EDA of FIFA dataset(up to 2014).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cc411e1a",
   "metadata": {},
   "source": [
    "# EDA of FIFA World Cup data till 2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "87bc0350",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "28bd1545",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly as py\n",
    "import cufflinks as cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "48f09733",
   "metadata": {},
   "outputs": [],
   "source": [
    "from plotly.offline import iplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1352367d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.1.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.1.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "py.offline.init_notebook_mode(connected=True)\n",
    "cf.go_offline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a8325de4",
   "metadata": {},
   "outputs": [],
   "source": [
    "world_cups=pd.read_csv('E:/WorldCups.csv')\n",
    "players=pd.read_csv('E:/WorldCupPlayers.csv')\n",
    "matches=pd.read_csv('E:/WorldCupMatches.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "16607e90",
   "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>Year</th>\n",
       "      <th>Country</th>\n",
       "      <th>Winner</th>\n",
       "      <th>Runners-Up</th>\n",
       "      <th>Third</th>\n",
       "      <th>Fourth</th>\n",
       "      <th>GoalsScored</th>\n",
       "      <th>QualifiedTeams</th>\n",
       "      <th>MatchesPlayed</th>\n",
       "      <th>Attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1930</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>USA</td>\n",
       "      <td>Yugoslavia</td>\n",
       "      <td>70</td>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>590.549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1934</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Czechoslovakia</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Austria</td>\n",
       "      <td>70</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>363.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1938</td>\n",
       "      <td>France</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>84</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>375.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1950</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Spain</td>\n",
       "      <td>88</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>1.045.246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Germany FR</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>Austria</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>140</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>768.607</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year      Country      Winner      Runners-Up    Third      Fourth  \\\n",
       "0  1930      Uruguay     Uruguay       Argentina      USA  Yugoslavia   \n",
       "1  1934        Italy       Italy  Czechoslovakia  Germany     Austria   \n",
       "2  1938       France       Italy         Hungary   Brazil      Sweden   \n",
       "3  1950       Brazil     Uruguay          Brazil   Sweden       Spain   \n",
       "4  1954  Switzerland  Germany FR         Hungary  Austria     Uruguay   \n",
       "\n",
       "   GoalsScored  QualifiedTeams  MatchesPlayed Attendance  \n",
       "0           70              13             18    590.549  \n",
       "1           70              16             17    363.000  \n",
       "2           84              15             18    375.700  \n",
       "3           88              13             22  1.045.246  \n",
       "4          140              16             26    768.607  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_cups.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "50bde2a3",
   "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>RoundID</th>\n",
       "      <th>MatchID</th>\n",
       "      <th>Team Initials</th>\n",
       "      <th>Coach Name</th>\n",
       "      <th>Line-up</th>\n",
       "      <th>Shirt Number</th>\n",
       "      <th>Player Name</th>\n",
       "      <th>Position</th>\n",
       "      <th>Event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>201</td>\n",
       "      <td>1096</td>\n",
       "      <td>FRA</td>\n",
       "      <td>CAUDRON Raoul (FRA)</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>Alex THEPOT</td>\n",
       "      <td>GK</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201</td>\n",
       "      <td>1096</td>\n",
       "      <td>MEX</td>\n",
       "      <td>LUQUE Juan (MEX)</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>Oscar BONFIGLIO</td>\n",
       "      <td>GK</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201</td>\n",
       "      <td>1096</td>\n",
       "      <td>FRA</td>\n",
       "      <td>CAUDRON Raoul (FRA)</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>Marcel LANGILLER</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G40'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201</td>\n",
       "      <td>1096</td>\n",
       "      <td>MEX</td>\n",
       "      <td>LUQUE Juan (MEX)</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>Juan CARRENO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G70'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201</td>\n",
       "      <td>1096</td>\n",
       "      <td>FRA</td>\n",
       "      <td>CAUDRON Raoul (FRA)</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>Ernest LIBERATI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RoundID  MatchID Team Initials           Coach Name Line-up  Shirt Number  \\\n",
       "0      201     1096           FRA  CAUDRON Raoul (FRA)       S             0   \n",
       "1      201     1096           MEX     LUQUE Juan (MEX)       S             0   \n",
       "2      201     1096           FRA  CAUDRON Raoul (FRA)       S             0   \n",
       "3      201     1096           MEX     LUQUE Juan (MEX)       S             0   \n",
       "4      201     1096           FRA  CAUDRON Raoul (FRA)       S             0   \n",
       "\n",
       "        Player Name Position Event  \n",
       "0       Alex THEPOT       GK   NaN  \n",
       "1   Oscar BONFIGLIO       GK   NaN  \n",
       "2  Marcel LANGILLER      NaN  G40'  \n",
       "3      Juan CARRENO      NaN  G70'  \n",
       "4   Ernest LIBERATI      NaN   NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "46b38586",
   "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>Year</th>\n",
       "      <th>Datetime</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Stadium</th>\n",
       "      <th>City</th>\n",
       "      <th>Home Team Name</th>\n",
       "      <th>Home Team Goals</th>\n",
       "      <th>Away Team Goals</th>\n",
       "      <th>Away Team Name</th>\n",
       "      <th>Win conditions</th>\n",
       "      <th>Attendance</th>\n",
       "      <th>Half-time Home Goals</th>\n",
       "      <th>Half-time Away Goals</th>\n",
       "      <th>Referee</th>\n",
       "      <th>Assistant 1</th>\n",
       "      <th>Assistant 2</th>\n",
       "      <th>RoundID</th>\n",
       "      <th>MatchID</th>\n",
       "      <th>Home Team Initials</th>\n",
       "      <th>Away Team Initials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>13 Jul 1930 - 15:00</td>\n",
       "      <td>Group 1</td>\n",
       "      <td>Pocitos</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>France</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td></td>\n",
       "      <td>4444.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>LOMBARDI Domingo (URU)</td>\n",
       "      <td>CRISTOPHE Henry (BEL)</td>\n",
       "      <td>REGO Gilberto (BRA)</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1096.0</td>\n",
       "      <td>FRA</td>\n",
       "      <td>MEX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>13 Jul 1930 - 15:00</td>\n",
       "      <td>Group 4</td>\n",
       "      <td>Parque Central</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>USA</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Belgium</td>\n",
       "      <td></td>\n",
       "      <td>18346.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>MACIAS Jose (ARG)</td>\n",
       "      <td>MATEUCCI Francisco (URU)</td>\n",
       "      <td>WARNKEN Alberto (CHI)</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>BEL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>14 Jul 1930 - 12:45</td>\n",
       "      <td>Group 2</td>\n",
       "      <td>Parque Central</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Yugoslavia</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td></td>\n",
       "      <td>24059.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TEJADA Anibal (URU)</td>\n",
       "      <td>VALLARINO Ricardo (URU)</td>\n",
       "      <td>BALWAY Thomas (FRA)</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1093.0</td>\n",
       "      <td>YUG</td>\n",
       "      <td>BRA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>14 Jul 1930 - 14:50</td>\n",
       "      <td>Group 3</td>\n",
       "      <td>Pocitos</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Romania</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Peru</td>\n",
       "      <td></td>\n",
       "      <td>2549.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WARNKEN Alberto (CHI)</td>\n",
       "      <td>LANGENUS Jean (BEL)</td>\n",
       "      <td>MATEUCCI Francisco (URU)</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>ROU</td>\n",
       "      <td>PER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>15 Jul 1930 - 16:00</td>\n",
       "      <td>Group 1</td>\n",
       "      <td>Parque Central</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>France</td>\n",
       "      <td></td>\n",
       "      <td>23409.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>REGO Gilberto (BRA)</td>\n",
       "      <td>SAUCEDO Ulises (BOL)</td>\n",
       "      <td>RADULESCU Constantin (ROU)</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1085.0</td>\n",
       "      <td>ARG</td>\n",
       "      <td>FRA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year              Datetime    Stage         Stadium         City  \\\n",
       "0  1930.0  13 Jul 1930 - 15:00   Group 1         Pocitos  Montevideo    \n",
       "1  1930.0  13 Jul 1930 - 15:00   Group 4  Parque Central  Montevideo    \n",
       "2  1930.0  14 Jul 1930 - 12:45   Group 2  Parque Central  Montevideo    \n",
       "3  1930.0  14 Jul 1930 - 14:50   Group 3         Pocitos  Montevideo    \n",
       "4  1930.0  15 Jul 1930 - 16:00   Group 1  Parque Central  Montevideo    \n",
       "\n",
       "  Home Team Name  Home Team Goals  Away Team Goals Away Team Name  \\\n",
       "0         France              4.0              1.0         Mexico   \n",
       "1            USA              3.0              0.0        Belgium   \n",
       "2     Yugoslavia              2.0              1.0         Brazil   \n",
       "3        Romania              3.0              1.0           Peru   \n",
       "4      Argentina              1.0              0.0         France   \n",
       "\n",
       "  Win conditions  Attendance  Half-time Home Goals  Half-time Away Goals  \\\n",
       "0                     4444.0                   3.0                   0.0   \n",
       "1                    18346.0                   2.0                   0.0   \n",
       "2                    24059.0                   2.0                   0.0   \n",
       "3                     2549.0                   1.0                   0.0   \n",
       "4                    23409.0                   0.0                   0.0   \n",
       "\n",
       "                  Referee               Assistant 1  \\\n",
       "0  LOMBARDI Domingo (URU)     CRISTOPHE Henry (BEL)   \n",
       "1       MACIAS Jose (ARG)  MATEUCCI Francisco (URU)   \n",
       "2     TEJADA Anibal (URU)   VALLARINO Ricardo (URU)   \n",
       "3   WARNKEN Alberto (CHI)       LANGENUS Jean (BEL)   \n",
       "4     REGO Gilberto (BRA)      SAUCEDO Ulises (BOL)   \n",
       "\n",
       "                  Assistant 2  RoundID  MatchID Home Team Initials  \\\n",
       "0         REGO Gilberto (BRA)    201.0   1096.0                FRA   \n",
       "1       WARNKEN Alberto (CHI)    201.0   1090.0                USA   \n",
       "2         BALWAY Thomas (FRA)    201.0   1093.0                YUG   \n",
       "3    MATEUCCI Francisco (URU)    201.0   1098.0                ROU   \n",
       "4  RADULESCU Constantin (ROU)    201.0   1085.0                ARG   \n",
       "\n",
       "  Away Team Initials  \n",
       "0                MEX  \n",
       "1                BEL  \n",
       "2                BRA  \n",
       "3                PER  \n",
       "4                FRA  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "791b11c8",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f4d70191",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping nul values\n",
    "\n",
    "matches.dropna(subset=['Year'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b9effd84",
   "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>Year</th>\n",
       "      <th>Datetime</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Stadium</th>\n",
       "      <th>City</th>\n",
       "      <th>Home Team Name</th>\n",
       "      <th>Home Team Goals</th>\n",
       "      <th>Away Team Goals</th>\n",
       "      <th>Away Team Name</th>\n",
       "      <th>Win conditions</th>\n",
       "      <th>Attendance</th>\n",
       "      <th>Half-time Home Goals</th>\n",
       "      <th>Half-time Away Goals</th>\n",
       "      <th>Referee</th>\n",
       "      <th>Assistant 1</th>\n",
       "      <th>Assistant 2</th>\n",
       "      <th>RoundID</th>\n",
       "      <th>MatchID</th>\n",
       "      <th>Home Team Initials</th>\n",
       "      <th>Away Team Initials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>847</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>05 Jul 2014 - 17:00</td>\n",
       "      <td>Quarter-finals</td>\n",
       "      <td>Arena Fonte Nova</td>\n",
       "      <td>Salvador</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>Netherlands win on penalties (4 - 3)</td>\n",
       "      <td>51179.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Ravshan IRMATOV (UZB)</td>\n",
       "      <td>RASULOV Abduxamidullo (UZB)</td>\n",
       "      <td>KOCHKAROV Bakhadyr (KGZ)</td>\n",
       "      <td>255953.0</td>\n",
       "      <td>300186488.0</td>\n",
       "      <td>NED</td>\n",
       "      <td>CRC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>08 Jul 2014 - 17:00</td>\n",
       "      <td>Semi-finals</td>\n",
       "      <td>Estadio Mineirao</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Germany</td>\n",
       "      <td></td>\n",
       "      <td>58141.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>RODRIGUEZ Marco (MEX)</td>\n",
       "      <td>TORRENTERA Marvin (MEX)</td>\n",
       "      <td>QUINTERO Marcos (MEX)</td>\n",
       "      <td>255955.0</td>\n",
       "      <td>300186474.0</td>\n",
       "      <td>BRA</td>\n",
       "      <td>GER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>849</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>09 Jul 2014 - 17:00</td>\n",
       "      <td>Semi-finals</td>\n",
       "      <td>Arena de Sao Paulo</td>\n",
       "      <td>Sao Paulo</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Argentina win on penalties (2 - 4)</td>\n",
       "      <td>63267.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C�neyt �AKIR (TUR)</td>\n",
       "      <td>DURAN Bahattin (TUR)</td>\n",
       "      <td>ONGUN Tarik (TUR)</td>\n",
       "      <td>255955.0</td>\n",
       "      <td>300186490.0</td>\n",
       "      <td>NED</td>\n",
       "      <td>ARG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>12 Jul 2014 - 17:00</td>\n",
       "      <td>Play-off for third place</td>\n",
       "      <td>Estadio Nacional</td>\n",
       "      <td>Brasilia</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td></td>\n",
       "      <td>68034.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>HAIMOUDI Djamel (ALG)</td>\n",
       "      <td>ACHIK Redouane (MAR)</td>\n",
       "      <td>ETCHIALI Abdelhak (ALG)</td>\n",
       "      <td>255957.0</td>\n",
       "      <td>300186502.0</td>\n",
       "      <td>BRA</td>\n",
       "      <td>NED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>13 Jul 2014 - 16:00</td>\n",
       "      <td>Final</td>\n",
       "      <td>Estadio do Maracana</td>\n",
       "      <td>Rio De Janeiro</td>\n",
       "      <td>Germany</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Germany win after extra time</td>\n",
       "      <td>74738.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Nicola RIZZOLI (ITA)</td>\n",
       "      <td>Renato FAVERANI (ITA)</td>\n",
       "      <td>Andrea STEFANI (ITA)</td>\n",
       "      <td>255959.0</td>\n",
       "      <td>300186501.0</td>\n",
       "      <td>GER</td>\n",
       "      <td>ARG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year              Datetime                     Stage  \\\n",
       "847  2014.0  05 Jul 2014 - 17:00             Quarter-finals   \n",
       "848  2014.0  08 Jul 2014 - 17:00                Semi-finals   \n",
       "849  2014.0  09 Jul 2014 - 17:00                Semi-finals   \n",
       "850  2014.0  12 Jul 2014 - 17:00   Play-off for third place   \n",
       "851  2014.0  13 Jul 2014 - 16:00                      Final   \n",
       "\n",
       "                 Stadium             City Home Team Name  Home Team Goals  \\\n",
       "847     Arena Fonte Nova        Salvador     Netherlands              0.0   \n",
       "848     Estadio Mineirao  Belo Horizonte          Brazil              1.0   \n",
       "849   Arena de Sao Paulo       Sao Paulo     Netherlands              0.0   \n",
       "850     Estadio Nacional        Brasilia          Brazil              0.0   \n",
       "851  Estadio do Maracana  Rio De Janeiro         Germany              1.0   \n",
       "\n",
       "     Away Team Goals Away Team Name                         Win conditions  \\\n",
       "847              0.0     Costa Rica  Netherlands win on penalties (4 - 3)    \n",
       "848              7.0        Germany                                          \n",
       "849              0.0      Argentina    Argentina win on penalties (2 - 4)    \n",
       "850              3.0    Netherlands                                          \n",
       "851              0.0      Argentina          Germany win after extra time    \n",
       "\n",
       "     Attendance  Half-time Home Goals  Half-time Away Goals  \\\n",
       "847     51179.0                   0.0                   0.0   \n",
       "848     58141.0                   0.0                   5.0   \n",
       "849     63267.0                   0.0                   0.0   \n",
       "850     68034.0                   0.0                   2.0   \n",
       "851     74738.0                   0.0                   0.0   \n",
       "\n",
       "                   Referee                  Assistant 1  \\\n",
       "847  Ravshan IRMATOV (UZB)  RASULOV Abduxamidullo (UZB)   \n",
       "848  RODRIGUEZ Marco (MEX)      TORRENTERA Marvin (MEX)   \n",
       "849     C�neyt �AKIR (TUR)         DURAN Bahattin (TUR)   \n",
       "850  HAIMOUDI Djamel (ALG)         ACHIK Redouane (MAR)   \n",
       "851   Nicola RIZZOLI (ITA)        Renato FAVERANI (ITA)   \n",
       "\n",
       "                  Assistant 2   RoundID      MatchID Home Team Initials  \\\n",
       "847  KOCHKAROV Bakhadyr (KGZ)  255953.0  300186488.0                NED   \n",
       "848     QUINTERO Marcos (MEX)  255955.0  300186474.0                BRA   \n",
       "849         ONGUN Tarik (TUR)  255955.0  300186490.0                NED   \n",
       "850   ETCHIALI Abdelhak (ALG)  255957.0  300186502.0                BRA   \n",
       "851      Andrea STEFANI (ITA)  255959.0  300186501.0                GER   \n",
       "\n",
       "    Away Team Initials  \n",
       "847                CRC  \n",
       "848                GER  \n",
       "849                ARG  \n",
       "850                NED  \n",
       "851                ARG  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches.tail() #null has been removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1d7a7d46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brazil                     82\n",
       "Italy                      57\n",
       "Argentina                  54\n",
       "Germany FR                 43\n",
       "England                    35\n",
       "                           ..\n",
       "Ukraine                     1\n",
       "rn\">Trinidad and Tobago     1\n",
       "IR Iran                     1\n",
       "Canada                      1\n",
       "Norway                      1\n",
       "Name: Home Team Name, Length: 78, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# some team names have been changed over the years\n",
    "\n",
    "matches['Home Team Name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f690914c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fixing country names like rn\"> , FR in germany FR etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5ce28177",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "847    False\n",
       "848    False\n",
       "849    False\n",
       "850    False\n",
       "851    False\n",
       "Name: Home Team Name, Length: 852, dtype: bool"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches['Home Team Name'].str.contains('rn\">')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "868ba600",
   "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>Year</th>\n",
       "      <th>Datetime</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Stadium</th>\n",
       "      <th>City</th>\n",
       "      <th>Home Team Name</th>\n",
       "      <th>Home Team Goals</th>\n",
       "      <th>Away Team Goals</th>\n",
       "      <th>Away Team Name</th>\n",
       "      <th>Win conditions</th>\n",
       "      <th>Attendance</th>\n",
       "      <th>Half-time Home Goals</th>\n",
       "      <th>Half-time Away Goals</th>\n",
       "      <th>Referee</th>\n",
       "      <th>Assistant 1</th>\n",
       "      <th>Assistant 2</th>\n",
       "      <th>RoundID</th>\n",
       "      <th>MatchID</th>\n",
       "      <th>Home Team Initials</th>\n",
       "      <th>Away Team Initials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1990.0</td>\n",
       "      <td>09 Jun 1990 - 17:00</td>\n",
       "      <td>Group D</td>\n",
       "      <td>Renato Dall Ara</td>\n",
       "      <td>Bologna</td>\n",
       "      <td>rn\"&gt;United Arab Emirates</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Colombia</td>\n",
       "      <td></td>\n",
       "      <td>30791.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>COURTNEY George (ENG)</td>\n",
       "      <td>TAKADA Shizuo (JPN)</td>\n",
       "      <td>SNODDY Alan (NIR)</td>\n",
       "      <td>322.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>UAE</td>\n",
       "      <td>COL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>1990.0</td>\n",
       "      <td>17 Jun 1990 - 17:00</td>\n",
       "      <td>Group F</td>\n",
       "      <td>Della Favorita</td>\n",
       "      <td>Palermo</td>\n",
       "      <td>rn\"&gt;Republic of Ireland</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Egypt</td>\n",
       "      <td></td>\n",
       "      <td>33288.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>VAN LANGENHOVE Marcel (BEL)</td>\n",
       "      <td>QUINIOU Joel (FRA)</td>\n",
       "      <td>LO BELLO Rosario (ITA)</td>\n",
       "      <td>322.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>IRL</td>\n",
       "      <td>EGY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>1990.0</td>\n",
       "      <td>21 Jun 1990 - 21:00</td>\n",
       "      <td>Group F</td>\n",
       "      <td>Della Favorita</td>\n",
       "      <td>Palermo</td>\n",
       "      <td>rn\"&gt;Republic of Ireland</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td></td>\n",
       "      <td>33288.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>VAUTROT Michel (FRA)</td>\n",
       "      <td>LORENC Richard (AUS)</td>\n",
       "      <td>HANSAL Mohamed (ALG)</td>\n",
       "      <td>322.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>IRL</td>\n",
       "      <td>NED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>1990.0</td>\n",
       "      <td>25 Jun 1990 - 17:00</td>\n",
       "      <td>Round of 16</td>\n",
       "      <td>Luigi Ferraris</td>\n",
       "      <td>Genoa</td>\n",
       "      <td>rn\"&gt;Republic of Ireland</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Romania</td>\n",
       "      <td>Republic of Ireland win on penalties (5 - 4)</td>\n",
       "      <td>31818.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>RAMIZ WRIGHT Jose (BRA)</td>\n",
       "      <td>MACIEL Carlos (PAR)</td>\n",
       "      <td>MANDI Jassim (BHR)</td>\n",
       "      <td>323.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>IRL</td>\n",
       "      <td>ROU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>1994.0</td>\n",
       "      <td>28 Jun 1994 - 12:30</td>\n",
       "      <td>Group E</td>\n",
       "      <td>Giants Stadium</td>\n",
       "      <td>New York/New Jersey</td>\n",
       "      <td>rn\"&gt;Republic of Ireland</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Norway</td>\n",
       "      <td></td>\n",
       "      <td>72404.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TORRES CADENA Jose Joaquin (COL)</td>\n",
       "      <td>ALVES Paulo Jorge (BRA)</td>\n",
       "      <td>PARK Hae-Yong (KOR)</td>\n",
       "      <td>337.0</td>\n",
       "      <td>3077.0</td>\n",
       "      <td>IRL</td>\n",
       "      <td>NOR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>2002.0</td>\n",
       "      <td>01 Jun 2002 - 15:30</td>\n",
       "      <td>Group E</td>\n",
       "      <td>Niigata Stadium Big Swan</td>\n",
       "      <td>Niigata</td>\n",
       "      <td>rn\"&gt;Republic of Ireland</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cameroon</td>\n",
       "      <td></td>\n",
       "      <td>33679.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>KAMIKAWA Toru (JPN)</td>\n",
       "      <td>AWANG HAMAT Mat Lazim (MAS)</td>\n",
       "      <td>VAN NYLEN Roland (BEL)</td>\n",
       "      <td>43950100.0</td>\n",
       "      <td>43950002.0</td>\n",
       "      <td>IRL</td>\n",
       "      <td>CMR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>2006.0</td>\n",
       "      <td>10 Jun 2006 - 18:00</td>\n",
       "      <td>Group B</td>\n",
       "      <td>FIFA World Cup Stadium, Dortmund</td>\n",
       "      <td>Dortmund</td>\n",
       "      <td>rn\"&gt;Trinidad and Tobago</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sweden</td>\n",
       "      <td></td>\n",
       "      <td>62959.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>MAIDIN Shamsul (SIN)</td>\n",
       "      <td>PERMPANICH Prachya (THA)</td>\n",
       "      <td>GHULOUM Eisa (UAE)</td>\n",
       "      <td>97410100.0</td>\n",
       "      <td>97410004.0</td>\n",
       "      <td>TRI</td>\n",
       "      <td>SWE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>2006.0</td>\n",
       "      <td>11 Jun 2006 - 15:00</td>\n",
       "      <td>Group C</td>\n",
       "      <td>Zentralstadion</td>\n",
       "      <td>Leipzig</td>\n",
       "      <td>rn\"&gt;Serbia and Montenegro</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td></td>\n",
       "      <td>43000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>MERK Markus (GER)</td>\n",
       "      <td>SCHRAER Christian (GER)</td>\n",
       "      <td>SALVER Jan-Hendrik (GER)</td>\n",
       "      <td>97410100.0</td>\n",
       "      <td>97410006.0</td>\n",
       "      <td>SCG</td>\n",
       "      <td>NED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>813</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>25 Jun 2014 - 13:00</td>\n",
       "      <td>Group F</td>\n",
       "      <td>Arena Fonte Nova</td>\n",
       "      <td>Salvador</td>\n",
       "      <td>rn\"&gt;Bosnia and Herzegovina</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>IR Iran</td>\n",
       "      <td></td>\n",
       "      <td>48011.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Carlos VELASCO CARBALLO (ESP)</td>\n",
       "      <td>ALONSO FERNANDEZ Roberto (ESP)</td>\n",
       "      <td>YUSTE Juan (ESP)</td>\n",
       "      <td>255931.0</td>\n",
       "      <td>300186464.0</td>\n",
       "      <td>BIH</td>\n",
       "      <td>IRN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year              Datetime        Stage  \\\n",
       "414  1990.0  09 Jun 1990 - 17:00       Group D   \n",
       "433  1990.0  17 Jun 1990 - 17:00       Group F   \n",
       "446  1990.0  21 Jun 1990 - 21:00       Group F   \n",
       "452  1990.0  25 Jun 1990 - 17:00   Round of 16   \n",
       "492  1994.0  28 Jun 1994 - 12:30       Group E   \n",
       "582  2002.0  01 Jun 2002 - 15:30       Group E   \n",
       "647  2006.0  10 Jun 2006 - 18:00       Group B   \n",
       "649  2006.0  11 Jun 2006 - 15:00       Group C   \n",
       "813  2014.0  25 Jun 2014 - 13:00       Group F   \n",
       "\n",
       "                              Stadium                  City  \\\n",
       "414                   Renato Dall Ara              Bologna    \n",
       "433                    Della Favorita              Palermo    \n",
       "446                    Della Favorita              Palermo    \n",
       "452                    Luigi Ferraris                Genoa    \n",
       "492                    Giants Stadium  New York/New Jersey    \n",
       "582          Niigata Stadium Big Swan              Niigata    \n",
       "647  FIFA World Cup Stadium, Dortmund             Dortmund    \n",
       "649                    Zentralstadion              Leipzig    \n",
       "813                  Arena Fonte Nova             Salvador    \n",
       "\n",
       "                 Home Team Name  Home Team Goals  Away Team Goals  \\\n",
       "414    rn\">United Arab Emirates              0.0              2.0   \n",
       "433     rn\">Republic of Ireland              0.0              0.0   \n",
       "446     rn\">Republic of Ireland              1.0              1.0   \n",
       "452     rn\">Republic of Ireland              0.0              0.0   \n",
       "492     rn\">Republic of Ireland              0.0              0.0   \n",
       "582     rn\">Republic of Ireland              1.0              1.0   \n",
       "647     rn\">Trinidad and Tobago              0.0              0.0   \n",
       "649   rn\">Serbia and Montenegro              0.0              1.0   \n",
       "813  rn\">Bosnia and Herzegovina              3.0              1.0   \n",
       "\n",
       "    Away Team Name                                 Win conditions  Attendance  \\\n",
       "414       Colombia                                                    30791.0   \n",
       "433          Egypt                                                    33288.0   \n",
       "446    Netherlands                                                    33288.0   \n",
       "452        Romania  Republic of Ireland win on penalties (5 - 4)      31818.0   \n",
       "492         Norway                                                    72404.0   \n",
       "582       Cameroon                                                    33679.0   \n",
       "647         Sweden                                                    62959.0   \n",
       "649    Netherlands                                                    43000.0   \n",
       "813        IR Iran                                                    48011.0   \n",
       "\n",
       "     Half-time Home Goals  Half-time Away Goals  \\\n",
       "414                   0.0                   0.0   \n",
       "433                   0.0                   0.0   \n",
       "446                   0.0                   1.0   \n",
       "452                   0.0                   0.0   \n",
       "492                   0.0                   0.0   \n",
       "582                   0.0                   1.0   \n",
       "647                   0.0                   0.0   \n",
       "649                   0.0                   1.0   \n",
       "813                   1.0                   0.0   \n",
       "\n",
       "                              Referee                     Assistant 1  \\\n",
       "414             COURTNEY George (ENG)             TAKADA Shizuo (JPN)   \n",
       "433       VAN LANGENHOVE Marcel (BEL)              QUINIOU Joel (FRA)   \n",
       "446              VAUTROT Michel (FRA)            LORENC Richard (AUS)   \n",
       "452           RAMIZ WRIGHT Jose (BRA)             MACIEL Carlos (PAR)   \n",
       "492  TORRES CADENA Jose Joaquin (COL)         ALVES Paulo Jorge (BRA)   \n",
       "582               KAMIKAWA Toru (JPN)     AWANG HAMAT Mat Lazim (MAS)   \n",
       "647              MAIDIN Shamsul (SIN)        PERMPANICH Prachya (THA)   \n",
       "649                 MERK Markus (GER)         SCHRAER Christian (GER)   \n",
       "813     Carlos VELASCO CARBALLO (ESP)  ALONSO FERNANDEZ Roberto (ESP)   \n",
       "\n",
       "                  Assistant 2     RoundID      MatchID Home Team Initials  \\\n",
       "414         SNODDY Alan (NIR)       322.0        119.0                UAE   \n",
       "433    LO BELLO Rosario (ITA)       322.0        152.0                IRL   \n",
       "446      HANSAL Mohamed (ALG)       322.0        228.0                IRL   \n",
       "452        MANDI Jassim (BHR)       323.0        248.0                IRL   \n",
       "492       PARK Hae-Yong (KOR)       337.0       3077.0                IRL   \n",
       "582    VAN NYLEN Roland (BEL)  43950100.0   43950002.0                IRL   \n",
       "647        GHULOUM Eisa (UAE)  97410100.0   97410004.0                TRI   \n",
       "649  SALVER Jan-Hendrik (GER)  97410100.0   97410006.0                SCG   \n",
       "813          YUSTE Juan (ESP)    255931.0  300186464.0                BIH   \n",
       "\n",
       "    Away Team Initials  \n",
       "414                COL  \n",
       "433                EGY  \n",
       "446                NED  \n",
       "452                ROU  \n",
       "492                NOR  \n",
       "582                CMR  \n",
       "647                SWE  \n",
       "649                NED  \n",
       "813                IRN  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#keeipng the above in a dataframe\n",
    "matches[matches['Home Team Name'].str.contains('rn\">')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e1e9fbb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rn\">Republic of Ireland       5\n",
       "rn\">Trinidad and Tobago       1\n",
       "rn\">United Arab Emirates      1\n",
       "rn\">Serbia and Montenegro     1\n",
       "rn\">Bosnia and Herzegovina    1\n",
       "Name: Home Team Name, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches[matches['Home Team Name'].str.contains('rn\">')]['Home Team Name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9490fe42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['rn\">Republic of Ireland', 'rn\">Trinidad and Tobago',\n",
       "       'rn\">United Arab Emirates', 'rn\">Serbia and Montenegro',\n",
       "       'rn\">Bosnia and Herzegovina'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names=matches[matches['Home Team Name'].str.contains('rn\">')]['Home Team Name'].value_counts()\n",
    "names.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "43ac8b79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['rn\">Republic of Ireland',\n",
       " 'rn\">Trinidad and Tobago',\n",
       " 'rn\">United Arab Emirates',\n",
       " 'rn\">Serbia and Montenegro',\n",
       " 'rn\">Bosnia and Herzegovina']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wrong_list=list(names.index)\n",
    "wrong_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c278150e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Republic of Ireland',\n",
       " 'Trinidad and Tobago',\n",
       " 'United Arab Emirates',\n",
       " 'Serbia and Montenegro',\n",
       " 'Bosnia and Herzegovina']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correct_list= [name.split('>')[1] for name in wrong_list]\n",
    "correct_list\n",
    "#here we have the corrected names of teams with rn\"> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "555794f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# correcting the other ones\n",
    "old = ['Germany FR', 'Maracan� - Est�dio Jornalista M�rio Filho', 'Estadio do Maracana']\n",
    "new = ['Germany', 'Maracanã Stadium', 'Maracanã Stadium']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c5b9bb83",
   "metadata": {},
   "outputs": [],
   "source": [
    "wrong_list=wrong_list+old\n",
    "correct_list=correct_list+new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ef0b7a27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['rn\">Republic of Ireland',\n",
       "  'rn\">Trinidad and Tobago',\n",
       "  'rn\">United Arab Emirates',\n",
       "  'rn\">Serbia and Montenegro',\n",
       "  'rn\">Bosnia and Herzegovina',\n",
       "  'Germany FR',\n",
       "  'Maracan� - Est�dio Jornalista M�rio Filho',\n",
       "  'Estadio do Maracana'],\n",
       " ['Republic of Ireland',\n",
       "  'Trinidad and Tobago',\n",
       "  'United Arab Emirates',\n",
       "  'Serbia and Montenegro',\n",
       "  'Bosnia and Herzegovina',\n",
       "  'Germany',\n",
       "  'Maracanã Stadium',\n",
       "  'Maracanã Stadium'])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wrong_list, correct_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbaf9d0e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a5266aeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, wr in enumerate(wrong_list):\n",
    "    world_cups=world_cups.replace(wrong_list[index],correct_list[index])\n",
    "\n",
    "for index, wr in enumerate(wrong_list):\n",
    "    players=players.replace(wrong_list[index],correct_list[index])\n",
    "\n",
    "for index, wr in enumerate(wrong_list):\n",
    "    matches=matches.replace(wrong_list[index],correct_list[index])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1265f776",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: Home Team Name, dtype: int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches[matches['Home Team Name'].str.contains('rn\">')]['Home Team Name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00acfe2e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "40305609",
   "metadata": {},
   "source": [
    "## Most number of world cup winning title(up to 2014)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0f608e14",
   "metadata": {},
   "outputs": [],
   "source": [
    "winner=world_cups['Winner'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1cd9e1f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brazil       5\n",
       "Germany      4\n",
       "Italy        4\n",
       "Uruguay      2\n",
       "Argentina    2\n",
       "England      1\n",
       "Spain        1\n",
       "France       1\n",
       "Name: Winner, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "winner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fc50c30b",
   "metadata": {},
   "outputs": [],
   "source": [
    "runnerup=world_cups['Runners-Up'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1ee0da8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Germany           4\n",
       "Argentina         3\n",
       "Netherlands       3\n",
       "Hungary           2\n",
       "Czechoslovakia    2\n",
       "Brazil            2\n",
       "Italy             2\n",
       "France            1\n",
       "Sweden            1\n",
       "Name: Runners-Up, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runnerup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "aae704a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "third=world_cups['Third'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6c5a74d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Germany        4\n",
       "France         2\n",
       "Poland         2\n",
       "Sweden         2\n",
       "Brazil         2\n",
       "Austria        1\n",
       "Italy          1\n",
       "USA            1\n",
       "Netherlands    1\n",
       "Portugal       1\n",
       "Chile          1\n",
       "Turkey         1\n",
       "Croatia        1\n",
       "Name: Third, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "third"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "32526fcc",
   "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>Winner</th>\n",
       "      <th>Runners-Up</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>England</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czechoslovakia</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Croatia</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Winner  Runners-Up  Third\n",
       "Brazil               5           2      2\n",
       "Germany              4           4      4\n",
       "Italy                4           2      1\n",
       "Uruguay              2           0      0\n",
       "Argentina            2           3      0\n",
       "England              1           0      0\n",
       "Spain                1           0      0\n",
       "France               1           1      2\n",
       "Netherlands          0           3      1\n",
       "Hungary              0           2      0\n",
       "Czechoslovakia       0           2      0\n",
       "Sweden               0           1      2\n",
       "Poland               0           0      2\n",
       "Austria              0           0      1\n",
       "USA                  0           0      1\n",
       "Portugal             0           0      1\n",
       "Chile                0           0      1\n",
       "Turkey               0           0      1\n",
       "Croatia              0           0      1"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concatenate all three columns to create a dataframe\n",
    "teams=pd.concat([winner,runnerup,third],axis=1)\n",
    "teams.fillna(0,inplace=True)  #this is done to  fill NA values as 0\n",
    "teams=teams.astype(int)\n",
    "teams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "47f4b26b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
        {
         "marker": {
          "color": "rgba(255, 153, 51, 0.6)",
          "line": {
           "color": "rgba(255, 153, 51, 1.0)",
           "width": 1
          }
         },
         "name": "Winner",
         "orientation": "v",
         "text": "",
         "type": "bar",
         "x": [
          "Brazil",
          "Germany",
          "Italy",
          "Uruguay",
          "Argentina",
          "England",
          "Spain",
          "France",
          "Netherlands",
          "Hungary",
          "Czechoslovakia",
          "Sweden",
          "Poland",
          "Austria",
          "USA",
          "Portugal",
          "Chile",
          "Turkey",
          "Croatia"
         ],
         "y": [
          5,
          4,
          4,
          2,
          2,
          1,
          1,
          1,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0,
          0
         ]
        },
        {
         "marker": {
          "color": "rgba(55, 128, 191, 0.6)",
          "line": {
           "color": "rgba(55, 128, 191, 1.0)",
           "width": 1
          }
         },
         "name": "Runners-Up",
         "orientation": "v",
         "text": "",
         "type": "bar",
         "x": [
          "Brazil",
          "Germany",
          "Italy",
          "Uruguay",
          "Argentina",
          "England",
          "Spain",
          "France",
          "Netherlands",
          "Hungary",
          "Czechoslovakia",
          "Sweden",
          "Poland",
          "Austria",
          "USA",
          "Portugal",
          "Chile",
          "Turkey",
          "Croatia"
         ],
         "y": [
          2,
          4,
          2,
          0,
          3,
          0,
          0,
          1,
          3,
          2,
          2,
          1,
          0,
          0,
          0,
          0,
          0,
          0,
          0
         ]
        },
        {
         "marker": {
          "color": "rgba(50, 171, 96, 0.6)",
          "line": {
           "color": "rgba(50, 171, 96, 1.0)",
           "width": 1
          }
         },
         "name": "Third",
         "orientation": "v",
         "text": "",
         "type": "bar",
         "x": [
          "Brazil",
          "Germany",
          "Italy",
          "Uruguay",
          "Argentina",
          "England",
          "Spain",
          "France",
          "Netherlands",
          "Hungary",
          "Czechoslovakia",
          "Sweden",
          "Poland",
          "Austria",
          "USA",
          "Portugal",
          "Chile",
          "Turkey",
          "Croatia"
         ],
         "y": [
          2,
          4,
          1,
          0,
          0,
          0,
          0,
          2,
          1,
          0,
          0,
          2,
          2,
          1,
          1,
          1,
          1,
          1,
          1
         ]
        }
       ],
       "layout": {
        "legend": {
         "bgcolor": "#F5F6F9",
         "font": {
          "color": "#4D5663"
         }
        },
        "paper_bgcolor": "#F5F6F9",
        "plot_bgcolor": "#F5F6F9",
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "font": {
          "color": "#4D5663"
         },
         "text": "FIFA world cup winning count"
        },
        "xaxis": {
         "gridcolor": "#E1E5ED",
         "showgrid": true,
         "tickfont": {
          "color": "#4D5663"
         },
         "title": {
          "font": {
           "color": "#4D5663"
          },
          "text": "Teams"
         },
         "zerolinecolor": "#E1E5ED"
        },
        "yaxis": {
         "gridcolor": "#E1E5ED",
         "showgrid": true,
         "tickfont": {
          "color": "#4D5663"
         },
         "title": {
          "font": {
           "color": "#4D5663"
          },
          "text": "Count"
         },
         "zerolinecolor": "#E1E5ED"
        }
       }
      },
      "text/html": [
       "<div>                            <div id=\"480dfb84-24fb-4d21-85e8-0c378e272d02\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
       "                    window.PLOTLYENV.BASE_URL='https://plot.ly';                                    if (document.getElementById(\"480dfb84-24fb-4d21-85e8-0c378e272d02\")) {                    Plotly.newPlot(                        \"480dfb84-24fb-4d21-85e8-0c378e272d02\",                        [{\"marker\":{\"color\":\"rgba(255, 153, 51, 0.6)\",\"line\":{\"color\":\"rgba(255, 153, 51, 1.0)\",\"width\":1}},\"name\":\"Winner\",\"orientation\":\"v\",\"text\":\"\",\"type\":\"bar\",\"x\":[\"Brazil\",\"Germany\",\"Italy\",\"Uruguay\",\"Argentina\",\"England\",\"Spain\",\"France\",\"Netherlands\",\"Hungary\",\"Czechoslovakia\",\"Sweden\",\"Poland\",\"Austria\",\"USA\",\"Portugal\",\"Chile\",\"Turkey\",\"Croatia\"],\"y\":[5,4,4,2,2,1,1,1,0,0,0,0,0,0,0,0,0,0,0]},{\"marker\":{\"color\":\"rgba(55, 128, 191, 0.6)\",\"line\":{\"color\":\"rgba(55, 128, 191, 1.0)\",\"width\":1}},\"name\":\"Runners-Up\",\"orientation\":\"v\",\"text\":\"\",\"type\":\"bar\",\"x\":[\"Brazil\",\"Germany\",\"Italy\",\"Uruguay\",\"Argentina\",\"England\",\"Spain\",\"France\",\"Netherlands\",\"Hungary\",\"Czechoslovakia\",\"Sweden\",\"Poland\",\"Austria\",\"USA\",\"Portugal\",\"Chile\",\"Turkey\",\"Croatia\"],\"y\":[2,4,2,0,3,0,0,1,3,2,2,1,0,0,0,0,0,0,0]},{\"marker\":{\"color\":\"rgba(50, 171, 96, 0.6)\",\"line\":{\"color\":\"rgba(50, 171, 96, 1.0)\",\"width\":1}},\"name\":\"Third\",\"orientation\":\"v\",\"text\":\"\",\"type\":\"bar\",\"x\":[\"Brazil\",\"Germany\",\"Italy\",\"Uruguay\",\"Argentina\",\"England\",\"Spain\",\"France\",\"Netherlands\",\"Hungary\",\"Czechoslovakia\",\"Sweden\",\"Poland\",\"Austria\",\"USA\",\"Portugal\",\"Chile\",\"Turkey\",\"Croatia\"],\"y\":[2,4,1,0,0,0,0,2,1,0,0,2,2,1,1,1,1,1,1]}],                        {\"legend\":{\"bgcolor\":\"#F5F6F9\",\"font\":{\"color\":\"#4D5663\"}},\"paper_bgcolor\":\"#F5F6F9\",\"plot_bgcolor\":\"#F5F6F9\",\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"title\":{\"font\":{\"color\":\"#4D5663\"},\"text\":\"FIFA world cup winning count\"},\"xaxis\":{\"gridcolor\":\"#E1E5ED\",\"showgrid\":true,\"tickfont\":{\"color\":\"#4D5663\"},\"title\":{\"font\":{\"color\":\"#4D5663\"},\"text\":\"Teams\"},\"zerolinecolor\":\"#E1E5ED\"},\"yaxis\":{\"gridcolor\":\"#E1E5ED\",\"showgrid\":true,\"tickfont\":{\"color\":\"#4D5663\"},\"title\":{\"font\":{\"color\":\"#4D5663\"},\"text\":\"Count\"},\"zerolinecolor\":\"#E1E5ED\"}},                        {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('480dfb84-24fb-4d21-85e8-0c378e272d02');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "teams.iplot(kind='bar',xTitle='Teams',yTitle='Count',title=\"FIFA world cup winning count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e40aa25e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "81e04596",
   "metadata": {},
   "source": [
    "## Number of goals per country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9024a0e3",
   "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>Year</th>\n",
       "      <th>Datetime</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Stadium</th>\n",
       "      <th>City</th>\n",
       "      <th>Home Team Name</th>\n",
       "      <th>Home Team Goals</th>\n",
       "      <th>Away Team Goals</th>\n",
       "      <th>Away Team Name</th>\n",
       "      <th>Win conditions</th>\n",
       "      <th>Attendance</th>\n",
       "      <th>Half-time Home Goals</th>\n",
       "      <th>Half-time Away Goals</th>\n",
       "      <th>Referee</th>\n",
       "      <th>Assistant 1</th>\n",
       "      <th>Assistant 2</th>\n",
       "      <th>RoundID</th>\n",
       "      <th>MatchID</th>\n",
       "      <th>Home Team Initials</th>\n",
       "      <th>Away Team Initials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>13 Jul 1930 - 15:00</td>\n",
       "      <td>Group 1</td>\n",
       "      <td>Pocitos</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>France</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td></td>\n",
       "      <td>4444.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>LOMBARDI Domingo (URU)</td>\n",
       "      <td>CRISTOPHE Henry (BEL)</td>\n",
       "      <td>REGO Gilberto (BRA)</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1096.0</td>\n",
       "      <td>FRA</td>\n",
       "      <td>MEX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>13 Jul 1930 - 15:00</td>\n",
       "      <td>Group 4</td>\n",
       "      <td>Parque Central</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>USA</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Belgium</td>\n",
       "      <td></td>\n",
       "      <td>18346.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>MACIAS Jose (ARG)</td>\n",
       "      <td>MATEUCCI Francisco (URU)</td>\n",
       "      <td>WARNKEN Alberto (CHI)</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>BEL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>14 Jul 1930 - 12:45</td>\n",
       "      <td>Group 2</td>\n",
       "      <td>Parque Central</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Yugoslavia</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td></td>\n",
       "      <td>24059.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TEJADA Anibal (URU)</td>\n",
       "      <td>VALLARINO Ricardo (URU)</td>\n",
       "      <td>BALWAY Thomas (FRA)</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1093.0</td>\n",
       "      <td>YUG</td>\n",
       "      <td>BRA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>14 Jul 1930 - 14:50</td>\n",
       "      <td>Group 3</td>\n",
       "      <td>Pocitos</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Romania</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Peru</td>\n",
       "      <td></td>\n",
       "      <td>2549.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WARNKEN Alberto (CHI)</td>\n",
       "      <td>LANGENUS Jean (BEL)</td>\n",
       "      <td>MATEUCCI Francisco (URU)</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>ROU</td>\n",
       "      <td>PER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>15 Jul 1930 - 16:00</td>\n",
       "      <td>Group 1</td>\n",
       "      <td>Parque Central</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>France</td>\n",
       "      <td></td>\n",
       "      <td>23409.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>REGO Gilberto (BRA)</td>\n",
       "      <td>SAUCEDO Ulises (BOL)</td>\n",
       "      <td>RADULESCU Constantin (ROU)</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1085.0</td>\n",
       "      <td>ARG</td>\n",
       "      <td>FRA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year              Datetime    Stage         Stadium         City  \\\n",
       "0  1930.0  13 Jul 1930 - 15:00   Group 1         Pocitos  Montevideo    \n",
       "1  1930.0  13 Jul 1930 - 15:00   Group 4  Parque Central  Montevideo    \n",
       "2  1930.0  14 Jul 1930 - 12:45   Group 2  Parque Central  Montevideo    \n",
       "3  1930.0  14 Jul 1930 - 14:50   Group 3         Pocitos  Montevideo    \n",
       "4  1930.0  15 Jul 1930 - 16:00   Group 1  Parque Central  Montevideo    \n",
       "\n",
       "  Home Team Name  Home Team Goals  Away Team Goals Away Team Name  \\\n",
       "0         France              4.0              1.0         Mexico   \n",
       "1            USA              3.0              0.0        Belgium   \n",
       "2     Yugoslavia              2.0              1.0         Brazil   \n",
       "3        Romania              3.0              1.0           Peru   \n",
       "4      Argentina              1.0              0.0         France   \n",
       "\n",
       "  Win conditions  Attendance  Half-time Home Goals  Half-time Away Goals  \\\n",
       "0                     4444.0                   3.0                   0.0   \n",
       "1                    18346.0                   2.0                   0.0   \n",
       "2                    24059.0                   2.0                   0.0   \n",
       "3                     2549.0                   1.0                   0.0   \n",
       "4                    23409.0                   0.0                   0.0   \n",
       "\n",
       "                  Referee               Assistant 1  \\\n",
       "0  LOMBARDI Domingo (URU)     CRISTOPHE Henry (BEL)   \n",
       "1       MACIAS Jose (ARG)  MATEUCCI Francisco (URU)   \n",
       "2     TEJADA Anibal (URU)   VALLARINO Ricardo (URU)   \n",
       "3   WARNKEN Alberto (CHI)       LANGENUS Jean (BEL)   \n",
       "4     REGO Gilberto (BRA)      SAUCEDO Ulises (BOL)   \n",
       "\n",
       "                  Assistant 2  RoundID  MatchID Home Team Initials  \\\n",
       "0         REGO Gilberto (BRA)    201.0   1096.0                FRA   \n",
       "1       WARNKEN Alberto (CHI)    201.0   1090.0                USA   \n",
       "2         BALWAY Thomas (FRA)    201.0   1093.0                YUG   \n",
       "3    MATEUCCI Francisco (URU)    201.0   1098.0                ROU   \n",
       "4  RADULESCU Constantin (ROU)    201.0   1085.0                ARG   \n",
       "\n",
       "  Away Team Initials  \n",
       "0                MEX  \n",
       "1                BEL  \n",
       "2                BRA  \n",
       "3                PER  \n",
       "4                FRA  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4b30fd00",
   "metadata": {},
   "outputs": [],
   "source": [
    "home=matches[['Home Team Name','Home Team Goals']].dropna()\n",
    "away=matches[['Away Team Name','Away Team Goals']].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "403b6dcd",
   "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>Home Team Name</th>\n",
       "      <th>Home Team Goals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>France</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USA</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yugoslavia</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Romania</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>847</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>849</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>Germany</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>852 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Home Team Name  Home Team Goals\n",
       "0           France              4.0\n",
       "1              USA              3.0\n",
       "2       Yugoslavia              2.0\n",
       "3          Romania              3.0\n",
       "4        Argentina              1.0\n",
       "..             ...              ...\n",
       "847    Netherlands              0.0\n",
       "848         Brazil              1.0\n",
       "849    Netherlands              0.0\n",
       "850         Brazil              0.0\n",
       "851        Germany              1.0\n",
       "\n",
       "[852 rows x 2 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "home"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1c5fe273",
   "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>Away Team Name</th>\n",
       "      <th>Away Team Goals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Peru</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>847</th>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848</th>\n",
       "      <td>Germany</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>849</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>852 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Away Team Name  Away Team Goals\n",
       "0           Mexico              1.0\n",
       "1          Belgium              0.0\n",
       "2           Brazil              1.0\n",
       "3             Peru              1.0\n",
       "4           France              0.0\n",
       "..             ...              ...\n",
       "847     Costa Rica              0.0\n",
       "848        Germany              7.0\n",
       "849      Argentina              0.0\n",
       "850    Netherlands              3.0\n",
       "851      Argentina              0.0\n",
       "\n",
       "[852 rows x 2 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "away"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a852aa50",
   "metadata": {},
   "outputs": [],
   "source": [
    "home.columns=['Countries','Goals']\n",
    "away.columns=home.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fbc7129c",
   "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>Countries</th>\n",
       "      <th>Goals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Peru</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>847</th>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848</th>\n",
       "      <td>Germany</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>849</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>852 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Countries  Goals\n",
       "0         Mexico    1.0\n",
       "1        Belgium    0.0\n",
       "2         Brazil    1.0\n",
       "3           Peru    1.0\n",
       "4         France    0.0\n",
       "..           ...    ...\n",
       "847   Costa Rica    0.0\n",
       "848      Germany    7.0\n",
       "849    Argentina    0.0\n",
       "850  Netherlands    3.0\n",
       "851    Argentina    0.0\n",
       "\n",
       "[852 rows x 2 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "away"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c17c892c",
   "metadata": {},
   "outputs": [],
   "source": [
    "goals=home.append(away,ignore_index=True)  #indexes won't be in count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "054b19e1",
   "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>Countries</th>\n",
       "      <th>Goals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>France</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USA</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yugoslavia</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Romania</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Germany</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Countries  Goals\n",
       "0          France    4.0\n",
       "1             USA    3.0\n",
       "2      Yugoslavia    2.0\n",
       "3         Romania    3.0\n",
       "4       Argentina    1.0\n",
       "...           ...    ...\n",
       "1699   Costa Rica    0.0\n",
       "1700      Germany    7.0\n",
       "1701    Argentina    0.0\n",
       "1702  Netherlands    3.0\n",
       "1703    Argentina    0.0\n",
       "\n",
       "[1704 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "goals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b62a9c57",
   "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>Goals</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Countries</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>133.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wales</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yugoslavia</th>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zaire</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Goals\n",
       "Countries                  \n",
       "Algeria                14.0\n",
       "Angola                  1.0\n",
       "Argentina             133.0\n",
       "Australia              11.0\n",
       "Austria                43.0\n",
       "...                     ...\n",
       "United Arab Emirates    2.0\n",
       "Uruguay                80.0\n",
       "Wales                   4.0\n",
       "Yugoslavia             60.0\n",
       "Zaire                   0.0\n",
       "\n",
       "[82 rows x 1 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "goals=goals.groupby('Countries').sum()\n",
    "goals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7bcb9b4f",
   "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>Goals</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Countries</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>133.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trinidad and Tobago</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China PR</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dutch East Indies</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zaire</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Goals\n",
       "Countries                 \n",
       "Germany              235.0\n",
       "Brazil               225.0\n",
       "Argentina            133.0\n",
       "Italy                128.0\n",
       "France               108.0\n",
       "...                    ...\n",
       "Trinidad and Tobago    0.0\n",
       "Canada                 0.0\n",
       "China PR               0.0\n",
       "Dutch East Indies      0.0\n",
       "Zaire                  0.0\n",
       "\n",
       "[82 rows x 1 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sorting by goals\n",
    "goals=goals.sort_values(by='Goals',ascending=False)\n",
    "goals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a5e957e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
        {
         "marker": {
          "color": "rgba(255, 153, 51, 0.6)",
          "line": {
           "color": "rgba(255, 153, 51, 1.0)",
           "width": 1
          }
         },
         "name": "Goals",
         "orientation": "v",
         "text": "",
         "type": "bar",
         "x": [
          "Germany",
          "Brazil",
          "Argentina",
          "Italy",
          "France",
          "Spain",
          "Netherlands",
          "Hungary",
          "Uruguay",
          "England",
          "Sweden",
          "Yugoslavia",
          "Mexico",
          "Belgium",
          "Soviet Union",
          "Switzerland",
          "Czechoslovakia",
          "Poland",
          "Portugal",
          "Austria"
         ],
         "y": [
          235,
          225,
          133,
          128,
          108,
          92,
          91,
          87,
          80,
          79,
          74,
          60,
          58,
          54,
          53,
          45,
          44,
          44,
          43,
          43
         ]
        }
       ],
       "layout": {
        "legend": {
         "bgcolor": "#F5F6F9",
         "font": {
          "color": "#4D5663"
         }
        },
        "paper_bgcolor": "#F5F6F9",
        "plot_bgcolor": "#F5F6F9",
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "font": {
          "color": "#4D5663"
         },
         "text": "Top 20 goalscoring nations"
        },
        "xaxis": {
         "gridcolor": "#E1E5ED",
         "showgrid": true,
         "tickfont": {
          "color": "#4D5663"
         },
         "title": {
          "font": {
           "color": "#4D5663"
          },
          "text": "Country Names"
         },
         "zerolinecolor": "#E1E5ED"
        },
        "yaxis": {
         "gridcolor": "#E1E5ED",
         "showgrid": true,
         "tickfont": {
          "color": "#4D5663"
         },
         "title": {
          "font": {
           "color": "#4D5663"
          },
          "text": "Goals"
         },
         "zerolinecolor": "#E1E5ED"
        }
       }
      },
      "text/html": [
       "<div>                            <div id=\"cdb8882b-888f-496f-a988-6d1c6721cc72\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
       "                    window.PLOTLYENV.BASE_URL='https://plot.ly';                                    if (document.getElementById(\"cdb8882b-888f-496f-a988-6d1c6721cc72\")) {                    Plotly.newPlot(                        \"cdb8882b-888f-496f-a988-6d1c6721cc72\",                        [{\"marker\":{\"color\":\"rgba(255, 153, 51, 0.6)\",\"line\":{\"color\":\"rgba(255, 153, 51, 1.0)\",\"width\":1}},\"name\":\"Goals\",\"orientation\":\"v\",\"text\":\"\",\"type\":\"bar\",\"x\":[\"Germany\",\"Brazil\",\"Argentina\",\"Italy\",\"France\",\"Spain\",\"Netherlands\",\"Hungary\",\"Uruguay\",\"England\",\"Sweden\",\"Yugoslavia\",\"Mexico\",\"Belgium\",\"Soviet Union\",\"Switzerland\",\"Czechoslovakia\",\"Poland\",\"Portugal\",\"Austria\"],\"y\":[235.0,225.0,133.0,128.0,108.0,92.0,91.0,87.0,80.0,79.0,74.0,60.0,58.0,54.0,53.0,45.0,44.0,44.0,43.0,43.0]}],                        {\"legend\":{\"bgcolor\":\"#F5F6F9\",\"font\":{\"color\":\"#4D5663\"}},\"paper_bgcolor\":\"#F5F6F9\",\"plot_bgcolor\":\"#F5F6F9\",\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"title\":{\"font\":{\"color\":\"#4D5663\"},\"text\":\"Top 20 goalscoring nations\"},\"xaxis\":{\"gridcolor\":\"#E1E5ED\",\"showgrid\":true,\"tickfont\":{\"color\":\"#4D5663\"},\"title\":{\"font\":{\"color\":\"#4D5663\"},\"text\":\"Country Names\"},\"zerolinecolor\":\"#E1E5ED\"},\"yaxis\":{\"gridcolor\":\"#E1E5ED\",\"showgrid\":true,\"tickfont\":{\"color\":\"#4D5663\"},\"title\":{\"font\":{\"color\":\"#4D5663\"},\"text\":\"Goals\"},\"zerolinecolor\":\"#E1E5ED\"}},                        {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('cdb8882b-888f-496f-a988-6d1c6721cc72');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "goals[:20].iplot(kind='bar',xTitle='Country Names',yTitle='Goals',title='Top 20 goalscoring nations')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8a99b6e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "797f8270",
   "metadata": {},
   "source": [
    "## Attendance, Number of teams, Goals and Matches per cup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "698dcd2f",
   "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>Year</th>\n",
       "      <th>Country</th>\n",
       "      <th>Winner</th>\n",
       "      <th>Runners-Up</th>\n",
       "      <th>Third</th>\n",
       "      <th>Fourth</th>\n",
       "      <th>GoalsScored</th>\n",
       "      <th>QualifiedTeams</th>\n",
       "      <th>MatchesPlayed</th>\n",
       "      <th>Attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1930</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>USA</td>\n",
       "      <td>Yugoslavia</td>\n",
       "      <td>70</td>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>590.549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1934</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Czechoslovakia</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Austria</td>\n",
       "      <td>70</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>363.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1938</td>\n",
       "      <td>France</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>84</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>375.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1950</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Spain</td>\n",
       "      <td>88</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>1.045.246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>Austria</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>140</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>768.607</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year      Country   Winner      Runners-Up    Third      Fourth  \\\n",
       "0  1930      Uruguay  Uruguay       Argentina      USA  Yugoslavia   \n",
       "1  1934        Italy    Italy  Czechoslovakia  Germany     Austria   \n",
       "2  1938       France    Italy         Hungary   Brazil      Sweden   \n",
       "3  1950       Brazil  Uruguay          Brazil   Sweden       Spain   \n",
       "4  1954  Switzerland  Germany         Hungary  Austria     Uruguay   \n",
       "\n",
       "   GoalsScored  QualifiedTeams  MatchesPlayed Attendance  \n",
       "0           70              13             18    590.549  \n",
       "1           70              16             17    363.000  \n",
       "2           84              15             18    375.700  \n",
       "3           88              13             22  1.045.246  \n",
       "4          140              16             26    768.607  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_cups.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d620418a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-44-a78e96642b83>:3: FutureWarning:\n",
      "\n",
      "The default value of regex will change from True to False in a future version. In addition, single character regular expressions will*not* be treated as literal strings when regex=True.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# fixing attendance. It has \",\" replace it to \"\"\n",
    "\n",
    "world_cups['Attendance']=world_cups['Attendance'].str.replace(\".\",\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4a03cc79",
   "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>Year</th>\n",
       "      <th>Country</th>\n",
       "      <th>Winner</th>\n",
       "      <th>Runners-Up</th>\n",
       "      <th>Third</th>\n",
       "      <th>Fourth</th>\n",
       "      <th>GoalsScored</th>\n",
       "      <th>QualifiedTeams</th>\n",
       "      <th>MatchesPlayed</th>\n",
       "      <th>Attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1930</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>USA</td>\n",
       "      <td>Yugoslavia</td>\n",
       "      <td>70</td>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>590549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1934</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Czechoslovakia</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Austria</td>\n",
       "      <td>70</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>363000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1938</td>\n",
       "      <td>France</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>84</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>375700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1950</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Spain</td>\n",
       "      <td>88</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>1045246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>Austria</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>140</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>768607</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year      Country   Winner      Runners-Up    Third      Fourth  \\\n",
       "0  1930      Uruguay  Uruguay       Argentina      USA  Yugoslavia   \n",
       "1  1934        Italy    Italy  Czechoslovakia  Germany     Austria   \n",
       "2  1938       France    Italy         Hungary   Brazil      Sweden   \n",
       "3  1950       Brazil  Uruguay          Brazil   Sweden       Spain   \n",
       "4  1954  Switzerland  Germany         Hungary  Austria     Uruguay   \n",
       "\n",
       "   GoalsScored  QualifiedTeams  MatchesPlayed Attendance  \n",
       "0           70              13             18     590549  \n",
       "1           70              16             17     363000  \n",
       "2           84              15             18     375700  \n",
       "3           88              13             22    1045246  \n",
       "4          140              16             26     768607  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world_cups.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6d69b67a",
   "metadata": {},
   "outputs": [],
   "source": [
    "world_cups['Attendance']=world_cups['Attendance'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "783a8928",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='Attendance'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(figsize=(10,5))  #creates a box\n",
    "sns.despine(right=True)  #removes top and right lines from the box\n",
    "\n",
    "sns.barplot(x='Year',y='Attendance', data=world_cups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "85270408",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# rotating the upper plot's x-axis markings\n",
    "\n",
    "g=sns.barplot(x='Year',y='Attendance', data=world_cups)\n",
    "g.set_xticklabels(g.get_xticklabels(), rotation=80)\n",
    "g.set_title('Attendance per year') #setting the title\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "78247fa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl4AAAFeCAYAAABUyREbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAqN0lEQVR4nO3deZgtVX3u8e/LIKAg42EQZVJUEAXiEXGIAyKiEQEZVBBRzAVx5F4zGJOrqEk0xuEax2BEEdSIDILKGIwDOCAgIjI5oSKHwySjigK/+8eqlp3DGfrAruo++3w/z9NPd9feu95V1dW9f73WqqpUFZIkSerfCjPdAEmSpOWFhZckSdJALLwkSZIGYuElSZI0EAsvSZKkgVh4SZIkDcTCS9JSS/LyJGePfH9bki26r1dL8qUkNyf5QpL9k5wxjhxJWtZZeEkTqitafpjkt0muSfKRJGv2kVVVq1fVz7pv9wY2ANatqn2q6jNVtcu4M5NslqSSrDTudUtSXyy8pAmU5I3AvwB/DawJ7AhsBpyRZOWe4zcFrqiqO3vOWe7MZJGZZMWZypYmiYWXNGGSPBh4G/C6qjqtqv5YVVcC+wKbA/t1z/tUkn8ced0zklw18v2bkvw0ya1JLkmy52IyK8kjkrwNeAvwom748ZULGZZ8dJIzk9yY5PIk+448tm6Sk5PckuRc4OGL2dRvdJ9v6rKe1K3joCSXJvlNktOTbDqy/g8k+VW3/vOT/PnIY4d3Q6PHdNv8wySPTPJ3Sa7tXrfLyPNfnuRn3XN/nmT/Reybw5Mcl+Tz3XMvSLLtyOMPSXJ8kuu69bx+Ia89JsktwMsXWPcTkswfLciS7JXkwu7rFUZ+jjckOTbJOiPP/ULXG3pzkm8keczIY59K8tEkpyS5HXjmYn4WkqbJwkuaPE8GVgVOGF1YVbcBpwLTHfb7KfDntB6ztwHHJNlocS+oqrcC/wx8vht+/MTo40keBJwJfBZYH3gJ8JGRN/wPA78HNgIO6j4W5Wnd57W6rG8n2QN4M/BCYA7wTeBzI6/5HrAdsE7Xhi8kWXXk8d2Ao4G1ge8Dp9P+Tm4MvB3495Ht+DfguVW1Bm2fX7iYtu4OfGEk94tJVk6yAvAl4AddxrOAw5I8Z4HXHgesBXxmdKVV9T3gBuDZI4tf2m0DwOuBPYCnAw8BfkPbx1NOBbak/SwuWHD9tCL9n4A1AOfaSWNg4SVNnvWA6xcx1DePVpAsUVV9oaqurqq7q+rzwI+BHe5n254PXFlVn6yqO6vqAuB4YO9uKGsv4C1VdXtVXQwctZTrPwR4Z1Vd2m3/PwPbTfV6VdUxVXVDl/1eYBXgUSOv/2ZVnd699gu0ffWuqvoj8J/AZknW6p57N7BNktWqal5V/Wgx7Tq/qo7r1vM+WmG8I/AEYE5Vvb2q/tDNk/s48OKR1367qr7Y/Rx+t5B1H0Urtuh6s55DK+6m9sffV9VVVXUHcDhtX6/U7Y8jq+rWkce2XWAe4ElVdU6X/fvFbJ+kabLwkibP9cB6i5gPtBFw3XRWkuRlSS5MclOSm4BtaEXd/bEp8MSpdXbr3R/YkFbkrAT8auT5v7gP6//AyLpvBELrTSLJG7thyJu7x9fkf27T/JGvf0crYO8a+R5g9aq6HXgR8CpgXpKvJHn0Ytr1p22qqruBq2g9UJsCD1lgf7yZdnLCvV67CMcAuyVZnTac/M2qmjeyP04cWfelwF3ABklWTPKubhjyFuDK7jWj+2NJ2ZKWkoWXNHm+DdxBG277k2547LnA17tFtwMPHHnKhiPP3ZTW8/Ja2tmJawEX04qY++NXwNeraq2Rj9Wr6lBaQXgn8LCR52+ymHXVItZ/yALrX62qvtXN5/pbWnGydrdNN9/Xbep6xp5NK2Yvo+2vRfnTNnXDiw8Fru7a+/MF2rtGVT1vCds52o5f037mewIHcM8wI936n7vA+lftXrMfbRhzZ1oButlUE6ebLWnpWXhJE6aqbqbNyfpgkl27uUSb0YbOrueeeTwXAs9Lsk6SDYHDRlbzINqb7nUASV5B6/G6v74MPDLJAV27Vu4miG/V9SydABye5IFJtgYOXMy6rqMN920xsuxjwN9NzRlLsmaSfbrH1qAVdtcBKyV5C/Dg+7IRSTZI8oKumL0DuI3Wk7Qoj0/ywq4X8rDuNd8BzgVuSfK3adc/WzHJNkmesJRN+jTwN8BjgRNHln8M+KepodYkc5Ls3j22RteOG2gF+D8vZaak+8DCS5pAVfVu2pDVe4BbgZ/T3lx37obJoPWM/IA2xHQG8PmR118CvJfWkzKf9oZ+zhjadSttcv+LaT0+19Aue7FK95TXAqt3yz8FfHIx6/otbeL3Od1Q2o5VdWK3vv/shs8upvXyQZsofypwBW0I8/fc96G0FYA3dttwI23y+qsX8/yTaEOTv6H1Sr2wO9v0LtqE/u1oP6Prgf+g9UAtjRPphhVHfr4AHwBOpl1G5FZasffE7rFP0/bDr4FLusck9SxV9iRLky7JQbResKdU1S9nuj3LkySHA4+oqpf2nPNT2jDrf/WZI+n+8YrP0nKgqo5M8kfaZQ8svCZMkr1oQ8Nfnem2SFo8Cy9pOVFVRy/5WVrWJPkasDVwQHfGpKRZzKFGSZKkgTi5XpIkaSAWXpIkSQNZJuZ47brrrnXaaafNdDMkSZKmY5EXZl4meryuv/76mW6CJEnS/bZMFF6SJEmTwMJLkiRpIBZekiRJA7HwkiRJGoiFlyRJ0kAsvCRJkgZi4SVJkjQQCy9JkqSBWHhJkiQNxMJLkiRpIBZekiRJA1kmbpItSdJscern+7t/8HNftN69ln3/P67tJWv7v1x/ocvnvfvXveRt9DcbL3T5/P93fi95Gxz2+IUuv/ZDZ/SSt/5rd5nW8+zxkiRJGoiFlyRJ0kAsvCRJkgZi4SVJkjQQCy9JkqSBWHhJkiQNxMJLkiRpIBZekiRJA7HwkiRJGoiFlyRJ0kAsvCRJkgbSW+GVZNUk5yb5QZIfJXlbt3ydJGcm+XH3ee2+2iBJkjSb9NnjdQewU1VtC2wH7JpkR+BNwFlVtSVwVve9JEnSxOut8Krmtu7blbuPAnYHjuqWHwXs0VcbJEmSZpNe53glWTHJhcC1wJlV9V1gg6qaB9B9Xr/PNkiSJM0WvRZeVXVXVW0HPBTYIck2031tkoOTnJfkvOuuu663NkqSJA1lkLMaq+om4GvArsD8JBsBdJ+vXcRrjqiquVU1d86cOUM0U5IkqVd9ntU4J8la3derATsDlwEnAwd2TzsQOKmvNkiSJM0mK/W47o2Ao5KsSCvwjq2qLyf5NnBsklcCvwT26bENkiRJs0ZvhVdVXQRsv5DlNwDP6itXkiRptvLK9ZIkSQOx8JIkSRqIhZckSdJALLwkSZIGYuElSZI0EAsvSZKkgVh4SZIkDcTCS5IkaSAWXpIkSQOx8JIkSRqIhZckSdJALLwkSZIGYuElSZI0EAsvSZKkgVh4SZIkDcTCS5IkaSAWXpIkSQOx8JIkSRqIhZckSdJALLwkSZIGYuElSZI0EAsvSZKkgVh4SZIkDcTCS5IkaSAWXpIkSQOx8JIkSRqIhZckSdJALLwkSZIGYuElSZI0EAsvSZKkgVh4SZIkDcTCS5IkaSC9FV5JHpbkv5NcmuRHSd7QLT88ya+TXNh9PK+vNkiSJM0mK/W47juBN1bVBUnWAM5Pcmb32Pur6j09ZkuSJM06vRVeVTUPmNd9fWuSS4GN+8qTJEma7QaZ45VkM2B74LvdotcmuSjJkUnWHqINkiRJM633wivJ6sDxwGFVdQvwUeDhwHa0HrH3LuJ1Byc5L8l51113Xd/NlCRJ6l2vhVeSlWlF12eq6gSAqppfVXdV1d3Ax4EdFvbaqjqiquZW1dw5c+b02UxJkqRB9HlWY4BPAJdW1ftGlm808rQ9gYv7aoMkSdJs0udZjU8BDgB+mOTCbtmbgZck2Q4o4ErgkB7bIEmSNGv0eVbj2UAW8tApfWVKkiTNZl65XpIkaSAWXpIkSQOx8JIkSRpIn5PrJUmL8ILjvtTLek/ee7eFLt/z+LN7yTtxr6fea9mLTvhJL1kAn3/hI+617MMnzu8t7zV7btDburV8ssdLkiRpIBZekiRJA7HwkiRJGoiFlyRJ0kAsvCRJkgZi4SVJkjQQCy9JkqSBWHhJkiQNxMJLkiRpIBZekiRJA7HwkiRJGoiFlyRJ0kAsvCRJkgZi4SVJkjQQCy9JkqSBWHhJkiQNxMJLkiRpIBZekiRJA7HwkiRJGoiFlyRJ0kAsvCRJkgYyrcIryRuSPDjNJ5JckGSXvhsnSZI0Sabb43VQVd0C7ALMAV4BvKu3VkmSJE2g6RZe6T4/D/hkVf1gZJkkSZKmYbqF1/lJzqAVXqcnWQO4u79mSZIkTZ6Vpvm8VwLbAT+rqt8mWZc23ChJkqRpmlbhVVV3J7kTeFqS0ddc1E+zJEmSJs+0Cq8kRwKPA37EPUOMBZywmNc8DPg0sGH3miOq6gNJ1gE+D2wGXAnsW1W/uY/tlyRJWmZMd6hxx6raeinXfSfwxqq6oJsTdn6SM4GXA2dV1buSvAl4E/C3S7luSZKkZc50J9d/O8lSFV5VNa+qLui+vhW4FNgY2B04qnvaUcAeS7NeSZKkZdV0e7yOohVf1wB30C4lUVX1uOm8OMlmwPbAd4ENqmoebQXzkqy/1K2WJElaBk238DoSOAD4IUt5GYkkqwPHA4dV1S3J9C7/leRg4GCATTbZZGkiJUmSZqXpFl6/rKqTl3blSVamFV2fqaqpifjzk2zU9XZtBFy7sNdW1RHAEQBz586tpc2WJEmabaZbeF2W5LPAl2hDjQCMFFP3kta19Qng0qp638hDJwMH0m45dCBw0tI2WpIkaVk03cJrNVrBNXpj7MVeTgJ4Ct3wZJILu2VvphVcxyZ5JfBLYJ+labAkSdKyaroXUF3qq9RX1dks+n6Oz1ra9UmSJC3rpnsB1VVptw16DLDq1PKqOqindkmSJE2c6V7H62jaFeifA3wdeChwa1+NkiRJmkTTLbweUVX/F7i9qo4C/gJ4bH/NkiRJmjzTLbz+2H2+Kck2wJq0ey1KkiRpmqZ7VuMRSdYG/i/tchCrA2/prVWSJEkTaLpnNf5H9+XXgS36a44kSdLkmtZQY5INknwiyand91t31+GSJEnSNE13jtengNOBh3TfXwEc1kN7JEmSJtZ0C6/1qupYuhtkV9WdwF29tUqSJGkCLbbwSjI1B+z2JOvSbhNEkh2Bm3tumyRJ0kRZ0uT6c4E/A95IO5vx4UnOAeYAe/fcNkmSpImypMIrAFV1fpKnA4/qll1eVX9c7CslSZL0Pyyp8JqT5P8sZPkuSaiq9/XRKEmSpEm0pMJrRdrFUjNAWyRJkibakgqveVX19kFaIkmSNOGWdDkJe7okSZLGZEk9XrskWWdRD1bVjWNujyRJ0sSazuUkitbztQnwm+7rtYBfApv32ThJkqRJstihxqravKq2oN0uaLeqWq+q1gWeD5wwRAMlSZImxXRvGfSEqjpl6puqOhV4ej9NkiRJmkxLGmqccn2SfwCOoQ09vhS4obdWSZIkTaDp9ni9hHaboBO7jzndMkmSJE3TtHq8urMX35Bk9aq6rec2SZIkTaRp9XgleXKSS4BLuu+3TfKRXlsmSZI0YaY71Ph+4Dl087qq6gfA0/pqlCRJ0iSabuFFVf1qgUV3jbktkiRJE226ZzX+KsmTgUryAOD1wKX9NUuSJGnyTLfH61XAa4CNgauA7brvJUmSNE3TPavxemD/ntsyK13z0X/sZb0bHvoPvax3Njvuk7v2tu69X3HavZb9+9HP6S3vkANOv9eyw4/tJ+/wfe+dBfDck/bqJe/U3Y9f6PLnndjP78Ipey78d+EvTvhoL3lfeeGhC13+/OM+00vel/deLv90SlqExRZeSf6mqt6d5IO0C6f+D1X1+t5aJkmSNGGW1ON1Sff5vL4bIkmSNOmWVHi9CPgysFZVfWCA9kiSJE2sJU2uf3ySTYGDkqydZJ3Rj8W9MMmRSa5NcvHIssOT/DrJhd3H88axEZIkScuCJfV4fQw4DdgCOB/IyGPVLV+UTwEfAj69wPL3V9V7lq6ZkiRJy77F9nhV1b9V1VbAkVW1RVVtPvKxuKKLqvoGcOM4GytJkrQsW2zhleTB3Zd/v+Aw45KGGhfjtUku6oYi176P65AkSVrmLGmo8bPA82nDjMXSDTUuzEeBd3SvfQfwXuCghT0xycHAwQCbbLLJUsYsuy778O69rPfRrzmpl/VKkqTpW2zhVVXP7z5vPo6wqpo/9XWSj9POmFzUc48AjgCYO3fuva4hJkmStKxZ0gVU/2xxj1fVBUsTlmSjqprXfbsncPHini9JkjRJljTU+N7FPFbATot6MMnngGcA6yW5Cngr8Iwk23WvvRI4ZCnaKkmStExb0lDjM+/riqvqJQtZ/In7uj5JkqRl3bRukg2QZBtga2DVqWVVteA1uiRJkrQI0yq8kryVNmy4NXAK8FzgbO59cVRJkiQtwpJuGTRlb+BZwDVV9QpgW2CV3lolSZI0gaZbeP2uqu4G7uwuqnotS38NL0mSpOXadOd4nZdkLeDjtIup3gac21ejJEmSJtG0Cq+qenX35ceSnAY8uKou6q9ZkiRJk2e6k+uftrBl3Y2wJUmSNA3THWr865GvVwV2oA05LvICqpIkSfqfpjvUuNvo90keBry7lxZJkiRNqOme1bigq4BtxtkQSZKkSTfdOV4fpN1fEVqxtj3wg74aJUmSNImmO8frMmDF7usbgM9V1Tn9NGnxrvvoMb2sd86hL+1lvbPZ1z7+F72t+xn/6yu9rVuSpGXVYguvJCsD/wq8DLgSCLA+8EHgnCTbV9X3+26kJEnSJFhSj9d7gQcCm1bVrQDdlevfk+SjwK7A5v02UZIkaTIsqfB6HrBlVU3N76KqbklyKHA97WbZkiRJmoYlndV492jRNaWq7gKuq6rv9NMsSZKkybOkwuuSJC9bcGGSlwKX9tMkSZKkybSkocbXACckOYh2pfoCngCsBuzZc9skSZImymILr6r6NfDEJDsBj6Gd1XhqVZ01ROMkSZImyXRvGfRV4Ks9t0WSJGmi3ddbBkmSJGkpWXhJkiQNxMJLkiRpIBZekiRJA7HwkiRJGoiFlyRJ0kAsvCRJkgZi4SVJkjQQCy9JkqSBWHhJkiQNxMJLkiRpIL0VXkmOTHJtkotHlq2T5MwkP+4+r91XviRJ0mzTZ4/Xp4BdF1j2JuCsqtoSOKv7XpIkabnQW+FVVd8Ablxg8e7AUd3XRwF79JUvSZI02ww9x2uDqpoH0H1ef+B8SZKkGTNrJ9cnOTjJeUnOu+6662a6OZIkSffb0IXX/CQbAXSfr13UE6vqiKqaW1Vz58yZM1gDJUmS+jJ04XUycGD39YHASQPnS5IkzZg+LyfxOeDbwKOSXJXklcC7gGcn+THw7O57SZKk5cJKfa24ql6yiIee1VemJEnSbDZrJ9dLkiRNGgsvSZKkgVh4SZIkDcTCS5IkaSAWXpIkSQOx8JIkSRqIhZckSdJALLwkSZIGYuElSZI0EAsvSZKkgVh4SZIkDcTCS5IkaSAWXpIkSQOx8JIkSRqIhZckSdJALLwkSZIGYuElSZI0EAsvSZKkgVh4SZIkDcTCS5IkaSAWXpIkSQOx8JIkSRqIhZckSdJALLwkSZIGYuElSZI0EAsvSZKkgVh4SZIkDcTCS5IkaSAWXpIkSQOx8JIkSRqIhZckSdJALLwkSZIGstJMhCa5ErgVuAu4s6rmzkQ7JEmShjQjhVfnmVV1/QzmS5IkDcqhRkmSpIHMVOFVwBlJzk9y8Ay1QZIkaVAzNdT4lKq6Osn6wJlJLquqb4w+oSvIDgbYZJNNZqKNkiRJYzUjPV5VdXX3+VrgRGCHhTzniKqaW1Vz58yZM3QTJUmSxm7wwivJg5KsMfU1sAtw8dDtkCRJGtpMDDVuAJyYZCr/s1V12gy0Q5IkaVCDF15V9TNg26FzJUmSZpqXk5AkSRqIhZckSdJALLwkSZIGYuElSZI0EAsvSZKkgVh4SZIkDcTCS5IkaSAWXpIkSQOx8JIkSRqIhZckSdJALLwkSZIGYuElSZI0EAsvSZKkgVh4SZIkDcTCS5IkaSAWXpIkSQOx8JIkSRqIhZckSdJALLwkSZIGYuElSZI0EAsvSZKkgVh4SZIkDcTCS5IkaSAWXpIkSQOx8JIkSRqIhZckSdJALLwkSZIGYuElSZI0EAsvSZKkgVh4SZIkDcTCS5IkaSAzUngl2TXJ5Ul+kuRNM9EGSZKkoQ1eeCVZEfgw8Fxga+AlSbYeuh2SJElDm4kerx2An1TVz6rqD8B/ArvPQDskSZIGNROF18bAr0a+v6pbJkmSNNFSVcMGJvsAz6mqv+y+PwDYoapet8DzDgYO7r59FHD5fYhbD7j+fjTXvOUnb5K3zTzzzJu5vEneNvMW7fqq2nVhD6x0/9pzn1wFPGzk+4cCVy/4pKo6Ajji/gQlOa+q5t6fdZi3fORN8raZZ555M5c3ydtm3n0zE0ON3wO2TLJ5kgcALwZOnoF2SJIkDWrwHq+qujPJa4HTgRWBI6vqR0O3Q5IkaWgzMdRIVZ0CnDJA1P0aqjRvucqb5G0zzzzzZi5vkrfNvPtg8Mn1kiRJyytvGSRJkjQQCy9JkqSBWHhJkqRlQjoz3Y77Y2ILr4yY6bb0Ycjtcl9qaXi8aLo8VpZN3T2Xp74edBur03dOkhW6j5XHvm4n149HkhWAACtU1R8HyswQB+DQlpd92XdmkpWq6s4hsmaSx8vY1j/xx8ukHitJVgK2p91+74yq+m3PeQcA5wA/r6pK8gRgVeCHVXVTD3kPALYBdgJWA34C/BC4bOqYHWPWulV1wwLLHl5VPx3Xz3LiCq8kqwNzgacBdwFXAD+g3Zj77h7y1quq6xdY9qiquryPX7gkDwf2BS6pqpOSbAU8ALi8qn4/5iz35XjzHki7SfzqVfXlca9/IXl/CXxxap8meSZwB3BRVd3WQ57Hy3jzJvZ48VgZryQfAR4LXAJsBLy5qi7uHtsHOLmq7hhj3s+BR1XVH5K8GvjzLvf3wKuq6spxZXV5n+rWfyatwNsYWAX4elUdNeasK4EbgZtoBd53gL8Hdq6qn48lY5IKryQbAR/tvv0vYF3aD2tF4EtVNdYr5CfZHLgQuAy4DfgRcCVwKLBVD5X4ZsDngG8A6wOX0n7Z1gB+TPtlG8t/ce7L8e3LLm8D4ANd1pW0Pxp/U1W/7v6b26Oqjh1j3lbAsVX12CQPBl5De6ObQ7tt16uq6pYx5nm8eLxMN8tjZbzHyqOAo6tqhyQPBQ4EHgm8AfgDcFZVPWmMeQ8HPl9Vc7ttPQnYp6quSHIg8Pyq2meMeVt1eY9LsiatUH8gbZ/+b+Dcqnr7mLLWBD5OK7yOBp4IPBnYFfg5cEtVPeX+5kzaHK9nA3dV1R60nfZe4G3AacDrkxw05ryVad2tpwFvBX4B7AasA/woyTFjztuR1rX7t8B3gb2BDwF/DazZtWFc3JfjtRNtesJOwNuB67osgO2AA8ac9yjaf2tT2c8CXgU8nXbD+bH8oRrh8TJek3y8eKyM12OBXwJU1VXAP9N6nl4HbAn8bsx5dwKXJNm5W/d/VdUV3WOXAGuNOW8d4OpuKPzmqrqtqq6tqrNox80zxxVUVTcDLwFuAParqvcB7wAuqKrHAoeMI2dGrlzfoxWBW+BPOxDafzjHJ1mf1m1/5DiCuu7pK5LsD/wrsFNVvT3JrcCPq+rgJFuOI2vEHGBe9/WjgYur6rtde75LG+MfF/fleG1DewOjqq5M8nbgiCT7dY/PW+Qr75urgd91+20+cGpVXQeQ5GpggzHnrYTHyzhtQxt+G/J4+f1Ax4t/W8brTuCnXW/NLd2cq7cA7wSOofVIjUW3P3+R5P3A+2jbumrXC/uftCLzB+PKA6iqc5J8DbgiyfeA82m9T/O6vJ8s5uVLpdu+u7r99+ok/xv4M1qvJbTC8n6btB6vTwN3JLkiyaeTvDHJ3kmeQesqvGhcQd3BvUJV/QZ4PUCSQ4CX024EDmM8IDonANsluZv2X9wfk+zR/eF4Jq0rfVyOZth9ueLA+/JEYNuB9iXAtcBN3R8oqupG4J9o/3n/I/DVcQV1fzzOBT5DG/L4D+CQJG/rfn470/4TH6dPMezxkoGPl+O59+/e7j0fL79Jsgr86Xj5R/o9Xo7hnuPl4B6Pl6m/0z9OcnSSvxrw73SSvAp4BcP8nV4FuLPPvy1V9UXg74Bbp+aqVdV8WqH5UOA3Y8ya+t37PrAHrVftk7Qe07fTjsu3jCsP/nR8vot2HJ5K6zXcizYU/zPgb8aVNbL/7qK9B/6Otm2nTTVnHDkTNcdrSpLH0sZlHwFsBjyMdhB+pY+JjV3mJsB+wD7A/lV1WXfAjH0HJ9mC9sv0bOD5wFNobwzvXnAC6RiytgaeSuuy3oxh9uXDgP0ZZl9uSvvve4h9+YCq+kP39QpVdXf3ZvNV4OlV9c1x5nU5c4An0bZrW+BW2s/vgnHPbenytqH97m1BO2Y2Zpjj5cW0IYL9ej5eNqdNut2J9sbzZPo7XlYG7uze7Fauqj8m2Yk2L6qv42V92lDZ02jHyy3Av9DD8ZI2d+eJtGNlK2AT4N30e6xsQDtWXga8tKou7flvy/W0Y2Vv2gT04xjzsZJ2RuNKwIa0Yb5bgOur6pbu9/+Occ3P6/JWoBWUawMPAn4z7mN/MdkrAqtW1e0952SqyKQdo5fVGM/WnKjCK+0soBWr6taB8lbr8m4bWfbUqjq7p7wH0U6DvnVk2b1OfR1DToANq2rcwxmLy9ugqq5ZYHkv+3JR29fHvuzWuyJt6OiH1Z2xlWQtYP1uGOQhwPzuv6xx5W1NO6PqrpHlK437zbNb78q0N7Njq+qObv8+sK8/jl3ei7q8qUJ2NdoQzndqzGfFdXkvoU3wvWNk+dq0oZ2x/NwWyJwDrFtVl40sW7+qrh338dKte13a8XjpEp98/7MeQOs13IB2htpdVXV5j3krd3kPpfVgrAKsV1Xf6SlvFVohtF73+RpgpZFh1XFmrUSbi/d/aFMZrqb9c3U1cEp1Zzb2lHcFbbjvNto2njTuvC5zfeDGhf3tSrLVOI/ZobImpvDq/pN5I+0sh/8GDqO9GdxCO6vj+z3n/RWt+3M+cE5VjXWce4G8r9EO/P1oB/zXq+rCMWY9DnhDVb2yexP9M9qZMr+gTaQc97YtmPcE2rZdDpxdVT/sMW8F2tlbL6cNAXytqsY21NHlPQn4WFVt2xXP+9AmL88Hvl9Vn+kxb3Xaae070yZon9tD3g60U67PAz5cI6d3T/XsDZXXhwXyPlJVn+o5bzNaL9MzaMNh76P9PlxF+9tyZo9553V5L6W9eX9tnHldQfmX3PO3eS3gZODXwAkL/vPVQ956tB7Kn9AKk2t7zLuVdsbml2hnM57cDQGOM++ptN+Bbbv3iI1pPYg70XpjD6luflmPeQ+nDaH2kbcV8EHamai30gq9q2h/q1cFjquqsczRW0zWFbTC/fhxZU3SHK89af+xrUybfPceoGhnfHwkyaN7zntnt3xH4GM9550HvIt2jZ3HAx8dc95c2lkxAM+hFXwBHtNljXsy6oJ5h9Em4D4R+Pee83bp8u6k9Zj0kfdY2s8M4HnA7rQ5AxcBByZ5aY95zwVeAJxCO077yHs07aytVwL7JjkryUFJ1h530bWQvH26vFd2vTZ9GM3be2T71ukp7/m0+TobABfQ5u9cAdwNvCPJrj3mnQ+8iTaZ+I4e8l4EPLKqtqUVd1+nvXk/FXhnRq6I3lPeWbTpEs8B/qXnvP27vPVphdC7esjbgG7OWFXNr6oLquq4qno17Wy8vxsg7ws95u1M66n8Km16zYa0f1oPpc3xumqArEOAfxtn1iQVXtvS/khBO/30sqp6a1UdCJxB62XoM+/yLu9lA+Yd3lPek4GpbvEdaL0yr6uqg2gH5YvHmLW4vJcPmPf6qnoF7Q/luPN+S+uOB9iUdsbYZ6rq07QCbOue806pqmN6zHsS7ffth7RhiONplyF4TZJHwp+Gd/vIO7TLexpw6IB5T6ed9dRH3kNpZ21Bu77Vt6vq3dWuVXQibVvHaci8jbnnjfsXXe63qmo/2nF76BizFpV3TlW9ZMC8b/WY9z1gpSSfTbJbkrlJHtP98/hE7vm5Lqt51wCfoL3HfQj4CO06Wx+m9UqNcyRrsKxJupzEHdzzX/7xwE9HHtuM9h+/edPPenGSp9PedPYdeWwT4CtjzJr4vKo6JskuSU6l/WF6SJIbacMdOwJfXJbzaFfkPq/L/hWth/lcWk/iN5LsW1XfMG/avgPsn+Q4YHVaXfco2hvD4xn/z2/IvG8DL0/yMlqRtzPt5AtoPUPjntM50XlV9csk+9KGN19AG5m4lTY68QvaEPKynPeFrpfw7mrzouZ1HySZz3jPgB0sa5LmeK0P3NxN7n0AbZjx7mrX5Pg67crL456EN8l5q9H+e3sMraftqqqqtOupHDruSbiTntdlPofW27Yj7XIBc2g9CkdXD/dWGyovyVzg/G7/rUx3XHaPbQP8osZ4wsuk53XrfRrtrNBv0i6xsBlt6GMV4PVV9etlNS/JbrQh998CX6adEPG77u/YoVU1lmslLUd5D6qq27te1w1pBd5Pq4fbgs1Q3uqLWncy3jNSh8qamMILFr7TugmAT66qE81bqqw1FnwzSZuo/ZSqOn2cWctJ3prVndWUNqF/zWrXFurFDOTda3/2aTnIW7VGLqmQ5Nm0mwOfUv2cmTpYXkYuqzKyLLSzOMd+WYJJzev+AX8mbZrJU2lnbF5Imy7xtWpXsR+bGczbm3vu6Xkp7XIqX+56n5e5LJiQwittkuufAy+kXa/oRtrVc0+j3fR1rKd7T3LeSNZeXdYNI1kn1xjvMbYc5T11JO8a2lyBM4Cv1pgvuTADeevS9uee3LM/L6Ltz7Ffj2k5yBs9Pp9Mm+R7Lu3srf8eZ9bQeSNZe9N6Ya+nvbl9mbYvx3YT5+Uk75m06579Pe1v2JZd7uNptxD6pxrv9btmOu8RtDm5c2kT3ceWN2QWTM7k+v1p19l5O+3mrvNpQ3HPp/0SjHvy6yTnTWW9DXj1AlkvHHPW8pK3H/f87G6i9STsBfzFBOTtRzsZYSrvWu7Zny8wb6mNHp+voRXOqwCvSPLiZTxvKutw4LW0wuRO2tX49xhz1vKQNzUEfgZwbVWdXVXvqTaRf03gH8aYNRvyzqmq91fV/j3kDZk1MZPrn0Trrfgp7Z5VT6ZNYvwZ8P+SzKvxTn6d5LxJ3rbZmHeNeeYtRd7Vy3DebPvdW9bzLgIel+RZ1W4YPeoO2qT3cZrkvEG3bVIKr+8Dj06yBq06fRZtzPmnSValnalj3uzLMs8885afvEnetkHzkqSqTu/W+86022bNo13u4XbaiUPHmDe7sv6kqpb5D+DBtEss3EQ7Q+3FwAO6x84GNjZv9mWZZ555y0/eJG/bDOVtTjt7cmfgIbSLQb8Z+F/AWuPMmvS8wbdt3Cuc6Q/alXVXoF35fEPgZebN/izzzDNv+cmb5G0bIq8rDo4HvkG7JdE5tPujTj3+CvNmX9bUxzI/uT7JFkn2SrJ12n3h5lfV1HV2/kC7n5N5syzLPPPMW37yJnnbZiKPdueEP1bV06pqN+BY4NgkqybZHniFebMyC5iMOV6vBl5Gu5fZmklupl2t++u063GsAhxh3qzLMs8885afvEnetpnI25p2CQfSLur7b7RLILyGduHWsV1lfTnIG3rbJqLw2ph2KvRHaOO0W9BuErwl7Yalh5k3K7PMM8+85SdvkrdtJvIuBB6QZKXqrj2Y5F20yx68EPhr82ZlVjPuscuhP2gH+VbAigt57Fu0K5GbN8uyzDPPvOUnb5K3bSbyuvWuxj0XQZ/6vCPtPr47mDc7s6pqMq5cvyhpFwD8Yo356tLLY94kb5t55pk3c3mTvG0zlLcOcFNV3W3e7Mya6MJLkiRpNlnmz2qUJElaVlh4SZIkDcTCS9JESXN2kueOLNs3yWkz2S5JAud4SZpASbYBvgBsT7t6+IXArtVuYLy061qx2oUwJel+s/CSNJGSvJt2k9sHdZ83pV1baSXg8Ko6KclmwNHdcwBeW1XfSvIM4K20m+VuV1VbD9t6SZPKwkvSREryINrNiv8AfBn4UVUdk2Qt4Fxab1gBd1fV75NsCXyuquZ2hddXgG2q6ucz0X5Jk2kSrlwvSfdSVbcn+TxwG7AvsFuSv+oeXhXYBLga+FCS7YC7gEeOrOJciy5J42bhJWmS3d19BNirqi4ffTDJ4cB8YFvayUajF7m8faA2SlqOeFajpOXB6cDrkgQgyfbd8jWBed2VqQ+gTcSXpN5YeElaHrwDWBm4KMnF3ffQbmp8YJLv0IYZ7eWS1Csn10uSJA3EHi9JkqSBWHhJkiQNxMJLkiRpIBZekiRJA7HwkiRJGoiFlyRJ0kAsvCRJkgZi4SVJkjSQ/w94T73FSji4QQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting qualified teams per year\n",
    "\n",
    "fig,ax=plt.subplots(figsize=(10,5))  \n",
    "sns.despine(right=True)  \n",
    "g=sns.barplot(x='Year',y='QualifiedTeams', data=world_cups)\n",
    "g.set_xticklabels(g.get_xticklabels(), rotation=80)\n",
    "g.set_title('Qualified teams per year') \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "35bb34e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Goals scored\n",
    "\n",
    "fig,ax=plt.subplots(figsize=(10,5))  \n",
    "sns.despine(right=True)  \n",
    "g=sns.barplot(x='Year',y='GoalsScored', data=world_cups)\n",
    "g.set_xticklabels(g.get_xticklabels(), rotation=80)\n",
    "g.set_title('Goals scored by teams per year') \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "54290464",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Matches played by teams\n",
    "\n",
    "fig,ax=plt.subplots(figsize=(10,5))  \n",
    "sns.despine(right=True)  \n",
    "g=sns.barplot(x='Year',y='MatchesPlayed', data=world_cups)\n",
    "g.set_xticklabels(g.get_xticklabels(), rotation=80)\n",
    "g.set_title('Matches played by teams per year') \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7cd41bb",
   "metadata": {},
   "source": [
    "## Goals per team per World cup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3c3e4eec",
   "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>Year</th>\n",
       "      <th>Datetime</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Stadium</th>\n",
       "      <th>City</th>\n",
       "      <th>Home Team Name</th>\n",
       "      <th>Home Team Goals</th>\n",
       "      <th>Away Team Goals</th>\n",
       "      <th>Away Team Name</th>\n",
       "      <th>Win conditions</th>\n",
       "      <th>Attendance</th>\n",
       "      <th>Half-time Home Goals</th>\n",
       "      <th>Half-time Away Goals</th>\n",
       "      <th>Referee</th>\n",
       "      <th>Assistant 1</th>\n",
       "      <th>Assistant 2</th>\n",
       "      <th>RoundID</th>\n",
       "      <th>MatchID</th>\n",
       "      <th>Home Team Initials</th>\n",
       "      <th>Away Team Initials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>13 Jul 1930 - 15:00</td>\n",
       "      <td>Group 1</td>\n",
       "      <td>Pocitos</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>France</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td></td>\n",
       "      <td>4444.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>LOMBARDI Domingo (URU)</td>\n",
       "      <td>CRISTOPHE Henry (BEL)</td>\n",
       "      <td>REGO Gilberto (BRA)</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1096.0</td>\n",
       "      <td>FRA</td>\n",
       "      <td>MEX</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year              Datetime    Stage  Stadium         City Home Team Name  \\\n",
       "0  1930.0  13 Jul 1930 - 15:00   Group 1  Pocitos  Montevideo          France   \n",
       "\n",
       "   Home Team Goals  Away Team Goals Away Team Name Win conditions  Attendance  \\\n",
       "0              4.0              1.0         Mexico                     4444.0   \n",
       "\n",
       "   Half-time Home Goals  Half-time Away Goals                 Referee  \\\n",
       "0                   3.0                   0.0  LOMBARDI Domingo (URU)   \n",
       "\n",
       "             Assistant 1          Assistant 2  RoundID  MatchID  \\\n",
       "0  CRISTOPHE Henry (BEL)  REGO Gilberto (BRA)    201.0   1096.0   \n",
       "\n",
       "  Home Team Initials Away Team Initials  \n",
       "0                FRA                MEX  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches.head(1) \n",
    "#team by team and year by year how many total goals have been hit by each team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "299478bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year    Home Team Name\n",
       "1930.0  Argentina         16.0\n",
       "        Brazil             4.0\n",
       "        Chile              4.0\n",
       "        France             4.0\n",
       "        Paraguay           1.0\n",
       "                          ... \n",
       "2014.0  Russia             1.0\n",
       "        Spain              1.0\n",
       "        Switzerland        4.0\n",
       "        USA                2.0\n",
       "        Uruguay            3.0\n",
       "Name: Home Team Goals, Length: 366, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "home=matches.groupby(['Year','Home Team Name'])['Home Team Goals'].sum()\n",
    "home"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "35ee6bf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year    Away Team Name\n",
       "1930.0  Argentina         2.0\n",
       "        Belgium           0.0\n",
       "        Bolivia           0.0\n",
       "        Brazil            1.0\n",
       "        Chile             1.0\n",
       "                         ... \n",
       "2014.0  Russia            1.0\n",
       "        Spain             3.0\n",
       "        Switzerland       3.0\n",
       "        USA               4.0\n",
       "        Uruguay           1.0\n",
       "Name: Away Team Goals, Length: 411, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "away=matches.groupby(['Year','Away Team Name'])['Away Team Goals'].sum()\n",
    "away"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7024700e",
   "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></th>\n",
       "      <th>Home Team Goals</th>\n",
       "      <th>Away Team Goals</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1930.0</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bolivia</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2014.0</th>\n",
       "      <th>Russia</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>427 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Home Team Goals  Away Team Goals\n",
       "Year                                                \n",
       "1930.0 Argentina               16.0              2.0\n",
       "       Belgium                  NaN              0.0\n",
       "       Bolivia                  NaN              0.0\n",
       "       Brazil                   4.0              1.0\n",
       "       Chile                    4.0              1.0\n",
       "...                             ...              ...\n",
       "2014.0 Russia                   1.0              1.0\n",
       "       Spain                    1.0              3.0\n",
       "       Switzerland              4.0              3.0\n",
       "       USA                      2.0              4.0\n",
       "       Uruguay                  3.0              1.0\n",
       "\n",
       "[427 rows x 2 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#concatenate Home and Away team stats\n",
    "\n",
    "goals=pd.concat([home,away],axis=1)\n",
    "goals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a28d7a0b",
   "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></th>\n",
       "      <th>Home Team Goals</th>\n",
       "      <th>Away Team Goals</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1930.0</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bolivia</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2014.0</th>\n",
       "      <th>Russia</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>427 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Home Team Goals  Away Team Goals\n",
       "Year                                                \n",
       "1930.0 Argentina               16.0              2.0\n",
       "       Belgium                  0.0              0.0\n",
       "       Bolivia                  0.0              0.0\n",
       "       Brazil                   4.0              1.0\n",
       "       Chile                    4.0              1.0\n",
       "...                             ...              ...\n",
       "2014.0 Russia                   1.0              1.0\n",
       "       Spain                    1.0              3.0\n",
       "       Switzerland              4.0              3.0\n",
       "       USA                      2.0              4.0\n",
       "       Uruguay                  3.0              1.0\n",
       "\n",
       "[427 rows x 2 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filling null values\n",
    "goals.fillna(0,inplace=True)\n",
    "goals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f29f2627",
   "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></th>\n",
       "      <th>Home Team Goals</th>\n",
       "      <th>Away Team Goals</th>\n",
       "      <th>Goals</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1930.0</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bolivia</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2014.0</th>\n",
       "      <th>Russia</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>427 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Home Team Goals  Away Team Goals  Goals\n",
       "Year                                                       \n",
       "1930.0 Argentina               16.0              2.0   18.0\n",
       "       Belgium                  0.0              0.0    0.0\n",
       "       Bolivia                  0.0              0.0    0.0\n",
       "       Brazil                   4.0              1.0    5.0\n",
       "       Chile                    4.0              1.0    5.0\n",
       "...                             ...              ...    ...\n",
       "2014.0 Russia                   1.0              1.0    2.0\n",
       "       Spain                    1.0              3.0    4.0\n",
       "       Switzerland              4.0              3.0    7.0\n",
       "       USA                      2.0              4.0    6.0\n",
       "       Uruguay                  3.0              1.0    4.0\n",
       "\n",
       "[427 rows x 3 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#adding Home and Away goals for every country\n",
    "goals['Goals']=goals['Home Team Goals']+goals['Away Team Goals']\n",
    "goals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5674205d",
   "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></th>\n",
       "      <th>Goals</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1930.0</th>\n",
       "      <th>Argentina</th>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bolivia</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2014.0</th>\n",
       "      <th>Russia</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>427 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Goals\n",
       "Year                     \n",
       "1930.0 Argentina     18.0\n",
       "       Belgium        0.0\n",
       "       Bolivia        0.0\n",
       "       Brazil         5.0\n",
       "       Chile          5.0\n",
       "...                   ...\n",
       "2014.0 Russia         2.0\n",
       "       Spain          4.0\n",
       "       Switzerland    7.0\n",
       "       USA            6.0\n",
       "       Uruguay        4.0\n",
       "\n",
       "[427 rows x 1 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping Home Team goals and Away team goals column\n",
    "goals=goals.drop(labels=['Home Team Goals','Away Team Goals'], axis=1) \n",
    "# axis=1 is necessary to find which columns need to be dropped\n",
    "goals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1bc12fde",
   "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>Year</th>\n",
       "      <th>Country</th>\n",
       "      <th>Goals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>Bolivia</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>Chile</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>Russia</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>Spain</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>427 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year      Country  Goals\n",
       "0    1930.0    Argentina   18.0\n",
       "1    1930.0      Belgium    0.0\n",
       "2    1930.0      Bolivia    0.0\n",
       "3    1930.0       Brazil    5.0\n",
       "4    1930.0        Chile    5.0\n",
       "..      ...          ...    ...\n",
       "422  2014.0       Russia    2.0\n",
       "423  2014.0        Spain    4.0\n",
       "424  2014.0  Switzerland    7.0\n",
       "425  2014.0          USA    6.0\n",
       "426  2014.0      Uruguay    4.0\n",
       "\n",
       "[427 rows x 3 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Resetting indexes and changing column names\n",
    "goals=goals.reset_index()\n",
    "goals.columns=['Year','Country','Goals']\n",
    "goals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f73a51b6",
   "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>Year</th>\n",
       "      <th>Country</th>\n",
       "      <th>Goals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>Yugoslavia</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>Russia</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>Cameroon</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>Honduras</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>IR Iran</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>427 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year     Country  Goals\n",
       "0    1930.0   Argentina   18.0\n",
       "11   1930.0     Uruguay   15.0\n",
       "10   1930.0         USA    7.0\n",
       "12   1930.0  Yugoslavia    7.0\n",
       "3    1930.0      Brazil    5.0\n",
       "..      ...         ...    ...\n",
       "416  2014.0       Japan    2.0\n",
       "422  2014.0      Russia    2.0\n",
       "401  2014.0    Cameroon    1.0\n",
       "413  2014.0    Honduras    1.0\n",
       "414  2014.0     IR Iran    1.0\n",
       "\n",
       "[427 rows x 3 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sorting goals by Year in ascending and then by goals in descending\n",
    "goals=goals.sort_values(by=['Year','Goals'],ascending=[True,False])\n",
    "goals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b62205c7",
   "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>Year</th>\n",
       "      <th>Country</th>\n",
       "      <th>Goals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>Yugoslavia</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>Germany</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>France</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year      Country  Goals\n",
       "0    1930.0    Argentina   18.0\n",
       "11   1930.0      Uruguay   15.0\n",
       "10   1930.0          USA    7.0\n",
       "12   1930.0   Yugoslavia    7.0\n",
       "3    1930.0       Brazil    5.0\n",
       "..      ...          ...    ...\n",
       "410  2014.0      Germany   29.0\n",
       "419  2014.0  Netherlands   20.0\n",
       "400  2014.0       Brazil   15.0\n",
       "403  2014.0     Colombia   15.0\n",
       "409  2014.0       France   12.0\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#top 5 countries for each year\n",
    "\n",
    "top5=goals.groupby('Year').head(5)\n",
    "top5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4a82fab5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f354f65e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to create a stacked bar chart for the countries\n",
    "x,y=goals['Year'].values,goals['Goals'].values\n",
    "data=[]\n",
    "for team in top5['Country'].drop_duplicates().values:\n",
    "    #print(team)  this prints team list\n",
    "    year=top5[top5['Country']==team]['Year']\n",
    "    goal=top5[top5['Country']==team]['Goals']\n",
    "    \n",
    "    data.append(go.Bar(x=year,y=goal,name=team))\n",
    "\n",
    "layout=go.Layout(barmode='stack',title='Top 5 teams with most goals',showlegend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7e47991d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "name": "Argentina",
         "type": "bar",
         "x": [
          1930,
          1974,
          1978,
          1986,
          1998,
          2006,
          2010
         ],
         "y": [
          18,
          9,
          15,
          14,
          10,
          11,
          10
         ]
        },
        {
         "name": "Uruguay",
         "type": "bar",
         "x": [
          1930,
          1950,
          1954,
          2010
         ],
         "y": [
          15,
          15,
          16,
          11
         ]
        },
        {
         "name": "USA",
         "type": "bar",
         "x": [
          1930
         ],
         "y": [
          7
         ]
        },
        {
         "name": "Yugoslavia",
         "type": "bar",
         "x": [
          1930,
          1950,
          1962,
          1974,
          1990
         ],
         "y": [
          7,
          7,
          10,
          12,
          8
         ]
        },
        {
         "name": "Brazil",
         "type": "bar",
         "x": [
          1930,
          1938,
          1950,
          1958,
          1962,
          1970,
          1978,
          1982,
          1994,
          1998,
          2002,
          2006,
          2010,
          2014
         ],
         "y": [
          5,
          14,
          22,
          16,
          14,
          19,
          10,
          15,
          11,
          14,
          18,
          10,
          9,
          15
         ]
        },
        {
         "name": "Italy",
         "type": "bar",
         "x": [
          1934,
          1938,
          1970,
          1978,
          1982,
          1990,
          2006
         ],
         "y": [
          12,
          11,
          10,
          9,
          12,
          10,
          12
         ]
        },
        {
         "name": "Germany",
         "type": "bar",
         "x": [
          1934,
          1954,
          1958,
          1966,
          1970,
          1974,
          1978,
          1982,
          1990,
          2002,
          2006,
          2010,
          2014
         ],
         "y": [
          11,
          25,
          12,
          15,
          17,
          13,
          10,
          12,
          15,
          14,
          14,
          16,
          29
         ]
        },
        {
         "name": "Czechoslovakia",
         "type": "bar",
         "x": [
          1934,
          1958,
          1990
         ],
         "y": [
          9,
          9,
          10
         ]
        },
        {
         "name": "Austria",
         "type": "bar",
         "x": [
          1934,
          1954
         ],
         "y": [
          7,
          17
         ]
        },
        {
         "name": "Hungary",
         "type": "bar",
         "x": [
          1934,
          1938,
          1954,
          1962,
          1966,
          1982
         ],
         "y": [
          5,
          15,
          27,
          8,
          8,
          12
         ]
        },
        {
         "name": "Sweden",
         "type": "bar",
         "x": [
          1938,
          1950,
          1958,
          1994
         ],
         "y": [
          11,
          11,
          12,
          15
         ]
        },
        {
         "name": "Cuba",
         "type": "bar",
         "x": [
          1938
         ],
         "y": [
          5
         ]
        },
        {
         "name": "Spain",
         "type": "bar",
         "x": [
          1950,
          1986,
          1994,
          2002
         ],
         "y": [
          10,
          11,
          10,
          10
         ]
        },
        {
         "name": "Switzerland",
         "type": "bar",
         "x": [
          1954
         ],
         "y": [
          11
         ]
        },
        {
         "name": "France",
         "type": "bar",
         "x": [
          1958,
          1982,
          1986,
          1998,
          2006,
          2014
         ],
         "y": [
          23,
          16,
          12,
          15,
          9,
          12
         ]
        },
        {
         "name": "Chile",
         "type": "bar",
         "x": [
          1962
         ],
         "y": [
          10
         ]
        },
        {
         "name": "Soviet Union",
         "type": "bar",
         "x": [
          1962,
          1966,
          1986
         ],
         "y": [
          9,
          10,
          12
         ]
        },
        {
         "name": "Portugal",
         "type": "bar",
         "x": [
          1966
         ],
         "y": [
          17
         ]
        },
        {
         "name": "England",
         "type": "bar",
         "x": [
          1966,
          1990
         ],
         "y": [
          11,
          8
         ]
        },
        {
         "name": "Peru",
         "type": "bar",
         "x": [
          1970
         ],
         "y": [
          9
         ]
        },
        {
         "name": "Mexico",
         "type": "bar",
         "x": [
          1970
         ],
         "y": [
          6
         ]
        },
        {
         "name": "Poland",
         "type": "bar",
         "x": [
          1974
         ],
         "y": [
          16
         ]
        },
        {
         "name": "Netherlands",
         "type": "bar",
         "x": [
          1974,
          1978,
          1998,
          2010,
          2014
         ],
         "y": [
          15,
          15,
          13,
          12,
          20
         ]
        },
        {
         "name": "Belgium",
         "type": "bar",
         "x": [
          1986
         ],
         "y": [
          12
         ]
        },
        {
         "name": "Bulgaria",
         "type": "bar",
         "x": [
          1994
         ],
         "y": [
          10
         ]
        },
        {
         "name": "Romania",
         "type": "bar",
         "x": [
          1994
         ],
         "y": [
          10
         ]
        },
        {
         "name": "Croatia",
         "type": "bar",
         "x": [
          1998
         ],
         "y": [
          11
         ]
        },
        {
         "name": "Turkey",
         "type": "bar",
         "x": [
          2002
         ],
         "y": [
          10
         ]
        },
        {
         "name": "Korea Republic",
         "type": "bar",
         "x": [
          2002
         ],
         "y": [
          8
         ]
        },
        {
         "name": "Colombia",
         "type": "bar",
         "x": [
          2014
         ],
         "y": [
          15
         ]
        }
       ],
       "layout": {
        "barmode": "stack",
        "showlegend": false,
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Top 5 teams with most goals"
        }
       }
      },
      "text/html": [
       "<div>                            <div id=\"78b7d5bf-9976-46a0-b199-61d34a12f589\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"78b7d5bf-9976-46a0-b199-61d34a12f589\")) {                    Plotly.newPlot(                        \"78b7d5bf-9976-46a0-b199-61d34a12f589\",                        [{\"name\":\"Argentina\",\"type\":\"bar\",\"x\":[1930.0,1974.0,1978.0,1986.0,1998.0,2006.0,2010.0],\"y\":[18.0,9.0,15.0,14.0,10.0,11.0,10.0]},{\"name\":\"Uruguay\",\"type\":\"bar\",\"x\":[1930.0,1950.0,1954.0,2010.0],\"y\":[15.0,15.0,16.0,11.0]},{\"name\":\"USA\",\"type\":\"bar\",\"x\":[1930.0],\"y\":[7.0]},{\"name\":\"Yugoslavia\",\"type\":\"bar\",\"x\":[1930.0,1950.0,1962.0,1974.0,1990.0],\"y\":[7.0,7.0,10.0,12.0,8.0]},{\"name\":\"Brazil\",\"type\":\"bar\",\"x\":[1930.0,1938.0,1950.0,1958.0,1962.0,1970.0,1978.0,1982.0,1994.0,1998.0,2002.0,2006.0,2010.0,2014.0],\"y\":[5.0,14.0,22.0,16.0,14.0,19.0,10.0,15.0,11.0,14.0,18.0,10.0,9.0,15.0]},{\"name\":\"Italy\",\"type\":\"bar\",\"x\":[1934.0,1938.0,1970.0,1978.0,1982.0,1990.0,2006.0],\"y\":[12.0,11.0,10.0,9.0,12.0,10.0,12.0]},{\"name\":\"Germany\",\"type\":\"bar\",\"x\":[1934.0,1954.0,1958.0,1966.0,1970.0,1974.0,1978.0,1982.0,1990.0,2002.0,2006.0,2010.0,2014.0],\"y\":[11.0,25.0,12.0,15.0,17.0,13.0,10.0,12.0,15.0,14.0,14.0,16.0,29.0]},{\"name\":\"Czechoslovakia\",\"type\":\"bar\",\"x\":[1934.0,1958.0,1990.0],\"y\":[9.0,9.0,10.0]},{\"name\":\"Austria\",\"type\":\"bar\",\"x\":[1934.0,1954.0],\"y\":[7.0,17.0]},{\"name\":\"Hungary\",\"type\":\"bar\",\"x\":[1934.0,1938.0,1954.0,1962.0,1966.0,1982.0],\"y\":[5.0,15.0,27.0,8.0,8.0,12.0]},{\"name\":\"Sweden\",\"type\":\"bar\",\"x\":[1938.0,1950.0,1958.0,1994.0],\"y\":[11.0,11.0,12.0,15.0]},{\"name\":\"Cuba\",\"type\":\"bar\",\"x\":[1938.0],\"y\":[5.0]},{\"name\":\"Spain\",\"type\":\"bar\",\"x\":[1950.0,1986.0,1994.0,2002.0],\"y\":[10.0,11.0,10.0,10.0]},{\"name\":\"Switzerland\",\"type\":\"bar\",\"x\":[1954.0],\"y\":[11.0]},{\"name\":\"France\",\"type\":\"bar\",\"x\":[1958.0,1982.0,1986.0,1998.0,2006.0,2014.0],\"y\":[23.0,16.0,12.0,15.0,9.0,12.0]},{\"name\":\"Chile\",\"type\":\"bar\",\"x\":[1962.0],\"y\":[10.0]},{\"name\":\"Soviet Union\",\"type\":\"bar\",\"x\":[1962.0,1966.0,1986.0],\"y\":[9.0,10.0,12.0]},{\"name\":\"Portugal\",\"type\":\"bar\",\"x\":[1966.0],\"y\":[17.0]},{\"name\":\"England\",\"type\":\"bar\",\"x\":[1966.0,1990.0],\"y\":[11.0,8.0]},{\"name\":\"Peru\",\"type\":\"bar\",\"x\":[1970.0],\"y\":[9.0]},{\"name\":\"Mexico\",\"type\":\"bar\",\"x\":[1970.0],\"y\":[6.0]},{\"name\":\"Poland\",\"type\":\"bar\",\"x\":[1974.0],\"y\":[16.0]},{\"name\":\"Netherlands\",\"type\":\"bar\",\"x\":[1974.0,1978.0,1998.0,2010.0,2014.0],\"y\":[15.0,15.0,13.0,12.0,20.0]},{\"name\":\"Belgium\",\"type\":\"bar\",\"x\":[1986.0],\"y\":[12.0]},{\"name\":\"Bulgaria\",\"type\":\"bar\",\"x\":[1994.0],\"y\":[10.0]},{\"name\":\"Romania\",\"type\":\"bar\",\"x\":[1994.0],\"y\":[10.0]},{\"name\":\"Croatia\",\"type\":\"bar\",\"x\":[1998.0],\"y\":[11.0]},{\"name\":\"Turkey\",\"type\":\"bar\",\"x\":[2002.0],\"y\":[10.0]},{\"name\":\"Korea Republic\",\"type\":\"bar\",\"x\":[2002.0],\"y\":[8.0]},{\"name\":\"Colombia\",\"type\":\"bar\",\"x\":[2014.0],\"y\":[15.0]}],                        {\"barmode\":\"stack\",\"showlegend\":false,\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"title\":{\"text\":\"Top 5 teams with most goals\"}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('78b7d5bf-9976-46a0-b199-61d34a12f589');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=go.Figure(data=data, layout=layout)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0575eaba",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b525031f",
   "metadata": {},
   "source": [
    "## Matches with Highest Number of Attendance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "6196e810",
   "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>Year</th>\n",
       "      <th>Datetime</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Stadium</th>\n",
       "      <th>City</th>\n",
       "      <th>Home Team Name</th>\n",
       "      <th>Home Team Goals</th>\n",
       "      <th>Away Team Goals</th>\n",
       "      <th>Away Team Name</th>\n",
       "      <th>Win conditions</th>\n",
       "      <th>Attendance</th>\n",
       "      <th>Half-time Home Goals</th>\n",
       "      <th>Half-time Away Goals</th>\n",
       "      <th>Referee</th>\n",
       "      <th>Assistant 1</th>\n",
       "      <th>Assistant 2</th>\n",
       "      <th>RoundID</th>\n",
       "      <th>MatchID</th>\n",
       "      <th>Home Team Initials</th>\n",
       "      <th>Away Team Initials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>13 Jul 1930 - 15:00</td>\n",
       "      <td>Group 1</td>\n",
       "      <td>Pocitos</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>France</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td></td>\n",
       "      <td>4444.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>LOMBARDI Domingo (URU)</td>\n",
       "      <td>CRISTOPHE Henry (BEL)</td>\n",
       "      <td>REGO Gilberto (BRA)</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1096.0</td>\n",
       "      <td>FRA</td>\n",
       "      <td>MEX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>13 Jul 1930 - 15:00</td>\n",
       "      <td>Group 4</td>\n",
       "      <td>Parque Central</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>USA</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Belgium</td>\n",
       "      <td></td>\n",
       "      <td>18346.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>MACIAS Jose (ARG)</td>\n",
       "      <td>MATEUCCI Francisco (URU)</td>\n",
       "      <td>WARNKEN Alberto (CHI)</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>BEL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year              Datetime    Stage         Stadium         City  \\\n",
       "0  1930.0  13 Jul 1930 - 15:00   Group 1         Pocitos  Montevideo    \n",
       "1  1930.0  13 Jul 1930 - 15:00   Group 4  Parque Central  Montevideo    \n",
       "\n",
       "  Home Team Name  Home Team Goals  Away Team Goals Away Team Name  \\\n",
       "0         France              4.0              1.0         Mexico   \n",
       "1            USA              3.0              0.0        Belgium   \n",
       "\n",
       "  Win conditions  Attendance  Half-time Home Goals  Half-time Away Goals  \\\n",
       "0                     4444.0                   3.0                   0.0   \n",
       "1                    18346.0                   2.0                   0.0   \n",
       "\n",
       "                  Referee               Assistant 1            Assistant 2  \\\n",
       "0  LOMBARDI Domingo (URU)     CRISTOPHE Henry (BEL)    REGO Gilberto (BRA)   \n",
       "1       MACIAS Jose (ARG)  MATEUCCI Francisco (URU)  WARNKEN Alberto (CHI)   \n",
       "\n",
       "   RoundID  MatchID Home Team Initials Away Team Initials  \n",
       "0    201.0   1096.0                FRA                MEX  \n",
       "1    201.0   1090.0                USA                BEL  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "8c00e63d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#changing into date time format\n",
    "matches['Datetime']=pd.to_datetime(matches['Datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f5ba1356",
   "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>Year</th>\n",
       "      <th>Datetime</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Stadium</th>\n",
       "      <th>City</th>\n",
       "      <th>Home Team Name</th>\n",
       "      <th>Home Team Goals</th>\n",
       "      <th>Away Team Goals</th>\n",
       "      <th>Away Team Name</th>\n",
       "      <th>Win conditions</th>\n",
       "      <th>Attendance</th>\n",
       "      <th>Half-time Home Goals</th>\n",
       "      <th>Half-time Away Goals</th>\n",
       "      <th>Referee</th>\n",
       "      <th>Assistant 1</th>\n",
       "      <th>Assistant 2</th>\n",
       "      <th>RoundID</th>\n",
       "      <th>MatchID</th>\n",
       "      <th>Home Team Initials</th>\n",
       "      <th>Away Team Initials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1930.0</td>\n",
       "      <td>13 Jul, 1930</td>\n",
       "      <td>Group 1</td>\n",
       "      <td>Pocitos</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>France</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td></td>\n",
       "      <td>4444.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>LOMBARDI Domingo (URU)</td>\n",
       "      <td>CRISTOPHE Henry (BEL)</td>\n",
       "      <td>REGO Gilberto (BRA)</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1096.0</td>\n",
       "      <td>FRA</td>\n",
       "      <td>MEX</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year      Datetime    Stage  Stadium         City Home Team Name  \\\n",
       "0  1930.0  13 Jul, 1930  Group 1  Pocitos  Montevideo          France   \n",
       "\n",
       "   Home Team Goals  Away Team Goals Away Team Name Win conditions  Attendance  \\\n",
       "0              4.0              1.0         Mexico                     4444.0   \n",
       "\n",
       "   Half-time Home Goals  Half-time Away Goals                 Referee  \\\n",
       "0                   3.0                   0.0  LOMBARDI Domingo (URU)   \n",
       "\n",
       "             Assistant 1          Assistant 2  RoundID  MatchID  \\\n",
       "0  CRISTOPHE Henry (BEL)  REGO Gilberto (BRA)    201.0   1096.0   \n",
       "\n",
       "  Home Team Initials Away Team Initials  \n",
       "0                FRA                MEX  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removed time from date-time\n",
    "matches['Datetime']=matches['Datetime'].apply(lambda x: x.strftime('%d %b, %Y'))\n",
    "matches.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "347c05be",
   "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>Year</th>\n",
       "      <th>Datetime</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Stadium</th>\n",
       "      <th>City</th>\n",
       "      <th>Home Team Name</th>\n",
       "      <th>Home Team Goals</th>\n",
       "      <th>Away Team Goals</th>\n",
       "      <th>Away Team Name</th>\n",
       "      <th>Win conditions</th>\n",
       "      <th>Attendance</th>\n",
       "      <th>Half-time Home Goals</th>\n",
       "      <th>Half-time Away Goals</th>\n",
       "      <th>Referee</th>\n",
       "      <th>Assistant 1</th>\n",
       "      <th>Assistant 2</th>\n",
       "      <th>RoundID</th>\n",
       "      <th>MatchID</th>\n",
       "      <th>Home Team Initials</th>\n",
       "      <th>Away Team Initials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>16 Jul, 1950</td>\n",
       "      <td>Group 6</td>\n",
       "      <td>Maracanã Stadium</td>\n",
       "      <td>Rio De Janeiro</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td></td>\n",
       "      <td>173850.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>READER George (ENG)</td>\n",
       "      <td>ELLIS Arthur (ENG)</td>\n",
       "      <td>MITCHELL George (SCO)</td>\n",
       "      <td>209.0</td>\n",
       "      <td>1190.0</td>\n",
       "      <td>URU</td>\n",
       "      <td>BRA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>13 Jul, 1950</td>\n",
       "      <td>Group 6</td>\n",
       "      <td>Maracanã Stadium</td>\n",
       "      <td>Rio De Janeiro</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Spain</td>\n",
       "      <td></td>\n",
       "      <td>152772.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>LEAFE Reginald (ENG)</td>\n",
       "      <td>MITCHELL George (SCO)</td>\n",
       "      <td>DA COSTA VIEIRA Jose (POR)</td>\n",
       "      <td>209.0</td>\n",
       "      <td>1186.0</td>\n",
       "      <td>BRA</td>\n",
       "      <td>ESP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>01 Jul, 1950</td>\n",
       "      <td>Group 1</td>\n",
       "      <td>Maracanã Stadium</td>\n",
       "      <td>Rio De Janeiro</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yugoslavia</td>\n",
       "      <td></td>\n",
       "      <td>142429.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>GRIFFITHS Benjamin (WAL)</td>\n",
       "      <td>BERANEK Alois (AUT)</td>\n",
       "      <td>DA COSTA VIEIRA Jose (POR)</td>\n",
       "      <td>208.0</td>\n",
       "      <td>1191.0</td>\n",
       "      <td>BRA</td>\n",
       "      <td>YUG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>09 Jul, 1950</td>\n",
       "      <td>Group 6</td>\n",
       "      <td>Maracanã Stadium</td>\n",
       "      <td>Rio De Janeiro</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Sweden</td>\n",
       "      <td></td>\n",
       "      <td>138886.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ELLIS Arthur (ENG)</td>\n",
       "      <td>GARCIA Prudencio (USA)</td>\n",
       "      <td>DE LA SALLE Charles (FRA)</td>\n",
       "      <td>209.0</td>\n",
       "      <td>1189.0</td>\n",
       "      <td>BRA</td>\n",
       "      <td>SWE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>1986.0</td>\n",
       "      <td>07 Jun, 1986</td>\n",
       "      <td>Group B</td>\n",
       "      <td>Estadio Azteca</td>\n",
       "      <td>Mexico City</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Paraguay</td>\n",
       "      <td></td>\n",
       "      <td>114600.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>COURTNEY George (ENG)</td>\n",
       "      <td>FREDRIKSSON Erik (SWE)</td>\n",
       "      <td>IGNA Ioan (ROU)</td>\n",
       "      <td>308.0</td>\n",
       "      <td>680.0</td>\n",
       "      <td>MEX</td>\n",
       "      <td>PAR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>1986.0</td>\n",
       "      <td>29 Jun, 1986</td>\n",
       "      <td>Final</td>\n",
       "      <td>Estadio Azteca</td>\n",
       "      <td>Mexico City</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Germany</td>\n",
       "      <td></td>\n",
       "      <td>114600.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ARPPI FILHO Romualdo (BRA)</td>\n",
       "      <td>FREDRIKSSON Erik (SWE)</td>\n",
       "      <td>ULLOA MORERA Berny (CRC)</td>\n",
       "      <td>3467.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>ARG</td>\n",
       "      <td>FRG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>1986.0</td>\n",
       "      <td>15 Jun, 1986</td>\n",
       "      <td>Round of 16</td>\n",
       "      <td>Estadio Azteca</td>\n",
       "      <td>Mexico City</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td></td>\n",
       "      <td>114580.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ARPPI FILHO Romualdo (BRA)</td>\n",
       "      <td>MENDEZ MOLINA Romulo (GUA)</td>\n",
       "      <td>IGNA Ioan (ROU)</td>\n",
       "      <td>309.0</td>\n",
       "      <td>463.0</td>\n",
       "      <td>MEX</td>\n",
       "      <td>BUL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>1986.0</td>\n",
       "      <td>22 Jun, 1986</td>\n",
       "      <td>Quarter-finals</td>\n",
       "      <td>Estadio Azteca</td>\n",
       "      <td>Mexico City</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>England</td>\n",
       "      <td></td>\n",
       "      <td>114580.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BENNACEUR Ali (TUN)</td>\n",
       "      <td>ULLOA MORERA Berny (CRC)</td>\n",
       "      <td>DOTCHEV Bogdan (BUL)</td>\n",
       "      <td>714.0</td>\n",
       "      <td>392.0</td>\n",
       "      <td>ARG</td>\n",
       "      <td>ENG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>1986.0</td>\n",
       "      <td>25 Jun, 1986</td>\n",
       "      <td>Semi-finals</td>\n",
       "      <td>Estadio Azteca</td>\n",
       "      <td>Mexico City</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Belgium</td>\n",
       "      <td></td>\n",
       "      <td>114500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>MARQUEZ RAMIREZ Antonio (MEX)</td>\n",
       "      <td>MENDEZ MOLINA Romulo (GUA)</td>\n",
       "      <td>SILVA VALENTE Carlos Alberto (POR)</td>\n",
       "      <td>3469.0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>ARG</td>\n",
       "      <td>BEL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>1986.0</td>\n",
       "      <td>03 Jun, 1986</td>\n",
       "      <td>Group B</td>\n",
       "      <td>Estadio Azteca</td>\n",
       "      <td>Mexico City</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td></td>\n",
       "      <td>110000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>ESPOSITO Carlos (ARG)</td>\n",
       "      <td>SILVA VALENTE Carlos Alberto (POR)</td>\n",
       "      <td>MENDEZ MOLINA Romulo (GUA)</td>\n",
       "      <td>308.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>BEL</td>\n",
       "      <td>MEX</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year      Datetime           Stage           Stadium             City  \\\n",
       "74   1950.0  16 Jul, 1950         Group 6  Maracanã Stadium  Rio De Janeiro    \n",
       "71   1950.0  13 Jul, 1950         Group 6  Maracanã Stadium  Rio De Janeiro    \n",
       "63   1950.0  01 Jul, 1950         Group 1  Maracanã Stadium  Rio De Janeiro    \n",
       "70   1950.0  09 Jul, 1950         Group 6  Maracanã Stadium  Rio De Janeiro    \n",
       "379  1986.0  07 Jun, 1986         Group B    Estadio Azteca     Mexico City    \n",
       "411  1986.0  29 Jun, 1986           Final    Estadio Azteca     Mexico City    \n",
       "397  1986.0  15 Jun, 1986     Round of 16    Estadio Azteca     Mexico City    \n",
       "407  1986.0  22 Jun, 1986  Quarter-finals    Estadio Azteca     Mexico City    \n",
       "409  1986.0  25 Jun, 1986     Semi-finals    Estadio Azteca     Mexico City    \n",
       "367  1986.0  03 Jun, 1986         Group B    Estadio Azteca     Mexico City    \n",
       "\n",
       "    Home Team Name  Home Team Goals  Away Team Goals Away Team Name  \\\n",
       "74         Uruguay              2.0              1.0         Brazil   \n",
       "71          Brazil              6.0              1.0          Spain   \n",
       "63          Brazil              2.0              0.0     Yugoslavia   \n",
       "70          Brazil              7.0              1.0         Sweden   \n",
       "379         Mexico              1.0              1.0       Paraguay   \n",
       "411      Argentina              3.0              2.0        Germany   \n",
       "397         Mexico              2.0              0.0       Bulgaria   \n",
       "407      Argentina              2.0              1.0        England   \n",
       "409      Argentina              2.0              0.0        Belgium   \n",
       "367        Belgium              1.0              2.0         Mexico   \n",
       "\n",
       "    Win conditions  Attendance  Half-time Home Goals  Half-time Away Goals  \\\n",
       "74                    173850.0                   0.0                   0.0   \n",
       "71                    152772.0                   3.0                   0.0   \n",
       "63                    142429.0                   1.0                   0.0   \n",
       "70                    138886.0                   3.0                   0.0   \n",
       "379                   114600.0                   1.0                   0.0   \n",
       "411                   114600.0                   1.0                   0.0   \n",
       "397                   114580.0                   1.0                   0.0   \n",
       "407                   114580.0                   0.0                   0.0   \n",
       "409                   114500.0                   0.0                   0.0   \n",
       "367                   110000.0                   1.0                   2.0   \n",
       "\n",
       "                           Referee                         Assistant 1  \\\n",
       "74             READER George (ENG)                  ELLIS Arthur (ENG)   \n",
       "71            LEAFE Reginald (ENG)               MITCHELL George (SCO)   \n",
       "63        GRIFFITHS Benjamin (WAL)                 BERANEK Alois (AUT)   \n",
       "70              ELLIS Arthur (ENG)              GARCIA Prudencio (USA)   \n",
       "379          COURTNEY George (ENG)              FREDRIKSSON Erik (SWE)   \n",
       "411     ARPPI FILHO Romualdo (BRA)              FREDRIKSSON Erik (SWE)   \n",
       "397     ARPPI FILHO Romualdo (BRA)          MENDEZ MOLINA Romulo (GUA)   \n",
       "407            BENNACEUR Ali (TUN)            ULLOA MORERA Berny (CRC)   \n",
       "409  MARQUEZ RAMIREZ Antonio (MEX)          MENDEZ MOLINA Romulo (GUA)   \n",
       "367          ESPOSITO Carlos (ARG)  SILVA VALENTE Carlos Alberto (POR)   \n",
       "\n",
       "                            Assistant 2  RoundID  MatchID Home Team Initials  \\\n",
       "74                MITCHELL George (SCO)    209.0   1190.0                URU   \n",
       "71           DA COSTA VIEIRA Jose (POR)    209.0   1186.0                BRA   \n",
       "63           DA COSTA VIEIRA Jose (POR)    208.0   1191.0                BRA   \n",
       "70            DE LA SALLE Charles (FRA)    209.0   1189.0                BRA   \n",
       "379                     IGNA Ioan (ROU)    308.0    680.0                MEX   \n",
       "411            ULLOA MORERA Berny (CRC)   3467.0    393.0                ARG   \n",
       "397                     IGNA Ioan (ROU)    309.0    463.0                MEX   \n",
       "407                DOTCHEV Bogdan (BUL)    714.0    392.0                ARG   \n",
       "409  SILVA VALENTE Carlos Alberto (POR)   3469.0    388.0                ARG   \n",
       "367          MENDEZ MOLINA Romulo (GUA)    308.0    428.0                BEL   \n",
       "\n",
       "    Away Team Initials  \n",
       "74                 BRA  \n",
       "71                 ESP  \n",
       "63                 YUG  \n",
       "70                 SWE  \n",
       "379                PAR  \n",
       "411                FRG  \n",
       "397                BUL  \n",
       "407                ENG  \n",
       "409                BEL  \n",
       "367                MEX  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 10 matches by attendance\n",
    "top10=matches.sort_values(by='Attendance', ascending=False)[:10]\n",
    "top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "132e815b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# top 10 matches \n",
    "top10['vs']=top10['Home Team Name']+'vs'+top10['Away Team Name']\n",
    "\n",
    "plt.figure(figsize=(12,9))\n",
    "\n",
    "ax=sns.barplot(y=top10['vs'], x=top10['Attendance'])\n",
    "sns.despine(right=True)\n",
    "\n",
    "plt.ylabel('Match Teams')\n",
    "plt.xlabel('Attendance')\n",
    "plt.title('Matches with highest number of attendance')\n",
    "\n",
    "#get the stadium name and date on each bar\n",
    "for i,s in enumerate('Stadium: '+top10['Stadium']+', Date:'+top10['Datetime']):\n",
    "    ax.text(2000, i, s, fontsize=12, color='white')   #2000 is  a tested number\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5e409dc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3a6e956e",
   "metadata": {},
   "source": [
    "## Stadiums with Highest Average Attendance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "753028e3",
   "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>Stadium</th>\n",
       "      <th>City</th>\n",
       "      <th>Attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Estadio Azteca</td>\n",
       "      <td>Mexico City</td>\n",
       "      <td>100923.684211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Rose Bowl</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>92600.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Wembley Stadium</td>\n",
       "      <td>London</td>\n",
       "      <td>86448.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Maracanã Stadium</td>\n",
       "      <td>Rio De Janeiro</td>\n",
       "      <td>86417.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Soccer City Stadium</td>\n",
       "      <td>Johannesburg</td>\n",
       "      <td>83857.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Giorgio Ascarelli</td>\n",
       "      <td>Naples</td>\n",
       "      <td>8000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Independencia</td>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>7590.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Eucaliptos</td>\n",
       "      <td>Porto Alegre</td>\n",
       "      <td>7329.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Fort Carree</td>\n",
       "      <td>Antibes</td>\n",
       "      <td>7000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Pocitos</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>3496.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>182 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Stadium             City     Attendance\n",
       "32        Estadio Azteca     Mexico City   100923.684211\n",
       "136            Rose Bowl     Los Angeles    92600.875000\n",
       "178      Wembley Stadium          London    86448.222222\n",
       "98      Maracanã Stadium  Rio De Janeiro    86417.333333\n",
       "150  Soccer City Stadium    Johannesburg    83857.375000\n",
       "..                   ...              ...            ...\n",
       "64     Giorgio Ascarelli          Naples     8000.000000\n",
       "75         Independencia  Belo Horizonte     7590.333333\n",
       "48            Eucaliptos    Porto Alegre     7329.000000\n",
       "57           Fort Carree         Antibes     7000.000000\n",
       "125              Pocitos      Montevideo     3496.500000\n",
       "\n",
       "[182 rows x 3 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches['Year']=matches['Year'].astype(int)\n",
    "\n",
    "std=matches.groupby(['Stadium','City'])['Attendance'].mean().reset_index().sort_values(by='Attendance', ascending=False)\n",
    "#grouping by Stadium and city by the mean values, resetting the index and sorting in descending\n",
    "std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "3f2612de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top10=std[:10]\n",
    "\n",
    "plt.figure(figsize=(12,9))\n",
    "ax=sns.barplot(y=top10['Stadium'], x=top10['Attendance'])\n",
    "sns.despine(right=True)\n",
    "\n",
    "plt.ylabel('Stadium Names')\n",
    "plt.xlabel('Attendance')\n",
    "plt.title('Stadium with highest number of attendance')\n",
    "\n",
    "#get the city name on each bar\n",
    "for i,s in enumerate('City: '+top10['City']):\n",
    "    ax.text(2000, i, s, fontsize=12, color='white')   #2000 is  a tested number\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "d9fc285f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
        {
         "marker": {
          "color": "rgba(255, 153, 51, 0.6)",
          "line": {
           "color": "rgba(255, 153, 51, 1.0)",
           "width": 1
          }
         },
         "name": "City",
         "orientation": "v",
         "text": "",
         "type": "bar",
         "x": [
          "Mexico City ",
          "Montevideo ",
          "Rio De Janeiro ",
          "Guadalajara ",
          "Johannesburg ",
          "Sao Paulo ",
          "Buenos Aires ",
          "Munich ",
          "Leon ",
          "Belo Horizonte ",
          "London ",
          "Dortmund ",
          "Brasilia ",
          "Santiago De Chile ",
          "Stuttgart ",
          "Frankfurt/Main ",
          "Gelsenkirchen ",
          "Rome ",
          "Hanover ",
          "Milan "
         ],
         "y": [
          23,
          18,
          18,
          17,
          15,
          14,
          12,
          11,
          11,
          11,
          10,
          10,
          10,
          10,
          10,
          10,
          10,
          9,
          9,
          9
         ]
        }
       ],
       "layout": {
        "legend": {
         "bgcolor": "#F5F6F9",
         "font": {
          "color": "#4D5663"
         }
        },
        "paper_bgcolor": "#F5F6F9",
        "plot_bgcolor": "#F5F6F9",
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "font": {
          "color": "#4D5663"
         }
        },
        "xaxis": {
         "gridcolor": "#E1E5ED",
         "showgrid": true,
         "tickfont": {
          "color": "#4D5663"
         },
         "title": {
          "font": {
           "color": "#4D5663"
          },
          "text": ""
         },
         "zerolinecolor": "#E1E5ED"
        },
        "yaxis": {
         "gridcolor": "#E1E5ED",
         "showgrid": true,
         "tickfont": {
          "color": "#4D5663"
         },
         "title": {
          "font": {
           "color": "#4D5663"
          },
          "text": ""
         },
         "zerolinecolor": "#E1E5ED"
        }
       }
      },
      "text/html": [
       "<div>                            <div id=\"87f30b70-f17f-4428-9a36-247ae586a22d\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
       "                    window.PLOTLYENV.BASE_URL='https://plot.ly';                                    if (document.getElementById(\"87f30b70-f17f-4428-9a36-247ae586a22d\")) {                    Plotly.newPlot(                        \"87f30b70-f17f-4428-9a36-247ae586a22d\",                        [{\"marker\":{\"color\":\"rgba(255, 153, 51, 0.6)\",\"line\":{\"color\":\"rgba(255, 153, 51, 1.0)\",\"width\":1}},\"name\":\"City\",\"orientation\":\"v\",\"text\":\"\",\"type\":\"bar\",\"x\":[\"Mexico City \",\"Montevideo \",\"Rio De Janeiro \",\"Guadalajara \",\"Johannesburg \",\"Sao Paulo \",\"Buenos Aires \",\"Munich \",\"Leon \",\"Belo Horizonte \",\"London \",\"Dortmund \",\"Brasilia \",\"Santiago De Chile \",\"Stuttgart \",\"Frankfurt/Main \",\"Gelsenkirchen \",\"Rome \",\"Hanover \",\"Milan \"],\"y\":[23,18,18,17,15,14,12,11,11,11,10,10,10,10,10,10,10,9,9,9]}],                        {\"legend\":{\"bgcolor\":\"#F5F6F9\",\"font\":{\"color\":\"#4D5663\"}},\"paper_bgcolor\":\"#F5F6F9\",\"plot_bgcolor\":\"#F5F6F9\",\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"title\":{\"font\":{\"color\":\"#4D5663\"}},\"xaxis\":{\"gridcolor\":\"#E1E5ED\",\"showgrid\":true,\"tickfont\":{\"color\":\"#4D5663\"},\"title\":{\"font\":{\"color\":\"#4D5663\"},\"text\":\"\"},\"zerolinecolor\":\"#E1E5ED\"},\"yaxis\":{\"gridcolor\":\"#E1E5ED\",\"showgrid\":true,\"tickfont\":{\"color\":\"#4D5663\"},\"title\":{\"font\":{\"color\":\"#4D5663\"},\"text\":\"\"},\"zerolinecolor\":\"#E1E5ED\"}},                        {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\", \"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('87f30b70-f17f-4428-9a36-247ae586a22d');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Cities which hosted the highest world cup matches\n",
    "matches['City'].value_counts()[:20].iplot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d0ee10a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "235a23b4",
   "metadata": {},
   "source": [
    "## Match Outcomes by Home and Away teams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ae1465c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to get labels of home and away team wins\n",
    "def getlabels(matches):\n",
    "    if matches['Home Team Goals']>matches['Away Team Goals']:\n",
    "        return 'Home Team Wins'\n",
    "    if matches['Home Team Goals']<matches['Away Team Goals']:\n",
    "        return 'Away Team Wins'\n",
    "    else:\n",
    "        return 'DRAW'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "f6024021",
   "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>Year</th>\n",
       "      <th>Datetime</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Stadium</th>\n",
       "      <th>City</th>\n",
       "      <th>Home Team Name</th>\n",
       "      <th>Home Team Goals</th>\n",
       "      <th>Away Team Goals</th>\n",
       "      <th>Away Team Name</th>\n",
       "      <th>Win conditions</th>\n",
       "      <th>...</th>\n",
       "      <th>Half-time Home Goals</th>\n",
       "      <th>Half-time Away Goals</th>\n",
       "      <th>Referee</th>\n",
       "      <th>Assistant 1</th>\n",
       "      <th>Assistant 2</th>\n",
       "      <th>RoundID</th>\n",
       "      <th>MatchID</th>\n",
       "      <th>Home Team Initials</th>\n",
       "      <th>Away Team Initials</th>\n",
       "      <th>outcomes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1930</td>\n",
       "      <td>13 Jul, 1930</td>\n",
       "      <td>Group 1</td>\n",
       "      <td>Pocitos</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>France</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>LOMBARDI Domingo (URU)</td>\n",
       "      <td>CRISTOPHE Henry (BEL)</td>\n",
       "      <td>REGO Gilberto (BRA)</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1096.0</td>\n",
       "      <td>FRA</td>\n",
       "      <td>MEX</td>\n",
       "      <td>Home Team Wins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1930</td>\n",
       "      <td>13 Jul, 1930</td>\n",
       "      <td>Group 4</td>\n",
       "      <td>Parque Central</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>USA</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Belgium</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>MACIAS Jose (ARG)</td>\n",
       "      <td>MATEUCCI Francisco (URU)</td>\n",
       "      <td>WARNKEN Alberto (CHI)</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1090.0</td>\n",
       "      <td>USA</td>\n",
       "      <td>BEL</td>\n",
       "      <td>Home Team Wins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1930</td>\n",
       "      <td>14 Jul, 1930</td>\n",
       "      <td>Group 2</td>\n",
       "      <td>Parque Central</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Yugoslavia</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TEJADA Anibal (URU)</td>\n",
       "      <td>VALLARINO Ricardo (URU)</td>\n",
       "      <td>BALWAY Thomas (FRA)</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1093.0</td>\n",
       "      <td>YUG</td>\n",
       "      <td>BRA</td>\n",
       "      <td>Home Team Wins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1930</td>\n",
       "      <td>14 Jul, 1930</td>\n",
       "      <td>Group 3</td>\n",
       "      <td>Pocitos</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Romania</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Peru</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>WARNKEN Alberto (CHI)</td>\n",
       "      <td>LANGENUS Jean (BEL)</td>\n",
       "      <td>MATEUCCI Francisco (URU)</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>ROU</td>\n",
       "      <td>PER</td>\n",
       "      <td>Home Team Wins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1930</td>\n",
       "      <td>15 Jul, 1930</td>\n",
       "      <td>Group 1</td>\n",
       "      <td>Parque Central</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>France</td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>REGO Gilberto (BRA)</td>\n",
       "      <td>SAUCEDO Ulises (BOL)</td>\n",
       "      <td>RADULESCU Constantin (ROU)</td>\n",
       "      <td>201.0</td>\n",
       "      <td>1085.0</td>\n",
       "      <td>ARG</td>\n",
       "      <td>FRA</td>\n",
       "      <td>Home Team Wins</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year      Datetime    Stage         Stadium         City Home Team Name  \\\n",
       "0  1930  13 Jul, 1930  Group 1         Pocitos  Montevideo          France   \n",
       "1  1930  13 Jul, 1930  Group 4  Parque Central  Montevideo             USA   \n",
       "2  1930  14 Jul, 1930  Group 2  Parque Central  Montevideo      Yugoslavia   \n",
       "3  1930  14 Jul, 1930  Group 3         Pocitos  Montevideo         Romania   \n",
       "4  1930  15 Jul, 1930  Group 1  Parque Central  Montevideo       Argentina   \n",
       "\n",
       "   Home Team Goals  Away Team Goals Away Team Name Win conditions  ...  \\\n",
       "0              4.0              1.0         Mexico                 ...   \n",
       "1              3.0              0.0        Belgium                 ...   \n",
       "2              2.0              1.0         Brazil                 ...   \n",
       "3              3.0              1.0           Peru                 ...   \n",
       "4              1.0              0.0         France                 ...   \n",
       "\n",
       "   Half-time Home Goals  Half-time Away Goals                 Referee  \\\n",
       "0                   3.0                   0.0  LOMBARDI Domingo (URU)   \n",
       "1                   2.0                   0.0       MACIAS Jose (ARG)   \n",
       "2                   2.0                   0.0     TEJADA Anibal (URU)   \n",
       "3                   1.0                   0.0   WARNKEN Alberto (CHI)   \n",
       "4                   0.0                   0.0     REGO Gilberto (BRA)   \n",
       "\n",
       "                Assistant 1                 Assistant 2 RoundID  MatchID  \\\n",
       "0     CRISTOPHE Henry (BEL)         REGO Gilberto (BRA)   201.0   1096.0   \n",
       "1  MATEUCCI Francisco (URU)       WARNKEN Alberto (CHI)   201.0   1090.0   \n",
       "2   VALLARINO Ricardo (URU)         BALWAY Thomas (FRA)   201.0   1093.0   \n",
       "3       LANGENUS Jean (BEL)    MATEUCCI Francisco (URU)   201.0   1098.0   \n",
       "4      SAUCEDO Ulises (BOL)  RADULESCU Constantin (ROU)   201.0   1085.0   \n",
       "\n",
       "   Home Team Initials Away Team Initials        outcomes  \n",
       "0                 FRA                MEX  Home Team Wins  \n",
       "1                 USA                BEL  Home Team Wins  \n",
       "2                 YUG                BRA  Home Team Wins  \n",
       "3                 ROU                PER  Home Team Wins  \n",
       "4                 ARG                FRA  Home Team Wins  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches['outcomes']=matches.apply(lambda x: getlabels(x), axis=1)\n",
    "matches.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "181bbfb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Home Team Wins    488\n",
       "DRAW              190\n",
       "Away Team Wins    174\n",
       "Name: outcomes, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "nt=matches['outcomes'].value_counts()\n",
    "nt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "f4e103fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='outcomes'>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nt.plot.pie(autopct='%1.0f%%') \n",
    "# autopct gives the percentage inside the pie-plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "cb4552f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='outcomes'>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#modifications on pie-plot\n",
    "nt.plot.pie(autopct='%1.0f%%', colors=sns.color_palette('winter_r'), shadow=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "32d354d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "\n",
    "nt.plot.pie(autopct='%1.0f%%', colors=sns.color_palette('winter_r'), shadow=True)\n",
    "\n",
    "#makes a Donut plot\n",
    "c=plt.Circle((0,0), 0.6, color='white')  #0.6 determines the thickness\n",
    "plt.gca().add_artist(c)\n",
    "plt.title('Match titles by home and away teams')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b7b389e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17ec1246",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bda9d38",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}