zhahunte/FootballMatches.ipynb

## FootballMatches.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# First the necessary packages are imported"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import rc",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Then the csv file is loaded into a dataframe"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df = pd.read_csv(\"Football_Matches.csv\")",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "#With this dataframe we can look up all the matches ever played in the world cup\ndf.head()",
      "execution_count": 3,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>home_team</th>\n      <th>away_team</th>\n      <th>home_score</th>\n      <th>away_score</th>\n      <th>tournament</th>\n      <th>city</th>\n      <th>country</th>\n      <th>neutral</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>1872-11-30</td>\n      <td>Scotland</td>\n      <td>England</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Friendly</td>\n      <td>Glasgow</td>\n      <td>Scotland</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>1873-03-08</td>\n      <td>England</td>\n      <td>Scotland</td>\n      <td>4</td>\n      <td>2</td>\n      <td>Friendly</td>\n      <td>London</td>\n      <td>England</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>1874-03-07</td>\n      <td>Scotland</td>\n      <td>England</td>\n      <td>2</td>\n      <td>1</td>\n      <td>Friendly</td>\n      <td>Glasgow</td>\n      <td>Scotland</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>1875-03-06</td>\n      <td>England</td>\n      <td>Scotland</td>\n      <td>2</td>\n      <td>2</td>\n      <td>Friendly</td>\n      <td>London</td>\n      <td>England</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>1876-03-04</td>\n      <td>Scotland</td>\n      <td>England</td>\n      <td>3</td>\n      <td>0</td>\n      <td>Friendly</td>\n      <td>Glasgow</td>\n      <td>Scotland</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "         date home_team away_team  home_score  away_score tournament     city  \\\n0  1872-11-30  Scotland   England           0           0   Friendly  Glasgow   \n1  1873-03-08   England  Scotland           4           2   Friendly   London   \n2  1874-03-07  Scotland   England           2           1   Friendly  Glasgow   \n3  1875-03-06   England  Scotland           2           2   Friendly   London   \n4  1876-03-04  Scotland   England           3           0   Friendly  Glasgow   \n\n    country  neutral  \n0  Scotland    False  \n1   England    False  \n2  Scotland    False  \n3   England    False  \n4  Scotland    False  "
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "dfw = pd.read_csv(\"WorldCups.csv\")",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "#With this dataframe we can look up all the world cup winners\ndfw.head()",
      "execution_count": 5,
      "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      <td>0</td>\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      <td>1</td>\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      <td>2</td>\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      <td>3</td>\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      <td>4</td>\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  \\\n0  1930      Uruguay     Uruguay       Argentina      USA  Yugoslavia   \n1  1934        Italy       Italy  Czechoslovakia  Germany     Austria   \n2  1938       France       Italy         Hungary   Brazil      Sweden   \n3  1950       Brazil     Uruguay          Brazil   Sweden       Spain   \n4  1954  Switzerland  Germany FR         Hungary  Austria     Uruguay   \n\n   GoalsScored  QualifiedTeams  MatchesPlayed Attendance  \n0           70              13             18    590.549  \n1           70              16             17    363.000  \n2           84              15             18    375.700  \n3           88              13             22  1.045.246  \n4          140              16             26    768.607  "
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Let's find the correct value for the world cup"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "#This command gives us a list of all the different football tournaments \ndf['tournament'].unique()",
      "execution_count": 6,
      "outputs": [
        {
          "data": {
            "text/plain": "array(['Friendly', 'British Championship', 'Copa Lipton', 'Copa Newton',\n       'Copa Premio Honor Argentino', 'Copa Premio Honor Uruguayo',\n       'Copa Roca', 'Copa América', 'Copa Chevallier Boutell',\n       'Nordic Championship', 'International Cup', 'Baltic Cup',\n       'Balkan Cup', 'FIFA World Cup', 'Copa Rio Branco',\n       'FIFA World Cup qualification', 'CCCF Championship',\n       'NAFU Championship', 'Copa Oswaldo Cruz',\n       'Pan American Championship', 'Copa del Pacífico',\n       \"Copa Bernardo O'Higgins\", 'AFC Asian Cup qualification',\n       'Atlantic Cup', 'AFC Asian Cup', 'African Cup of Nations',\n       'Copa Paz del Chaco', 'Merdeka Tournament',\n       'UEFA Euro qualification', 'UEFA Euro',\n       'Windward Islands Tournament',\n       'African Cup of Nations qualification', 'Vietnam Independence Cup',\n       'Copa Carlos Dittborn', 'CONCACAF Championship',\n       'Copa Juan Pinto Durán', 'UAFA Cup', 'South Pacific Games',\n       'CONCACAF Championship qualification', 'Copa Artigas', 'GaNEFo',\n       \"King's Cup\", 'Gulf Cup', 'Indonesia Tournament', 'Korea Cup',\n       'Brazil Independence Cup', 'Copa Ramón Castilla',\n       'Oceania Nations Cup', 'CECAFA Cup', 'Copa Félix Bogado',\n       'Kirin Cup', 'CFU Caribbean Cup qualification',\n       'CFU Caribbean Cup', 'Amílcar Cabral Cup', 'Mundialito',\n       'West African Cup', 'Nehru Cup', 'Merlion Cup', 'UDEAC Cup',\n       'Rous Cup', 'Lunar New Year Cup', 'Tournoi de France',\n       'Malta International Tournament', 'Island Games', 'Dynasty Cup',\n       'UNCAF Cup', 'Gold Cup', 'USA Cup',\n       'Jordan International Tournament', 'Confederations Cup',\n       'United Arab Emirates Friendship Tournament',\n       'Oceania Nations Cup qualification', 'Simba Tournament',\n       'SAFF Cup', 'AFF Championship', 'King Hassan II Tournament',\n       'Cyprus International Tournament', 'Dunhill Cup', 'COSAFA Cup',\n       'Gold Cup qualification', 'SKN Football Festival', 'UNIFFAC Cup',\n       'WAFF Championship', 'Millennium Cup', \"Prime Minister's Cup\",\n       'EAFF Championship', 'AFC Challenge Cup', 'FIFI Wild Cup',\n       'ELF Cup', 'Viva World Cup', 'UAFA Cup qualification',\n       'AFC Challenge Cup qualification', 'African Nations Championship',\n       'VFF Cup', 'Dragon Cup', 'ABCS Tournament',\n       'Nile Basin Tournament', 'Nations Cup', 'Pacific Games', 'OSN Cup',\n       'CONIFA World Football Cup', 'CONIFA European Football Cup',\n       'Copa América qualification', 'World Unity Cup',\n       'Intercontinental Cup', 'AFF Championship qualification',\n       'UEFA Nations League', 'Atlantic Heritage Cup',\n       'Inter Games Football Tournament'], dtype=object)"
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "#### It's: \"FIFA World Cup\""
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "#### We create a new dataframe with just the world cup"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df = df[df.tournament == 'FIFA World Cup']",
      "execution_count": 7,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "#### We want to create a new dataframe with just the winners"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Out of the 'winners dataframe' we want to create a new dataframe that just displays the winners\ndfw['titles'] = dfw.groupby('Winner')['Winner'].transform('count')\ndfw = dfw[['Winner', 'titles']]\ndfw = dfw.drop_duplicates(['Winner'], keep='last')\ndfw.set_index('Winner')",
      "execution_count": 8,
      "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>titles</th>\n    </tr>\n    <tr>\n      <th>Winner</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>Uruguay</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <td>England</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <td>Argentina</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <td>Germany FR</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <td>France</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <td>Brazil</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <td>Italy</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <td>Spain</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <td>Germany</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "            titles\nWinner            \nUruguay          2\nEngland          1\nArgentina        2\nGermany FR       3\nFrance           1\nBrazil           5\nItaly            4\nSpain            1\nGermany          1"
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# We want to create a dataframe that displays the amount of games a country played at the WC"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "#With this piece of code we create a list and a dataframe with all the countries that have ever participated in the world cup\nhome = df['home_team'].unique()\naway = df['away_team'].unique()\ncountries = [y for x in [home, away] for y in x] \ncountries = list(set(countries)) \ndfc = pd.DataFrame(countries,columns=['country'])",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df1 = df\ndf2 = df",
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df1['count'] = df1.groupby('home_team')['home_team'].transform('count')\ndf_temp = df1[['home_team', 'count']]\ndf_temp = df_temp.drop_duplicates(['home_team'], keep='last')\ndf_temp.rename(columns={'home_team': 'country'}, inplace = True)\ndf_temp =  pd.merge(df_temp, dfc, on='country', how='outer')\ndf_temp.set_index('country')\ndf_temp = df_temp.fillna(0)",
      "execution_count": 11,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df2['count'] = df2.groupby('away_team')['away_team'].transform('count')\ndf_temp2 = df2[['away_team', 'count']]\ndf_temp2 = df_temp2.drop_duplicates(['away_team'], keep='last')\ndf_temp2.rename(columns={'away_team': 'country'}, inplace = True)\ndf_temp2 =  pd.merge(df_temp2, dfc, on='country', how='outer')\ndf_temp2.set_index('country')\ndf_temp2 = df_temp2.fillna(0)",
      "execution_count": 12,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_games = pd.merge(df_temp, df_temp2, on='country', how='outer')\ndf_games['count'] = df_games['count_x'] + df_games['count_y']\ndf_games.drop(['count_x', 'count_y'], axis =1, inplace = True) \ndf_games",
      "execution_count": 13,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>Cuba</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>El Salvador</td>\n      <td>6.0</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>Israel</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>Haiti</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>DR Congo</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <td>76</td>\n      <td>Belgium</td>\n      <td>48.0</td>\n    </tr>\n    <tr>\n      <td>77</td>\n      <td>France</td>\n      <td>66.0</td>\n    </tr>\n    <tr>\n      <td>78</td>\n      <td>Kuwait</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <td>79</td>\n      <td>Wales</td>\n      <td>5.0</td>\n    </tr>\n    <tr>\n      <td>80</td>\n      <td>Indonesia</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>81 rows × 2 columns</p>\n</div>",
            "text/plain": "        country  count\n0          Cuba    3.0\n1   El Salvador    6.0\n2        Israel    3.0\n3         Haiti    3.0\n4      DR Congo    3.0\n..          ...    ...\n76      Belgium   48.0\n77       France   66.0\n78       Kuwait    3.0\n79        Wales    5.0\n80    Indonesia    1.0\n\n[81 rows x 2 columns]"
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# We also want to see the goals against, goals scored, goal difference in one df per country"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_ho = df.groupby(['home_team']).sum()\ndf_ho.reset_index(inplace = True)\ndf_ho.rename(columns={'away_score': 'GA', 'home_score': 'GS', 'home_team': 'country'}, inplace = True)\ndf_ho = pd.merge(df_ho, dfc, on='country', how='outer')\ndf_ho = df_ho.fillna(0)",
      "execution_count": 14,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_aw = df.groupby(['away_team']).sum()\ndf_aw.reset_index(inplace = True)\ndf_aw.rename(columns={'away_score': 'GS', 'home_score': 'GA', 'away_team': 'country'}, inplace = True)\ndf_aw = pd.merge(df_aw, dfc, on='country', how='outer')\ndf_aw = df_aw.fillna(0)",
      "execution_count": 15,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_wc = pd.merge(df_ho, df_aw, on='country', how='outer')\ndf_wc['GS'] = df_wc['GS_x'] + df_wc['GS_y']\ndf_wc['GA'] = df_wc['GA_x'] + df_wc['GA_y']\ndf_wc['GD'] = df_wc['GS'] - df_wc['GA']\ndf_wc.drop(['GS_x', 'GA_x', 'neutral_x', 'GA_y', 'GS_y', 'neutral_y', 'count_x', 'count_y'], axis =1, inplace = True) ",
      "execution_count": 16,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_wc",
      "execution_count": 17,
      "outputs": [
        {
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>country</th>\n      <th>GS</th>\n      <th>GA</th>\n      <th>GD</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>Algeria</td>\n      <td>13.0</td>\n      <td>19.0</td>\n      <td>-6.0</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>Angola</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>-1.0</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>Argentina</td>\n      <td>137.0</td>\n      <td>93.0</td>\n      <td>44.0</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>Australia</td>\n      <td>13.0</td>\n      <td>31.0</td>\n      <td>-18.0</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>Austria</td>\n      <td>43.0</td>\n      <td>47.0</td>\n      <td>-4.0</td>\n    </tr>\n    <tr>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <td>76</td>\n      <td>Uruguay</td>\n      <td>87.0</td>\n      <td>74.0</td>\n      <td>13.0</td>\n    </tr>\n    <tr>\n      <td>77</td>\n      <td>Yugoslavia</td>\n      <td>55.0</td>\n      <td>42.0</td>\n      <td>13.0</td>\n    </tr>\n    <tr>\n      <td>78</td>\n      <td>Kuwait</td>\n      <td>2.0</td>\n      <td>6.0</td>\n      <td>-4.0</td>\n    </tr>\n    <tr>\n      <td>79</td>\n      <td>Wales</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <td>80</td>\n      <td>Indonesia</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>-6.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>81 rows × 4 columns</p>\n</div>",
            "text/plain": "       country     GS    GA    GD\n0      Algeria   13.0  19.0  -6.0\n1       Angola    1.0   2.0  -1.0\n2    Argentina  137.0  93.0  44.0\n3    Australia   13.0  31.0 -18.0\n4      Austria   43.0  47.0  -4.0\n..         ...    ...   ...   ...\n76     Uruguay   87.0  74.0  13.0\n77  Yugoslavia   55.0  42.0  13.0\n78      Kuwait    2.0   6.0  -4.0\n79       Wales    4.0   4.0   0.0\n80   Indonesia    0.0   6.0  -6.0\n\n[81 rows x 4 columns]"
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Let's merge the appearances df and the goals df"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_wc = df_wc.merge(df_games, left_on='country', right_on='country')\n#We want the index to be the countries, not a numerical index\ndf_wc.set_index('country')",
      "execution_count": 18,
      "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>GS</th>\n      <th>GA</th>\n      <th>GD</th>\n      <th>count</th>\n    </tr>\n    <tr>\n      <th>country</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>Algeria</td>\n      <td>13.0</td>\n      <td>19.0</td>\n      <td>-6.0</td>\n      <td>13.0</td>\n    </tr>\n    <tr>\n      <td>Angola</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>-1.0</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <td>Argentina</td>\n      <td>137.0</td>\n      <td>93.0</td>\n      <td>44.0</td>\n      <td>81.0</td>\n    </tr>\n    <tr>\n      <td>Australia</td>\n      <td>13.0</td>\n      <td>31.0</td>\n      <td>-18.0</td>\n      <td>16.0</td>\n    </tr>\n    <tr>\n      <td>Austria</td>\n      <td>43.0</td>\n      <td>47.0</td>\n      <td>-4.0</td>\n      <td>29.0</td>\n    </tr>\n    <tr>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <td>Uruguay</td>\n      <td>87.0</td>\n      <td>74.0</td>\n      <td>13.0</td>\n      <td>56.0</td>\n    </tr>\n    <tr>\n      <td>Yugoslavia</td>\n      <td>55.0</td>\n      <td>42.0</td>\n      <td>13.0</td>\n      <td>33.0</td>\n    </tr>\n    <tr>\n      <td>Kuwait</td>\n      <td>2.0</td>\n      <td>6.0</td>\n      <td>-4.0</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <td>Wales</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n    </tr>\n    <tr>\n      <td>Indonesia</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>-6.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>81 rows × 4 columns</p>\n</div>",
            "text/plain": "               GS    GA    GD  count\ncountry                             \nAlgeria      13.0  19.0  -6.0   13.0\nAngola        1.0   2.0  -1.0    3.0\nArgentina   137.0  93.0  44.0   81.0\nAustralia    13.0  31.0 -18.0   16.0\nAustria      43.0  47.0  -4.0   29.0\n...           ...   ...   ...    ...\nUruguay      87.0  74.0  13.0   56.0\nYugoslavia   55.0  42.0  13.0   33.0\nKuwait        2.0   6.0  -4.0    3.0\nWales         4.0   4.0   0.0    5.0\nIndonesia     0.0   6.0  -6.0    1.0\n\n[81 rows x 4 columns]"
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Let's create a dataframe with just the World Cup winners"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Germany is in the dataframe both as 'Germany' and 'Germany FR'\ndic = {'Germany FR':'Germany'}\ndfw['Winner'].replace(dic,inplace=True)\ndfw = dfw.groupby('Winner').sum().reset_index()",
      "execution_count": 19,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "dfw",
      "execution_count": 20,
      "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>titles</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>Argentina</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>Brazil</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>England</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>France</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>Germany</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <td>5</td>\n      <td>Italy</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <td>6</td>\n      <td>Spain</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <td>7</td>\n      <td>Uruguay</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      Winner  titles\n0  Argentina       2\n1     Brazil       5\n2    England       1\n3     France       1\n4    Germany       4\n5      Italy       4\n6      Spain       1\n7    Uruguay       2"
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Now let's create a dataframe with the top 10 countries with the most appearances"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df_sort = df_wc.sort_values(by=['count'], ascending = False)\ndf_sort.set_index('country')\ndf_top10 = df_sort.iloc[0:10]",
      "execution_count": 21,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Let's look at the top 10 countries with the most appearances in the FIFA World Cup"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "ax_top_count = df_top10.plot.barh(x='country', y='count', rot=0, figsize=(20,10), color=['midnightblue', 'mediumblue', 'slateblue'])\nplt.xlabel('Appearances in the FIFA world Cup', size = 20)\nplt.ylabel('Country', size = 20)\nplt.title('Top 10 countries that have played the most FIFA games', size = 20)\nplt.xticks(size = 20)\nplt.yticks(size = 20)\nplt.gca().invert_yaxis()",
      "execution_count": 22,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 1440x720 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "labels = df_top10['country']\ngoals_scored = df_top10['GS']\ngoals_against = df_top10['GA']\n\n\n\nx = np.arange(len(labels))  \nwidth = 0.35  \n\nfig, ax = plt.subplots(figsize=(15,8))\nrects1 = ax.bar(x - width/2, goals_scored, width, label='Goals Scored')\nrects2 = ax.bar(x + width/2, goals_against, width, label='Goals Against')\n\n\nax.set_ylabel('Goals')\nax.set_title('GS and GA per country')\nax.set_xticks(x)\nax.set_xticklabels(labels)\nax.legend()\n\n\ndef autolabel(rects):\n    for rect in rects:\n        height = rect.get_height()\n        ax.annotate('{}'.format(height),\n                    xy=(rect.get_x() + rect.get_width() / 2, height),\n                    xytext=(0, 3), \n                    textcoords=\"offset points\",\n                    ha='center', va='bottom')\n\n\nautolabel(rects1)\nautolabel(rects2)\n\n\nfig.tight_layout()\nplt.savefig('GSGA.png')\nplt.show()",
      "execution_count": 23,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 1080x576 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Let's plot graphs for all the winners"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "dfw.sort_values(by=['titles'], ascending = False)",
      "execution_count": 24,
      "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>titles</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>1</td>\n      <td>Brazil</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>Germany</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <td>5</td>\n      <td>Italy</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <td>0</td>\n      <td>Argentina</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <td>7</td>\n      <td>Uruguay</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>England</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>France</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <td>6</td>\n      <td>Spain</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      Winner  titles\n1     Brazil       5\n4    Germany       4\n5      Italy       4\n0  Argentina       2\n7    Uruguay       2\n2    England       1\n3     France       1\n6      Spain       1"
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "ax_top_titles = dfw.plot.bar(x='Winner', y='titles', rot=0, figsize=(20,10), color=['midnightblue', 'mediumblue', 'slateblue'])\nplt.xlabel('World Cup winners', size = 20)\nplt.ylabel('Titles', size = 20)\nplt.title('Number of World Cup Titles', size = 20)\nplt.xticks(size = 20)\nplt.yticks(size = 20)\nplt.savefig('titles.png')",
      "execution_count": 25,
      "outputs": [
        {
          "data": {
            "image/png": 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uohIAAAAA3UQlAAAAALpZUwkAAACAbo5UAgAAAKDbTtMeYLnc9ra3bbvvvvu0xwAAAAC4wfj85z9/SWtt3aTbbjBRaffdd8+mTZumPQYAAADADUZVnb/YbU5/AwAAAKCbqAQAAABAN1EJAAAAgG43mDWVAAAAAJbLj3/841x44YW58sorpz3KqlizZk1222237Lzzzku+j6gEAAAAsMCFF16YW97yltl9991TVdMeZ0W11nLppZfmwgsvzB577LHk+zn9DQAAAGCBK6+8MmvXrr3BB6UkqaqsXbu2+6gsUQkAAABgghtDUJpzfV6rqAQAAAAwYy677LK87nWvS5Js3rw5j3rUo5IkZ555Zk455ZRr9zvxxBPz1Kc+dSozWlMJAAAAYBt23fWoZX28zZtfvtXb56LSk5/85Oy66645+eSTkwxRadOmTXnQgx60rPNcH45UAgAAAJgxRx99dM4999zst99+OeKII7L33nvnRz/6UZ7//OfnpJNOyn777ZeTTjppi/tcfPHFeeQjH5kDDzwwBx54YD71qU8lSc4444zst99+2W+//bL//vvn8ssvX5YZHakEAAAAMGNOOOGEnHXWWTnzzDNz3nnn5cEPfnB+5md+Jscff3w2bdqU17zmNUmG09/mPP3pT89RRx2Vgw46KBdccEHuf//755xzzslLX/rSvPa1r83GjRtzxRVXZM2aNcsyo6gEAAAAcANw6qmn5uyzz772+ve+971cfvnl2bhxY/7oj/4oj370o3P44Ydnt912W5bnE5UAAAAAbgCuueaafOYzn8kuu+yyxfajjz46hx12WE455ZTc/e53z6mnnpo73/nO2/181lQCAAAAmDG3vOUtJ659tNj2JDn00EOvPS0uGRb1TpJzzz03d73rXfOsZz0rGzZsyJe//OVlmVFUAgAAAJgxa9euzcaNG7P33nvnmc985rXbDznkkJx99tkTF+p+1atelU2bNmWfffbJXnvtlde//vVJkle84hXZe++9s++++2aXXXbJAx/4wGWZsVpry/JA07Zhw4a2adOmaY8BAAAA3ACcc8452XPPPac9xqqa9Jqr6vOttQ2T9nekEgAAAADdRCUAAAAAus1EVKqq86qqLfJ10bTnAwAAAGBLO017gHm+m+QVE7ZfsdqDAAAAALTWUlXTHmNVXJ81t2cpKl3WWjtu2kMAAAAArFmzJpdeemnWrl17gw9LrbVceumlWbNmTdf9ZikqAQAAAMyE3XbbLRdeeGEuvvjiaY+yKtasWZPddtut6z6zFJVuVlW/nWR9ku8n+VKSj7fWfjLdsQAAAIAbm5133jl77LHHtMeYabMUlW6X5O0Ltn29qp7QWjtjGgMBAAAAMNmsRKW3JPlEkn9LcnmSOyR5apIjk3y4qu7RWvuXhXeqqiPHfbJ+/frVmxaWSdUXpj3CTGrtgGmPAAAAwDbcZNoDJElr7QWttY+11r7VWvtBa+2s1tr/TvKyJLskOW6R+72htbahtbZh3bp1qzkyAAAAwI3aTESlrXj9eHnvqU4BAAAAwBZmPSr913h586lOAQAAAMAWZj0q3WO8/NpUpwAAAABgC1OPSlV1l6r6uQnbfynJa8ar71jdqQAAAADYmln49LcjkhxdVacl+XqGT3+7Y5LDkqxJckqSl05vPAAAAAAWmoWodFqSX02yf4bT3W6e5LIkn0zy9iRvb6216Y0HAAAAwEJTj0qttTOSnDHtOQAAAABYuqmvqQQAAADAjkdUAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoNpNRqaoeU1Vt/HrStOcBAAAAYEszF5Wq6vZJXp3kimnPAgAAAMBkMxWVqqqSvCXJpUleP+VxAAAAAFjETEWlJE9Lct8kT0jy/SnPAgAAAMAiZiYqVdWeSU5I8srW2senPQ8AAAAAi5uJqFRVOyV5e5ILkjxnyuMAAAAAsA07TXuA0fOT7J/koNbaD6c9DAAAAABbN/WoVFW/luHopL9orX2m875HJjkySdavX78C0wEA3LBVfWHaI8yk1g6Y9ggz5ymP9rMyyWvf6WcFuPGa6ulv8057+2qSY3rv31p7Q2ttQ2ttw7p165Z9PgAAAAAmm/aaSrdIcqckeya5sqra3FeSY8d9/nLc9oqpTQkAAADAFqZ9+ttVSd60yG0HZFhn6ZNJvpKk69Q4AAAAAFbOVKPSuCj3kybdVlXHZYhKb22tvXE15wIAAABg66Z9+hsAAAAAOyBRCQAAAIBuMxuVWmvHtdbKqW8AAAAAs2dmoxIAAAAAs0tUAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0G0molJV/WlVfbSq/rOqflhV366qL1bVsVW1dtrzAQAAALClmYhKSY5KcvMk/5jklUnemeTqJMcl+VJV3X56owEAAACw0E7THmB0q9balQs3VtWLkjwnybOTPHnVpwIAAABgopk4UmlSUBq9a7z8ldWaBQAAAIBtm4motBUPGS+/NNUpAAAAANjCrJz+liSpqj9Ocoskt06yIclBGYLSCdOcCwAAAIAtzVRUSvLHSX5h3vWPJHl8a+3iSTtX1ZFJjkyS9evXr/x0AAAAACSZsdPfWmu3a61VktslOTzJHZJ8saoOWGT/N7TWNrTWNqxbt241RwUAAAC4UZupqDSntfat1tp7kxyaZG2St015JAAAAADmmcmoNKe1dn6Ss5PcpapuO+15AAAAABjMdFQa7Tpe/mSqUwAAAABwralHpaq6c1XdbsL2m1TVi5L8fJJPt9a+s/rTAQAAADDJLHz62wOS/HlVfTzJuUkuzfAJcPfJsFD3RUl+d3rjAQAAALDQLESlU5O8IcnGJPsmuU2S7yf5apK3J3lVa+3b0xsPAAAAgIWmHpVaa2clecq05wAAAABg6aa+phIAAAAAOx5RCQAAAIBuohIAAAAA3UQlAAAAALqJSgAAAAB0W3JUqqr9q+rJVXXredtuXlVvrarLqmpzVT19ZcYEAAAAYJb0HKn0rCTPba19d962lyR5zPg4a5O8rKoOXcb5AAAAAJhBPVFpQ5LT565U1c5JHpfkc0l+PskeSS5J8rRlnA8AAACAGdQTlX4+yX/Ou74hyS2T/L/W2pWttc1J/i7JPss4HwAAAAAzqCcqtSQ7zbt+0LjtjHnbLk6ybhnmAgAAAGCG9USlC5Lcfd71hyW5sLX2tXnbdk3yneUYDAAAAIDZ1ROV3pXknlV1clW9I8k9kpy8YJ+9k5y7XMMBAAAAMJt22vYu13p5kgckOXy8fmaS4+durKq9ktwtyYuXbToAAAAAZtKSo1Jr7YokG6tq73HT2a21a+bt8oMkj0iyaRnnAwAAAGAG9RyplCRprZ21yPbzkpy3nfMAAAAAsAPojkpVtXOS+yXZM8ktWmsvHLevSXKrJJcsOIIJAAAAgBuYnoW6U1UPyHA00oeS/EWS4+bdvF+Sbyb5H8s0GwAAAAAzaslRqao2JHlfkpbkqCR/Nf/21tpnk3w9w7pKAAAAANyA9RypdEyGxbg3tNZeleTfJ+zzz0n2XY7BAAAAAJhdPVFpY5L3tdYu2so+/5nkF7dvJAAAAABmXU9UukWSS7axz892PiYAAAAAO6CeAPSNJHfZxj77Jfna9R8HAAAAgB1BT1T6cJL7V9VBk26sqgcmuWeSDy7HYAAAAADMrp6o9JIklyX5h6r60yR7JUlVHTZef3eSbyZ52bJPCQAAAMBM2WmpO7bWvlFVhyZ5V5Jnzrvp/UkqyblJDm+tbWvdJQAAAAB2cEuOSknSWvtCVf1qksOS3CPJ2iTfTfLZJH/XWrt6+UcEAAAAYNZ0RaUkaa39JMPRSe9f/nEAAAAA2BH0rKkEAAAAAEm2cqRSVT32+j5oa+1t1/e+AAAAAMy+rZ3+dmKS1vl4Nd5HVAIAAAC4AdtaVHrCqk0BAAAAwA5l0ajUWnvrag4CAAAAwI5jyQt1V9W9q2r9Nva5fVXde/vHAgAAAGCW9Xz622lJHr+NfR477gcAAADADVhPVKol7tO7uDcAAAAAO5ieqLQU65NcvsyPCQAAAMCM2dqnv6Wqnr9g08FVEw9YummGoPQ/k3xyeUYDAAAAYFZtNSolOW7ev1uSg8evxXwjydHbNREAAAAAM29bUemQ8bKSfCzJiUneOmG/nyS5NMlXWmvXLNt0AAAAAMykrUal1toZc/+uqrcmed/8bQAAAADcOG3rSKVrtdaesJKDAAAAALDjWO5PfwMAAADgRmDRI5Wq6pok1yTZq7X21fF6W8Jjttbako+AAgAAAGDHs7X48/EMEekHC64DAAAAcCO3aFRqrR28tesAAAAA3HhtdU2lqnpsVe2zWsMAAAAAsGPY1kLdJyZ5+CrMAQAAAMAOxKe/AQAAANBNVAIAAACgm6gEAAAAQLdFP/1tnttU1fqeB22tXXA95wEAAABgB7CUqPT08Wup2hIfFwAAAIAd1FLiz/eSXLbSgwAAAACw41hKVHp5a+34FZ8EAAAAgB2GhboBAAAA6CYqAQAAANBNVJYZl+UAACAASURBVAIAAACgm6gEAAAAQLetLtTdWhOdAAAAAPgpohEAAAAA3UQlAAAAALqJSgAAAAB0E5UAAAAA6CYqAQAAANBNVAIAAACgm6gEAAAAQDdRCQAAAIBuohIAAAAA3UQlAAAAALqJSgAAAAB0E5UAAAAA6CYqAQAAANBNVAIAAACgm6gEAAAAQDdRCQAAAIBuohIAAAAA3UQlAAAAALqJSgAAAAB0E5UAAAAA6CYqAQAAANBNVAIAAACgm6gEAAAAQDdRCQAAAIBuohIAAAAA3UQlAAAAALqJSgAAAAB0E5UAAAAA6CYqAQAAANBt6lGpqtZW1ZOq6r1V9R9V9cOq+m5VfbKqfqeqpj4jAAAAAFvaadoDJDkiyf9N8s0kpyW5IMkvJDk8yRuTPLCqjmittemNCAAAAMB8sxCVvprkoUk+1Fq7Zm5jVT0nyeeSPDJDYHrPdMYDAAAAYKGpn1rWWvtYa+0D84PSuP2iJK8frx686oMBAAAAsKipR6Vt+PF4efVUpwAAAABgCzMblapqpySPHa9+ZJqzAAAAALClmY1KSU5IsneSU1prfz/tYQAAAAC4ziws1P1TquppSZ6R5MtJHrOV/Y5McmSSrF+/fnWGW4Jddz1q2iPMpM2bXz7tEQAAAJghVV+Y9ggzqbUDpj3CkszckUpV9ZQkr0xydpJDWmvfXmzf1tobWmsbWmsb1q1bt2ozAgAAANzYzVRUqqo/TPKaJGdlCEoXTXkkAAAAACaYmahUVc9K8vIkZ2YISv815ZEAAAAAWMRMRKWqOibDwtyfT3K/1tolUx4JAAAAgK2Y+kLdVfW4JMcn+UmSTyR5WlUt3O281tqJqzwaAAAAAIuYelRKssd4edMkf7jIPmckOXFVpgEAAABgm6Z++ltr7bjWWm3j6+BpzwkAAADAdaYelQAAAADY8YhKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAuolKAAAAAHQTlQAAAADoJioBAAAA0E1UAgAAAKCbqAQAAABAN1EJAAAAgG6iEgAAAADdRCUAAAAAus1EVKqqR1XVq6vqE1X1vapqVfWOac8FAAAAwGQ7TXuA0fOS7JvkiiQXJrnzdMcBAAAAYGtm4kilJEcluVOSWyX5/SnPAgAAAMA2zMSRSq210+b+XVXTHAUAAACAJZiVI5UAAAAA2IGISgAAAAB0m4nT366vqjoyyZFJsn79+ilPA7CynvLoL0x7hJn02nceMO0RZs6uux417RFm0ubNL5/2CAA3Gv63aDL/W8QNzQ59pFJr7Q2ttQ2ttQ3r1q2b9jgAAAAANxo7dFQCAAAAYDpEJQAAAAC6iUoAAAAAdBOVAAAAAOg2E5/+VlUPT/Lw8ertxst7VNWJ478vaa398aoPBgAAAMBEMxGVkuyX5HELtt1h/EqS85OISgAAAAAzYiZOf2utHddaq6187T7tGQEAAAC4zkxEJQAAAAB2LKISAAAAAN1EJQAAAAC6iUoAAAAAdBOVAAAAAOgmKgEAAADQTVQCAAAAoJuoBAAAAEA3UQkAAACAbqISAAAAAN1EJQAAAAC6iUoAAAAAdBOVAAAAAOgmKgEAAADQTVQCAAAAoJuoBAAAAEA3UQkAAACAbqISAAAAAN1EJQAAAAC6iUoAAAAAdBOVAAAAAOgmKgEAAADQTVQCAAAAoJuoBAAAAEA3UQkAAACAbqISAAAAAN1EJQAAAAC6iUoAAAAAdBOVAAAAAOgmKgEAAADQTVQCAAAAoJuoBAAAAEA3UQkAAACAbqISAAAAAN1EJQAAAAC6iUoAAAAAdBOVAAAAAOgmKgEAAADQTVQCAAAAoJuoBAAAAEA3UQkAAACAbqISAAAAAN1EJQAAAAC6iUoAAAAAdBOVAAAAAOgmKgEAAADQTVQCAAAAoJuoBAAAAEA3UQkAAACAbqISAAAAAN1EJQAAAAC6iUoAAAAAdBOVAAAAAOgmKgEAAADQTVQCAAAAoJuoBAAAAEA3UQkAAACAbqISAAAAAN1EJQAAAAC6iUoAAAAAdBOVAAAAAOgmKgEAAADQTVQCAAAAoJuoBAAAAEA3UQkAAACAbqISAAAAAN1EJQAAAAC6iUoAAAAAdBOVAAAAAOgmKgEAAADQTVQCAAAAoJuoBAAAAEA3UQkAAACAbqISAAAAAN1EJQAAAAC6iUoAAAAAdBOVAAAAAOgmKgEAAADQTVQCAAAAoJuoBAAAAEA3UQkAAACAbqISAAAAAN1EJQAAAAC6iUoAAAAAdBOVAAAAAOgmKgEAAADQTVQCAAAAoJuoBAAAAEC3mYlKVbVbVb25qjZX1VVVdV5VveL/t3fe4ZYU1d5+f0QBcZB0QUQHRMDIJUmWGTISROGKeK86SBQDRpQwMpiuOaJ4UWFMqCgijIiCMIMX+AQliSD5HiWDREEy6/tjVTN9enqfs/ecsM8wv/d5+ulzqqqr1+6qrrC61ipJz++3bMYYY4wxxhhjjDFmMIv1WwAASS8BLgRWBk4DrgFeAxwK7CRpi4i4p48iGmOMMcYYY4wxxpgaE2Wl0jdJhdJ7I2KPiPhoRGwDfBlYB/hUX6UzxhhjjDHGGGOMMYPou1JJ0prADsAA8I1G9NHAw8BbJS0zzqIZY4wxxhhjjDHGmA70XakEbFPOZ0XE0/WIiPgncAGwNLDpeAtmjDHGGGOMMcYYY9qZCEqldcr5ug7x15fz2uMgizHGGGOMMcYYY4zpgomgVJpUzg90iK/ClxsHWYwxxhhjjDHGGGNMF0yI3d+GQeUc80RIBwIHln8fknTtuEm14LAi8I9+CwEgfaXfIpihmUB1pd8SmC6YMPXlmyf1WwIzDBOmrrgfWiCYQPWl3xKYYZgwdcX90ALBhKkv7osmPBOorvRbgkG8uFPERFAqVSuRJnWIf14j3TNExPHA8WMh1LMFSX+KiI36LYeZ+LiumF5wfTHd4rpiesH1xXSL64rpBdcX0y2uK70zEczfqtVFnXwmvbScO/lcMsYYY4wxxhhjjDHjzERQKs0u5x0kDZJH0rLAFsAjwB/GWzBjjDHGGGOMMcYY007flUoRcSNwFjAZeFcj+hhgGeD7EfHwOIv2bMHmgaZbXFdML7i+mG5xXTG94PpiusV1xfSC64vpFteVHlHEPP6vx18I6SXAhcDKwGnAX4FNgKmk2dvmEXFP/yQ0xhhjjDHGGGOMMXUmhFIJQNLqwMeBnYAVgNuBXwLHRMS9/ZTNGGOMMcYYY4wxxgym7+ZvFRFxc0TsGxGrRsQSEfHiiDjUCqWJg6RpkkLStH7LYiYukiaXejKzET6zhE/ui2Bm1CjlOKffctTpVO+MMcYsfEgakDTQbznMgoekKWU8MaPfshizoDBhlEoLCpKOLA1NSFqn3/KMJm5EJya1+lY/HisDpu9Jelm/ZTRjQ4eybx5T+i2nmdh0UYem9VtGM/ZIWlvSlyRdKuleSU+U80WSviBpw37LaJ79VO1OI8wfBRZiJC0q6QBJ59Xaprsk/VnSdyTt3m8ZzehRm2/OGSJN1SYMjJ9kZiQs1m8BFiQkCdgPCEDAAcCH+irU+HIquQvf7f0WZCHlmNrfk4DXAG8D9pS0ZURc3h+x5uFW4GXAA/0W5FnEMUPEDYyXEGaBp1M9mihthxkDytjlY+VYBLgU+ClwL7As8GrgPcAHJb07Ir7RL1mNMQsXkhYFfkW6P7kfOAO4BVgeeAnwFmBd4PRxFOtichz7j3G8pzELNFYq9cYOwBrATGBn4O2SjoiIx/sq1TgREQ9gRUHfiIgZzTBJXwfeDbwPmDbOIrUSEU8A1/RbjmcTbWVvTK+4Hi20fAyYAdwM7BMRFzQTSFqZ7Ecmja9oxpiFnH1IhdIVwNZlrvEMkpYmN28aNyLiX3gca0xP2PytNw4o528DPwJWBN7QTCRpRmWWIuktZWn5Q/UlfEoOlXS1pEcl3SrpWEmThrIDl7SPpNmS7ivX/VXSUZKWbEkbkuZIWlHS8ZJuL2ZTV0nat5F2JjC7/Ht0m3mNOvhUquSVtLSkz0v6e7nPDZI+Ur6SNmWbJukUSTdJekTSg5IukPRfHZ69aeescl6pHlgvK0k7lXrwQH3JuaQ9JP1Q0nWSHi519BJJ75W0SIf8hjoml7Rext4nGm3PXpIulvSvspz8J5JW63DdxpLOkvTP8i7+TtJm9fy6uPcLJH2svMd3SHpc0m2STlKLiWa9npS/fyLpH6Vd+5OkXTvcZ1mlCc8tJe01kj6A+7NRobQJIelFpY+6srTRvyvxS0p6j6QzJf2ttPX3Sjpb0o4d8ryl9AfLSPpirY+4XtKH2vqIct2mkk4u9aiqT7+VtFdL2s1Kn1LVvZslfUvSqqP7hBYsJK0JHAU8DuzcplACiIi7IuII4HON65eWdLiky2v9xP+TtE/LvZ4xoZf0GklnlLpR7x+q8cJzJX25lNMjJf89SprFJB1R6sejkm6U9O6W+y0h6d2Sft2oi7+TtHOH59H1eEXSukX2c4d4vlcqTXVW6ZTGDI3S5cL/lX/frhbT3Pkp65b7HFzy/FiH+FVKWV45Gr/LdM3m5TyzqVCCVPBERDU/aY5vd5F0YWmb7pP0c0kvbeahNP39jHJscXepP39Tzo1e2JK+1R2IciwdjTbqsdKOfVbSEiN/HKYTGmZ+3ancate3zq+Vc++vqDGulLSm2n3EzlHDhLcW12muPLXUt6uV4+xHJP1F0tGSntNI+5mSx9s63GPDEj+r48PqA16p1CWS/g3YHbguIi6U9CDwAeBAchl5Gx8EtgdmkQqb+hfAbwDvBG4DjicHfLuTJk2LA0+0yPBd4B3kstBfkMtENwU+AWwrafuIeLJx2XLABSX/nwPPAfYCTpD0dER8r6T7ZTm/HTgPmFPLY6DD76uzOKngeAFwJvAksAfwmXLPptnFccDVwO9Jc7oVgNcBP5C0TkRM7+KeBrYr5z91iN+L/AJ0JvAtYHIt7jPA08BFpMnaJGAb4KvAxsBba2kvp910ZhJwKGkS+uj8/AAzJhxCtienk+/zJsDewHqS/j0iHqsSStqKfHcXB04BbgReRbZZHSdTLbwW+Gi57hTgIeClZB3cXdIWEXFFy3UvJpea3wT8gFzyvjdwmqTtGoPJJYFzyPp5BancXw6YDmzdg6xmeL4BbAn8mjRHqFbkrgR8BbgQOBu4G1iVrG9nSnpHRMxsyW8J4HfAyiXPp8iPMp8HlgQ+VU8s6eAiwxNkPb6hXLsxcDDZn1VpDyDbt0dK2luAtckPQbtK2iQibp3vJ7Fgsy851jspIq4aLnF9DCFpObINWJ80mTuBVN7uCJwk6RURcVRLNpsBhwPnl2tWZG79gWxrzibf9dPIurEPcIqkHcj2axOy33oM+A/g65Lujoj6eGt5sr9q1sXdgF9LOiAivtMiX1fjlYi4RtJsYKqktSPiunomkjYHXgmcEhF3tNzHdMccsh0/lGzXf1mLq0xz57es6/wQ+Cywv6RPRcRTjfh3kO/K/8z/TzHzwT3lvHaP172RtBo5laxD/w7sSb6vm0fEtY20B5PjkwvJ9ugVwP7AbpI26rGPOAnYimw/HiTnL4eRfdS+Q1xnRoeh5tc9URQ65wIbAJeR48pJwJFkGY8WHyHNOC8kx1TPAbYgVxFPKePdqk36FvBh4CDg+y15HVTOE6utiggfXRzkZCmAw2thl5CT8rUaaWeUtA8D67fktVWJvxZYrha+BKlkCWCgcc20Ev4LYKkO9zu0ER7l+A6waC385eQg6upG+ikl/YwOz6CSYVojfKCE/7ouG9m43l+OxRvXvKQl/yXICeMTwGr9LvOJctTKcUbt+BLwv6X+zQKW7VBWTwM7dci3rQwWAb5Xrt1kGLkWJyeJg+oeqbgK8qtTPf3MEj653890QTk6lH39+GgjfdUWPAi8qhF3Uol7U6O8ry/hOzfSH1y7/5QWueY0wlZu1sMSvh6pYDqzET65lv/RjbgdqzalEX5ECT8FWKQWvgbpH2aeeudj2Ho0rZH2hyXtzcCLW/J6Di3tMzkp/Cs52VuyEXdLyXMWg/uIVUpdvZfBfdSryT7qHuBlLfdavfb3y8gJwrXAqo10O5DKq5/1uwz6WPbnlme/33xcW7XZh7XUgd+Q/cu/18Kn1OraQR3yHKjVhSVr4dW46F7gjwweG61ZyviyRl5LAi9succk4C8lr+Z4qbp/V+MVUikewBeGeD7b97ucF6SjqiONsMlDtd8jKOuBRtix5T67NsJFfth4GJjU72e0MB2k0vrx0p78gFQAvXiI9NNq7UyzHKuPnOc0wlej0S+V8KqPOK4RXrVlMxrhc0r4JcDytfBlyA8fTwGr9PuZTvSj9nznDJGmahMGamEzGHp+3Vputfi2NmF6uebHgGrhq5Pjmbb5zJxmG9ZSP6c1wtes518L/0RJv3cj/FclvDmWfy7wT+Dv1MZNE+HouwALwlE6m6qxWK0W/p5S4J9ppK8q/Zc75PedEv+2lrgtmi9RCb+MVLYs13LNoqQzuYsb4dWL97yWa84r8cvWwoZ7GTu9KAMlfK2WayoFxSu7fNZv7PRsFtaDuZ1n23EV8JYhyurU+bjfBuXajw2T7rsl3dca4ZM7NMIzsVJpNMs+gPsb6au255MteU2lMTkiV6IEcG5L+kXIiXrQhVJpmN9xOrmSrT5Zq+rJAC0dI/A34B+NsOvJdrhNIVr99pndyrWwHMPUoTmNtJVS6V3zcZ/DyrWbN8IrpdLklmt+VOLWrYUdV8Le08U9v17S7tghfhbZdy7T73LoU9lfXZ7PPB8Xyjs4o3G8r8StQCr2/tgh3/VKvp+rhU0pYZcNIc9ASdP2Dt9U4rZpiZtd5OlqEE2uJA/gtR3u39V4hVy5cis5xqorwZYD/kWODeeZKPgYsmyCHpVKIyjrgUbYK0raWY3w6kPGCf1+PgvjAbyJtFqo9033kKuQdmuknUaL4qjELVreyWAIxVTjmj8DNzXCqrZsRiN8TgnfriWfY2hRdPlofebV850zRJqqTRiohc1g6Pl1a7nV4tvahGp+P7kl/ZFt7RLzoVQa4neu0Nb2ALuU8K83wg+iizlaPw6bv3XHNuQOBL+NwcsjTwK+AEyTND3SQXGdizvkt345n98S9wdy4PQMSid165GDmvep3f3EY+QX2ybXR8SDLeE3l/NypMZzpDwQETcMcZ/n1wMlvYhcCrgt8CJgqcZ1rb5fFmYiou7rYRlycPQZ4EfFDOHIlss61UEkrUAur3wdqUFfppGkYxlIOpJcKj6LdO5qxpB62XdJmzlk27vYsS2KiKclXUgPS9Il7UKucNqINHlp9jErMu/ukZfHvGYIlbyb1fJeFlgLuDkibmxJPwc4ultZF0Z6rEdDtR2vItuOLUkToqZPv7a2456IGGgJb6uXm5bzmV3IWdWRqZI2a4mv6uFapGnNwkZV5tESN5l535m/keaNG5MTtE7+KRYv57ZxR8e6U7i/wzt8G7nq8JKWuFuLPKuUvwGQ9AqyLr6WNId6TuO6trrY9XglIp6U9B3S2fme5LgP0jx8KeD4KCN9M7bMZ1kPIiKukvR7YGdJq0dEVeYHlvO3Rkte0z0RcbKkU8mPX1uSY5MtSbPUPSR9n5yk19+181ryeUrS+eScbX2yPat2wPxPcsK/HvmOL1q7tNcNl7odY5mxYbg+piskPY+sKzd3GJ+0zdPn917LkCvp3kCOq5dlbv8M87ZfZ5K+5t4q6SORzuMh26qnyAUqEworlbqj6mxm1gMj4p7iJGtP4PXUfDwUOtnYV7afdzYjSoN4TyP4+WTFW4neJ033dwivFFeLdojvla7vo3QcejH5u/6X9G3wAEVTTPp1msfxuJlLRDwMXCzpjeQqgMMkfas2QKporYPFV8YfyQH8xaTN7r1keVW+DVrLQOmg9RPkwH+fiHh65L/IjDJt72PbO9+xLRomfB4kvZf0eXEf6fPi7+SX/CAHhuvRXqeGajvqzreHk9U+TUaXTm3HFqTZ6yKkufJp5IeJp8lVjrvReznD4Hq5XDl34+NihXL+yDDpnttFXs9Gbif9OMwz4Y6IOZRBraTFGOzLsXquG5ejE23Pdbh3sdMusk8Wudriq3pSKbOQtClp3rcYWRdPJ80pnyb9q7yekddFSL+XR5BfiCul0oHkRPTEDnmZUWQEZd3GN0nF1P7kxjSrkD7hLo+IUZmsmt4pH+bPKgeSFiXnVycAbyNXLdX9bQ03Fqj72fkS+QH0duC3ZN/ySImbRvp27EXWbsdYpp1q3jDUBitVXNscY7TGe88r5xGPgYdC0uJk+/Ua0lz3p6RpXdXnHk2j/Sofdv+HXDywN3CipA3JcdYvI+K20ZBtNLFSaRgkrUROiAB+LOnHHZIeyLxKpU5fr6qVQ/9GLveu329RcjBXH0xXA6zLImKDbuSe4HyA/I37RsOha1FYvL0fQi2IRMT9kq4lG5kNmPul5JkkHS7dn1QoHRONbcbL1/5D2y4qTp1PLPfZrSi3zIJLvS1qo1P4IMqE9Biyo98gIm5vxLetIOmVqh3sJJN3XxpdOrUd08kVAltFxKCveJKmk0qlkVIN2Fcjl6YPRVUvlql9yTNzuYD8+r8tOTnrluq5fjkiPtDjPcdr5c5R5GqhqUVB9gySDicVDSMmIm4tHxDfoNzJ8vmkg+6fRsTdo3EPMyyjWda/ICeL+0n6OHbQPSEpK5hPLitjjyKtRupKpeHGAg8ASFoZeC85md88IgZZZ6hlJ0sz5lT9ywpDpFmxnNsUeJ36mEoB1Um/MYnBHzXmdwz8NOTYN9o3yGryelKh9L2ImFaPUO5Q22nByAnk2Pogcu41MR10F7wF8/C8nXQgfQnpQ6btuBvYTtIaXeZ5WTlv2RK3KY2XISIeIn3nvELS8r3+gB6oTFDGWsu+Vjmf0hK39Rjf+9lItdS2l/e55zJQbtN6KmlquUtTcWAWSDq2RZIWYe5Wv8OxItmRXtiiUHouqfAcEWUgeAOwmqSXtCSZMtJ7mK5YC7irqVAqjFb7/Ydy7mar8CrtaO7S8mxiJvkFfa+iEOmWi8mB80R+rmsB9zaVDIXRHkt8s5wPZO7q9Qk5sF9AGW78OWplXVbEfIdUWu9GfmR7iPTvZiYelRKoab49T7mXD/PVeKYa36xJjo/PalEovbDEm/HlWnIusXZxxdFG9TGyF7P1+8p59WaEpLVoKHyKe5ibyHHl5Jb82ubpQ96HdP/QZL7mveWjxc+BTcoq8X1Iv1Bndbqmn1ipNDz7l/MhEbF/20EOLFRLOxzV9oBHSnpmeaakJYBPd7jmS6Ry64RiujQISc+XNNKJW2V296IR5jMcA+U8pR4oaUe6f4YGkLQHueLoCXKbym4ZKOcpjfzWJ7eCbt5nRXK3nEnAXhHxl/kQ10w8LgBuJP3RNCfwB9K9P6W7SFO3DYsSCXhmye9XmfvFaaScSPZbny1Kr+o+a5BfIs3YMwCsVPybPIOkg8jVMKPBN8lJ5gxJ6zYjJdVNub5OKk2+WgaNzbRLSOo0MHzWU3wXfZIcP5wpqZOiuDnYvoucZG8kaXpZjTgISS/p4WPaWDAALC/p1fVASfuRjpdHk3OA68gPjW8CrouI2aN8j4WZ+8jVB53GnwOMblkfT7Yxx5JjqJOaCgczPkjaR9L29T69FrcKcED59/eN6G0k7doIezfpI2d2RPythA2U85ZF6VTl/Vzg29hqZ9yJiEeBn5DP/vNqOAsuyr4Pl39n9pD1NeTqo9eXFWpVfksBX+twzffJceV/1+WQtDqdfcZWZrIH1AMlbUsqfpoMlPOURvo1gc92uEfFceX8U9Lc/PiJ6nbEL9IQSJoCrANcOYyd9XdJD/H7ShrW51FEnCfpeHLSdpWkU0ilwG7ksrzbaNiQRsQJxZbyEOBGSb8lfZYsT3aIryUnXAf39CMHcy1pdvdmSY+X/AP4Qa1xHg2+CewL/Kz89lvJpeQ7ASeTtqOmQcNZ6jLAy5n7Jf+IiOjF9vf7ZIP9FUlTyV21XgrsSi4Nb5bBx0lN+6XAFkVj3uQrHezMzQjp4Ci34pcRcfn85Ftstvcntwc/vbyPN5Jbum9POgrcmXab9mY+XwM+Clwp6TRyEjuVbKNml79HyhdJc+Q9gUtLOziJrK+/J/1imLHly6Ty6EJJJ5MDuNeQXxVPIctmRETElZLeQ074Li/16UZyqfzGpP+37Uraq0od/jZwtaQzyfZs8kzXjgAADuZJREFUSXKCuhXZp75ypHItwHyc/PA1HbhA0iXkoPheUpk0mfI8GTxxezfZL3ycdBZ6Pmk29ALSQffG5AD6/8b+J7TyFVKhcH6piw+QX4m3JL/u7jVaN4qIkPQt8gMfeJXSqBIRD0m6CNhK0o9IBd5TwOkR8WdGuawj4u+SzmBun+Hy7B+bkC4X7ihtTNWerEHugLUU6buv6WJkFnCq0sH3DaTfxteR7dohVaKIuEPST4A3k/3JWeS4YXtyV9rLSb9cZnz5INmH7AtsJulscjzxYtJcbFngsxExj0P2TkTEE5K+SvZ1l5W6sRhZ1reVo8nnyHHlm4F1avXjTWR/uAfzjoFPJOdQh0taj9xldW1yvHwq846DZpF19APFnPMycnyyK3AGQyzmiIgLJF1B1u8n6M2MfVyxUmloKg3kkB7WI2JA0u/IStutP4l3khrVg0hFULV15hGk4+V5dkaJiHeVAfPB5ABwObLx/DvweXIr6PmmOAl/A+kU7E3M9Ux/PmUHhdEgIv5cFBmfJDuAxcjljW8kbWetVGqnrrB8ijS7nAUcGxFn95JRRNxW/CN9hhyU7UjWx0NIJ7zNMli6nCvfTW3MpLMDVDMyhlJWD5CDovkiIuZI2pp8H3cpwReRSqD/LP+37SDZZDpZJ/cn27UHSIfdR5E24SMmIh6TtB25reze5EB0oMh+KlYqjTkRcYak15MfUt5MrhK6mPwCty6joFQq9zlO0p+BD5F18Q3kDqhXkKsM6mm/J+ly0l/fFPIDxcPkAPKn5VhoKTsmzVD6hDyYfJ5vIT9O/JMcbxxHfkC6tHbdg6VtOLCk35P0p3Unqbh7P/mO94WI+I2k3cg2Zm+yX7yY/H1rMopKpcJMcsffJ4DvjXLeJnfU+zL5/u5Djj9vAf48RmV9Atln/Kle782480WyPdmO/KC1I9nO3EPu6noSuZKs6UfnF2RfcCQ5dnmihB0eEdc10u5HmjntDbyLHKucTu7q2GaSZMaYstnVJuQq8zeQDtOXIsv9POC4iPj1fGR9NLly/gCy77qDXBU1g1T+NOV4pMxJP062I+8nFZufJjeT2oPGGDgi7ip94+fJRR1bkzsCbk8qQ/dspH9Y0jbknGsK+bHrJnLToy8x/Lz3RFKxflqPCwjGFXkn1IlF8VtzHfCTiLDzOGNM35B0AfkVcZKdshtjFmbK6vXZwA8j4q19FseMkLIC+Ghg/4j4bp/FMV0iaRo5yZ5nsx9jRhNJB5CKy4Mjom+rGSXNJE2vt4uIc/olx3DYp1KfkLRK035Y0tKkJhLyq7sxxowpkpbu4KdtGumo+ywrlIwxhsPK+di+SmFGjKRlyVV79wKddnU2xiwESHpBS9jq5Ar8J4FfjbtQg+V4M/BX4Nx+ydENNn/rH+8D9pE0B7id3AJzW+CFpB+Tn/VPNGPMQsSLSNvzs0mb78WA9UmzyPtJu3djjFnoKP4vdgU2JP1l/CoiLuqvVGZ+kbQLacK/G7ld+Ici4l/9lcoY02dOKRvLXEKOeyeT7f7SpDnlreMtkKS3kH6a3kz6iJzeYgI6obBSqX+cTTrd2oF0ZPskafb2NdLh8YSuOMaYZw13krs8bU36p1iStEE/EfhU2T3KGGMWRjYkfWs8SH7sO2To5GaC8x+kGcmdwH+TPpyMMQs3PyB9uu1JOul+iPQtemxE/KJPMh1I+mu6GXh/REx431/2qWSMMcYYY4wxxhhjesY+lYwxxhhjjDHGGGNMz1ipZIwxxhhjjDHGGGN6xkolY4wxxhhjjDHGGNMzVioZY4wxZkIj6XxJT/aQfjFJIel3YynXswFJt0i6od9yGGOMMWbBxEolY4wxZiFF0oZF+fKHDvFvKfEhaY2W+KUkPSrpX5KWHHuJxx5JL5d0rKS/SHpA0uOSbpM0S9K+z5bfaYwxxhgzGizWbwGMMcYY0zcuA+4DNpL0vIh4sBG/DRCAyt/fbcRvASwJnB0Rj421sGOJJAEzgOnk770QOJfcXngVYAqwK3AQsGlfhBwbtibL2BhjjDGmZ7xSyRhjjFlIiYingTnAoqRyock2Jf6e8ndbPMA5YyDeeDMd+Bjwd+A1EbFFRLw3Io6IiHdExJrA7sDDfZVylImIGyPipn7LYYwxxpgFEyuVjDHGmIWbSiE0SGkkaTKwRok/D5jacm2rUknScyQdUUzIHpH0oKTfS9qrmYGktYp53XckrSPpZ5LulvS0pC2HElzSkpKOlnRTMcO7SdLHgSW6+eG1fF5CKpUeA3aOiD+2pYuIWcDratdtV2Q/qkO+8/grkrR/uea/JO0u6f9JeljSvZJOLrJ0I/MrSj7fa4SvXTNZ3KwR96US/toeZNxW0nmSHirmgLMkrdMizw/LNatLOqSU/aOS7pD0LUnP6/A7Vpf0zVJ2j0m6R9JpkjZsSfvJco8tJb1V0sVFrhtqafaQdG6572OSbpU0R9LB3TxXY4wxxvSGlUrGGGPMws255bxtI3zbWvxsYFVJL68ii5JgI+B+4NJa+JLA2cCnSDOyY4EfAi8DflaUPm2sDVwMvLCk/zbwz05CF3O1U0iTtSfLfc4ADgR+PMTvbeMdpEuAn0XEX4dKOIpmfv9Byv834KvARSXsD5JeOtzFEXEVcAfzlltdOdgW9y+g1YdWC3sAvyHL+DjSJHBX4DxJy3e45otk2V8GfKPIeBD5WwchaSPgcuBg4Brga8As0tTwQkk7dLjHR4DjgQGy3H9T8jsEOBVYFzi9yHImsAzw9i5/szHGGGN6wD6VjDHGmIWYiPirpNuAV0paKSLuLlHbkP6E/gg8WAu7uvy9NWk2N7uY0VUcBmxJKgfeGBFPAhRl0h+BoySdEREXNUTZCvhERHysS9HfCuwCXABsWyl7JB1T7tML1Yqo8TTj251cFfWbKkDSB4EvkIqSHbvIYzawj6R1IuLaErYtcCdwV/n7kyXvFYFXk/6vHu9SxtcD20fEnJqMnwc+BEwDvtRyzcbAqyLilpJ+cXKl23aSNoiIS2vhJwNLA6+NiPNr9ziKLMMTJK3ZIu9UYNOIuKIRfhDwKPDqiPhHPaL8fmOMMcaMMl6pZIwxxpjZ5KqiuonbVOB/I+LJsirmLgavgunkT+kdwNPAByuFEkBE3EEqOATs1yLDbSW+W/Yt58Prq4eKMuFTPeQDsGo539LjdSPhrLpCqfBVcvXNDpJW6yKP6tlvC8+s3ppaws8BNpO0VEmzDfnse1Gc/aiuUCocX86v6XDNMZVCCSAingBObLlmd9K88it1hVK55hZSubYauWqpyXEtCqWKJ8oxiKaSyRhjjDGjg5VKxhhjjBnkV6mYua1KKpsq5gBTJC1STwv8rkog6fnAZODmiLi+5T6Vqd36LXGX97CCBmAD0uztwpa4OT3kA6lsgfHdBe28ZkBRwl1Q/m17Rk0GKZWA9YAVSvi55M581SqsqrzOpXv+1BJ2czk/f4TXVP6e1pA0o3mQppWQZpNNLu5w7x8BywJXF/9Rr/cKJWOMMWZssfmbMcYYY5rKiTYFxBzgTcD6kv4GvAq4tWZ2BTCpnG/vcJ8qfLmWuDt6EZhUHtwZEU+NQl63AWuR/pzGizs7hFeyT+oQ/wwRMSDp/4CpRdlX94N1L/BUCTu7nAf5v+qC+1vCqtVni47wmhXKee9hZHhuS1hr+UbE5yTdBbwTeB/wfiAkzQY+XJneGWOMMWb08EolY4wxZiEnIv4O3AisJWl1Uql0P+lsuaJatbQNnU2pHijnVTrcatVGukFi9Cj2P4EVJbUpNzrdvxOV+VXTsfVwVL6kOn2kG0ox9G8dwivZ255RG+eSK4A2IOW/MSIGIuJBctXQdqVM1wLmNPxf9ZPq9+0SERriaDNl7FhXImJmRGxCKq12JU3vpgK/lbRCp+uMMcYYM39YqWSMMcYYmKsg2o50wn1eXQEREdeQK40qpVL9mirNfeRuZi+StGbLPSqfTaOxYuRSUpmzeUvclB7zOoFcTfMmSesOlbDsbldxXzmv3pJuXdpX2VRs3XLNYsAW5d/LmvEdqMpgJ9LZ+TmNuPXJXeVgfB2RD0e1A91WY5F5RNwXEWdExH7AD4AVmWsKaIwxxphRwkolY4wxxsBcU7f3A8sz2J9SxRxSCVBt9d6mpDiBHF98vuZ/CUkrA0fW0oyUyvnzp+uKnuJD58j2S9qJiBuBT5A+iH4taYO2dJJeB/yqFnQ1uUPeG+q+eyQtTTrdHoodJO3UCDuU9El1dkTc2qX4Vbm9h1RindOIWwT4SCPtROBU0in5eyW17nQnaXNJz+k2Q0k7FcVcPUzAyuXff82nrMYYY4zpgH0qGWOMMQZS4RCkr6Tq/yazgX3IXbuu7aD4+Cy5auaNwBWSzgSWIVfLrAR8OiL+0HJdr/yA9PG0C3ClpNNJpdBepCPnyT3m9wnS58904E+SLiTNxx4mTdW2Jk3InpE9Ih6T9HXgcOBySacCiwM7kiu2OvlNApgFzJJ0CnATuaJoJ+Ae4F3dCh0Rd0q6CngFWX51ZeAFwKOkUuX2iLi623zHmvLs3gj8BviNpAuAy4FHgBcBG5P1bCXyN3TDz4F/SjqfVFgtSipBNyLrRJui1BhjjDEjwCuVjDHGGENE3A1cWf79B/CXlmT1SXmrKVVEPEb69plOjjPeC7wNuBZ4c0T0tIpoCHkD2BM4hlTkvIf0ofNtUvHVc34RcTTwSuA40k/RO4APk4qr64H9mNds7ahyPA4cBLwO+AmwM3MdVLdxcpF/MrlCaVNSKbJph53zhqIqiytLOVa/6VHm7o434RQqEXEZ8Grgc8x93u8k/UNdAvwXc00Mu+Ew4CJgQ1IxN41ULB0GbFt21zPGGGPMKKIckxljjDHGmLFG0v6k4uutEfHDfstjjDHGGDMSvFLJGGOMMcYYY4wxxvSMlUrGGGOMMcYYY4wxpmesVDLGGGOMMcYYY4wxPWOfSsYYY4wxxhhjjDGmZ7xSyRhjjDHGGGOMMcb0jJVKxhhjjDHGGGOMMaZnrFQyxhhjjDHGGGOMMT1jpZIxxhhjjDHGGGOM6RkrlYwxxhhjjDHGGGNMz1ipZIwxxhhjjDHGGGN65v8DEbddFQd9fxcAAAAASUVORK5CYII=\n",
            "text/plain": "<Figure size 1440x720 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "#We save the dataframes to disk\ndf_wc.to_pickle(\"df_wc.pkl\")\ndfw.to_pickle(\"dfw.pkl\")\ndf_top10.to_pickle(\"df_top10.pkl\")",
      "execution_count": 26,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.7.3",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "latex_envs": {
      "eqNumInitial": 1,
      "eqLabelWithNumbers": true,
      "current_citInitial": 1,
      "cite_by": "apalike",
      "bibliofile": "biblio.bib",
      "LaTeX_envs_menu_present": true,
      "labels_anchors": false,
      "latex_user_defs": false,
      "user_envs_cfg": false,
      "report_style_numbering": false,
      "autoclose": false,
      "autocomplete": true,
      "hotkeys": {
        "equation": "Ctrl-E",
        "itemize": "Ctrl-I"
      }
    },
    "gist": {
      "id": "",
      "data": {
        "description": "Desktop/Projects/FIFA/FootballMatches.ipynb",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}