footie.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f4ec7cee",
   "metadata": {},
   "source": [
    "## This project aims to gain insights from football matches\n",
    "The dataset was got from kaggle in January 2022.\n",
    "\n",
    "A couple of questions are to be answered such as\n",
    "1. Which team(s) scored the most goals in FIFA World Cup 2014?\n",
    "2. Country with most world cup wins ever\n",
    "3. The tournament with most goals\n",
    "4. Best teams in major tournaments\n",
    "5. Correlation between home ground and home_win\n",
    "\n",
    "\n",
    "\n",
    "Other insights will be derived during the course of the analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d9c2038",
   "metadata": {},
   "source": [
    "Firstly, all the necessary libraries were imported"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2b6731f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14af2017",
   "metadata": {},
   "source": [
    "The csv file was read into a pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4e725480",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('results.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa14b770",
   "metadata": {},
   "source": [
    "A quick check on the first five rows of the dataframe using the head function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "59923786",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bea59a64",
   "metadata": {},
   "source": [
    "It can be observed that the first match here was played in 1872. A quick Google search confirms that this is the first official football match in history."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "029a3d6a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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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>43081</th>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Doha</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43082</th>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Doha</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43083</th>\n",
       "      <td>2021-12-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Doha</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43084</th>\n",
       "      <td>2021-12-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Al Khor</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43085</th>\n",
       "      <td>2021-12-18</td>\n",
       "      <td>United States</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>United States</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date      home_team               away_team  home_score  \\\n",
       "43081  2021-12-15        Tunisia                   Egypt         NaN   \n",
       "43082  2021-12-15          Qatar                 Algeria         NaN   \n",
       "43083  2021-12-18            NaN                     NaN         NaN   \n",
       "43084  2021-12-18            NaN                     NaN         NaN   \n",
       "43085  2021-12-18  United States  Bosnia and Herzegovina         NaN   \n",
       "\n",
       "       away_score tournament         city        country neutral  \n",
       "43081         NaN   Arab Cup         Doha          Qatar    True  \n",
       "43082         NaN   Arab Cup         Doha          Qatar   False  \n",
       "43083         NaN   Arab Cup         Doha          Qatar     NaN  \n",
       "43084         NaN   Arab Cup      Al Khor          Qatar     NaN  \n",
       "43085         NaN   Friendly  Los Angeles  United States   False  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a9d3b25",
   "metadata": {},
   "source": [
    "It can be observed that the data was sorted using the date column from earliest football matches to the latest as at the time. 1872 to 2021."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3592b0a7",
   "metadata": {},
   "source": [
    "The info function was used to check the total number of columns, the datatype of each column as well as the number of rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b54f17e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 43086 entries, 0 to 43085\n",
      "Data columns (total 9 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   date        43086 non-null  object \n",
      " 1   home_team   43084 non-null  object \n",
      " 2   away_team   43084 non-null  object \n",
      " 3   home_score  43081 non-null  float64\n",
      " 4   away_score  43081 non-null  float64\n",
      " 5   tournament  43086 non-null  object \n",
      " 6   city        43086 non-null  object \n",
      " 7   country     43086 non-null  object \n",
      " 8   neutral     43084 non-null  object \n",
      "dtypes: float64(2), object(7)\n",
      "memory usage: 3.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48d9f1a0",
   "metadata": {},
   "source": [
    "the number of rows and columns can also be checked using the shape function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b31584ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(43086, 9)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ed4fdfe4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'home_team', 'away_team', 'home_score', 'away_score',\n",
       "       'tournament', 'city', 'country', 'neutral'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51db5544",
   "metadata": {},
   "source": [
    "The dataframe was checked for null/missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c78ac42f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date          0\n",
       "home_team     2\n",
       "away_team     2\n",
       "home_score    5\n",
       "away_score    5\n",
       "tournament    0\n",
       "city          0\n",
       "country       0\n",
       "neutral       2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "820a1f0a",
   "metadata": {},
   "source": [
    "It can be seen that there are a few missing values. I proceeded to check for the missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "93fea5fd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43081</th>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Doha</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43082</th>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Doha</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43083</th>\n",
       "      <td>2021-12-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Doha</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43084</th>\n",
       "      <td>2021-12-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Al Khor</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43085</th>\n",
       "      <td>2021-12-18</td>\n",
       "      <td>United States</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>United States</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date      home_team               away_team  home_score  \\\n",
       "43081  2021-12-15        Tunisia                   Egypt         NaN   \n",
       "43082  2021-12-15          Qatar                 Algeria         NaN   \n",
       "43083  2021-12-18            NaN                     NaN         NaN   \n",
       "43084  2021-12-18            NaN                     NaN         NaN   \n",
       "43085  2021-12-18  United States  Bosnia and Herzegovina         NaN   \n",
       "\n",
       "       away_score tournament         city        country neutral  \n",
       "43081         NaN   Arab Cup         Doha          Qatar    True  \n",
       "43082         NaN   Arab Cup         Doha          Qatar   False  \n",
       "43083         NaN   Arab Cup         Doha          Qatar     NaN  \n",
       "43084         NaN   Arab Cup      Al Khor          Qatar     NaN  \n",
       "43085         NaN   Friendly  Los Angeles  United States   False  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['home_score'].isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44f42d20",
   "metadata": {},
   "source": [
    "By digging into the 5 missing values in the home_score column, I found out that all the other missing values are contained here as well. 2  missing values in home_team, 2 in away_team, 5 in away_team and 2 in neutral."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b3ded70",
   "metadata": {},
   "source": [
    "I proceeded to drop these 5 rows from the dataframe and continue the analysis using clean data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "91301ea5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cleaned = df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7d6738a",
   "metadata": {},
   "source": [
    "A quick check for missing values after dropping the missing rows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "62b53897",
   "metadata": {
    "scrolled": true
   },
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cleaned.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9817fc04",
   "metadata": {},
   "source": [
    "The cleaned data now has 43,081 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0fc03321",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(43081, 9)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cleaned.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d2ae47b",
   "metadata": {},
   "source": [
    "creating a copy of the cleaned data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f415660f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=df_cleaned.copy() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9e645f95",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43076</th>\n",
       "      <td>2021-12-10</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>Oman</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Al Rayyan</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43077</th>\n",
       "      <td>2021-12-10</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Al Khor</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43078</th>\n",
       "      <td>2021-12-11</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Al Wakrah</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43079</th>\n",
       "      <td>2021-12-11</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Doha</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43080</th>\n",
       "      <td>2021-12-11</td>\n",
       "      <td>El Salvador</td>\n",
       "      <td>Chile</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>United States</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43081 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date    home_team             away_team  home_score  away_score  \\\n",
       "0      1872-11-30     Scotland               England         0.0         0.0   \n",
       "1      1873-03-08      England              Scotland         4.0         2.0   \n",
       "2      1874-03-07     Scotland               England         2.0         1.0   \n",
       "3      1875-03-06      England              Scotland         2.0         2.0   \n",
       "4      1876-03-04     Scotland               England         3.0         0.0   \n",
       "...           ...          ...                   ...         ...         ...   \n",
       "43076  2021-12-10      Tunisia                  Oman         2.0         1.0   \n",
       "43077  2021-12-10        Qatar  United Arab Emirates         5.0         0.0   \n",
       "43078  2021-12-11        Egypt                Jordan         3.0         1.0   \n",
       "43079  2021-12-11      Morocco               Algeria         2.0         2.0   \n",
       "43080  2021-12-11  El Salvador                 Chile         0.0         1.0   \n",
       "\n",
       "      tournament         city        country neutral  \n",
       "0       Friendly      Glasgow       Scotland   False  \n",
       "1       Friendly       London        England   False  \n",
       "2       Friendly      Glasgow       Scotland   False  \n",
       "3       Friendly       London        England   False  \n",
       "4       Friendly      Glasgow       Scotland   False  \n",
       "...          ...          ...            ...     ...  \n",
       "43076   Arab Cup    Al Rayyan          Qatar    True  \n",
       "43077   Arab Cup      Al Khor          Qatar   False  \n",
       "43078   Arab Cup    Al Wakrah          Qatar    True  \n",
       "43079   Arab Cup         Doha          Qatar    True  \n",
       "43080   Friendly  Los Angeles  United States    True  \n",
       "\n",
       "[43081 rows x 9 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49d94718",
   "metadata": {},
   "source": [
    "Using value_counts function to check the top 20 tournaments with most matches"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51117119",
   "metadata": {},
   "source": [
    "a function to return the home_score and away_score as integers and not floats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "82015819",
   "metadata": {},
   "outputs": [],
   "source": [
    "def score_to_int(score):\n",
    "    score=int(score)\n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9f66ff8d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df2['home_score'] = df2.apply(lambda x: score_to_int(x['home_score']), axis=1)\n",
    "df2['away_score'] = df2.apply(lambda x: score_to_int(x['away_score']), axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d126ad2e",
   "metadata": {},
   "source": [
    "Checking the datatypes of the columns after conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6641ea31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date          object\n",
       "home_team     object\n",
       "away_team     object\n",
       "home_score     int64\n",
       "away_score     int64\n",
       "tournament    object\n",
       "city          object\n",
       "country       object\n",
       "neutral       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ca55b790",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '17243'),\n",
       " Text(0, 0, '7879'),\n",
       " Text(0, 0, '2582'),\n",
       " Text(0, 0, '1719'),\n",
       " Text(0, 0, '900'),\n",
       " Text(0, 0, '841'),\n",
       " Text(0, 0, '728'),\n",
       " Text(0, 0, '690'),\n",
       " Text(0, 0, '620'),\n",
       " Text(0, 0, '606'),\n",
       " Text(0, 0, '595'),\n",
       " Text(0, 0, '505'),\n",
       " Text(0, 0, '380'),\n",
       " Text(0, 0, '370'),\n",
       " Text(0, 0, '358'),\n",
       " Text(0, 0, '350'),\n",
       " Text(0, 0, '337'),\n",
       " Text(0, 0, '309'),\n",
       " Text(0, 0, '308'),\n",
       " Text(0, 0, '293')]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_barp = df2['tournament'].value_counts().head(20).reset_index()\n",
    "df_barp = df_barp.rename(columns={'index':'Tournament','tournament':'Matches_Played'})\n",
    "plt.figure(figsize=(15,8))\n",
    "ax = sns.barplot(y='Tournament',x='Matches_Played', data=df_barp)\n",
    "ax.bar_label(ax.containers[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20348e13",
   "metadata": {},
   "source": [
    "A function to show the winning team name or return a draw in the case of a drawn match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ce9d619a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def winning_team(home_score, away_score, home_team, away_team):\n",
    "    if (home_score > away_score):\n",
    "        winner = home_team\n",
    "    elif (home_score < away_score):\n",
    "        winner = away_team\n",
    "    else:\n",
    "        winner = 'Draw'\n",
    "    return winner"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65283918",
   "metadata": {},
   "source": [
    "A function to show the losing team name or return a draw in the case of a drawn match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6a2d517b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def losing_team(home_score, away_score, home_team, away_team):\n",
    "    if (home_score < away_score):\n",
    "        loser = home_team\n",
    "    elif (home_score > away_score):\n",
    "        loser = away_team\n",
    "    else:\n",
    "        loser = 'Draw'\n",
    "    return loser"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d34e5bc4",
   "metadata": {},
   "source": [
    "Creating a new column to show the winner and loser using the above function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1216e66a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df2['winner']=df2.apply(lambda x: winning_team(x['home_score'], x['away_score'], x['home_team'], x['away_team']), axis=1)\n",
    "df2['loser']=df2.apply(lambda x: losing_team(x['home_score'], x['away_score'], x['home_team'], x['away_team']), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8c8bbb7",
   "metadata": {},
   "source": [
    "Checking for the newly added winner column and its contents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5299989f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "      <th>winner</th>\n",
       "      <th>loser</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1872-11-30</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "      <td>Draw</td>\n",
       "      <td>Draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1873-03-08</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1874-03-07</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1875-03-06</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "      <td>Draw</td>\n",
       "      <td>Draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1876-03-04</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43076</th>\n",
       "      <td>2021-12-10</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>Oman</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Al Rayyan</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>True</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>Oman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43077</th>\n",
       "      <td>2021-12-10</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Al Khor</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>False</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43078</th>\n",
       "      <td>2021-12-11</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Al Wakrah</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>True</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>Jordan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43079</th>\n",
       "      <td>2021-12-11</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Doha</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>True</td>\n",
       "      <td>Draw</td>\n",
       "      <td>Draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43080</th>\n",
       "      <td>2021-12-11</td>\n",
       "      <td>El Salvador</td>\n",
       "      <td>Chile</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>United States</td>\n",
       "      <td>True</td>\n",
       "      <td>Chile</td>\n",
       "      <td>El Salvador</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43081 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date    home_team             away_team  home_score  away_score  \\\n",
       "0      1872-11-30     Scotland               England           0           0   \n",
       "1      1873-03-08      England              Scotland           4           2   \n",
       "2      1874-03-07     Scotland               England           2           1   \n",
       "3      1875-03-06      England              Scotland           2           2   \n",
       "4      1876-03-04     Scotland               England           3           0   \n",
       "...           ...          ...                   ...         ...         ...   \n",
       "43076  2021-12-10      Tunisia                  Oman           2           1   \n",
       "43077  2021-12-10        Qatar  United Arab Emirates           5           0   \n",
       "43078  2021-12-11        Egypt                Jordan           3           1   \n",
       "43079  2021-12-11      Morocco               Algeria           2           2   \n",
       "43080  2021-12-11  El Salvador                 Chile           0           1   \n",
       "\n",
       "      tournament         city        country neutral    winner  \\\n",
       "0       Friendly      Glasgow       Scotland   False      Draw   \n",
       "1       Friendly       London        England   False   England   \n",
       "2       Friendly      Glasgow       Scotland   False  Scotland   \n",
       "3       Friendly       London        England   False      Draw   \n",
       "4       Friendly      Glasgow       Scotland   False  Scotland   \n",
       "...          ...          ...            ...     ...       ...   \n",
       "43076   Arab Cup    Al Rayyan          Qatar    True   Tunisia   \n",
       "43077   Arab Cup      Al Khor          Qatar   False     Qatar   \n",
       "43078   Arab Cup    Al Wakrah          Qatar    True     Egypt   \n",
       "43079   Arab Cup         Doha          Qatar    True      Draw   \n",
       "43080   Friendly  Los Angeles  United States    True     Chile   \n",
       "\n",
       "                      loser  \n",
       "0                      Draw  \n",
       "1                  Scotland  \n",
       "2                   England  \n",
       "3                      Draw  \n",
       "4                   England  \n",
       "...                     ...  \n",
       "43076                  Oman  \n",
       "43077  United Arab Emirates  \n",
       "43078                Jordan  \n",
       "43079                  Draw  \n",
       "43080           El Salvador  \n",
       "\n",
       "[43081 rows x 11 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbe8c899",
   "metadata": {},
   "source": [
    "Defining a function to return the scoreline using the home_score and away_score as inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1d9c7121",
   "metadata": {},
   "outputs": [],
   "source": [
    "def scoreline(score1, score2):\n",
    "    scores = str(score1) + \"-\" + str(score2)\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7df60920",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2-1'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#testing the code by passing 2 integers\n",
    "scoreline(2,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc204b65",
   "metadata": {},
   "source": [
    "Using the code to create a new column scoreline which returns the match scoreline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a71cbce6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df2['scoreline'] = df2.apply(lambda x: scoreline(x['home_score'], x['away_score']), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5dc6f0f",
   "metadata": {},
   "source": [
    "A quick check of the added column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "559f3fbc",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>2</td>\n",
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       "      <td>England</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>43076</th>\n",
       "      <td>2021-12-10</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>Oman</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Arab Cup</td>\n",
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       "      <td>Qatar</td>\n",
       "      <td>True</td>\n",
       "      <td>Tunisia</td>\n",
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       "      <td>2021-12-10</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Al Khor</td>\n",
       "      <td>Qatar</td>\n",
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       "      <td>Qatar</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>5-0</td>\n",
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       "    <tr>\n",
       "      <th>43078</th>\n",
       "      <td>2021-12-11</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Arab Cup</td>\n",
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       "      <td>Qatar</td>\n",
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       "      <th>43079</th>\n",
       "      <td>2021-12-11</td>\n",
       "      <td>Morocco</td>\n",
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       "      <td>2</td>\n",
       "      <td>Arab Cup</td>\n",
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       "      <td>True</td>\n",
       "      <td>Chile</td>\n",
       "      <td>El Salvador</td>\n",
       "      <td>0-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>43081 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date    home_team             away_team  home_score  away_score  \\\n",
       "0      1872-11-30     Scotland               England           0           0   \n",
       "1      1873-03-08      England              Scotland           4           2   \n",
       "2      1874-03-07     Scotland               England           2           1   \n",
       "3      1875-03-06      England              Scotland           2           2   \n",
       "4      1876-03-04     Scotland               England           3           0   \n",
       "...           ...          ...                   ...         ...         ...   \n",
       "43076  2021-12-10      Tunisia                  Oman           2           1   \n",
       "43077  2021-12-10        Qatar  United Arab Emirates           5           0   \n",
       "43078  2021-12-11        Egypt                Jordan           3           1   \n",
       "43079  2021-12-11      Morocco               Algeria           2           2   \n",
       "43080  2021-12-11  El Salvador                 Chile           0           1   \n",
       "\n",
       "      tournament         city        country neutral    winner  \\\n",
       "0       Friendly      Glasgow       Scotland   False      Draw   \n",
       "1       Friendly       London        England   False   England   \n",
       "2       Friendly      Glasgow       Scotland   False  Scotland   \n",
       "3       Friendly       London        England   False      Draw   \n",
       "4       Friendly      Glasgow       Scotland   False  Scotland   \n",
       "...          ...          ...            ...     ...       ...   \n",
       "43076   Arab Cup    Al Rayyan          Qatar    True   Tunisia   \n",
       "43077   Arab Cup      Al Khor          Qatar   False     Qatar   \n",
       "43078   Arab Cup    Al Wakrah          Qatar    True     Egypt   \n",
       "43079   Arab Cup         Doha          Qatar    True      Draw   \n",
       "43080   Friendly  Los Angeles  United States    True     Chile   \n",
       "\n",
       "                      loser scoreline  \n",
       "0                      Draw       0-0  \n",
       "1                  Scotland       4-2  \n",
       "2                   England       2-1  \n",
       "3                      Draw       2-2  \n",
       "4                   England       3-0  \n",
       "...                     ...       ...  \n",
       "43076                  Oman       2-1  \n",
       "43077  United Arab Emirates       5-0  \n",
       "43078                Jordan       3-1  \n",
       "43079                  Draw       2-2  \n",
       "43080           El Salvador       0-1  \n",
       "\n",
       "[43081 rows x 12 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4db1c063",
   "metadata": {},
   "source": [
    "A function to determine the total number of goals scored in a match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bf47fec1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def goals_sum(score1,score2):\n",
    "    scores = score1 + score2\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee23731c",
   "metadata": {},
   "source": [
    "Applying the function to create a new column 'total_goals' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b7c8bec6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
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       "      <th>total_goals</th>\n",
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       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "      <td>Draw</td>\n",
       "      <td>Draw</td>\n",
       "      <td>0-0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>1873-03-08</td>\n",
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       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>4-2</td>\n",
       "      <td>6</td>\n",
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       "      <th>2</th>\n",
       "      <td>1874-03-07</td>\n",
       "      <td>Scotland</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
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       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "      <td>Scotland</td>\n",
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       "      <td>2-1</td>\n",
       "      <td>3</td>\n",
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       "      <th>3</th>\n",
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       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
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       "      <td>Draw</td>\n",
       "      <td>Draw</td>\n",
       "      <td>2-2</td>\n",
       "      <td>4</td>\n",
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       "      <th>4</th>\n",
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       "      <td>Arab Cup</td>\n",
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       "      <td>2021-12-10</td>\n",
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       "      <td>Al Khor</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>False</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>5-0</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>43078</th>\n",
       "      <td>2021-12-11</td>\n",
       "      <td>Egypt</td>\n",
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       "      <td>1</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Al Wakrah</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>True</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>3-1</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>43079</th>\n",
       "      <td>2021-12-11</td>\n",
       "      <td>Morocco</td>\n",
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       "      <td>2</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Doha</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>True</td>\n",
       "      <td>Draw</td>\n",
       "      <td>Draw</td>\n",
       "      <td>2-2</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>43080</th>\n",
       "      <td>2021-12-11</td>\n",
       "      <td>El Salvador</td>\n",
       "      <td>Chile</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>United States</td>\n",
       "      <td>True</td>\n",
       "      <td>Chile</td>\n",
       "      <td>El Salvador</td>\n",
       "      <td>0-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43081 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date    home_team             away_team  home_score  away_score  \\\n",
       "0      1872-11-30     Scotland               England           0           0   \n",
       "1      1873-03-08      England              Scotland           4           2   \n",
       "2      1874-03-07     Scotland               England           2           1   \n",
       "3      1875-03-06      England              Scotland           2           2   \n",
       "4      1876-03-04     Scotland               England           3           0   \n",
       "...           ...          ...                   ...         ...         ...   \n",
       "43076  2021-12-10      Tunisia                  Oman           2           1   \n",
       "43077  2021-12-10        Qatar  United Arab Emirates           5           0   \n",
       "43078  2021-12-11        Egypt                Jordan           3           1   \n",
       "43079  2021-12-11      Morocco               Algeria           2           2   \n",
       "43080  2021-12-11  El Salvador                 Chile           0           1   \n",
       "\n",
       "      tournament         city        country neutral    winner  \\\n",
       "0       Friendly      Glasgow       Scotland   False      Draw   \n",
       "1       Friendly       London        England   False   England   \n",
       "2       Friendly      Glasgow       Scotland   False  Scotland   \n",
       "3       Friendly       London        England   False      Draw   \n",
       "4       Friendly      Glasgow       Scotland   False  Scotland   \n",
       "...          ...          ...            ...     ...       ...   \n",
       "43076   Arab Cup    Al Rayyan          Qatar    True   Tunisia   \n",
       "43077   Arab Cup      Al Khor          Qatar   False     Qatar   \n",
       "43078   Arab Cup    Al Wakrah          Qatar    True     Egypt   \n",
       "43079   Arab Cup         Doha          Qatar    True      Draw   \n",
       "43080   Friendly  Los Angeles  United States    True     Chile   \n",
       "\n",
       "                      loser scoreline  total_goals  \n",
       "0                      Draw       0-0            0  \n",
       "1                  Scotland       4-2            6  \n",
       "2                   England       2-1            3  \n",
       "3                      Draw       2-2            4  \n",
       "4                   England       3-0            3  \n",
       "...                     ...       ...          ...  \n",
       "43076                  Oman       2-1            3  \n",
       "43077  United Arab Emirates       5-0            5  \n",
       "43078                Jordan       3-1            4  \n",
       "43079                  Draw       2-2            4  \n",
       "43080           El Salvador       0-1            1  \n",
       "\n",
       "[43081 rows x 13 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['total_goals'] = df2.apply(lambda x: goals_sum(x['home_score'], x['away_score']), axis=1)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "57799df0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "             date    home_team             away_team  home_score  away_score  \\\n",
       "0      1872-11-30     Scotland               England           0           0   \n",
       "1      1873-03-08      England              Scotland           4           2   \n",
       "2      1874-03-07     Scotland               England           2           1   \n",
       "3      1875-03-06      England              Scotland           2           2   \n",
       "4      1876-03-04     Scotland               England           3           0   \n",
       "...           ...          ...                   ...         ...         ...   \n",
       "43076  2021-12-10      Tunisia                  Oman           2           1   \n",
       "43077  2021-12-10        Qatar  United Arab Emirates           5           0   \n",
       "43078  2021-12-11        Egypt                Jordan           3           1   \n",
       "43079  2021-12-11      Morocco               Algeria           2           2   \n",
       "43080  2021-12-11  El Salvador                 Chile           0           1   \n",
       "\n",
       "      tournament         city        country neutral    winner  \\\n",
       "0       Friendly      Glasgow       Scotland   False      Draw   \n",
       "1       Friendly       London        England   False   England   \n",
       "2       Friendly      Glasgow       Scotland   False  Scotland   \n",
       "3       Friendly       London        England   False      Draw   \n",
       "4       Friendly      Glasgow       Scotland   False  Scotland   \n",
       "...          ...          ...            ...     ...       ...   \n",
       "43076   Arab Cup    Al Rayyan          Qatar    True   Tunisia   \n",
       "43077   Arab Cup      Al Khor          Qatar   False     Qatar   \n",
       "43078   Arab Cup    Al Wakrah          Qatar    True     Egypt   \n",
       "43079   Arab Cup         Doha          Qatar    True      Draw   \n",
       "43080   Friendly  Los Angeles  United States    True     Chile   \n",
       "\n",
       "                      loser scoreline  total_goals  \n",
       "0                      Draw       0-0            0  \n",
       "1                  Scotland       4-2            6  \n",
       "2                   England       2-1            3  \n",
       "3                      Draw       2-2            4  \n",
       "4                   England       3-0            3  \n",
       "...                     ...       ...          ...  \n",
       "43076                  Oman       2-1            3  \n",
       "43077  United Arab Emirates       5-0            5  \n",
       "43078                Jordan       3-1            4  \n",
       "43079                  Draw       2-2            4  \n",
       "43080           El Salvador       0-1            1  \n",
       "\n",
       "[43081 rows x 13 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['total_goals'] = df2.apply(lambda x: goals_sum(x['home_score'], x['away_score']), axis=1)\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab670c75",
   "metadata": {},
   "source": [
    "Calculating the total number of goals scored in the cleaned dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "84474af4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "126091"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['total_goals'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff8dd7ee",
   "metadata": {},
   "source": [
    "Drilling down to the most popular sporting event in the world - The FIFA World Cup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7d17adc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Friendly', 'British Championship', 'Copa Lipton', 'Copa Newton',\n",
       "       'Copa Premio Honor Argentino', 'Copa Premio Honor Uruguayo',\n",
       "       'Copa Roca', 'Copa América', 'Copa Chevallier Boutell',\n",
       "       'Nordic Championship', 'International Cup', 'Baltic Cup',\n",
       "       'Balkan Cup', 'FIFA World Cup', 'Copa Rio Branco',\n",
       "       'FIFA World Cup qualification', 'Bolivarian Games',\n",
       "       'CCCF Championship', 'NAFU Championship', 'Copa Oswaldo Cruz',\n",
       "       'Pan American Championship', 'Copa del Pacífico',\n",
       "       \"Copa Bernardo O'Higgins\", 'AFC Asian Cup qualification',\n",
       "       'Atlantic Cup', 'AFC Asian Cup', 'African Cup of Nations',\n",
       "       'Copa Paz del Chaco', 'Merdeka Tournament',\n",
       "       'UEFA Euro qualification', 'UEFA Euro',\n",
       "       'Windward Islands Tournament',\n",
       "       'African Cup of Nations qualification', 'Vietnam Independence Cup',\n",
       "       'Copa Carlos Dittborn', 'CONCACAF Championship',\n",
       "       'Copa Juan Pinto Durán', 'Arab Cup', 'South Pacific Games',\n",
       "       'CONCACAF Championship qualification', 'Copa Artigas', 'GaNEFo',\n",
       "       \"King's Cup\", 'Gulf Cup', 'Indonesia Tournament', 'Korea Cup',\n",
       "       'Palestine Cup', 'Brazil Independence Cup', 'Copa Ramón Castilla',\n",
       "       'Oceania Nations Cup', 'CECAFA Cup', 'Copa Félix Bogado',\n",
       "       'Beijing International Friendship Tournament', 'Kirin Cup',\n",
       "       'CFU Caribbean Cup qualification', 'CFU Caribbean Cup',\n",
       "       'Amílcar Cabral Cup', 'FIFA 75th Anniversary Cup',\n",
       "       'Guangzhou International Friendship Tournament', 'Mundialito',\n",
       "       'West African Cup', 'Nehru Cup', 'Merlion Cup', 'Great Wall Cup',\n",
       "       'UDEAC Cup', 'Rous Cup', 'Lunar New Year Cup',\n",
       "       'Arab Cup qualification', 'Tournoi de France',\n",
       "       'Malta International Tournament', 'Matthews Cup', 'Island Games',\n",
       "       'Dynasty Cup', 'Dakar Tournament', 'UNCAF Cup', 'Gold Cup',\n",
       "       'USA Cup', 'Jordan International Tournament', 'Confederations Cup',\n",
       "       'United Arab Emirates Friendship Tournament',\n",
       "       'Oceania Nations Cup qualification', 'Simba Tournament',\n",
       "       'SAFF Cup', 'AFF Championship', 'King Hassan II Tournament',\n",
       "       'Cyprus International Tournament', 'Dunhill Cup',\n",
       "       'COSAFA Cup qualification', 'COSAFA Cup', 'Gold Cup qualification',\n",
       "       'SKN Football Festival', 'UNIFFAC Cup', 'WAFF Championship',\n",
       "       'Millennium Cup', 'Cup of Ancient Civilizations',\n",
       "       \"Prime Minister's Cup\", 'EAFF Championship', 'TIFOCO Tournament',\n",
       "       'Afro-Asian Games', 'AFC Challenge Cup', 'FIFI Wild Cup',\n",
       "       'ELF Cup', 'Viva World Cup', 'AFC Challenge Cup qualification',\n",
       "       'African Nations Championship', 'VFF Cup', 'Dragon Cup',\n",
       "       'ABCS Tournament', 'Nile Basin Tournament', 'Nations Cup',\n",
       "       'Pacific Games', 'OSN Cup', 'CONIFA World Football Cup',\n",
       "       'CONIFA European Football Cup', 'Copa América qualification',\n",
       "       'World Unity Cup', 'Intercontinental Cup',\n",
       "       'AFF Championship qualification', 'UEFA Nations League',\n",
       "       'CONCACAF Nations League qualification',\n",
       "       'African Nations Championship qualification',\n",
       "       'Atlantic Heritage Cup', 'Inter Games Football Tournament',\n",
       "       'CONCACAF Nations League', 'Three Nations Cup',\n",
       "       'Mahinda Rajapaksa Cup'], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['tournament'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c3192216",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "      <th>winner</th>\n",
       "      <th>loser</th>\n",
       "      <th>scoreline</th>\n",
       "      <th>total_goals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1314</th>\n",
       "      <td>1930-07-13</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>United States</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>United States</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>0-3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1315</th>\n",
       "      <td>1930-07-13</td>\n",
       "      <td>France</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>France</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>4-1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>1930-07-14</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Yugoslavia</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>Yugoslavia</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>1-2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>1930-07-14</td>\n",
       "      <td>Peru</td>\n",
       "      <td>Romania</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>Romania</td>\n",
       "      <td>Peru</td>\n",
       "      <td>1-3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>1930-07-15</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>France</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>True</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>France</td>\n",
       "      <td>1-0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  home_team      away_team  home_score  away_score  \\\n",
       "1314  1930-07-13    Belgium  United States           0           3   \n",
       "1315  1930-07-13     France         Mexico           4           1   \n",
       "1316  1930-07-14     Brazil     Yugoslavia           1           2   \n",
       "1317  1930-07-14       Peru        Romania           1           3   \n",
       "1318  1930-07-15  Argentina         France           1           0   \n",
       "\n",
       "          tournament        city  country neutral         winner    loser  \\\n",
       "1314  FIFA World Cup  Montevideo  Uruguay    True  United States  Belgium   \n",
       "1315  FIFA World Cup  Montevideo  Uruguay    True         France   Mexico   \n",
       "1316  FIFA World Cup  Montevideo  Uruguay    True     Yugoslavia   Brazil   \n",
       "1317  FIFA World Cup  Montevideo  Uruguay    True        Romania     Peru   \n",
       "1318  FIFA World Cup  Montevideo  Uruguay    True      Argentina   France   \n",
       "\n",
       "     scoreline  total_goals  \n",
       "1314       0-3            3  \n",
       "1315       4-1            5  \n",
       "1316       1-2            3  \n",
       "1317       1-3            4  \n",
       "1318       1-0            1  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wc = df2[df2['tournament']=='FIFA World Cup']\n",
    "df_wc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3706d282",
   "metadata": {},
   "source": [
    "Total number of goals scored in the world cup since its inception "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "12ffb50b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2548"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wc['total_goals'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ed866b9",
   "metadata": {},
   "source": [
    "The distribution of total goals scored in a FIFA World Cup match. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c2b8e430",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='tournament', ylabel='total_goals'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,6))\n",
    "#plt.xticks(rotation = 45)\n",
    "sns.boxplot(x=\"tournament\", y=\"total_goals\", data=df_wc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c72d16a4",
   "metadata": {},
   "source": [
    "The boxplot shows that the distribution of the total goals scored in a FIFA World cup match is concentrated betweem 1 and 4 with  a few outliers of up to 8 to 12 goals. Let's check for matches where 8 goals and above were scored. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "197e9b3f",
   "metadata": {},
   "source": [
    "Further drilling down to the world cup in 2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6a6eb8b1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "      <th>winner</th>\n",
       "      <th>loser</th>\n",
       "      <th>scoreline</th>\n",
       "      <th>total_goals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36219</th>\n",
       "      <td>2014-06-12</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>São Paulo</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>False</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>3-1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36220</th>\n",
       "      <td>2014-06-13</td>\n",
       "      <td>Chile</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Cuiabá</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>True</td>\n",
       "      <td>Chile</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3-1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36221</th>\n",
       "      <td>2014-06-13</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Cameroon</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Natal</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>True</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Cameroon</td>\n",
       "      <td>1-0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36222</th>\n",
       "      <td>2014-06-13</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Salvador</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>True</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Spain</td>\n",
       "      <td>1-5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36223</th>\n",
       "      <td>2014-06-14</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>Greece</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>True</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>Greece</td>\n",
       "      <td>3-0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36284</th>\n",
       "      <td>2014-07-05</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Salvador</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>True</td>\n",
       "      <td>Draw</td>\n",
       "      <td>Draw</td>\n",
       "      <td>0-0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36286</th>\n",
       "      <td>2014-07-08</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Germany</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>False</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>1-7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36287</th>\n",
       "      <td>2014-07-09</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>São Paulo</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>True</td>\n",
       "      <td>Draw</td>\n",
       "      <td>Draw</td>\n",
       "      <td>0-0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36289</th>\n",
       "      <td>2014-07-12</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Brasília</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>False</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0-3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36291</th>\n",
       "      <td>2014-07-13</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>True</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>1-0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date    home_team    away_team  home_score  away_score  \\\n",
       "36219  2014-06-12       Brazil      Croatia           3           1   \n",
       "36220  2014-06-13        Chile    Australia           3           1   \n",
       "36221  2014-06-13       Mexico     Cameroon           1           0   \n",
       "36222  2014-06-13        Spain  Netherlands           1           5   \n",
       "36223  2014-06-14     Colombia       Greece           3           0   \n",
       "...           ...          ...          ...         ...         ...   \n",
       "36284  2014-07-05  Netherlands   Costa Rica           0           0   \n",
       "36286  2014-07-08       Brazil      Germany           1           7   \n",
       "36287  2014-07-09  Netherlands    Argentina           0           0   \n",
       "36289  2014-07-12       Brazil  Netherlands           0           3   \n",
       "36291  2014-07-13      Germany    Argentina           1           0   \n",
       "\n",
       "           tournament            city country neutral       winner      loser  \\\n",
       "36219  FIFA World Cup       São Paulo  Brazil   False       Brazil    Croatia   \n",
       "36220  FIFA World Cup          Cuiabá  Brazil    True        Chile  Australia   \n",
       "36221  FIFA World Cup           Natal  Brazil    True       Mexico   Cameroon   \n",
       "36222  FIFA World Cup        Salvador  Brazil    True  Netherlands      Spain   \n",
       "36223  FIFA World Cup  Belo Horizonte  Brazil    True     Colombia     Greece   \n",
       "...               ...             ...     ...     ...          ...        ...   \n",
       "36284  FIFA World Cup        Salvador  Brazil    True         Draw       Draw   \n",
       "36286  FIFA World Cup  Belo Horizonte  Brazil   False      Germany     Brazil   \n",
       "36287  FIFA World Cup       São Paulo  Brazil    True         Draw       Draw   \n",
       "36289  FIFA World Cup        Brasília  Brazil   False  Netherlands     Brazil   \n",
       "36291  FIFA World Cup  Rio de Janeiro  Brazil    True      Germany  Argentina   \n",
       "\n",
       "      scoreline  total_goals  \n",
       "36219       3-1            4  \n",
       "36220       3-1            4  \n",
       "36221       1-0            1  \n",
       "36222       1-5            6  \n",
       "36223       3-0            3  \n",
       "...         ...          ...  \n",
       "36284       0-0            0  \n",
       "36286       1-7            8  \n",
       "36287       0-0            0  \n",
       "36289       0-3            3  \n",
       "36291       1-0            1  \n",
       "\n",
       "[64 rows x 13 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wc2014 = df_wc[(df_wc['date']>'2014-01-01') & (df_wc['date']<'2015-01-01')]\n",
    "df_wc2014"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2a8721b",
   "metadata": {},
   "source": [
    "checking for the match that produced most goals in the 2014 FIFA world cup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8662c530",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#firstly, we get the max number of goals scored in a match in FIFA world cup 2014\n",
    "df_wc2014['total_goals'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ea276938",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
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       "      <th>loser</th>\n",
       "      <th>scoreline</th>\n",
       "      <th>total_goals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36286</th>\n",
       "      <td>2014-07-08</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Germany</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>False</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>1-7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date home_team away_team  home_score  away_score      tournament  \\\n",
       "36286  2014-07-08    Brazil   Germany           1           7  FIFA World Cup   \n",
       "\n",
       "                 city country neutral   winner   loser scoreline  total_goals  \n",
       "36286  Belo Horizonte  Brazil   False  Germany  Brazil       1-7            8  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#then we save this in a variable and use it to get the full details of the match in the cleaned dataset\n",
    "max_goals_wc2014 = df_wc2014['total_goals'].max()\n",
    "df_wc2014[df_wc2014['total_goals']==max_goals_wc2014]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f5d9f6f",
   "metadata": {},
   "source": [
    "Checking for the top 10 matches that produced most goals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f5ca20de",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>away_score</th>\n",
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       "      <th>winner</th>\n",
       "      <th>loser</th>\n",
       "      <th>scoreline</th>\n",
       "      <th>total_goals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36286</th>\n",
       "      <td>2014-07-08</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Germany</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>False</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>1-7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36245</th>\n",
       "      <td>2014-06-20</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>France</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Salvador</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>True</td>\n",
       "      <td>France</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>2-5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36252</th>\n",
       "      <td>2014-06-22</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Porto Alegre</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>True</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>2-4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36222</th>\n",
       "      <td>2014-06-13</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Salvador</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>True</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Spain</td>\n",
       "      <td>1-5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36266</th>\n",
       "      <td>2014-06-25</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Porto Alegre</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>True</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>2-3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36255</th>\n",
       "      <td>2014-06-23</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Cameroon</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Brasília</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>False</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Cameroon</td>\n",
       "      <td>4-1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36236</th>\n",
       "      <td>2014-06-18</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Curitiba</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>True</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Australia</td>\n",
       "      <td>2-3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36261</th>\n",
       "      <td>2014-06-24</td>\n",
       "      <td>Japan</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Cuiabá</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>True</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1-4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36262</th>\n",
       "      <td>2014-06-25</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>Iran</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Salvador</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>True</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>Iran</td>\n",
       "      <td>3-1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36253</th>\n",
       "      <td>2014-06-22</td>\n",
       "      <td>United States</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Manaus</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>True</td>\n",
       "      <td>Draw</td>\n",
       "      <td>Draw</td>\n",
       "      <td>2-2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date               home_team    away_team  home_score  \\\n",
       "36286  2014-07-08                  Brazil      Germany           1   \n",
       "36245  2014-06-20             Switzerland       France           2   \n",
       "36252  2014-06-22             South Korea      Algeria           2   \n",
       "36222  2014-06-13                   Spain  Netherlands           1   \n",
       "36266  2014-06-25                 Nigeria    Argentina           2   \n",
       "36255  2014-06-23                  Brazil     Cameroon           4   \n",
       "36236  2014-06-18               Australia  Netherlands           2   \n",
       "36261  2014-06-24                   Japan     Colombia           1   \n",
       "36262  2014-06-25  Bosnia and Herzegovina         Iran           3   \n",
       "36253  2014-06-22           United States     Portugal           2   \n",
       "\n",
       "       away_score      tournament            city country neutral  \\\n",
       "36286           7  FIFA World Cup  Belo Horizonte  Brazil   False   \n",
       "36245           5  FIFA World Cup        Salvador  Brazil    True   \n",
       "36252           4  FIFA World Cup    Porto Alegre  Brazil    True   \n",
       "36222           5  FIFA World Cup        Salvador  Brazil    True   \n",
       "36266           3  FIFA World Cup    Porto Alegre  Brazil    True   \n",
       "36255           1  FIFA World Cup        Brasília  Brazil   False   \n",
       "36236           3  FIFA World Cup        Curitiba  Brazil    True   \n",
       "36261           4  FIFA World Cup          Cuiabá  Brazil    True   \n",
       "36262           1  FIFA World Cup        Salvador  Brazil    True   \n",
       "36253           2  FIFA World Cup          Manaus  Brazil    True   \n",
       "\n",
       "                       winner        loser scoreline  total_goals  \n",
       "36286                 Germany       Brazil       1-7            8  \n",
       "36245                  France  Switzerland       2-5            7  \n",
       "36252                 Algeria  South Korea       2-4            6  \n",
       "36222             Netherlands        Spain       1-5            6  \n",
       "36266               Argentina      Nigeria       2-3            5  \n",
       "36255                  Brazil     Cameroon       4-1            5  \n",
       "36236             Netherlands    Australia       2-3            5  \n",
       "36261                Colombia        Japan       1-4            5  \n",
       "36262  Bosnia and Herzegovina         Iran       3-1            4  \n",
       "36253                    Draw         Draw       2-2            4  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches_with_most_goals_wc2014 = df_wc2014.sort_values('total_goals',ascending=False)\n",
    "matches_with_most_goals_wc2014.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "75a60904",
   "metadata": {},
   "outputs": [
    {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "      <th>winner</th>\n",
       "      <th>loser</th>\n",
       "      <th>scoreline</th>\n",
       "      <th>total_goals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36286</th>\n",
       "      <td>2014-07-08</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Germany</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>False</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>1-7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date home_team away_team  home_score  away_score      tournament  \\\n",
       "36286  2014-07-08    Brazil   Germany           1           7  FIFA World Cup   \n",
       "\n",
       "                 city country neutral   winner   loser scoreline  total_goals  \n",
       "36286  Belo Horizonte  Brazil   False  Germany  Brazil       1-7            8  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#similarly, the match with most goals could have simply been shown using the above approach\n",
    "match_with_most_goals_wc2014 = df_wc2014.sort_values('total_goals',ascending=False)\n",
    "match_with_most_goals_wc2014.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2642f61d",
   "metadata": {},
   "source": [
    "Checking for the total number of teams that played at least a game as home team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "58e36224",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_wc2014['home_team'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "586b2b0f",
   "metadata": {},
   "source": [
    "Checking for the total number of teams that played at least a game as away team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "183c325d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_wc2014['away_team'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec406155",
   "metadata": {},
   "source": [
    "#### It is clear that there is a slight discrepancy here. 32 teams partook in the world cup and only one team played all its games as a home_team. The host nation Brazil."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ba71cb98",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Brazil',\n",
       " 'Chile',\n",
       " 'Mexico',\n",
       " 'Spain',\n",
       " 'Colombia',\n",
       " 'England',\n",
       " 'Ivory Coast',\n",
       " 'Uruguay',\n",
       " 'Argentina',\n",
       " 'France',\n",
       " 'Switzerland',\n",
       " 'Germany',\n",
       " 'Ghana',\n",
       " 'Iran',\n",
       " 'Belgium',\n",
       " 'Russia',\n",
       " 'Australia',\n",
       " 'Cameroon',\n",
       " 'Japan',\n",
       " 'Honduras',\n",
       " 'Italy',\n",
       " 'Nigeria',\n",
       " 'South Korea',\n",
       " 'United States',\n",
       " 'Croatia',\n",
       " 'Netherlands',\n",
       " 'Costa Rica',\n",
       " 'Greece',\n",
       " 'Bosnia and Herzegovina',\n",
       " 'Ecuador',\n",
       " 'Algeria',\n",
       " 'Portugal',\n",
       " 'Croatia',\n",
       " 'Australia',\n",
       " 'Cameroon',\n",
       " 'Netherlands',\n",
       " 'Greece',\n",
       " 'Italy',\n",
       " 'Japan',\n",
       " 'Costa Rica',\n",
       " 'Bosnia and Herzegovina',\n",
       " 'Honduras',\n",
       " 'Ecuador',\n",
       " 'Portugal',\n",
       " 'United States',\n",
       " 'Nigeria',\n",
       " 'Algeria',\n",
       " 'Mexico',\n",
       " 'South Korea',\n",
       " 'Chile',\n",
       " 'Ivory Coast',\n",
       " 'England',\n",
       " 'France',\n",
       " 'Iran',\n",
       " 'Ghana',\n",
       " 'Russia',\n",
       " 'Spain',\n",
       " 'Uruguay',\n",
       " 'Colombia',\n",
       " 'Switzerland',\n",
       " 'Argentina',\n",
       " 'Belgium',\n",
       " 'Germany']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "home_team_list_wc2014 = df_wc2014['home_team'].unique().tolist()\n",
    "away_team_list_wc2014 = df_wc2014['away_team'].unique().tolist()\n",
    "team_list_wc2014 = home_team_list_wc2014 + away_team_list_wc2014\n",
    "team_list_wc2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1e967694",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(team_list_wc2014)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cec1ae82",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Brazil', 'Chile', 'Mexico', 'Spain', 'Colombia', 'England',\n",
       "       'Ivory Coast', 'Uruguay', 'Argentina', 'France', 'Switzerland',\n",
       "       'Germany', 'Ghana', 'Iran', 'Belgium', 'Russia', 'Australia',\n",
       "       'Cameroon', 'Japan', 'Honduras', 'Italy', 'Nigeria', 'South Korea',\n",
       "       'United States', 'Croatia', 'Netherlands', 'Costa Rica', 'Greece',\n",
       "       'Bosnia and Herzegovina', 'Ecuador', 'Algeria', 'Portugal',\n",
       "       'Croatia', 'Australia', 'Cameroon', 'Netherlands', 'Greece',\n",
       "       'Italy', 'Japan', 'Costa Rica', 'Bosnia and Herzegovina',\n",
       "       'Honduras', 'Ecuador', 'Portugal', 'United States', 'Nigeria',\n",
       "       'Algeria', 'Mexico', 'South Korea', 'Chile', 'Ivory Coast',\n",
       "       'England', 'France', 'Iran', 'Ghana', 'Russia', 'Spain', 'Uruguay',\n",
       "       'Colombia', 'Switzerland', 'Argentina', 'Belgium', 'Germany'],\n",
       "      dtype='<U22')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "team_array_wc2014 = np.array(team_list_wc2014)\n",
    "team_array_wc2014"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13252ecd",
   "metadata": {},
   "source": [
    "Combining the two lists gives us a total of 63, there are repeated values here however because most teams are contained in the two lists. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3e3b015f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(team_array_wc2014)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c456407",
   "metadata": {},
   "source": [
    "The total number of distinct teams in the world cup 2014 is determined using the numpy unique function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0750f96e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.unique(team_array_wc2014))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "364759ff",
   "metadata": {},
   "source": [
    "A function to return the total number of goals scored by a country as well as details of each match is shown below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3c7ac10f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Argentina scored 8 goals in the FIFA World Cup 2014\n"
     ]
    }
   ],
   "source": [
    "all_countries = np.unique(team_array_wc2014).tolist()\n",
    "all_countries\n",
    "country='Argentina'\n",
    "\n",
    "country_goals_home = df_wc2014[df_wc2014['home_team']==country]['home_score'].values\n",
    "country_goals_away = df_wc2014[df_wc2014['away_team']==country]['away_score'].values\n",
    "country_goals_home = country_goals_home.tolist()\n",
    "country_goals_away = country_goals_away.tolist()\n",
    "country_goals = country_goals_home + country_goals_away\n",
    "def goals_scored(country_goals):\n",
    "    for i in country_goals:\n",
    "        total_goals = sum(country_goals)\n",
    "        #return total_goals\n",
    "    print (country, \"scored\", total_goals, \"goals in the FIFA World Cup 2014\")\n",
    "    \n",
    "goals_scored(country_goals)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c206e5e6",
   "metadata": {},
   "source": [
    "You can save another country in the country variable and run the goals_scored function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "340c2311",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "      <th>winner</th>\n",
       "      <th>loser</th>\n",
       "      <th>scoreline</th>\n",
       "      <th>total_goals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36227</th>\n",
       "      <td>2014-06-15</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>True</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>2-1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36246</th>\n",
       "      <td>2014-06-21</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Iran</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>True</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Iran</td>\n",
       "      <td>1-0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36266</th>\n",
       "      <td>2014-06-25</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Porto Alegre</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>True</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>2-3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36278</th>\n",
       "      <td>2014-07-01</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>São Paulo</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>True</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>1-0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36283</th>\n",
       "      <td>2014-07-05</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Brasília</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>True</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>1-0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36287</th>\n",
       "      <td>2014-07-09</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>São Paulo</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>True</td>\n",
       "      <td>Draw</td>\n",
       "      <td>Draw</td>\n",
       "      <td>0-0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36291</th>\n",
       "      <td>2014-07-13</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>True</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>1-0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date    home_team               away_team  home_score  \\\n",
       "36227  2014-06-15    Argentina  Bosnia and Herzegovina           2   \n",
       "36246  2014-06-21    Argentina                    Iran           1   \n",
       "36266  2014-06-25      Nigeria               Argentina           2   \n",
       "36278  2014-07-01    Argentina             Switzerland           1   \n",
       "36283  2014-07-05    Argentina                 Belgium           1   \n",
       "36287  2014-07-09  Netherlands               Argentina           0   \n",
       "36291  2014-07-13      Germany               Argentina           1   \n",
       "\n",
       "       away_score      tournament            city country neutral     winner  \\\n",
       "36227           1  FIFA World Cup  Rio de Janeiro  Brazil    True  Argentina   \n",
       "36246           0  FIFA World Cup  Belo Horizonte  Brazil    True  Argentina   \n",
       "36266           3  FIFA World Cup    Porto Alegre  Brazil    True  Argentina   \n",
       "36278           0  FIFA World Cup       São Paulo  Brazil    True  Argentina   \n",
       "36283           0  FIFA World Cup        Brasília  Brazil    True  Argentina   \n",
       "36287           0  FIFA World Cup       São Paulo  Brazil    True       Draw   \n",
       "36291           0  FIFA World Cup  Rio de Janeiro  Brazil    True    Germany   \n",
       "\n",
       "                        loser scoreline  total_goals  \n",
       "36227  Bosnia and Herzegovina       2-1            3  \n",
       "36246                    Iran       1-0            1  \n",
       "36266                 Nigeria       2-3            5  \n",
       "36278             Switzerland       1-0            1  \n",
       "36283                 Belgium       1-0            1  \n",
       "36287                    Draw       0-0            0  \n",
       "36291               Argentina       1-0            1  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wc2014[(df_wc2014['away_team']==country)|(df_wc2014['home_team']==country)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1d8b03ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Algeria scored a total of 7 goal(s).\n",
      "Argentina scored a total of 8 goal(s).\n",
      "Australia scored a total of 3 goal(s).\n",
      "Belgium scored a total of 6 goal(s).\n",
      "Bosnia and Herzegovina scored a total of 4 goal(s).\n",
      "Brazil scored a total of 11 goal(s).\n",
      "Cameroon scored a total of 1 goal(s).\n",
      "Chile scored a total of 6 goal(s).\n",
      "Colombia scored a total of 12 goal(s).\n",
      "Costa Rica scored a total of 5 goal(s).\n",
      "Croatia scored a total of 6 goal(s).\n",
      "Ecuador scored a total of 3 goal(s).\n",
      "England scored a total of 2 goal(s).\n",
      "France scored a total of 10 goal(s).\n",
      "Germany scored a total of 18 goal(s).\n",
      "Ghana scored a total of 4 goal(s).\n",
      "Greece scored a total of 3 goal(s).\n",
      "Honduras scored a total of 1 goal(s).\n",
      "Iran scored a total of 1 goal(s).\n",
      "Italy scored a total of 2 goal(s).\n",
      "Ivory Coast scored a total of 4 goal(s).\n",
      "Japan scored a total of 2 goal(s).\n",
      "Mexico scored a total of 5 goal(s).\n",
      "Netherlands scored a total of 15 goal(s).\n",
      "Nigeria scored a total of 3 goal(s).\n",
      "Portugal scored a total of 4 goal(s).\n",
      "Russia scored a total of 2 goal(s).\n",
      "South Korea scored a total of 3 goal(s).\n",
      "Spain scored a total of 4 goal(s).\n",
      "Switzerland scored a total of 7 goal(s).\n",
      "United States scored a total of 5 goal(s).\n",
      "Uruguay scored a total of 4 goal(s).\n"
     ]
    }
   ],
   "source": [
    "all_countries = np.unique(team_array_wc2014).tolist()\n",
    "count_dicti={}\n",
    "\n",
    "def count_my_goals(country):\n",
    "    country_goals_home = df_wc2014[df_wc2014['home_team']==country]['home_score'].values\n",
    "    country_goals_away = df_wc2014[df_wc2014['away_team']==country]['away_score'].values\n",
    "    country_goals_home = country_goals_home.tolist()\n",
    "    country_goals_away = country_goals_away.tolist()\n",
    "    country_goals = country_goals_home + country_goals_away\n",
    "    total_goals = sum(country_goals)\n",
    "    count_dicti[country] = total_goals\n",
    "    print(country, \"scored a total of\", total_goals, \"goal(s).\")\n",
    "    \n",
    "    \n",
    "for country in all_countries:\n",
    "    count_my_goals(country)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "623ae45f",
   "metadata": {},
   "source": [
    "## 1. Country with most goals in FIFA World Cup 2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "23c95bf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# count_dicti\n",
    "new = pd.DataFrame.from_dict([count_dicti]).T.sort_values(by=0, ascending=False)\n",
    "new = new.rename(columns={ 0:'Goals scored'})\n",
    "new = new.reset_index().head(6)\n",
    "new = new.rename(columns={ 'index':'Country'})\n",
    "plt.figure(figsize=(16,6))\n",
    "for index, value in enumerate(new['Goals scored'].sort_values()):\n",
    "    plt.text(value, index,\n",
    "             str(value)  +\" goals scored\")\n",
    "plt.barh('Country', 'Goals scored', data=new.sort_values('Goals scored'), color=['c', 'b', 'y', 'm', 'orange', 'k'])\n",
    "plt.xlabel(\"Goals\")\n",
    "plt.ylabel(\"Countries\")\n",
    "plt.title(\"Most Prolific Teams in FIFA World Cup 2014\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5158235f",
   "metadata": {},
   "source": [
    "The last four matches of the tournament are shown below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "26bcc175",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "      <th>winner</th>\n",
       "      <th>loser</th>\n",
       "      <th>scoreline</th>\n",
       "      <th>total_goals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36286</th>\n",
       "      <td>2014-07-08</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Germany</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>False</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>1-7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36287</th>\n",
       "      <td>2014-07-09</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>São Paulo</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>True</td>\n",
       "      <td>Draw</td>\n",
       "      <td>Draw</td>\n",
       "      <td>0-0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36289</th>\n",
       "      <td>2014-07-12</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Brasília</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>False</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0-3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36291</th>\n",
       "      <td>2014-07-13</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>FIFA World Cup</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>True</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>1-0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date    home_team    away_team  home_score  away_score  \\\n",
       "36286  2014-07-08       Brazil      Germany           1           7   \n",
       "36287  2014-07-09  Netherlands    Argentina           0           0   \n",
       "36289  2014-07-12       Brazil  Netherlands           0           3   \n",
       "36291  2014-07-13      Germany    Argentina           1           0   \n",
       "\n",
       "           tournament            city country neutral       winner      loser  \\\n",
       "36286  FIFA World Cup  Belo Horizonte  Brazil   False      Germany     Brazil   \n",
       "36287  FIFA World Cup       São Paulo  Brazil    True         Draw       Draw   \n",
       "36289  FIFA World Cup        Brasília  Brazil   False  Netherlands     Brazil   \n",
       "36291  FIFA World Cup  Rio de Janeiro  Brazil    True      Germany  Argentina   \n",
       "\n",
       "      scoreline  total_goals  \n",
       "36286       1-7            8  \n",
       "36287       0-0            0  \n",
       "36289       0-3            3  \n",
       "36291       1-0            1  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wc2014.tail(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c8f0cbb",
   "metadata": {},
   "source": [
    "Most common scoreline in FIFA World Cup 2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5d646eac",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2-1    12\n",
       "0-0     7\n",
       "1-0     7\n",
       "0-1     5\n",
       "1-1     4\n",
       "3-1     3\n",
       "1-2     3\n",
       "2-0     3\n",
       "0-3     3\n",
       "3-0     2\n",
       "1-3     2\n",
       "2-3     2\n",
       "2-2     2\n",
       "2-4     1\n",
       "1-4     1\n",
       "4-1     1\n",
       "0-4     1\n",
       "2-5     1\n",
       "0-2     1\n",
       "4-0     1\n",
       "1-5     1\n",
       "1-7     1\n",
       "Name: scoreline, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wc2014['scoreline'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73e0dedc",
   "metadata": {},
   "source": [
    "## 2. Country with most FIFA world cup wins ever"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e884fa14",
   "metadata": {},
   "source": [
    "The answer to this lies in the winner column created. This column however includes information for drawn games. A new dataframe which filters out the drawn games is created and the value_counts function is used to get the countries with most wins in FIFA World cup history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a9963a24",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_wc_wins = df_wc[df_wc['winner']!='Draw']['winner']\n",
    "df_most_wc_matches_won = df_wc_wins.value_counts().head().reset_index()\n",
    "df_most_wc_matches_won = df_most_wc_matches_won.rename(columns={'index':'Country','winner':'Number of wins'})\n",
    "df_most_wc_matches_won\n",
    "plt.figure(figsize=(16,6))\n",
    "for index, value in enumerate(df_most_wc_matches_won['Number of wins'].sort_values()):\n",
    "    plt.text(value, index,\n",
    "             str(value)  +\" world cup wins\")\n",
    "plt.barh('Country', 'Number of wins', data=df_most_wc_matches_won.sort_values('Number of wins'), color=['b', 'c', 'g', 'k', 'y'])\n",
    "plt.xlabel(\"Goals\")\n",
    "plt.ylabel(\"Country\")\n",
    "plt.title(\"Most Wins by Teams in FIFA World Cup History\")\n",
    "plt.figure(figsize=(16,4))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c0646c5",
   "metadata": {},
   "source": [
    "## 3. The tournament with most goals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce56bef5",
   "metadata": {},
   "source": [
    "There are lots of tournaments in this dataset, value_counts will also be used here to determine which tournament has most goals. The newly created column of total goals will be used here acoompanied with the groupby function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "bf659a98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "      <th>winner</th>\n",
       "      <th>loser</th>\n",
       "      <th>scoreline</th>\n",
       "      <th>total_goals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1872-11-30</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "      <td>Draw</td>\n",
       "      <td>Draw</td>\n",
       "      <td>0-0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1873-03-08</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>4-2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1874-03-07</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>2-1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1875-03-06</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "      <td>Draw</td>\n",
       "      <td>Draw</td>\n",
       "      <td>2-2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1876-03-04</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>3-0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43076</th>\n",
       "      <td>2021-12-10</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>Oman</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Al Rayyan</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>True</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>Oman</td>\n",
       "      <td>2-1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43077</th>\n",
       "      <td>2021-12-10</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Al Khor</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>False</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>5-0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43078</th>\n",
       "      <td>2021-12-11</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Al Wakrah</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>True</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>3-1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43079</th>\n",
       "      <td>2021-12-11</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Doha</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>True</td>\n",
       "      <td>Draw</td>\n",
       "      <td>Draw</td>\n",
       "      <td>2-2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43080</th>\n",
       "      <td>2021-12-11</td>\n",
       "      <td>El Salvador</td>\n",
       "      <td>Chile</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>United States</td>\n",
       "      <td>True</td>\n",
       "      <td>Chile</td>\n",
       "      <td>El Salvador</td>\n",
       "      <td>0-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43081 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date    home_team             away_team  home_score  away_score  \\\n",
       "0      1872-11-30     Scotland               England           0           0   \n",
       "1      1873-03-08      England              Scotland           4           2   \n",
       "2      1874-03-07     Scotland               England           2           1   \n",
       "3      1875-03-06      England              Scotland           2           2   \n",
       "4      1876-03-04     Scotland               England           3           0   \n",
       "...           ...          ...                   ...         ...         ...   \n",
       "43076  2021-12-10      Tunisia                  Oman           2           1   \n",
       "43077  2021-12-10        Qatar  United Arab Emirates           5           0   \n",
       "43078  2021-12-11        Egypt                Jordan           3           1   \n",
       "43079  2021-12-11      Morocco               Algeria           2           2   \n",
       "43080  2021-12-11  El Salvador                 Chile           0           1   \n",
       "\n",
       "      tournament         city        country neutral    winner  \\\n",
       "0       Friendly      Glasgow       Scotland   False      Draw   \n",
       "1       Friendly       London        England   False   England   \n",
       "2       Friendly      Glasgow       Scotland   False  Scotland   \n",
       "3       Friendly       London        England   False      Draw   \n",
       "4       Friendly      Glasgow       Scotland   False  Scotland   \n",
       "...          ...          ...            ...     ...       ...   \n",
       "43076   Arab Cup    Al Rayyan          Qatar    True   Tunisia   \n",
       "43077   Arab Cup      Al Khor          Qatar   False     Qatar   \n",
       "43078   Arab Cup    Al Wakrah          Qatar    True     Egypt   \n",
       "43079   Arab Cup         Doha          Qatar    True      Draw   \n",
       "43080   Friendly  Los Angeles  United States    True     Chile   \n",
       "\n",
       "                      loser scoreline  total_goals  \n",
       "0                      Draw       0-0            0  \n",
       "1                  Scotland       4-2            6  \n",
       "2                   England       2-1            3  \n",
       "3                      Draw       2-2            4  \n",
       "4                   England       3-0            3  \n",
       "...                     ...       ...          ...  \n",
       "43076                  Oman       2-1            3  \n",
       "43077  United Arab Emirates       5-0            5  \n",
       "43078                Jordan       3-1            4  \n",
       "43079                  Draw       2-2            4  \n",
       "43080           El Salvador       0-1            1  \n",
       "\n",
       "[43081 rows x 13 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "89578482",
   "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>tournament</th>\n",
       "      <th>total_goals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Friendly</td>\n",
       "      <td>49877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>FIFA World Cup qualification</td>\n",
       "      <td>22749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>UEFA Euro qualification</td>\n",
       "      <td>7306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>African Cup of Nations qualification</td>\n",
       "      <td>4083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Copa América</td>\n",
       "      <td>2671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Matthews Cup</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AFF Championship qualification</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Cup of Ancient Civilizations</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>TIFOCO Tournament</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>FIFA 75th Anniversary Cup</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>126 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               tournament  total_goals\n",
       "66                               Friendly        49877\n",
       "64           FIFA World Cup qualification        22749\n",
       "113               UEFA Euro qualification         7306\n",
       "8    African Cup of Nations qualification         4083\n",
       "36                           Copa América         2671\n",
       "..                                    ...          ...\n",
       "86                           Matthews Cup            5\n",
       "6          AFF Championship qualification            5\n",
       "54           Cup of Ancient Civilizations            4\n",
       "108                     TIFOCO Tournament            2\n",
       "62              FIFA 75th Anniversary Cup            0\n",
       "\n",
       "[126 rows x 2 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tournament_total_goals = df2.groupby('tournament')['total_goals'].sum().reset_index()\n",
    "df_tournament_total_goals = df_tournament_total_goals.sort_values('total_goals', ascending=False)\n",
    "df_tournament_total_goals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e90b6945",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,6))\n",
    "for index, value in enumerate(df_tournament_total_goals['total_goals'].head().sort_values()):\n",
    "    plt.text(value, index,\n",
    "             str(value)  +\" goals.\")\n",
    "plt.barh('tournament', 'total_goals', data=df_tournament_total_goals.head().sort_values('total_goals'),\n",
    "         color=['b', 'c', 'g', 'k', 'y'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a9eadba",
   "metadata": {},
   "source": [
    "## 4. Best teams in major tournaments"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49d1a025",
   "metadata": {},
   "source": [
    "Major tournaments are the apex tournaments held in each continental region as well as the world cup. The qualification tournaments for this major tournaments will be excluded. Friendlies are not major tournaments. UEFA(Europe) seem to have 2 major tournaments), the UEFA cup as well as the UEFA Nations league. The UEFA Nations league will be excluded from the major tournaments.Similarly there is a lesser championship in Africa compared to the majot African Cup of Nations tournament. These lesser tournaments are not included. \n",
    "\n",
    "There are 5 qualification regions and the sinlge major tournament for these regions are include.\n",
    "The FIFA World Cup is inlcuded too as well as the confederations cup.\n",
    "\n",
    "Africa - African Cup of Nations\n",
    "Europe - UEFA Euro\n",
    "Asia (Oceania) - AFC Asian Cup\n",
    "Oceania - Oceania Nations Cup\n",
    "North and Central America and the Caribbean - Gold Cup\n",
    "South America - Copa América\n",
    "World - FIFA World Cup\n",
    "World - Confederations Cup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c4e0840d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "126"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df2['tournament'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e085addf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Friendly                                 17243\n",
       "FIFA World Cup qualification              7879\n",
       "UEFA Euro qualification                   2582\n",
       "African Cup of Nations qualification      1719\n",
       "FIFA World Cup                             900\n",
       "Copa América                               841\n",
       "AFC Asian Cup qualification                728\n",
       "African Cup of Nations                     690\n",
       "CECAFA Cup                                 620\n",
       "CFU Caribbean Cup qualification            606\n",
       "Merdeka Tournament                         595\n",
       "British Championship                       505\n",
       "Gulf Cup                                   380\n",
       "AFC Asian Cup                              370\n",
       "Gold Cup                                   358\n",
       "Island Games                               350\n",
       "UEFA Euro                                  337\n",
       "COSAFA Cup                                 309\n",
       "UEFA Nations League                        308\n",
       "AFF Championship                           293\n",
       "Nordic Championship                        283\n",
       "African Nations Championship               264\n",
       "CFU Caribbean Cup                          251\n",
       "Amílcar Cabral Cup                         235\n",
       "King's Cup                                 234\n",
       "South Pacific Games                        205\n",
       "UNCAF Cup                                  164\n",
       "Korea Cup                                  159\n",
       "SAFF Cup                                   147\n",
       "Confederations Cup                         140\n",
       "Arab Cup                                   140\n",
       "International Cup                          126\n",
       "CCCF Championship                          123\n",
       "EAFF Championship                          122\n",
       "CONCACAF Nations League                    106\n",
       "Windward Islands Tournament                101\n",
       "CONIFA World Football Cup                  101\n",
       "Oceania Nations Cup                        101\n",
       "AFC Challenge Cup                          100\n",
       "WAFF Championship                           97\n",
       "Baltic Cup                                  94\n",
       "AFC Challenge Cup qualification             92\n",
       "Nehru Cup                                   89\n",
       "Balkan Cup                                  87\n",
       "Indonesia Tournament                        75\n",
       "Oceania Nations Cup qualification           70\n",
       "Cyprus International Tournament             70\n",
       "Kirin Cup                                   69\n",
       "CONCACAF Nations League qualification       68\n",
       "Gold Cup qualification                      65\n",
       "Name: tournament, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['tournament'].value_counts().head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "65ee20a7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "      <th>winner</th>\n",
       "      <th>loser</th>\n",
       "      <th>scoreline</th>\n",
       "      <th>total_goals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>1916-07-02</td>\n",
       "      <td>Chile</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Copa América</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>True</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Chile</td>\n",
       "      <td>0-4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>1916-07-06</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Chile</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Copa América</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>False</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Chile</td>\n",
       "      <td>6-1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>1916-07-08</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Chile</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Copa América</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>True</td>\n",
       "      <td>Draw</td>\n",
       "      <td>Draw</td>\n",
       "      <td>1-1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>1916-07-10</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Copa América</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>False</td>\n",
       "      <td>Draw</td>\n",
       "      <td>Draw</td>\n",
       "      <td>1-1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>1916-07-12</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Copa América</td>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>True</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>1-2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42555</th>\n",
       "      <td>2021-07-25</td>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>Canada</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Gold Cup</td>\n",
       "      <td>Arlington</td>\n",
       "      <td>United States</td>\n",
       "      <td>True</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>0-2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42556</th>\n",
       "      <td>2021-07-25</td>\n",
       "      <td>United States</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Gold Cup</td>\n",
       "      <td>Arlington</td>\n",
       "      <td>United States</td>\n",
       "      <td>False</td>\n",
       "      <td>United States</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>1-0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42557</th>\n",
       "      <td>2021-07-29</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Canada</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Gold Cup</td>\n",
       "      <td>Houston</td>\n",
       "      <td>United States</td>\n",
       "      <td>True</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Canada</td>\n",
       "      <td>2-1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42558</th>\n",
       "      <td>2021-07-29</td>\n",
       "      <td>United States</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Gold Cup</td>\n",
       "      <td>Austin</td>\n",
       "      <td>United States</td>\n",
       "      <td>False</td>\n",
       "      <td>United States</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>1-0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42559</th>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>United States</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Gold Cup</td>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>United States</td>\n",
       "      <td>False</td>\n",
       "      <td>United States</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>1-0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3737 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date      home_team away_team  home_score  away_score  \\\n",
       "438    1916-07-02          Chile   Uruguay           0           4   \n",
       "440    1916-07-06      Argentina     Chile           6           1   \n",
       "441    1916-07-08         Brazil     Chile           1           1   \n",
       "442    1916-07-10      Argentina    Brazil           1           1   \n",
       "444    1916-07-12         Brazil   Uruguay           1           2   \n",
       "...           ...            ...       ...         ...         ...   \n",
       "42555  2021-07-25     Costa Rica    Canada           0           2   \n",
       "42556  2021-07-25  United States   Jamaica           1           0   \n",
       "42557  2021-07-29         Mexico    Canada           2           1   \n",
       "42558  2021-07-29  United States     Qatar           1           0   \n",
       "42559  2021-08-01  United States    Mexico           1           0   \n",
       "\n",
       "         tournament          city        country neutral         winner  \\\n",
       "438    Copa América  Buenos Aires      Argentina    True        Uruguay   \n",
       "440    Copa América  Buenos Aires      Argentina   False      Argentina   \n",
       "441    Copa América  Buenos Aires      Argentina    True           Draw   \n",
       "442    Copa América  Buenos Aires      Argentina   False           Draw   \n",
       "444    Copa América  Buenos Aires      Argentina    True        Uruguay   \n",
       "...             ...           ...            ...     ...            ...   \n",
       "42555      Gold Cup     Arlington  United States    True         Canada   \n",
       "42556      Gold Cup     Arlington  United States   False  United States   \n",
       "42557      Gold Cup       Houston  United States    True         Mexico   \n",
       "42558      Gold Cup        Austin  United States   False  United States   \n",
       "42559      Gold Cup     Las Vegas  United States   False  United States   \n",
       "\n",
       "            loser scoreline  total_goals  \n",
       "438         Chile       0-4            4  \n",
       "440         Chile       6-1            7  \n",
       "441          Draw       1-1            2  \n",
       "442          Draw       1-1            2  \n",
       "444        Brazil       1-2            3  \n",
       "...           ...       ...          ...  \n",
       "42555  Costa Rica       0-2            2  \n",
       "42556     Jamaica       1-0            1  \n",
       "42557      Canada       2-1            3  \n",
       "42558       Qatar       1-0            1  \n",
       "42559      Mexico       1-0            1  \n",
       "\n",
       "[3737 rows x 13 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_major_tournaments = df2[(df2['tournament']=='FIFA World Cup')|(df2['tournament']=='Copa América')\n",
    "    |(df2['tournament']=='African Cup of Nations')|(df2['tournament']=='AFC Asian Cup')\n",
    "    |(df2['tournament']=='Gold Cup')|(df2['tournament']=='UEFA Euro')\n",
    "   |(df2['tournament']=='Confederations Cup')|(df2['tournament']=='Oceania Nations Cup')\n",
    "    ]\n",
    "df_major_tournaments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2fedbc29",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_major_tournaments[df_major_tournaments['winner']=='Draw']\n",
    "df_major_tournaments_ = df_major_tournaments[df_major_tournaments['winner']!='Draw']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "56564fd1",
   "metadata": {
    "scrolled": false
   },
   "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>Country</th>\n",
       "      <th>Wins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Germany</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>United States</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Chile</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Paraguay</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Italy</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>France</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Peru</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country  Wins\n",
       "0          Brazil   212\n",
       "1       Argentina   175\n",
       "2         Uruguay   141\n",
       "3          Mexico   103\n",
       "4         Germany   102\n",
       "5   United States    86\n",
       "6           Chile    79\n",
       "7        Paraguay    72\n",
       "8           Italy    69\n",
       "9          France    64\n",
       "10           Peru    63\n",
       "11       Colombia    63\n",
       "12        Nigeria    59"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wc_most_wins = df_major_tournaments_['winner'].value_counts().head(13).reset_index()\n",
    "wc_most_wins = wc_most_wins.rename(columns={'index':'Country','winner':'Wins'})\n",
    "wc_most_wins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e8263c85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '212'),\n",
       " Text(0, 0, '175'),\n",
       " Text(0, 0, '141'),\n",
       " Text(0, 0, '103'),\n",
       " Text(0, 0, '102'),\n",
       " Text(0, 0, '86'),\n",
       " Text(0, 0, '79'),\n",
       " Text(0, 0, '72'),\n",
       " Text(0, 0, '69'),\n",
       " Text(0, 0, '64'),\n",
       " Text(0, 0, '63'),\n",
       " Text(0, 0, '63'),\n",
       " Text(0, 0, '59')]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.xticks(rotation=90)\n",
    "ax1 = sns.barplot(y='Country',x='Wins',data=wc_most_wins)\n",
    "ax1.bar_label(ax1.containers[0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2c6c779",
   "metadata": {},
   "source": [
    "It can be seen that Brazil has the most wins in major tournaments. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c81f561b",
   "metadata": {},
   "source": [
    "## 5. Compare home wins and away wins"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "449e7b8e",
   "metadata": {},
   "source": [
    "We create a dataframe of only home wins. The length of this data frame is simply the number of home wins. Same ting was done for the away wins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "95139ad7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "      <th>winner</th>\n",
       "      <th>loser</th>\n",
       "      <th>scoreline</th>\n",
       "      <th>total_goals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1873-03-08</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>4-2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1874-03-07</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>2-1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1876-03-04</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>3-0</td>\n",
       "      <td>3</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</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>Wales</td>\n",
       "      <td>4-0</td>\n",
       "      <td>4</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</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>7-2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43071</th>\n",
       "      <td>2021-12-07</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Doha</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>True</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>1-0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43073</th>\n",
       "      <td>2021-12-07</td>\n",
       "      <td>Lebanon</td>\n",
       "      <td>Sudan</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Al Rayyan</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>True</td>\n",
       "      <td>Lebanon</td>\n",
       "      <td>Sudan</td>\n",
       "      <td>1-0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43076</th>\n",
       "      <td>2021-12-10</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>Oman</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Al Rayyan</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>True</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>Oman</td>\n",
       "      <td>2-1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43077</th>\n",
       "      <td>2021-12-10</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Al Khor</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>False</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>5-0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43078</th>\n",
       "      <td>2021-12-11</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Al Wakrah</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>True</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>3-1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20954 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date home_team             away_team  home_score  away_score  \\\n",
       "1      1873-03-08   England              Scotland           4           2   \n",
       "2      1874-03-07  Scotland               England           2           1   \n",
       "4      1876-03-04  Scotland               England           3           0   \n",
       "5      1876-03-25  Scotland                 Wales           4           0   \n",
       "8      1878-03-02  Scotland               England           7           2   \n",
       "...           ...       ...                   ...         ...         ...   \n",
       "43071  2021-12-07   Morocco          Saudi Arabia           1           0   \n",
       "43073  2021-12-07   Lebanon                 Sudan           1           0   \n",
       "43076  2021-12-10   Tunisia                  Oman           2           1   \n",
       "43077  2021-12-10     Qatar  United Arab Emirates           5           0   \n",
       "43078  2021-12-11     Egypt                Jordan           3           1   \n",
       "\n",
       "      tournament       city   country neutral    winner                 loser  \\\n",
       "1       Friendly     London   England   False   England              Scotland   \n",
       "2       Friendly    Glasgow  Scotland   False  Scotland               England   \n",
       "4       Friendly    Glasgow  Scotland   False  Scotland               England   \n",
       "5       Friendly    Glasgow  Scotland   False  Scotland                 Wales   \n",
       "8       Friendly    Glasgow  Scotland   False  Scotland               England   \n",
       "...          ...        ...       ...     ...       ...                   ...   \n",
       "43071   Arab Cup       Doha     Qatar    True   Morocco          Saudi Arabia   \n",
       "43073   Arab Cup  Al Rayyan     Qatar    True   Lebanon                 Sudan   \n",
       "43076   Arab Cup  Al Rayyan     Qatar    True   Tunisia                  Oman   \n",
       "43077   Arab Cup    Al Khor     Qatar   False     Qatar  United Arab Emirates   \n",
       "43078   Arab Cup  Al Wakrah     Qatar    True     Egypt                Jordan   \n",
       "\n",
       "      scoreline  total_goals  \n",
       "1           4-2            6  \n",
       "2           2-1            3  \n",
       "4           3-0            3  \n",
       "5           4-0            4  \n",
       "8           7-2            9  \n",
       "...         ...          ...  \n",
       "43071       1-0            1  \n",
       "43073       1-0            1  \n",
       "43076       2-1            3  \n",
       "43077       5-0            5  \n",
       "43078       3-1            4  \n",
       "\n",
       "[20954 rows x 13 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2['home_score']>df2['away_score']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "324e1667",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20954"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df2[df2['home_score']>df2['away_score']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "258cb4f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12199"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df2[df2['home_score']<df2['away_score']])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d484c672",
   "metadata": {},
   "source": [
    "We can also create a new column that will help aggregate all the home wins, away wins as well as draws. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "67402e92",
   "metadata": {},
   "outputs": [],
   "source": [
    "def outcome( home_team, away_team, winner):\n",
    "    if (home_team == winner):\n",
    "        result = \"home win\"\n",
    "    elif (away_team == winner):\n",
    "        result = \"away win\"\n",
    "    else:\n",
    "        result = 'Draw'\n",
    "    return result\n",
    "\n",
    "df2['result']=df2.apply(lambda x: outcome(x['home_team'], x['away_team'], x['winner']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "510bd40c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "      <th>winner</th>\n",
       "      <th>loser</th>\n",
       "      <th>scoreline</th>\n",
       "      <th>total_goals</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1872-11-30</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "      <td>Draw</td>\n",
       "      <td>Draw</td>\n",
       "      <td>0-0</td>\n",
       "      <td>0</td>\n",
       "      <td>Draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1873-03-08</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>4-2</td>\n",
       "      <td>6</td>\n",
       "      <td>home win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1874-03-07</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>2-1</td>\n",
       "      <td>3</td>\n",
       "      <td>home win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1875-03-06</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "      <td>Draw</td>\n",
       "      <td>Draw</td>\n",
       "      <td>2-2</td>\n",
       "      <td>4</td>\n",
       "      <td>Draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1876-03-04</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>3-0</td>\n",
       "      <td>3</td>\n",
       "      <td>home win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43076</th>\n",
       "      <td>2021-12-10</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>Oman</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Al Rayyan</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>True</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>Oman</td>\n",
       "      <td>2-1</td>\n",
       "      <td>3</td>\n",
       "      <td>home win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43077</th>\n",
       "      <td>2021-12-10</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Al Khor</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>False</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>5-0</td>\n",
       "      <td>5</td>\n",
       "      <td>home win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43078</th>\n",
       "      <td>2021-12-11</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Al Wakrah</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>True</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>Jordan</td>\n",
       "      <td>3-1</td>\n",
       "      <td>4</td>\n",
       "      <td>home win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43079</th>\n",
       "      <td>2021-12-11</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Arab Cup</td>\n",
       "      <td>Doha</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>True</td>\n",
       "      <td>Draw</td>\n",
       "      <td>Draw</td>\n",
       "      <td>2-2</td>\n",
       "      <td>4</td>\n",
       "      <td>Draw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43080</th>\n",
       "      <td>2021-12-11</td>\n",
       "      <td>El Salvador</td>\n",
       "      <td>Chile</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>United States</td>\n",
       "      <td>True</td>\n",
       "      <td>Chile</td>\n",
       "      <td>El Salvador</td>\n",
       "      <td>0-1</td>\n",
       "      <td>1</td>\n",
       "      <td>away win</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43081 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date    home_team             away_team  home_score  away_score  \\\n",
       "0      1872-11-30     Scotland               England           0           0   \n",
       "1      1873-03-08      England              Scotland           4           2   \n",
       "2      1874-03-07     Scotland               England           2           1   \n",
       "3      1875-03-06      England              Scotland           2           2   \n",
       "4      1876-03-04     Scotland               England           3           0   \n",
       "...           ...          ...                   ...         ...         ...   \n",
       "43076  2021-12-10      Tunisia                  Oman           2           1   \n",
       "43077  2021-12-10        Qatar  United Arab Emirates           5           0   \n",
       "43078  2021-12-11        Egypt                Jordan           3           1   \n",
       "43079  2021-12-11      Morocco               Algeria           2           2   \n",
       "43080  2021-12-11  El Salvador                 Chile           0           1   \n",
       "\n",
       "      tournament         city        country neutral    winner  \\\n",
       "0       Friendly      Glasgow       Scotland   False      Draw   \n",
       "1       Friendly       London        England   False   England   \n",
       "2       Friendly      Glasgow       Scotland   False  Scotland   \n",
       "3       Friendly       London        England   False      Draw   \n",
       "4       Friendly      Glasgow       Scotland   False  Scotland   \n",
       "...          ...          ...            ...     ...       ...   \n",
       "43076   Arab Cup    Al Rayyan          Qatar    True   Tunisia   \n",
       "43077   Arab Cup      Al Khor          Qatar   False     Qatar   \n",
       "43078   Arab Cup    Al Wakrah          Qatar    True     Egypt   \n",
       "43079   Arab Cup         Doha          Qatar    True      Draw   \n",
       "43080   Friendly  Los Angeles  United States    True     Chile   \n",
       "\n",
       "                      loser scoreline  total_goals    result  \n",
       "0                      Draw       0-0            0      Draw  \n",
       "1                  Scotland       4-2            6  home win  \n",
       "2                   England       2-1            3  home win  \n",
       "3                      Draw       2-2            4      Draw  \n",
       "4                   England       3-0            3  home win  \n",
       "...                     ...       ...          ...       ...  \n",
       "43076                  Oman       2-1            3  home win  \n",
       "43077  United Arab Emirates       5-0            5  home win  \n",
       "43078                Jordan       3-1            4  home win  \n",
       "43079                  Draw       2-2            4      Draw  \n",
       "43080           El Salvador       0-1            1  away win  \n",
       "\n",
       "[43081 rows x 14 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "119cc06d",
   "metadata": {},
   "source": [
    "To view just the home and away wins, we can simply filter out the draws."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ce7205b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '20954'), Text(0, 0, '12199')]"
      ]
     },
     "execution_count": 68,
     "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": [
    "df_home_away = df2[df2['result']!=\"Draw\"]['result'].value_counts().reset_index()\n",
    "df_home_away = df_home_away.rename(columns={'index':'result','result':'matches'})\n",
    "ax2 = sns.barplot(x='result',y='matches',data=df_home_away)\n",
    "ax2.bar_label(ax2.containers[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "fe7e559f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '20954'), Text(0, 0, '12199'), Text(0, 0, '9928')]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Oc2uVya3rLOCZ2NGuQmlmVmYNedHGY4FzgKmSJqfY1cANwMOSBgLvAt8AiIjpkh4GZpCd8XVRRKxJ5S4E7gF2B/6UHpAlqvskzSJrifRrwP0xM7MCGiyRRMRfKDyGAdCrjjJDgCEF4jVApwLxT0iJyMzMysO/bDczs5I4kZiZWUmcSMzMrCROJGZmVhInEjMzK4kTiZmZlcSJxMzMSuJEYmZmJXEiMTOzkjiRmJlZSZxIzMysJE4kZmZWEicSMzMriROJmZmVxInEzMxK4kRiZmYlachb7d4taaGkaXmxhyRNTo/ZuTsnSmoj6e95836bV6abpKmSZkkalm63S7ol70Mp/qKkNg21L2ZmVreGbJHcA/TJD0TEtyKiKiKqgFHAY3mz38zNi4jBefHhwCCye7i3zVvnQGBpRBwO3Azc2CB7YWZm9WqwRBIRE8nuo76B1Kr4JvBAfeuQ1AJoGhHPR0QA9wJnpNmnAyPT9KNAr1xrxczMGk+5xkiOBxZExBt5sUMlvSJpgqTjU6wSmJu3zNwUy82bAxARq4EPgP0LbUzSIEk1kmoWLVq0JffDzGyHV65E0p/1WyPzgYMj4ijgh8DvJTUFCrUwIv2tb976wYgREVEdEdXNmzcvodpmZlZbRWNvUFIF8HWgWy4WESuBlWl6kqQ3gXZkLZBWecVbAfPS9FygNTA3rbMZdXSlmZlZwylHi+Qk4LWIWNdlJam5pJ3T9OfIBtXfioj5wApJPdL4x7nA6FRsDDAgTZ8FPJPGUczMrBE15Om/DwDPA0dImitpYJrVjw0H2XsCUyS9SjZwPjgicq2LC4F/B2YBbwJ/SvG7gP0lzSLrDruyofbFzMzq1mBdWxHRv474dwrERpGdDlxo+RqgU4H4J8A3SqulmZmVyr9sNzOzkjiRmJlZSZxIzMysJE4kZmZWEicSMzMriROJmZmVxInEzMxK4kRiZmYlcSIxM7OSOJGYmVlJnEjMzKwkTiRmZlYSJxIzMyuJE4mZmZXEicTMzErSkDe2ulvSQknT8mLXSXpP0uT0OCVv3lWSZkl6XdLJefFukqamecPSnRKRtJukh1L8RUltGmpfzMysbg3ZIrkH6FMgfnNEVKXHHwEkdSC7c2LHVOaO3K13geHAILLb77bNW+dAYGlEHA7cDNzYUDtiZmZ1a7BEEhETgfc3umDmdODBiFgZEW+T3Va3u6QWQNOIeD7dj/1e4Iy8MiPT9KNAr1xrxczMGk85xkguljQldX3tm2KVwJy8ZeamWGWarh1fr0xErAY+APZvyIqbmdmGGjuRDAcOA6qA+cCvUrxQSyLqiddXZgOSBkmqkVSzaNGiTaqwmZnVr1ETSUQsiIg1EbEWuBPonmbNBVrnLdoKmJfirQrE1ysjqQJoRh1daRExIiKqI6K6efPmW2p3zMyMRk4kacwjpy+QO6NrDNAvnYl1KNmg+ksRMR9YIalHGv84FxidV2ZAmj4LeCaNo5iZWSOqaKgVS3oAOAE4QNJc4FrgBElVZF1Qs4HvAUTEdEkPAzOA1cBFEbEmrepCsjPAdgf+lB4AdwH3SZpF1hLp11D7YmZmdWuwRBIR/QuE76pn+SHAkALxGqBTgfgnwDdKqaOZmZXOv2w3M7OSOJGYmVlJnEjMzKwkTiRmZlYSJxIzMyuJE4mZmZXEicTMzEriRGJmZiVxIjEzs5I4kZiZWUmcSMzMrCROJGZmVhInEjMzK4kTidkWdv7553PggQfSqdNnF62+7LLLOPLII+nSpQt9+/Zl2bJlACxZsoQTTzyRvfbai4svvni99Tz00EN06dKFjh07cvnll6+Lv/POO/Tq1YsuXbpwwgknMHfuXMzKyYnEbAv7zne+w1NPPbVerHfv3kybNo0pU6bQrl07hg4dCkCTJk24/vrruemmm9ZbfsmSJVx22WWMGzeO6dOns2DBAsaNGwfAj370I84991ymTJnCNddcw1VXXdU4O2ZWh6ISiaQ9Je2UpttJOk3SLg1bNbNtU8+ePdlvv/3Wi335y1+moiK7/U+PHj3WtSL23HNPjjvuOJo0abLe8m+99Rbt2rUjd2vok046iVGjRgEwY8YMevXqBcCJJ57I6NGjMSunYlskE4EmkiqBccB5ZHctrJOkuyUtlDQtL/ZLSa9JmiLpcUn7pHgbSX+XNDk9fptXppukqZJmSRqWbrlLui3vQyn+oqQ2m7LjZuVy991385WvfKXeZQ4//HBee+01Zs+ezerVq3niiSeYM2cOAF27dl2XVB5//HFWrFjBkiVLGrzeZnUpNpEoIj4Gvg78JiL6Ah02UuYeoE+t2FigU0R0Af4XyG+TvxkRVekxOC8+HBhEdh/3tnnrHAgsjYjDgZuBG4vcF7OyGTJkCBUVFZx99tn1LrfvvvsyfPhwvvWtb3H88cfTpk2bdS2am266iQkTJnDUUUcxYcIEKisr180zK4di332SdAxwNtkH+EbLRsTE2q2EiHg67+kLwFkb2WgLoGlEPJ+e3wucQXbf9tOB69KijwK3SVJERBH7Y9boRo4cyZNPPsm4ceNIDet6nXrqqZx66qkAjBgxgp133hmAli1b8thjjwHw4YcfMmrUKJo1a9ZwFTfbiGJbJD8gaz08HhHTJX0O+HOJ2z6fLCHkHCrpFUkTJB2fYpVA/ikpc1MsN28OQESsBj4A9i+0IUmDJNVIqlm0aFGJ1TbbdE899RQ33ngjY8aMYY899iiqzMKFCwFYunQpd9xxBxdccAEAixcvZu3atQAMHTqU888/v2EqbVakolokETEBmCBpz/T8LeD7m7tRSf8KrAbuT6H5wMERsURSN+AJSR2BQl/bci2O+ubVrv8IYARAdXW1WyzWoPr378/48eNZvHgxrVq14qc//SlDhw5l5cqV9O7dG8gG3H/722wosE2bNixfvpxPP/2UJ554gqeffpoOHTpwySWX8OqrrwJwzTXX0K5dOwDGjx/PVVddhSR69uzJ7bffXp4dNUuKSiSpW+suYC/gYEldge9FxD9v6gYlDQC+BvTKdUNFxEpgZZqeJOlNoB1ZC6RVXvFWwLw0PRdoDcyVVAE0A97f1PqYbWkPPPDABrGBAwcWWDIze/bsotcDcNZZZ3HWWfX2Cps1qmLHSG4BTgbGAETEq5J6burGJPUBrgC+mAbvc/HmwPsRsSZ1m7UF3oqI9yWtkNQDeBE4F/hNKjYGGAA8TzbW8ozHRyzfuz/rXO4qbPcOvmZquatgW4GiT/WIiDm1BgjX1Le8pAeAE4ADJM0FriUbZ9kNGJvW9UI6Q6sn8DNJq9N6B0dErnVxIdkZYLuTjankxlXuAu6TNIusJdKv2H0xM7Mtp9hEMkfSF4CQtCvZ+MjM+gpERP8C4bvqWHYUMKqOeTVApwLxT4BvbKTeZmbWwIo9a2swcBGfnUVVlZ6bmdkOrtizthaT/YbEzMxsPcWetdUc+C7QJr9MRPgEdjOzHVyxYySjgWeB/2Ejg+xmZrZjKTaR7BERVzRoTczMbJtU7GD7k5JOadCamJnZNqneFomkFWSXHRFwtaSVwKr0PCKiacNX0czMtmYbu4Lv3o1VETMz2zYVe4fEvpKa5T3fR9IZDVYrMzPbZhQ7RnJtRHyQexIRy8gueWJmZju4YhNJoeV8SzYzMys6kdRI+rWkwyR9TtLNwKSGrJiZWbndeuutdOrUiY4dO3LLLbcA8Oqrr3LMMcfQuXNnTj31VJYvXw7A2LFj6datG507d6Zbt24888wz69bzwAMP0LlzZ7p06UKfPn1YvHhxOXanwRSbSP4f8CnwEPAw8Hdgk+9FYma2rZg2bRp33nknL730Eq+++ipPPvkkb7zxBhdccAE33HADU6dOpW/fvvzyl78E4IADDuAPf/gDU6dOZeTIkZxzzjkArF69mksuuYQ///nPTJkyhS5dunDbbbeVc9e2uGITySkRcWVEVKfH1cBXG7JiZmblNHPmTHr06MEee+xBRUUFX/ziF3n88cd5/fXX6dkzux1T7969GTUqu3D5UUcdRcuWLQHo2LEjn3zyCStXriQiiAg++ugjIoLly5evW257UWwiuarImJnZdqFTp05MnDiRJUuW8PHHH/PHP/6ROXPm0KlTJ8aMGQPAI488wpw5czYoO2rUKI466ih22203dtllF4YPH07nzp1p2bIlM2bMqPeOmduiehOJpK9I+g1QKWlY3uMesnuum5ltl9q3b88VV1xB79696dOnD127dqWiooK7776b22+/nW7durFixQp23XXX9cpNnz6dK664gt/97ncArFq1iuHDh/PKK68wb948unTpwtChQ8uxSw1mYy2SeUAN8AnZ4HruMYbs1rt1knS3pIWSpuXF9pM0VtIb6e++efOukjRL0uuSTs6Ld5M0Nc0bpnRrRUm7SXooxV+U1GYT993MrF4DBw7k5ZdfZuLEiey33360bduWI488kqeffppJkybRv39/DjvssHXLz507l759+3Lvvfeui0+ePBmAww47DEl885vf5K9//Ws5dqfB1JtIIuLViBgJHB4RI/Mej0XE0o2s+x6gT63YlcC4iGgLjEvPkdSB7Fa5HVOZOyTtnMoMBwaR3ce9bd46BwJLI+Jw4Gbgxo3urZnZJli4cCEA7777Lo899hj9+/dfF1u7di0///nPGTx4MADLli3jq1/9KkOHDuXYY49dt47KykpmzJjBokWLgOzsrvbt2zfynjSsYsdI2kh6VNIMSW/lHvUViIiJZPdSz3c6MDJNjwTOyIs/GBErI+JtYBbQXVILoGlEPB8RAdxbq0xuXY8CvXKtFTOzLeHMM8+kQ4cOnHrqqdx+++3su+++PPDAA7Rr144jjzySli1bct555wFw2223MWvWLK6//nqqqqqoqqpi4cKFtGzZkmuvvZaePXvSpUsXJk+ezNVXX13mPduyiv1R4X+Q/ZL9ZuBE4DyyCzduqoMiYj5ARMyXdGCKVwIv5C03N8VWpena8VyZOWldqyV9AOwPbHCCtqRBZK0aDj744M2otpntiJ599tkNYpdccgmXXHLJBvEf//jH/PjHPy64nsGDB69ruWyPik0ku0fEOEmKiHeA6yQ9y5a7TEqhpBT1xOsrs2EwYgQwAqC6urrgMma29Tj2N8dufCEr2XP/77ktsp5iE8knknYC3pB0MfAecOBGyhSyQFKL1BppASxM8blA67zlWpEN9M9N07Xj+WXmSqoAmrFhV5qZmTWwYsdIfgDsAXwf6AZ8Gzh3M7Y3BhiQpgeQ3cI3F++XzsQ6lGxQ/aXUDbZCUo80/nFurTK5dZ0FPJPGUczMrBEV2yIJ4D7gEGCXFLsT6FJXAUkPACcAB0iaS9YNdgPwsKSBwLvANwAiYrqkh4EZZL9PuSgicveGv5DsDLDdgT+lB8BdwH2SZpG1RPoVuS9mZrYFFZtI7gcuA6YCa4spEBH965jVq47lhwBDCsRrgE4F4p+QEpGZmZVPsYlkUUSMadCamJnZNqnYRHKtpH8n+xHhylwwIh5rkFqZmdk2o9hEch5wJNn4SK5rKwAnEjOzHVyxiaRrRHRu0JqYmdk2qdjTf19I18MyMzNbT7EtkuOAAZLeJhsjERARUefpv2ZmtmMoNpHUvoqvmZkZUGQiSdfXMjMz20CxYyRmZmYFOZGYmVlJnEjMzKwkTiRmZlYSJxIzMyuJE4mZmZXEicTMzEriRGJmZiVp9EQi6QhJk/MeyyX9QNJ1kt7Li5+SV+YqSbMkvS7p5Lx4N0lT07xh6Xa8ZmbWiBo9kUTE6xFRFRFVZPd//xh4PM2+OTcvIv4IkC4W2Q/oSHapljsk7ZyWHw4MIrvHe1t8KRczs0ZX7q6tXsCbG7kEy+nAgxGxMiLeBmYB3SW1AJpGxPMREcC9wBkNXmMzM1tPuRNJP+CBvOcXS5oi6W5J+6ZYJTAnb5m5KVaZpmvHNyBpkKQaSTWLFi3acrU3M7PyJRJJuwKnAY+k0HDgMKAKmA/8KrdogeJRT3zDYMSIiKiOiOrmzZuXUm0zM6ulnC2SrwAvR8QCgIhYEBFrImItcCfQPS03F2idV64VMC/FWxWIm5lZIypnIulPXrdWGvPI6QtMS9NjgH6SdpN0KNmg+ksRMR9YIalHOlvrXGB041TdzMxyir2x1RYlaQ+gN/C9vPC/Saoi656anZsXEdMlPQzMAFYDF0XEmlTmQuAeYHfgT+lhZmaNqCyJJCI+BvavFTunnuWHAEMKxGuATlu8gmZmVrRyn7VlZmbbOCcSMzMriROJmZmVxInEzMxK4kRiZmYlcSIxM7OSOJGYmVlJnEjMzKwkTiRmZlYSJxIzMyuJE4mZmZXEicTMzEriRGJmZiVxIjEzs5I4kZiZWUnKkkgkzZY0VdJkSTUptp+ksZLeSH/3zVv+KkmzJL0u6eS8eLe0nlmShqU7JZqZWSMqZ4vkxIioiojq9PxKYFxEtAXGpedI6gD0AzoCfYA7JO2cygwHBpHdfrdtmm9mZo1oa+raOh0YmaZHAmfkxR+MiJUR8TYwC+ie7vHeNCKej4gA7s0rY2ZmjaRciSSApyVNkjQoxQ6KiPkA6e+BKV4JzMkrOzfFKtN07fgGJA2SVCOpZtGiRVtwN8zMrCz3bAeOjYh5kg4Exkp6rZ5lC417RD3xDYMRI4ARANXV1QWXMTOzzVOWFklEzEt/FwKPA92BBam7ivR3YVp8LtA6r3grYF6KtyoQNzOzRtToiUTSnpL2zk0DXwamAWOAAWmxAcDoND0G6CdpN0mHkg2qv5S6v1ZI6pHO1jo3r4yZmTWScnRtHQQ8ns7UrQB+HxFPSfob8LCkgcC7wDcAImK6pIeBGcBq4KKIWJPWdSFwD7A78Kf0MDOzRtToiSQi3gK6FogvAXrVUWYIMKRAvAbotKXraGZmxduaTv81M7NtkBOJmZmVxInEzMxK4kRiZmYlcSIxM7OSOJGYmVlJnEjMzKwkTiRmZlYSJxIzMyuJE4mZmZXEicTMzEriRGJmZiVxIjEzs5I4kZiZWUmcSMzMrCROJGZmVpJy3Gq3taQ/S5opabqkS1L8OknvSZqcHqfklblK0ixJr0s6OS/eTdLUNG9YuuWumZk1onLcanc1cGlEvJzu3T5J0tg07+aIuCl/YUkdgH5AR6Al8D+S2qXb7Q4HBgEvAH8E+uDb7ZqZNapGb5FExPyIeDlNrwBmApX1FDkdeDAiVkbE28AsoLukFkDTiHg+IgK4FzijYWtvZma1lXWMRFIb4CjgxRS6WNIUSXdL2jfFKoE5ecXmplhlmq4dL7SdQZJqJNUsWrRoS+6CmdkOr2yJRNJewCjgBxGxnKyb6jCgCpgP/Cq3aIHiUU98w2DEiIiojojq5s2bl1p1MzPLU5ZEImkXsiRyf0Q8BhARCyJiTUSsBe4EuqfF5wKt84q3AualeKsCcTMza0TlOGtLwF3AzIj4dV68Rd5ifYFpaXoM0E/SbpIOBdoCL0XEfGCFpB5pnecCoxtlJ8zMbJ1ynLV1LHAOMFXS5BS7GugvqYqse2o28D2AiJgu6WFgBtkZXxelM7YALgTuAXYnO1vLZ2yZmTWyRk8kEfEXCo9v/LGeMkOAIQXiNUCnLVc7MzPbVP5lu5mZlcSJxMzMSuJEYmZmJXEiMTOzkjiRmJlZSZxIzMysJE4kZmZWEicSMzMriROJmZmVxInEzMxK4kRiZmYlcSIxM7OSOJGYmVlJnEjMzKwkTiRmZlYSJxIzMyvJNp9IJPWR9LqkWZKuLHd9zMx2NNt0IpG0M3A78BWgA9ntejuUt1ZmZjuWbTqRAN2BWRHxVkR8CjwInF7mOpmZ7VAUEeWuw2aTdBbQJyIuSM/PAY6OiItrLTcIGJSeHgG83qgVbVwHAIvLXQnbLH7ttm3b++t3SEQ0LzSjorFrsoWpQGyDzBgRI4ARDV+d8pNUExHV5a6HbTq/dtu2Hfn129a7tuYCrfOetwLmlakuZmY7pG09kfwNaCvpUEm7Av2AMWWuk5nZDmWb7tqKiNWSLgb+G9gZuDsippe5WuW2Q3Thbaf82m3bdtjXb5sebDczs/Lb1ru2zMyszJxIzMysJE4kjUxSG0nTyl2Pukj6maSTyl0P2zSSBks6t9z12J5IWiNpsqTpkl6V9ENJ/swswGMkjUxSG+DJiOhU7rqYWd0kfRgRe6XpA4HfA89FxLW1lquIiNXlqOPWwtm1PHaWdGf6pvO0pN0BJFVJekHSFEmPS9o3xcdLulnSREkzJf2jpMckvSHp57mVSvq2pJfSt6jfpWuRkTe/u6TH0vTpkv4uaVdJTSS9leL3pCsGIGm2pJ9KelnSVElHNtYB2ppJekLSpPT6DUqxb0r6dZq+JO94HibpL2n6Gkl/kzRN0ghlDpP0ct6620qaVGt7B+ZikrpKCkkHp+dvStpD0nWSfpRi4yXdmN4L/yvp+MY4LtuziFhIdnWMi9Pr9h1Jj0j6A/C0pL0kjcv7XzkdQNLlkr6fpm+W9Eya7iXpP8u2Q1uYE0l5tAVuj4iOwDLgzBS/F7giIroAU4H8bz6fRkRP4LfAaOAioBPwHUn7S2oPfAs4NiKqgDXA2bW2+zJwVJo+HpgG/CNwNPBiHXVdHBGfB4YDP9qsvd3+nB8R3YBq4PuS9gcmkh1T0t8lkiqB44BnU/y2iPjH1BrdHfhaRLwJfCCpKi1zHnBP/sbSh1gTSU3TumuA4yUdAiyMiI8L1LEiIroDP2D995Ftpoh4i+wz88AUOgYYEBFfAj4B+qb/lROBX0kS678vqoG9JO3C+u+Lbd42/TuSbdjbETE5TU8C2khqBuwTERNSfCTwSF6Z3A8tpwLTI2I+QPrm25rsjdkN+Fv2/mV3YGH+RtPvbmalpNMd+DXQk+w3OHW9qR/Lq+fXN31Xt0vfl9Q3TbcG2kbEC+lb6d4p9nuyY3s8nx3DEyVdDuwB7AdMB/4A/DtwnqQfkn0Z6F5gm38Fjk3r/AXQh+wSQcW8bm02cz9tQ/mXZRobEe/nxX8hqSewFqgEDiI7/t3S+2Il2Ze5arL3xfcbrdYNzImkPFbmTa8h+9AvtszaWuXXkr2OAkZGxFUbWc+zZJfdXwX8D9m3352pu7WR29Ya/H5B0gnAScAxEfGxpPFAkzT7ebIWxetkx/l8sm+tl0pqAtwBVEfEHEnX5ZUbRdZqeAaYFBFLCmz6WbIPn0PIWqRXkF1X7sk6qurXbQuT9Dmy45n7gvZR3uyzgeZAt4hYJWk20CRv+jyyLwNTyFoshwEzG6nqDc5dW1uJiPgAWJrXn30OMKGeIrWNA85Kg4JI2i91fdQ2kay74/mIWATsDxxJ9u3YNq4ZsDQlkSOBHnnzJpIl5InAK2QfGCvTa5tLGosl7QWclSsUEZ+QXZ1hOPAfdWx3IvBt4I2IWAu8D5wCPLeldszqJqk5WbfybVH4DKVmZN2MqySdSJbwc/LfF88Cg4HJdaxnm+RvKluXAcBvJe0BvEX2LaYoETFD0o/JBv52ImtxXAS8U2vRF8ma3BPT8ylk/wDbzZu6gT0FDJY0hazl8ULevGfJurUmRsQaSXOA1wAiYpmkO8m6JmeTXScu3/1kXYdPF9poRMxOXZa51+0vQKuIWLoldsoK2l3SZGAXYDVwH1l3cCH3A3+QVANMJr3uybPAv5J9eftI0idsR+Mj4NN/zbYK6YyrZhHxk3LXxWxTuUViVmaSHifrM/9SuetitjncIjEzs5J4sN3MzEriRGJmZiVxIjEzs5I4kZhtI9L1nW5L02dI6lDuOpmBE4lZg0sX+dvS/2tnAE4ktlVwIjFrAMruOzNT0h1k11f6Sbry7xRJP03L7Cnpv5Td62KapG+l+GxJB6Tp6nQZlvx1fwE4Dfilsis9H9aoO2dWi39HYtZwjiC7OsETZJdE6U52TbQx6eJ+zYF5EfFVgHThzo2KiL9KGkN2X5tHG6LiZpvCLRKzhvNORLwAfDk9XiFrnRxJdiuBqcBJ6d4hx6drcpltc9wiMWs4uavDChgaEb+rvYCkbmQXXxwq6emI+BnZdZ1yX/Ka1C5jtrVxi8Ss4f03cH666i+SKtNdD1sCH0fEfwI3AZ9Py88mu7cMfHbTs9pWAHs3XJXNiudEYtbAIuJpshtdPS9pKvAoWRLoDLyUrjD7r0Dutsk/BW6V9CzZ/S8KeRC4TNIrHmy3cvO1tszMrCRukZiZWUmcSMzMrCROJGZmVhInEjMzK4kTiZmZlcSJxMzMSuJEYmZmJfn/pYtZNzG6ZvcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_home_away = df2['result'].value_counts().reset_index()\n",
    "df_home_away = df_home_away.rename(columns={'index':'result','result':'matches'})\n",
    "plt.title(\"Bar Chart of Wins, Draws and Losses\")\n",
    "ax2 = sns.barplot(x='result',y='matches',data=df_home_away)\n",
    "ax2.bar_label(ax2.containers[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "43478930",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Pie Chart Showing The Percentages of Wins, Losses and Draws\")\n",
    "plt.pie(df_home_away['matches'], labels = df_home_away['result'], autopct='%.2f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "6d5bf443",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '15181'), Text(0, 0, '8518'), Text(0, 0, '7196')]"
      ]
     },
     "execution_count": 71,
     "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": [
    "df_home_away_friendly = df2[(df2['tournament']==\"Friendly\") | (df2['tournament']==\"FIFA World Cup qualification\")\n",
    "                            | (df2['tournament']==\"FIFA World Cup qualification\")\n",
    "                            | (df2['tournament']==\"UEFA Euro qualification\")\n",
    "                            | (df2['tournament']==\"African Cup of Nations qualification\")\n",
    "                            | (df2['tournament']==\"AFC Asian Cup qualification\")\n",
    "                            | (df2['tournament']==\"CFU Caribbean Cup qualification\")\n",
    "                            | (df2['tournament']==\"Oceania Nations Cup qualification\")\n",
    "                            | (df2['tournament']==\"CONCACAF Nations League qualification\")]['result'].value_counts().reset_index()\n",
    "df_home_away_friendly = df_home_away_friendly.rename(columns={'index':'result','result':'matches'})\n",
    "df_home_away_friendly\n",
    "ax2 = sns.barplot(x='result',y='matches',data=df_home_away_friendly)\n",
    "ax2.bar_label(ax2.containers[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "3bc7ca2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Pie Chart Showing The Percentages of Wins, Losses and Draws in Friendly and Qualifying Matches\")\n",
    "plt.pie(df_home_away_friendly['matches'], labels = df_home_away_friendly['result'], autopct='%.2f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a4adfd16",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Friendly                                17243\n",
       "FIFA World Cup qualification             7879\n",
       "UEFA Euro qualification                  2582\n",
       "African Cup of Nations qualification     1719\n",
       "FIFA World Cup                            900\n",
       "Copa América                              841\n",
       "AFC Asian Cup qualification               728\n",
       "African Cup of Nations                    690\n",
       "CECAFA Cup                                620\n",
       "CFU Caribbean Cup qualification           606\n",
       "Merdeka Tournament                        595\n",
       "British Championship                      505\n",
       "Gulf Cup                                  380\n",
       "AFC Asian Cup                             370\n",
       "Gold Cup                                  358\n",
       "Island Games                              350\n",
       "UEFA Euro                                 337\n",
       "COSAFA Cup                                309\n",
       "UEFA Nations League                       308\n",
       "AFF Championship                          293\n",
       "Nordic Championship                       283\n",
       "African Nations Championship              264\n",
       "CFU Caribbean Cup                         251\n",
       "Amílcar Cabral Cup                        235\n",
       "King's Cup                                234\n",
       "South Pacific Games                       205\n",
       "UNCAF Cup                                 164\n",
       "Korea Cup                                 159\n",
       "SAFF Cup                                  147\n",
       "Confederations Cup                        140\n",
       "Name: tournament, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['tournament'].value_counts().head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "eb3c9a75",
   "metadata": {},
   "outputs": [],
   "source": [
    "def year_extract(the_date):\n",
    "    year_date = the_date[:4]\n",
    "    return year_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "6092ff5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3=df2.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85f93b23",
   "metadata": {},
   "source": [
    "Creating a new column which shows only the year in which a match was played."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "6e9c177f",
   "metadata": {
    "scrolled": true
   },
   "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>year_date</th>\n",
       "      <th>total_goals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1885</td>\n",
       "      <td>5.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1890</td>\n",
       "      <td>5.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1891</td>\n",
       "      <td>5.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1873</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1899</td>\n",
       "      <td>6.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1880</td>\n",
       "      <td>6.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1893</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1888</td>\n",
       "      <td>7.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1882</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1878</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year_date  total_goals\n",
       "13      1885     5.428571\n",
       "18      1890     5.500000\n",
       "19      1891     5.666667\n",
       "1       1873     6.000000\n",
       "27      1899     6.500000\n",
       "8       1880     6.666667\n",
       "21      1893     7.000000\n",
       "16      1888     7.142857\n",
       "10      1882     8.000000\n",
       "6       1878     9.000000"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['year_date'] = df3.apply(lambda x: year_extract(x['date']), axis=1)\n",
    "df3.groupby('year_date')['total_goals'].mean().reset_index().sort_values('total_goals').tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "4f297f98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "      <th>winner</th>\n",
       "      <th>loser</th>\n",
       "      <th>scoreline</th>\n",
       "      <th>total_goals</th>\n",
       "      <th>result</th>\n",
       "      <th>year_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1878-03-02</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>7-2</td>\n",
       "      <td>9</td>\n",
       "      <td>home win</td>\n",
       "      <td>1878</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</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>Wales</td>\n",
       "      <td>9-0</td>\n",
       "      <td>9</td>\n",
       "      <td>home win</td>\n",
       "      <td>1878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date home_team away_team  home_score  away_score tournament     city  \\\n",
       "8  1878-03-02  Scotland   England           7           2   Friendly  Glasgow   \n",
       "9  1878-03-23  Scotland     Wales           9           0   Friendly  Glasgow   \n",
       "\n",
       "    country neutral    winner    loser scoreline  total_goals    result  \\\n",
       "8  Scotland   False  Scotland  England       7-2            9  home win   \n",
       "9  Scotland   False  Scotland    Wales       9-0            9  home win   \n",
       "\n",
       "  year_date  \n",
       "8      1878  \n",
       "9      1878  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3[df3['year_date']=='1878']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "5ab34392",
   "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>year_date</th>\n",
       "      <th>total_goals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1872</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>1982</td>\n",
       "      <td>2.605744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>1983</td>\n",
       "      <td>2.600897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>1984</td>\n",
       "      <td>2.553672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>1985</td>\n",
       "      <td>2.425569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>1986</td>\n",
       "      <td>2.379845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>1987</td>\n",
       "      <td>2.416201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>1988</td>\n",
       "      <td>2.215356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>1989</td>\n",
       "      <td>2.328814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>1990</td>\n",
       "      <td>2.360277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>1991</td>\n",
       "      <td>2.648536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>1992</td>\n",
       "      <td>2.561760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>1994</td>\n",
       "      <td>2.622807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2002</td>\n",
       "      <td>2.626316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>2009</td>\n",
       "      <td>2.671053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>2010</td>\n",
       "      <td>2.580796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2013</td>\n",
       "      <td>2.618802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>2014</td>\n",
       "      <td>2.615116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>2018</td>\n",
       "      <td>2.609031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>2020</td>\n",
       "      <td>2.487805</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year_date  total_goals\n",
       "0        1872     0.000000\n",
       "110      1982     2.605744\n",
       "111      1983     2.600897\n",
       "112      1984     2.553672\n",
       "113      1985     2.425569\n",
       "114      1986     2.379845\n",
       "115      1987     2.416201\n",
       "116      1988     2.215356\n",
       "117      1989     2.328814\n",
       "118      1990     2.360277\n",
       "119      1991     2.648536\n",
       "120      1992     2.561760\n",
       "122      1994     2.622807\n",
       "130      2002     2.626316\n",
       "137      2009     2.671053\n",
       "138      2010     2.580796\n",
       "141      2013     2.618802\n",
       "142      2014     2.615116\n",
       "146      2018     2.609031\n",
       "148      2020     2.487805"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['year_date'] = df3.apply(lambda x: year_extract(x['date']), axis=1)\n",
    "df3.groupby('year_date')['total_goals'].mean().reset_index().sort_values('total_goals').head(20).sort_values('year_date')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "202fb95f",
   "metadata": {},
   "source": [
    "It can be observed here that the years that experienced the most goals on average in football history are all in the 1800s. \n",
    "The years with the least goals on average are pretty recent with 19 out of the 20 with least average goals being in the last 40 years."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba59b1b8",
   "metadata": {},
   "source": [
    "### Thanks for viewing!"
   ]
  }
 ],
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