Result dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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 pandas_profiling import ProfileReport\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Res =pd.read_csv('results.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1872-11-30</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1873-03-08</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1874-03-07</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1875-03-06</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1876-03-04</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1876-03-25</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>Wales</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1877-03-03</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1877-03-05</td>\n",
       "      <td>Wales</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Wrexham</td>\n",
       "      <td>Wales</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1878-03-02</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1878-03-23</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>Wales</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date home_team away_team  home_score  away_score tournament     city  \\\n",
       "0  1872-11-30  Scotland   England         0.0         0.0   Friendly  Glasgow   \n",
       "1  1873-03-08   England  Scotland         4.0         2.0   Friendly   London   \n",
       "2  1874-03-07  Scotland   England         2.0         1.0   Friendly  Glasgow   \n",
       "3  1875-03-06   England  Scotland         2.0         2.0   Friendly   London   \n",
       "4  1876-03-04  Scotland   England         3.0         0.0   Friendly  Glasgow   \n",
       "5  1876-03-25  Scotland     Wales         4.0         0.0   Friendly  Glasgow   \n",
       "6  1877-03-03   England  Scotland         1.0         3.0   Friendly   London   \n",
       "7  1877-03-05     Wales  Scotland         0.0         2.0   Friendly  Wrexham   \n",
       "8  1878-03-02  Scotland   England         7.0         2.0   Friendly  Glasgow   \n",
       "9  1878-03-23  Scotland     Wales         9.0         0.0   Friendly  Glasgow   \n",
       "\n",
       "    country  neutral  \n",
       "0  Scotland    False  \n",
       "1   England    False  \n",
       "2  Scotland    False  \n",
       "3   England    False  \n",
       "4  Scotland    False  \n",
       "5  Scotland    False  \n",
       "6   England    False  \n",
       "7     Wales    False  \n",
       "8  Scotland    False  \n",
       "9  Scotland    False  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Res.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42364</th>\n",
       "      <td>2021-06-27</td>\n",
       "      <td>El Salvador</td>\n",
       "      <td>Guatemala</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>United States</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42365</th>\n",
       "      <td>2021-06-28</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Bolivia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Copa América</td>\n",
       "      <td>Cuiaba</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42366</th>\n",
       "      <td>2021-06-28</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Paraguay</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Copa América</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42367</th>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Panama</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Nashville</td>\n",
       "      <td>United States</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42368</th>\n",
       "      <td>2021-07-02</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Nashville</td>\n",
       "      <td>United States</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date    home_team  away_team  home_score  away_score  \\\n",
       "42364  2021-06-27  El Salvador  Guatemala         0.0         0.0   \n",
       "42365  2021-06-28    Argentina    Bolivia         NaN         NaN   \n",
       "42366  2021-06-28      Uruguay   Paraguay         NaN         NaN   \n",
       "42367  2021-06-30       Mexico     Panama         NaN         NaN   \n",
       "42368  2021-07-02       Mexico    Nigeria         NaN         NaN   \n",
       "\n",
       "         tournament            city        country  neutral  \n",
       "42364      Friendly     Los Angeles  United States     True  \n",
       "42365  Copa América          Cuiaba         Brazil     True  \n",
       "42366  Copa América  Rio de Janeiro         Brazil     True  \n",
       "42367      Friendly       Nashville  United States     True  \n",
       "42368      Friendly       Nashville  United States     True  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Res.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>42365.000000</td>\n",
       "      <td>42365.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.743940</td>\n",
       "      <td>1.185861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.753251</td>\n",
       "      <td>1.404554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         home_score    away_score\n",
       "count  42365.000000  42365.000000\n",
       "mean       1.743940      1.185861\n",
       "std        1.753251      1.404554\n",
       "min        0.000000      0.000000\n",
       "25%        1.000000      0.000000\n",
       "50%        1.000000      1.000000\n",
       "75%        2.000000      2.000000\n",
       "max       31.000000     21.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Res.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date          0\n",
       "home_team     0\n",
       "away_team     0\n",
       "home_score    4\n",
       "away_score    4\n",
       "tournament    0\n",
       "city          0\n",
       "country       0\n",
       "neutral       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Res.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7439395727605334"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Res['home_score'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Res['home_score'].fillna(value=Res['home_score'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0.00000\n",
       "1        4.00000\n",
       "2        2.00000\n",
       "3        2.00000\n",
       "4        3.00000\n",
       "          ...   \n",
       "42364    0.00000\n",
       "42365    1.74394\n",
       "42366    1.74394\n",
       "42367    1.74394\n",
       "42368    1.74394\n",
       "Name: home_score, Length: 42369, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Res['home_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Res['home_score'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "Res['home_score']= Res['home_score'].round(decimals = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1872-11-30</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1873-03-08</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1874-03-07</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1875-03-06</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1876-03-04</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date home_team away_team  home_score  away_score tournament     city  \\\n",
       "0  1872-11-30  Scotland   England         0.0         0.0   Friendly  Glasgow   \n",
       "1  1873-03-08   England  Scotland         4.0         2.0   Friendly   London   \n",
       "2  1874-03-07  Scotland   England         2.0         1.0   Friendly  Glasgow   \n",
       "3  1875-03-06   England  Scotland         2.0         2.0   Friendly   London   \n",
       "4  1876-03-04  Scotland   England         3.0         0.0   Friendly  Glasgow   \n",
       "\n",
       "    country  neutral  \n",
       "0  Scotland    False  \n",
       "1   England    False  \n",
       "2  Scotland    False  \n",
       "3   England    False  \n",
       "4  Scotland    False  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Res.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1858609701404461"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Res['away_score'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "Res['away_score'].fillna(value=Res['away_score'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0.000000\n",
       "1        2.000000\n",
       "2        1.000000\n",
       "3        2.000000\n",
       "4        0.000000\n",
       "           ...   \n",
       "42364    0.000000\n",
       "42365    1.185861\n",
       "42366    1.185861\n",
       "42367    1.185861\n",
       "42368    1.185861\n",
       "Name: away_score, Length: 42369, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Res['away_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Res['away_score'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "Res['away_score']= Res['away_score'].round(decimals = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1872-11-30</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1873-03-08</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1874-03-07</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1875-03-06</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1876-03-04</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date home_team away_team  home_score  away_score tournament     city  \\\n",
       "0  1872-11-30  Scotland   England         0.0         0.0   Friendly  Glasgow   \n",
       "1  1873-03-08   England  Scotland         4.0         2.0   Friendly   London   \n",
       "2  1874-03-07  Scotland   England         2.0         1.0   Friendly  Glasgow   \n",
       "3  1875-03-06   England  Scotland         2.0         2.0   Friendly   London   \n",
       "4  1876-03-04  Scotland   England         3.0         0.0   Friendly  Glasgow   \n",
       "\n",
       "    country  neutral  \n",
       "0  Scotland    False  \n",
       "1   England    False  \n",
       "2  Scotland    False  \n",
       "3   England    False  \n",
       "4  Scotland    False  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Res.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date          0\n",
       "home_team     0\n",
       "away_team     0\n",
       "home_score    0\n",
       "away_score    0\n",
       "tournament    0\n",
       "city          0\n",
       "country       0\n",
       "neutral       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Res.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 42369 entries, 0 to 42368\n",
      "Data columns (total 9 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   date        42369 non-null  object \n",
      " 1   home_team   42369 non-null  object \n",
      " 2   away_team   42369 non-null  object \n",
      " 3   home_score  42369 non-null  float64\n",
      " 4   away_score  42369 non-null  float64\n",
      " 5   tournament  42369 non-null  object \n",
      " 6   city        42369 non-null  object \n",
      " 7   country     42369 non-null  object \n",
      " 8   neutral     42369 non-null  bool   \n",
      "dtypes: bool(1), float64(2), object(6)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "Res.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Res['neutral'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brazil                           578\n",
       "Argentina                        555\n",
       "Mexico                           520\n",
       "Germany                          518\n",
       "England                          510\n",
       "                                ... \n",
       "Central Spain                      1\n",
       "St Vincent and the Grenadines      1\n",
       "Chameria                           1\n",
       "Madrid                             1\n",
       "Sark                               1\n",
       "Name: home_team, Length: 309, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Res['home_team'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .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>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>neutral</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>home_score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.137437</td>\n",
       "      <td>-0.033528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>away_score</th>\n",
       "      <td>-0.137437</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.086065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neutral</th>\n",
       "      <td>-0.033528</td>\n",
       "      <td>0.086065</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            home_score  away_score   neutral\n",
       "home_score    1.000000   -0.137437 -0.033528\n",
       "away_score   -0.137437    1.000000  0.086065\n",
       "neutral      -0.033528    0.086065  1.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Res.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2add19b64c0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(Res['home_score'], kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2add1b2f880>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(Res['away_score'], kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2add1b14280>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(Res['neutral'], kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2add1d2e520>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=Res['home_score'], label=\"home_score\")\n",
    "sns.lineplot(data=Res['away_score'], label=\"away_score\")\n",
    "sns.lineplot(data=Res['neutral'], label=\"neutral\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2add1d82880>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=Res['home_score'], y=Res['away_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2add2172df0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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f/79GLKYZl1zVMPzs8hU5phUN4+YeduCIxWTrjyqPHPYAFkXEgxGxErgYmNmi3KeBS4HHK4zFzMyGoMrkMBX4fd3wQP7sRZKmAocAczrNSNIsSfMlzV+6dOmwB2pmZo2qTA6tnvQSTcNfBk6MiNWdZhQR50REf0T09/X1DVuANjpssIXYYMv0bmajQ5X3OQwA29YNbwMsaSrTD1wsCWAKcICkVRFxRYVx2Siz1UzfbmM22lT5q7wF2EHS9sBi4HDgw/UFIuLF5z1KOh+4yonBzKz3KksOEbFK0gmkq5AmAOdFxEJJx+XxHc8zmJlZ71R6PB8R84B5TZ+1TAoRcUyVsZiZWXm+Q9rMzAqcHMzMrMCXiZgNk9mzZzM4OMjWW2/N6aef3utwzNaJk4PZMBkcHGTx4sVtxx94yQ8bhp9fvhyAJcuXN4y76rAPVhOg2RC4WcnMzAqcHMzMrMDJwczMCpwczMyswMnBzMwKnBzMzKzAycHMzAp8n4OtH7bYOD1gZIuNex2J2Zjg5GDrhQ1n7tbrEMzGFDcrmZlZgZODmZkVODmYmVmBk4OZmRU4OZiZWYGTg5mZFVSaHCRNl3SfpEWSTmox/iOSFuTXLyXtUmU8ZmZWTmXJQdIE4Cxgf2An4AhJOzUVewh4R0TsDJwKnFNVPGZmVl6VRw57AIsi4sGIWAlcDMysLxARv4yIp/LgTcA2FcZjZmYlVZkcpgK/rxseyJ+18wng6lYjJM2SNF/S/KVLlw5jiGZm1kqVyUEtPouWBaV3kpLDia3GR8Q5EdEfEf19fX3DGKKZmbVSZd9KA8C2dcPbAEuaC0naGTgX2D8inqwwHrNhdeCl5zcMP7/8GQCWLH+mYdxVhx4zckGZDZMqjxxuAXaQtL2kDYHDgbn1BSRNAy4DjoqI+yuMxczMhqCyI4eIWCXpBOAaYAJwXkQslHRcHj8HOAV4OfB1SQCrIqK/qpjMytIWmza8m61vKu2yOyLmAfOaPptT9/exwLFVxmC2Njac8fZeh2DWU75D2szMCpwczMyswMnBzMwKnBzMzKzAycHMzAqcHMzMrKDSS1nNrLc0aTIvye9mQ+HkYDaObTbjyF6HYGOUm5XMzKzAycHMzAqcHMzMrMDnHMzGCE3aouHdrEpODmZjxCYHHdrrEGw94uRgNkw0afOGd7OxzMnBbJhsNOPdQyqvSZMa3s1GEycHsx7Z6KD39ToEs7Z8tZKZmRU4OZiZWYGTg5mZFVSaHCRNl3SfpEWSTmoxXpK+kscvkLRblfGYmVk5lSUHSROAs4D9gZ2AIyTt1FRsf2CH/JoFnF1VPGZmVl6VRw57AIsi4sGIWAlcDMxsKjMT+G4kNwGTJb2ywpjMzKwERUQ1M5YOA6ZHxLF5+CjgzRFxQl2Zq4B/jYj/zsPXAydGxPymec0iHVkAvA64r0WVU4AnhhjmUKcZjXWMxphGoo7RGNNI1DEaYxqJOkZjTCNRx3DG9OqI6Cs9l4io5AV8ADi3bvgo4KtNZX4M7FU3fD3wprWsb37V04zGOkZjTP7eo6f8eKljNMY0Xr53u1eVzUoDwLZ1w9sAS9aijJmZjbAqk8MtwA6Stpe0IXA4MLepzFzg6HzV0p7Asoh4tMKYzMyshMq6z4iIVZJOAK4BJgDnRcRCScfl8XOAecABwCLgWeBj61DlOSMwzWisYzTGNBJ1jMaYRqKO0RjTSNQxGmMaiTpGIqaWKjshbWZmY5fvkDYzswInBzMzKxqOS55G8gVMJ93nsAg4qcV4AV/J4xcAVwKPA3e1mZ+AhcAq4HlgtxZl/g64Pb/uAlYDL8vjtgV+nqf9I/DZEjHtCdwM3JHr/mKJaXYjnbv5DXBVyfIPA3fmuAuXt7WYZm/gEuBe4B7gLR3K35dftf/LM8Dflojpc/k73wVcBGzcZZozctmFzfPvtPyAlwHXAb/N7y+tm+a82jpRtz49CjwGvAD0t6inNs3DdevfDfl/tQC4HJjcpY4/5HpuB64FXtWl/CLgJOD/AgFMKRHTT4HFdcvlgBJ1XJn/XgicXqKOO+rm/zBwe5c6Hsmv24H5wB4lyv+OtO7+CNiirnzt93ZPLvNYjumLHZZ3u2kuzN+5YZk3lV9I6rnhPuCpHGur5d2ujpPy+IZlCGxM4zbgglzHg/k7NHyPDuU7rrdlt5stp+n1xn4oL9LG8QHgNcCG+R+1U1OZA4CrSRuNPYG7SRuldsnhAOCmXOYB4NddYjgI+Fnd8CuBfwe+n+u9v0RMvwY2z+Mm5uE9S0zzv3M9rZJDq/IP07RB6TLNUuDYPG7D+pW/XR11y2WQdJNNp/K3AQ8Bm+TxPwSO6TDNh0kXKmxKunjip8AOZZYfcDprfpgnAafVTbN3bZ2oW592Jm3ob271I8vT9AN/qlv/HgD+Oo8/rUQdLyevs8BngDldym9IWn//m7TBaU4OrWIapGkD36WO95B2anbJZbYqUceLvzvSun9KlzquI230dsrL6xddys8nbcR2Aj4OnNr0e6vtKD2Y16ddSBvt/2izvNtNc28tHhqTwytZs5OxJbASeG8uW1t+zcu7XR13AO8iXZjz4jIkrd+1bcBGpB2b9wP/Rtrg71T/PTqU77jelt1utnqNtWaltemSY0J+tTMTOJO0V/cc3bvwOIK0x1szAdgVOJd0RHEPMLVLTJOB2uO/JuZX85UBzdNMAQ7J9bT7Hs11dPreDdOQNkJbkm5MJCJWRsTT3erI/6t9gQci4nddym9JWjk3kbQBaaPffF9LfUx/Jv0wt4yIVaQ99UNalG+1/GYC38llvgMcXJsgIm7M5Tdhzfq0IJeb0uoflafZFlhZt/6dCxyYi9xEuk+nUx1Psmad3Yy6Zd6m/ErSOnUrxfWjXUx3AW/o8B0a6gA+CfyAtOEjIh4vUcfFwExJAj5I3e+hTR0v5P/PTNI6sKRL+R2Bb+Xy1wGH1pV/NCJuI20Lfkvai9+KtPF8PhdrXt7tpvkOaQPe/H+qlYe0kX46fRzzWLP8mpd3uzouJq2fs2lc3hERy/PgW0jr+WLSzue3WbP+HtypfLf1Niuz3SwYa8lhKvD7uuEBihviVmW2Xsd5AiBpU9Lh2aV1H3+ZtOBfIK3gbyTttXerY1tJt5P2eK6LiG7TTAK+kesp+z1eAlwr6dbcBUmnaV5D2rieI+k3ks6VtFmJOqaS7mG5iKLm8g/nco+Q9o6WRcS1Haa5i5RMdsr/+wNovGmyU0yviHzPTH7fqkV8E1tMu1GLcjVbkxJWc12Q9nCvLlHHnsDfAx8BTulUXtIM0oa0U5JvjukZYK/cy/F5kl7aJaYdSUc0n5V0g6TdS9RR+95vBx6LiN92qeNvSXvef0/aM/58l/J3kZbXVFJPC83LnDzuKdb83jYFtoCOy7t5mra/9Tq75nnXfp+1adot7+Y6+oAXIuKO5oKSJuRtwNXAQ3kb8AryDmbz92hTvhZTp/W29Dau3lhLDmrxWfMeVZkya1v+IOB/IuIPAJIOBB6PiFtJieFNpHbxZ0rUsToidiXtfewhqXlv78Vpcj0rSSvNUL7HxyNiN1Lvt8dL2rvDNBuQEtAlEfFGYAXpsLZbHROAGcB/lohpA+AdwPbAq4DNJB3ZbpqIuIeUUL4G/IR0OLyqREzrcn32kNcVSSeT4rqwxPwvAc7PZU9oVygnw5NJOyJDiel64LukjdqjpGafTmpHcP9JOrf2w3xE0KkOckzNR9HtfIr0fc8nnXP6VpfyHwfeTToqmURa95ttksvU/966LfchTSNpc9K5gpuaftNvov3yfrGOXGYG6SijICJq24DjgL6mbUCrI8Uhla//Kq2q71AeGHvJYW275HhsHedZ07yH/DZghqSHgStIP7L3D6WO3HTzC9IRSbtp3kbao7iCdEj4LkkXlKhjQa7jcdIJtD06TDNAasKo7clfQmpD7VbH64HbIqLV/7i5/F8Av42IpRHxZ+Ay4K1dppkAvCsi9iY1PzTvpbb73z5Wax7M749T9OcW07baENU8StrDrS+/Nalp6SO5KaxMHUtI544O7VD+taQkehpwZJ7uNknNR8HNMW1Bam54AfgmxWXeHNMAqS18SUTcTDoybW6iaPW9B0nr+g9azL+5jo/m8ktISahjTBFxL+n/8yXS7+2B+oKSJgLHA09ExGX542dJ505aLu8207T9refylwJXkX4XNTNIOzaF5d2ijteS1o8P5m1Eu2V4f45/Omlb9XpgSYf1tr587Xt0Wm/XqpuisZYc1qpLDtKJ1nbmAkfnvzehTRcekrYk7fVeWfssIj5P+qffSLqq4tqIaN4TbhXTClITDpJqexr3tpsm1/mbiNg2f+eftainuY4/AstzHZuRDuvv6lDHdqQ22y3yuH1J5yE61bGMtIK223tsLv80sIukTXOd+1I8Gir8ryLiUUnTSBuj5rraLb+5pI0S+f1Kip6juD516gFzAbBRXflPkhLojIh4ts009XXsxJp1dgbFZf5iedKy24bUBLcX6Qe+W0QMdonpSNb8Jg6huMwbYiKttzOAuZJ2JDXjNf8Pmus4nPSbujciBrp9b9KG6BM5rndRTPAN5SXVmip/BPwDMKdWKK833yI12WxYF1OQruiBpuXdYZpW24/68vcA/6curgNJrQcfaV7ereogXR30AKlz0e2oW4aS+iRNzpPfyZrmqKtIPUXU1t8r8/xbli+53pbZbhbFMF1JNFIvUrvz/fmffnL+7DjguFhzVv+sPP5O0lUCj5L2TAZIK2lz+ftJh4BBytwNZXK5Y4CLW8SzV57uAdLG8vYcY6eYDiddkrqA9OM9pcT36M+f70O+WqlL+ZmkZpjapW9l/lcfJl0psoB0lPLSLuXfBjxJOmFMme9AuuTw3vy9v0dqK+00zW9ISeoOYN82dbRafi8nNbH8Nr/XLj1+FWljVVsnniDt1Q7m5fenPI9r6srPIyWlR3M9fybt0T1JasutXdY5p0sdy/O8F5A2fFO7lK9fxx9mzZUunWL6Tf6/LSBtAF5Zoo7aJdq3kY7SutVxMqmJ6Lim30K7Ohbn1x2kjeebupR/Ir/uB/6VNT05vAr4VV7OC3Lsz+fp/6XD8m43zYWkbULzMp9ZV/72PM1AjvHpNsu7XR0n1/1/6pfhvqT1rbYN+F7+vg/l6Zu/R7vyHdfbTtvNbi93n2FmZgVjrVnJzMxGgJODmZkVODmYmVmBk4OZmRU4OZiZWYGTg5mZFTg52LglaTtJrW4CM7MunBzMxghJ3XrZNRs2Tg423k2Q9E1JCyVdK2kTSbtKukmp59LLlXsulfQLSV+SdKOkeyTtLukySb+V9C+1GUo6UtLNkm6X9I12G22lXjTPl3SXpDslfS5//heSfirpDkm3SXpt7i7kjLqyH8pl95H0c0nfB+7M8zxD0i05/r8Zgf+hrYecHGy82wE4KyL+itT1waGkXktPjIidSV1NfKGu/Mrcyd8cUr82x5Oej3CMpJdLej3wIeBtkXrIXE3qfruVXUldZLwhIv6a1E8/pG4bzoqIXUgdDz5K6jdqV9LzBd4NnKE1zxXZg9TlwU6krkGWRcTuwO7AJ3M/PmbDaoNeB2BWsYci4vb8962knjInR8QN+bPv0NjdeK1DsjuBhbVOGCU9SOpkcS9Sl823pL7W2ITWPWdCeiLYayR9lfQQpWslTSIljMsBIuL5PP+9gIsiYjWpR9kbSBv/Z4CbI+KhPM/3AjtLOiwPb0lKgLXxZsPCycHGuz/V/b2a9IS8MuVfaJr2BdLvRcB3IvXI21FEPCVpF2A/0hHIB0n9/LfSqs/9mhVN5T4dEdd0q99sXbhZydY3y0hdHb89Dx9FevxoWdcDh0naCkDSyyS9ulVBSVOAl0TEpcA/krprfgYYkHRwLrOR0oN9bgQ+lM8p9JGerXxzi9leA3xK6dkBSNpRxSf2ma0zHznY+uijwJy8UX6Q1H9+KRFxt6R/IDURvYTUjfPxpAfmNJsKfDuXgzWPxzwK+Iakf87Tf4D0MKa3kLq1DmB27vf/L5vmeS7p2Ru35WcILKXueclmw8VddpuZWYGblczMrMDNSmbDQNKvSU+1q3dURNzZi3jM1pWblczMrMDNSmZmVuDkYGZmBU4OZtmFcuQAAAAOSURBVGZW4ORgZmYF/x9LgwLsBZubUQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=Res['home_score'], y=Res['away_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}