data_visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IMPORTS\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "import tqdm\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Config for the libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "pd.options.display.max_columns = None\n",
    "pd.options.display.max_rows = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up Variables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "ROOT_PATH = os.path.dirname(os.path.dirname(\n",
    "    os.path.abspath(\"visualize_data.ipynb\")))\n",
    "DATA_PATH = os.path.join(ROOT_PATH, \"player-recommendation\", \"data\")\n",
    "DATA_DESC = {\n",
    "    'Rk': 'Rank',\n",
    "    'Player': \"Player's name\",\n",
    "    'Nation': \"Player's nation\",\n",
    "    'Pos': 'Position',\n",
    "    'Squad': \"Squad’s name\",\n",
    "    'Comp': 'League that squat occupies',\n",
    "    'Age': \"Player's age\",\n",
    "    'Born': 'Year of birth',\n",
    "    'MP': 'Matches played',\n",
    "    'Starts': 'Matches started',\n",
    "    'Min': 'Minutes played',\n",
    "    '90s': 'Minutes played divided by 90',\n",
    "    'Goals': 'Goals scored or allowed',\n",
    "    'Shots': 'Shots total (Does not include penalty kicks)',\n",
    "    'SoT': 'Shots on target (Does not include penalty kicks)',\n",
    "    'SoT%': 'Shots on target percentage (Does not include penalty kicks)',\n",
    "    'G/Sh': 'Goals per shot',\n",
    "    'G/SoT': 'Goals per shot on target (Does not include penalty kicks)',\n",
    "    'ShoDist': 'Average distance, in yards, from goal of all shots taken (Does not include penalty kicks)',\n",
    "    'ShoFK': 'Shots from free kicks',\n",
    "    'ShoPK': 'Penalty kicks made',\n",
    "    'PKatt': 'Penalty kicks attempted',\n",
    "    'PasTotCmp': 'Passes completed',\n",
    "    'PasTotAtt': 'Passes attempted',\n",
    "    'PasTotCmp%': 'Pass completion percentage',\n",
    "    'PasTotDist': 'Total distance, in yards, that completed passes have traveled in any direction',\n",
    "    'PasTotPrgDist': 'Total distance, in yards, that completed passes have traveled towards the opponent\\'s goal',\n",
    "    'PasShoCmp': 'Passes completed (Passes between 5 and 15 yards)',\n",
    "    'PasShoAtt': 'Passes attempted (Passes between 5 and 15 yards)',\n",
    "    'PasShoCmp%': 'Pass completion percentage (Passes between 5 and 15 yards)',\n",
    "    'PasMedCmp': 'Passes completed (Passes between 15 and 30 yards)',\n",
    "    'PasMedAtt': 'Passes attempted (Passes between 15 and 30 yards)',\n",
    "    'PasMedCmp%': 'Pass completion percentage (Passes between 15 and 30 yards)',\n",
    "    'PasLonCmp': 'Passes completed (Passes longer than 30 yards)',\n",
    "    'PasLonAtt': 'Passes attempted (Passes longer than 30 yards)',\n",
    "    'PasLonCmp%': 'Pass completion percentage (Passes longer than 30 yards)',\n",
    "    'Assists': 'Assists',\n",
    "    'PasAss': 'Passes that directly lead to a shot (assisted shots)',\n",
    "    'Pas3rd': 'Completed passes that enter the 1/3 of the pitch closest to the goal',\n",
    "    'PPA': 'Completed passes into the 18-yard box',\n",
    "    'CrsPA': 'Completed crosses into the 18-yard box',\n",
    "    'PasProg': 'Completed passes that move the ball towards the opponent\\'s goal at least 10 yards from its furthest point in the last six passes, or any completed pass into the penalty area',\n",
    "    'PasAtt': 'Passes attempted',\n",
    "    'PasLive': 'Live-ball passes',\n",
    "    'PasDead': 'Dead-ball passes',\n",
    "    'PasFK': 'Passes attempted from free kicks',\n",
    "    'TB': 'Completed pass sent between back defenders into open space',\n",
    "    'Sw': 'Passes that travel more than 40 yards of the width of the pitch',\n",
    "    'PasCrs': 'Crosses',\n",
    "    'TI': 'Throw-Ins taken',\n",
    "    'CK': 'Corner kicks',\n",
    "    'CkIn': 'Inswinging corner kicks',\n",
    "    'CkOut': 'Outswinging corner kicks',\n",
    "    'CkStr': 'Straight corner kicks',\n",
    "    'PasCmp': 'Passes completed',\n",
    "    'PasOff': 'Offsides',\n",
    "    'PasBlocks': 'Blocked by the opponent who was standing it the path',\n",
    "    'SCA': 'Shot-creating actions',\n",
    "    'ScaPassLive': 'Completed live-ball passes that lead to a shot attempt',\n",
    "    'ScaPassDead': 'Completed dead-ball passes that lead to a shot attempt',\n",
    "    'ScaDrib': 'Successful dribbles that lead to a shot attempt',\n",
    "    'ScaSh': 'Shots that lead to another shot attempt',\n",
    "    'ScaFld': 'Fouls drawn that lead to a shot attempt',\n",
    "    'ScaDef': 'Defensive actions that lead to a shot attempt',\n",
    "    'GCA': 'Goal-creating actions',\n",
    "    'GcaPassLive': 'Completed live-ball passes that lead to a goal',\n",
    "    'GcaPassDead': 'Completed dead-ball passes that lead to a goal',\n",
    "    'GcaDrib': 'Successful dribbles that lead to a goal',\n",
    "    'GcaSh': 'Shots that lead to another goal-scoring shot',\n",
    "    'GcaFld': 'Fouls drawn that lead to a goal',\n",
    "    'GcaDef': 'Defensive actions that lead to a goal',\n",
    "    'Tkl': 'Number of players tackled',\n",
    "    'TklWon': 'Tackles in which the tackler\\'s team won possession of the ball',\n",
    "    'TklDef3rd': 'Tackles in defensive 1/3',\n",
    "    'TklMid3rd': 'Tackles in middle 1/3',\n",
    "    'TklAtt3rd': 'Tackles in attacking 1/3',\n",
    "    'TklDri': 'Number of dribblers tackled',\n",
    "    'TklDriAtt': 'Number of times dribbled past plus number of tackles',\n",
    "    'TklDri%': 'Percentage of dribblers tackled',\n",
    "    'TklDriPast': 'Number of times dribbled past by an opposing player',\n",
    "    'Blocks': 'Number of times blocking the ball by standing in its path',\n",
    "    'BlkSh': 'Number of times blocking a shot by standing in its path',\n",
    "    'BlkPass': 'Number of times blocking a pass by standing in its path',\n",
    "    'Int': 'Interceptions',\n",
    "    'Tkl+Int': 'Number of players tackled plus number of interceptions',\n",
    "    'Clr': 'Clearances',\n",
    "    'Err': 'Mistakes leading to an opponent\\'s shot',\n",
    "    'Touches': 'Number of times a player touched the ball. Note: Receiving a pass, then dribbling, then sending a pass counts as one touch',\n",
    "    'TouDefPen': 'Touches in defensive penalty area',\n",
    "    'TouDef3rd': 'Touches in defensive 1/3',\n",
    "    'TouMid3rd': 'Touches in middle 1/3',\n",
    "    'TouAtt3rd': 'Touches in attacking 1/3',\n",
    "    'TouAttPen': 'Touches in attacking penalty area',\n",
    "    'TouLive': 'Live-ball touches. Does not include corner kicks, free kicks, throw-ins, kick-offs, goal kicks or penalty kicks.',\n",
    "    'ToAtt': 'Number of attempts to take on defenders while dribbling',\n",
    "    'ToSuc': 'Number of defenders taken on successfully, by dribbling past them',\n",
    "    'ToSuc%': 'Percentage of take-ons Completed Successfully',\n",
    "    'ToTkl': 'Number of times tackled by a defender during a take-on attempt',\n",
    "    'ToTkl%': 'Percentage of time tackled by a defender during a take-on attempt',\n",
    "    'Carries': 'Number of times the player controlled the ball with their feet',\n",
    "    'CarTotDist': 'Total distance, in yards, a player moved the ball while controlling it with their feet, in any direction',\n",
    "    'CarPrgDist': 'Total distance, in yards, a player moved the ball while controlling it with their feet towards the opponent\\'s goal',\n",
    "    'CarProg': 'Carries that move the ball towards the opponent\\'s goal at least 5 yards, or any carry into the penalty area',\n",
    "    'Car3rd': 'Carries that enter the 1/3 of the pitch closest to the goal',\n",
    "    'CPA': 'Carries into the 18-yard box',\n",
    "    'CarMis': 'Number of times a player failed when attempting to gain control of a ball',\n",
    "    'CarDis': 'Number of times a player loses control of the ball after being tackled by an opposing player',\n",
    "    'Rec': 'Number of times a player successfully received a pass',\n",
    "    'RecProg': 'Completed passes that move the ball towards the opponent\\'s goal at least 10 yards from its furthest point in the last six passes, or any completed pass into the penalty area',\n",
    "    'CrdY': 'Yellow cards',\n",
    "    'CrdR': 'Red cards',\n",
    "    '2CrdY': 'Second yellow card',\n",
    "    'Fls': 'Fouls committed',\n",
    "    'Fld': 'Fouls drawn',\n",
    "    'Off': 'Offsides',\n",
    "    'Crs': 'Crosses',\n",
    "    'TklW': 'Tackles in which the tackler\\'s team won possession of the ball',\n",
    "    'PKwon': 'Penalty kicks won',\n",
    "    'PKcon': 'Penalty kicks conceded',\n",
    "    'OG': 'Own goals',\n",
    "    'Recov': 'Number of loose balls recovered',\n",
    "    'AerWon': 'Aerials won',\n",
    "    'AerLost': 'Aerials lost',\n",
    "    'AerWon%': 'Percentage of aerials won'\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Data from CSV files\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2689 entries, 0 to 2688\n",
      "Columns: 124 entries, Rk to AerWon%\n",
      "dtypes: float64(112), int64(7), object(5)\n",
      "memory usage: 2.5+ MB\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>Rk</th>\n",
       "      <th>Age</th>\n",
       "      <th>Born</th>\n",
       "      <th>MP</th>\n",
       "      <th>Starts</th>\n",
       "      <th>Min</th>\n",
       "      <th>90s</th>\n",
       "      <th>Goals</th>\n",
       "      <th>Shots</th>\n",
       "      <th>SoT</th>\n",
       "      <th>SoT%</th>\n",
       "      <th>G/Sh</th>\n",
       "      <th>G/SoT</th>\n",
       "      <th>ShoDist</th>\n",
       "      <th>ShoFK</th>\n",
       "      <th>ShoPK</th>\n",
       "      <th>PKatt</th>\n",
       "      <th>PasTotCmp</th>\n",
       "      <th>PasTotAtt</th>\n",
       "      <th>PasTotCmp%</th>\n",
       "      <th>PasTotDist</th>\n",
       "      <th>PasTotPrgDist</th>\n",
       "      <th>PasShoCmp</th>\n",
       "      <th>PasShoAtt</th>\n",
       "      <th>PasShoCmp%</th>\n",
       "      <th>PasMedCmp</th>\n",
       "      <th>PasMedAtt</th>\n",
       "      <th>PasMedCmp%</th>\n",
       "      <th>PasLonCmp</th>\n",
       "      <th>PasLonAtt</th>\n",
       "      <th>PasLonCmp%</th>\n",
       "      <th>Assists</th>\n",
       "      <th>PasAss</th>\n",
       "      <th>Pas3rd</th>\n",
       "      <th>PPA</th>\n",
       "      <th>CrsPA</th>\n",
       "      <th>PasProg</th>\n",
       "      <th>PasAtt</th>\n",
       "      <th>PasLive</th>\n",
       "      <th>PasDead</th>\n",
       "      <th>PasFK</th>\n",
       "      <th>TB</th>\n",
       "      <th>Sw</th>\n",
       "      <th>PasCrs</th>\n",
       "      <th>TI</th>\n",
       "      <th>CK</th>\n",
       "      <th>CkIn</th>\n",
       "      <th>CkOut</th>\n",
       "      <th>CkStr</th>\n",
       "      <th>PasCmp</th>\n",
       "      <th>PasOff</th>\n",
       "      <th>PasBlocks</th>\n",
       "      <th>SCA</th>\n",
       "      <th>ScaPassLive</th>\n",
       "      <th>ScaPassDead</th>\n",
       "      <th>ScaDrib</th>\n",
       "      <th>ScaSh</th>\n",
       "      <th>ScaFld</th>\n",
       "      <th>ScaDef</th>\n",
       "      <th>GCA</th>\n",
       "      <th>GcaPassLive</th>\n",
       "      <th>GcaPassDead</th>\n",
       "      <th>GcaDrib</th>\n",
       "      <th>GcaSh</th>\n",
       "      <th>GcaFld</th>\n",
       "      <th>GcaDef</th>\n",
       "      <th>Tkl</th>\n",
       "      <th>TklWon</th>\n",
       "      <th>TklDef3rd</th>\n",
       "      <th>TklMid3rd</th>\n",
       "      <th>TklAtt3rd</th>\n",
       "      <th>TklDri</th>\n",
       "      <th>TklDriAtt</th>\n",
       "      <th>TklDri%</th>\n",
       "      <th>TklDriPast</th>\n",
       "      <th>Blocks</th>\n",
       "      <th>BlkSh</th>\n",
       "      <th>BlkPass</th>\n",
       "      <th>Int</th>\n",
       "      <th>Tkl+Int</th>\n",
       "      <th>Clr</th>\n",
       "      <th>Err</th>\n",
       "      <th>Touches</th>\n",
       "      <th>TouDefPen</th>\n",
       "      <th>TouDef3rd</th>\n",
       "      <th>TouMid3rd</th>\n",
       "      <th>TouAtt3rd</th>\n",
       "      <th>TouAttPen</th>\n",
       "      <th>TouLive</th>\n",
       "      <th>ToAtt</th>\n",
       "      <th>ToSuc</th>\n",
       "      <th>ToSuc%</th>\n",
       "      <th>ToTkl</th>\n",
       "      <th>ToTkl%</th>\n",
       "      <th>Carries</th>\n",
       "      <th>CarTotDist</th>\n",
       "      <th>CarPrgDist</th>\n",
       "      <th>CarProg</th>\n",
       "      <th>Car3rd</th>\n",
       "      <th>CPA</th>\n",
       "      <th>CarMis</th>\n",
       "      <th>CarDis</th>\n",
       "      <th>Rec</th>\n",
       "      <th>RecProg</th>\n",
       "      <th>CrdY</th>\n",
       "      <th>CrdR</th>\n",
       "      <th>2CrdY</th>\n",
       "      <th>Fls</th>\n",
       "      <th>Fld</th>\n",
       "      <th>Off</th>\n",
       "      <th>Crs</th>\n",
       "      <th>TklW</th>\n",
       "      <th>PKwon</th>\n",
       "      <th>PKcon</th>\n",
       "      <th>OG</th>\n",
       "      <th>Recov</th>\n",
       "      <th>AerWon</th>\n",
       "      <th>AerLost</th>\n",
       "      <th>AerWon%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.00000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.00000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.00000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "      <td>2689.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1345.000000</td>\n",
       "      <td>26.011157</td>\n",
       "      <td>1996.155820</td>\n",
       "      <td>11.833023</td>\n",
       "      <td>8.476013</td>\n",
       "      <td>760.451097</td>\n",
       "      <td>8.450465</td>\n",
       "      <td>1.027520</td>\n",
       "      <td>1.245787</td>\n",
       "      <td>0.411261</td>\n",
       "      <td>24.222164</td>\n",
       "      <td>0.064210</td>\n",
       "      <td>0.167374</td>\n",
       "      <td>14.141726</td>\n",
       "      <td>0.035920</td>\n",
       "      <td>0.008111</td>\n",
       "      <td>0.010945</td>\n",
       "      <td>33.348204</td>\n",
       "      <td>42.701882</td>\n",
       "      <td>75.489773</td>\n",
       "      <td>577.364187</td>\n",
       "      <td>198.385950</td>\n",
       "      <td>15.389275</td>\n",
       "      <td>17.784968</td>\n",
       "      <td>84.270807</td>\n",
       "      <td>13.590718</td>\n",
       "      <td>16.197352</td>\n",
       "      <td>78.697285</td>\n",
       "      <td>3.269732</td>\n",
       "      <td>5.964154</td>\n",
       "      <td>51.159799</td>\n",
       "      <td>0.083466</td>\n",
       "      <td>0.866936</td>\n",
       "      <td>2.529621</td>\n",
       "      <td>0.738103</td>\n",
       "      <td>0.197713</td>\n",
       "      <td>3.331097</td>\n",
       "      <td>42.701882</td>\n",
       "      <td>38.572090</td>\n",
       "      <td>3.955508</td>\n",
       "      <td>1.005110</td>\n",
       "      <td>0.108167</td>\n",
       "      <td>0.312916</td>\n",
       "      <td>1.661636</td>\n",
       "      <td>1.832904</td>\n",
       "      <td>0.41315</td>\n",
       "      <td>0.175627</td>\n",
       "      <td>0.157441</td>\n",
       "      <td>0.003079</td>\n",
       "      <td>33.348204</td>\n",
       "      <td>0.173871</td>\n",
       "      <td>0.913009</td>\n",
       "      <td>2.071179</td>\n",
       "      <td>1.498059</td>\n",
       "      <td>0.167683</td>\n",
       "      <td>0.112588</td>\n",
       "      <td>0.14881</td>\n",
       "      <td>0.108676</td>\n",
       "      <td>0.035459</td>\n",
       "      <td>0.209583</td>\n",
       "      <td>0.144102</td>\n",
       "      <td>0.010242</td>\n",
       "      <td>0.013942</td>\n",
       "      <td>0.026021</td>\n",
       "      <td>0.012622</td>\n",
       "      <td>0.00267</td>\n",
       "      <td>1.701655</td>\n",
       "      <td>0.990569</td>\n",
       "      <td>0.816422</td>\n",
       "      <td>0.648635</td>\n",
       "      <td>0.236664</td>\n",
       "      <td>0.750398</td>\n",
       "      <td>1.444533</td>\n",
       "      <td>44.535106</td>\n",
       "      <td>0.694080</td>\n",
       "      <td>1.079762</td>\n",
       "      <td>0.275385</td>\n",
       "      <td>0.804522</td>\n",
       "      <td>0.820331</td>\n",
       "      <td>2.521261</td>\n",
       "      <td>1.574704</td>\n",
       "      <td>0.022350</td>\n",
       "      <td>53.651023</td>\n",
       "      <td>4.613027</td>\n",
       "      <td>15.713856</td>\n",
       "      <td>24.237754</td>\n",
       "      <td>14.262763</td>\n",
       "      <td>2.161078</td>\n",
       "      <td>53.639903</td>\n",
       "      <td>1.817869</td>\n",
       "      <td>0.729093</td>\n",
       "      <td>34.399702</td>\n",
       "      <td>0.860033</td>\n",
       "      <td>37.415210</td>\n",
       "      <td>31.611711</td>\n",
       "      <td>170.765946</td>\n",
       "      <td>82.638732</td>\n",
       "      <td>1.679989</td>\n",
       "      <td>1.270476</td>\n",
       "      <td>0.434883</td>\n",
       "      <td>1.682049</td>\n",
       "      <td>1.056794</td>\n",
       "      <td>34.369636</td>\n",
       "      <td>3.970714</td>\n",
       "      <td>0.246222</td>\n",
       "      <td>0.020688</td>\n",
       "      <td>0.006761</td>\n",
       "      <td>1.319788</td>\n",
       "      <td>1.146991</td>\n",
       "      <td>0.204697</td>\n",
       "      <td>1.661636</td>\n",
       "      <td>0.990569</td>\n",
       "      <td>0.009249</td>\n",
       "      <td>0.015173</td>\n",
       "      <td>0.003142</td>\n",
       "      <td>4.951967</td>\n",
       "      <td>1.312064</td>\n",
       "      <td>1.497356</td>\n",
       "      <td>43.583600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>776.391761</td>\n",
       "      <td>4.446259</td>\n",
       "      <td>4.450108</td>\n",
       "      <td>6.864278</td>\n",
       "      <td>6.994383</td>\n",
       "      <td>591.094260</td>\n",
       "      <td>6.567484</td>\n",
       "      <td>2.013714</td>\n",
       "      <td>1.424619</td>\n",
       "      <td>0.754716</td>\n",
       "      <td>24.249242</td>\n",
       "      <td>0.118826</td>\n",
       "      <td>0.263501</td>\n",
       "      <td>8.677582</td>\n",
       "      <td>0.198595</td>\n",
       "      <td>0.041589</td>\n",
       "      <td>0.051776</td>\n",
       "      <td>17.027779</td>\n",
       "      <td>18.447543</td>\n",
       "      <td>13.113075</td>\n",
       "      <td>324.007185</td>\n",
       "      <td>145.782161</td>\n",
       "      <td>8.486637</td>\n",
       "      <td>9.050574</td>\n",
       "      <td>15.280160</td>\n",
       "      <td>9.142460</td>\n",
       "      <td>9.683369</td>\n",
       "      <td>19.294651</td>\n",
       "      <td>2.952956</td>\n",
       "      <td>4.975162</td>\n",
       "      <td>24.030708</td>\n",
       "      <td>0.276581</td>\n",
       "      <td>1.078650</td>\n",
       "      <td>2.207206</td>\n",
       "      <td>1.065411</td>\n",
       "      <td>0.518301</td>\n",
       "      <td>2.629106</td>\n",
       "      <td>18.447543</td>\n",
       "      <td>17.567568</td>\n",
       "      <td>4.005617</td>\n",
       "      <td>1.176429</td>\n",
       "      <td>0.381662</td>\n",
       "      <td>0.566358</td>\n",
       "      <td>2.319000</td>\n",
       "      <td>3.333445</td>\n",
       "      <td>1.07893</td>\n",
       "      <td>0.643655</td>\n",
       "      <td>0.504091</td>\n",
       "      <td>0.028629</td>\n",
       "      <td>17.027779</td>\n",
       "      <td>0.407284</td>\n",
       "      <td>1.087060</td>\n",
       "      <td>1.913766</td>\n",
       "      <td>1.453089</td>\n",
       "      <td>0.480929</td>\n",
       "      <td>0.447581</td>\n",
       "      <td>0.45827</td>\n",
       "      <td>0.270573</td>\n",
       "      <td>0.156976</td>\n",
       "      <td>0.462712</td>\n",
       "      <td>0.344309</td>\n",
       "      <td>0.052678</td>\n",
       "      <td>0.136515</td>\n",
       "      <td>0.249190</td>\n",
       "      <td>0.054875</td>\n",
       "      <td>0.02362</td>\n",
       "      <td>1.729327</td>\n",
       "      <td>1.235965</td>\n",
       "      <td>1.156952</td>\n",
       "      <td>0.909804</td>\n",
       "      <td>0.566928</td>\n",
       "      <td>1.000632</td>\n",
       "      <td>1.520987</td>\n",
       "      <td>30.431628</td>\n",
       "      <td>0.972013</td>\n",
       "      <td>1.188531</td>\n",
       "      <td>0.484980</td>\n",
       "      <td>1.094028</td>\n",
       "      <td>0.882421</td>\n",
       "      <td>2.103961</td>\n",
       "      <td>1.729149</td>\n",
       "      <td>0.070363</td>\n",
       "      <td>19.242087</td>\n",
       "      <td>7.412558</td>\n",
       "      <td>12.185479</td>\n",
       "      <td>13.530122</td>\n",
       "      <td>10.592654</td>\n",
       "      <td>2.489662</td>\n",
       "      <td>19.245770</td>\n",
       "      <td>2.263393</td>\n",
       "      <td>1.201242</td>\n",
       "      <td>27.935466</td>\n",
       "      <td>1.276899</td>\n",
       "      <td>28.986632</td>\n",
       "      <td>13.996602</td>\n",
       "      <td>94.941583</td>\n",
       "      <td>56.734133</td>\n",
       "      <td>1.960935</td>\n",
       "      <td>1.537054</td>\n",
       "      <td>0.863519</td>\n",
       "      <td>1.890601</td>\n",
       "      <td>1.528011</td>\n",
       "      <td>15.358018</td>\n",
       "      <td>4.152683</td>\n",
       "      <td>0.608983</td>\n",
       "      <td>0.296473</td>\n",
       "      <td>0.108943</td>\n",
       "      <td>1.465974</td>\n",
       "      <td>1.261866</td>\n",
       "      <td>0.552376</td>\n",
       "      <td>2.319000</td>\n",
       "      <td>1.235965</td>\n",
       "      <td>0.043781</td>\n",
       "      <td>0.077399</td>\n",
       "      <td>0.022607</td>\n",
       "      <td>2.901833</td>\n",
       "      <td>1.579539</td>\n",
       "      <td>1.830391</td>\n",
       "      <td>26.673092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1981.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>673.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>194.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>30.400000</td>\n",
       "      <td>70.400000</td>\n",
       "      <td>339.400000</td>\n",
       "      <td>85.800000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>12.100000</td>\n",
       "      <td>81.200000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.350000</td>\n",
       "      <td>72.800000</td>\n",
       "      <td>1.110000</td>\n",
       "      <td>2.350000</td>\n",
       "      <td>41.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.670000</td>\n",
       "      <td>30.400000</td>\n",
       "      <td>26.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>21.700000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>1.140000</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.800000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>5.560000</td>\n",
       "      <td>16.200000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>40.800000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>110.900000</td>\n",
       "      <td>49.600000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>25.100000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.330000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>27.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1345.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>1996.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>684.000000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.860000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.800000</td>\n",
       "      <td>41.700000</td>\n",
       "      <td>77.400000</td>\n",
       "      <td>558.400000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>14.600000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>87.400000</td>\n",
       "      <td>12.200000</td>\n",
       "      <td>15.300000</td>\n",
       "      <td>82.800000</td>\n",
       "      <td>2.650000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>53.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>2.190000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.130000</td>\n",
       "      <td>41.700000</td>\n",
       "      <td>37.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.800000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>1.820000</td>\n",
       "      <td>1.320000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>0.690000</td>\n",
       "      <td>2.430000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>53.300000</td>\n",
       "      <td>2.120000</td>\n",
       "      <td>12.900000</td>\n",
       "      <td>23.800000</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>1.340000</td>\n",
       "      <td>53.300000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>41.700000</td>\n",
       "      <td>29.900000</td>\n",
       "      <td>155.300000</td>\n",
       "      <td>74.100000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.260000</td>\n",
       "      <td>0.710000</td>\n",
       "      <td>32.800000</td>\n",
       "      <td>3.330000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>0.910000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>46.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1245.000000</td>\n",
       "      <td>13.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.850000</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>20.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>53.500000</td>\n",
       "      <td>83.300000</td>\n",
       "      <td>763.600000</td>\n",
       "      <td>274.400000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.700000</td>\n",
       "      <td>91.600000</td>\n",
       "      <td>18.300000</td>\n",
       "      <td>21.400000</td>\n",
       "      <td>90.300000</td>\n",
       "      <td>4.690000</td>\n",
       "      <td>8.330000</td>\n",
       "      <td>66.700000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>1.270000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>4.550000</td>\n",
       "      <td>53.500000</td>\n",
       "      <td>48.400000</td>\n",
       "      <td>6.320000</td>\n",
       "      <td>1.620000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1.240000</td>\n",
       "      <td>0.13000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>2.970000</td>\n",
       "      <td>2.120000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.18000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>2.290000</td>\n",
       "      <td>1.320000</td>\n",
       "      <td>1.130000</td>\n",
       "      <td>0.890000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>1.960000</td>\n",
       "      <td>66.700000</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>1.410000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>1.230000</td>\n",
       "      <td>3.420000</td>\n",
       "      <td>2.310000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>64.700000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>23.700000</td>\n",
       "      <td>31.300000</td>\n",
       "      <td>20.100000</td>\n",
       "      <td>3.240000</td>\n",
       "      <td>64.700000</td>\n",
       "      <td>2.530000</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.220000</td>\n",
       "      <td>55.600000</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>216.300000</td>\n",
       "      <td>104.500000</td>\n",
       "      <td>2.380000</td>\n",
       "      <td>1.790000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>2.330000</td>\n",
       "      <td>1.430000</td>\n",
       "      <td>42.100000</td>\n",
       "      <td>6.190000</td>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.670000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1.320000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.270000</td>\n",
       "      <td>1.790000</td>\n",
       "      <td>1.830000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2689.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>2070.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>46.900000</td>\n",
       "      <td>6.670000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.910000</td>\n",
       "      <td>235.000000</td>\n",
       "      <td>235.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>3735.000000</td>\n",
       "      <td>1230.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>40.300000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>235.000000</td>\n",
       "      <td>235.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>20.00000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>235.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.00000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.430000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>0.83000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>245.000000</td>\n",
       "      <td>44.400000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>245.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>1070.000000</td>\n",
       "      <td>780.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.870000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Rk          Age         Born           MP       Starts  \\\n",
       "count  2689.000000  2689.000000  2689.000000  2689.000000  2689.000000   \n",
       "mean   1345.000000    26.011157  1996.155820    11.833023     8.476013   \n",
       "std     776.391761     4.446259     4.450108     6.864278     6.994383   \n",
       "min       1.000000    15.000000  1981.000000     1.000000     0.000000   \n",
       "25%     673.000000    23.000000  1993.000000     5.000000     2.000000   \n",
       "50%    1345.000000    26.000000  1996.000000    13.000000     7.000000   \n",
       "75%    2017.000000    29.000000  2000.000000    18.000000    14.000000   \n",
       "max    2689.000000    41.000000  2007.000000    23.000000    23.000000   \n",
       "\n",
       "               Min          90s        Goals        Shots          SoT  \\\n",
       "count  2689.000000  2689.000000  2689.000000  2689.000000  2689.000000   \n",
       "mean    760.451097     8.450465     1.027520     1.245787     0.411261   \n",
       "std     591.094260     6.567484     2.013714     1.424619     0.754716   \n",
       "min       1.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%     194.000000     2.200000     0.000000     0.260000     0.000000   \n",
       "50%     684.000000     7.600000     0.000000     0.860000     0.180000   \n",
       "75%    1245.000000    13.800000     1.000000     1.850000     0.590000   \n",
       "max    2070.000000    23.000000    25.000000    15.000000    10.000000   \n",
       "\n",
       "              SoT%         G/Sh        G/SoT      ShoDist        ShoFK  \\\n",
       "count  2689.000000  2689.000000  2689.000000  2689.000000  2689.000000   \n",
       "mean     24.222164     0.064210     0.167374    14.141726     0.035920   \n",
       "std      24.249242     0.118826     0.263501     8.677582     0.198595   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     9.500000     0.000000   \n",
       "50%      24.000000     0.000000     0.000000    15.500000     0.000000   \n",
       "75%      40.000000     0.100000     0.320000    20.100000     0.000000   \n",
       "max     100.000000     1.000000     1.000000    46.900000     6.670000   \n",
       "\n",
       "             ShoPK        PKatt    PasTotCmp    PasTotAtt   PasTotCmp%  \\\n",
       "count  2689.000000  2689.000000  2689.000000  2689.000000  2689.000000   \n",
       "mean      0.008111     0.010945    33.348204    42.701882    75.489773   \n",
       "std       0.041589     0.051776    17.027779    18.447543    13.113075   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000    21.600000    30.400000    70.400000   \n",
       "50%       0.000000     0.000000    31.800000    41.700000    77.400000   \n",
       "75%       0.000000     0.000000    43.000000    53.500000    83.300000   \n",
       "max       0.740000     0.910000   235.000000   235.000000   100.000000   \n",
       "\n",
       "        PasTotDist  PasTotPrgDist    PasShoCmp    PasShoAtt   PasShoCmp%  \\\n",
       "count  2689.000000    2689.000000  2689.000000  2689.000000  2689.000000   \n",
       "mean    577.364187     198.385950    15.389275    17.784968    84.270807   \n",
       "std     324.007185     145.782161     8.486637     9.050574    15.280160   \n",
       "min       0.000000       0.000000     0.000000     0.000000     0.000000   \n",
       "25%     339.400000      85.800000    10.000000    12.100000    81.200000   \n",
       "50%     558.400000     175.000000    14.600000    17.200000    87.400000   \n",
       "75%     763.600000     274.400000    20.000000    22.700000    91.600000   \n",
       "max    3735.000000    1230.000000   115.000000   115.000000   100.000000   \n",
       "\n",
       "         PasMedCmp    PasMedAtt   PasMedCmp%    PasLonCmp    PasLonAtt  \\\n",
       "count  2689.000000  2689.000000  2689.000000  2689.000000  2689.000000   \n",
       "mean     13.590718    16.197352    78.697285     3.269732     5.964154   \n",
       "std       9.142460     9.683369    19.294651     2.952956     4.975162   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       7.000000     9.350000    72.800000     1.110000     2.350000   \n",
       "50%      12.200000    15.300000    82.800000     2.650000     5.000000   \n",
       "75%      18.300000    21.400000    90.300000     4.690000     8.330000   \n",
       "max     100.000000   120.000000   100.000000    35.000000    40.300000   \n",
       "\n",
       "        PasLonCmp%      Assists       PasAss       Pas3rd          PPA  \\\n",
       "count  2689.000000  2689.000000  2689.000000  2689.000000  2689.000000   \n",
       "mean     51.159799     0.083466     0.866936     2.529621     0.738103   \n",
       "std      24.030708     0.276581     1.078650     2.207206     1.065411   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%      41.200000     0.000000     0.110000     1.000000     0.000000   \n",
       "50%      53.600000     0.000000     0.670000     2.190000     0.500000   \n",
       "75%      66.700000     0.110000     1.270000     3.500000     1.040000   \n",
       "max     100.000000    10.000000    20.000000    25.000000    20.000000   \n",
       "\n",
       "             CrsPA      PasProg       PasAtt      PasLive      PasDead  \\\n",
       "count  2689.000000  2689.000000  2689.000000  2689.000000  2689.000000   \n",
       "mean      0.197713     3.331097    42.701882    38.572090     3.955508   \n",
       "std       0.518301     2.629106    18.447543    17.567568     4.005617   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     1.670000    30.400000    26.800000     1.000000   \n",
       "50%       0.000000     3.130000    41.700000    37.300000     2.300000   \n",
       "75%       0.250000     4.550000    53.500000    48.400000     6.320000   \n",
       "max      10.000000    35.000000   235.000000   235.000000    25.000000   \n",
       "\n",
       "             PasFK           TB           Sw       PasCrs           TI  \\\n",
       "count  2689.000000  2689.000000  2689.000000  2689.000000  2689.000000   \n",
       "mean      1.005110     0.108167     0.312916     1.661636     1.832904   \n",
       "std       1.176429     0.381662     0.566358     2.319000     3.333445   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.670000     0.000000     0.140000     0.760000     0.220000   \n",
       "75%       1.620000     0.120000     0.430000     2.500000     1.240000   \n",
       "max      10.000000    10.000000    10.000000    30.000000    25.000000   \n",
       "\n",
       "               CK         CkIn        CkOut        CkStr       PasCmp  \\\n",
       "count  2689.00000  2689.000000  2689.000000  2689.000000  2689.000000   \n",
       "mean      0.41315     0.175627     0.157441     0.003079    33.348204   \n",
       "std       1.07893     0.643655     0.504091     0.028629    17.027779   \n",
       "min       0.00000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.00000     0.000000     0.000000     0.000000    21.600000   \n",
       "50%       0.00000     0.000000     0.000000     0.000000    31.800000   \n",
       "75%       0.13000     0.000000     0.000000     0.000000    43.000000   \n",
       "max      20.00000    20.000000    10.000000     0.770000   235.000000   \n",
       "\n",
       "            PasOff    PasBlocks          SCA  ScaPassLive  ScaPassDead  \\\n",
       "count  2689.000000  2689.000000  2689.000000  2689.000000  2689.000000   \n",
       "mean      0.173871     0.913009     2.071179     1.498059     0.167683   \n",
       "std       0.407284     1.087060     1.913766     1.453089     0.480929   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.250000     0.700000     0.510000     0.000000   \n",
       "50%       0.090000     0.770000     1.820000     1.320000     0.000000   \n",
       "75%       0.230000     1.250000     2.970000     2.120000     0.110000   \n",
       "max      10.000000    20.000000    30.000000    20.000000    10.000000   \n",
       "\n",
       "           ScaDrib       ScaSh       ScaFld       ScaDef          GCA  \\\n",
       "count  2689.000000  2689.00000  2689.000000  2689.000000  2689.000000   \n",
       "mean      0.112588     0.14881     0.108676     0.035459     0.209583   \n",
       "std       0.447581     0.45827     0.270573     0.156976     0.462712   \n",
       "min       0.000000     0.00000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.00000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.00000     0.000000     0.000000     0.080000   \n",
       "75%       0.100000     0.18000     0.130000     0.000000     0.300000   \n",
       "max      10.000000    10.00000     5.000000     5.000000    10.000000   \n",
       "\n",
       "       GcaPassLive  GcaPassDead      GcaDrib        GcaSh       GcaFld  \\\n",
       "count  2689.000000  2689.000000  2689.000000  2689.000000  2689.000000   \n",
       "mean      0.144102     0.010242     0.013942     0.026021     0.012622   \n",
       "std       0.344309     0.052678     0.136515     0.249190     0.054875   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.200000     0.000000     0.000000     0.000000     0.000000   \n",
       "max      10.000000     1.430000     5.000000    10.000000     1.300000   \n",
       "\n",
       "           GcaDef          Tkl       TklWon    TklDef3rd    TklMid3rd  \\\n",
       "count  2689.00000  2689.000000  2689.000000  2689.000000  2689.000000   \n",
       "mean      0.00267     1.701655     0.990569     0.816422     0.648635   \n",
       "std       0.02362     1.729327     1.235965     1.156952     0.909804   \n",
       "min       0.00000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.00000     0.720000     0.300000     0.100000     0.070000   \n",
       "50%       0.00000     1.500000     0.830000     0.630000     0.500000   \n",
       "75%       0.00000     2.290000     1.320000     1.130000     0.890000   \n",
       "max       0.83000    20.000000    20.000000    20.000000    15.000000   \n",
       "\n",
       "         TklAtt3rd       TklDri    TklDriAtt      TklDri%   TklDriPast  \\\n",
       "count  2689.000000  2689.000000  2689.000000  2689.000000  2689.000000   \n",
       "mean      0.236664     0.750398     1.444533    44.535106     0.694080   \n",
       "std       0.566928     1.000632     1.520987    30.431628     0.972013   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.110000     0.560000    21.700000     0.140000   \n",
       "50%       0.110000     0.600000     1.250000    50.000000     0.500000   \n",
       "75%       0.310000     1.050000     1.960000    66.700000     0.920000   \n",
       "max      10.000000    20.000000    30.000000   100.000000    10.000000   \n",
       "\n",
       "            Blocks        BlkSh      BlkPass          Int      Tkl+Int  \\\n",
       "count  2689.000000  2689.000000  2689.000000  2689.000000  2689.000000   \n",
       "mean      1.079762     0.275385     0.804522     0.820331     2.521261   \n",
       "std       1.188531     0.484980     1.094028     0.882421     2.103961   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.490000     0.000000     0.240000     0.090000     1.140000   \n",
       "50%       0.990000     0.110000     0.670000     0.690000     2.430000   \n",
       "75%       1.410000     0.400000     1.050000     1.230000     3.420000   \n",
       "max      20.000000    10.000000    20.000000    10.000000    20.000000   \n",
       "\n",
       "               Clr          Err      Touches    TouDefPen    TouDef3rd  \\\n",
       "count  2689.000000  2689.000000  2689.000000  2689.000000  2689.000000   \n",
       "mean      1.574704     0.022350    53.651023     4.613027    15.713856   \n",
       "std       1.729149     0.070363    19.242087     7.412558    12.185479   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.370000     0.000000    40.800000     0.750000     5.560000   \n",
       "50%       1.000000     0.000000    53.300000     2.120000    12.900000   \n",
       "75%       2.310000     0.000000    64.700000     5.000000    23.700000   \n",
       "max      20.000000     1.000000   245.000000    44.400000    95.000000   \n",
       "\n",
       "         TouMid3rd    TouAtt3rd    TouAttPen      TouLive        ToAtt  \\\n",
       "count  2689.000000  2689.000000  2689.000000  2689.000000  2689.000000   \n",
       "mean     24.237754    14.262763     2.161078    53.639903     1.817869   \n",
       "std      13.530122    10.592654     2.489662    19.245770     2.263393   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%      16.200000     5.500000     0.480000    40.800000     0.290000   \n",
       "50%      23.800000    14.200000     1.340000    53.300000     1.200000   \n",
       "75%      31.300000    20.100000     3.240000    64.700000     2.530000   \n",
       "max     200.000000    85.000000    30.000000   245.000000    30.000000   \n",
       "\n",
       "             ToSuc       ToSuc%        ToTkl       ToTkl%      Carries  \\\n",
       "count  2689.000000  2689.000000  2689.000000  2689.000000  2689.000000   \n",
       "mean      0.729093    34.399702     0.860033    37.415210    31.611711   \n",
       "std       1.201242    27.935466     1.276899    28.986632    13.996602   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000    22.500000   \n",
       "50%       0.410000    34.000000     0.490000    41.700000    29.900000   \n",
       "75%       0.960000    50.000000     1.220000    55.600000    38.500000   \n",
       "max      20.000000   100.000000    20.000000   100.000000   150.000000   \n",
       "\n",
       "        CarTotDist   CarPrgDist      CarProg       Car3rd          CPA  \\\n",
       "count  2689.000000  2689.000000  2689.000000  2689.000000  2689.000000   \n",
       "mean    170.765946    82.638732     1.679989     1.270476     0.434883   \n",
       "std      94.941583    56.734133     1.960935     1.537054     0.863519   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%     110.900000    49.600000     0.380000     0.270000     0.000000   \n",
       "50%     155.300000    74.100000     1.250000     1.000000     0.100000   \n",
       "75%     216.300000   104.500000     2.380000     1.790000     0.560000   \n",
       "max    1070.000000   780.000000    30.000000    30.000000    15.000000   \n",
       "\n",
       "            CarMis       CarDis          Rec      RecProg         CrdY  \\\n",
       "count  2689.000000  2689.000000  2689.000000  2689.000000  2689.000000   \n",
       "mean      1.682049     1.056794    34.369636     3.970714     0.246222   \n",
       "std       1.890601     1.528011    15.358018     4.152683     0.608983   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.490000     0.140000    25.100000     0.390000     0.000000   \n",
       "50%       1.260000     0.710000    32.800000     3.330000     0.140000   \n",
       "75%       2.330000     1.430000    42.100000     6.190000     0.290000   \n",
       "max      20.000000    20.000000   230.000000    50.000000    10.000000   \n",
       "\n",
       "              CrdR        2CrdY          Fls          Fld          Off  \\\n",
       "count  2689.000000  2689.000000  2689.000000  2689.000000  2689.000000   \n",
       "mean      0.020688     0.006761     1.319788     1.146991     0.204697   \n",
       "std       0.296473     0.108943     1.465974     1.261866     0.552376   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.590000     0.380000     0.000000   \n",
       "50%       0.000000     0.000000     1.100000     0.910000     0.000000   \n",
       "75%       0.000000     0.000000     1.670000     1.600000     0.210000   \n",
       "max      10.000000     5.000000    20.000000    20.000000    10.000000   \n",
       "\n",
       "               Crs         TklW        PKwon        PKcon           OG  \\\n",
       "count  2689.000000  2689.000000  2689.000000  2689.000000  2689.000000   \n",
       "mean      1.661636     0.990569     0.009249     0.015173     0.003142   \n",
       "std       2.319000     1.235965     0.043781     0.077399     0.022607   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.300000     0.000000     0.000000     0.000000   \n",
       "50%       0.760000     0.830000     0.000000     0.000000     0.000000   \n",
       "75%       2.500000     1.320000     0.000000     0.000000     0.000000   \n",
       "max      30.000000    20.000000     0.870000     2.000000     0.500000   \n",
       "\n",
       "             Recov       AerWon      AerLost      AerWon%  \n",
       "count  2689.000000  2689.000000  2689.000000  2689.000000  \n",
       "mean      4.951967     1.312064     1.497356    43.583600  \n",
       "std       2.901833     1.579539     1.830391    26.673092  \n",
       "min       0.000000     0.000000     0.000000     0.000000  \n",
       "25%       3.330000     0.330000     0.550000    27.600000  \n",
       "50%       5.000000     0.930000     1.100000    46.400000  \n",
       "75%       6.270000     1.790000     1.830000    60.000000  \n",
       "max      30.000000    25.000000    30.000000   100.000000  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(os.path.join(DATA_PATH,\n",
    "                              \"2022-2023 Football Player Stats.csv\"), encoding='latin-1', delimiter=';')\n",
    "df.info()\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking if there are any null values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Nation']"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[feature for feature in df.columns if df[feature].isnull().sum() > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## By the league\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Number of players in each league from the dataset'),\n",
       " Text(0.5, 0, 'League'),\n",
       " Text(0, 0.5, 'Count')]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_by_leagues = df.Comp.value_counts().index.tolist()\n",
    "sns.countplot(x='Comp', data=df, label='Count', order=data_by_leagues, hue='Comp', palette='Set2').set(\n",
    "    title='Number of players in each league from the dataset', xlabel='League', ylabel='Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## By the teams/squads\n"
   ]
  },
  {
   "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>Squad</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cádiz</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Schalke 04</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hellas Verona</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sevilla</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Spezia</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Augsburg</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Nott'ham Forest</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ajaccio</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Rennes</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Valladolid</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Chelsea</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Hoffenheim</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Sampdoria</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Wolves</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Angers</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Southampton</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Real Sociedad</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Cremonese</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Stuttgart</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Elche</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Nice</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Espanyol</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Auxerre</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Betis</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Fulham</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Villarreal</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Reims</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Montpellier</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Sassuolo</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Marseille</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Monza</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Union Berlin</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Fiorentina</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Girona</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Everton</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Rayo Vallecano</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Brest</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Salernitana</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Leeds United</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Troyes</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Empoli</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Strasbourg</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Dortmund</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Toulouse</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Nantes</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Lorient</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Lens</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Leicester City</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Liverpool</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Almería</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Bayern Munich</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Torino</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Milan</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Tottenham</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Roma</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Lyon</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Bochum</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Köln</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Leverkusen</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Lecce</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>M'Gladbach</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Mallorca</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Valencia</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Hertha BSC</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Clermont Foot</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Mainz 05</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Juventus</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Eint Frankfurt</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Udinese</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>RB Leipzig</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Paris S-G</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Bologna</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Monaco</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Barcelona</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Getafe</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Brentford</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Newcastle Utd</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Wolfsburg</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Athletic Club</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>West Ham</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Manchester Utd</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Atlético Madrid</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Werder Bremen</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Napoli</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Brighton</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Atalanta</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Lille</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Inter</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Osasuna</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Celta Vigo</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Freiburg</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Manchester City</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Lazio</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Squad  Count\n",
       "1             Cádiz     34\n",
       "2        Schalke 04     34\n",
       "3     Hellas Verona     34\n",
       "4           Sevilla     33\n",
       "5            Spezia     33\n",
       "6          Augsburg     33\n",
       "7   Nott'ham Forest     33\n",
       "8           Ajaccio     33\n",
       "9            Rennes     32\n",
       "10       Valladolid     32\n",
       "11          Chelsea     32\n",
       "12       Hoffenheim     31\n",
       "13        Sampdoria     31\n",
       "14           Wolves     31\n",
       "15           Angers     31\n",
       "16      Southampton     31\n",
       "17    Real Sociedad     30\n",
       "18        Cremonese     30\n",
       "19        Stuttgart     30\n",
       "20            Elche     30\n",
       "21             Nice     30\n",
       "22         Espanyol     30\n",
       "23          Auxerre     30\n",
       "24            Betis     30\n",
       "25           Fulham     29\n",
       "26       Villarreal     29\n",
       "27            Reims     29\n",
       "28      Montpellier     29\n",
       "29         Sassuolo     29\n",
       "30        Marseille     29\n",
       "31      Bournemouth     29\n",
       "32            Monza     29\n",
       "33     Union Berlin     28\n",
       "34       Fiorentina     28\n",
       "35           Girona     28\n",
       "36          Everton     28\n",
       "37   Rayo Vallecano     28\n",
       "38            Brest     28\n",
       "39      Salernitana     28\n",
       "40     Leeds United     28\n",
       "41           Troyes     28\n",
       "42           Empoli     28\n",
       "43       Strasbourg     28\n",
       "44         Dortmund     28\n",
       "45         Toulouse     28\n",
       "46           Nantes     28\n",
       "47          Lorient     28\n",
       "48             Lens     28\n",
       "49   Leicester City     27\n",
       "50        Liverpool     27\n",
       "51          Almería     27\n",
       "52    Bayern Munich     27\n",
       "53           Torino     27\n",
       "54            Milan     27\n",
       "55        Tottenham     27\n",
       "56             Roma     26\n",
       "57             Lyon     26\n",
       "58           Bochum     26\n",
       "59             Köln     26\n",
       "60       Leverkusen     26\n",
       "61            Lecce     26\n",
       "62       M'Gladbach     26\n",
       "63         Mallorca     26\n",
       "64         Valencia     26\n",
       "65       Hertha BSC     26\n",
       "66    Clermont Foot     26\n",
       "67         Mainz 05     26\n",
       "68         Juventus     26\n",
       "69   Eint Frankfurt     26\n",
       "70          Udinese     25\n",
       "71       RB Leipzig     25\n",
       "72        Paris S-G     25\n",
       "73          Arsenal     25\n",
       "74          Bologna     25\n",
       "75           Monaco     25\n",
       "76        Barcelona     25\n",
       "77           Getafe     25\n",
       "78        Brentford     25\n",
       "79    Newcastle Utd     25\n",
       "80        Wolfsburg     25\n",
       "81    Athletic Club     25\n",
       "82      Aston Villa     25\n",
       "83         West Ham     25\n",
       "84   Manchester Utd     25\n",
       "85  Atlético Madrid     24\n",
       "86    Werder Bremen     24\n",
       "87           Napoli     24\n",
       "88         Brighton     24\n",
       "89         Atalanta     24\n",
       "90            Lille     24\n",
       "91            Inter     23\n",
       "92          Osasuna     23\n",
       "93       Celta Vigo     23\n",
       "94   Crystal Palace     23\n",
       "95         Freiburg     23\n",
       "96      Real Madrid     22\n",
       "97  Manchester City     21\n",
       "98            Lazio     21"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a new dataframe from the value counts output\n",
    "squad_counts = pd.DataFrame(df.Squad.value_counts())\n",
    "\n",
    "# rename the column\n",
    "squad_counts.columns = ['Count']\n",
    "\n",
    "# create a column from the index\n",
    "squad_counts.reset_index(inplace=True)\n",
    "squad_counts.index = range(1, len(squad_counts)+1)\n",
    "\n",
    "display(squad_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "forward_features = [\"Goals\", \"Shots\", \"SoT\", \"G/Sh\", \"G/SoT\", \"ShoDist\", \"GCA\", \"SCA\", \"Off\", \"PKwon\", \"ScaDrib\", \"Assists\",\n",
    "                    \"ScaPassLive\", \"Car3rd\", \"ScaFld\", \"ToAtt\", \"ToSuc\", \"Carries\", \"CarTotDist\", \"CarPrgDist\", 'CPA', \"CarMis\", \"CarDis\"]\n",
    "midfielder_features = [\"Goals\", \"PasTotCmp\", \"PasTotCmp%\", \"PasTotDist\", \"PasTotPrgDist\", \"Assists\", \"PasAss\", \"Pas3rd\", \"Crs\", \"PasCmp\",\n",
    "                       \"PasOff\", \"PasBlocks\", \"SCA\", \"ScaPassLive\", \"ScaPassDead\", \"ScaDrib\", \"ScaSh\", \"ScaFld\", \"GCA\", \"GcaPassLive\",\n",
    "                       \"GcaPassDead\", \"GcaDrib\", \"GcaSh\", \"GcaFld\", \"Tkl\", \"TklWon\", \"TklDef3rd\", \"TklMid3rd\", \"TklAtt3rd\", \"TklDri\",\n",
    "                       \"TklDriAtt\", \"TklDri%\", \"TklDriPast\", \"Blocks\", \"BlkSh\", \"Int\", \"Recov\", \"Carries\", \"CarTotDist\", \"CarPrgDist\", \"Fld\"]\n",
    "defender_features = [\"PasTotCmp\", \"PasTotDist\", \"PasTotPrgDist\", \"Tkl\", \"TklWon\", \"TklDef3rd\", \"TklMid3rd\", \"TklAtt3rd\", \"TklDri\", \"TklDriAtt\", \"TklDriPast\", \"Blocks\",\n",
    "                     \"BlkSh\", \"Int\", \"Tkl+Int\", \"Recov\", \"AerWon\", \"AerLost\", \"Carries\", \"CarTotDist\", \"CarPrgDist\", \"CrdY\", \"CrdR\", \"Fls\", \"Clr\"]\n",
    "\n",
    "general_info = [\"Player\", \"Nation\", \"Pos\", \"Squad\", \"Age\", \"Born\"]\n",
    "playing_time = [\"MP\", \"Starts\", \"Min\"]\n",
    "goals_info = [\"Goals\", \"Shots\", \"SoT\", \"G/Sh\", \"G/SoT\", \"ShoDist\", \"ShoPK\", \"PKatt\", \"GCA\",\n",
    "              \"SCA\", \"Off\", \"PKwon\", \"PKcon\"]\n",
    "\n",
    "passes_info = [\"PasTotCmp\", \"PasTotDist\", \"PasTotPrgDist\", \"Assists\",\n",
    "               \"PasAss\", \"Pas3rd\", \"PPA\", \"PasCrs\", \"CrsPA\", \"PasAtt\", \"PasLive\", \"PasDead\", \"PasOff\",\n",
    "               \"PasBlocks\", \"Crs\"]\n",
    "skill_info = [\"SCA\", \"ScaDrib\", \"ScaFld\", \"ToAtt\", \"ToSuc\", \"Carries\",\n",
    "              \"CarTotDist\", \"CarPrgDist\", \"Car3rd\", 'CPA', \"CarMis\", \"CarDis\", \"Fld\"]\n",
    "defense_info = [\"Tkl\", \"TklWon\", \"TklDri\", \"Blocks\", \"Int\", \"Clr\", \"Err\", \"CrdY\", \"CrdR\", \"Fls\", \"OG\", \"Recov\", \"AerWon\",\n",
    "                \"AerLost\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>Comp</th>\n",
       "      <th>Age</th>\n",
       "      <th>Born</th>\n",
       "      <th>MP</th>\n",
       "      <th>Starts</th>\n",
       "      <th>Min</th>\n",
       "      <th>90s</th>\n",
       "      <th>Goals</th>\n",
       "      <th>Shots</th>\n",
       "      <th>SoT</th>\n",
       "      <th>SoT%</th>\n",
       "      <th>G/Sh</th>\n",
       "      <th>G/SoT</th>\n",
       "      <th>ShoDist</th>\n",
       "      <th>ShoFK</th>\n",
       "      <th>ShoPK</th>\n",
       "      <th>PKatt</th>\n",
       "      <th>PasTotCmp</th>\n",
       "      <th>PasTotAtt</th>\n",
       "      <th>PasTotCmp%</th>\n",
       "      <th>PasTotDist</th>\n",
       "      <th>PasTotPrgDist</th>\n",
       "      <th>PasShoCmp</th>\n",
       "      <th>PasShoAtt</th>\n",
       "      <th>PasShoCmp%</th>\n",
       "      <th>PasMedCmp</th>\n",
       "      <th>PasMedAtt</th>\n",
       "      <th>PasMedCmp%</th>\n",
       "      <th>PasLonCmp</th>\n",
       "      <th>PasLonAtt</th>\n",
       "      <th>PasLonCmp%</th>\n",
       "      <th>Assists</th>\n",
       "      <th>PasAss</th>\n",
       "      <th>Pas3rd</th>\n",
       "      <th>PPA</th>\n",
       "      <th>CrsPA</th>\n",
       "      <th>PasProg</th>\n",
       "      <th>PasAtt</th>\n",
       "      <th>PasLive</th>\n",
       "      <th>PasDead</th>\n",
       "      <th>PasFK</th>\n",
       "      <th>TB</th>\n",
       "      <th>Sw</th>\n",
       "      <th>PasCrs</th>\n",
       "      <th>TI</th>\n",
       "      <th>CK</th>\n",
       "      <th>CkIn</th>\n",
       "      <th>CkOut</th>\n",
       "      <th>CkStr</th>\n",
       "      <th>PasCmp</th>\n",
       "      <th>PasOff</th>\n",
       "      <th>PasBlocks</th>\n",
       "      <th>SCA</th>\n",
       "      <th>ScaPassLive</th>\n",
       "      <th>ScaPassDead</th>\n",
       "      <th>ScaDrib</th>\n",
       "      <th>ScaSh</th>\n",
       "      <th>ScaFld</th>\n",
       "      <th>ScaDef</th>\n",
       "      <th>GCA</th>\n",
       "      <th>GcaPassLive</th>\n",
       "      <th>GcaPassDead</th>\n",
       "      <th>GcaDrib</th>\n",
       "      <th>GcaSh</th>\n",
       "      <th>GcaFld</th>\n",
       "      <th>GcaDef</th>\n",
       "      <th>Tkl</th>\n",
       "      <th>TklWon</th>\n",
       "      <th>TklDef3rd</th>\n",
       "      <th>TklMid3rd</th>\n",
       "      <th>TklAtt3rd</th>\n",
       "      <th>TklDri</th>\n",
       "      <th>TklDriAtt</th>\n",
       "      <th>TklDri%</th>\n",
       "      <th>TklDriPast</th>\n",
       "      <th>Blocks</th>\n",
       "      <th>BlkSh</th>\n",
       "      <th>BlkPass</th>\n",
       "      <th>Int</th>\n",
       "      <th>Tkl+Int</th>\n",
       "      <th>Clr</th>\n",
       "      <th>Err</th>\n",
       "      <th>Touches</th>\n",
       "      <th>TouDefPen</th>\n",
       "      <th>TouDef3rd</th>\n",
       "      <th>TouMid3rd</th>\n",
       "      <th>TouAtt3rd</th>\n",
       "      <th>TouAttPen</th>\n",
       "      <th>TouLive</th>\n",
       "      <th>ToAtt</th>\n",
       "      <th>ToSuc</th>\n",
       "      <th>ToSuc%</th>\n",
       "      <th>ToTkl</th>\n",
       "      <th>ToTkl%</th>\n",
       "      <th>Carries</th>\n",
       "      <th>CarTotDist</th>\n",
       "      <th>CarPrgDist</th>\n",
       "      <th>CarProg</th>\n",
       "      <th>Car3rd</th>\n",
       "      <th>CPA</th>\n",
       "      <th>CarMis</th>\n",
       "      <th>CarDis</th>\n",
       "      <th>Rec</th>\n",
       "      <th>RecProg</th>\n",
       "      <th>CrdY</th>\n",
       "      <th>CrdR</th>\n",
       "      <th>2CrdY</th>\n",
       "      <th>Fls</th>\n",
       "      <th>Fld</th>\n",
       "      <th>Off</th>\n",
       "      <th>Crs</th>\n",
       "      <th>TklW</th>\n",
       "      <th>PKwon</th>\n",
       "      <th>PKcon</th>\n",
       "      <th>OG</th>\n",
       "      <th>Recov</th>\n",
       "      <th>AerWon</th>\n",
       "      <th>AerLost</th>\n",
       "      <th>AerWon%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>238</td>\n",
       "      <td>Youcef Belaïli</td>\n",
       "      <td>ALG</td>\n",
       "      <td>MFFW</td>\n",
       "      <td>Ajaccio</td>\n",
       "      <td>Ligue 1</td>\n",
       "      <td>30</td>\n",
       "      <td>1992</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>780</td>\n",
       "      <td>8.7</td>\n",
       "      <td>5</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.69</td>\n",
       "      <td>31.6</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.17</td>\n",
       "      <td>21.9</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.57</td>\n",
       "      <td>25.9</td>\n",
       "      <td>35.9</td>\n",
       "      <td>72.1</td>\n",
       "      <td>437.9</td>\n",
       "      <td>170.9</td>\n",
       "      <td>12.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>80.7</td>\n",
       "      <td>8.05</td>\n",
       "      <td>10.7</td>\n",
       "      <td>75.3</td>\n",
       "      <td>3.33</td>\n",
       "      <td>6.09</td>\n",
       "      <td>54.7</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.72</td>\n",
       "      <td>2.76</td>\n",
       "      <td>1.95</td>\n",
       "      <td>0.34</td>\n",
       "      <td>4.02</td>\n",
       "      <td>35.9</td>\n",
       "      <td>30.2</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>3.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.9</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.80</td>\n",
       "      <td>3.79</td>\n",
       "      <td>1.72</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.61</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.69</td>\n",
       "      <td>1.38</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.84</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.4</td>\n",
       "      <td>0.34</td>\n",
       "      <td>4.71</td>\n",
       "      <td>23.0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>5.06</td>\n",
       "      <td>48.9</td>\n",
       "      <td>4.14</td>\n",
       "      <td>1.72</td>\n",
       "      <td>41.7</td>\n",
       "      <td>1.84</td>\n",
       "      <td>44.4</td>\n",
       "      <td>36.8</td>\n",
       "      <td>240.2</td>\n",
       "      <td>94.6</td>\n",
       "      <td>2.64</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.84</td>\n",
       "      <td>3.91</td>\n",
       "      <td>1.38</td>\n",
       "      <td>34.3</td>\n",
       "      <td>5.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.72</td>\n",
       "      <td>1.95</td>\n",
       "      <td>0.46</td>\n",
       "      <td>3.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.71</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.80</td>\n",
       "      <td>41.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>239</td>\n",
       "      <td>Youcef Belaïli</td>\n",
       "      <td>ALG</td>\n",
       "      <td>FWMF</td>\n",
       "      <td>Brest</td>\n",
       "      <td>Ligue 1</td>\n",
       "      <td>30</td>\n",
       "      <td>1992</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>356</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>11.1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>28.5</td>\n",
       "      <td>39.5</td>\n",
       "      <td>72.2</td>\n",
       "      <td>556.0</td>\n",
       "      <td>142.3</td>\n",
       "      <td>11.8</td>\n",
       "      <td>13.0</td>\n",
       "      <td>90.4</td>\n",
       "      <td>10.50</td>\n",
       "      <td>12.5</td>\n",
       "      <td>84.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>10.50</td>\n",
       "      <td>47.6</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>3.50</td>\n",
       "      <td>39.5</td>\n",
       "      <td>34.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.75</td>\n",
       "      <td>5.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.75</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.75</td>\n",
       "      <td>28.6</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>5.50</td>\n",
       "      <td>21.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>3.25</td>\n",
       "      <td>53.0</td>\n",
       "      <td>3.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>26.7</td>\n",
       "      <td>1.75</td>\n",
       "      <td>46.7</td>\n",
       "      <td>37.3</td>\n",
       "      <td>255.8</td>\n",
       "      <td>121.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>36.0</td>\n",
       "      <td>7.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>5.25</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>66.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rk          Player Nation   Pos    Squad     Comp  Age  Born  MP  \\\n",
       "237  238  Youcef Belaïli    ALG  MFFW  Ajaccio  Ligue 1   30  1992  12   \n",
       "238  239  Youcef Belaïli    ALG  FWMF    Brest  Ligue 1   30  1992   6   \n",
       "\n",
       "     Starts  Min  90s  Goals  Shots   SoT  SoT%  G/Sh  G/SoT  ShoDist  ShoFK  \\\n",
       "237      10  780  8.7      5   2.18  0.69  31.6  0.05   0.17     21.9   0.23   \n",
       "238       4  356  4.0      0   2.25  0.25  11.1  0.00   0.00     16.0   0.25   \n",
       "\n",
       "     ShoPK  PKatt  PasTotCmp  PasTotAtt  PasTotCmp%  PasTotDist  \\\n",
       "237   0.46   0.57       25.9       35.9        72.1       437.9   \n",
       "238   0.00   0.00       28.5       39.5        72.2       556.0   \n",
       "\n",
       "     PasTotPrgDist  PasShoCmp  PasShoAtt  PasShoCmp%  PasMedCmp  PasMedAtt  \\\n",
       "237          170.9       12.5       15.5        80.7       8.05       10.7   \n",
       "238          142.3       11.8       13.0        90.4      10.50       12.5   \n",
       "\n",
       "     PasMedCmp%  PasLonCmp  PasLonAtt  PasLonCmp%  Assists  PasAss  Pas3rd  \\\n",
       "237        75.3       3.33       6.09        54.7     0.34    1.72    2.76   \n",
       "238        84.0       5.00      10.50        47.6     0.50    2.00    1.00   \n",
       "\n",
       "      PPA  CrsPA  PasProg  PasAtt  PasLive  PasDead  PasFK    TB    Sw  \\\n",
       "237  1.95   0.34     4.02    35.9     30.2      5.4   2.18  0.23  0.23   \n",
       "238  2.00   0.25     3.50    39.5     34.0      5.5   1.75  0.25  1.75   \n",
       "\n",
       "     PasCrs    TI    CK  CkIn  CkOut  CkStr  PasCmp  PasOff  PasBlocks   SCA  \\\n",
       "237    3.68  1.03  1.38  0.34   0.57    0.0    25.9    0.23       0.80  3.79   \n",
       "238    5.25  0.25  3.50  2.50   1.00    0.0    28.5    0.00       1.75  3.75   \n",
       "\n",
       "     ScaPassLive  ScaPassDead  ScaDrib  ScaSh  ScaFld  ScaDef   GCA  \\\n",
       "237         1.72          0.8     0.23   0.46    0.57     0.0  1.03   \n",
       "238         2.00          1.5     0.00   0.00    0.25     0.0  0.50   \n",
       "\n",
       "     GcaPassLive  GcaPassDead  GcaDrib  GcaSh  GcaFld  GcaDef   Tkl  TklWon  \\\n",
       "237         0.46          0.0     0.23   0.11    0.23     0.0  1.61    1.03   \n",
       "238         0.00          0.5     0.00   0.00    0.00     0.0  1.25    0.50   \n",
       "\n",
       "     TklDef3rd  TklMid3rd  TklAtt3rd  TklDri  TklDriAtt  TklDri%  TklDriPast  \\\n",
       "237       0.11        0.8       0.69    0.69       1.38     50.0        0.69   \n",
       "238       0.75        0.5       0.00    0.50       1.75     28.6        1.25   \n",
       "\n",
       "     Blocks  BlkSh  BlkPass   Int  Tkl+Int   Clr  Err  Touches  TouDefPen  \\\n",
       "237    0.92   0.11      0.8  0.23     1.84  0.46  0.0     49.4       0.34   \n",
       "238    0.50   0.00      0.5  0.25     1.50  0.25  0.0     53.0       0.50   \n",
       "\n",
       "     TouDef3rd  TouMid3rd  TouAtt3rd  TouAttPen  TouLive  ToAtt  ToSuc  \\\n",
       "237       4.71       23.0       22.4       5.06     48.9   4.14   1.72   \n",
       "238       5.50       21.0       27.5       3.25     53.0   3.75   1.00   \n",
       "\n",
       "     ToSuc%  ToTkl  ToTkl%  Carries  CarTotDist  CarPrgDist  CarProg  Car3rd  \\\n",
       "237    41.7   1.84    44.4     36.8       240.2        94.6     2.64    1.95   \n",
       "238    26.7   1.75    46.7     37.3       255.8       121.0     4.00    2.50   \n",
       "\n",
       "      CPA  CarMis  CarDis   Rec  RecProg  CrdY  CrdR  2CrdY   Fls   Fld   Off  \\\n",
       "237  1.84    3.91    1.38  34.3     5.75  0.00   0.0    0.0  1.72  1.95  0.46   \n",
       "238  1.00    4.00    1.00  36.0     7.75  0.75   0.0    0.0  2.75  2.00  0.50   \n",
       "\n",
       "      Crs  TklW  PKwon  PKcon   OG  Recov  AerWon  AerLost  AerWon%  \n",
       "237  3.68  1.03   0.23    0.0  0.0   4.71    0.57     0.80     41.7  \n",
       "238  5.25  0.50   0.00    0.0  0.0   4.75    0.50     0.25     66.7  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for duplicates\n",
    "df['Player'].duplicated().sum()\n",
    "\n",
    "# list duplicates\n",
    "df[df['Player'].duplicated()]\n",
    "\n",
    "df[df['Player'] == 'Youcef Belaïli']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Brenden Aaronson (Leeds United)': 0,\n",
       " 'Yunis Abdelhamid (Reims)': 1,\n",
       " 'Himad Abdelli (Angers)': 2,\n",
       " 'Salis Abdul Samed (Lens)': 3,\n",
       " 'Laurent Abergel (Lorient)': 4,\n",
       " 'Oliver Abildgaard (Hellas Verona)': 5,\n",
       " 'Matthis Abline (Auxerre)': 6,\n",
       " 'Matthis Abline (Rennes)': 7,\n",
       " 'Abner (Betis)': 8,\n",
       " 'Zakaria Aboukhlal (Toulouse)': 9,\n",
       " 'Tammy Abraham (Roma)': 10,\n",
       " 'Francesco Acerbi (Inter)': 11,\n",
       " 'Mohamed Achi (Nantes)': 12,\n",
       " 'Marcos Acuña (Sevilla)': 13,\n",
       " 'Che Adams (Southampton)': 14,\n",
       " 'Tyler Adams (Leeds United)': 15,\n",
       " 'Sargis Adamyan (Köln)': 16,\n",
       " 'Tosin Adarabioyo (Fulham)': 17,\n",
       " 'Martin Adeline (Reims)': 18,\n",
       " 'Karim Adeyemi (Dortmund)': 19,\n",
       " 'Amine Adli (Leverkusen)': 20,\n",
       " 'Yacine Adli (Milan)': 21,\n",
       " 'Michel Aebischer (Bologna)': 22,\n",
       " 'Felix Afena-Gyan (Cremonese)': 23,\n",
       " 'Emmanuel Agbadou (Reims)': 24,\n",
       " 'Lucien Agoume (Troyes)': 25,\n",
       " 'Felix Agu (Werder Bremen)': 26,\n",
       " 'Álvaro Aguado (Valladolid)': 27,\n",
       " 'Kevin Agudelo (Spezia)': 28,\n",
       " 'Nayef Aguerd (West Ham)': 29,\n",
       " 'Ruben Aguilar (Monaco)': 30,\n",
       " 'Naouirou Ahamada (Crystal Palace)': 31,\n",
       " 'Naouirou Ahamada (Stuttgart)': 32,\n",
       " 'Amar Ahmed (Troyes)': 33,\n",
       " 'Jean-Eudes Aholou (Strasbourg)': 34,\n",
       " 'Joseph Aidoo (Celta Vigo)': 35,\n",
       " 'Ola Aina (Torino)': 36,\n",
       " 'Rayan Aït Nouri (Wolves)': 37,\n",
       " 'Emanuel Aiwum (Cremonese)': 38,\n",
       " 'Kristoffer Ajer (Brentford)': 39,\n",
       " 'Ludovic Ajorque (Strasbourg)': 40,\n",
       " 'Ludovic Ajorque (Mainz 05)': 41,\n",
       " 'Manuel Akanji (Manchester City)': 42,\n",
       " 'Nathan Aké (Manchester City)': 43,\n",
       " 'Sergio Akieme (Almería)': 44,\n",
       " 'Maghnes Akliouche (Monaco)': 45,\n",
       " 'Paul Akouokou (Betis)': 46,\n",
       " 'Jean-Daniel Akpa-Akpro (Empoli)': 47,\n",
       " 'Kevin Akpoguma (Hoffenheim)': 48,\n",
       " 'David Alaba (Real Madrid)': 49,\n",
       " 'Tomás Alarcón (Cádiz)': 50,\n",
       " 'Lucas Alario (Eint Frankfurt)': 51,\n",
       " 'Jordi Alba (Barcelona)': 52,\n",
       " 'Luis Alberto (Lazio)': 53,\n",
       " 'Raúl Albiol (Villarreal)': 54,\n",
       " 'Marc Albrighton (Leicester City)': 55,\n",
       " 'Thiago Alcántara (Liverpool)': 56,\n",
       " 'Carlos Alcaraz (Southampton)': 57,\n",
       " 'Rubén Alcaraz (Cádiz)': 58,\n",
       " 'Omar Alderete (Getafe)': 59,\n",
       " 'Iván Alejo (Cádiz)': 60,\n",
       " 'Doðan Alemdar (Rennes)': 61,\n",
       " 'Carles Aleñá (Getafe)': 62,\n",
       " 'Trent Alexander-Arnold (Liverpool)': 63,\n",
       " 'Alejandro Alfaro (Elche)': 64,\n",
       " 'Ángel Algobia (Getafe)': 65,\n",
       " 'Mohamed Ali Cho (Real Sociedad)': 66,\n",
       " 'Faride Alidou (Eint Frankfurt)': 67,\n",
       " 'Alisson (Liverpool)': 68,\n",
       " 'Jim Allevinah (Clermont Foot)': 69,\n",
       " 'Dele Alli (Everton)': 70,\n",
       " 'Domingos André Ribeiro Almeida (Valencia)': 71,\n",
       " 'Miguel Almirón (Newcastle Utd)': 72,\n",
       " 'Marcos Alonso (Barcelona)': 73,\n",
       " 'Mickaël Alphonse (Ajaccio)': 74,\n",
       " 'Éder Álvarez Balanta (Schalke 04)': 75,\n",
       " 'Carlos Álvarez (Sevilla)': 76,\n",
       " 'Gastón Álvarez (Getafe)': 77,\n",
       " 'Julián Álvarez (Manchester City)': 78,\n",
       " 'Agustín Álvarez Martínez (Sassuolo)': 79,\n",
       " 'Yeray Álvarez (Athletic Club)': 80,\n",
       " 'Ibrahim Amadou (Angers)': 81,\n",
       " 'Daniel Amartey (Leicester City)': 82,\n",
       " 'Amath (Mallorca)': 83,\n",
       " 'Jordan Amavi (Getafe)': 84,\n",
       " 'Kelvin Amian (Spezia)': 85,\n",
       " 'Bruno Amione (Hellas Verona)': 86,\n",
       " 'Bruno Amione (Sampdoria)': 87,\n",
       " 'Nadiem Amiri (Leverkusen)': 88,\n",
       " 'Ethan Ampadu (Spezia)': 89,\n",
       " 'Sofyan Amrabat (Fiorentina)': 90,\n",
       " 'Joachim Andersen (Crystal Palace)': 91,\n",
       " 'Elliot Anderson (Newcastle Utd)': 92,\n",
       " 'Felipe Anderson (Lazio)': 93,\n",
       " 'Benjamin André (Lille)': 94,\n",
       " 'Komnen Andri? (Clermont Foot)': 95,\n",
       " 'Robert Andrich (Leverkusen)': 96,\n",
       " 'Angeliño (Hoffenheim)': 97,\n",
       " 'Fabrizio Angileri (Getafe)': 98,\n",
       " 'Jaidon Anthony (Bournemouth)': 99,\n",
       " 'Janis Antiste (Sassuolo)': 100,\n",
       " 'Waldemar Anton (Stuttgart)': 101,\n",
       " 'Marcos Antônio (Lazio)': 102,\n",
       " 'Michail Antonio (West Ham)': 103,\n",
       " 'Antony (Manchester Utd)': 104,\n",
       " 'Valentin Antov (Monza)': 105,\n",
       " 'Christopher Antwi-Adjei (Bochum)': 106,\n",
       " 'Anuar (Valladolid)': 107,\n",
       " 'Houssem Aouar (Lyon)': 108,\n",
       " 'Adil Aouchiche (Lorient)': 109,\n",
       " 'Dennis Appiah (Nantes)': 110,\n",
       " 'Mauro Arambarri (Getafe)': 111,\n",
       " 'Charles Aránguiz (Leverkusen)': 112,\n",
       " 'Ronald Araújo (Barcelona)': 113,\n",
       " 'Cameron Archer (Aston Villa)': 114,\n",
       " 'Taïryk Arconte (Brest)': 115,\n",
       " 'Alphonse Areola (West Ham)': 116,\n",
       " 'Joe Aribo (Southampton)': 117,\n",
       " 'Adam Armstrong (Southampton)': 118,\n",
       " 'Stuart Armstrong (Southampton)': 119,\n",
       " 'Marko Arnautovi? (Bologna)': 120,\n",
       " 'Maximilian Arnold (Wolfsburg)': 121,\n",
       " 'Kepa Arrizabalaga (Chelsea)': 122,\n",
       " 'Roberto Arroyo (Valladolid)': 123,\n",
       " 'Tolgay Arslan (Udinese)': 124,\n",
       " 'Arthur (Fiorentina)': 125,\n",
       " 'Santiago Arzamendia (Cádiz)': 126,\n",
       " 'Takuma Asano (Bochum)': 127,\n",
       " 'Santiago Ascacíbar (Cremonese)': 128,\n",
       " 'Sergio Asenjo (Valladolid)': 129,\n",
       " 'Marco Asensio (Real Madrid)': 130,\n",
       " 'Kristoffer Askildsen (Lecce)': 131,\n",
       " 'Fisnik Asllani (Hoffenheim)': 132,\n",
       " 'Kristjan Asllani (Inter)': 133,\n",
       " 'Iago Aspas (Celta Vigo)': 134,\n",
       " 'Lorenz Assignon (Rennes)': 135,\n",
       " 'Youcef Atal (Nice)': 136,\n",
       " 'Pierre-Emerick Aubameyang (Chelsea)': 137,\n",
       " 'Pierre-Emerick Aubameyang (Barcelona)': 138,\n",
       " 'Emil Audero (Sampdoria)': 139,\n",
       " 'Tommaso Augello (Sampdoria)': 140,\n",
       " 'Ludwig Augustinsson (Aston Villa)': 141,\n",
       " 'Ludwig Augustinsson (Mallorca)': 142,\n",
       " \"Serge Aurier (Nott'ham Forest)\": 143,\n",
       " 'Mathias Autret (Auxerre)': 144,\n",
       " 'Ezequiel Ávila (Osasuna)': 145,\n",
       " 'Cédric Avinel (Ajaccio)': 146,\n",
       " \"Taiwo Awoniyi (Nott'ham Forest)\": 147,\n",
       " 'Mehmet-Can Aydin (Schalke 04)': 148,\n",
       " \"André Ayew (Nott'ham Forest)\": 149,\n",
       " 'Jordan Ayew (Crystal Palace)': 150,\n",
       " 'Kaan Ayhan (Sassuolo)': 151,\n",
       " 'Luke Ayling (Leeds United)': 152,\n",
       " 'Ayman Azhil (Leverkusen)': 153,\n",
       " 'Sardar Azmoun (Leverkusen)': 154,\n",
       " 'César Azpilicueta (Chelsea)': 155,\n",
       " 'Sanoussy Ba (RB Leipzig)': 156,\n",
       " 'Iddrisu Baba (Mallorca)': 157,\n",
       " 'Sr?an Babi? (Almería)': 158,\n",
       " 'Loïc Bade (Sevilla)': 159,\n",
       " 'Loïc Bade (Rennes)': 160,\n",
       " 'Édgar Badía (Elche)': 161,\n",
       " 'Benoît Badiashile (Chelsea)': 162,\n",
       " 'Benoît Badiashile (Monaco)': 163,\n",
       " 'Alex Baena (Villarreal)': 164,\n",
       " 'Jaime Báez (Cremonese)': 165,\n",
       " 'Leon Bailey (Aston Villa)': 166,\n",
       " 'Eric Bailly (Marseille)': 167,\n",
       " 'Dénys Bain (Auxerre)': 168,\n",
       " 'Brandon Baiye (Clermont Foot)': 169,\n",
       " 'Stefan Bajcetic (Liverpool)': 170,\n",
       " 'Nedim Bajrami (Empoli)': 171,\n",
       " 'Nedim Bajrami (Sassuolo)': 172,\n",
       " 'Cédric Bakambu (Marseille)': 173,\n",
       " 'Mitchel Bakker (Leverkusen)': 174,\n",
       " 'Ridle Baku (Wolfsburg)': 175,\n",
       " 'Tommaso Baldanzi (Empoli)': 176,\n",
       " 'Alejandro Balde (Barcelona)': 177,\n",
       " 'Thierno Balde (Troyes)': 178,\n",
       " 'Carlos Baleba (Lille)': 179,\n",
       " 'Mikel Balenziaga (Athletic Club)': 180,\n",
       " 'Leonardo Balerdi (Marseille)': 181,\n",
       " 'Iván Balliu (Rayo Vallecano)': 182,\n",
       " 'Fodé Ballo-Touré (Milan)': 183,\n",
       " 'Folarin Balogun (Reims)': 184,\n",
       " 'Abdoulaye Bamba (Angers)': 185,\n",
       " 'Jonathan Bamba (Lille)': 186,\n",
       " 'Patrick Bamford (Leeds United)': 187,\n",
       " 'Lameck Banda (Lecce)': 188,\n",
       " 'Filippo Bandinelli (Empoli)': 189,\n",
       " 'Léo Baptistão (Almería)': 190,\n",
       " 'Shandon Baptiste (Brentford)': 191,\n",
       " 'Antonín Barák (Hellas Verona)': 192,\n",
       " 'Antonín Barák (Fiorentina)': 193,\n",
       " 'Andrea Barberis (Monza)': 194,\n",
       " 'Bradley Barcola (Lyon)': 195,\n",
       " 'Melvin Bard (Nice)': 196,\n",
       " 'Keidi Bare (Espanyol)': 197,\n",
       " 'Nicolò Barella (Inter)': 198,\n",
       " 'Kike Barja (Osasuna)': 199,\n",
       " 'Ross Barkley (Nice)': 200,\n",
       " 'Aymen Barkok (Mainz 05)': 201,\n",
       " 'Harvey Barnes (Leicester City)': 202,\n",
       " 'Leandro Barreiro Martins (Mainz 05)': 203,\n",
       " 'Ander Barrenetxea (Real Sociedad)': 204,\n",
       " 'Mickaël Barreto (Ajaccio)': 205,\n",
       " 'Pablo Barrios (Atlético Madrid)': 206,\n",
       " 'Musa Barrow (Bologna)': 207,\n",
       " 'Federico Baschirotto (Lecce)': 208,\n",
       " 'Toma Ba\\x9ai? (Lazio)': 209,\n",
       " 'Gil Bastião Dias (Stuttgart)': 210,\n",
       " 'Alessandro Bastoni (Inter)': 211,\n",
       " 'Simone Bastoni (Spezia)': 212,\n",
       " 'Rodrigo Battaglia (Mallorca)': 213,\n",
       " 'Maximilian Bauer (Augsburg)': 214,\n",
       " 'Oliver Baumann (Hoffenheim)': 215,\n",
       " 'Timo Baumgartl (Union Berlin)': 216,\n",
       " 'Julian Baumgartlinger (Augsburg)': 217,\n",
       " 'Christoph Baumgartner (Hoffenheim)': 218,\n",
       " 'Cyrille Bayala (Ajaccio)': 219,\n",
       " 'Brian Bayeye (Torino)': 220,\n",
       " 'Noah Joel Sarenren Bazee (Augsburg)': 221,\n",
       " 'Gavin Bazunu (Southampton)': 222,\n",
       " 'Bebé (Rayo Vallecano)': 223,\n",
       " 'Ihlas Bebou (Hoffenheim)': 224,\n",
       " 'Rodrigo Becão (Udinese)': 225,\n",
       " 'Mads Bech Sørensen (Brentford)': 226,\n",
       " 'Julius Beck (Spezia)': 227,\n",
       " 'Sheraldo Becker (Union Berlin)': 228,\n",
       " 'Jan Bednarek (Southampton)': 229,\n",
       " 'Jan Bednarek (Aston Villa)': 230,\n",
       " 'Donny van de Beek (Manchester Utd)': 231,\n",
       " 'Asmir Begovi? (Everton)': 232,\n",
       " 'Yanis Begraoui (Toulouse)': 233,\n",
       " 'Kevin Behrens (Union Berlin)': 234,\n",
       " 'Alexis Beka Beka (Nice)': 235,\n",
       " 'Jérémie Bela (Clermont Foot)': 236,\n",
       " 'Youcef Belaïli (Ajaccio)': 237,\n",
       " 'Youcef Belaïli (Brest)': 238,\n",
       " 'Haris Belkebla (Brest)': 239,\n",
       " 'Stefan Bell (Mainz 05)': 240,\n",
       " 'Armel Bella Kotchap (Southampton)': 241,\n",
       " 'Raoul Bellanova (Inter)': 242,\n",
       " 'Karim Bellarabi (Leverkusen)': 243,\n",
       " 'Jean-Ricner Bellegarde (Strasbourg)': 244,\n",
       " 'Héctor Bellerín (Barcelona)': 245,\n",
       " 'Jude Bellingham (Dortmund)': 246,\n",
       " 'Jeanöel Belocian (Rennes)': 247,\n",
       " 'Andrea Belotti (Roma)': 248,\n",
       " 'Fran Beltrán (Celta Vigo)': 249,\n",
       " 'Eliesse Ben Seghir (Monaco)': 250,\n",
       " 'Salim Ben Seghir (Marseille)': 251,\n",
       " 'Wissam Ben Yedder (Monaco)': 252,\n",
       " 'Marco Benassi (Fiorentina)': 253,\n",
       " 'Marco Benassi (Cremonese)': 254,\n",
       " 'Ismaël Bennacer (Milan)': 255,\n",
       " 'Saïd Benrahma (West Ham)': 256,\n",
       " \"Ramy Bensebaini (M'Gladbach)\": 257,\n",
       " 'Nabil Bentaleb (Angers)': 258,\n",
       " 'Rodrigo Bentancur (Tottenham)': 259,\n",
       " 'Karim Benzema (Real Madrid)': 260,\n",
       " 'Domenico Berardi (Sassuolo)': 261,\n",
       " 'Yuri Berchiche (Athletic Club)': 262,\n",
       " 'Álex Berenguer (Athletic Club)': 263,\n",
       " 'Bartosz Bereszy?ski (Sampdoria)': 264,\n",
       " 'Patrick Berg (Lens)': 265,\n",
       " 'Mitchell van Bergen (Reims)': 266,\n",
       " 'Mergim Berisha (Augsburg)': 267,\n",
       " 'Quentin Bernard (Auxerre)': 268,\n",
       " 'Paul Bernardoni (Angers)': 269,\n",
       " 'Juan Bernat (Paris S-G)': 270,\n",
       " 'Victor Bernth Kristiansen (Leicester City)': 271,\n",
       " 'Jason Berthomier (Clermont Foot)': 272,\n",
       " 'Beto (Udinese)': 273,\n",
       " 'Owen Bevan (Bournemouth)': 274,\n",
       " 'Matteo Bianchetti (Cremonese)': 275,\n",
       " 'Alessandro Bianco (Fiorentina)': 276,\n",
       " \"Giulian Biancone (Nott'ham Forest)\": 277,\n",
       " 'Ermin Bi?ak?i? (Hoffenheim)': 278,\n",
       " 'Pedro Bigas (Elche)': 279,\n",
       " 'Jaka Bijol (Udinese)': 280,\n",
       " 'El Bilal Touré (Almería)': 281,\n",
       " 'El Bilal Touré (Reims)': 282,\n",
       " 'Philip Billing (Bournemouth)': 283,\n",
       " 'Jean-Claude Billong (Clermont Foot)': 284,\n",
       " 'Cristiano Biraghi (Fiorentina)': 285,\n",
       " 'Samuele Birindelli (Monza)': 286,\n",
       " 'Veljko Birman?evi? (Toulouse)': 287,\n",
       " 'Tom Bischof (Hoffenheim)': 288,\n",
       " 'Yves Bissouma (Tottenham)': 289,\n",
       " 'Kristijan Bistrovi? (Lecce)': 290,\n",
       " 'Leonardo Bittencourt (Werder Bremen)': 291,\n",
       " 'Marco Bizot (Brest)': 292,\n",
       " 'Blanco (Cádiz)': 293,\n",
       " 'Lautaro Blanco (Elche)': 294,\n",
       " 'Rubén Blanco (Marseille)': 295,\n",
       " 'Ludovic Blas (Nantes)': 296,\n",
       " 'Janis Blaswich (RB Leipzig)': 297,\n",
       " 'Miha Bla?i? (Angers)': 298,\n",
       " 'Joaquín Blázquez (Brest)': 299,\n",
       " 'Alexis Blin (Lecce)': 300,\n",
       " 'Daley Blind (Bayern Munich)': 301,\n",
       " 'Myron Boadu (Monaco)': 302,\n",
       " 'Jérôme Boateng (Lyon)': 303,\n",
       " 'Kevin-Prince Boateng (Hertha BSC)': 304,\n",
       " 'Antonin Bobichon (Angers)': 305,\n",
       " 'Jean-Paul Boëtius (Hertha BSC)': 306,\n",
       " 'Jeremie Boga (Atalanta)': 307,\n",
       " 'Emil Bohinen (Salernitana)': 308,\n",
       " 'Quentin Boisgard (Lorient)': 309,\n",
       " \"Willy Boly (Nott'ham Forest)\": 310,\n",
       " 'Giacomo Bonaventura (Fiorentina)': 311,\n",
       " 'Federico Bonazzoli (Salernitana)': 312,\n",
       " 'Warren Bondo (Monza)': 313,\n",
       " 'Theo Bongonda (Cádiz)': 314,\n",
       " 'Kevin Bonifazi (Bologna)': 315,\n",
       " 'Leonardo Bonucci (Juventus)': 316,\n",
       " 'Neto Borges (Clermont Foot)': 317,\n",
       " \"Yvandro Borges Sanches (M'Gladbach)\": 318,\n",
       " 'Sebastiaan Bornauw (Wolfsburg)': 319,\n",
       " 'Rafael Borré (Eint Frankfurt)': 320,\n",
       " 'Erik Botheim (Salernitana)': 321,\n",
       " 'Sven Botman (Newcastle Utd)': 322,\n",
       " 'Kenji-Van Boto (Auxerre)': 323,\n",
       " 'Kouamé Botué (Ajaccio)': 324,\n",
       " 'Badredine Bouanani (Nice)': 325,\n",
       " 'Hicham Boudaoui (Nice)': 326,\n",
       " 'Franci Bouebari (Strasbourg)': 327,\n",
       " 'Sofiane Boufal (Angers)': 328,\n",
       " 'Wassim Boullif (Espanyol)': 329,\n",
       " 'Yassine Bounou (Sevilla)': 330,\n",
       " 'Ismaëlben Boura (Lens)': 331,\n",
       " 'Mehdi Bourabia (Spezia)': 332,\n",
       " 'Benjamin Bourigeaud (Rennes)': 333,\n",
       " 'Edoardo Bove (Roma)': 334,\n",
       " 'Jarrod Bowen (West Ham)': 335,\n",
       " 'Lucas Boyé (Elche)': 336,\n",
       " 'Jayden Braaf (Hellas Verona)': 337,\n",
       " 'Domagoj Bradari? (Salernitana)': 338,\n",
       " 'Bilal Brahimi (Nice)': 339,\n",
       " 'Martin Braithwaite (Espanyol)': 340,\n",
       " 'Juan Brandáriz (Almería)': 341,\n",
       " 'Julian Brandt (Dortmund)': 342,\n",
       " 'Darko Bra\\x9aanac (Osasuna)': 343,\n",
       " 'Lilian Brassier (Brest)': 344,\n",
       " 'Claudio Bravo (Betis)': 345,\n",
       " 'Fabian Bredlow (Stuttgart)': 346,\n",
       " 'James Bree (Southampton)': 347,\n",
       " 'Josip Brekalo (Fiorentina)': 348,\n",
       " 'Josip Brekalo (Wolfsburg)': 349,\n",
       " 'Gleison Bremer (Juventus)': 350,\n",
       " 'Armando Broja (Chelsea)': 351,\n",
       " 'Dylan Bronn (Salernitana)': 352,\n",
       " 'John Brooks (Hoffenheim)': 353,\n",
       " 'Moritz Broschinski (Bochum)': 354,\n",
       " 'Marcelo Brozovi? (Inter)': 355,\n",
       " 'Cédric Brunner (Schalke 04)': 356,\n",
       " 'Lewis Brunt (Leicester City)': 357,\n",
       " 'Jacob Bruun Larsen (Hoffenheim)': 358,\n",
       " 'Andreas Bruus (Troyes)': 359,\n",
       " 'Bryan (Tottenham)': 360,\n",
       " 'Bryan (Sevilla)': 361,\n",
       " 'Joe Bryan (Nice)': 362,\n",
       " 'Lee Buchanan (Werder Bremen)': 363,\n",
       " 'Ante Budimir (Osasuna)': 364,\n",
       " 'Emi Buendía (Aston Villa)': 365,\n",
       " 'Hugo Bueno (Wolves)': 366,\n",
       " 'Santiago Bueno (Girona)': 367,\n",
       " 'Adam Buksa (Lens)': 368,\n",
       " 'Marius Bülter (Schalke 04)': 369,\n",
       " 'Cristian Buonaiuto (Cremonese)': 370,\n",
       " 'Facundo Buonanotte (Brighton)': 371,\n",
       " 'Alessandro Buongiorno (Torino)': 372,\n",
       " 'Delano Burgzorg (Mainz 05)': 373,\n",
       " 'Jonathan Burkardt (Mainz 05)': 374,\n",
       " 'Oliver Burke (Werder Bremen)': 375,\n",
       " 'Dan Burn (Newcastle Utd)': 376,\n",
       " 'Maxime Busi (Reims)': 377,\n",
       " 'Sergio Busquets (Barcelona)': 378,\n",
       " 'Aurélio Buta (Eint Frankfurt)': 379,\n",
       " 'Marcin Bu?ka (Nice)': 380,\n",
       " 'Jamie Bynoe-Gittens (Dortmund)': 381,\n",
       " 'Juan Cabal (Hellas Verona)': 382,\n",
       " 'Rémy Cabella (Lille)': 383,\n",
       " 'Jimmy Cabot (Lens)': 384,\n",
       " 'Leandro Cabrera (Espanyol)': 385,\n",
       " 'Liberato Cacace (Empoli)': 386,\n",
       " 'Anthony Caci (Mainz 05)': 387,\n",
       " \"Cafú (Nott'ham Forest)\": 388,\n",
       " 'Moisés Caicedo (Brighton)': 389,\n",
       " 'Tom Cairney (Fulham)': 390,\n",
       " 'Jens Cajuste (Reims)': 391,\n",
       " 'Cala (Cádiz)': 392,\n",
       " 'Davide Calabria (Milan)': 393,\n",
       " 'Mattia Caldara (Spezia)': 394,\n",
       " 'Luca Caldirola (Monza)': 395,\n",
       " 'Fernando Calero (Espanyol)': 396,\n",
       " 'Duje ?aleta-Car (Southampton)': 397,\n",
       " 'Duje ?aleta-Car (Marseille)': 398,\n",
       " 'Hakan Çalhanoðlu (Inter)': 399,\n",
       " 'Kerim Calhanoglu (Schalke 04)': 400,\n",
       " 'Daniel Caligiuri (Augsburg)': 401,\n",
       " 'Alexander Callens (Girona)': 402,\n",
       " 'Dominic Calvert-Lewin (Everton)': 403,\n",
       " 'Mahdi Camara (Brest)': 404,\n",
       " 'Mohamed Camara (Roma)': 405,\n",
       " 'Mohamed Camara (Monaco)': 406,\n",
       " 'Ousmane Camara (Auxerre)': 407,\n",
       " 'Ousmane Camara (Angers)': 408,\n",
       " 'Eduardo Camavinga (Real Madrid)': 409,\n",
       " 'Nicolò Cambiaghi (Empoli)': 410,\n",
       " 'Andrea Cambiaso (Bologna)': 411,\n",
       " 'Axel Camblan (Brest)': 412,\n",
       " 'Sergio Camello (Rayo Vallecano)': 413,\n",
       " 'Chem Campbell (Wolves)': 414,\n",
       " 'Emre Can (Dortmund)': 415,\n",
       " 'Sergio Canales (Betis)': 416,\n",
       " 'Matteo Cancellieri (Lazio)': 417,\n",
       " 'João Cancelo (Manchester City)': 418,\n",
       " 'João Cancelo (Bayern Munich)': 419,\n",
       " 'Antonio Candreva (Salernitana)': 420,\n",
       " 'Thomas Cannon (Everton)': 421,\n",
       " 'Sergi Canós (Brentford)': 422,\n",
       " 'Ander Capa (Athletic Club)': 423,\n",
       " 'Pierrick Capelle (Angers)': 424,\n",
       " 'Étienne Capoue (Villarreal)': 425,\n",
       " 'Gianluca Caprari (Monza)': 426,\n",
       " 'Francesco Caputo (Sampdoria)': 427,\n",
       " 'Francesco Caputo (Empoli)': 428,\n",
       " 'Maxence Caqueret (Lyon)': 429,\n",
       " 'Andrea Carboni (Monza)': 430,\n",
       " 'Valentin Carboni (Inter)': 431,\n",
       " 'Isaac Carcelen (Cádiz)': 432,\n",
       " 'Irvin Cardona (Brest)': 433,\n",
       " 'Irvin Cardona (Augsburg)': 434,\n",
       " 'Igor Carioca (Lorient)': 435,\n",
       " 'Carlos (Monza)': 436,\n",
       " 'Diego Carlos (Aston Villa)': 437,\n",
       " 'Juan Carlos (Girona)': 438,\n",
       " 'José Carlos Lazo (Almería)': 439,\n",
       " 'José Carlos Lazo (Espanyol)': 440,\n",
       " 'Carmona (Elche)': 441,\n",
       " 'Carmona (Sevilla)': 442,\n",
       " 'Marco Carnesecchi (Cremonese)': 443,\n",
       " 'Yannick Carrasco (Atlético Madrid)': 444,\n",
       " 'Dani Carvajal (Real Madrid)': 445,\n",
       " 'Fabio Carvalho (Liverpool)': 446,\n",
       " 'Matis Carvalho (Montpellier)': 447,\n",
       " 'William Carvalho (Betis)': 448,\n",
       " 'Nicolò Casale (Lazio)': 449,\n",
       " 'Casemiro (Manchester Utd)': 450,\n",
       " 'Casemiro (Real Madrid)': 451,\n",
       " 'Matty Cash (Aston Villa)': 452,\n",
       " 'Timothy Castagne (Leicester City)': 453,\n",
       " 'Michele Castagnetti (Cremonese)': 454,\n",
       " 'Koen Casteels (Wolfsburg)': 455,\n",
       " 'Valentín Castellanos (Girona)': 456,\n",
       " 'Jean-Charles Castelletto (Nantes)': 457,\n",
       " 'Samu Castillejo (Valencia)': 458,\n",
       " 'Jonny Castro (Wolves)': 459,\n",
       " 'Gaetano Castrovilli (Fiorentina)': 460,\n",
       " 'Danilo Cataldi (Lazio)': 461,\n",
       " 'Catena (Rayo Vallecano)': 462,\n",
       " 'Yoann Cathline (Lorient)': 463,\n",
       " 'Maximiliano Caufriez (Clermont Foot)': 464,\n",
       " 'Edinson Cavani (Valencia)': 465,\n",
       " 'Dani Ceballos (Real Madrid)': 466,\n",
       " 'Federico Ceccherini (Hellas Verona)': 467,\n",
       " 'Assan Ceesay (Lecce)': 468,\n",
       " 'Emil Ceide (Sassuolo)': 469,\n",
       " 'Zeki Çelik (Roma)': 470,\n",
       " 'Fabien Centonze (Nantes)': 471,\n",
       " 'Franco Cervi (Celta Vigo)': 472,\n",
       " 'Julio César Enciso (Brighton)': 473,\n",
       " 'Leonardo César Jardim (Lille)': 474,\n",
       " 'Julian Chabot (Köln)': 475,\n",
       " 'Florian Chabrolle (Ajaccio)': 476,\n",
       " 'El Chadaille Bitshiabu (Paris S-G)': 477,\n",
       " 'Fares Chaïbi (Toulouse)': 478,\n",
       " 'Nathaniel Chalobah (Fulham)': 479,\n",
       " 'Trevoh Chalobah (Chelsea)': 480,\n",
       " 'Calum Chambers (Aston Villa)': 481,\n",
       " 'Timothy Chandler (Eint Frankfurt)': 482,\n",
       " 'Gaëtan Charbonnier (Auxerre)': 483,\n",
       " 'Brendan Chardonnet (Brest)': 484,\n",
       " 'Xavier Chavalerin (Troyes)': 485,\n",
       " 'Pep Chavarría (Rayo Vallecano)': 486,\n",
       " 'Rayan Cherki (Lyon)': 487,\n",
       " 'Ilyes Cheti (Angers)': 488,\n",
       " 'Lucas Chevalier (Lille)': 489,\n",
       " 'Fabio Chiarodia (Werder Bremen)': 490,\n",
       " 'Federico Chiesa (Juventus)': 491,\n",
       " 'Ben Chilwell (Chelsea)': 492,\n",
       " 'Vlad Chiriche? (Cremonese)': 493,\n",
       " 'Pedro Chirivella (Nantes)': 494,\n",
       " 'Joris Chotard (Montpellier)': 495,\n",
       " 'Andreas Christensen (Barcelona)': 496,\n",
       " 'Oliver Christensen (Hertha BSC)': 497,\n",
       " 'Ryan Christie (Bournemouth)': 498,\n",
       " 'Carney Chukwuemeka (Chelsea)': 499,\n",
       " 'Samuel Chukwueze (Villarreal)': 500,\n",
       " 'Darko Churlinov (Stuttgart)': 501,\n",
       " 'Víctor Chust (Cádiz)': 502,\n",
       " 'Tolga Ciðerci (Hertha BSC)': 503,\n",
       " 'Yanis Cimignani (Ajaccio)': 504,\n",
       " 'Daniel Ciofani (Cremonese)': 505,\n",
       " 'Tio Cipot (Spezia)': 506,\n",
       " 'Pathé Ciss (Rayo Vallecano)': 507,\n",
       " 'Souleymane Cissé (Clermont Foot)': 508,\n",
       " 'Patrick Ciurria (Monza)': 509,\n",
       " 'Bobby Clark (Liverpool)': 510,\n",
       " 'Alexis Claude-Maurice (Nice)': 511,\n",
       " 'Alexis Claude-Maurice (Lens)': 512,\n",
       " 'Jonathan Clauss (Marseille)': 513,\n",
       " 'Carlos Clerc (Elche)': 514,\n",
       " 'Nathaniel Clyne (Crystal Palace)': 515,\n",
       " 'Conor Coady (Everton)': 516,\n",
       " 'Marcus Coco (Nantes)': 517,\n",
       " 'Alexandre Coeff (Auxerre)': 518,\n",
       " \"Jack Colback (Nott'ham Forest)\": 519,\n",
       " 'Séamus Coleman (Everton)': 520,\n",
       " 'David ?olina (Augsburg)': 521,\n",
       " 'Álex Collado (Elche)': 522,\n",
       " 'Diego Collado (Villarreal)': 523,\n",
       " 'Omar Colley (Sampdoria)': 524,\n",
       " 'Nathan Collins (Wolves)': 525,\n",
       " 'Lorenzo Colombo (Lecce)': 526,\n",
       " 'Andrea Colpani (Monza)': 527,\n",
       " 'Levi Colwill (Brighton)': 528,\n",
       " 'Kingsley Coman (Bayern Munich)': 529,\n",
       " 'Eray Cömert (Valencia)': 530,\n",
       " 'Santi Comesaña (Rayo Vallecano)': 531,\n",
       " 'Andrea Consigli (Sassuolo)': 532,\n",
       " 'Abdu Conté (Troyes)': 533,\n",
       " 'Andrea Conti (Sampdoria)': 534,\n",
       " 'Lewis Cook (Bournemouth)': 535,\n",
       " \"Steve Cook (Nott'ham Forest)\": 536,\n",
       " 'Liam Cooper (Leeds United)': 537,\n",
       " 'Copete (Mallorca)': 538,\n",
       " 'Diego Coppola (Hellas Verona)': 539,\n",
       " 'Francis Coquelin (Villarreal)': 540,\n",
       " 'Sébastien Corchia (Nantes)': 541,\n",
       " 'Maxwel Cornet (West Ham)': 542,\n",
       " 'Jesús Corona (Sevilla)': 543,\n",
       " 'Ángel Correa (Atlético Madrid)': 544,\n",
       " 'Joaquín Correa (Inter)': 545,\n",
       " 'Thierry Correia (Valencia)': 546,\n",
       " 'Alessandro Cortinovis (Hellas Verona)': 547,\n",
       " 'Danny da Costa (Mainz 05)': 548,\n",
       " 'David Pereira da Costa (Lens)': 549,\n",
       " 'Diego Costa (Wolves)': 550,\n",
       " 'Jaume Costa (Mallorca)': 551,\n",
       " 'Logan Costa (Toulouse)': 552,\n",
       " 'Nuno da Costa (Auxerre)': 553,\n",
       " 'Samu Costa (Almería)': 554,\n",
       " 'Benoît Costil (Auxerre)': 555,\n",
       " 'Vladimír Coufal (West Ham)': 556,\n",
       " 'Lassana Coulibaly (Salernitana)': 557,\n",
       " 'Tanguy Coulibaly (Stuttgart)': 558,\n",
       " 'Thibaut Courtois (Real Madrid)': 559,\n",
       " 'Mathieu Coutadeur (Ajaccio)': 560,\n",
       " 'Philippe Coutinho (Aston Villa)': 561,\n",
       " 'Yan Couto (Girona)': 562,\n",
       " 'Conor Coventry (West Ham)': 563,\n",
       " 'Nicolas Cozza (Montpellier)': 564,\n",
       " 'Aaron Cresswell (West Ham)': 565,\n",
       " 'Bryan Cristante (Roma)': 566,\n",
       " 'Domen ?rnigoj (Salernitana)': 567,\n",
       " 'Juan Cruz Armada (Osasuna)': 568,\n",
       " 'Juan Cruz (Betis)': 569,\n",
       " 'Juan Cuadrado (Juventus)': 570,\n",
       " 'Marc Cucurella (Chelsea)': 571,\n",
       " 'Jorge Cuenca (Villarreal)': 572,\n",
       " 'Braian Cufré (Mallorca)': 573,\n",
       " 'Mickaël Cuisance (Sampdoria)': 574,\n",
       " 'Matheus Cunha (Wolves)': 575,\n",
       " 'Matheus Cunha (Atlético Madrid)': 576,\n",
       " 'Curro (Almería)': 577,\n",
       " \"Marco D'Alessandro (Monza)\": 578,\n",
       " \"Danilo D'Ambrosio (Inter)\": 579,\n",
       " \"Luca D'Andrea (Sassuolo)\": 580,\n",
       " 'Damien Da Silva (Lyon)': 581,\n",
       " 'Moanes Dabbur (Hoffenheim)': 582,\n",
       " 'Colin Dagba (Strasbourg)': 583,\n",
       " 'Finn Dahmen (Mainz 05)': 584,\n",
       " 'Mahmoud Dahoud (Dortmund)': 585,\n",
       " 'Patson Daka (Leicester City)': 586,\n",
       " 'Thijs Dallinga (Toulouse)': 587,\n",
       " 'Diogo Dalot (Manchester Utd)': 588,\n",
       " 'Muhammed Damar (Hoffenheim)': 589,\n",
       " 'Mikkel Damsgaard (Brentford)': 590,\n",
       " 'Flavius Daniliuc (Salernitana)': 591,\n",
       " 'Flavius Daniliuc (Nice)': 592,\n",
       " \"Danilo (Nott'ham Forest)\": 593,\n",
       " 'Danilo (Juventus)': 594,\n",
       " 'Arnaut Danjuma (Tottenham)': 595,\n",
       " 'Arnaut Danjuma (Villarreal)': 596,\n",
       " 'Kevin Danso (Lens)': 597,\n",
       " 'Dante (Nice)': 598,\n",
       " 'Márton Dárdai (Hertha BSC)': 599,\n",
       " 'Sergi Darder (Espanyol)': 600,\n",
       " 'Achraf Dari (Brest)': 601,\n",
       " 'Vladimír Darida (Hertha BSC)': 602,\n",
       " 'Matteo Darmian (Inter)': 603,\n",
       " 'Josh Dasilva (Brentford)': 604,\n",
       " 'Jonathan David (Lille)': 605,\n",
       " 'Alphonso Davies (Bayern Munich)': 606,\n",
       " 'Ben Davies (Tottenham)': 607,\n",
       " 'Tom Davies (Everton)': 608,\n",
       " 'Pawe? Dawidowicz (Hellas Verona)': 609,\n",
       " 'Craig Dawson (West Ham)': 610,\n",
       " 'Craig Dawson (Wolves)': 611,\n",
       " 'Kevin De Bruyne (Manchester City)': 612,\n",
       " 'Charles De Ketelaere (Milan)': 613,\n",
       " 'Manuel De Luca (Sampdoria)': 614,\n",
       " 'Rodrigo De Paul (Atlético Madrid)': 615,\n",
       " 'Mattia De Sciglio (Juventus)': 616,\n",
       " 'Lorenzo De Silvestri (Bologna)': 617,\n",
       " 'Thibault De Smet (Reims)': 618,\n",
       " 'Koni De Winter (Empoli)': 619,\n",
       " 'Grégoire Defrel (Sassuolo)': 620,\n",
       " 'Duccio Degli Innocenti (Empoli)': 621,\n",
       " 'Brecht Dejaegere (Toulouse)': 622,\n",
       " 'Romain Del Castillo (Brest)': 623,\n",
       " 'Alberto Del Moral (Villarreal)': 624,\n",
       " 'Thomas Delaine (Strasbourg)': 625,\n",
       " 'Thomas Delaney (Sevilla)': 626,\n",
       " 'Thomas Delaney (Hoffenheim)': 627,\n",
       " 'Sacha Delaye (Montpellier)': 628,\n",
       " 'Fran Delgado (Betis)': 629,\n",
       " 'Andy Delort (Nice)': 630,\n",
       " 'Andy Delort (Nantes)': 631,\n",
       " 'Karamoko Dembele (Brest)': 632,\n",
       " 'Moussa Dembélé (Lyon)': 633,\n",
       " 'Ousmane Dembélé (Barcelona)': 634,\n",
       " 'Siriki Dembélé (Bournemouth)': 635,\n",
       " 'Siriki Dembélé (Auxerre)': 636,\n",
       " 'Merih Demiral (Atalanta)': 637,\n",
       " 'Kerem Demirbay (Leverkusen)': 638,\n",
       " 'Ermedin Demirovi? (Augsburg)': 639,\n",
       " 'Diego Demme (Napoli)': 640,\n",
       " 'Sepp van den Berg (Schalke 04)': 641,\n",
       " 'Branco van den Boomen (Toulouse)': 642,\n",
       " 'Leander Dendoncker (Aston Villa)': 643,\n",
       " 'Leander Dendoncker (Wolves)': 644,\n",
       " \"Emmanuel Dennis (Nott'ham Forest)\": 645,\n",
       " 'Fabio Depaoli (Hellas Verona)': 646,\n",
       " 'Fabio Depaoli (Sampdoria)': 647,\n",
       " 'Kastriot Dermaku (Lecce)': 648,\n",
       " 'Halil Derviþoðlu (Brentford)': 649,\n",
       " 'Rémy Descamps (Nantes)': 650,\n",
       " 'Mikkel Desler (Toulouse)': 651,\n",
       " 'Cyriel Dessers (Cremonese)': 652,\n",
       " 'Sergiño Dest (Milan)': 653,\n",
       " 'Mattia Destro (Empoli)': 654,\n",
       " 'Gerard Deulofeu (Udinese)': 655,\n",
       " 'Kiernan Dewsbury-Hall (Leicester City)': 656,\n",
       " 'Samuel Di Carmine (Cremonese)': 657,\n",
       " 'Federico Di Francesco (Lecce)': 658,\n",
       " 'Michele Di Gregorio (Monza)': 659,\n",
       " 'Giovanni Di Lorenzo (Napoli)': 660,\n",
       " 'Ángel Di María (Juventus)': 661,\n",
       " 'Boulaye Dia (Salernitana)': 662,\n",
       " 'Moussa Diaby (Leverkusen)': 663,\n",
       " 'Mouctar Diakhaby (Valencia)': 664,\n",
       " 'Bafodé Diakité (Lille)': 665,\n",
       " 'Ibrahim Diakité (Reims)': 666,\n",
       " 'Abdou Diallo (RB Leipzig)': 667,\n",
       " 'Baila Diallo (Clermont Foot)': 668,\n",
       " 'Habib Diallo (Strasbourg)': 669,\n",
       " 'Ibrahima Diallo (Southampton)': 670,\n",
       " 'Ismael Diallo (Ajaccio)': 671,\n",
       " 'Mamady Diarra (Cádiz)': 672,\n",
       " 'Mouhamadou Diarra (Strasbourg)': 673,\n",
       " 'Moussa Diarra (Toulouse)': 674,\n",
       " 'Stéphane Diarra (Lorient)': 675,\n",
       " 'Youba Diarra (Cádiz)': 676,\n",
       " 'Rúben Dias (Manchester City)': 677,\n",
       " 'Krépin Diatta (Monaco)': 678,\n",
       " 'Mory Diaw (Clermont Foot)': 679,\n",
       " 'Brahim Díaz (Milan)': 680,\n",
       " 'Luis Díaz (Liverpool)': 681,\n",
       " 'Justin Diehl (Köln)': 682,\n",
       " 'Bamba Dieng (Marseille)': 683,\n",
       " 'Bamba Dieng (Lorient)': 684,\n",
       " 'Eric Dier (Tottenham)': 685,\n",
       " 'Florian Dietz (Köln)': 686,\n",
       " 'Lucas Digne (Aston Villa)': 687,\n",
       " 'Federico Dimarco (Inter)': 688,\n",
       " 'Stole Dimitrievski (Rayo Vallecano)': 689,\n",
       " 'Junior Dina Ebimbe (Eint Frankfurt)': 690,\n",
       " 'Tristan Dingomé (Troyes)': 691,\n",
       " 'Eren Dinkçi (Werder Bremen)': 692,\n",
       " 'Sinaly Diomande (Lyon)': 693,\n",
       " 'Lois Diony (Angers)': 694,\n",
       " 'Issa Diop (Fulham)': 695,\n",
       " 'Sofiane Diop (Nice)': 696,\n",
       " 'Sofiane Diop (Monaco)': 697,\n",
       " 'Yehvann Diouf (Reims)': 698,\n",
       " 'Axel Disasi (Monaco)': 699,\n",
       " 'Malcom Abdulai Ares Djalo (Athletic Club)': 700,\n",
       " 'Tiago Djaló (Lille)': 701,\n",
       " 'Djené (Getafe)': 702,\n",
       " 'Moussa Djenepo (Southampton)': 703,\n",
       " 'Koffi Djidji (Torino)': 704,\n",
       " 'Alexander Djiku (Strasbourg)': 705,\n",
       " 'Berat Djimsiti (Atalanta)': 706,\n",
       " 'Moussa Djitte (Ajaccio)': 707,\n",
       " 'Marko Dmitrovi? (Sevilla)': 708,\n",
       " 'Alan Do Marcolino (Rennes)': 709,\n",
       " 'Ben Doak (Liverpool)': 710,\n",
       " 'Ritsu Doan (Freiburg)': 711,\n",
       " 'Dodô (Fiorentina)': 712,\n",
       " 'Danilho Doekhi (Union Berlin)': 713,\n",
       " 'Matt Doherty (Tottenham)': 714,\n",
       " 'Josh Doig (Hellas Verona)': 715,\n",
       " 'Jeremy Doku (Rennes)': 716,\n",
       " 'Kasper Dolberg (Sevilla)': 717,\n",
       " 'Kasper Dolberg (Hoffenheim)': 718,\n",
       " 'Carlos Dominguez (Celta Vigo)': 719,\n",
       " 'Nicolás Domínguez (Bologna)': 720,\n",
       " 'John Donald (Elche)': 721,\n",
       " 'Giulio Donati (Monza)': 722,\n",
       " 'Kyliane Dong (Troyes)': 723,\n",
       " 'Gianluigi Donnarumma (Paris S-G)': 724,\n",
       " 'Niklas Dorsch (Augsburg)': 725,\n",
       " 'Gabriel Dos Santos (Arsenal)': 726,\n",
       " 'Jodel Dossou (Clermont Foot)': 727,\n",
       " 'Lohann Doucet (Nantes)': 728,\n",
       " 'Abdoulaye Doucouré (Everton)': 729,\n",
       " 'Cheick Doucouré (Crystal Palace)': 730,\n",
       " 'Ckene Doucouré (Lorient)': 731,\n",
       " 'Désiré Doué (Rennes)': 732,\n",
       " 'Guela Doué (Rennes)': 733,\n",
       " 'Ismaël Doukouré (Strasbourg)': 734,\n",
       " 'Kamory Doumbia (Reims)': 735,\n",
       " 'Souleyman Doumbia (Angers)': 736,\n",
       " 'Flynn Downes (West Ham)': 737,\n",
       " 'Bart?omiej Dr?gowski (Spezia)': 738,\n",
       " 'Cody Drameh (Leeds United)': 739,\n",
       " 'Dion Drena Beljo (Augsburg)': 740,\n",
       " 'Dominick Drexler (Schalke 04)': 741,\n",
       " 'Domingos Duarte (Getafe)': 742,\n",
       " 'Marvin Ducksch (Werder Bremen)': 743,\n",
       " 'Ondrej Duda (Hellas Verona)': 744,\n",
       " 'Ondrej Duda (Köln)': 745,\n",
       " 'Shane Duffy (Fulham)': 746,\n",
       " 'Rémy Dugimont (Auxerre)': 747,\n",
       " 'Denzel Dumfries (Inter)': 748,\n",
       " 'Alfred Duncan (Fiorentina)': 749,\n",
       " 'Lewis Dunk (Brighton)': 750,\n",
       " 'Maxime Dupé (Toulouse)': 751,\n",
       " 'Jhon Durán (Aston Villa)': 752,\n",
       " 'Pablo Durán (Celta Vigo)': 753,\n",
       " 'Hugo Duro (Valencia)': 754,\n",
       " 'Jean-Kevin Duverne (Brest)': 755,\n",
       " 'Paulo Dybala (Roma)': 756,\n",
       " 'Edin D?eko (Inter)': 757,\n",
       " 'Malcolm Ebiowei (Crystal Palace)': 758,\n",
       " 'Festy Ebosele (Udinese)': 759,\n",
       " 'Enzo Ebosse (Udinese)': 760,\n",
       " 'Tyronne Ebuehi (Empoli)': 761,\n",
       " 'Ederson (Manchester City)': 762,\n",
       " 'Éderson (Atalanta)': 763,\n",
       " 'Odsonne Édouard (Crystal Palace)': 764,\n",
       " 'Samuel Edozie (Southampton)': 765,\n",
       " 'Maximilian Eggestein (Freiburg)': 766,\n",
       " 'Lilian Egloff (Stuttgart)': 767,\n",
       " 'Íñigo Eguaras (Almería)': 768,\n",
       " 'Kingsley Ehizibue (Udinese)': 769,\n",
       " 'Kingsley Ehizibue (Köln)': 770,\n",
       " 'Chidera Ejuke (Hertha BSC)': 771,\n",
       " 'Albin Ekdal (Spezia)': 772,\n",
       " 'Hugo Ekitike (Paris S-G)': 773,\n",
       " 'Emmanuel Ekong (Empoli)': 774,\n",
       " 'Anthony Elanga (Manchester Utd)': 775,\n",
       " 'Alberth Elis (Brest)': 776,\n",
       " 'Mikael Ellertsson (Spezia)': 777,\n",
       " 'Harvey Elliott (Liverpool)': 778,\n",
       " 'Elif Elmas (Napoli)': 779,\n",
       " 'Mohamed Elneny (Arsenal)': 780,\n",
       " 'Aritz Elustondo (Real Sociedad)': 781,\n",
       " \"Nico Elvedi (M'Gladbach)\": 782,\n",
       " 'Rodrigo Ely (Almería)': 783,\n",
       " 'Sepe Elye Wahi (Montpellier)': 784,\n",
       " 'Mohamed Elyounoussi (Southampton)': 785,\n",
       " 'Adri Embarba (Almería)': 786,\n",
       " 'Adri Embarba (Espanyol)': 787,\n",
       " 'Breel Embolo (Monaco)': 788,\n",
       " 'Emerson (Tottenham)': 789,\n",
       " 'Youssef En-Nesyri (Sevilla)': 790,\n",
       " 'Wataru Endo (Stuttgart)': 791,\n",
       " 'Arne Engels (Augsburg)': 792,\n",
       " 'Christian Eriksen (Manchester Utd)': 793,\n",
       " 'Martin Erlic (Sassuolo)': 794,\n",
       " 'Gonzalo Escalante (Cremonese)': 795,\n",
       " 'Gonzalo Escalante (Cádiz)': 796,\n",
       " 'Sergio Escudero (Valladolid)': 797,\n",
       " 'Alfonso Espino (Cádiz)': 798,\n",
       " 'Bernardo Espinosa (Girona)': 799,\n",
       " 'Salvatore Esposito (Spezia)': 800,\n",
       " 'Maxime Estève (Montpellier)': 801,\n",
       " 'Pervis Estupiñán (Brighton)': 802,\n",
       " 'Jonny Evans (Leicester City)': 803,\n",
       " 'Edu Expósito (Espanyol)': 804,\n",
       " 'Eberechi Eze (Crystal Palace)': 805,\n",
       " 'Nicolás Ezequiel Fernández (Elche)': 806,\n",
       " 'Abdessamad Ezzalzouli (Osasuna)': 807,\n",
       " '?ukasz Fabia?ski (West Ham)': 808,\n",
       " 'Fabinho (Liverpool)': 809,\n",
       " 'Fábio (Nantes)': 810,\n",
       " 'Noah Fadiga (Brest)': 811,\n",
       " 'Wout Faes (Leicester City)': 812,\n",
       " 'Wout Faes (Reims)': 813,\n",
       " 'Nicolò Fagioli (Juventus)': 814,\n",
       " 'Ralf Fährmann (Schalke 04)': 815,\n",
       " 'Romain Faivre (Lorient)': 816,\n",
       " 'Romain Faivre (Lyon)': 817,\n",
       " 'Radamel Falcao (Rayo Vallecano)': 818,\n",
       " 'Wladimiro Falcone (Lecce)': 819,\n",
       " 'Fali (Cádiz)': 820,\n",
       " 'Mamadou Fall (Villarreal)': 821,\n",
       " 'Davide Faraoni (Hellas Verona)': 822,\n",
       " 'Ansu Fati (Barcelona)': 823,\n",
       " 'Khalil Fayad (Montpellier)': 824,\n",
       " 'Cheikh Faye (Montpellier)': 825,\n",
       " 'Federico Fazio (Salernitana)': 826,\n",
       " 'Jacopo Fazzini (Empoli)': 827,\n",
       " 'Zouhair Feddal (Valladolid)': 828,\n",
       " 'Nabil Fekir (Betis)': 829,\n",
       " \"Felipe (Nott'ham Forest)\": 830,\n",
       " 'Felipe (Atlético Madrid)': 831,\n",
       " 'Luiz Felipe (Betis)': 832,\n",
       " 'João Félix (Chelsea)': 833,\n",
       " 'João Félix (Atlético Madrid)': 834,\n",
       " 'Ohis Felix Uduokhai (Augsburg)': 835,\n",
       " 'Kiko Femenía (Villarreal)': 836,\n",
       " 'Evan Ferguson (Brighton)': 837,\n",
       " 'Lewis Ferguson (Bologna)': 838,\n",
       " 'Bruno Fernandes (Manchester Utd)': 839,\n",
       " 'Edimilson Fernandes (Mainz 05)': 840,\n",
       " 'Aitor Fernández (Osasuna)': 841,\n",
       " 'Álex Fernández (Cádiz)': 842,\n",
       " 'Álvaro Fernández (Espanyol)': 843,\n",
       " 'Carlos Fernández (Real Sociedad)': 844,\n",
       " 'Enzo Fernández (Chelsea)': 845,\n",
       " 'Federico Fernández (Elche)': 846,\n",
       " 'Joaquín Fernández (Valladolid)': 847,\n",
       " 'Mateo Fernández (Leeds United)': 848,\n",
       " 'Fernando (Almería)': 849,\n",
       " 'Fernando (Sevilla)': 850,\n",
       " 'Alex Ferrari (Sampdoria)': 851,\n",
       " 'Alex Ferrari (Cremonese)': 852,\n",
       " 'Salvador Ferrer (Spezia)': 853,\n",
       " 'Jordan Ferri (Montpellier)': 854,\n",
       " 'Fidel (Elche)': 855,\n",
       " 'Karol Fila (Strasbourg)': 856,\n",
       " 'Roberto Firmino (Liverpool)': 857,\n",
       " 'Junior Firpo (Leeds United)': 858,\n",
       " 'Kilian Fischer (Wolfsburg)': 859,\n",
       " 'Mark Flekken (Freiburg)': 860,\n",
       " 'Florian Flick (Schalke 04)': 861,\n",
       " 'Alexis Flips (Reims)': 862,\n",
       " 'Alessandro Florenzi (Milan)': 863,\n",
       " 'Phil Foden (Manchester City)': 864,\n",
       " 'Datro Fofana (Chelsea)': 865,\n",
       " 'Seko Fofana (Lens)': 866,\n",
       " 'Wesley Fofana (Leicester City)': 867,\n",
       " 'Wesley Fofana (Chelsea)': 868,\n",
       " 'Yahia Fofana (Angers)': 869,\n",
       " 'Youssouf Fofana (Monaco)': 870,\n",
       " 'Thomas Foket (Reims)': 871,\n",
       " 'José Fonte (Lille)': 872,\n",
       " 'Pablo Fornals (West Ham)': 873,\n",
       " 'Emil Forsberg (RB Leipzig)': 874,\n",
       " 'Adam Forshaw (Leeds United)': 875,\n",
       " 'Fraser Forster (Tottenham)': 876,\n",
       " 'Philipp Förster (Bochum)': 877,\n",
       " 'Steven Fortes (Lens)': 878,\n",
       " 'Timothy Fosu-Mensah (Leverkusen)': 879,\n",
       " 'Dimitri Foulquier (Valencia)': 880,\n",
       " 'Juan Foyth (Villarreal)': 881,\n",
       " 'Raphael Framberger (Augsburg)': 882,\n",
       " 'Tyrese Francois (Fulham)': 883,\n",
       " 'Bartol Franji? (Wolfsburg)': 884,\n",
       " 'Przemys?aw Frankowski (Lens)': 885,\n",
       " 'Ryan Fraser (Newcastle Utd)': 886,\n",
       " 'Davide Frattesi (Sassuolo)': 887,\n",
       " 'Fred (Manchester Utd)': 888,\n",
       " 'Ryan Fredericks (Bournemouth)': 889,\n",
       " 'Iván Fresneda (Valladolid)': 890,\n",
       " \"Remo Freuler (Nott'ham Forest)\": 891,\n",
       " 'Michael Frey (Schalke 04)': 892,\n",
       " 'Marco Friedl (Werder Bremen)': 893,\n",
       " \"Marvin Friedrich (M'Gladbach)\": 894,\n",
       " 'Jeremie Frimpong (Leverkusen)': 895,\n",
       " 'Chris Führich (Stuttgart)': 896,\n",
       " 'Angelo Fulgini (Lens)': 897,\n",
       " 'Angelo Fulgini (Mainz 05)': 898,\n",
       " 'Niclas Füllkrug (Werder Bremen)': 899,\n",
       " 'Matteo Gabbia (Milan)': 900,\n",
       " 'Manolo Gabbiadini (Sampdoria)': 901,\n",
       " 'Gianluca Gaetano (Napoli)': 902,\n",
       " 'Roberto Gagliardini (Inter)': 903,\n",
       " 'Adolfo Gaich (Hellas Verona)': 904,\n",
       " 'Cody Gakpo (Liverpool)': 905,\n",
       " 'Javi Galán (Celta Vigo)': 906,\n",
       " 'Iñigo Ruiz de Galarreta (Mallorca)': 907,\n",
       " 'Pablo Galdames Millán (Cremonese)': 908,\n",
       " 'Conor Gallagher (Chelsea)': 909,\n",
       " 'Antonino Gallo (Lecce)': 910,\n",
       " 'Gauthier Gallon (Troyes)': 911,\n",
       " 'Cristian Gamboa (Bochum)': 912,\n",
       " 'Kévin Gameiro (Strasbourg)': 913,\n",
       " 'Idrissa Gana Gueye (Everton)': 914,\n",
       " 'Ignatius Ganago (Nantes)': 915,\n",
       " 'Ignatius Ganago (Lens)': 916,\n",
       " \"Silvère Ganvoula M'Boussy (Bochum)\": 917,\n",
       " 'Aleix García (Girona)': 918,\n",
       " 'Álvaro García (Rayo Vallecano)': 919,\n",
       " 'Borja García (Girona)': 920,\n",
       " 'Carlos García (Cádiz)': 921,\n",
       " 'Dani García (Athletic Club)': 922,\n",
       " 'David García (Osasuna)': 923,\n",
       " 'Eric García (Barcelona)': 924,\n",
       " 'Francisco Garcia (Rayo Vallecano)': 925,\n",
       " 'Raúl García (Athletic Club)': 926,\n",
       " 'Rubén García (Osasuna)': 927,\n",
       " 'Unai García (Osasuna)': 928,\n",
       " 'Alejandro Garnacho (Manchester Utd)': 929,\n",
       " 'James Garner (Everton)': 930,\n",
       " 'Felix Garreta (Betis)': 931,\n",
       " 'Sékou Gassama (Valladolid)': 932,\n",
       " 'Johan Gastien (Clermont Foot)': 933,\n",
       " 'Federico Gatti (Juventus)': 934,\n",
       " 'Gavi (Barcelona)': 935,\n",
       " 'Paulo Gazzaniga (Girona)': 936,\n",
       " 'David de Gea (Manchester Utd)': 937,\n",
       " 'Dennis Geiger (Hoffenheim)': 938,\n",
       " 'Joe Gelhardt (Leeds United)': 939,\n",
       " 'Valentin Gendrey (Lecce)': 940,\n",
       " 'Denis Genreau (Toulouse)': 941,\n",
       " 'Yannick Gerhardt (Wolfsburg)': 942,\n",
       " 'Valère Germain (Montpellier)': 943,\n",
       " 'Gerson (Marseille)': 944,\n",
       " 'Ismaël Gharbi (Paris S-G)': 945,\n",
       " 'Paolo Ghiglione (Cremonese)': 946,\n",
       " 'Saman Ghoddos (Brentford)': 947,\n",
       " 'Faouzi Ghoulam (Angers)': 948,\n",
       " 'Morgan Gibbs-White (Wolves)': 949,\n",
       " \"Morgan Gibbs-White (Nott'ham Forest)\": 950,\n",
       " 'Niko Gießelmann (Union Berlin)': 951,\n",
       " 'Samuel Gigot (Marseille)': 952,\n",
       " 'Rafa? Gikiewicz (Augsburg)': 953,\n",
       " 'Óscar Gil (Espanyol)': 954,\n",
       " 'Mario Gila (Lazio)': 955,\n",
       " 'Billy Gilmour (Brighton)': 956,\n",
       " 'Álvaro Giménez (Cádiz)': 957,\n",
       " 'Gvidas Gineitis (Torino)': 958,\n",
       " 'Matthias Ginter (Freiburg)': 959,\n",
       " 'Andrei Girotto (Nantes)': 960,\n",
       " 'Olivier Giroud (Milan)': 961,\n",
       " 'Serge Gnabry (Bayern Munich)': 962,\n",
       " 'Degnand Gnonto (Leeds United)': 963,\n",
       " 'Ben Godfrey (Everton)': 964,\n",
       " 'Pierluigi Gollini (Fiorentina)': 965,\n",
       " 'Aleksandr Golovin (Monaco)': 966,\n",
       " 'André Gomes (Lille)': 967,\n",
       " 'Angel Gomes (Lille)': 968,\n",
       " 'João Gomes (Wolves)': 969,\n",
       " 'Toti Gomes (Wolves)': 970,\n",
       " 'Dani Gómez (Espanyol)': 971,\n",
       " 'Joe Gomez (Liverpool)': 972,\n",
       " 'Maxi Gómez (Valencia)': 973,\n",
       " 'Moi Gómez (Osasuna)': 974,\n",
       " 'Papu Gómez (Sevilla)': 975,\n",
       " 'Sergi Gómez (Espanyol)': 976,\n",
       " 'Sergio Gómez (Manchester City)': 977,\n",
       " 'Maxime Gonalons (Clermont Foot)': 978,\n",
       " 'Diego González (Elche)': 979,\n",
       " 'Édgar González (Betis)': 980,\n",
       " 'Giovanni González (Mallorca)': 981,\n",
       " 'Joan Gonzàlez (Lecce)': 982,\n",
       " 'Nicolás González (Fiorentina)': 983,\n",
       " 'Nicolás González (Valencia)': 984,\n",
       " 'Oumar Gonzalez (Ajaccio)': 985,\n",
       " 'Roberto González (Betis)': 986,\n",
       " 'Jacek Góralski (Bochum)': 987,\n",
       " 'Anthony Gordon (Newcastle Utd)': 988,\n",
       " 'Anthony Gordon (Everton)': 989,\n",
       " 'Leon Goretzka (Bayern Munich)': 990,\n",
       " 'Andoni Gorosabel (Real Sociedad)': 991,\n",
       " 'Robin Gosens (Inter)': 992,\n",
       " 'Mario Götze (Eint Frankfurt)': 993,\n",
       " 'Amine Gouiri (Nice)': 994,\n",
       " 'Amine Gouiri (Rennes)': 995,\n",
       " 'Jeffrey Gouweleeuw (Augsburg)': 996,\n",
       " 'Jonathan Gradit (Lens)': 997,\n",
       " 'José Gragera (Espanyol)': 998,\n",
       " 'Alberto Grassi (Empoli)': 999,\n",
       " ...}"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropping duplicates\n",
    "# or make them unique by adding club name\n",
    "# as players can move from one club to another in the same season\n",
    "# as shown in the figure\n",
    "\n",
    "# df.drop_duplicates(subset='Player', inplace=True)\n",
    "\n",
    "players = []\n",
    "\n",
    "for i in range(len(df)):\n",
    "    players.append(df['Player'][i] +\n",
    "                   \" \" + f\"({df['Squad'][i]})\")\n",
    "\n",
    "players_ID = dict(zip(players, df.index))\n",
    "players_ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/beyond/Desktop/Projects/player-recommendation-demo/venv/lib/python3.11/site-packages/seaborn/axisgrid.py:854: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  func(*plot_args, **plot_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Age Distribution')"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(df, height=6, aspect=10 /\n",
    "              6).map(sns.distplot, 'Age', bins=25)\n",
    "plt.title(\"Age Distribution\", size=24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Age variance among leagues')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x='Comp', y='Age', hue='Comp', data=df,\n",
    "                 palette='Set2')\n",
    "plt.xlabel('League Competition')\n",
    "plt.title('Age variance among leagues', size=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standardized data [[-1.36685925e-02  1.99538528e-01 -1.73952938e-01 ... -3.16987108e-02\n",
      "  -6.15524478e-01 -1.67949399e-01]\n",
      " [-5.10355690e-01 -2.70850217e-01 -4.78759923e-01 ...  5.81820673e-01\n",
      "   5.49589220e-01 -1.46092079e-01]\n",
      " [-5.10355690e-01 -1.37456394e-01 -8.11855946e-02 ...  1.09883139e+00\n",
      "  -2.41928238e-01 -2.44450016e-01]\n",
      " ...\n",
      " [ 3.46314109e+00  1.04202583e+00  5.94690764e-01 ...  8.20436468e-02\n",
      "  -5.26874523e-01 -4.35701561e-01]\n",
      " [-1.36685925e-02 -1.51497849e-01 -2.00457893e-01 ... -1.00023212e+00\n",
      "   8.35078529e+00  1.14348976e+00]\n",
      " [ 4.83018505e-01 -6.02283908e-02  9.10966145e-02 ...  6.21540836e-03\n",
      "  -4.88881685e-01 -3.37343624e-01]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 't-SNE - Outfield players')"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 391.75x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "# selecting only numerical metrics\n",
    "stats = df.iloc[:, 12:-1]\n",
    "# position types\n",
    "labels = df['Pos']\n",
    "\n",
    "# standardizing the data\n",
    "data = StandardScaler().fit_transform(stats)\n",
    "print('Standardized data', data)\n",
    "\n",
    "# configuring tSNE params\n",
    "model = TSNE(n_components=2, perplexity=30, random_state=0)\n",
    "tsne_data = model.fit_transform(data)\n",
    "\n",
    "# creating a new df to plot the result data\n",
    "tsne_data = np.vstack((tsne_data.T, labels)).T\n",
    "tsne_df = pd.DataFrame(data=tsne_data, columns=(\n",
    "    \"Dimension 1\", \"Dimension 2\", \"Positions\"))\n",
    "\n",
    "# ploting the result of tSNE\n",
    "ax = sns.FacetGrid(tsne_df, hue=\"Positions\", aspect=20/20)\\\n",
    "        .map(plt.scatter, 'Dimension 1', 'Dimension 2').add_legend()\n",
    "plt.title('t-SNE - Outfield players', size=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'PCA: components selection')"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import decomposition\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# standardizing the data\n",
    "data = StandardScaler().fit_transform(stats)\n",
    "\n",
    "# setting up and running PCA\n",
    "pca = decomposition.PCA()\n",
    "pca.n_components = 111\n",
    "pca_data = pca.fit_transform(data)\n",
    "\n",
    "# % variance explained per components\n",
    "percentage_var_explained = pca.explained_variance_ / \\\n",
    "    np.sum(pca.explained_variance_)\n",
    "\n",
    "# cumulative variance explained\n",
    "cum_var_explained = np.cumsum(percentage_var_explained)\n",
    "\n",
    "plt.figure(1, figsize=(6, 4))\n",
    "plt.plot(cum_var_explained, linewidth=2)\n",
    "plt.axis('tight')\n",
    "plt.grid()\n",
    "plt.xlabel('principal components')\n",
    "plt.ylabel('Cumulative variance explained')\n",
    "plt.title('PCA: components selection')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Position distribution among leagues')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Pos', hue='Comp', data=df)\n",
    "plt.xlabel('Position')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Position distribution among leagues', size=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats = pca_data[:, :80]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'module' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[119], line 26\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[38;5;66;03m# player-similarities hash table\u001b[39;00m\n\u001b[1;32m     25\u001b[0m engine \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m---> 26\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m query \u001b[38;5;129;01min\u001b[39;00m \u001b[43mtqdm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mplayers\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m     27\u001b[0m     metric \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m     28\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m player \u001b[38;5;129;01min\u001b[39;00m players:\n",
      "\u001b[0;31mTypeError\u001b[0m: 'module' object is not callable"
     ]
    }
   ],
   "source": [
    "from scipy.spatial import distance\n",
    "import numpy as np\n",
    "\n",
    "# fetch the player vector\n",
    "\n",
    "\n",
    "def getStats(name):\n",
    "    idx = player_ID[name]\n",
    "    return stats[idx, :]\n",
    "\n",
    "# fetch cosine similarity between two player vectors\n",
    "\n",
    "\n",
    "def similarity(player1, player2):\n",
    "    return 1 - distance.cosine(getStats(player1), getStats(player2))\n",
    "\n",
    "# normalize an array to a scale of 0 to 100\n",
    "\n",
    "\n",
    "def normalize(array):\n",
    "    return np.array([round(num, 2) for num in (array - min(array))*100/(max(array)-min(array))])\n",
    "\n",
    "\n",
    "# player-similarities hash table\n",
    "engine = {}\n",
    "for query in tqdm(players):\n",
    "    metric = []\n",
    "    for player in players:\n",
    "        value = similarity(query, player)\n",
    "        metric.append(value)\n",
    "    metric = normalize(metric)\n",
    "    # adding normalized similarity values to the 'player name' key\n",
    "    engine[query] = metric\n",
    "\n",
    "# saving hash table/dict as a pickle\n",
    "with open(r'data\\engine.pickle', 'wb') as file:\n",
    "    pickle.dump(engine, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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