Radar chart comparing players (usually used for evaluations during scouting).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from soccerplots.radar_chart import Radar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rk</th>\n",
       "      <th>Player</th>\n",
       "      <th>Nation</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Squad</th>\n",
       "      <th>Age</th>\n",
       "      <th>Born</th>\n",
       "      <th>MP</th>\n",
       "      <th>Starts</th>\n",
       "      <th>Min</th>\n",
       "      <th>...</th>\n",
       "      <th>Gls</th>\n",
       "      <th>Ast</th>\n",
       "      <th>G+A</th>\n",
       "      <th>G-PK</th>\n",
       "      <th>G+A-PK</th>\n",
       "      <th>xG</th>\n",
       "      <th>xA</th>\n",
       "      <th>xG+xA</th>\n",
       "      <th>npxG</th>\n",
       "      <th>npxG+xA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Sabit Abdulai\\Sabit-Abdulai</td>\n",
       "      <td>gh GHA</td>\n",
       "      <td>MF</td>\n",
       "      <td>Getafe</td>\n",
       "      <td>21-343</td>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Marcos Acuña\\Marcos-Acuna</td>\n",
       "      <td>ar ARG</td>\n",
       "      <td>DF</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>29-173</td>\n",
       "      <td>1991</td>\n",
       "      <td>24</td>\n",
       "      <td>21</td>\n",
       "      <td>1848</td>\n",
       "      <td>...</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.05</td>\n",
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       "      <td>0.05</td>\n",
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       "      <td>0.15</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Bobby Adekanye\\Bobby-Adekanye</td>\n",
       "      <td>nl NED</td>\n",
       "      <td>FWMF</td>\n",
       "      <td>Cádiz</td>\n",
       "      <td>22-064</td>\n",
       "      <td>1999</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>...</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>0.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Martin Agirregabiria\\Martin-Agirregabiria</td>\n",
       "      <td>es ESP</td>\n",
       "      <td>DFMF</td>\n",
       "      <td>Alavés</td>\n",
       "      <td>24-344</td>\n",
       "      <td>1996</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>1104</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Joseph Aidoo\\Joseph-Aidoo</td>\n",
       "      <td>gh GHA</td>\n",
       "      <td>DF</td>\n",
       "      <td>Celta Vigo</td>\n",
       "      <td>25-202</td>\n",
       "      <td>1995</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "      <td>1041</td>\n",
       "      <td>...</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>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <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>563</th>\n",
       "      <td>564</td>\n",
       "      <td>Joseba Zaldúa\\Joseba-Zaldua</td>\n",
       "      <td>es ESP</td>\n",
       "      <td>DF</td>\n",
       "      <td>Real Sociedad</td>\n",
       "      <td>28-299</td>\n",
       "      <td>1992</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>493</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.00</td>\n",
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       "      <td>0.01</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>565</td>\n",
       "      <td>Oier Zarraga\\Oier-Zarraga</td>\n",
       "      <td>es ESP</td>\n",
       "      <td>MF</td>\n",
       "      <td>Athletic Club</td>\n",
       "      <td>22-105</td>\n",
       "      <td>1999</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>566</td>\n",
       "      <td>Igor Zubeldia\\Igor-Zubeldia</td>\n",
       "      <td>es ESP</td>\n",
       "      <td>DF</td>\n",
       "      <td>Real Sociedad</td>\n",
       "      <td>24-020</td>\n",
       "      <td>1997</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
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       "      <td>0.05</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>567</td>\n",
       "      <td>Martín Zubimendi\\Martin-Zubimendi</td>\n",
       "      <td>es ESP</td>\n",
       "      <td>MF</td>\n",
       "      <td>Real Sociedad</td>\n",
       "      <td>22-076</td>\n",
       "      <td>1999</td>\n",
       "      <td>24</td>\n",
       "      <td>13</td>\n",
       "      <td>1374</td>\n",
       "      <td>...</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>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>568</td>\n",
       "      <td>Martin Ødegaard\\Martin-Odegaard</td>\n",
       "      <td>no NOR</td>\n",
       "      <td>MFFW</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>22-123</td>\n",
       "      <td>1998</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>234</td>\n",
       "      <td>...</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>0.04</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>568 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rk                                     Player  Nation   Pos  \\\n",
       "0      1                Sabit Abdulai\\Sabit-Abdulai  gh GHA    MF   \n",
       "1      2                  Marcos Acuña\\Marcos-Acuna  ar ARG    DF   \n",
       "2      3              Bobby Adekanye\\Bobby-Adekanye  nl NED  FWMF   \n",
       "3      4  Martin Agirregabiria\\Martin-Agirregabiria  es ESP  DFMF   \n",
       "4      5                  Joseph Aidoo\\Joseph-Aidoo  gh GHA    DF   \n",
       "..   ...                                        ...     ...   ...   \n",
       "563  564                Joseba Zaldúa\\Joseba-Zaldua  es ESP    DF   \n",
       "564  565                  Oier Zarraga\\Oier-Zarraga  es ESP    MF   \n",
       "565  566                Igor Zubeldia\\Igor-Zubeldia  es ESP    DF   \n",
       "566  567          Martín Zubimendi\\Martin-Zubimendi  es ESP    MF   \n",
       "567  568            Martin Ødegaard\\Martin-Odegaard  no NOR  MFFW   \n",
       "\n",
       "             Squad     Age  Born  MP  Starts   Min  ...   Gls   Ast   G+A  \\\n",
       "0           Getafe  21-343  1999   1       0     6  ...  0.00  0.00  0.00   \n",
       "1          Sevilla  29-173  1991  24      21  1848  ...  0.05  0.10  0.15   \n",
       "2            Cádiz  22-064  1999   3       0    36  ...  0.00  0.00  0.00   \n",
       "3           Alavés  24-344  1996  20      11  1104  ...  0.00  0.08  0.08   \n",
       "4       Celta Vigo  25-202  1995  21      11  1041  ...  0.00  0.00  0.00   \n",
       "..             ...     ...   ...  ..     ...   ...  ...   ...   ...   ...   \n",
       "563  Real Sociedad  28-299  1992   8       5   493  ...  0.00  0.00  0.00   \n",
       "564  Athletic Club  22-105  1999   5       1   100  ...  0.00  0.00  0.00   \n",
       "565  Real Sociedad  24-020  1997  22      20  1847  ...  0.00  0.05  0.05   \n",
       "566  Real Sociedad  22-076  1999  24      13  1374  ...  0.00  0.00  0.00   \n",
       "567    Real Madrid  22-123  1998   7       3   234  ...  0.00  0.00  0.00   \n",
       "\n",
       "     G-PK  G+A-PK    xG    xA  xG+xA  npxG  npxG+xA  \n",
       "0    0.00    0.00  0.00  0.00   0.00  0.00     0.00  \n",
       "1    0.05    0.15  0.05  0.10   0.15  0.05     0.15  \n",
       "2    0.00    0.00  0.43  0.00   0.43  0.43     0.43  \n",
       "3    0.00    0.08  0.02  0.07   0.09  0.02     0.09  \n",
       "4    0.00    0.00  0.05  0.05   0.11  0.05     0.11  \n",
       "..    ...     ...   ...   ...    ...   ...      ...  \n",
       "563  0.00    0.00  0.01  0.09   0.10  0.01     0.10  \n",
       "564  0.00    0.00  0.08  0.04   0.13  0.08     0.13  \n",
       "565  0.00    0.05  0.04  0.04   0.07  0.04     0.07  \n",
       "566  0.00    0.00  0.03  0.01   0.05  0.03     0.05  \n",
       "567  0.00    0.00  0.04  0.15   0.19  0.04     0.19  \n",
       "\n",
       "[568 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats = pd.read_csv(\"2020-21 La Liga player stats (per 90s) - Sheet1.csv\")\n",
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rk</th>\n",
       "      <th>Player</th>\n",
       "      <th>Nation</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Squad</th>\n",
       "      <th>Age</th>\n",
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       "      <th>MP</th>\n",
       "      <th>Starts</th>\n",
       "      <th>Min</th>\n",
       "      <th>...</th>\n",
       "      <th>Gls</th>\n",
       "      <th>Ast</th>\n",
       "      <th>G+A</th>\n",
       "      <th>G-PK</th>\n",
       "      <th>G+A-PK</th>\n",
       "      <th>xG</th>\n",
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       "      <th>xG+xA</th>\n",
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       "      <th>npxG+xA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>[Sabit Abdulai, Sabit-Abdulai]</td>\n",
       "      <td>gh GHA</td>\n",
       "      <td>MF</td>\n",
       "      <td>Getafe</td>\n",
       "      <td>21-343</td>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>[Marcos Acuña, Marcos-Acuna]</td>\n",
       "      <td>ar ARG</td>\n",
       "      <td>DF</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>29-173</td>\n",
       "      <td>1991</td>\n",
       "      <td>24</td>\n",
       "      <td>21</td>\n",
       "      <td>1848</td>\n",
       "      <td>...</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.15</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>[Bobby Adekanye, Bobby-Adekanye]</td>\n",
       "      <td>nl NED</td>\n",
       "      <td>FWMF</td>\n",
       "      <td>Cádiz</td>\n",
       "      <td>22-064</td>\n",
       "      <td>1999</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>[Martin Agirregabiria, Martin-Agirregabiria]</td>\n",
       "      <td>es ESP</td>\n",
       "      <td>DFMF</td>\n",
       "      <td>Alavés</td>\n",
       "      <td>24-344</td>\n",
       "      <td>1996</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>1104</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>[Joseph Aidoo, Joseph-Aidoo]</td>\n",
       "      <td>gh GHA</td>\n",
       "      <td>DF</td>\n",
       "      <td>Celta Vigo</td>\n",
       "      <td>25-202</td>\n",
       "      <td>1995</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "      <td>1041</td>\n",
       "      <td>...</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>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.11</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>564</td>\n",
       "      <td>[Joseba Zaldúa, Joseba-Zaldua]</td>\n",
       "      <td>es ESP</td>\n",
       "      <td>DF</td>\n",
       "      <td>Real Sociedad</td>\n",
       "      <td>28-299</td>\n",
       "      <td>1992</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>493</td>\n",
       "      <td>...</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>0.01</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>565</td>\n",
       "      <td>[Oier Zarraga, Oier-Zarraga]</td>\n",
       "      <td>es ESP</td>\n",
       "      <td>MF</td>\n",
       "      <td>Athletic Club</td>\n",
       "      <td>22-105</td>\n",
       "      <td>1999</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>...</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>0.08</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>566</td>\n",
       "      <td>[Igor Zubeldia, Igor-Zubeldia]</td>\n",
       "      <td>es ESP</td>\n",
       "      <td>DF</td>\n",
       "      <td>Real Sociedad</td>\n",
       "      <td>24-020</td>\n",
       "      <td>1997</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1847</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>567</td>\n",
       "      <td>[Martín Zubimendi, Martin-Zubimendi]</td>\n",
       "      <td>es ESP</td>\n",
       "      <td>MF</td>\n",
       "      <td>Real Sociedad</td>\n",
       "      <td>22-076</td>\n",
       "      <td>1999</td>\n",
       "      <td>24</td>\n",
       "      <td>13</td>\n",
       "      <td>1374</td>\n",
       "      <td>...</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>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>568</td>\n",
       "      <td>[Martin Ødegaard, Martin-Odegaard]</td>\n",
       "      <td>no NOR</td>\n",
       "      <td>MFFW</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>22-123</td>\n",
       "      <td>1998</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>234</td>\n",
       "      <td>...</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>0.04</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>568 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rk                                        Player  Nation   Pos  \\\n",
       "0      1                [Sabit Abdulai, Sabit-Abdulai]  gh GHA    MF   \n",
       "1      2                  [Marcos Acuña, Marcos-Acuna]  ar ARG    DF   \n",
       "2      3              [Bobby Adekanye, Bobby-Adekanye]  nl NED  FWMF   \n",
       "3      4  [Martin Agirregabiria, Martin-Agirregabiria]  es ESP  DFMF   \n",
       "4      5                  [Joseph Aidoo, Joseph-Aidoo]  gh GHA    DF   \n",
       "..   ...                                           ...     ...   ...   \n",
       "563  564                [Joseba Zaldúa, Joseba-Zaldua]  es ESP    DF   \n",
       "564  565                  [Oier Zarraga, Oier-Zarraga]  es ESP    MF   \n",
       "565  566                [Igor Zubeldia, Igor-Zubeldia]  es ESP    DF   \n",
       "566  567          [Martín Zubimendi, Martin-Zubimendi]  es ESP    MF   \n",
       "567  568            [Martin Ødegaard, Martin-Odegaard]  no NOR  MFFW   \n",
       "\n",
       "             Squad     Age  Born  MP  Starts   Min  ...   Gls   Ast   G+A  \\\n",
       "0           Getafe  21-343  1999   1       0     6  ...  0.00  0.00  0.00   \n",
       "1          Sevilla  29-173  1991  24      21  1848  ...  0.05  0.10  0.15   \n",
       "2            Cádiz  22-064  1999   3       0    36  ...  0.00  0.00  0.00   \n",
       "3           Alavés  24-344  1996  20      11  1104  ...  0.00  0.08  0.08   \n",
       "4       Celta Vigo  25-202  1995  21      11  1041  ...  0.00  0.00  0.00   \n",
       "..             ...     ...   ...  ..     ...   ...  ...   ...   ...   ...   \n",
       "563  Real Sociedad  28-299  1992   8       5   493  ...  0.00  0.00  0.00   \n",
       "564  Athletic Club  22-105  1999   5       1   100  ...  0.00  0.00  0.00   \n",
       "565  Real Sociedad  24-020  1997  22      20  1847  ...  0.00  0.05  0.05   \n",
       "566  Real Sociedad  22-076  1999  24      13  1374  ...  0.00  0.00  0.00   \n",
       "567    Real Madrid  22-123  1998   7       3   234  ...  0.00  0.00  0.00   \n",
       "\n",
       "     G-PK  G+A-PK    xG    xA  xG+xA  npxG  npxG+xA  \n",
       "0    0.00    0.00  0.00  0.00   0.00  0.00     0.00  \n",
       "1    0.05    0.15  0.05  0.10   0.15  0.05     0.15  \n",
       "2    0.00    0.00  0.43  0.00   0.43  0.43     0.43  \n",
       "3    0.00    0.08  0.02  0.07   0.09  0.02     0.09  \n",
       "4    0.00    0.00  0.05  0.05   0.11  0.05     0.11  \n",
       "..    ...     ...   ...   ...    ...   ...      ...  \n",
       "563  0.00    0.00  0.01  0.09   0.10  0.01     0.10  \n",
       "564  0.00    0.00  0.08  0.04   0.13  0.08     0.13  \n",
       "565  0.00    0.05  0.04  0.04   0.07  0.04     0.07  \n",
       "566  0.00    0.00  0.03  0.01   0.05  0.03     0.05  \n",
       "567  0.00    0.00  0.04  0.15   0.19  0.04     0.19  \n",
       "\n",
       "[568 rows x 21 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats['Player'] = stats['Player'].str.split('\\\\')\n",
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>name1</th>\n",
       "      <th>name2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sabit Abdulai</td>\n",
       "      <td>Sabit-Abdulai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Marcos Acuña</td>\n",
       "      <td>Marcos-Acuna</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bobby Adekanye</td>\n",
       "      <td>Bobby-Adekanye</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Martin Agirregabiria</td>\n",
       "      <td>Martin-Agirregabiria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Joseph Aidoo</td>\n",
       "      <td>Joseph-Aidoo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>Joseba Zaldúa</td>\n",
       "      <td>Joseba-Zaldua</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>Oier Zarraga</td>\n",
       "      <td>Oier-Zarraga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>Igor Zubeldia</td>\n",
       "      <td>Igor-Zubeldia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>Martín Zubimendi</td>\n",
       "      <td>Martin-Zubimendi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>Martin Ødegaard</td>\n",
       "      <td>Martin-Odegaard</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>568 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name1                 name2\n",
       "0           Sabit Abdulai         Sabit-Abdulai\n",
       "1            Marcos Acuña          Marcos-Acuna\n",
       "2          Bobby Adekanye        Bobby-Adekanye\n",
       "3    Martin Agirregabiria  Martin-Agirregabiria\n",
       "4            Joseph Aidoo          Joseph-Aidoo\n",
       "..                    ...                   ...\n",
       "563         Joseba Zaldúa         Joseba-Zaldua\n",
       "564          Oier Zarraga          Oier-Zarraga\n",
       "565         Igor Zubeldia         Igor-Zubeldia\n",
       "566      Martín Zubimendi      Martin-Zubimendi\n",
       "567       Martin Ødegaard       Martin-Odegaard\n",
       "\n",
       "[568 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = stats['Player']\n",
    "names = pd.DataFrame(names.to_list(), columns=['name1', 'name2'])\n",
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rk</th>\n",
       "      <th>Player</th>\n",
       "      <th>Nation</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Squad</th>\n",
       "      <th>Age</th>\n",
       "      <th>Born</th>\n",
       "      <th>MP</th>\n",
       "      <th>Starts</th>\n",
       "      <th>Min</th>\n",
       "      <th>...</th>\n",
       "      <th>Gls</th>\n",
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       "      <th>G+A</th>\n",
       "      <th>G-PK</th>\n",
       "      <th>G+A-PK</th>\n",
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       "      <th>xA</th>\n",
       "      <th>xG+xA</th>\n",
       "      <th>npxG</th>\n",
       "      <th>npxG+xA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Sabit Abdulai</td>\n",
       "      <td>gh GHA</td>\n",
       "      <td>MF</td>\n",
       "      <td>Getafe</td>\n",
       "      <td>21-343</td>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
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       "      <td>0.00</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Marcos Acuña</td>\n",
       "      <td>ar ARG</td>\n",
       "      <td>DF</td>\n",
       "      <td>Sevilla</td>\n",
       "      <td>29-173</td>\n",
       "      <td>1991</td>\n",
       "      <td>24</td>\n",
       "      <td>21</td>\n",
       "      <td>1848</td>\n",
       "      <td>...</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.05</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Bobby Adekanye</td>\n",
       "      <td>nl NED</td>\n",
       "      <td>FWMF</td>\n",
       "      <td>Cádiz</td>\n",
       "      <td>22-064</td>\n",
       "      <td>1999</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Martin Agirregabiria</td>\n",
       "      <td>es ESP</td>\n",
       "      <td>DFMF</td>\n",
       "      <td>Alavés</td>\n",
       "      <td>24-344</td>\n",
       "      <td>1996</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>1104</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.02</td>\n",
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       "      <td>0.02</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Joseph Aidoo</td>\n",
       "      <td>gh GHA</td>\n",
       "      <td>DF</td>\n",
       "      <td>Celta Vigo</td>\n",
       "      <td>25-202</td>\n",
       "      <td>1995</td>\n",
       "      <td>21</td>\n",
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       "      <td>0.05</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.11</td>\n",
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       "      <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>563</th>\n",
       "      <td>564</td>\n",
       "      <td>Joseba Zaldúa</td>\n",
       "      <td>es ESP</td>\n",
       "      <td>DF</td>\n",
       "      <td>Real Sociedad</td>\n",
       "      <td>28-299</td>\n",
       "      <td>1992</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>493</td>\n",
       "      <td>...</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>0.01</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>565</td>\n",
       "      <td>Oier Zarraga</td>\n",
       "      <td>es ESP</td>\n",
       "      <td>MF</td>\n",
       "      <td>Athletic Club</td>\n",
       "      <td>22-105</td>\n",
       "      <td>1999</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>...</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>0.08</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>566</td>\n",
       "      <td>Igor Zubeldia</td>\n",
       "      <td>es ESP</td>\n",
       "      <td>DF</td>\n",
       "      <td>Real Sociedad</td>\n",
       "      <td>24-020</td>\n",
       "      <td>1997</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1847</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>567</td>\n",
       "      <td>Martín Zubimendi</td>\n",
       "      <td>es ESP</td>\n",
       "      <td>MF</td>\n",
       "      <td>Real Sociedad</td>\n",
       "      <td>22-076</td>\n",
       "      <td>1999</td>\n",
       "      <td>24</td>\n",
       "      <td>13</td>\n",
       "      <td>1374</td>\n",
       "      <td>...</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>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>568</td>\n",
       "      <td>Martin Ødegaard</td>\n",
       "      <td>no NOR</td>\n",
       "      <td>MFFW</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>22-123</td>\n",
       "      <td>1998</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>234</td>\n",
       "      <td>...</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>0.04</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>568 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rk                Player  Nation   Pos          Squad     Age  Born  MP  \\\n",
       "0      1         Sabit Abdulai  gh GHA    MF         Getafe  21-343  1999   1   \n",
       "1      2          Marcos Acuña  ar ARG    DF        Sevilla  29-173  1991  24   \n",
       "2      3        Bobby Adekanye  nl NED  FWMF          Cádiz  22-064  1999   3   \n",
       "3      4  Martin Agirregabiria  es ESP  DFMF         Alavés  24-344  1996  20   \n",
       "4      5          Joseph Aidoo  gh GHA    DF     Celta Vigo  25-202  1995  21   \n",
       "..   ...                   ...     ...   ...            ...     ...   ...  ..   \n",
       "563  564         Joseba Zaldúa  es ESP    DF  Real Sociedad  28-299  1992   8   \n",
       "564  565          Oier Zarraga  es ESP    MF  Athletic Club  22-105  1999   5   \n",
       "565  566         Igor Zubeldia  es ESP    DF  Real Sociedad  24-020  1997  22   \n",
       "566  567      Martín Zubimendi  es ESP    MF  Real Sociedad  22-076  1999  24   \n",
       "567  568       Martin Ødegaard  no NOR  MFFW    Real Madrid  22-123  1998   7   \n",
       "\n",
       "     Starts   Min  ...   Gls   Ast   G+A  G-PK  G+A-PK    xG    xA  xG+xA  \\\n",
       "0         0     6  ...  0.00  0.00  0.00  0.00    0.00  0.00  0.00   0.00   \n",
       "1        21  1848  ...  0.05  0.10  0.15  0.05    0.15  0.05  0.10   0.15   \n",
       "2         0    36  ...  0.00  0.00  0.00  0.00    0.00  0.43  0.00   0.43   \n",
       "3        11  1104  ...  0.00  0.08  0.08  0.00    0.08  0.02  0.07   0.09   \n",
       "4        11  1041  ...  0.00  0.00  0.00  0.00    0.00  0.05  0.05   0.11   \n",
       "..      ...   ...  ...   ...   ...   ...   ...     ...   ...   ...    ...   \n",
       "563       5   493  ...  0.00  0.00  0.00  0.00    0.00  0.01  0.09   0.10   \n",
       "564       1   100  ...  0.00  0.00  0.00  0.00    0.00  0.08  0.04   0.13   \n",
       "565      20  1847  ...  0.00  0.05  0.05  0.00    0.05  0.04  0.04   0.07   \n",
       "566      13  1374  ...  0.00  0.00  0.00  0.00    0.00  0.03  0.01   0.05   \n",
       "567       3   234  ...  0.00  0.00  0.00  0.00    0.00  0.04  0.15   0.19   \n",
       "\n",
       "     npxG  npxG+xA  \n",
       "0    0.00     0.00  \n",
       "1    0.05     0.15  \n",
       "2    0.43     0.43  \n",
       "3    0.02     0.09  \n",
       "4    0.05     0.11  \n",
       "..    ...      ...  \n",
       "563  0.01     0.10  \n",
       "564  0.08     0.13  \n",
       "565  0.04     0.07  \n",
       "566  0.03     0.05  \n",
       "567  0.04     0.19  \n",
       "\n",
       "[568 rows x 21 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats['Player'] = names['name1']\n",
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sabit Abdulai', 'Marcos Acuña', 'Bobby Adekanye',\n",
       "       'Martin Agirregabiria', 'Joseph Aidoo', 'Carlos Akapo',\n",
       "       'Paul Akouokou', 'Jony Álamo', 'Jordi Alba', 'Raúl Albiol',\n",
       "       'Paco Alcácer', 'Pedro Alcalá', 'Rubén Alcaraz', 'Iván Alejo',\n",
       "       'Carles Aleñá', 'Alfon', 'Sergio Álvarez', 'Yeray Álvarez',\n",
       "       'Jon Ander Garrido', 'Ángel', 'Miguel Ángel Rubio', 'José Ángel',\n",
       "       'José Antonio Miranda', 'Aritz Arambarri', 'Mauro Arambarri',\n",
       "       'Néstor Araujo', 'Ronald Araújo', 'Anaitz Arbilla',\n",
       "       'Unai Arietaleanizbeaskoa', 'Sergio Arribas', 'Sergio Asenjo',\n",
       "       'Marco Asensio', 'Iago Aspas', 'Miguel Atienza', 'Ezequiel Ávila',\n",
       "       'Ramon Azeez', 'Carlos Bacca', 'Édgar Badía', 'Alex Baena',\n",
       "       'Miguel Baeza', 'Mikel Balenziaga', 'Barcia', 'Enis Bardhi',\n",
       "       'Kike Barja', 'Marc Baró Ortiz', 'Antonio Barragán',\n",
       "       'Ander Barrenetxea', 'Marc Bartra', 'Rodrigo Battaglia',\n",
       "       'Jon Bautista', 'Fran Beltrán', 'Sergio Benito', 'Karim Benzema',\n",
       "       'Yuri Berchiche', 'Álex Berenguer', 'Pedro Bigas', 'Blanco',\n",
       "       'Alejandro Blanco', 'Rubén Blanco', 'Yann Bodiger',\n",
       "       'Yassine Bounou', 'Lucas Boyé', 'Martin Braithwaite',\n",
       "       'Darko Brašanac', 'Álvaro Bravo', 'Claudio Bravo',\n",
       "       'Yan Brice Eteki', 'Juan Brunet', 'Bryan', 'Ante Budimir',\n",
       "       'Esteban Burgos', 'Burgui', 'Sergio Busquets', 'Erick Cabaco',\n",
       "       'Cala', 'Jonathan Calleri', 'Dani Calvo', 'José Campaña',\n",
       "       'Sergio Canales', 'Ander Capa', 'Étienne Capoue', 'Isaac Carcelen',\n",
       "       'Cárdenas', 'Marc Cardona', 'Diego Carlos', 'Juan Carlos',\n",
       "       'Yannick Carrasco', 'Sergio Carreira', 'Guido Carrillo',\n",
       "       'Dani Carvajal', 'William Carvalho', 'Casemiro', 'Sofian Chakla',\n",
       "       'Chema', 'Denis Cheryshev', 'Samuel Chukwueze', 'Víctor Chust',\n",
       "       'Cifu', 'Alberto Cifuentes', 'Jasper Cillessen', 'Carlos Clerc',\n",
       "       'Coke', 'Francis Coquelin', 'Iñigo Córdoba', 'Ángel Correa',\n",
       "       'Rober Correa', 'Thierry Correia', 'Diego Costa', 'Jaume Costa',\n",
       "       'Thibaut Courtois', 'Philippe Coutinho', 'Juan Cruz Armada',\n",
       "       'Sergio Cubero Ezcurra', 'Cucho', 'Marc Cucurella',\n",
       "       'Patrick Cutrone', 'Moussa Dembélé', 'Ousmane Dembélé',\n",
       "       'Sergiño Dest', 'Deyverson', 'Abdoulay Diaby', 'Mouctar Diakhaby',\n",
       "       'Víctor Díaz', 'Papakouli Diop', 'Djené', 'Marko Dmitrović',\n",
       "       'Jaume Doménech', 'John Donald', 'Cheick Doukouré',\n",
       "       'Idrissa Doumbia', 'Domingos Duarte', 'Óscar Duarte',\n",
       "       'Rubén Duarte', 'Unai Dufur', 'Hugo Duro', 'Aritz Elustondo',\n",
       "       'Rodrigo Ely', 'Emerson', 'Youssef En-Nesyri', 'Sergi Enrich',\n",
       "       'Dani Escriche', 'Sergio Escudero', 'Alfonso Espino',\n",
       "       'Vicente Esquerdo', 'Pervis Estupiñán', 'Xabier Etxeita',\n",
       "       'Edu Expósito', 'Fali', 'Ansu Fati', 'Nabil Fekir', 'Felipe',\n",
       "       'João Félix', 'Aitor Fernández', 'Álex Fernández',\n",
       "       'Álvaro Fernández', 'Andrés Fernández', 'Augusto Fernández',\n",
       "       'Carlos Fernández', 'Joaquín Fernández', 'Fernando',\n",
       "       'David Ferreiro', 'Facundo Ferreyra', 'Fidel', 'Junior Firpo',\n",
       "       'Kingsley Fobi', 'Ramón Folch', 'José Fontán', 'Dimitri Foulquier',\n",
       "       'Juan Foyth', 'Jorge de Frutos', 'Ramiro Funes Mori', 'Javi Galán',\n",
       "       'Enric Gallego', 'Kévin Gameiro', 'Aleix García', 'Borja García',\n",
       "       'Dani García', 'David García', 'Manu García', 'Raúl García',\n",
       "       'Rubén García', 'Sergi García', 'Unai García', 'Mario Gaspar',\n",
       "       'Paulo Gazzaniga', 'David Gil', 'Álvaro Giménez', 'Dani Gómez',\n",
       "       'Ibai Gómez', 'Maxi Gómez', 'Moi Gómez', 'Papu Gómez',\n",
       "       'Sergi Gómez', 'Sergio Gómez', 'Maxime Gonalons', 'Bruno González',\n",
       "       'Diego González', 'Sergio González', 'Urko González',\n",
       "       'Andoni Gorosabel', 'Antoine Griezmann', 'Andrés Guardado',\n",
       "       'Sergi Guardiola', 'Nemanja Gudelj', 'Gonçalo Guedes',\n",
       "       'Ander Guevara', 'John Guidetti', 'Hugo Guillamón', 'Jon Guridi',\n",
       "       'Raúl Guti', 'Marcos Mauro López Gutiérrez', 'Munir El Haddadi',\n",
       "       'Eden Hazard', 'Mario Hermoso', 'Aridane Hernández',\n",
       "       'Héctor Herrera', 'Sergio Herrera', 'Yangel Herrera',\n",
       "       'Pablo Hervías', 'Jordan Holsgrove', 'Vicente Iborra',\n",
       "       'Oussama Idrissi', 'Iglesias', 'Borja Iglesias', 'Ilaix',\n",
       "       'Asier Illarramendi', 'Pablo Insua', 'Takashi Inui',\n",
       "       'Alexander Isak', 'Isco', 'Jairo Izquierdo', 'Saidy Janko',\n",
       "       'Adnan Januzaj', 'Jason', 'Luis Javier Suárez', 'Joaquín',\n",
       "       'John Joe', 'Frenkie de Jong', 'Luuk de Jong', 'Jony',\n",
       "       'Joan Jordán', 'Josan', 'Willian José', 'Joselu', 'Josema', 'Jota',\n",
       "       'Luka Jović', 'Juanmi', 'Vinicius Júnior', 'Jens Jønsson',\n",
       "       'Damian Kądzior', 'Lee Kangin', 'Stephane Keller', 'Kenedy',\n",
       "       'Kike', 'Giorgi Kochorashvili', 'Kenan Kodro', 'Koba Koindredi',\n",
       "       'Koke', 'Geoffrey Kondogbia', 'Youssouf Koné', 'Jules Koundé',\n",
       "       'Toni Kroos', 'Takefusa Kubo', 'Kuki', 'Víctor Laguardia',\n",
       "       'Diego Lainez', 'Toni Lato', 'Lauti', 'Robin Le Normand',\n",
       "       'Jeremías Ledesma', 'Florian Lejeune', 'Iñigo Lekue',\n",
       "       'Thomas Lemar', 'Clément Lenglet', 'Pedro León', 'Sergio León',\n",
       "       'Diego Llorente', 'Marcos Llorente', 'Renan Lodi',\n",
       "       'Pedro López Muñoz', 'Adrián López', 'Javi López', 'Roberto López',\n",
       "       'Unai López', 'Anthony Lozano', 'Jeison Lucumí', 'José Luis Gayà',\n",
       "       'José Luis Morales', 'Luisinho', 'Luismi', 'Darwin Machís',\n",
       "       'Pablo Maffeo', 'Abdallahi Mahmoud', 'Nemanja Maksimović',\n",
       "       'Filip Malbašić', 'Hugo Mallo', 'Mickael Malsa', 'Aïssa Mandi',\n",
       "       'Eliaquim Mangala', 'Maranhão', 'Marcelo', 'Iván Marcone',\n",
       "       'Óscar de Marcos', 'José Mari', 'José María Giménez', 'Mariano',\n",
       "       'Adrián Marín', 'Roger Martí', 'Aarón Martín', 'Álex Martín',\n",
       "       'Iñigo Martínez', 'Javi Martínez', 'Marvin', 'Jordi Masip',\n",
       "       'Jaime Mata', 'Borja Mayoral', 'Gonzalo Melero', 'Brais Méndez',\n",
       "       'Édgar Méndez', 'Eñaut Mendía', 'Ferland Mendy', 'Mikel Merino',\n",
       "       'Martín Merquelanz', 'Roque Mesa', 'Lionel Messi', 'Omenuke Mfulu',\n",
       "       'Míchel', 'Éder Militão', 'Luis Milla', 'Pere Milla',\n",
       "       'Alejandro Millán', 'Santi Mina', 'Óscar Mingueza', 'Rafa Mir',\n",
       "       'Jorge Miramón', 'Juan Miranda', 'Luka Modrić', 'Johan Mojica',\n",
       "       'Guillem Molina', 'Jorge Molina', 'Victor Mollejo',\n",
       "       'Jon Moncayola', 'Nacho Monreal', 'Ángel Montoro',\n",
       "       'Martín Montoya', 'Emre Mor', 'Jon Morcillo', 'Alberto Moreno',\n",
       "       'Álex Moreno', 'Gerard Moreno', 'Tete Morente', 'Loren Morón',\n",
       "       'Pedro Mosquera', 'Javi Moyano', 'Iker Muniain', 'Aihen Muñoz',\n",
       "       'Joaquín Muñoz', 'Jeison Murillo', 'Yunus Musah', 'Yoshinori Mutō',\n",
       "       'Nacho', 'Nano', 'Roberto Navarro', 'Ximo Navarro', 'Jesús Navas',\n",
       "       'Raúl Navas', 'Álvaro Negredo', 'Neto', 'Carlos Neva',\n",
       "       'Mamor Niang', 'Manuel Nieto', 'Saúl Ñíguez', 'Nino',\n",
       "       'Fernando Niño', 'Nolito', 'Unai Núñez', 'Kelechi Nwakali',\n",
       "       'Allan Nyom', 'Jan Oblak', 'Lucas Ocampos', 'Álvaro Odriozola',\n",
       "       'Shinji Okazaki', 'Lucas Olaza', 'Christian Oliva',\n",
       "       'Paulo Oliveira', 'Mathías Olivera', 'Javier Ontiveros',\n",
       "       'Fabián Orellana', 'Mikel Oyarzabal', 'Fernando Pacheco',\n",
       "       'Helibelton Palacios', 'Ante Palaversa', 'Daniel Parejo',\n",
       "       'Thomas Partey', 'Gabriel Paulista', 'Alfonso Pedraza', 'Pedri',\n",
       "       'Facundo Pellistri', 'Rubén Peña', 'Pepe', 'Alberto Perea',\n",
       "       'Iñigo Pérez', 'Juan Pérez', 'Kike Pérez', 'Lucas Pérez',\n",
       "       'Luis Pérez', 'Nehuén Pérez', 'Pablo Piatti', 'Cristiano Piccini',\n",
       "       'Tomás Pina Isla', 'Yeremi Pino', 'Gerard Piqué', 'Miralem Pjanić',\n",
       "       'Óscar Plano', 'Dani Plomer', 'Jorge Pombo', 'Pere Pons',\n",
       "       'Francisco Portillo', 'Portu', 'Sergio Postigo', 'Darío Poveda',\n",
       "       'Alejandro Pozo Pozo', 'Antonio Puertas', 'Riqui Puig',\n",
       "       'Jorge Pulido', 'Domingos Quina', 'Quini', 'Quique', 'Dani Raba',\n",
       "       'Uroš Račić', 'Nemanja Radoja', 'Ivan Rakitić', 'Jonás Ramalho',\n",
       "       'Sandro Ramírez', 'Sergio Ramos', 'Recio',\n",
       "       'Francisco Reis Ferreira', 'Karim Rekik', 'Álex Remiro',\n",
       "       'Oriol Rey', 'Ricard', 'Mikel Rico', 'Emiliano Rigoni',\n",
       "       'Luis Rioja', 'Rober', 'Roberto', 'Sergi Roberto', 'Joel Robles',\n",
       "       'Rubén Rochina', 'Rodrigo', 'Kévin Rodrigues',\n",
       "       'Óscar Rodríguez Arnaiz', 'Ángel Rodríguez', 'Guido Rodríguez',\n",
       "       'Miguel Rodríguez', 'Víctor Rodríguez', 'Rodrygo',\n",
       "       'Facundo Roncaglia', 'Rubén', 'Waldo Rubio', 'Aitor Ruibal',\n",
       "       'Isma Ruiz', 'Víctor Ruiz', 'Modibo Sagnan', 'Borja Sainz',\n",
       "       'Salvi', 'Fede San Emeterio', 'Antonio Sanabria', 'Oihan Sancet',\n",
       "       'Juan Sánchez Miño', 'Germán Sánchez', 'Javi Sánchez',\n",
       "       'Manuel Sánchez', 'Oier Sanjurjo', 'Ivan Šaponjić', 'Stefan Savić',\n",
       "       'Jaime Seoane', 'Edgar Sevikyan', 'Sidnei', 'David Silva',\n",
       "       'Gastón Silva', 'Rui Silva', 'Unai Simón', 'Dimitris Siovas',\n",
       "       'Rafa Soares', 'Rubén Sobrino', 'Augusto Solari',\n",
       "       'Roberto Soldado', 'Carlos Soler', 'Son', 'David Soria',\n",
       "       'Alberto Soro', 'Damián Suárez', 'Denis Suárez', 'Luis Suárez',\n",
       "       'Suso', 'Tachi', 'Renato Tapia', 'Tomás Tavares', 'Álvaro Tejero',\n",
       "       'Cristian Tello', 'Marc-André ter Stegen', 'David Timor', 'Toño',\n",
       "       'Lucas Torreira', 'Óliver Torres', 'Pau Torres', 'Roberto Torres',\n",
       "       'Lucas Torró', 'Manu Trigueros', 'Francisco Trincão',\n",
       "       'Kieran Trippier', 'Samuel Umtiti', 'Enes Ünal', 'Tomáš Vaclík',\n",
       "       'Eugeni Valderrama', 'Jesús Vallejo', 'Manu Vallejo',\n",
       "       'Federico Valverde', 'Raphaël Varane', 'Denis Vavro',\n",
       "       'Franco Vázquez', 'Kevin Vázquez', 'Lucas Vázquez', 'Koke Vegas',\n",
       "       'Gabriel Veiga', 'Unai Vencedor Paris', 'Gonzalo Verdú',\n",
       "       'Mikel Vesga', 'Rúben Vezo', 'Iñigo Vicente', 'Fede Vico',\n",
       "       'Aleix Vidal', 'Nacho Vidal', 'Toni Villa', 'Asier Villalibre',\n",
       "       'Iván Villar', 'Vitolo', 'Šime Vrsaljko', 'Nikola Vukčević',\n",
       "       'Daniel Wass', 'Shon Weissman', 'Iñaki Williams', 'Jawad El Yamiq',\n",
       "       'Rubén Yáñez', 'Yoel', 'Okay Yokuşlu', 'Joseba Zaldúa',\n",
       "       'Oier Zarraga', 'Igor Zubeldia', 'Martín Zubimendi',\n",
       "       'Martin Ødegaard'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.Player.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>Rk</th>\n",
       "      <th>Player</th>\n",
       "      <th>Nation</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Squad</th>\n",
       "      <th>Age</th>\n",
       "      <th>Born</th>\n",
       "      <th>MP</th>\n",
       "      <th>Starts</th>\n",
       "      <th>Min</th>\n",
       "      <th>...</th>\n",
       "      <th>Gls</th>\n",
       "      <th>Ast</th>\n",
       "      <th>G+A</th>\n",
       "      <th>G-PK</th>\n",
       "      <th>G+A-PK</th>\n",
       "      <th>xG</th>\n",
       "      <th>xA</th>\n",
       "      <th>xG+xA</th>\n",
       "      <th>npxG</th>\n",
       "      <th>npxG+xA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>272</td>\n",
       "      <td>Toni Kroos</td>\n",
       "      <td>de GER</td>\n",
       "      <td>MF</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>31-105</td>\n",
       "      <td>1990</td>\n",
       "      <td>27</td>\n",
       "      <td>23</td>\n",
       "      <td>2027</td>\n",
       "      <td>...</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>349</td>\n",
       "      <td>Luka Modrić</td>\n",
       "      <td>hr CRO</td>\n",
       "      <td>MF</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>35-222</td>\n",
       "      <td>1985</td>\n",
       "      <td>30</td>\n",
       "      <td>27</td>\n",
       "      <td>2322</td>\n",
       "      <td>...</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rk       Player  Nation Pos        Squad     Age  Born  MP  Starts  \\\n",
       "271  272   Toni Kroos  de GER  MF  Real Madrid  31-105  1990  27      23   \n",
       "348  349  Luka Modrić  hr CRO  MF  Real Madrid  35-222  1985  30      27   \n",
       "\n",
       "      Min  ...   Gls   Ast   G+A  G-PK  G+A-PK    xG    xA  xG+xA  npxG  \\\n",
       "271  2027  ...  0.13  0.36  0.49  0.13    0.49  0.10  0.28   0.37  0.10   \n",
       "348  2322  ...  0.12  0.12  0.23  0.12    0.23  0.09  0.13   0.22  0.09   \n",
       "\n",
       "     npxG+xA  \n",
       "271     0.37  \n",
       "348     0.22  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S = stats[(stats['Player'] == 'Luka Modrić') | (stats['Player'] == 'Toni Kroos')]\n",
    "S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Rk', 'Player', 'Nation', 'Pos', 'Squad', 'Age', 'Born', 'MP', 'Starts',\n",
       "       'Min', '90s', 'Gls', 'Ast', 'G+A', 'G-PK', 'G+A-PK', 'xG', 'xA',\n",
       "       'xG+xA', 'npxG', 'npxG+xA'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>index</th>\n",
       "      <th>Player</th>\n",
       "      <th>Gls</th>\n",
       "      <th>Ast</th>\n",
       "      <th>G+A</th>\n",
       "      <th>G-PK</th>\n",
       "      <th>G+A-PK</th>\n",
       "      <th>xG</th>\n",
       "      <th>xA</th>\n",
       "      <th>xG+xA</th>\n",
       "      <th>npxG</th>\n",
       "      <th>npxG+xA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>271</td>\n",
       "      <td>Toni Kroos</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>348</td>\n",
       "      <td>Luka Modrić</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index       Player   Gls   Ast   G+A  G-PK  G+A-PK    xG    xA  xG+xA  \\\n",
       "0    271   Toni Kroos  0.13  0.36  0.49  0.13    0.49  0.10  0.28   0.37   \n",
       "1    348  Luka Modrić  0.12  0.12  0.23  0.12    0.23  0.09  0.13   0.22   \n",
       "\n",
       "   npxG  npxG+xA  \n",
       "0  0.10     0.37  \n",
       "1  0.09     0.22  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S = S[['Player', 'Gls', 'Ast', 'G+A', 'G-PK', 'G+A-PK', 'xG', 'xA',\n",
    "       'xG+xA', 'npxG', 'npxG+xA']].reset_index()\n",
    "S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>index</th>\n",
       "      <th>Player</th>\n",
       "      <th>Goals</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Goals+Assists</th>\n",
       "      <th>Non Penalty Goals</th>\n",
       "      <th>Non Penalty Goals+Assists</th>\n",
       "      <th>Expected Goals</th>\n",
       "      <th>Expected Assists</th>\n",
       "      <th>xG+xA</th>\n",
       "      <th>Non Penalty Expected Goals</th>\n",
       "      <th>Non Penalty Expected Goals+Expected Assists</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>271</td>\n",
       "      <td>Toni Kroos</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>348</td>\n",
       "      <td>Luka Modrić</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index       Player  Goals  Assists  Goals+Assists  Non Penalty Goals  \\\n",
       "0    271   Toni Kroos   0.13     0.36           0.49               0.13   \n",
       "1    348  Luka Modrić   0.12     0.12           0.23               0.12   \n",
       "\n",
       "   Non Penalty Goals+Assists  Expected Goals  Expected Assists  xG+xA  \\\n",
       "0                       0.49            0.10              0.28   0.37   \n",
       "1                       0.23            0.09              0.13   0.22   \n",
       "\n",
       "   Non Penalty Expected Goals  Non Penalty Expected Goals+Expected Assists  \n",
       "0                        0.10                                         0.37  \n",
       "1                        0.09                                         0.22  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S = S.rename(columns={\"Gls\": \"Goals\", \"Ast\": \"Assists\", \"G+A\": \"Goals+Assists\", \"G-PK\": \"Non Penalty Goals\", \"G+A-PK\": \"Non Penalty Goals+Assists\",\n",
    "                     \"xG\": \"Expected Goals\", \"xA\": \"Expected Assists\", \"npxG\": \"Non Penalty Expected Goals\", \"npxG+xA\": \"Non Penalty Expected Goals+Expected Assists\"})\n",
    "S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'Player', 'Goals', 'Assists', 'Goals+Assists',\n",
       "       'Non Penalty Goals', 'Non Penalty Goals+Assists', 'Expected Goals',\n",
       "       'Expected Assists', 'xG+xA', 'Non Penalty Expected Goals',\n",
       "       'Non Penalty Expected Goals+Expected Assists'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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>Player</th>\n",
       "      <th>Goals</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Goals+Assists</th>\n",
       "      <th>Non Penalty Goals</th>\n",
       "      <th>Non Penalty Goals+Assists</th>\n",
       "      <th>Expected Goals</th>\n",
       "      <th>Expected Assists</th>\n",
       "      <th>xG+xA</th>\n",
       "      <th>Non Penalty Expected Goals</th>\n",
       "      <th>Non Penalty Expected Goals+Expected Assists</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toni Kroos</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Luka Modrić</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Player  Goals  Assists  Goals+Assists  Non Penalty Goals  \\\n",
       "0   Toni Kroos   0.13     0.36           0.49               0.13   \n",
       "1  Luka Modrić   0.12     0.12           0.23               0.12   \n",
       "\n",
       "   Non Penalty Goals+Assists  Expected Goals  Expected Assists  xG+xA  \\\n",
       "0                       0.49            0.10              0.28   0.37   \n",
       "1                       0.23            0.09              0.13   0.22   \n",
       "\n",
       "   Non Penalty Expected Goals  Non Penalty Expected Goals+Expected Assists  \n",
       "0                        0.10                                         0.37  \n",
       "1                        0.09                                         0.22  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S = S[['Player', 'Goals', 'Assists', 'Goals+Assists',\n",
    "       'Non Penalty Goals', 'Non Penalty Goals+Assists', 'Expected Goals',\n",
    "       'Expected Assists', 'xG+xA', 'Non Penalty Expected Goals',\n",
    "       'Non Penalty Expected Goals+Expected Assists']]\n",
    "S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Player',\n",
       " 'Goals',\n",
       " 'Assists',\n",
       " 'Goals+Assists',\n",
       " 'Non Penalty Goals',\n",
       " 'Non Penalty Goals+Assists',\n",
       " 'Expected Goals',\n",
       " 'Expected Assists',\n",
       " 'xG+xA',\n",
       " 'Non Penalty Expected Goals',\n",
       " 'Non Penalty Expected Goals+Expected Assists']"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_names = S.columns.to_list()\n",
    "col_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Goals',\n",
       " 'Assists',\n",
       " 'Goals+Assists',\n",
       " 'Non Penalty Goals',\n",
       " 'Non Penalty Goals+Assists',\n",
       " 'Expected Goals',\n",
       " 'Expected Assists',\n",
       " 'xG+xA',\n",
       " 'Non Penalty Expected Goals',\n",
       " 'Non Penalty Expected Goals+Expected Assists']"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = col_names[1:]\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.096, 0.156),\n",
       " (0.096, 0.432),\n",
       " (0.184, 0.588),\n",
       " (0.096, 0.156),\n",
       " (0.184, 0.588),\n",
       " (0.072, 0.12000000000000001),\n",
       " (0.10400000000000001, 0.336),\n",
       " (0.176, 0.444),\n",
       " (0.072, 0.12000000000000001),\n",
       " (0.176, 0.444)]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R = []\n",
    "for colnames in col_names[1:]:\n",
    "    Min = min(S[colnames])\n",
    "    Max = max(S[colnames])\n",
    "    R.append((Min - 0.2 * Min, Max + 0.2 * Max))\n",
    "R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.13, 0.36, 0.49, 0.13, 0.49, 0.1, 0.28, 0.37, 0.1, 0.37],\n",
       " [0.12, 0.12, 0.23, 0.12, 0.23, 0.09, 0.13, 0.22, 0.09, 0.22]]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S1 = []\n",
    "for i in range(len(S)):\n",
    "    S1 += [S.T[i].tolist()[1:], ]\n",
    "S1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'title_name': 'Toni Kroos',\n",
       " 'title_color': 'blue',\n",
       " 'subtitle_name': 'MF, Real Madrid',\n",
       " 'subtitle_color': 'blue',\n",
       " 'title_name_2': 'Luka Modrić',\n",
       " 'title_color_2': 'red',\n",
       " 'subtitle_name_2': 'MF, Real Madrid',\n",
       " 'subtitle_color_2': 'red'}"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T = dict(\n",
    "    title_name = 'Toni Kroos',\n",
    "    title_color = 'blue',\n",
    "    subtitle_name = 'MF, Real Madrid',\n",
    "    subtitle_color = 'blue',\n",
    "    title_name_2 = 'Luka Modrić',\n",
    "    title_color_2 = 'red',\n",
    "    subtitle_name_2 = 'MF, Real Madrid',\n",
    "    subtitle_color_2 = 'red'\n",
    ")\n",
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "note = '@indraghosh314 \\nFBREF data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "radar = Radar(background_color=\"black\", patch_color=\"#28252C\", label_color=\"white\", range_color=\"#BFE9BF\")\n",
    "fig, ax = radar.plot_radar(ranges = R, params = c, values = S1, alphas = [0.76, 0.6], \n",
    "                           title = T, endnote = note, radar_color=['#0f4c75', '#e94560'], compare=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}