.72   \n",
       "3            0.88            0.80                2.19                1.09   \n",
       "4            0.86            0.78                2.74                1.84   \n",
       "5            0.85            0.77                3.12                1.87   \n",
       "6            0.79            0.77                2.08                0.88   \n",
       "7            0.83            0.77                2.70                1.46   \n",
       "8            0.84            0.77                2.97                1.92   \n",
       "9            0.76            0.76                3.03                1.36   \n",
       "10           0.80            0.76                3.26                1.33   \n",
       "11           0.85            0.76                3.11                1.91   \n",
       "12           0.87            0.76                2.58                1.11   \n",
       "13           0.70            0.76                3.66                1.43   \n",
       "14           0.75            0.75                3.00                1.92   \n",
       "15           0.78            0.73                2.53                1.39   \n",
       "16           0.69            0.73                2.48                0.88   \n",
       "17           0.77            0.73                2.49                0.96   \n",
       "18           0.69            0.72                2.32                0.79   \n",
       "19           0.74            0.71                2.35                0.88   \n",
       "\n",
       "    total_goal_diff  avg_total_goal_diff  \n",
       "0                21                 3.00  \n",
       "1               385                 2.32  \n",
       "2               379                 3.18  \n",
       "3               253                 1.59  \n",
       "4               316                 2.26  \n",
       "5               311                 2.45  \n",
       "6               220                 1.53  \n",
       "7               310                 2.04  \n",
       "8               330                 2.39  \n",
       "9               290                 2.32  \n",
       "10              265                 2.30  \n",
       "11              350                 2.45  \n",
       "12              234                 1.76  \n",
       "13              147                 2.77  \n",
       "14              352                 2.50  \n",
       "15              249                 2.09  \n",
       "16              249                 1.79  \n",
       "17              241                 1.69  \n",
       "18              190                 1.60  \n",
       "19              207                 1.53  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show top 10 teams with highest win rate\n",
    "df_game_stat.sort_values(by=['total_win_rate'], ascending=False).head(20).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f64c994-415f-4bf8-955e-2dd1b27c3003",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Player Analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "10616cea-a85b-46ea-8745-b58c437d567b",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'E:\\\\COM480-DataVis\\\\datavis-project-2022-y3\\\\Data\\\\players_21_value.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/n3/gbdx6wws5kb_m82h5gt5pf440000gn/T/ipykernel_43918/3970394614.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_player\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'E:\\COM480-DataVis\\datavis-project-2022-y3\\Data\\players_21_value.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf_player\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 )\n\u001b[0;32m--> 311\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    584\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 482\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    484\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    809\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    810\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 811\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    812\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    813\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1038\u001b[0m             )\n\u001b[1;32m   1039\u001b[0m         \u001b[0;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1040\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1041\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1042\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/c_parser_wrapper.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0;31m# open handles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/base_parser.py\u001b[0m in \u001b[0;36m_open_handles\u001b[0;34m(self, src, kwds)\u001b[0m\n\u001b[1;32m    220\u001b[0m         \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m         \"\"\"\n\u001b[0;32m--> 222\u001b[0;31m         self.handles = get_handle(\n\u001b[0m\u001b[1;32m    223\u001b[0m             \u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m             \u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    700\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    701\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    703\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    704\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'E:\\\\COM480-DataVis\\\\datavis-project-2022-y3\\\\Data\\\\players_21_value.csv'"
     ]
    }
   ],
   "source": [
    "df_player = pd.read_csv('E:\\COM480-DataVis\\datavis-project-2022-y3\\Data\\players_21_value.csv')\n",
    "df_player.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9305b150-2ca3-4474-881f-9b15cf680631",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sofifa_id',\n",
       " 'player_url',\n",
       " 'short_name',\n",
       " 'long_name',\n",
       " 'player_positions',\n",
       " 'overall',\n",
       " 'potential',\n",
       " 'value_eur',\n",
       " 'wage_eur',\n",
       " 'age',\n",
       " 'dob',\n",
       " 'height_cm',\n",
       " 'weight_kg',\n",
       " 'club_team_id',\n",
       " 'club_name',\n",
       " 'league_name',\n",
       " 'league_level',\n",
       " 'club_position',\n",
       " 'club_jersey_number',\n",
       " 'club_loaned_from',\n",
       " 'club_joined',\n",
       " 'club_contract_valid_until',\n",
       " 'nationality_id',\n",
       " 'nationality_name',\n",
       " 'nation_team_id',\n",
       " 'nation_position',\n",
       " 'nation_jersey_number',\n",
       " 'preferred_foot',\n",
       " 'weak_foot',\n",
       " 'skill_moves',\n",
       " 'international_reputation',\n",
       " 'work_rate',\n",
       " 'body_type',\n",
       " 'real_face',\n",
       " 'release_clause_eur',\n",
       " 'player_tags',\n",
       " 'player_traits',\n",
       " 'pace',\n",
       " 'shooting',\n",
       " 'passing',\n",
       " 'dribbling',\n",
       " 'defending',\n",
       " 'physic',\n",
       " 'attacking_crossing',\n",
       " 'attacking_finishing',\n",
       " 'attacking_heading_accuracy',\n",
       " 'attacking_short_passing',\n",
       " 'attacking_volleys',\n",
       " 'skill_dribbling',\n",
       " 'skill_curve',\n",
       " 'skill_fk_accuracy',\n",
       " 'skill_long_passing',\n",
       " 'skill_ball_control',\n",
       " 'movement_acceleration',\n",
       " 'movement_sprint_speed',\n",
       " 'movement_agility',\n",
       " 'movement_reactions',\n",
       " 'movement_balance',\n",
       " 'power_shot_power',\n",
       " 'power_jumping',\n",
       " 'power_stamina',\n",
       " 'power_strength',\n",
       " 'power_long_shots',\n",
       " 'mentality_aggression',\n",
       " 'mentality_interceptions',\n",
       " 'mentality_positioning',\n",
       " 'mentality_vision',\n",
       " 'mentality_penalties',\n",
       " 'mentality_composure',\n",
       " 'defending_marking_awareness',\n",
       " 'defending_standing_tackle',\n",
       " 'defending_sliding_tackle',\n",
       " 'goalkeeping_diving',\n",
       " 'goalkeeping_handling',\n",
       " 'goalkeeping_kicking',\n",
       " 'goalkeeping_positioning',\n",
       " 'goalkeeping_reflexes',\n",
       " 'goalkeeping_speed',\n",
       " 'ls',\n",
       " 'st',\n",
       " 'rs',\n",
       " 'lw',\n",
       " 'lf',\n",
       " 'cf',\n",
       " 'rf',\n",
       " 'rw',\n",
       " 'lam',\n",
       " 'cam',\n",
       " 'ram',\n",
       " 'lm',\n",
       " 'lcm',\n",
       " 'cm',\n",
       " 'rcm',\n",
       " 'rm',\n",
       " 'lwb',\n",
       " 'ldm',\n",
       " 'cdm',\n",
       " 'rdm',\n",
       " 'rwb',\n",
       " 'lb',\n",
       " 'lcb',\n",
       " 'cb',\n",
       " 'rcb',\n",
       " 'rb',\n",
       " 'gk',\n",
       " 'player_face_url',\n",
       " 'club_logo_url',\n",
       " 'club_flag_url',\n",
       " 'nation_logo_url',\n",
       " 'nation_flag_url']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df_player.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b9715f21-8410-4936-9e8d-5d12076a5f3d",
   "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>sofifa_id</th>\n",
       "      <th>long_name</th>\n",
       "      <th>player_positions</th>\n",
       "      <th>overall</th>\n",
       "      <th>potential</th>\n",
       "      <th>value_eur</th>\n",
       "      <th>wage_eur</th>\n",
       "      <th>age</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>club_name</th>\n",
       "      <th>league_name</th>\n",
       "      <th>league_level</th>\n",
       "      <th>club_position</th>\n",
       "      <th>club_jersey_number</th>\n",
       "      <th>nationality_name</th>\n",
       "      <th>nation_position</th>\n",
       "      <th>nation_jersey_number</th>\n",
       "      <th>preferred_foot</th>\n",
       "      <th>weak_foot</th>\n",
       "      <th>...</th>\n",
       "      <th>power_shot_power</th>\n",
       "      <th>power_jumping</th>\n",
       "      <th>power_stamina</th>\n",
       "      <th>power_strength</th>\n",
       "      <th>power_long_shots</th>\n",
       "      <th>mentality_aggression</th>\n",
       "      <th>mentality_interceptions</th>\n",
       "      <th>mentality_positioning</th>\n",
       "      <th>mentality_vision</th>\n",
       "      <th>mentality_penalties</th>\n",
       "      <th>mentality_composure</th>\n",
       "      <th>defending_marking_awareness</th>\n",
       "      <th>defending_standing_tackle</th>\n",
       "      <th>defending_sliding_tackle</th>\n",
       "      <th>goalkeeping_diving</th>\n",
       "      <th>goalkeeping_handling</th>\n",
       "      <th>goalkeeping_kicking</th>\n",
       "      <th>goalkeeping_positioning</th>\n",
       "      <th>goalkeeping_reflexes</th>\n",
       "      <th>goalkeeping_speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>158023</td>\n",
       "      <td>Lionel Andrés Messi Cuccittini</td>\n",
       "      <td>RW, ST, CF</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>103500000.00</td>\n",
       "      <td>560000.00</td>\n",
       "      <td>33</td>\n",
       "      <td>170</td>\n",
       "      <td>72</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>Spain Primera Division</td>\n",
       "      <td>1.00</td>\n",
       "      <td>CAM</td>\n",
       "      <td>10.00</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>RW</td>\n",
       "      <td>10.00</td>\n",
       "      <td>Left</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>86</td>\n",
       "      <td>68</td>\n",
       "      <td>72</td>\n",
       "      <td>69</td>\n",
       "      <td>94</td>\n",
       "      <td>44</td>\n",
       "      <td>40</td>\n",
       "      <td>93</td>\n",
       "      <td>95</td>\n",
       "      <td>75</td>\n",
       "      <td>96</td>\n",
       "      <td>32</td>\n",
       "      <td>35</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20801</td>\n",
       "      <td>Cristiano Ronaldo dos Santos Aveiro</td>\n",
       "      <td>ST, LW</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>63000000.00</td>\n",
       "      <td>220000.00</td>\n",
       "      <td>35</td>\n",
       "      <td>187</td>\n",
       "      <td>83</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>Italian Serie A</td>\n",
       "      <td>1.00</td>\n",
       "      <td>LS</td>\n",
       "      <td>7.00</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>LS</td>\n",
       "      <td>7.00</td>\n",
       "      <td>Right</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>94</td>\n",
       "      <td>95</td>\n",
       "      <td>84</td>\n",
       "      <td>78</td>\n",
       "      <td>93</td>\n",
       "      <td>63</td>\n",
       "      <td>29</td>\n",
       "      <td>95</td>\n",
       "      <td>82</td>\n",
       "      <td>84</td>\n",
       "      <td>95</td>\n",
       "      <td>28</td>\n",
       "      <td>32</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>188545</td>\n",
       "      <td>Robert Lewandowski</td>\n",
       "      <td>ST</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>111000000.00</td>\n",
       "      <td>240000.00</td>\n",
       "      <td>31</td>\n",
       "      <td>184</td>\n",
       "      <td>80</td>\n",
       "      <td>FC Bayern München</td>\n",
       "      <td>German 1. Bundesliga</td>\n",
       "      <td>1.00</td>\n",
       "      <td>ST</td>\n",
       "      <td>9.00</td>\n",
       "      <td>Poland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Right</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>89</td>\n",
       "      <td>84</td>\n",
       "      <td>76</td>\n",
       "      <td>86</td>\n",
       "      <td>85</td>\n",
       "      <td>81</td>\n",
       "      <td>49</td>\n",
       "      <td>94</td>\n",
       "      <td>79</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>35</td>\n",
       "      <td>42</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>190871</td>\n",
       "      <td>Neymar da Silva Santos Júnior</td>\n",
       "      <td>LW, CAM</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>132000000.00</td>\n",
       "      <td>270000.00</td>\n",
       "      <td>28</td>\n",
       "      <td>175</td>\n",
       "      <td>68</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>French Ligue 1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>LW</td>\n",
       "      <td>10.00</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Right</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>80</td>\n",
       "      <td>62</td>\n",
       "      <td>81</td>\n",
       "      <td>50</td>\n",
       "      <td>84</td>\n",
       "      <td>51</td>\n",
       "      <td>36</td>\n",
       "      <td>87</td>\n",
       "      <td>90</td>\n",
       "      <td>92</td>\n",
       "      <td>93</td>\n",
       "      <td>35</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>192985</td>\n",
       "      <td>Kevin De Bruyne</td>\n",
       "      <td>CAM, CM</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>129000000.00</td>\n",
       "      <td>370000.00</td>\n",
       "      <td>29</td>\n",
       "      <td>181</td>\n",
       "      <td>70</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>English Premier League</td>\n",
       "      <td>1.00</td>\n",
       "      <td>RCM</td>\n",
       "      <td>17.00</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>RCM</td>\n",
       "      <td>7.00</td>\n",
       "      <td>Right</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>91</td>\n",
       "      <td>63</td>\n",
       "      <td>89</td>\n",
       "      <td>74</td>\n",
       "      <td>91</td>\n",
       "      <td>76</td>\n",
       "      <td>66</td>\n",
       "      <td>88</td>\n",
       "      <td>94</td>\n",
       "      <td>84</td>\n",
       "      <td>91</td>\n",
       "      <td>68</td>\n",
       "      <td>65</td>\n",
       "      <td>53</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   sofifa_id                            long_name player_positions  overall  \\\n",
       "0     158023       Lionel Andrés Messi Cuccittini       RW, ST, CF       93   \n",
       "1      20801  Cristiano Ronaldo dos Santos Aveiro           ST, LW       92   \n",
       "2     188545                   Robert Lewandowski               ST       91   \n",
       "3     190871        Neymar da Silva Santos Júnior          LW, CAM       91   \n",
       "4     192985                      Kevin De Bruyne          CAM, CM       91   \n",
       "\n",
       "   potential    value_eur  wage_eur  age  height_cm  weight_kg  \\\n",
       "0         93 103500000.00 560000.00   33        170         72   \n",
       "1         92  63000000.00 220000.00   35        187         83   \n",
       "2         91 111000000.00 240000.00   31        184         80   \n",
       "3         91 132000000.00 270000.00   28        175         68   \n",
       "4         91 129000000.00 370000.00   29        181         70   \n",
       "\n",
       "             club_name             league_name  league_level club_position  \\\n",
       "0         FC Barcelona  Spain Primera Division          1.00           CAM   \n",
       "1             Juventus         Italian Serie A          1.00            LS   \n",
       "2    FC Bayern München    German 1. Bundesliga          1.00            ST   \n",
       "3  Paris Saint-Germain          French Ligue 1          1.00            LW   \n",
       "4      Manchester City  English Premier League          1.00           RCM   \n",
       "\n",
       "   club_jersey_number nationality_name nation_position  nation_jersey_number  \\\n",
       "0               10.00        Argentina              RW                 10.00   \n",
       "1                7.00         Portugal              LS                  7.00   \n",
       "2                9.00           Poland             NaN                   NaN   \n",
       "3               10.00           Brazil             NaN                   NaN   \n",
       "4               17.00          Belgium             RCM                  7.00   \n",
       "\n",
       "  preferred_foot  weak_foot  ...  power_shot_power power_jumping  \\\n",
       "0           Left          4  ...                86            68   \n",
       "1          Right          4  ...                94            95   \n",
       "2          Right          4  ...                89            84   \n",
       "3          Right          5  ...                80            62   \n",
       "4          Right          5  ...                91            63   \n",
       "\n",
       "  power_stamina power_strength  power_long_shots  mentality_aggression  \\\n",
       "0            72             69                94                    44   \n",
       "1            84             78                93                    63   \n",
       "2            76             86                85                    81   \n",
       "3            81             50                84                    51   \n",
       "4            89             74                91                    76   \n",
       "\n",
       "   mentality_interceptions  mentality_positioning  mentality_vision  \\\n",
       "0                       40                     93                95   \n",
       "1                       29                     95                82   \n",
       "2                       49                     94                79   \n",
       "3                       36                     87                90   \n",
       "4                       66                     88                94   \n",
       "\n",
       "   mentality_penalties  mentality_composure  defending_marking_awareness  \\\n",
       "0                   75                   96                           32   \n",
       "1                   84                   95                           28   \n",
       "2                   88                   88                           35   \n",
       "3                   92                   93                           35   \n",
       "4                   84                   91                           68   \n",
       "\n",
       "   defending_standing_tackle  defending_sliding_tackle  goalkeeping_diving  \\\n",
       "0                         35                        24                   6   \n",
       "1                         32                        24                   7   \n",
       "2                         42                        19                  15   \n",
       "3                         30                        29                   9   \n",
       "4                         65                        53                  15   \n",
       "\n",
       "   goalkeeping_handling  goalkeeping_kicking  goalkeeping_positioning  \\\n",
       "0                    11                   15                       14   \n",
       "1                    11                   15                       14   \n",
       "2                     6                   12                        8   \n",
       "3                     9                   15                       15   \n",
       "4                    13                    5                       10   \n",
       "\n",
       "   goalkeeping_reflexes  goalkeeping_speed  \n",
       "0                     8                NaN  \n",
       "1                    11                NaN  \n",
       "2                    10                NaN  \n",
       "3                    11                NaN  \n",
       "4                    13                NaN  \n",
       "\n",
       "[5 rows x 65 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player_attr = ['sofifa_id','long_name','player_positions','overall','potential','value_eur','wage_eur','age','height_cm','weight_kg',\n",
    "               'club_name', 'league_name', 'league_level', 'club_position', 'club_jersey_number', 'nationality_name', 'nation_position', 'nation_jersey_number',\n",
    "               'preferred_foot', 'weak_foot', 'skill_moves', 'body_type', 'real_face', 'player_tags', 'pace', 'shooting', 'passing', 'dribbling', 'defending', 'physic',\n",
    "               'attacking_crossing', 'attacking_finishing', 'attacking_heading_accuracy', 'attacking_short_passing', 'attacking_volleys',\n",
    "               'skill_dribbling', 'skill_curve', 'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control',\n",
    "               'movement_acceleration', 'movement_sprint_speed', 'movement_agility', 'movement_reactions', 'movement_balance', \n",
    "               'power_shot_power', 'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots',\n",
    "               'mentality_aggression', 'mentality_interceptions', 'mentality_positioning', 'mentality_vision', 'mentality_penalties', 'mentality_composure',\n",
    "               'defending_marking_awareness', 'defending_standing_tackle', 'defending_sliding_tackle',\n",
    "               'goalkeeping_diving', 'goalkeeping_handling', 'goalkeeping_kicking', 'goalkeeping_positioning', 'goalkeeping_reflexes', 'goalkeeping_speed']\n",
    "\n",
    "df_player = df_player[player_attr]\n",
    "df_player.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "42473d66-9d1a-4c35-91d4-24902d667227",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop players where value_eur or wage_eur is missing\n",
    "df_player = df_player.dropna(subset=['value_eur', 'wage_eur'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9a06c406-e5ed-4a59-adad-ce7b1baea70b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter nation with more than 20 registered players (number of players in the national squad)\n",
    "df_player = df_player.groupby('nationality_name').filter(lambda x: x['nationality_name'].shape[0] > 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a24f3924-4d11-497b-9a2c-27d3c0b72d7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "d_player_value_wage = df_player.groupby('nationality_name').describe()[['value_eur','wage_eur']]\n",
    "d_player_value_wage.columns = d_player_value_wage.columns.get_level_values(0) + '_' + d_player_value_wage.columns.get_level_values(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "45ddecd7-9133-4a1f-bad2-42f51d779aad",
   "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>value_eur_count</th>\n",
       "      <th>value_eur_mean</th>\n",
       "      <th>value_eur_std</th>\n",
       "      <th>value_eur_min</th>\n",
       "      <th>value_eur_25%</th>\n",
       "      <th>value_eur_50%</th>\n",
       "      <th>value_eur_75%</th>\n",
       "      <th>value_eur_max</th>\n",
       "      <th>wage_eur_count</th>\n",
       "      <th>wage_eur_mean</th>\n",
       "      <th>wage_eur_std</th>\n",
       "      <th>wage_eur_min</th>\n",
       "      <th>wage_eur_25%</th>\n",
       "      <th>wage_eur_50%</th>\n",
       "      <th>wage_eur_75%</th>\n",
       "      <th>wage_eur_max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nationality_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>England</th>\n",
       "      <td>1683.00</td>\n",
       "      <td>2304447.42</td>\n",
       "      <td>7785459.69</td>\n",
       "      <td>25000.00</td>\n",
       "      <td>325000.00</td>\n",
       "      <td>600000.00</td>\n",
       "      <td>1400000.00</td>\n",
       "      <td>124000000.00</td>\n",
       "      <td>1683.00</td>\n",
       "      <td>9622.10</td>\n",
       "      <td>19388.86</td>\n",
       "      <td>500.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>7000.00</td>\n",
       "      <td>270000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1189.00</td>\n",
       "      <td>3018292.68</td>\n",
       "      <td>9138420.96</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>550000.00</td>\n",
       "      <td>975000.00</td>\n",
       "      <td>1900000.00</td>\n",
       "      <td>121000000.00</td>\n",
       "      <td>1189.00</td>\n",
       "      <td>9141.38</td>\n",
       "      <td>20156.54</td>\n",
       "      <td>500.00</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>310000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>1071.00</td>\n",
       "      <td>5237338.94</td>\n",
       "      <td>9543119.44</td>\n",
       "      <td>60000.00</td>\n",
       "      <td>925000.00</td>\n",
       "      <td>1700000.00</td>\n",
       "      <td>4100000.00</td>\n",
       "      <td>66500000.00</td>\n",
       "      <td>1071.00</td>\n",
       "      <td>14963.59</td>\n",
       "      <td>27094.08</td>\n",
       "      <td>500.00</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>18000.00</td>\n",
       "      <td>300000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>984.00</td>\n",
       "      <td>4761519.31</td>\n",
       "      <td>11746459.16</td>\n",
       "      <td>35000.00</td>\n",
       "      <td>600000.00</td>\n",
       "      <td>1200000.00</td>\n",
       "      <td>3000000.00</td>\n",
       "      <td>185500000.00</td>\n",
       "      <td>984.00</td>\n",
       "      <td>13652.39</td>\n",
       "      <td>27923.96</td>\n",
       "      <td>500.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>350000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>936.00</td>\n",
       "      <td>3453344.02</td>\n",
       "      <td>8488579.50</td>\n",
       "      <td>50000.00</td>\n",
       "      <td>600000.00</td>\n",
       "      <td>1300000.00</td>\n",
       "      <td>2800000.00</td>\n",
       "      <td>109000000.00</td>\n",
       "      <td>936.00</td>\n",
       "      <td>10005.02</td>\n",
       "      <td>25542.22</td>\n",
       "      <td>500.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>11000.00</td>\n",
       "      <td>560000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>884.00</td>\n",
       "      <td>5575113.12</td>\n",
       "      <td>11358419.07</td>\n",
       "      <td>50000.00</td>\n",
       "      <td>1100000.00</td>\n",
       "      <td>1900000.00</td>\n",
       "      <td>4300000.00</td>\n",
       "      <td>132000000.00</td>\n",
       "      <td>884.00</td>\n",
       "      <td>16683.31</td>\n",
       "      <td>26678.13</td>\n",
       "      <td>500.00</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>9000.00</td>\n",
       "      <td>19000.00</td>\n",
       "      <td>310000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>486.00</td>\n",
       "      <td>1113631.69</td>\n",
       "      <td>1542665.96</td>\n",
       "      <td>35000.00</td>\n",
       "      <td>406250.00</td>\n",
       "      <td>775000.00</td>\n",
       "      <td>1300000.00</td>\n",
       "      <td>14000000.00</td>\n",
       "      <td>486.00</td>\n",
       "      <td>3750.62</td>\n",
       "      <td>6403.56</td>\n",
       "      <td>500.00</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>79000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>432.00</td>\n",
       "      <td>4270011.57</td>\n",
       "      <td>10823959.64</td>\n",
       "      <td>80000.00</td>\n",
       "      <td>675000.00</td>\n",
       "      <td>1200000.00</td>\n",
       "      <td>2900000.00</td>\n",
       "      <td>113000000.00</td>\n",
       "      <td>432.00</td>\n",
       "      <td>11046.30</td>\n",
       "      <td>22381.63</td>\n",
       "      <td>500.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>11000.00</td>\n",
       "      <td>210000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>421.00</td>\n",
       "      <td>4748040.38</td>\n",
       "      <td>9736905.98</td>\n",
       "      <td>45000.00</td>\n",
       "      <td>775000.00</td>\n",
       "      <td>1600000.00</td>\n",
       "      <td>3400000.00</td>\n",
       "      <td>93500000.00</td>\n",
       "      <td>421.00</td>\n",
       "      <td>15142.16</td>\n",
       "      <td>21674.06</td>\n",
       "      <td>500.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>18000.00</td>\n",
       "      <td>135000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>378.00</td>\n",
       "      <td>1304603.17</td>\n",
       "      <td>2672690.18</td>\n",
       "      <td>90000.00</td>\n",
       "      <td>356250.00</td>\n",
       "      <td>700000.00</td>\n",
       "      <td>1300000.00</td>\n",
       "      <td>41500000.00</td>\n",
       "      <td>378.00</td>\n",
       "      <td>4026.98</td>\n",
       "      <td>8452.07</td>\n",
       "      <td>500.00</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>83000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>369.00</td>\n",
       "      <td>1387439.02</td>\n",
       "      <td>2955715.63</td>\n",
       "      <td>50000.00</td>\n",
       "      <td>350000.00</td>\n",
       "      <td>700000.00</td>\n",
       "      <td>1400000.00</td>\n",
       "      <td>35000000.00</td>\n",
       "      <td>369.00</td>\n",
       "      <td>4180.35</td>\n",
       "      <td>9109.70</td>\n",
       "      <td>500.00</td>\n",
       "      <td>700.00</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>100000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China PR</th>\n",
       "      <td>363.00</td>\n",
       "      <td>584517.91</td>\n",
       "      <td>1738076.54</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>110000.00</td>\n",
       "      <td>250000.00</td>\n",
       "      <td>525000.00</td>\n",
       "      <td>21000000.00</td>\n",
       "      <td>363.00</td>\n",
       "      <td>3444.77</td>\n",
       "      <td>3804.62</td>\n",
       "      <td>500.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>35000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>361.00</td>\n",
       "      <td>2184265.93</td>\n",
       "      <td>4530277.18</td>\n",
       "      <td>60000.00</td>\n",
       "      <td>550000.00</td>\n",
       "      <td>1000000.00</td>\n",
       "      <td>1900000.00</td>\n",
       "      <td>45000000.00</td>\n",
       "      <td>361.00</td>\n",
       "      <td>9563.99</td>\n",
       "      <td>12828.28</td>\n",
       "      <td>500.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>11000.00</td>\n",
       "      <td>140000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>360.00</td>\n",
       "      <td>1633652.78</td>\n",
       "      <td>6154704.37</td>\n",
       "      <td>60000.00</td>\n",
       "      <td>350000.00</td>\n",
       "      <td>700000.00</td>\n",
       "      <td>1300000.00</td>\n",
       "      <td>92000000.00</td>\n",
       "      <td>360.00</td>\n",
       "      <td>3727.78</td>\n",
       "      <td>10384.78</td>\n",
       "      <td>500.00</td>\n",
       "      <td>650.00</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>135000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>358.00</td>\n",
       "      <td>1763240.22</td>\n",
       "      <td>7042157.94</td>\n",
       "      <td>50000.00</td>\n",
       "      <td>350000.00</td>\n",
       "      <td>625000.00</td>\n",
       "      <td>1300000.00</td>\n",
       "      <td>111000000.00</td>\n",
       "      <td>358.00</td>\n",
       "      <td>5311.03</td>\n",
       "      <td>16588.73</td>\n",
       "      <td>500.00</td>\n",
       "      <td>850.00</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>240000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>353.00</td>\n",
       "      <td>6137577.90</td>\n",
       "      <td>12123955.28</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>925000.00</td>\n",
       "      <td>1800000.00</td>\n",
       "      <td>5000000.00</td>\n",
       "      <td>105500000.00</td>\n",
       "      <td>353.00</td>\n",
       "      <td>14238.10</td>\n",
       "      <td>28521.64</td>\n",
       "      <td>500.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>13000.00</td>\n",
       "      <td>230000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>344.00</td>\n",
       "      <td>1790959.30</td>\n",
       "      <td>3244755.57</td>\n",
       "      <td>70000.00</td>\n",
       "      <td>425000.00</td>\n",
       "      <td>825000.00</td>\n",
       "      <td>1500000.00</td>\n",
       "      <td>29000000.00</td>\n",
       "      <td>344.00</td>\n",
       "      <td>9411.63</td>\n",
       "      <td>12640.90</td>\n",
       "      <td>500.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>4500.00</td>\n",
       "      <td>9250.00</td>\n",
       "      <td>71000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korea Republic</th>\n",
       "      <td>340.00</td>\n",
       "      <td>1088617.65</td>\n",
       "      <td>4727854.01</td>\n",
       "      <td>70000.00</td>\n",
       "      <td>300000.00</td>\n",
       "      <td>500000.00</td>\n",
       "      <td>1000000.00</td>\n",
       "      <td>85000000.00</td>\n",
       "      <td>340.00</td>\n",
       "      <td>3301.32</td>\n",
       "      <td>9792.62</td>\n",
       "      <td>500.00</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>165000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Republic of Ireland</th>\n",
       "      <td>338.00</td>\n",
       "      <td>902278.11</td>\n",
       "      <td>2002160.37</td>\n",
       "      <td>25000.00</td>\n",
       "      <td>220000.00</td>\n",
       "      <td>400000.00</td>\n",
       "      <td>800000.00</td>\n",
       "      <td>23500000.00</td>\n",
       "      <td>338.00</td>\n",
       "      <td>4660.06</td>\n",
       "      <td>10881.85</td>\n",
       "      <td>500.00</td>\n",
       "      <td>500.00</td>\n",
       "      <td>875.00</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>95000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colombia</th>\n",
       "      <td>336.00</td>\n",
       "      <td>2744851.19</td>\n",
       "      <td>4714591.02</td>\n",
       "      <td>35000.00</td>\n",
       "      <td>525000.00</td>\n",
       "      <td>1200000.00</td>\n",
       "      <td>2550000.00</td>\n",
       "      <td>36500000.00</td>\n",
       "      <td>336.00</td>\n",
       "      <td>7105.36</td>\n",
       "      <td>14190.79</td>\n",
       "      <td>500.00</td>\n",
       "      <td>600.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>7000.00</td>\n",
       "      <td>105000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     value_eur_count  value_eur_mean  value_eur_std  \\\n",
       "nationality_name                                                      \n",
       "England                      1683.00      2304447.42     7785459.69   \n",
       "Germany                      1189.00      3018292.68     9138420.96   \n",
       "Spain                        1071.00      5237338.94     9543119.44   \n",
       "France                        984.00      4761519.31    11746459.16   \n",
       "Argentina                     936.00      3453344.02     8488579.50   \n",
       "Brazil                        884.00      5575113.12    11358419.07   \n",
       "Japan                         486.00      1113631.69     1542665.96   \n",
       "Netherlands                   432.00      4270011.57    10823959.64   \n",
       "Italy                         421.00      4748040.38     9736905.98   \n",
       "United States                 378.00      1304603.17     2672690.18   \n",
       "Sweden                        369.00      1387439.02     2955715.63   \n",
       "China PR                      363.00       584517.91     1738076.54   \n",
       "Mexico                        361.00      2184265.93     4530277.18   \n",
       "Norway                        360.00      1633652.78     6154704.37   \n",
       "Poland                        358.00      1763240.22     7042157.94   \n",
       "Portugal                      353.00      6137577.90    12123955.28   \n",
       "Turkey                        344.00      1790959.30     3244755.57   \n",
       "Korea Republic                340.00      1088617.65     4727854.01   \n",
       "Republic of Ireland           338.00       902278.11     2002160.37   \n",
       "Colombia                      336.00      2744851.19     4714591.02   \n",
       "\n",
       "                     value_eur_min  value_eur_25%  value_eur_50%  \\\n",
       "nationality_name                                                   \n",
       "England                   25000.00      325000.00      600000.00   \n",
       "Germany                   15000.00      550000.00      975000.00   \n",
       "Spain                     60000.00      925000.00     1700000.00   \n",
       "France                    35000.00      600000.00     1200000.00   \n",
       "Argentina                 50000.00      600000.00     1300000.00   \n",
       "Brazil                    50000.00     1100000.00     1900000.00   \n",
       "Japan                     35000.00      406250.00      775000.00   \n",
       "Netherlands               80000.00      675000.00     1200000.00   \n",
       "Italy                     45000.00      775000.00     1600000.00   \n",
       "United States             90000.00      356250.00      700000.00   \n",
       "Sweden                    50000.00      350000.00      700000.00   \n",
       "China PR                  15000.00      110000.00      250000.00   \n",
       "Mexico                    60000.00      550000.00     1000000.00   \n",
       "Norway                    60000.00      350000.00      700000.00   \n",
       "Poland                    50000.00      350000.00      625000.00   \n",
       "Portugal                 100000.00      925000.00     1800000.00   \n",
       "Turkey                    70000.00      425000.00      825000.00   \n",
       "Korea Republic            70000.00      300000.00      500000.00   \n",
       "Republic of Ireland       25000.00      220000.00      400000.00   \n",
       "Colombia                  35000.00      525000.00     1200000.00   \n",
       "\n",
       "                     value_eur_75%  value_eur_max  wage_eur_count  \\\n",
       "nationality_name                                                    \n",
       "England                 1400000.00   124000000.00         1683.00   \n",
       "Germany                 1900000.00   121000000.00         1189.00   \n",
       "Spain                   4100000.00    66500000.00         1071.00   \n",
       "France                  3000000.00   185500000.00          984.00   \n",
       "Argentina               2800000.00   109000000.00          936.00   \n",
       "Brazil                  4300000.00   132000000.00          884.00   \n",
       "Japan                   1300000.00    14000000.00          486.00   \n",
       "Netherlands             2900000.00   113000000.00          432.00   \n",
       "Italy                   3400000.00    93500000.00          421.00   \n",
       "United States           1300000.00    41500000.00          378.00   \n",
       "Sweden                  1400000.00    35000000.00          369.00   \n",
       "China PR                 525000.00    21000000.00          363.00   \n",
       "Mexico                  1900000.00    45000000.00          361.00   \n",
       "Norway                  1300000.00    92000000.00          360.00   \n",
       "Poland                  1300000.00   111000000.00          358.00   \n",
       "Portugal                5000000.00   105500000.00          353.00   \n",
       "Turkey                  1500000.00    29000000.00          344.00   \n",
       "Korea Republic          1000000.00    85000000.00          340.00   \n",
       "Republic of Ireland      800000.00    23500000.00          338.00   \n",
       "Colombia                2550000.00    36500000.00          336.00   \n",
       "\n",
       "                     wage_eur_mean  wage_eur_std  wage_eur_min  wage_eur_25%  \\\n",
       "nationality_name                                                               \n",
       "England                    9622.10      19388.86        500.00       2000.00   \n",
       "Germany                    9141.38      20156.54        500.00       1000.00   \n",
       "Spain                     14963.59      27094.08        500.00       3000.00   \n",
       "France                    13652.39      27923.96        500.00       2000.00   \n",
       "Argentina                 10005.02      25542.22        500.00       2000.00   \n",
       "Brazil                    16683.31      26678.13        500.00       4000.00   \n",
       "Japan                      3750.62       6403.56        500.00       1000.00   \n",
       "Netherlands               11046.30      22381.63        500.00       2000.00   \n",
       "Italy                     15142.16      21674.06        500.00       2000.00   \n",
       "United States              4026.98       8452.07        500.00       1000.00   \n",
       "Sweden                     4180.35       9109.70        500.00        700.00   \n",
       "China PR                   3444.77       3804.62        500.00       2000.00   \n",
       "Mexico                     9563.99      12828.28        500.00       2000.00   \n",
       "Norway                     3727.78      10384.78        500.00        650.00   \n",
       "Poland                     5311.03      16588.73        500.00        850.00   \n",
       "Portugal                  14238.10      28521.64        500.00       2000.00   \n",
       "Turkey                     9411.63      12640.90        500.00       2000.00   \n",
       "Korea Republic             3301.32       9792.62        500.00       1000.00   \n",
       "Republic of Ireland        4660.06      10881.85        500.00        500.00   \n",
       "Colombia                   7105.36      14190.79        500.00        600.00   \n",
       "\n",
       "                     wage_eur_50%  wage_eur_75%  wage_eur_max  \n",
       "nationality_name                                               \n",
       "England                   3000.00       7000.00     270000.00  \n",
       "Germany                   3000.00       8000.00     310000.00  \n",
       "Spain                     6000.00      18000.00     300000.00  \n",
       "France                    4000.00      15000.00     350000.00  \n",
       "Argentina                 4000.00      11000.00     560000.00  \n",
       "Brazil                    9000.00      19000.00     310000.00  \n",
       "Japan                     2000.00       4000.00      79000.00  \n",
       "Netherlands               4000.00      11000.00     210000.00  \n",
       "Italy                     6000.00      18000.00     135000.00  \n",
       "United States             2000.00       4000.00      83000.00  \n",
       "Sweden                    1000.00       3000.00     100000.00  \n",
       "China PR                  2000.00       4000.00      35000.00  \n",
       "Mexico                    5000.00      11000.00     140000.00  \n",
       "Norway                    1000.00       2000.00     135000.00  \n",
       "Poland                    1000.00       3000.00     240000.00  \n",
       "Portugal                  5000.00      13000.00     230000.00  \n",
       "Turkey                    4500.00       9250.00      71000.00  \n",
       "Korea Republic            1500.00       3000.00     165000.00  \n",
       "Republic of Ireland        875.00       3000.00      95000.00  \n",
       "Colombia                  2000.00       7000.00     105000.00  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dispay top 40 countries statistics by count of registered players\n",
    "d_nation_top_count = d_player_value_wage.sort_values(by=['value_eur_count'], ascending=False).head(20)\n",
    "d_nation_top_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "73eb2630-acee-4760-a294-26027a9c061e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualisation\n",
    "\n",
    "f, ax = plt.subplots(figsize = (20,8))\n",
    "\n",
    "sns.set_context('paper')\n",
    "\n",
    "sns.set_color_codes('muted')\n",
    "sns.barplot(x = 'value_eur_count', y = 'nationality_name',\n",
    "           data = d_nation_top_count.reset_index(), color = 'b', edgecolor = 'black')\n",
    "\n",
    "sns.despine(left = True, bottom = True)\n",
    "plt.title(\"Top 20 nations with most players\")\n",
    "plt.xlabel(\"Number of Player Records\")\n",
    "plt.ylabel(\"Nation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43c0c468-28d8-4fb4-a938-86b786148d0a",
   "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>value_eur_count</th>\n",
       "      <th>value_eur_mean</th>\n",
       "      <th>value_eur_std</th>\n",
       "      <th>value_eur_min</th>\n",
       "      <th>value_eur_25%</th>\n",
       "      <th>value_eur_50%</th>\n",
       "      <th>value_eur_75%</th>\n",
       "      <th>value_eur_max</th>\n",
       "      <th>wage_eur_count</th>\n",
       "      <th>wage_eur_mean</th>\n",
       "      <th>wage_eur_std</th>\n",
       "      <th>wage_eur_min</th>\n",
       "      <th>wage_eur_25%</th>\n",
       "      <th>wage_eur_50%</th>\n",
       "      <th>wage_eur_75%</th>\n",
       "      <th>wage_eur_max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nationality_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>353.00</td>\n",
       "      <td>6137577.90</td>\n",
       "      <td>12123955.28</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>925000.00</td>\n",
       "      <td>1800000.00</td>\n",
       "      <td>5000000.00</td>\n",
       "      <td>105500000.00</td>\n",
       "      <td>353.00</td>\n",
       "      <td>14238.10</td>\n",
       "      <td>28521.64</td>\n",
       "      <td>500.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>13000.00</td>\n",
       "      <td>230000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovenia</th>\n",
       "      <td>45.00</td>\n",
       "      <td>6005555.56</td>\n",
       "      <td>18561823.06</td>\n",
       "      <td>250000.00</td>\n",
       "      <td>525000.00</td>\n",
       "      <td>1300000.00</td>\n",
       "      <td>2900000.00</td>\n",
       "      <td>120000000.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>14012.22</td>\n",
       "      <td>27639.27</td>\n",
       "      <td>550.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>125000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>52.00</td>\n",
       "      <td>5789134.62</td>\n",
       "      <td>9070134.50</td>\n",
       "      <td>110000.00</td>\n",
       "      <td>937500.00</td>\n",
       "      <td>2400000.00</td>\n",
       "      <td>6250000.00</td>\n",
       "      <td>53000000.00</td>\n",
       "      <td>52.00</td>\n",
       "      <td>21576.92</td>\n",
       "      <td>32728.44</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>9500.00</td>\n",
       "      <td>27000.00</td>\n",
       "      <td>210000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>884.00</td>\n",
       "      <td>5575113.12</td>\n",
       "      <td>11358419.07</td>\n",
       "      <td>50000.00</td>\n",
       "      <td>1100000.00</td>\n",
       "      <td>1900000.00</td>\n",
       "      <td>4300000.00</td>\n",
       "      <td>132000000.00</td>\n",
       "      <td>884.00</td>\n",
       "      <td>16683.31</td>\n",
       "      <td>26678.13</td>\n",
       "      <td>500.00</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>9000.00</td>\n",
       "      <td>19000.00</td>\n",
       "      <td>310000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morocco</th>\n",
       "      <td>80.00</td>\n",
       "      <td>5286062.50</td>\n",
       "      <td>9972261.17</td>\n",
       "      <td>160000.00</td>\n",
       "      <td>1075000.00</td>\n",
       "      <td>1900000.00</td>\n",
       "      <td>4650000.00</td>\n",
       "      <td>62000000.00</td>\n",
       "      <td>80.00</td>\n",
       "      <td>15186.88</td>\n",
       "      <td>22083.56</td>\n",
       "      <td>600.00</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>8500.00</td>\n",
       "      <td>17000.00</td>\n",
       "      <td>140000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>1071.00</td>\n",
       "      <td>5237338.94</td>\n",
       "      <td>9543119.44</td>\n",
       "      <td>60000.00</td>\n",
       "      <td>925000.00</td>\n",
       "      <td>1700000.00</td>\n",
       "      <td>4100000.00</td>\n",
       "      <td>66500000.00</td>\n",
       "      <td>1071.00</td>\n",
       "      <td>14963.59</td>\n",
       "      <td>27094.08</td>\n",
       "      <td>500.00</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>18000.00</td>\n",
       "      <td>300000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>65.00</td>\n",
       "      <td>5035692.31</td>\n",
       "      <td>6548560.90</td>\n",
       "      <td>170000.00</td>\n",
       "      <td>1400000.00</td>\n",
       "      <td>2400000.00</td>\n",
       "      <td>5500000.00</td>\n",
       "      <td>34000000.00</td>\n",
       "      <td>65.00</td>\n",
       "      <td>7129.23</td>\n",
       "      <td>15663.93</td>\n",
       "      <td>500.00</td>\n",
       "      <td>550.00</td>\n",
       "      <td>950.00</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>95000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Croatia</th>\n",
       "      <td>132.00</td>\n",
       "      <td>5029924.24</td>\n",
       "      <td>8433926.20</td>\n",
       "      <td>250000.00</td>\n",
       "      <td>1000000.00</td>\n",
       "      <td>1650000.00</td>\n",
       "      <td>4200000.00</td>\n",
       "      <td>47000000.00</td>\n",
       "      <td>132.00</td>\n",
       "      <td>15164.39</td>\n",
       "      <td>30539.99</td>\n",
       "      <td>500.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>15250.00</td>\n",
       "      <td>260000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>984.00</td>\n",
       "      <td>4761519.31</td>\n",
       "      <td>11746459.16</td>\n",
       "      <td>35000.00</td>\n",
       "      <td>600000.00</td>\n",
       "      <td>1200000.00</td>\n",
       "      <td>3000000.00</td>\n",
       "      <td>185500000.00</td>\n",
       "      <td>984.00</td>\n",
       "      <td>13652.39</td>\n",
       "      <td>27923.96</td>\n",
       "      <td>500.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>15000.00</td>\n",
       "      <td>350000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>421.00</td>\n",
       "      <td>4748040.38</td>\n",
       "      <td>9736905.98</td>\n",
       "      <td>45000.00</td>\n",
       "      <td>775000.00</td>\n",
       "      <td>1600000.00</td>\n",
       "      <td>3400000.00</td>\n",
       "      <td>93500000.00</td>\n",
       "      <td>421.00</td>\n",
       "      <td>15142.16</td>\n",
       "      <td>21674.06</td>\n",
       "      <td>500.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>18000.00</td>\n",
       "      <td>135000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>298.00</td>\n",
       "      <td>4692701.34</td>\n",
       "      <td>12293917.72</td>\n",
       "      <td>110000.00</td>\n",
       "      <td>725000.00</td>\n",
       "      <td>1300000.00</td>\n",
       "      <td>2875000.00</td>\n",
       "      <td>129000000.00</td>\n",
       "      <td>298.00</td>\n",
       "      <td>14862.08</td>\n",
       "      <td>37956.48</td>\n",
       "      <td>500.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>11750.00</td>\n",
       "      <td>370000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>66.00</td>\n",
       "      <td>4577348.48</td>\n",
       "      <td>6082875.60</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>1000000.00</td>\n",
       "      <td>2050000.00</td>\n",
       "      <td>5000000.00</td>\n",
       "      <td>27000000.00</td>\n",
       "      <td>66.00</td>\n",
       "      <td>21498.48</td>\n",
       "      <td>16563.91</td>\n",
       "      <td>500.00</td>\n",
       "      <td>6250.00</td>\n",
       "      <td>20000.00</td>\n",
       "      <td>35000.00</td>\n",
       "      <td>63000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serbia</th>\n",
       "      <td>130.00</td>\n",
       "      <td>4574730.77</td>\n",
       "      <td>9039743.78</td>\n",
       "      <td>120000.00</td>\n",
       "      <td>787500.00</td>\n",
       "      <td>1600000.00</td>\n",
       "      <td>3450000.00</td>\n",
       "      <td>74500000.00</td>\n",
       "      <td>130.00</td>\n",
       "      <td>15124.23</td>\n",
       "      <td>23497.00</td>\n",
       "      <td>500.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>5500.00</td>\n",
       "      <td>20000.00</td>\n",
       "      <td>140000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senegal</th>\n",
       "      <td>128.00</td>\n",
       "      <td>4402031.25</td>\n",
       "      <td>13305350.76</td>\n",
       "      <td>160000.00</td>\n",
       "      <td>712500.00</td>\n",
       "      <td>1200000.00</td>\n",
       "      <td>2450000.00</td>\n",
       "      <td>120500000.00</td>\n",
       "      <td>128.00</td>\n",
       "      <td>12717.58</td>\n",
       "      <td>27877.84</td>\n",
       "      <td>500.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>11000.00</td>\n",
       "      <td>250000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Côte d'Ivoire</th>\n",
       "      <td>98.00</td>\n",
       "      <td>4309081.63</td>\n",
       "      <td>6945226.55</td>\n",
       "      <td>130000.00</td>\n",
       "      <td>831250.00</td>\n",
       "      <td>2100000.00</td>\n",
       "      <td>3575000.00</td>\n",
       "      <td>40500000.00</td>\n",
       "      <td>98.00</td>\n",
       "      <td>14761.22</td>\n",
       "      <td>21419.57</td>\n",
       "      <td>500.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>6500.00</td>\n",
       "      <td>17000.00</td>\n",
       "      <td>100000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>432.00</td>\n",
       "      <td>4270011.57</td>\n",
       "      <td>10823959.64</td>\n",
       "      <td>80000.00</td>\n",
       "      <td>675000.00</td>\n",
       "      <td>1200000.00</td>\n",
       "      <td>2900000.00</td>\n",
       "      <td>113000000.00</td>\n",
       "      <td>432.00</td>\n",
       "      <td>11046.30</td>\n",
       "      <td>22381.63</td>\n",
       "      <td>500.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>11000.00</td>\n",
       "      <td>210000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cameroon</th>\n",
       "      <td>74.00</td>\n",
       "      <td>3835810.81</td>\n",
       "      <td>7388804.80</td>\n",
       "      <td>250000.00</td>\n",
       "      <td>831250.00</td>\n",
       "      <td>1550000.00</td>\n",
       "      <td>3075000.00</td>\n",
       "      <td>48000000.00</td>\n",
       "      <td>74.00</td>\n",
       "      <td>11643.92</td>\n",
       "      <td>18302.31</td>\n",
       "      <td>500.00</td>\n",
       "      <td>2250.00</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>14750.00</td>\n",
       "      <td>115000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>26.00</td>\n",
       "      <td>3828269.23</td>\n",
       "      <td>7779463.60</td>\n",
       "      <td>90000.00</td>\n",
       "      <td>625000.00</td>\n",
       "      <td>1150000.00</td>\n",
       "      <td>2175000.00</td>\n",
       "      <td>36000000.00</td>\n",
       "      <td>26.00</td>\n",
       "      <td>10150.00</td>\n",
       "      <td>15778.71</td>\n",
       "      <td>500.00</td>\n",
       "      <td>1250.00</td>\n",
       "      <td>4000.00</td>\n",
       "      <td>13500.00</td>\n",
       "      <td>65000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Republic</th>\n",
       "      <td>111.00</td>\n",
       "      <td>3765495.50</td>\n",
       "      <td>5361533.28</td>\n",
       "      <td>25000.00</td>\n",
       "      <td>862500.00</td>\n",
       "      <td>2100000.00</td>\n",
       "      <td>3750000.00</td>\n",
       "      <td>34500000.00</td>\n",
       "      <td>111.00</td>\n",
       "      <td>6452.25</td>\n",
       "      <td>12225.20</td>\n",
       "      <td>500.00</td>\n",
       "      <td>650.00</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>58000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mali</th>\n",
       "      <td>50.00</td>\n",
       "      <td>3646000.00</td>\n",
       "      <td>4561508.10</td>\n",
       "      <td>275000.00</td>\n",
       "      <td>1100000.00</td>\n",
       "      <td>1750000.00</td>\n",
       "      <td>3300000.00</td>\n",
       "      <td>19000000.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>12764.00</td>\n",
       "      <td>12906.06</td>\n",
       "      <td>500.00</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>17750.00</td>\n",
       "      <td>49000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  value_eur_count  value_eur_mean  value_eur_std  \\\n",
       "nationality_name                                                   \n",
       "Portugal                   353.00      6137577.90    12123955.28   \n",
       "Slovenia                    45.00      6005555.56    18561823.06   \n",
       "Algeria                     52.00      5789134.62     9070134.50   \n",
       "Brazil                     884.00      5575113.12    11358419.07   \n",
       "Morocco                     80.00      5286062.50     9972261.17   \n",
       "Spain                     1071.00      5237338.94     9543119.44   \n",
       "Ukraine                     65.00      5035692.31     6548560.90   \n",
       "Croatia                    132.00      5029924.24     8433926.20   \n",
       "France                     984.00      4761519.31    11746459.16   \n",
       "Italy                      421.00      4748040.38     9736905.98   \n",
       "Belgium                    298.00      4692701.34    12293917.72   \n",
       "Russia                      66.00      4577348.48     6082875.60   \n",
       "Serbia                     130.00      4574730.77     9039743.78   \n",
       "Senegal                    128.00      4402031.25    13305350.76   \n",
       "Côte d'Ivoire               98.00      4309081.63     6945226.55   \n",
       "Netherlands                432.00      4270011.57    10823959.64   \n",
       "Cameroon                    74.00      3835810.81     7388804.80   \n",
       "Hungary                     26.00      3828269.23     7779463.60   \n",
       "Czech Republic             111.00      3765495.50     5361533.28   \n",
       "Mali                        50.00      3646000.00     4561508.10   \n",
       "\n",
       "                  value_eur_min  value_eur_25%  value_eur_50%  value_eur_75%  \\\n",
       "nationality_name                                                               \n",
       "Portugal              100000.00      925000.00     1800000.00     5000000.00   \n",
       "Slovenia              250000.00      525000.00     1300000.00     2900000.00   \n",
       "Algeria               110000.00      937500.00     2400000.00     6250000.00   \n",
       "Brazil                 50000.00     1100000.00     1900000.00     4300000.00   \n",
       "Morocco               160000.00     1075000.00     1900000.00     4650000.00   \n",
       "Spain                  60000.00      925000.00     1700000.00     4100000.00   \n",
       "Ukraine               170000.00     1400000.00     2400000.00     5500000.00   \n",
       "Croatia               250000.00     1000000.00     1650000.00     4200000.00   \n",
       "France                 35000.00      600000.00     1200000.00     3000000.00   \n",
       "Italy                  45000.00      775000.00     1600000.00     3400000.00   \n",
       "Belgium               110000.00      725000.00     1300000.00     2875000.00   \n",
       "Russia                100000.00     1000000.00     2050000.00     5000000.00   \n",
       "Serbia                120000.00      787500.00     1600000.00     3450000.00   \n",
       "Senegal               160000.00      712500.00     1200000.00     2450000.00   \n",
       "Côte d'Ivoire         130000.00      831250.00     2100000.00     3575000.00   \n",
       "Netherlands            80000.00      675000.00     1200000.00     2900000.00   \n",
       "Cameroon              250000.00      831250.00     1550000.00     3075000.00   \n",
       "Hungary                90000.00      625000.00     1150000.00     2175000.00   \n",
       "Czech Republic         25000.00      862500.00     2100000.00     3750000.00   \n",
       "Mali                  275000.00     1100000.00     1750000.00     3300000.00   \n",
       "\n",
       "                  value_eur_max  wage_eur_count  wage_eur_mean  wage_eur_std  \\\n",
       "nationality_name                                                               \n",
       "Portugal           105500000.00          353.00       14238.10      28521.64   \n",
       "Slovenia           120000000.00           45.00       14012.22      27639.27   \n",
       "Algeria             53000000.00           52.00       21576.92      32728.44   \n",
       "Brazil             132000000.00          884.00       16683.31      26678.13   \n",
       "Morocco             62000000.00           80.00       15186.88      22083.56   \n",
       "Spain               66500000.00         1071.00       14963.59      27094.08   \n",
       "Ukraine             34000000.00           65.00        7129.23      15663.93   \n",
       "Croatia             47000000.00          132.00       15164.39      30539.99   \n",
       "France             185500000.00          984.00       13652.39      27923.96   \n",
       "Italy               93500000.00          421.00       15142.16      21674.06   \n",
       "Belgium            129000000.00          298.00       14862.08      37956.48   \n",
       "Russia              27000000.00           66.00       21498.48      16563.91   \n",
       "Serbia              74500000.00          130.00       15124.23      23497.00   \n",
       "Senegal            120500000.00          128.00       12717.58      27877.84   \n",
       "Côte d'Ivoire       40500000.00           98.00       14761.22      21419.57   \n",
       "Netherlands        113000000.00          432.00       11046.30      22381.63   \n",
       "Cameroon            48000000.00           74.00       11643.92      18302.31   \n",
       "Hungary             36000000.00           26.00       10150.00      15778.71   \n",
       "Czech Republic      34500000.00          111.00        6452.25      12225.20   \n",
       "Mali                19000000.00           50.00       12764.00      12906.06   \n",
       "\n",
       "                  wage_eur_min  wage_eur_25%  wage_eur_50%  wage_eur_75%  \\\n",
       "nationality_name                                                           \n",
       "Portugal                500.00       2000.00       5000.00      13000.00   \n",
       "Slovenia                550.00       2000.00       4000.00      10000.00   \n",
       "Algeria                1000.00       5000.00       9500.00      27000.00   \n",
       "Brazil                  500.00       4000.00       9000.00      19000.00   \n",
       "Morocco                 600.00       3000.00       8500.00      17000.00   \n",
       "Spain                   500.00       3000.00       6000.00      18000.00   \n",
       "Ukraine                 500.00        550.00        950.00       6000.00   \n",
       "Croatia                 500.00       2000.00       5000.00      15250.00   \n",
       "France                  500.00       2000.00       4000.00      15000.00   \n",
       "Italy                   500.00       2000.00       6000.00      18000.00   \n",
       "Belgium                 500.00       2000.00       5000.00      11750.00   \n",
       "Russia                  500.00       6250.00      20000.00      35000.00   \n",
       "Serbia                  500.00       2000.00       5500.00      20000.00   \n",
       "Senegal                 500.00       2000.00       4000.00      11000.00   \n",
       "Côte d'Ivoire           500.00       2000.00       6500.00      17000.00   \n",
       "Netherlands             500.00       2000.00       4000.00      11000.00   \n",
       "Cameroon                500.00       2250.00       5000.00      14750.00   \n",
       "Hungary                 500.00       1250.00       4000.00      13500.00   \n",
       "Czech Republic          500.00        650.00       1000.00       5000.00   \n",
       "Mali                    500.00       3000.00      10000.00      17750.00   \n",
       "\n",
       "                  wage_eur_max  \n",
       "nationality_name                \n",
       "Portugal             230000.00  \n",
       "Slovenia             125000.00  \n",
       "Algeria              210000.00  \n",
       "Brazil               310000.00  \n",
       "Morocco              140000.00  \n",
       "Spain                300000.00  \n",
       "Ukraine               95000.00  \n",
       "Croatia              260000.00  \n",
       "France               350000.00  \n",
       "Italy                135000.00  \n",
       "Belgium              370000.00  \n",
       "Russia                63000.00  \n",
       "Serbia               140000.00  \n",
       "Senegal              250000.00  \n",
       "Côte d'Ivoire        100000.00  \n",
       "Netherlands          210000.00  \n",
       "Cameroon             115000.00  \n",
       "Hungary               65000.00  \n",
       "Czech Republic        58000.00  \n",
       "Mali                  49000.00  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dispay top 20 countries statistics by registered player's value and wage  \n",
    "d_nation_top_value = d_player_value_wage.sort_values(by=['value_eur_mean', 'wage_eur_mean'], ascending=False).head(20)\n",
    "d_nation_top_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0e450386-8d39-41f0-96ad-ffd6d1ab9a52",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualisation\n",
    "\n",
    "f, ax = plt.subplots(figsize = (20,8))\n",
    "\n",
    "sns.set_context('paper')\n",
    "\n",
    "sns.set_color_codes('muted')\n",
    "sns.barplot(x = 'value_eur_mean', y = 'nationality_name',\n",
    "           data = d_nation_top_value.reset_index(), color = 'b', edgecolor = 'black')\n",
    "\n",
    "sns.despine(left = True, bottom = True)\n",
    "plt.title(\"Top 20 nations with highest average player's value\")\n",
    "plt.xlabel(\"Average player's value in Euro\")\n",
    "plt.ylabel(\"Nation\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a879c3e-ecf3-4e21-9cb2-01aa8d10f5b2",
   "metadata": {},
   "source": [
    "**Discussion**: In this section we have done some shown some general analysis on the value and wage of registered players from countries with more than 20 players in the past 30 years. Moreover, we have also plotted some top statistics to see, e.g. countries with the highest number of football players and countries with the highest average value. Consequently, we have gained the following insights from the aforementioned statistics and visualisations:\n",
    "- Top 20 countries with most players are all Eurpoean, Asian and South American countries. However, European and African took the vast majority among the top 20 countries with highest average value. African teams have showed their rising despite the total number of football players are lacking behind.\n",
    "- England as the birthplace of the modern football, has an overwhelming amount of football players among all nations, followed by Germany, Spain, France, Argentina and Brazil, whose amount of players are more than twice of the 7th country Japan. Besides, these countries have also wonned the World Cup since 1986.\n",
    "- **China** has a decent amount of football players (ranked 12th), but their players seems to be in a very unsatisfactory state to their nationals, which is also reflected by the mean player's value on the international transfer market. Nevertheless, this is not the real case as I can tell the Chinese football players is much much more expensive in the domestic transfer market, which will be further discussed and criticised in the following milestones."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4b5d81e-0491-4ed3-a5ad-6bdc27dadcc5",
   "metadata": {},
   "source": [
    "### Generate files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "e96034a0-a06b-4c56-9162-24dea703cfc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xuyixuan/opt/anaconda3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3444: DtypeWarning: Columns (25,108) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "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>sofifa_id</th>\n",
       "      <th>player_url</th>\n",
       "      <th>short_name</th>\n",
       "      <th>long_name</th>\n",
       "      <th>player_positions</th>\n",
       "      <th>overall</th>\n",
       "      <th>potential</th>\n",
       "      <th>value_eur</th>\n",
       "      <th>wage_eur</th>\n",
       "      <th>age</th>\n",
       "      <th>dob</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>club_team_id</th>\n",
       "      <th>club_name</th>\n",
       "      <th>league_name</th>\n",
       "      <th>league_level</th>\n",
       "      <th>club_position</th>\n",
       "      <th>club_jersey_number</th>\n",
       "      <th>club_loaned_from</th>\n",
       "      <th>...</th>\n",
       "      <th>lcm</th>\n",
       "      <th>cm</th>\n",
       "      <th>rcm</th>\n",
       "      <th>rm</th>\n",
       "      <th>lwb</th>\n",
       "      <th>ldm</th>\n",
       "      <th>cdm</th>\n",
       "      <th>rdm</th>\n",
       "      <th>rwb</th>\n",
       "      <th>lb</th>\n",
       "      <th>lcb</th>\n",
       "      <th>cb</th>\n",
       "      <th>rcb</th>\n",
       "      <th>rb</th>\n",
       "      <th>gk</th>\n",
       "      <th>player_face_url</th>\n",
       "      <th>club_logo_url</th>\n",
       "      <th>club_flag_url</th>\n",
       "      <th>nation_logo_url</th>\n",
       "      <th>nation_flag_url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>158023</td>\n",
       "      <td>https://sofifa.com/player/158023/lionel-messi/...</td>\n",
       "      <td>L. Messi</td>\n",
       "      <td>Lionel Andrés Messi Cuccittini</td>\n",
       "      <td>RW, ST, CF</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>78000000.00</td>\n",
       "      <td>320000.00</td>\n",
       "      <td>34</td>\n",
       "      <td>1987-06-24</td>\n",
       "      <td>170</td>\n",
       "      <td>72</td>\n",
       "      <td>73.00</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>French Ligue 1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>RW</td>\n",
       "      <td>30.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>87+3</td>\n",
       "      <td>87+3</td>\n",
       "      <td>87+3</td>\n",
       "      <td>91+2</td>\n",
       "      <td>66+3</td>\n",
       "      <td>64+3</td>\n",
       "      <td>64+3</td>\n",
       "      <td>64+3</td>\n",
       "      <td>66+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>50+3</td>\n",
       "      <td>50+3</td>\n",
       "      <td>50+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>19+3</td>\n",
       "      <td>https://cdn.sofifa.net/players/158/023/22_120.png</td>\n",
       "      <td>https://cdn.sofifa.net/teams/73/60.png</td>\n",
       "      <td>https://cdn.sofifa.net/flags/fr.png</td>\n",
       "      <td>https://cdn.sofifa.net/teams/1369/60.png</td>\n",
       "      <td>https://cdn.sofifa.net/flags/ar.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>188545</td>\n",
       "      <td>https://sofifa.com/player/188545/robert-lewand...</td>\n",
       "      <td>R. Lewandowski</td>\n",
       "      <td>Robert Lewandowski</td>\n",
       "      <td>ST</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>119500000.00</td>\n",
       "      <td>270000.00</td>\n",
       "      <td>32</td>\n",
       "      <td>1988-08-21</td>\n",
       "      <td>185</td>\n",
       "      <td>81</td>\n",
       "      <td>21.00</td>\n",
       "      <td>FC Bayern München</td>\n",
       "      <td>German 1. Bundesliga</td>\n",
       "      <td>1.00</td>\n",
       "      <td>ST</td>\n",
       "      <td>9.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>80+3</td>\n",
       "      <td>80+3</td>\n",
       "      <td>80+3</td>\n",
       "      <td>84+3</td>\n",
       "      <td>64+3</td>\n",
       "      <td>66+3</td>\n",
       "      <td>66+3</td>\n",
       "      <td>66+3</td>\n",
       "      <td>64+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>60+3</td>\n",
       "      <td>60+3</td>\n",
       "      <td>60+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>19+3</td>\n",
       "      <td>https://cdn.sofifa.net/players/188/545/22_120.png</td>\n",
       "      <td>https://cdn.sofifa.net/teams/21/60.png</td>\n",
       "      <td>https://cdn.sofifa.net/flags/de.png</td>\n",
       "      <td>https://cdn.sofifa.net/teams/1353/60.png</td>\n",
       "      <td>https://cdn.sofifa.net/flags/pl.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20801</td>\n",
       "      <td>https://sofifa.com/player/20801/c-ronaldo-dos-...</td>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>Cristiano Ronaldo dos Santos Aveiro</td>\n",
       "      <td>ST, LW</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>45000000.00</td>\n",
       "      <td>270000.00</td>\n",
       "      <td>36</td>\n",
       "      <td>1985-02-05</td>\n",
       "      <td>187</td>\n",
       "      <td>83</td>\n",
       "      <td>11.00</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>English Premier League</td>\n",
       "      <td>1.00</td>\n",
       "      <td>ST</td>\n",
       "      <td>7.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>78+3</td>\n",
       "      <td>78+3</td>\n",
       "      <td>78+3</td>\n",
       "      <td>86+3</td>\n",
       "      <td>63+3</td>\n",
       "      <td>59+3</td>\n",
       "      <td>59+3</td>\n",
       "      <td>59+3</td>\n",
       "      <td>63+3</td>\n",
       "      <td>60+3</td>\n",
       "      <td>53+3</td>\n",
       "      <td>53+3</td>\n",
       "      <td>53+3</td>\n",
       "      <td>60+3</td>\n",
       "      <td>20+3</td>\n",
       "      <td>https://cdn.sofifa.net/players/020/801/22_120.png</td>\n",
       "      <td>https://cdn.sofifa.net/teams/11/60.png</td>\n",
       "      <td>https://cdn.sofifa.net/flags/gb-eng.png</td>\n",
       "      <td>https://cdn.sofifa.net/teams/1354/60.png</td>\n",
       "      <td>https://cdn.sofifa.net/flags/pt.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>190871</td>\n",
       "      <td>https://sofifa.com/player/190871/neymar-da-sil...</td>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>Neymar da Silva Santos Júnior</td>\n",
       "      <td>LW, CAM</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>129000000.00</td>\n",
       "      <td>270000.00</td>\n",
       "      <td>29</td>\n",
       "      <td>1992-02-05</td>\n",
       "      <td>175</td>\n",
       "      <td>68</td>\n",
       "      <td>73.00</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>French Ligue 1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>LW</td>\n",
       "      <td>10.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>82+3</td>\n",
       "      <td>82+3</td>\n",
       "      <td>82+3</td>\n",
       "      <td>89+2</td>\n",
       "      <td>67+3</td>\n",
       "      <td>63+3</td>\n",
       "      <td>63+3</td>\n",
       "      <td>63+3</td>\n",
       "      <td>67+3</td>\n",
       "      <td>62+3</td>\n",
       "      <td>50+3</td>\n",
       "      <td>50+3</td>\n",
       "      <td>50+3</td>\n",
       "      <td>62+3</td>\n",
       "      <td>20+3</td>\n",
       "      <td>https://cdn.sofifa.net/players/190/871/22_120.png</td>\n",
       "      <td>https://cdn.sofifa.net/teams/73/60.png</td>\n",
       "      <td>https://cdn.sofifa.net/flags/fr.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://cdn.sofifa.net/flags/br.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>192985</td>\n",
       "      <td>https://sofifa.com/player/192985/kevin-de-bruy...</td>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>Kevin De Bruyne</td>\n",
       "      <td>CM, CAM</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>125500000.00</td>\n",
       "      <td>350000.00</td>\n",
       "      <td>30</td>\n",
       "      <td>1991-06-28</td>\n",
       "      <td>181</td>\n",
       "      <td>70</td>\n",
       "      <td>10.00</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>English Premier League</td>\n",
       "      <td>1.00</td>\n",
       "      <td>RCM</td>\n",
       "      <td>17.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>89+2</td>\n",
       "      <td>89+2</td>\n",
       "      <td>89+2</td>\n",
       "      <td>89+2</td>\n",
       "      <td>79+3</td>\n",
       "      <td>80+3</td>\n",
       "      <td>80+3</td>\n",
       "      <td>80+3</td>\n",
       "      <td>79+3</td>\n",
       "      <td>75+3</td>\n",
       "      <td>69+3</td>\n",
       "      <td>69+3</td>\n",
       "      <td>69+3</td>\n",
       "      <td>75+3</td>\n",
       "      <td>21+3</td>\n",
       "      <td>https://cdn.sofifa.net/players/192/985/22_120.png</td>\n",
       "      <td>https://cdn.sofifa.net/teams/10/60.png</td>\n",
       "      <td>https://cdn.sofifa.net/flags/gb-eng.png</td>\n",
       "      <td>https://cdn.sofifa.net/teams/1325/60.png</td>\n",
       "      <td>https://cdn.sofifa.net/flags/be.png</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 110 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   sofifa_id                                         player_url  \\\n",
       "0     158023  https://sofifa.com/player/158023/lionel-messi/...   \n",
       "1     188545  https://sofifa.com/player/188545/robert-lewand...   \n",
       "2      20801  https://sofifa.com/player/20801/c-ronaldo-dos-...   \n",
       "3     190871  https://sofifa.com/player/190871/neymar-da-sil...   \n",
       "4     192985  https://sofifa.com/player/192985/kevin-de-bruy...   \n",
       "\n",
       "          short_name                            long_name player_positions  \\\n",
       "0           L. Messi       Lionel Andrés Messi Cuccittini       RW, ST, CF   \n",
       "1     R. Lewandowski                   Robert Lewandowski               ST   \n",
       "2  Cristiano Ronaldo  Cristiano Ronaldo dos Santos Aveiro           ST, LW   \n",
       "3          Neymar Jr        Neymar da Silva Santos Júnior          LW, CAM   \n",
       "4       K. De Bruyne                      Kevin De Bruyne          CM, CAM   \n",
       "\n",
       "   overall  potential    value_eur  wage_eur  age         dob  height_cm  \\\n",
       "0       93         93  78000000.00 320000.00   34  1987-06-24        170   \n",
       "1       92         92 119500000.00 270000.00   32  1988-08-21        185   \n",
       "2       91         91  45000000.00 270000.00   36  1985-02-05        187   \n",
       "3       91         91 129000000.00 270000.00   29  1992-02-05        175   \n",
       "4       91         91 125500000.00 350000.00   30  1991-06-28        181   \n",
       "\n",
       "   weight_kg  club_team_id            club_name             league_name  \\\n",
       "0         72         73.00  Paris Saint-Germain          French Ligue 1   \n",
       "1         81         21.00    FC Bayern München    German 1. Bundesliga   \n",
       "2         83         11.00    Manchester United  English Premier League   \n",
       "3         68         73.00  Paris Saint-Germain          French Ligue 1   \n",
       "4         70         10.00      Manchester City  English Premier League   \n",
       "\n",
       "   league_level club_position  club_jersey_number club_loaned_from  ...   lcm  \\\n",
       "0          1.00            RW               30.00              NaN  ...  87+3   \n",
       "1          1.00            ST                9.00              NaN  ...  80+3   \n",
       "2          1.00            ST                7.00              NaN  ...  78+3   \n",
       "3          1.00            LW               10.00              NaN  ...  82+3   \n",
       "4          1.00           RCM               17.00              NaN  ...  89+2   \n",
       "\n",
       "     cm   rcm    rm   lwb   ldm   cdm   rdm   rwb    lb   lcb    cb   rcb  \\\n",
       "0  87+3  87+3  91+2  66+3  64+3  64+3  64+3  66+3  61+3  50+3  50+3  50+3   \n",
       "1  80+3  80+3  84+3  64+3  66+3  66+3  66+3  64+3  61+3  60+3  60+3  60+3   \n",
       "2  78+3  78+3  86+3  63+3  59+3  59+3  59+3  63+3  60+3  53+3  53+3  53+3   \n",
       "3  82+3  82+3  89+2  67+3  63+3  63+3  63+3  67+3  62+3  50+3  50+3  50+3   \n",
       "4  89+2  89+2  89+2  79+3  80+3  80+3  80+3  79+3  75+3  69+3  69+3  69+3   \n",
       "\n",
       "     rb    gk                                    player_face_url  \\\n",
       "0  61+3  19+3  https://cdn.sofifa.net/players/158/023/22_120.png   \n",
       "1  61+3  19+3  https://cdn.sofifa.net/players/188/545/22_120.png   \n",
       "2  60+3  20+3  https://cdn.sofifa.net/players/020/801/22_120.png   \n",
       "3  62+3  20+3  https://cdn.sofifa.net/players/190/871/22_120.png   \n",
       "4  75+3  21+3  https://cdn.sofifa.net/players/192/985/22_120.png   \n",
       "\n",
       "                            club_logo_url  \\\n",
       "0  https://cdn.sofifa.net/teams/73/60.png   \n",
       "1  https://cdn.sofifa.net/teams/21/60.png   \n",
       "2  https://cdn.sofifa.net/teams/11/60.png   \n",
       "3  https://cdn.sofifa.net/teams/73/60.png   \n",
       "4  https://cdn.sofifa.net/teams/10/60.png   \n",
       "\n",
       "                             club_flag_url  \\\n",
       "0      https://cdn.sofifa.net/flags/fr.png   \n",
       "1      https://cdn.sofifa.net/flags/de.png   \n",
       "2  https://cdn.sofifa.net/flags/gb-eng.png   \n",
       "3      https://cdn.sofifa.net/flags/fr.png   \n",
       "4  https://cdn.sofifa.net/flags/gb-eng.png   \n",
       "\n",
       "                            nation_logo_url  \\\n",
       "0  https://cdn.sofifa.net/teams/1369/60.png   \n",
       "1  https://cdn.sofifa.net/teams/1353/60.png   \n",
       "2  https://cdn.sofifa.net/teams/1354/60.png   \n",
       "3                                       NaN   \n",
       "4  https://cdn.sofifa.net/teams/1325/60.png   \n",
       "\n",
       "                       nation_flag_url  \n",
       "0  https://cdn.sofifa.net/flags/ar.png  \n",
       "1  https://cdn.sofifa.net/flags/pl.png  \n",
       "2  https://cdn.sofifa.net/flags/pt.png  \n",
       "3  https://cdn.sofifa.net/flags/br.png  \n",
       "4  https://cdn.sofifa.net/flags/be.png  \n",
       "\n",
       "[5 rows x 110 columns]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_player = pd.read_csv('./data/players_22_value.csv')\n",
    "df_player.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "3429a740-5d09-4d1b-946a-8dc35876be2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "countries = ['Argentina', 'Belgium', 'Brazil', 'Chile', 'Colombia', 'Croatia',\n",
    "             'Denmark', 'France', 'Germany', 'Iceland', 'Italy', 'Japan',\n",
    "             'Mexico', 'Netherlands', 'Nigeria', 'Poland', 'Portugal', 'Morocco',\n",
    "             'Saudi Arabia', 'Senegal', 'Serbia', 'Korea Republic', 'Spain', 'Sweden', \n",
    "             'Switzerland', 'Turkey', 'England', 'United States', 'Uruguay', 'China PR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "cdece1b0-0b32-46cd-8867-e5906edbde57",
   "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>sofifa_id</th>\n",
       "      <th>player_url</th>\n",
       "      <th>short_name</th>\n",
       "      <th>long_name</th>\n",
       "      <th>player_positions</th>\n",
       "      <th>overall</th>\n",
       "      <th>potential</th>\n",
       "      <th>value_eur</th>\n",
       "      <th>wage_eur</th>\n",
       "      <th>age</th>\n",
       "      <th>dob</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>club_team_id</th>\n",
       "      <th>club_name</th>\n",
       "      <th>league_name</th>\n",
       "      <th>league_level</th>\n",
       "      <th>club_position</th>\n",
       "      <th>club_jersey_number</th>\n",
       "      <th>club_loaned_from</th>\n",
       "      <th>...</th>\n",
       "      <th>lcm</th>\n",
       "      <th>cm</th>\n",
       "      <th>rcm</th>\n",
       "      <th>rm</th>\n",
       "      <th>lwb</th>\n",
       "      <th>ldm</th>\n",
       "      <th>cdm</th>\n",
       "      <th>rdm</th>\n",
       "      <th>rwb</th>\n",
       "      <th>lb</th>\n",
       "      <th>lcb</th>\n",
       "      <th>cb</th>\n",
       "      <th>rcb</th>\n",
       "      <th>rb</th>\n",
       "      <th>gk</th>\n",
       "      <th>player_face_url</th>\n",
       "      <th>club_logo_url</th>\n",
       "      <th>club_flag_url</th>\n",
       "      <th>nation_logo_url</th>\n",
       "      <th>nation_flag_url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>158023</td>\n",
       "      <td>https://sofifa.com/player/158023/lionel-messi/...</td>\n",
       "      <td>L. Messi</td>\n",
       "      <td>Lionel Andrés Messi Cuccittini</td>\n",
       "      <td>RW, ST, CF</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>78000000.00</td>\n",
       "      <td>320000.00</td>\n",
       "      <td>34</td>\n",
       "      <td>1987-06-24</td>\n",
       "      <td>170</td>\n",
       "      <td>72</td>\n",
       "      <td>73.00</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>French Ligue 1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>RW</td>\n",
       "      <td>30.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>87+3</td>\n",
       "      <td>87+3</td>\n",
       "      <td>87+3</td>\n",
       "      <td>91+2</td>\n",
       "      <td>66+3</td>\n",
       "      <td>64+3</td>\n",
       "      <td>64+3</td>\n",
       "      <td>64+3</td>\n",
       "      <td>66+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>50+3</td>\n",
       "      <td>50+3</td>\n",
       "      <td>50+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>19+3</td>\n",
       "      <td>https://cdn.sofifa.net/players/158/023/22_120.png</td>\n",
       "      <td>https://cdn.sofifa.net/teams/73/60.png</td>\n",
       "      <td>https://cdn.sofifa.net/flags/fr.png</td>\n",
       "      <td>https://cdn.sofifa.net/teams/1369/60.png</td>\n",
       "      <td>https://cdn.sofifa.net/flags/ar.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>188545</td>\n",
       "      <td>https://sofifa.com/player/188545/robert-lewand...</td>\n",
       "      <td>R. Lewandowski</td>\n",
       "      <td>Robert Lewandowski</td>\n",
       "      <td>ST</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>119500000.00</td>\n",
       "      <td>270000.00</td>\n",
       "      <td>32</td>\n",
       "      <td>1988-08-21</td>\n",
       "      <td>185</td>\n",
       "      <td>81</td>\n",
       "      <td>21.00</td>\n",
       "      <td>FC Bayern München</td>\n",
       "      <td>German 1. Bundesliga</td>\n",
       "      <td>1.00</td>\n",
       "      <td>ST</td>\n",
       "      <td>9.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>80+3</td>\n",
       "      <td>80+3</td>\n",
       "      <td>80+3</td>\n",
       "      <td>84+3</td>\n",
       "      <td>64+3</td>\n",
       "      <td>66+3</td>\n",
       "      <td>66+3</td>\n",
       "      <td>66+3</td>\n",
       "      <td>64+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>60+3</td>\n",
       "      <td>60+3</td>\n",
       "      <td>60+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>19+3</td>\n",
       "      <td>https://cdn.sofifa.net/players/188/545/22_120.png</td>\n",
       "      <td>https://cdn.sofifa.net/teams/21/60.png</td>\n",
       "      <td>https://cdn.sofifa.net/flags/de.png</td>\n",
       "      <td>https://cdn.sofifa.net/teams/1353/60.png</td>\n",
       "      <td>https://cdn.sofifa.net/flags/pl.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20801</td>\n",
       "      <td>https://sofifa.com/player/20801/c-ronaldo-dos-...</td>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>Cristiano Ronaldo dos Santos Aveiro</td>\n",
       "      <td>ST, LW</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>45000000.00</td>\n",
       "      <td>270000.00</td>\n",
       "      <td>36</td>\n",
       "      <td>1985-02-05</td>\n",
       "      <td>187</td>\n",
       "      <td>83</td>\n",
       "      <td>11.00</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>English Premier League</td>\n",
       "      <td>1.00</td>\n",
       "      <td>ST</td>\n",
       "      <td>7.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>78+3</td>\n",
       "      <td>78+3</td>\n",
       "      <td>78+3</td>\n",
       "      <td>86+3</td>\n",
       "      <td>63+3</td>\n",
       "      <td>59+3</td>\n",
       "      <td>59+3</td>\n",
       "      <td>59+3</td>\n",
       "      <td>63+3</td>\n",
       "      <td>60+3</td>\n",
       "      <td>53+3</td>\n",
       "      <td>53+3</td>\n",
       "      <td>53+3</td>\n",
       "      <td>60+3</td>\n",
       "      <td>20+3</td>\n",
       "      <td>https://cdn.sofifa.net/players/020/801/22_120.png</td>\n",
       "      <td>https://cdn.sofifa.net/teams/11/60.png</td>\n",
       "      <td>https://cdn.sofifa.net/flags/gb-eng.png</td>\n",
       "      <td>https://cdn.sofifa.net/teams/1354/60.png</td>\n",
       "      <td>https://cdn.sofifa.net/flags/pt.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>190871</td>\n",
       "      <td>https://sofifa.com/player/190871/neymar-da-sil...</td>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>Neymar da Silva Santos Júnior</td>\n",
       "      <td>LW, CAM</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>129000000.00</td>\n",
       "      <td>270000.00</td>\n",
       "      <td>29</td>\n",
       "      <td>1992-02-05</td>\n",
       "      <td>175</td>\n",
       "      <td>68</td>\n",
       "      <td>73.00</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>French Ligue 1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>LW</td>\n",
       "      <td>10.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>82+3</td>\n",
       "      <td>82+3</td>\n",
       "      <td>82+3</td>\n",
       "      <td>89+2</td>\n",
       "      <td>67+3</td>\n",
       "      <td>63+3</td>\n",
       "      <td>63+3</td>\n",
       "      <td>63+3</td>\n",
       "      <td>67+3</td>\n",
       "      <td>62+3</td>\n",
       "      <td>50+3</td>\n",
       "      <td>50+3</td>\n",
       "      <td>50+3</td>\n",
       "      <td>62+3</td>\n",
       "      <td>20+3</td>\n",
       "      <td>https://cdn.sofifa.net/players/190/871/22_120.png</td>\n",
       "      <td>https://cdn.sofifa.net/teams/73/60.png</td>\n",
       "      <td>https://cdn.sofifa.net/flags/fr.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://cdn.sofifa.net/flags/br.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>192985</td>\n",
       "      <td>https://sofifa.com/player/192985/kevin-de-bruy...</td>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>Kevin De Bruyne</td>\n",
       "      <td>CM, CAM</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>125500000.00</td>\n",
       "      <td>350000.00</td>\n",
       "      <td>30</td>\n",
       "      <td>1991-06-28</td>\n",
       "      <td>181</td>\n",
       "      <td>70</td>\n",
       "      <td>10.00</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>English Premier League</td>\n",
       "      <td>1.00</td>\n",
       "      <td>RCM</td>\n",
       "      <td>17.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>89+2</td>\n",
       "      <td>89+2</td>\n",
       "      <td>89+2</td>\n",
       "      <td>89+2</td>\n",
       "      <td>79+3</td>\n",
       "      <td>80+3</td>\n",
       "      <td>80+3</td>\n",
       "      <td>80+3</td>\n",
       "      <td>79+3</td>\n",
       "      <td>75+3</td>\n",
       "      <td>69+3</td>\n",
       "      <td>69+3</td>\n",
       "      <td>69+3</td>\n",
       "      <td>75+3</td>\n",
       "      <td>21+3</td>\n",
       "      <td>https://cdn.sofifa.net/players/192/985/22_120.png</td>\n",
       "      <td>https://cdn.sofifa.net/teams/10/60.png</td>\n",
       "      <td>https://cdn.sofifa.net/flags/gb-eng.png</td>\n",
       "      <td>https://cdn.sofifa.net/teams/1325/60.png</td>\n",
       "      <td>https://cdn.sofifa.net/flags/be.png</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",
       "      <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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19221</th>\n",
       "      <td>243790</td>\n",
       "      <td>https://sofifa.com/player/243790/zhenao-wang/2...</td>\n",
       "      <td>Wang Zhen'ao</td>\n",
       "      <td>王振澳</td>\n",
       "      <td>RW</td>\n",
       "      <td>47</td>\n",
       "      <td>57</td>\n",
       "      <td>110000.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>21</td>\n",
       "      <td>1999-08-10</td>\n",
       "      <td>170</td>\n",
       "      <td>58</td>\n",
       "      <td>112378.00</td>\n",
       "      <td>Dalian Professional Football Club</td>\n",
       "      <td>Chinese Super League</td>\n",
       "      <td>1.00</td>\n",
       "      <td>SUB</td>\n",
       "      <td>34.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>41+2</td>\n",
       "      <td>41+2</td>\n",
       "      <td>41+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>40+2</td>\n",
       "      <td>36+2</td>\n",
       "      <td>36+2</td>\n",
       "      <td>36+2</td>\n",
       "      <td>40+2</td>\n",
       "      <td>39+2</td>\n",
       "      <td>35+2</td>\n",
       "      <td>35+2</td>\n",
       "      <td>35+2</td>\n",
       "      <td>39+2</td>\n",
       "      <td>14+2</td>\n",
       "      <td>https://cdn.sofifa.net/players/243/790/22_120.png</td>\n",
       "      <td>https://cdn.sofifa.net/teams/112378/60.png</td>\n",
       "      <td>https://cdn.sofifa.net/flags/cn.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://cdn.sofifa.net/flags/cn.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19222</th>\n",
       "      <td>245024</td>\n",
       "      <td>https://sofifa.com/player/245024/shilong-wang/...</td>\n",
       "      <td>Wang Shilong</td>\n",
       "      <td>王世龙</td>\n",
       "      <td>CB</td>\n",
       "      <td>47</td>\n",
       "      <td>60</td>\n",
       "      <td>110000.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>20</td>\n",
       "      <td>2001-03-07</td>\n",
       "      <td>177</td>\n",
       "      <td>64</td>\n",
       "      <td>111839.00</td>\n",
       "      <td>Guangzhou FC</td>\n",
       "      <td>Chinese Super League</td>\n",
       "      <td>1.00</td>\n",
       "      <td>SUB</td>\n",
       "      <td>20.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>33+2</td>\n",
       "      <td>33+2</td>\n",
       "      <td>33+2</td>\n",
       "      <td>34+2</td>\n",
       "      <td>45+2</td>\n",
       "      <td>42+2</td>\n",
       "      <td>42+2</td>\n",
       "      <td>42+2</td>\n",
       "      <td>45+2</td>\n",
       "      <td>47+2</td>\n",
       "      <td>47+2</td>\n",
       "      <td>47+2</td>\n",
       "      <td>47+2</td>\n",
       "      <td>47+2</td>\n",
       "      <td>13+2</td>\n",
       "      <td>https://cdn.sofifa.net/players/245/024/22_120.png</td>\n",
       "      <td>https://cdn.sofifa.net/teams/111839/60.png</td>\n",
       "      <td>https://cdn.sofifa.net/flags/cn.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://cdn.sofifa.net/flags/cn.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19223</th>\n",
       "      <td>255615</td>\n",
       "      <td>https://sofifa.com/player/255615/xinfeng-fang/...</td>\n",
       "      <td>Fang Xinfeng</td>\n",
       "      <td>房欣峰</td>\n",
       "      <td>CDM</td>\n",
       "      <td>47</td>\n",
       "      <td>57</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>21</td>\n",
       "      <td>1999-07-21</td>\n",
       "      <td>189</td>\n",
       "      <td>79</td>\n",
       "      <td>112961.00</td>\n",
       "      <td>Qingdao FC</td>\n",
       "      <td>Chinese Super League</td>\n",
       "      <td>1.00</td>\n",
       "      <td>SUB</td>\n",
       "      <td>3.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>43+2</td>\n",
       "      <td>43+2</td>\n",
       "      <td>43+2</td>\n",
       "      <td>43+2</td>\n",
       "      <td>45+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>45+2</td>\n",
       "      <td>45+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>45+2</td>\n",
       "      <td>14+2</td>\n",
       "      <td>https://cdn.sofifa.net/players/255/615/22_120.png</td>\n",
       "      <td>https://cdn.sofifa.net/teams/112961/60.png</td>\n",
       "      <td>https://cdn.sofifa.net/flags/cn.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://cdn.sofifa.net/flags/cn.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19224</th>\n",
       "      <td>257687</td>\n",
       "      <td>https://sofifa.com/player/257687/xingyu-hu/220002</td>\n",
       "      <td>Hu Xingyu</td>\n",
       "      <td>胡兴雨</td>\n",
       "      <td>CM</td>\n",
       "      <td>47</td>\n",
       "      <td>55</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>800.00</td>\n",
       "      <td>19</td>\n",
       "      <td>2001-07-21</td>\n",
       "      <td>178</td>\n",
       "      <td>68</td>\n",
       "      <td>112165.00</td>\n",
       "      <td>Chongqing Liangjiang Athletic</td>\n",
       "      <td>Chinese Super League</td>\n",
       "      <td>1.00</td>\n",
       "      <td>SUB</td>\n",
       "      <td>21.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>47+2</td>\n",
       "      <td>47+2</td>\n",
       "      <td>47+2</td>\n",
       "      <td>47+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>44+2</td>\n",
       "      <td>44+2</td>\n",
       "      <td>44+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>15+2</td>\n",
       "      <td>https://cdn.sofifa.net/players/257/687/22_120.png</td>\n",
       "      <td>https://cdn.sofifa.net/teams/112165/60.png</td>\n",
       "      <td>https://cdn.sofifa.net/flags/cn.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://cdn.sofifa.net/flags/cn.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19234</th>\n",
       "      <td>261962</td>\n",
       "      <td>https://sofifa.com/player/261962/defu-song/220002</td>\n",
       "      <td>Song Defu</td>\n",
       "      <td>宋德福</td>\n",
       "      <td>CDM</td>\n",
       "      <td>47</td>\n",
       "      <td>52</td>\n",
       "      <td>70000.00</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>22</td>\n",
       "      <td>1999-01-02</td>\n",
       "      <td>180</td>\n",
       "      <td>64</td>\n",
       "      <td>112541.00</td>\n",
       "      <td>Wuhan FC</td>\n",
       "      <td>Chinese Super League</td>\n",
       "      <td>1.00</td>\n",
       "      <td>SUB</td>\n",
       "      <td>36.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>46+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>47+2</td>\n",
       "      <td>48+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>48+2</td>\n",
       "      <td>48+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>48+2</td>\n",
       "      <td>15+2</td>\n",
       "      <td>https://cdn.sofifa.net/players/261/962/22_120.png</td>\n",
       "      <td>https://cdn.sofifa.net/teams/112541/60.png</td>\n",
       "      <td>https://cdn.sofifa.net/flags/cn.png</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://cdn.sofifa.net/flags/cn.png</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13736 rows × 110 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       sofifa_id                                         player_url  \\\n",
       "0         158023  https://sofifa.com/player/158023/lionel-messi/...   \n",
       "1         188545  https://sofifa.com/player/188545/robert-lewand...   \n",
       "2          20801  https://sofifa.com/player/20801/c-ronaldo-dos-...   \n",
       "3         190871  https://sofifa.com/player/190871/neymar-da-sil...   \n",
       "4         192985  https://sofifa.com/player/192985/kevin-de-bruy...   \n",
       "...          ...                                                ...   \n",
       "19221     243790  https://sofifa.com/player/243790/zhenao-wang/2...   \n",
       "19222     245024  https://sofifa.com/player/245024/shilong-wang/...   \n",
       "19223     255615  https://sofifa.com/player/255615/xinfeng-fang/...   \n",
       "19224     257687  https://sofifa.com/player/257687/xingyu-hu/220002   \n",
       "19234     261962  https://sofifa.com/player/261962/defu-song/220002   \n",
       "\n",
       "              short_name                            long_name  \\\n",
       "0               L. Messi       Lionel Andrés Messi Cuccittini   \n",
       "1         R. Lewandowski                   Robert Lewandowski   \n",
       "2      Cristiano Ronaldo  Cristiano Ronaldo dos Santos Aveiro   \n",
       "3              Neymar Jr        Neymar da Silva Santos Júnior   \n",
       "4           K. De Bruyne                      Kevin De Bruyne   \n",
       "...                  ...                                  ...   \n",
       "19221       Wang Zhen'ao                                  王振澳   \n",
       "19222       Wang Shilong                                  王世龙   \n",
       "19223       Fang Xinfeng                                  房欣峰   \n",
       "19224          Hu Xingyu                                  胡兴雨   \n",
       "19234          Song Defu                                  宋德福   \n",
       "\n",
       "      player_positions  overall  potential    value_eur  wage_eur  age  \\\n",
       "0           RW, ST, CF       93         93  78000000.00 320000.00   34   \n",
       "1                   ST       92         92 119500000.00 270000.00   32   \n",
       "2               ST, LW       91         91  45000000.00 270000.00   36   \n",
       "3              LW, CAM       91         91 129000000.00 270000.00   29   \n",
       "4              CM, CAM       91         91 125500000.00 350000.00   30   \n",
       "...                ...      ...        ...          ...       ...  ...   \n",
       "19221               RW       47         57    110000.00   2000.00   21   \n",
       "19222               CB       47         60    110000.00   2000.00   20   \n",
       "19223              CDM       47         57    100000.00   1000.00   21   \n",
       "19224               CM       47         55    100000.00    800.00   19   \n",
       "19234              CDM       47         52     70000.00   1000.00   22   \n",
       "\n",
       "              dob  height_cm  weight_kg  club_team_id  \\\n",
       "0      1987-06-24        170         72         73.00   \n",
       "1      1988-08-21        185         81         21.00   \n",
       "2      1985-02-05        187         83         11.00   \n",
       "3      1992-02-05        175         68         73.00   \n",
       "4      1991-06-28        181         70         10.00   \n",
       "...           ...        ...        ...           ...   \n",
       "19221  1999-08-10        170         58     112378.00   \n",
       "19222  2001-03-07        177         64     111839.00   \n",
       "19223  1999-07-21        189         79     112961.00   \n",
       "19224  2001-07-21        178         68     112165.00   \n",
       "19234  1999-01-02        180         64     112541.00   \n",
       "\n",
       "                               club_name             league_name  \\\n",
       "0                    Paris Saint-Germain          French Ligue 1   \n",
       "1                      FC Bayern München    German 1. Bundesliga   \n",
       "2                      Manchester United  English Premier League   \n",
       "3                    Paris Saint-Germain          French Ligue 1   \n",
       "4                        Manchester City  English Premier League   \n",
       "...                                  ...                     ...   \n",
       "19221  Dalian Professional Football Club    Chinese Super League   \n",
       "19222                       Guangzhou FC    Chinese Super League   \n",
       "19223                         Qingdao FC    Chinese Super League   \n",
       "19224      Chongqing Liangjiang Athletic    Chinese Super League   \n",
       "19234                           Wuhan FC    Chinese Super League   \n",
       "\n",
       "       league_level club_position  club_jersey_number club_loaned_from  ...  \\\n",
       "0              1.00            RW               30.00              NaN  ...   \n",
       "1              1.00            ST                9.00              NaN  ...   \n",
       "2              1.00            ST                7.00              NaN  ...   \n",
       "3              1.00            LW               10.00              NaN  ...   \n",
       "4              1.00           RCM               17.00              NaN  ...   \n",
       "...             ...           ...                 ...              ...  ...   \n",
       "19221          1.00           SUB               34.00              NaN  ...   \n",
       "19222          1.00           SUB               20.00              NaN  ...   \n",
       "19223          1.00           SUB                3.00              NaN  ...   \n",
       "19224          1.00           SUB               21.00              NaN  ...   \n",
       "19234          1.00           SUB               36.00              NaN  ...   \n",
       "\n",
       "        lcm    cm   rcm    rm   lwb   ldm   cdm   rdm   rwb    lb   lcb    cb  \\\n",
       "0      87+3  87+3  87+3  91+2  66+3  64+3  64+3  64+3  66+3  61+3  50+3  50+3   \n",
       "1      80+3  80+3  80+3  84+3  64+3  66+3  66+3  66+3  64+3  61+3  60+3  60+3   \n",
       "2      78+3  78+3  78+3  86+3  63+3  59+3  59+3  59+3  63+3  60+3  53+3  53+3   \n",
       "3      82+3  82+3  82+3  89+2  67+3  63+3  63+3  63+3  67+3  62+3  50+3  50+3   \n",
       "4      89+2  89+2  89+2  89+2  79+3  80+3  80+3  80+3  79+3  75+3  69+3  69+3   \n",
       "...     ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   \n",
       "19221  41+2  41+2  41+2  46+2  40+2  36+2  36+2  36+2  40+2  39+2  35+2  35+2   \n",
       "19222  33+2  33+2  33+2  34+2  45+2  42+2  42+2  42+2  45+2  47+2  47+2  47+2   \n",
       "19223  43+2  43+2  43+2  43+2  45+2  46+2  46+2  46+2  45+2  45+2  46+2  46+2   \n",
       "19224  47+2  47+2  47+2  47+2  46+2  46+2  46+2  46+2  46+2  46+2  44+2  44+2   \n",
       "19234  46+2  46+2  46+2  47+2  48+2  46+2  46+2  46+2  48+2  48+2  46+2  46+2   \n",
       "\n",
       "        rcb    rb    gk                                    player_face_url  \\\n",
       "0      50+3  61+3  19+3  https://cdn.sofifa.net/players/158/023/22_120.png   \n",
       "1      60+3  61+3  19+3  https://cdn.sofifa.net/players/188/545/22_120.png   \n",
       "2      53+3  60+3  20+3  https://cdn.sofifa.net/players/020/801/22_120.png   \n",
       "3      50+3  62+3  20+3  https://cdn.sofifa.net/players/190/871/22_120.png   \n",
       "4      69+3  75+3  21+3  https://cdn.sofifa.net/players/192/985/22_120.png   \n",
       "...     ...   ...   ...                                                ...   \n",
       "19221  35+2  39+2  14+2  https://cdn.sofifa.net/players/243/790/22_120.png   \n",
       "19222  47+2  47+2  13+2  https://cdn.sofifa.net/players/245/024/22_120.png   \n",
       "19223  46+2  45+2  14+2  https://cdn.sofifa.net/players/255/615/22_120.png   \n",
       "19224  44+2  46+2  15+2  https://cdn.sofifa.net/players/257/687/22_120.png   \n",
       "19234  46+2  48+2  15+2  https://cdn.sofifa.net/players/261/962/22_120.png   \n",
       "\n",
       "                                    club_logo_url  \\\n",
       "0          https://cdn.sofifa.net/teams/73/60.png   \n",
       "1          https://cdn.sofifa.net/teams/21/60.png   \n",
       "2          https://cdn.sofifa.net/teams/11/60.png   \n",
       "3          https://cdn.sofifa.net/teams/73/60.png   \n",
       "4          https://cdn.sofifa.net/teams/10/60.png   \n",
       "...                                           ...   \n",
       "19221  https://cdn.sofifa.net/teams/112378/60.png   \n",
       "19222  https://cdn.sofifa.net/teams/111839/60.png   \n",
       "19223  https://cdn.sofifa.net/teams/112961/60.png   \n",
       "19224  https://cdn.sofifa.net/teams/112165/60.png   \n",
       "19234  https://cdn.sofifa.net/teams/112541/60.png   \n",
       "\n",
       "                                 club_flag_url  \\\n",
       "0          https://cdn.sofifa.net/flags/fr.png   \n",
       "1          https://cdn.sofifa.net/flags/de.png   \n",
       "2      https://cdn.sofifa.net/flags/gb-eng.png   \n",
       "3          https://cdn.sofifa.net/flags/fr.png   \n",
       "4      https://cdn.sofifa.net/flags/gb-eng.png   \n",
       "...                                        ...   \n",
       "19221      https://cdn.sofifa.net/flags/cn.png   \n",
       "19222      https://cdn.sofifa.net/flags/cn.png   \n",
       "19223      https://cdn.sofifa.net/flags/cn.png   \n",
       "19224      https://cdn.sofifa.net/flags/cn.png   \n",
       "19234      https://cdn.sofifa.net/flags/cn.png   \n",
       "\n",
       "                                nation_logo_url  \\\n",
       "0      https://cdn.sofifa.net/teams/1369/60.png   \n",
       "1      https://cdn.sofifa.net/teams/1353/60.png   \n",
       "2      https://cdn.sofifa.net/teams/1354/60.png   \n",
       "3                                           NaN   \n",
       "4      https://cdn.sofifa.net/teams/1325/60.png   \n",
       "...                                         ...   \n",
       "19221                                       NaN   \n",
       "19222                                       NaN   \n",
       "19223                                       NaN   \n",
       "19224                                       NaN   \n",
       "19234                                       NaN   \n",
       "\n",
       "                           nation_flag_url  \n",
       "0      https://cdn.sofifa.net/flags/ar.png  \n",
       "1      https://cdn.sofifa.net/flags/pl.png  \n",
       "2      https://cdn.sofifa.net/flags/pt.png  \n",
       "3      https://cdn.sofifa.net/flags/br.png  \n",
       "4      https://cdn.sofifa.net/flags/be.png  \n",
       "...                                    ...  \n",
       "19221  https://cdn.sofifa.net/flags/cn.png  \n",
       "19222  https://cdn.sofifa.net/flags/cn.png  \n",
       "19223  https://cdn.sofifa.net/flags/cn.png  \n",
       "19224  https://cdn.sofifa.net/flags/cn.png  \n",
       "19234  https://cdn.sofifa.net/flags/cn.png  \n",
       "\n",
       "[13736 rows x 110 columns]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filter = df_player[df_player.nationality_name.isin(countries)]\n",
    "df_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "a47e25ab-dbdd-441b-b416-17b0d615d04f",
   "metadata": {},
   "outputs": [],
   "source": [
    "attr = ['sofifa_id', 'short_name', 'overall', 'player_positions', 'value_eur', 'league_level', 'wage_eur', 'height_cm', 'weight_kg', 'club_name', 'nationality_name', 'real_face', 'player_face_url', 'shooting', 'passing', 'dribbling', 'defending', \n",
    "       'attacking_crossing', 'attacking_finishing', 'attacking_heading_accuracy', 'attacking_short_passing', 'attacking_volleys', \n",
    "       'skill_dribbling', 'skill_curve', 'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control',\n",
    "       'movement_acceleration', 'movement_sprint_speed', 'movement_agility', 'movement_reactions', 'movement_balance', \n",
    "       'power_shot_power', 'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots',\n",
    "       'mentality_aggression', 'mentality_interceptions', 'mentality_positioning', 'mentality_vision', 'mentality_penalties', 'mentality_composure',\n",
    "       'defending_marking_awareness', 'defending_standing_tackle', 'defending_sliding_tackle']\n",
    "\n",
    "\n",
    "df_filter = df_filter[attr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "da192bc4-4d83-4a68-ad54-7abbc6417c5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filter = df_filter[df_filter.league_level == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "4d129e18-4a09-447d-a4f9-37300a88811f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filter['attacking'] =  (df_filter['attacking_crossing'] + df_filter['attacking_finishing'] + df_filter['attacking_heading_accuracy'] + df_filter['attacking_short_passing'] + df_filter['attacking_volleys']) / 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "2bd5d7e0-aaf7-497c-8c8b-43c3c6cc62eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filter['skill'] =  (df_filter['skill_dribbling'] + df_filter['skill_curve'] + df_filter['skill_fk_accuracy'] + df_filter['skill_long_passing'] + df_filter['skill_ball_control']) / 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "9d3e9b90-adfb-4a9b-acc7-c6bec26d5712",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filter['movement'] =  (df_filter['movement_acceleration'] + df_filter['movement_sprint_speed'] + df_filter['movement_agility'] + df_filter['movement_reactions'] + df_filter['movement_balance']) / 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "aad3ce8f-f2c9-4285-ac8e-b84aac2bc6e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filter['power'] =  (df_filter['power_shot_power'] + df_filter['power_jumping'] + df_filter['power_stamina'] + df_filter['power_strength'] + df_filter['power_long_shots']) / 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "e8141a8c-3963-4ddd-a2c4-30a6505779dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filter['defending'] =  (df_filter['defending_marking_awareness'] + df_filter['defending_standing_tackle'] + df_filter['defending_sliding_tackle']) / 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "cdcdbd73-b66e-468d-af49-56e54ec9b73e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filter['mentality'] =  (df_filter['mentality_aggression'] + df_filter['mentality_interceptions'] + df_filter['mentality_positioning'] + df_filter['mentality_penalties'] + df_filter['mentality_vision'] + df_filter['mentality_composure']) / 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "2f3c617e-da14-4d50-8e96-3676d90c20b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filter = df_filter[['sofifa_id', 'short_name', 'overall', 'player_positions', 'value_eur', 'league_level', 'wage_eur', 'height_cm', 'weight_kg', 'player_face_url',\n",
    "                       'club_name', 'nationality_name', 'real_face', 'shooting', 'passing', 'dribbling', 'defending', 'attacking', \n",
    "                       'skill', 'movement', 'power', 'defending', 'mentality']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "d7dd9baf-0872-41a0-8b88-f80997fa5935",
   "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>sofifa_id</th>\n",
       "      <th>short_name</th>\n",
       "      <th>overall</th>\n",
       "      <th>player_positions</th>\n",
       "      <th>value_eur</th>\n",
       "      <th>league_level</th>\n",
       "      <th>wage_eur</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>player_face_url</th>\n",
       "      <th>club_name</th>\n",
       "      <th>nationality_name</th>\n",
       "      <th>real_face</th>\n",
       "      <th>shooting</th>\n",
       "      <th>passing</th>\n",
       "      <th>dribbling</th>\n",
       "      <th>defending</th>\n",
       "      <th>attacking</th>\n",
       "      <th>skill</th>\n",
       "      <th>movement</th>\n",
       "      <th>power</th>\n",
       "      <th>defending</th>\n",
       "      <th>mentality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>158023</td>\n",
       "      <td>L. Messi</td>\n",
       "      <td>93</td>\n",
       "      <td>RW, ST, CF</td>\n",
       "      <td>78000000.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>320000.00</td>\n",
       "      <td>170</td>\n",
       "      <td>72</td>\n",
       "      <td>https://cdn.sofifa.net/players/158/023/22_120.png</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Yes</td>\n",
       "      <td>92.00</td>\n",
       "      <td>91.00</td>\n",
       "      <td>95.00</td>\n",
       "      <td>26.33</td>\n",
       "      <td>85.80</td>\n",
       "      <td>94.00</td>\n",
       "      <td>90.20</td>\n",
       "      <td>77.80</td>\n",
       "      <td>26.33</td>\n",
       "      <td>73.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>188545</td>\n",
       "      <td>R. Lewandowski</td>\n",
       "      <td>92</td>\n",
       "      <td>ST</td>\n",
       "      <td>119500000.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>270000.00</td>\n",
       "      <td>185</td>\n",
       "      <td>81</td>\n",
       "      <td>https://cdn.sofifa.net/players/188/545/22_120.png</td>\n",
       "      <td>FC Bayern München</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Yes</td>\n",
       "      <td>92.00</td>\n",
       "      <td>79.00</td>\n",
       "      <td>86.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>86.00</td>\n",
       "      <td>81.40</td>\n",
       "      <td>81.60</td>\n",
       "      <td>84.80</td>\n",
       "      <td>32.00</td>\n",
       "      <td>80.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20801</td>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>91</td>\n",
       "      <td>ST, LW</td>\n",
       "      <td>45000000.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>270000.00</td>\n",
       "      <td>187</td>\n",
       "      <td>83</td>\n",
       "      <td>https://cdn.sofifa.net/players/020/801/22_120.png</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Yes</td>\n",
       "      <td>94.00</td>\n",
       "      <td>80.00</td>\n",
       "      <td>88.00</td>\n",
       "      <td>26.67</td>\n",
       "      <td>87.60</td>\n",
       "      <td>83.60</td>\n",
       "      <td>85.40</td>\n",
       "      <td>87.20</td>\n",
       "      <td>26.67</td>\n",
       "      <td>74.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>190871</td>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>91</td>\n",
       "      <td>LW, CAM</td>\n",
       "      <td>129000000.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>270000.00</td>\n",
       "      <td>175</td>\n",
       "      <td>68</td>\n",
       "      <td>https://cdn.sofifa.net/players/190/871/22_120.png</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Yes</td>\n",
       "      <td>83.00</td>\n",
       "      <td>86.00</td>\n",
       "      <td>94.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>80.60</td>\n",
       "      <td>89.20</td>\n",
       "      <td>90.20</td>\n",
       "      <td>71.80</td>\n",
       "      <td>32.00</td>\n",
       "      <td>77.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>192985</td>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>91</td>\n",
       "      <td>CM, CAM</td>\n",
       "      <td>125500000.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>350000.00</td>\n",
       "      <td>181</td>\n",
       "      <td>70</td>\n",
       "      <td>https://cdn.sofifa.net/players/192/985/22_120.png</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Yes</td>\n",
       "      <td>86.00</td>\n",
       "      <td>93.00</td>\n",
       "      <td>88.00</td>\n",
       "      <td>62.00</td>\n",
       "      <td>81.40</td>\n",
       "      <td>88.00</td>\n",
       "      <td>80.00</td>\n",
       "      <td>81.60</td>\n",
       "      <td>62.00</td>\n",
       "      <td>82.67</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19221</th>\n",
       "      <td>243790</td>\n",
       "      <td>Wang Zhen'ao</td>\n",
       "      <td>47</td>\n",
       "      <td>RW</td>\n",
       "      <td>110000.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>170</td>\n",
       "      <td>58</td>\n",
       "      <td>https://cdn.sofifa.net/players/243/790/22_120.png</td>\n",
       "      <td>Dalian Professional Football Club</td>\n",
       "      <td>China PR</td>\n",
       "      <td>No</td>\n",
       "      <td>49.00</td>\n",
       "      <td>41.00</td>\n",
       "      <td>48.00</td>\n",
       "      <td>33.33</td>\n",
       "      <td>43.00</td>\n",
       "      <td>38.80</td>\n",
       "      <td>50.80</td>\n",
       "      <td>47.00</td>\n",
       "      <td>33.33</td>\n",
       "      <td>39.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19222</th>\n",
       "      <td>245024</td>\n",
       "      <td>Wang Shilong</td>\n",
       "      <td>47</td>\n",
       "      <td>CB</td>\n",
       "      <td>110000.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>177</td>\n",
       "      <td>64</td>\n",
       "      <td>https://cdn.sofifa.net/players/245/024/22_120.png</td>\n",
       "      <td>Guangzhou FC</td>\n",
       "      <td>China PR</td>\n",
       "      <td>No</td>\n",
       "      <td>23.00</td>\n",
       "      <td>27.00</td>\n",
       "      <td>33.00</td>\n",
       "      <td>53.00</td>\n",
       "      <td>27.60</td>\n",
       "      <td>25.20</td>\n",
       "      <td>58.60</td>\n",
       "      <td>45.20</td>\n",
       "      <td>53.00</td>\n",
       "      <td>38.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19223</th>\n",
       "      <td>255615</td>\n",
       "      <td>Fang Xinfeng</td>\n",
       "      <td>47</td>\n",
       "      <td>CDM</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>189</td>\n",
       "      <td>79</td>\n",
       "      <td>https://cdn.sofifa.net/players/255/615/22_120.png</td>\n",
       "      <td>Qingdao FC</td>\n",
       "      <td>China PR</td>\n",
       "      <td>No</td>\n",
       "      <td>31.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>44.00</td>\n",
       "      <td>44.00</td>\n",
       "      <td>36.60</td>\n",
       "      <td>41.80</td>\n",
       "      <td>56.00</td>\n",
       "      <td>45.20</td>\n",
       "      <td>44.00</td>\n",
       "      <td>41.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19224</th>\n",
       "      <td>257687</td>\n",
       "      <td>Hu Xingyu</td>\n",
       "      <td>47</td>\n",
       "      <td>CM</td>\n",
       "      <td>100000.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>800.00</td>\n",
       "      <td>178</td>\n",
       "      <td>68</td>\n",
       "      <td>https://cdn.sofifa.net/players/257/687/22_120.png</td>\n",
       "      <td>Chongqing Liangjiang Athletic</td>\n",
       "      <td>China PR</td>\n",
       "      <td>No</td>\n",
       "      <td>33.00</td>\n",
       "      <td>47.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>38.40</td>\n",
       "      <td>40.80</td>\n",
       "      <td>60.20</td>\n",
       "      <td>49.20</td>\n",
       "      <td>40.00</td>\n",
       "      <td>43.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19234</th>\n",
       "      <td>261962</td>\n",
       "      <td>Song Defu</td>\n",
       "      <td>47</td>\n",
       "      <td>CDM</td>\n",
       "      <td>70000.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>180</td>\n",
       "      <td>64</td>\n",
       "      <td>https://cdn.sofifa.net/players/261/962/22_120.png</td>\n",
       "      <td>Wuhan FC</td>\n",
       "      <td>China PR</td>\n",
       "      <td>No</td>\n",
       "      <td>35.00</td>\n",
       "      <td>46.00</td>\n",
       "      <td>48.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>41.20</td>\n",
       "      <td>42.60</td>\n",
       "      <td>58.80</td>\n",
       "      <td>47.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>42.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10233 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       sofifa_id         short_name  overall player_positions    value_eur  \\\n",
       "0         158023           L. Messi       93       RW, ST, CF  78000000.00   \n",
       "1         188545     R. Lewandowski       92               ST 119500000.00   \n",
       "2          20801  Cristiano Ronaldo       91           ST, LW  45000000.00   \n",
       "3         190871          Neymar Jr       91          LW, CAM 129000000.00   \n",
       "4         192985       K. De Bruyne       91          CM, CAM 125500000.00   \n",
       "...          ...                ...      ...              ...          ...   \n",
       "19221     243790       Wang Zhen'ao       47               RW    110000.00   \n",
       "19222     245024       Wang Shilong       47               CB    110000.00   \n",
       "19223     255615       Fang Xinfeng       47              CDM    100000.00   \n",
       "19224     257687          Hu Xingyu       47               CM    100000.00   \n",
       "19234     261962          Song Defu       47              CDM     70000.00   \n",
       "\n",
       "       league_level  wage_eur  height_cm  weight_kg  \\\n",
       "0              1.00 320000.00        170         72   \n",
       "1              1.00 270000.00        185         81   \n",
       "2              1.00 270000.00        187         83   \n",
       "3              1.00 270000.00        175         68   \n",
       "4              1.00 350000.00        181         70   \n",
       "...             ...       ...        ...        ...   \n",
       "19221          1.00   2000.00        170         58   \n",
       "19222          1.00   2000.00        177         64   \n",
       "19223          1.00   1000.00        189         79   \n",
       "19224          1.00    800.00        178         68   \n",
       "19234          1.00   1000.00        180         64   \n",
       "\n",
       "                                         player_face_url  \\\n",
       "0      https://cdn.sofifa.net/players/158/023/22_120.png   \n",
       "1      https://cdn.sofifa.net/players/188/545/22_120.png   \n",
       "2      https://cdn.sofifa.net/players/020/801/22_120.png   \n",
       "3      https://cdn.sofifa.net/players/190/871/22_120.png   \n",
       "4      https://cdn.sofifa.net/players/192/985/22_120.png   \n",
       "...                                                  ...   \n",
       "19221  https://cdn.sofifa.net/players/243/790/22_120.png   \n",
       "19222  https://cdn.sofifa.net/players/245/024/22_120.png   \n",
       "19223  https://cdn.sofifa.net/players/255/615/22_120.png   \n",
       "19224  https://cdn.sofifa.net/players/257/687/22_120.png   \n",
       "19234  https://cdn.sofifa.net/players/261/962/22_120.png   \n",
       "\n",
       "                               club_name nationality_name real_face  shooting  \\\n",
       "0                    Paris Saint-Germain        Argentina       Yes     92.00   \n",
       "1                      FC Bayern München           Poland       Yes     92.00   \n",
       "2                      Manchester United         Portugal       Yes     94.00   \n",
       "3                    Paris Saint-Germain           Brazil       Yes     83.00   \n",
       "4                        Manchester City          Belgium       Yes     86.00   \n",
       "...                                  ...              ...       ...       ...   \n",
       "19221  Dalian Professional Football Club         China PR        No     49.00   \n",
       "19222                       Guangzhou FC         China PR        No     23.00   \n",
       "19223                         Qingdao FC         China PR        No     31.00   \n",
       "19224      Chongqing Liangjiang Athletic         China PR        No     33.00   \n",
       "19234                           Wuhan FC         China PR        No     35.00   \n",
       "\n",
       "       passing  dribbling  defending  attacking  skill  movement  power  \\\n",
       "0        91.00      95.00      26.33      85.80  94.00     90.20  77.80   \n",
       "1        79.00      86.00      32.00      86.00  81.40     81.60  84.80   \n",
       "2        80.00      88.00      26.67      87.60  83.60     85.40  87.20   \n",
       "3        86.00      94.00      32.00      80.60  89.20     90.20  71.80   \n",
       "4        93.00      88.00      62.00      81.40  88.00     80.00  81.60   \n",
       "...        ...        ...        ...        ...    ...       ...    ...   \n",
       "19221    41.00      48.00      33.33      43.00  38.80     50.80  47.00   \n",
       "19222    27.00      33.00      53.00      27.60  25.20     58.60  45.20   \n",
       "19223    43.00      44.00      44.00      36.60  41.80     56.00  45.20   \n",
       "19224    47.00      45.00      40.00      38.40  40.80     60.20  49.20   \n",
       "19234    46.00      48.00      43.00      41.20  42.60     58.80  47.00   \n",
       "\n",
       "       defending  mentality  \n",
       "0          26.33      73.83  \n",
       "1          32.00      80.67  \n",
       "2          26.67      74.33  \n",
       "3          32.00      77.00  \n",
       "4          62.00      82.67  \n",
       "...          ...        ...  \n",
       "19221      33.33      39.67  \n",
       "19222      53.00      38.17  \n",
       "19223      44.00      41.50  \n",
       "19224      40.00      43.67  \n",
       "19234      43.00      42.33  \n",
       "\n",
       "[10233 rows x 23 columns]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "4f986832-decd-49d9-bad1-bdc69a1e65c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filter.to_csv(r'./data/players.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e6b7911-e10a-4c22-aff2-3d7a19b25137",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "0fa0ce59-4063-4fe1-b962-929a3ea477c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1872-11-30</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1873-03-08</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1874-03-07</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1875-03-06</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1876-03-04</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date home_team away_team  home_score  away_score tournament\n",
       "0 1872-11-30  Scotland   England           0           0   Friendly\n",
       "1 1873-03-08   England  Scotland           4           2   Friendly\n",
       "2 1874-03-07  Scotland   England           2           1   Friendly\n",
       "3 1875-03-06   England  Scotland           2           2   Friendly\n",
       "4 1876-03-04  Scotland   England           3           0   Friendly"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_game = pd.read_csv('./Data/match_results.csv', \n",
    "                      parse_dates=['date'],\n",
    "                      usecols=['date', 'home_team', 'away_team', 'home_score', 'away_score', 'tournament'])\n",
    "df_game.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "ca296f5a-fd1a-440c-a30b-711550d38477",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>tournament</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40566</th>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>Oman</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40567</th>\n",
       "      <td>2019-01-05</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>AFC Asian Cup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40568</th>\n",
       "      <td>2019-01-06</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>India</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>40569</th>\n",
       "      <td>2019-01-06</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>AFC Asian Cup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40570</th>\n",
       "      <td>2019-01-06</td>\n",
       "      <td>Syria</td>\n",
       "      <td>Palestine</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>AFC Asian Cup</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "            date             home_team  away_team  home_score  away_score  \\\n",
       "40566 2019-01-02                  Oman   Thailand           2           0   \n",
       "40567 2019-01-05  United Arab Emirates    Bahrain           1           1   \n",
       "40568 2019-01-06              Thailand      India           1           4   \n",
       "40569 2019-01-06             Australia     Jordan           0           1   \n",
       "40570 2019-01-06                 Syria  Palestine           0           0   \n",
       "\n",
       "          tournament  \n",
       "40566       Friendly  \n",
       "40567  AFC Asian Cup  \n",
       "40568  AFC Asian Cup  \n",
       "40569  AFC Asian Cup  \n",
       "40570  AFC Asian Cup  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter data by year and tournament\n",
    "\n",
    "# Only keep games from 2019\n",
    "df_game = df_game[df_game.date.dt.year >= 2019]\n",
    "\n",
    "# Only keep tournament that are methods of qualifaction for world cup, that is:\n",
    "tournaments=['AFC Asian Cup', 'AFC Asian Cup qualification', 'African Cup of Nations', 'African Cup of Nations qualification', 'Copa América', 'Copa América qualification'\n",
    "             'FIFA World Cup', 'FIFA World Cup qualification', 'Oceania Nations Cup', 'UEFA Euro', 'UEFA Euro qualification', 'UEFA Nations League', 'Friendly']\n",
    "\n",
    "df_game = df_game[df_game.tournament.isin(tournaments)]\n",
    "df_game.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "3aaa9b33-6b0e-46c4-965d-1ed9e4e10fa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2166"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_game)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "39fe6790-53be-4f7d-9827-53e00d83de2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_game.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "eccfe5b7-4196-4002-bb99-526802672e22",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2</td>\n",
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       "      <td>2019-01-05</td>\n",
       "      <td>United Arab Emirates</td>\n",
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
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       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>2019-01-06</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>Palestine</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2161</th>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>Suriname</td>\n",
       "      <td>Guyana</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2162</th>\n",
       "      <td>2022-02-02</td>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>Senegal</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>African Cup of Nations</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2163</th>\n",
       "      <td>2022-02-03</td>\n",
       "      <td>Cameroon</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>African Cup of Nations</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2164</th>\n",
       "      <td>2022-02-05</td>\n",
       "      <td>Cameroon</td>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>African Cup of Nations</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2165</th>\n",
       "      <td>2022-02-06</td>\n",
       "      <td>Senegal</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>African Cup of Nations</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2166 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date             home_team     away_team  home_score  away_score  \\\n",
       "0    2019-01-02                  Oman      Thailand           2           0   \n",
       "1    2019-01-05  United Arab Emirates       Bahrain           1           1   \n",
       "2    2019-01-06              Thailand         India           1           4   \n",
       "3    2019-01-06             Australia        Jordan           0           1   \n",
       "4    2019-01-06                 Syria     Palestine           0           0   \n",
       "...         ...                   ...           ...         ...         ...   \n",
       "2161 2022-02-01              Suriname        Guyana           2           1   \n",
       "2162 2022-02-02          Burkina Faso       Senegal           1           3   \n",
       "2163 2022-02-03              Cameroon         Egypt           0           0   \n",
       "2164 2022-02-05              Cameroon  Burkina Faso           3           3   \n",
       "2165 2022-02-06               Senegal         Egypt           0           0   \n",
       "\n",
       "                  tournament  \n",
       "0                   Friendly  \n",
       "1              AFC Asian Cup  \n",
       "2              AFC Asian Cup  \n",
       "3              AFC Asian Cup  \n",
       "4              AFC Asian Cup  \n",
       "...                      ...  \n",
       "2161                Friendly  \n",
       "2162  African Cup of Nations  \n",
       "2163  African Cup of Nations  \n",
       "2164  African Cup of Nations  \n",
       "2165  African Cup of Nations  \n",
       "\n",
       "[2166 rows x 6 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "01217494-d9e3-4db8-aaea-5c9b94859cac",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_game = df_game[df_game.home_score + df_game.away_score != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0670f1f9-c883-49a0-a678-77b7c81934cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_game['delta_goal'] = df_game['home_score'].astype(int) - df_game['away_score'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "117490a8-cda3-46d6-854d-1586d8009b89",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_game['win'] = df_game.apply(lambda x: 1 if x['delta_goal'] > 0 else 0, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "534abc56-2bd1-46ce-af75-3c3e6812b3d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_game['lose'] = 1 - df_game['win']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "49523bac-f66e-417a-816a-a812de50621b",
   "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>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>delta_goal</th>\n",
       "      <th>win</th>\n",
       "      <th>lose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>Oman</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-05</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>AFC Asian Cup</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-01-06</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AFC Asian Cup</td>\n",
       "      <td>-3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-01-06</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>AFC Asian Cup</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>China PR</td>\n",
       "      <td>Kyrgyzstan</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>AFC Asian Cup</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2159</th>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>Nepal</td>\n",
       "      <td>Mauritius</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2160</th>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>Belize</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2161</th>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>Suriname</td>\n",
       "      <td>Guyana</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2162</th>\n",
       "      <td>2022-02-02</td>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>Senegal</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>African Cup of Nations</td>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2164</th>\n",
       "      <td>2022-02-05</td>\n",
       "      <td>Cameroon</td>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>African Cup of Nations</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1976 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date             home_team     away_team  home_score  away_score  \\\n",
       "0    2019-01-02                  Oman      Thailand           2           0   \n",
       "1    2019-01-05  United Arab Emirates       Bahrain           1           1   \n",
       "2    2019-01-06              Thailand         India           1           4   \n",
       "3    2019-01-06             Australia        Jordan           0           1   \n",
       "5    2019-01-07              China PR    Kyrgyzstan           2           1   \n",
       "...         ...                   ...           ...         ...         ...   \n",
       "2159 2022-02-01                 Nepal     Mauritius           1           0   \n",
       "2160 2022-02-01             Nicaragua        Belize           1           1   \n",
       "2161 2022-02-01              Suriname        Guyana           2           1   \n",
       "2162 2022-02-02          Burkina Faso       Senegal           1           3   \n",
       "2164 2022-02-05              Cameroon  Burkina Faso           3           3   \n",
       "\n",
       "                  tournament  delta_goal  win  lose  \n",
       "0                   Friendly           2    1     0  \n",
       "1              AFC Asian Cup           0    0     1  \n",
       "2              AFC Asian Cup          -3    0     1  \n",
       "3              AFC Asian Cup          -1    0     1  \n",
       "5              AFC Asian Cup           1    1     0  \n",
       "...                      ...         ...  ...   ...  \n",
       "2159                Friendly           1    1     0  \n",
       "2160                Friendly           0    0     1  \n",
       "2161                Friendly           1    1     0  \n",
       "2162  African Cup of Nations          -2    0     1  \n",
       "2164  African Cup of Nations           0    0     1  \n",
       "\n",
       "[1976 rows x 9 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "0bc56721-6dd5-4dd3-a3d9-0e127612f7ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_game.to_csv(r'./data/matches_since2019.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "f33ebd95-d2a5-41e9-8c49-b4823ef2b61c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_game.home_score.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "9e218cce-daeb-45d0-a83c-042991ea8cf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Oman', 'United Arab Emirates', 'Thailand', 'Australia',\n",
       "       'China PR', 'South Korea', 'Iran', 'Finland', 'Iraq',\n",
       "       'Saudi Arabia', 'Qatar', 'Japan', 'Uzbekistan', 'Bahrain',\n",
       "       'Jordan', 'Estonia', 'Iceland', 'Palestine', 'Philippines',\n",
       "       'Kyrgyzstan', 'Vietnam', 'Yemen', 'Lebanon', 'North Korea',\n",
       "       'Turkmenistan', 'India', 'United States', 'Kazakhstan', 'Cuba',\n",
       "       'Bolivia', 'Barbados', 'El Salvador', 'Cambodia', 'Bahamas',\n",
       "       'Fiji', 'Solomon Islands', 'Germany', 'Wales', 'Kosovo',\n",
       "       'Mauritius', 'Northern Ireland', 'Netherlands', 'Croatia',\n",
       "       'Slovakia', 'Austria', 'North Macedonia', 'Israel', 'Cyprus',\n",
       "       'Belgium', 'Argentina', 'Guatemala', 'Mexico', 'Peru', 'Bulgaria',\n",
       "       'England', 'Luxembourg', 'Albania', 'Andorra', 'Moldova',\n",
       "       'Botswana', 'Brazil', 'Malaysia', 'Georgia', 'Gibraltar', 'Malta',\n",
       "       'Sweden', 'Spain', 'Bosnia and Herzegovina', 'Italy',\n",
       "       'Liechtenstein', 'Senegal', 'Cameroon', 'Mali', 'Ghana', 'Niger',\n",
       "       'Zambia', 'Taiwan', 'Hungary', 'Poland', 'Slovenia', 'San Marino',\n",
       "       'Benin', 'Libya', 'Zimbabwe', 'Tanzania', 'Catalonia', 'Kuwait',\n",
       "       'Myanmar', 'Montenegro', 'Portugal', 'Turkey', 'France', 'Algeria',\n",
       "       'Costa Rica', 'Czech Republic', 'Ivory Coast', 'Morocco',\n",
       "       'Nicaragua', 'Nigeria', 'Paraguay', 'Republic of Ireland',\n",
       "       'Switzerland', 'Norway', 'Romania', 'Armenia', 'Yorkshire',\n",
       "       'Kernow', 'Cascadia', 'Laos', 'Venezuela', 'Colombia', 'Vanuatu',\n",
       "       'Bermuda', 'Chile', 'Tajikistan', 'Bhutan', 'Macau', 'Mongolia',\n",
       "       'Lithuania', 'Ukraine', 'Denmark', 'Faroe Islands', 'Latvia',\n",
       "       'Kenya', 'Tunisia', 'Uruguay', 'Belarus', 'Azerbaijan', 'Russia',\n",
       "       'Scotland', 'Greece', 'Angola', 'Singapore', 'Serbia', 'Hong Kong',\n",
       "       'Brunei', 'Guam', 'Pakistan', 'Sri Lanka', 'Ynys Môn', 'Egypt',\n",
       "       'Madagascar', 'Indonesia', 'Ecuador', 'DR Congo', 'Guinea',\n",
       "       'Uganda', 'South Africa', 'Burundi', 'Namibia', 'Guinea-Bissau',\n",
       "       'Equatorial Guinea', 'Saint Vincent and the Grenadines',\n",
       "       'Saint Kitts and Nevis', 'Belize', 'Djibouti', 'Eritrea',\n",
       "       'Liberia', 'São Tomé and Príncipe', 'South Sudan', 'Honduras',\n",
       "       'Chad', 'Seychelles', 'Somalia', 'Comoros', 'Gambia', 'Lesotho',\n",
       "       'Sierra Leone', 'Maldives', 'Afghanistan', 'Malawi', 'Mozambique',\n",
       "       'Rwanda', 'Togo', 'Bangladesh', 'Cape Verde', 'Gabon', 'Syria',\n",
       "       'Ethiopia', 'Trinidad and Tobago', 'Sudan',\n",
       "       'Central African Republic', 'Congo', 'Eswatini', 'Mauritania',\n",
       "       'Nepal', 'Canada', 'Burkina Faso', 'Basque Country',\n",
       "       'Dominican Republic', 'Antigua and Barbuda', 'Suriname', 'Curaçao',\n",
       "       'Panama', 'Haiti', 'United States Virgin Islands', 'Aruba',\n",
       "       'British Virgin Islands', 'Anguilla', 'Turks and Caicos Islands',\n",
       "       'Montserrat', 'Dominica', 'Puerto Rico', 'Cayman Islands',\n",
       "       'Grenada', 'Guyana', 'Jamaica', 'New Zealand'], dtype=object)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_game.home_team.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "848c1241-5920-4b21-8f05-36cefc82d18c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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