Top 5 Leagues.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a38fb5b7",
   "metadata": {},
   "source": [
    "# Top 5 Leagues - Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "412c3447",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install adjustText\n",
    "# %pip install highlight_text\n",
    "# %pip install mplsoccer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "02e9efc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from adjustText import adjust_text\n",
    "from highlight_text import fig_text\n",
    "import matplotlib.patches as patches\n",
    "import mplsoccer\n",
    "\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6bb4f7f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting basic color palette\n",
    "background = \"#171717\"\n",
    "font_color = 'white'\n",
    "pd.options.display.max_columns = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "26b65cbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing data\n",
    "df_gsc = pd.read_csv(\"../../Data/Men's Football Data/All 5 Leagues/Goal_Shot_Creation_20_21.csv\", \n",
    "                     index_col='Rk')\n",
    "df_sh = pd.read_csv(\"../../Data/Men's Football Data/All 5 Leagues/Shooting_20_21.csv\", index_col='Rk')\n",
    "df_pos = pd.read_csv(\"../../Data/Men's Football Data/Player Positions-FBRef format.csv\",\n",
    "                    index_col='S. No.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b0cda9e",
   "metadata": {},
   "source": [
    "### Merging two dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "52225a6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>Nation_x</th>\n",
       "      <th>Pos_x</th>\n",
       "      <th>Squad_x</th>\n",
       "      <th>Comp_x</th>\n",
       "      <th>Age_x</th>\n",
       "      <th>Born_x</th>\n",
       "      <th>90s_x</th>\n",
       "      <th>Gls</th>\n",
       "      <th>Sh</th>\n",
       "      <th>SoT</th>\n",
       "      <th>SoT%</th>\n",
       "      <th>Sh/90</th>\n",
       "      <th>SoT/90</th>\n",
       "      <th>G/Sh</th>\n",
       "      <th>G/SoT</th>\n",
       "      <th>Dist</th>\n",
       "      <th>FK</th>\n",
       "      <th>PK</th>\n",
       "      <th>PKatt</th>\n",
       "      <th>xG</th>\n",
       "      <th>npxG</th>\n",
       "      <th>npxG/Sh</th>\n",
       "      <th>G-xG</th>\n",
       "      <th>np:G-xG</th>\n",
       "      <th>Nation_y</th>\n",
       "      <th>Pos_y</th>\n",
       "      <th>Squad_y</th>\n",
       "      <th>Comp_y</th>\n",
       "      <th>Age_y</th>\n",
       "      <th>Born_y</th>\n",
       "      <th>90s_y</th>\n",
       "      <th>SCA</th>\n",
       "      <th>SCA90</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>GCA90</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2507</th>\n",
       "      <td>Alexis Saelemaekers\\Alexis-Saelemaekers</td>\n",
       "      <td>be BEL</td>\n",
       "      <td>FW</td>\n",
       "      <td>Milan</td>\n",
       "      <td>it Serie A</td>\n",
       "      <td>21</td>\n",
       "      <td>1999</td>\n",
       "      <td>22.4</td>\n",
       "      <td>0.09</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.31</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.29</td>\n",
       "      <td>17.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>be BEL</td>\n",
       "      <td>FW</td>\n",
       "      <td>Milan</td>\n",
       "      <td>it Serie A</td>\n",
       "      <td>21</td>\n",
       "      <td>1999</td>\n",
       "      <td>22.4</td>\n",
       "      <td>3.21</td>\n",
       "      <td>3.21</td>\n",
       "      <td>2.59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Player Nation_x Pos_x Squad_x  \\\n",
       "2507  Alexis Saelemaekers\\Alexis-Saelemaekers   be BEL    FW   Milan   \n",
       "\n",
       "          Comp_x  Age_x  Born_x  90s_x   Gls    Sh   SoT  SoT%  Sh/90  SoT/90  \\\n",
       "2507  it Serie A     21    1999   22.4  0.09  1.25  0.31  25.0   1.25    0.31   \n",
       "\n",
       "      G/Sh  G/SoT  Dist   FK   PK  PKatt    xG  npxG  npxG/Sh  G-xG  np:G-xG  \\\n",
       "2507  0.07   0.29  17.7  0.0  0.0    0.0  0.13  0.13      0.1 -0.04    -0.04   \n",
       "\n",
       "     Nation_y Pos_y Squad_y      Comp_y  Age_y  Born_y  90s_y   SCA  SCA90  \\\n",
       "2507   be BEL    FW   Milan  it Serie A     21    1999   22.4  3.21   3.21   \n",
       "\n",
       "      SCAPassLive  SCAPassDead  SCADrib  SCASh  SCAFld  SCADef   GCA  GCA90  \\\n",
       "2507         2.59          0.0     0.22    0.0    0.36    0.04  0.36   0.36   \n",
       "\n",
       "      GCAPassLive  GCAPassDead  GCADrib  GCASh  GCAFld  GCADef  \n",
       "2507         0.27          0.0      0.0    0.0    0.09     0.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df_sh, df_gsc, on='Player', how='outer')\n",
    "df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4b96fa27",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Player'] = df['Player'].apply(lambda x: x.split('\\\\')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8360fa36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>Position</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S. No.</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ismael Aaneba</td>\n",
       "      <td>Right-Back</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Patrick van Aanholt</td>\n",
       "      <td>Left-Back</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Issah Abbas</td>\n",
       "      <td>Centre-Forward</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yunis Abdelhamid</td>\n",
       "      <td>Centre-Back</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sabit Abdulai</td>\n",
       "      <td>Central Midfield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2818</th>\n",
       "      <td>Steven Zuber</td>\n",
       "      <td>Left Midfield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2819</th>\n",
       "      <td>Martín Zubimendi</td>\n",
       "      <td>Defensive Midfield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2820</th>\n",
       "      <td>Martin Ødegaard</td>\n",
       "      <td>Attacking Midfield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2821</th>\n",
       "      <td>Martin Ødegaard</td>\n",
       "      <td>Attacking Midfield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2822</th>\n",
       "      <td>Filip Đuričić</td>\n",
       "      <td>Attacking Midfield</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2822 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Player            Position\n",
       "S. No.                                         \n",
       "1             Ismael Aaneba          Right-Back\n",
       "2       Patrick van Aanholt           Left-Back\n",
       "3               Issah Abbas      Centre-Forward\n",
       "4          Yunis Abdelhamid         Centre-Back\n",
       "5             Sabit Abdulai    Central Midfield\n",
       "...                     ...                 ...\n",
       "2818           Steven Zuber       Left Midfield\n",
       "2819       Martín Zubimendi  Defensive Midfield\n",
       "2820        Martin Ødegaard  Attacking Midfield\n",
       "2821        Martin Ødegaard  Attacking Midfield\n",
       "2822          Filip Đuričić  Attacking Midfield\n",
       "\n",
       "[2822 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pos.rename(columns={\"Name\": \"Player\"}, inplace=True)\n",
    "df_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8224246b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>Position</th>\n",
       "      <th>Nation_x</th>\n",
       "      <th>Pos_x</th>\n",
       "      <th>Squad_x</th>\n",
       "      <th>Comp_x</th>\n",
       "      <th>Age_x</th>\n",
       "      <th>Born_x</th>\n",
       "      <th>90s_x</th>\n",
       "      <th>Gls</th>\n",
       "      <th>Sh</th>\n",
       "      <th>SoT</th>\n",
       "      <th>SoT%</th>\n",
       "      <th>Sh/90</th>\n",
       "      <th>SoT/90</th>\n",
       "      <th>G/Sh</th>\n",
       "      <th>G/SoT</th>\n",
       "      <th>Dist</th>\n",
       "      <th>FK</th>\n",
       "      <th>PK</th>\n",
       "      <th>PKatt</th>\n",
       "      <th>xG</th>\n",
       "      <th>npxG</th>\n",
       "      <th>npxG/Sh</th>\n",
       "      <th>G-xG</th>\n",
       "      <th>np:G-xG</th>\n",
       "      <th>Nation_y</th>\n",
       "      <th>Pos_y</th>\n",
       "      <th>Squad_y</th>\n",
       "      <th>Comp_y</th>\n",
       "      <th>Age_y</th>\n",
       "      <th>Born_y</th>\n",
       "      <th>90s_y</th>\n",
       "      <th>SCA</th>\n",
       "      <th>SCA90</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>GCA90</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ismael Aaneba</td>\n",
       "      <td>Right-Back</td>\n",
       "      <td>fr FRA</td>\n",
       "      <td>FWDF</td>\n",
       "      <td>Strasbourg</td>\n",
       "      <td>fr Ligue 1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>fr FRA</td>\n",
       "      <td>FWDF</td>\n",
       "      <td>Strasbourg</td>\n",
       "      <td>fr Ligue 1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Patrick van Aanholt</td>\n",
       "      <td>Left-Back</td>\n",
       "      <td>nl NED</td>\n",
       "      <td>DF</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>eng Premier League</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>19.7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.30</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.3</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.07</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>nl NED</td>\n",
       "      <td>DF</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>eng Premier League</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>19.7</td>\n",
       "      <td>1.32</td>\n",
       "      <td>1.32</td>\n",
       "      <td>1.12</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Issah Abbas</td>\n",
       "      <td>Centre-Forward</td>\n",
       "      <td>gh GHA</td>\n",
       "      <td>DFFW</td>\n",
       "      <td>Mainz 05</td>\n",
       "      <td>de Bundesliga</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>gh GHA</td>\n",
       "      <td>DFFW</td>\n",
       "      <td>Mainz 05</td>\n",
       "      <td>de Bundesliga</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yunis Abdelhamid</td>\n",
       "      <td>Centre-Back</td>\n",
       "      <td>ma MAR</td>\n",
       "      <td>DF</td>\n",
       "      <td>Reims</td>\n",
       "      <td>fr Ligue 1</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>32.1</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.16</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>10.2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>ma MAR</td>\n",
       "      <td>DF</td>\n",
       "      <td>Reims</td>\n",
       "      <td>fr Ligue 1</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>32.1</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sabit Abdulai</td>\n",
       "      <td>Central Midfield</td>\n",
       "      <td>gh GHA</td>\n",
       "      <td>MF</td>\n",
       "      <td>Getafe</td>\n",
       "      <td>es La Liga</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>gh GHA</td>\n",
       "      <td>MF</td>\n",
       "      <td>Getafe</td>\n",
       "      <td>es La Liga</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Player          Position Nation_x Pos_x         Squad_x  \\\n",
       "0        Ismael Aaneba        Right-Back   fr FRA  FWDF      Strasbourg   \n",
       "1  Patrick van Aanholt         Left-Back   nl NED    DF  Crystal Palace   \n",
       "2          Issah Abbas    Centre-Forward   gh GHA  DFFW        Mainz 05   \n",
       "3     Yunis Abdelhamid       Centre-Back   ma MAR    DF           Reims   \n",
       "4        Sabit Abdulai  Central Midfield   gh GHA    MF          Getafe   \n",
       "\n",
       "               Comp_x  Age_x  Born_x  90s_x   Gls    Sh   SoT  SoT%  Sh/90  \\\n",
       "0          fr Ligue 1   21.0  1999.0    0.1  0.00  0.00  0.00   NaN   0.00   \n",
       "1  eng Premier League   29.0  1990.0   19.7  0.00  0.91  0.30  33.3   0.91   \n",
       "2       de Bundesliga   21.0  1998.0    0.2  0.00  0.00  0.00   NaN   0.00   \n",
       "3          fr Ligue 1   32.0  1987.0   32.1  0.09  0.47  0.16  33.3   0.47   \n",
       "4          es La Liga   21.0  1999.0    0.7  0.00  0.00  0.00   NaN   0.00   \n",
       "\n",
       "   SoT/90  G/Sh  G/SoT  Dist    FK   PK  PKatt    xG  npxG  npxG/Sh  G-xG  \\\n",
       "0    0.00   NaN    NaN   NaN  0.00  0.0    0.0  0.00  0.00      NaN  0.00   \n",
       "1    0.30   0.0    0.0  20.3  0.15  0.0    0.0  0.06  0.06     0.07 -0.06   \n",
       "2    0.00   NaN    NaN   NaN  0.00  0.0    0.0  0.00  0.00      NaN  0.00   \n",
       "3    0.16   0.2    0.6  10.2  0.00  0.0    0.0  0.05  0.05     0.11  0.04   \n",
       "4    0.00   NaN    NaN   NaN  0.00  0.0    0.0  0.00  0.00      NaN  0.00   \n",
       "\n",
       "   np:G-xG Nation_y Pos_y         Squad_y              Comp_y  Age_y  Born_y  \\\n",
       "0     0.00   fr FRA  FWDF      Strasbourg          fr Ligue 1   21.0  1999.0   \n",
       "1    -0.06   nl NED    DF  Crystal Palace  eng Premier League   29.0  1990.0   \n",
       "2     0.00   gh GHA  DFFW        Mainz 05       de Bundesliga   21.0  1998.0   \n",
       "3     0.04   ma MAR    DF           Reims          fr Ligue 1   32.0  1987.0   \n",
       "4     0.00   gh GHA    MF          Getafe          es La Liga   21.0  1999.0   \n",
       "\n",
       "   90s_y   SCA  SCA90  SCAPassLive  SCAPassDead  SCADrib  SCASh  SCAFld  \\\n",
       "0    0.1  0.00   0.00         0.00         0.00     0.00   0.00     0.0   \n",
       "1   19.7  1.32   1.32         1.12         0.15     0.05   0.00     0.0   \n",
       "2    0.2  0.00   0.00         0.00         0.00     0.00   0.00     0.0   \n",
       "3   32.1  0.34   0.34         0.31         0.00     0.00   0.03     0.0   \n",
       "4    0.7  0.00   0.00         0.00         0.00     0.00   0.00     0.0   \n",
       "\n",
       "   SCADef   GCA  GCA90  GCAPassLive  GCAPassDead  GCADrib  GCASh  GCAFld  \\\n",
       "0     0.0  0.00   0.00          0.0         0.00      0.0    0.0     0.0   \n",
       "1     0.0  0.15   0.15          0.1         0.05      0.0    0.0     0.0   \n",
       "2     0.0  0.00   0.00          0.0         0.00      0.0    0.0     0.0   \n",
       "3     0.0  0.00   0.00          0.0         0.00      0.0    0.0     0.0   \n",
       "4     0.0  0.00   0.00          0.0         0.00      0.0    0.0     0.0   \n",
       "\n",
       "   GCADef  \n",
       "0     0.0  \n",
       "1     0.0  \n",
       "2     0.0  \n",
       "3     0.0  \n",
       "4     0.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df_pos, df, on='Player', how='outer')\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e920052",
   "metadata": {},
   "source": [
    "#### Dropping unused columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4666f9d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>Position</th>\n",
       "      <th>Squad_x</th>\n",
       "      <th>Age_x</th>\n",
       "      <th>90s_x</th>\n",
       "      <th>Gls</th>\n",
       "      <th>Sh</th>\n",
       "      <th>SoT</th>\n",
       "      <th>SoT%</th>\n",
       "      <th>Sh/90</th>\n",
       "      <th>SoT/90</th>\n",
       "      <th>G/Sh</th>\n",
       "      <th>G/SoT</th>\n",
       "      <th>Dist</th>\n",
       "      <th>FK</th>\n",
       "      <th>PK</th>\n",
       "      <th>PKatt</th>\n",
       "      <th>xG</th>\n",
       "      <th>npxG</th>\n",
       "      <th>npxG/Sh</th>\n",
       "      <th>G-xG</th>\n",
       "      <th>np:G-xG</th>\n",
       "      <th>SCA</th>\n",
       "      <th>SCA90</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>GCA90</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>Alessandro Deiola</td>\n",
       "      <td>Central Midfield</td>\n",
       "      <td>Spezia</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.6</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>2.06</td>\n",
       "      <td>2.05</td>\n",
       "      <td>1.62</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Player          Position Squad_x  Age_x  90s_x  Gls    Sh  \\\n",
       "820  Alessandro Deiola  Central Midfield  Spezia   24.0    4.0  0.0  1.25   \n",
       "\n",
       "      SoT  SoT%  Sh/90  SoT/90  G/Sh  G/SoT  Dist    FK   PK  PKatt    xG  \\\n",
       "820  0.25  20.0   1.25    0.25   0.0    0.0  23.6  0.25  0.0    0.0  0.05   \n",
       "\n",
       "     npxG  npxG/Sh  G-xG  np:G-xG   SCA  SCA90  SCAPassLive  SCAPassDead  \\\n",
       "820  0.05     0.04 -0.05    -0.05  2.06   2.05         1.62          0.0   \n",
       "\n",
       "     SCADrib  SCASh  SCAFld  SCADef   GCA  GCA90  GCAPassLive  GCAPassDead  \\\n",
       "820      0.0   0.29    0.15     0.0  0.15   0.15         0.15          0.0   \n",
       "\n",
       "     GCADrib  GCASh  GCAFld  GCADef  \n",
       "820      0.0    0.0     0.0     0.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop([ 'Nation_y', 'Pos_y', 'Squad_y', 'Comp_y', 'Age_y', 'Born_y',\n",
    "        '90s_y', 'Nation_x', 'Comp_x', 'Born_x', 'Pos_x'], inplace=True, axis=1)\n",
    "df.sample(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9f099dd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>Position</th>\n",
       "      <th>Squad</th>\n",
       "      <th>Age</th>\n",
       "      <th>90s</th>\n",
       "      <th>Gls</th>\n",
       "      <th>Sh</th>\n",
       "      <th>SoT</th>\n",
       "      <th>SoT%</th>\n",
       "      <th>Sh/90</th>\n",
       "      <th>SoT/90</th>\n",
       "      <th>G/Sh</th>\n",
       "      <th>G/SoT</th>\n",
       "      <th>Dist</th>\n",
       "      <th>FK</th>\n",
       "      <th>PK</th>\n",
       "      <th>PKatt</th>\n",
       "      <th>xG</th>\n",
       "      <th>npxG</th>\n",
       "      <th>npxG/Sh</th>\n",
       "      <th>G-xG</th>\n",
       "      <th>np:G-xG</th>\n",
       "      <th>SCA</th>\n",
       "      <th>SCA90</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>GCA90</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ismael Aaneba</td>\n",
       "      <td>Right-Back</td>\n",
       "      <td>Strasbourg</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Patrick van Aanholt</td>\n",
       "      <td>Left-Back</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>29.0</td>\n",
       "      <td>19.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.3</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.3</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.07</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>1.32</td>\n",
       "      <td>1.32</td>\n",
       "      <td>1.12</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Issah Abbas</td>\n",
       "      <td>Centre-Forward</td>\n",
       "      <td>Mainz 05</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Player        Position           Squad   Age   90s  Gls    Sh  \\\n",
       "0        Ismael Aaneba      Right-Back      Strasbourg  21.0   0.1  0.0  0.00   \n",
       "1  Patrick van Aanholt       Left-Back  Crystal Palace  29.0  19.7  0.0  0.91   \n",
       "2          Issah Abbas  Centre-Forward        Mainz 05  21.0   0.2  0.0  0.00   \n",
       "\n",
       "   SoT  SoT%  Sh/90  SoT/90  G/Sh  G/SoT  Dist    FK   PK  PKatt    xG  npxG  \\\n",
       "0  0.0   NaN   0.00     0.0   NaN    NaN   NaN  0.00  0.0    0.0  0.00  0.00   \n",
       "1  0.3  33.3   0.91     0.3   0.0    0.0  20.3  0.15  0.0    0.0  0.06  0.06   \n",
       "2  0.0   NaN   0.00     0.0   NaN    NaN   NaN  0.00  0.0    0.0  0.00  0.00   \n",
       "\n",
       "   npxG/Sh  G-xG  np:G-xG   SCA  SCA90  SCAPassLive  SCAPassDead  SCADrib  \\\n",
       "0      NaN  0.00     0.00  0.00   0.00         0.00         0.00     0.00   \n",
       "1     0.07 -0.06    -0.06  1.32   1.32         1.12         0.15     0.05   \n",
       "2      NaN  0.00     0.00  0.00   0.00         0.00         0.00     0.00   \n",
       "\n",
       "   SCASh  SCAFld  SCADef   GCA  GCA90  GCAPassLive  GCAPassDead  GCADrib  \\\n",
       "0    0.0     0.0     0.0  0.00   0.00          0.0         0.00      0.0   \n",
       "1    0.0     0.0     0.0  0.15   0.15          0.1         0.05      0.0   \n",
       "2    0.0     0.0     0.0  0.00   0.00          0.0         0.00      0.0   \n",
       "\n",
       "   GCASh  GCAFld  GCADef  \n",
       "0    0.0     0.0     0.0  \n",
       "1    0.0     0.0     0.0  \n",
       "2    0.0     0.0     0.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={\"Squad_x\": \"Squad\",\n",
    "                  'Age_x':'Age',\n",
    "                  '90s_x': '90s'}, inplace=True)\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da9da647",
   "metadata": {},
   "source": [
    "###### Updating missing values of two players as positions are missing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "64f201fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-11-e5c41f9c0b7e>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['Position'][df['Player'] == 'Silas Katompa'] = 'Right Midfield'\n",
      "<ipython-input-11-e5c41f9c0b7e>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['Position'][df['Player'] == 'Junior Dina Ebimbe'] = 'Central Midfield'\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>Player</th>\n",
       "      <th>Position</th>\n",
       "      <th>Squad</th>\n",
       "      <th>Age</th>\n",
       "      <th>90s</th>\n",
       "      <th>Gls</th>\n",
       "      <th>Sh</th>\n",
       "      <th>SoT</th>\n",
       "      <th>SoT%</th>\n",
       "      <th>Sh/90</th>\n",
       "      <th>SoT/90</th>\n",
       "      <th>G/Sh</th>\n",
       "      <th>G/SoT</th>\n",
       "      <th>Dist</th>\n",
       "      <th>FK</th>\n",
       "      <th>PK</th>\n",
       "      <th>PKatt</th>\n",
       "      <th>xG</th>\n",
       "      <th>npxG</th>\n",
       "      <th>npxG/Sh</th>\n",
       "      <th>G-xG</th>\n",
       "      <th>np:G-xG</th>\n",
       "      <th>SCA</th>\n",
       "      <th>SCA90</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>GCA90</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3590</th>\n",
       "      <td>Junior Dina Ebimbe</td>\n",
       "      <td>Central Midfield</td>\n",
       "      <td>Dijon</td>\n",
       "      <td>19.0</td>\n",
       "      <td>23.8</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.93</td>\n",
       "      <td>0.55</td>\n",
       "      <td>28.3</td>\n",
       "      <td>1.93</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.08</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.08</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>2.56</td>\n",
       "      <td>2.56</td>\n",
       "      <td>1.72</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3591</th>\n",
       "      <td>Silas Katompa</td>\n",
       "      <td>Right Midfield</td>\n",
       "      <td>Stuttgart</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.3</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.88</td>\n",
       "      <td>0.70</td>\n",
       "      <td>37.5</td>\n",
       "      <td>1.88</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.53</td>\n",
       "      <td>15.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.14</td>\n",
       "      <td>2.86</td>\n",
       "      <td>2.86</td>\n",
       "      <td>1.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Player          Position      Squad   Age   90s   Gls    Sh  \\\n",
       "3590  Junior Dina Ebimbe  Central Midfield      Dijon  19.0  23.8  0.04  1.93   \n",
       "3591       Silas Katompa    Right Midfield  Stuttgart  21.0  21.3  0.52  1.88   \n",
       "\n",
       "       SoT  SoT%  Sh/90  SoT/90  G/Sh  G/SoT  Dist   FK    PK  PKatt    xG  \\\n",
       "3590  0.55  28.3   1.93    0.55  0.02   0.08  17.0  0.0  0.00   0.00  0.16   \n",
       "3591  0.70  37.5   1.88    0.70  0.20   0.53  15.9  0.0  0.14   0.14  0.34   \n",
       "\n",
       "      npxG  npxG/Sh  G-xG  np:G-xG   SCA  SCA90  SCAPassLive  SCAPassDead  \\\n",
       "3590  0.16     0.08 -0.12    -0.12  2.56   2.56         1.72         0.04   \n",
       "3591  0.24     0.13  0.17     0.14  2.86   2.86         1.92         0.00   \n",
       "\n",
       "      SCADrib  SCASh  SCAFld  SCADef   GCA  GCA90  GCAPassLive  GCAPassDead  \\\n",
       "3590     0.42   0.21    0.17    0.00  0.13   0.13         0.08          0.0   \n",
       "3591     0.28   0.33    0.14    0.19  0.47   0.47         0.28          0.0   \n",
       "\n",
       "      GCADrib  GCASh  GCAFld  GCADef  \n",
       "3590     0.00   0.04     0.0    0.00  \n",
       "3591     0.05   0.05     0.0    0.09  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Position'][df['Player'] == 'Silas Katompa'] = 'Right Midfield'\n",
    "df['Position'][df['Player'] == 'Junior Dina Ebimbe'] = 'Central Midfield'\n",
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b224333a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9d52f005",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_m = df[df['Position'].apply(lambda x: 'Central Midfield' in x)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a2414853",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Player', 'Position', 'Squad', 'Age', '90s', 'Gls', 'Sh', 'SoT', 'SoT%',\n",
       "       'Sh/90', 'SoT/90', 'G/Sh', 'G/SoT', 'Dist', 'FK', 'PK', 'PKatt', 'xG',\n",
       "       'npxG', 'npxG/Sh', 'G-xG', 'np:G-xG', 'SCA', 'SCA90', 'SCAPassLive',\n",
       "       'SCAPassDead', 'SCADrib', 'SCASh', 'SCAFld', 'SCADef', 'GCA', 'GCA90',\n",
       "       'GCAPassLive', 'GCAPassDead', 'GCADrib', 'GCASh', 'GCAFld', 'GCADef'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_m = df_m[df_m['90s']>=10]\n",
    "df_m.drop_duplicates()\n",
    "df_m.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "97d7f393",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Right-Back', 'Left-Back', 'Centre-Forward', 'Centre-Back',\n",
       "       'Central Midfield', 'Defensive Midfield', 'Right Winger',\n",
       "       'Attacking Midfield', 'Goalkeeper', 'Left Winger',\n",
       "       'Right Midfield', 'Left Midfield', 'Second Striker', 'attack',\n",
       "       'midfield', 'Defender'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Position'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6cb49442",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rb = df[df['Position'].apply(lambda x: 'Right-Back' in x)]\n",
    "df_rb = df_rb[df_rb['90s'] > 10]\n",
    "df_rb.drop_duplicates(subset=['Player'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2693c625",
   "metadata": {},
   "outputs": [],
   "source": [
    "cutoff_pass = df_rb['SCAPassLive'].quantile(0.95)\n",
    "cutoff_drib = df_rb['SCADrib'].quantile(0.95)\n",
    "cutoff_both = (df_rb['SCAPassLive'] + df_rb['SCADrib']).quantile(0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8f0a0ff2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['Helvetica'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['Helvetica'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['Helvetica'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "fig.set_facecolor(background)\n",
    "ax.patch.set_facecolor(background)\n",
    "ax.grid(ls='--', alpha=0.09, c='white')\n",
    "\n",
    "x = (df_rb['SCAPassLive'])\n",
    "y = (df_rb['SCADrib'])\n",
    "ax.scatter(x, y, c='#BD4B4B', alpha=0.8, edgecolor='#FF4848', s=120)\n",
    "ax.set_ylim(bottom=0.01)\n",
    "ax.set_xlim(left=0)\n",
    "ax.set_xlabel('Shot Creating action from Open Play Passes', color='#FFD371', fontname='Helvetica', \n",
    "              size=20, labelpad=30.0, weight='book')\n",
    "ax.set_ylabel('Shot Creating action from Dribbles', color='#FFD371', fontname='Helvetica', size=20,\n",
    "             labelpad=30.0, weight='book')\n",
    "\n",
    "for axis in ['left', 'bottom', 'right', 'top']:\n",
    "    ax.spines[axis].set_color('#FFD371')\n",
    "    ax.spines[axis].set_linewidth(2)\n",
    "\n",
    "ax.tick_params(axis='both', colors='#FFD371', size=8, labelsize=14)\n",
    "\n",
    "\n",
    "texts = []\n",
    "for x, y, minutes, name in zip(df_rb['SCAPassLive'], df_rb['SCADrib'], df_rb['90s'], df_rb['Player']):\n",
    "    # Splitting the names to only get the last name of any kind of names\n",
    "    if len(name.split()) == 1:\n",
    "        name = name\n",
    "#         print(name)\n",
    "    else:\n",
    "        name = \"\".join(name.split()[1:])\n",
    "#         print(name)\n",
    "        \n",
    "    if (x+y)> cutoff_both or y>cutoff_drib:\n",
    "        ax.scatter(x, y, c='#F8485E')\n",
    "        texts.append(ax.text(x, y, name, fontdict=dict(color='white', size=15, fontname='Verdana')))\n",
    "        \n",
    "adjust_text(texts, force_points=0.5, force_text=0.9, expand_points=(2.2, 2.2),\n",
    "               expand_text=(0.9,0.9), autoalign=True,\n",
    "               arrowprops=dict(arrowstyle = '->',alpha=0.8, lw=2, color='#B4A5A5',))\n",
    "\n",
    "rectangle = patches.Rectangle((0.663,0.655),0.20,0.21, ls='--', lw=1.7, edgecolor='#FFE459', fill=False,\n",
    "                             alpha=0.6)\n",
    "rectangle.set_transform(ax.transAxes)\n",
    "rectangle.set_clip_on(False)\n",
    "ax.add_patch(rectangle)\n",
    "\n",
    "ax.text(2.32,0.359, 'HIGHLIGHTED PLAYERS\\n\\nABOVE $95^{th}$ PERCENTILE\\n\\nIN RESPECTIVE AXIS OR\\n\\nBOTH',  \n",
    "        size=12, color='#FFE459', fontname='Verdana', horizontalalignment='center', alpha=0.6, weight='bold')\n",
    "\n",
    "fig.text(0.504, 0.97, 'Shot Creating Action from Dribbles vs Open Play Passes per 90', size=22,\n",
    "         color='#FFD371', fontname='Helvetica', horizontalalignment='center', weight = 'bold')\n",
    "fig_text(0.175, 0.95, '<Right Backs> || Top 5 Leagues - 2020/21 || Minimum 900 minutes', size=21,\n",
    "         color='#FFD371', fontname='Helvetica', weight = 'bold',\n",
    "         highlight_textprops=[{'color':'#BD4B4B'}])\n",
    "fig.text(0.63, 0.89, 'Made by Snehangsu || Data from Fbref', \n",
    "         size=15, fontname='Tahoma', horizontalalignment='right', color='#595260')\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig('C:/Users/Snehangshu. D/Pictures/Football/Plots/sca20_21(2).png', facecolor=background, dpi=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d21d00b9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}