02_Add_Country_Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ad54925d",
   "metadata": {},
   "source": [
    "# FIFA World Rankings\n",
    "## Part 2 - Add Country Data\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8764c521",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary dependencies\n",
    "import re\n",
    "import time\n",
    "import pandas as pd\n",
    "import json\n",
    "from datetime import datetime, date, timedelta\n",
    "import numpy as np\n",
    "from datetime import datetime as dt\n",
    "import validators\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a214a5da",
   "metadata": {},
   "source": [
    "### Get country flag images\n",
    "Source: https://data.world/pbi/country-flag-database-from-wikipedia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a2ca4b72",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>ImageURL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country                                           ImageURL\n",
       "0  Afghanistan  https://upload.wikimedia.org/wikipedia/commons...\n",
       "1      Albania  https://upload.wikimedia.org/wikipedia/commons...\n",
       "2      Algeria  https://upload.wikimedia.org/wikipedia/commons...\n",
       "3      Andorra  https://upload.wikimedia.org/wikipedia/commons...\n",
       "4       Angola  https://upload.wikimedia.org/wikipedia/commons..."
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_flags = pd.read_excel('./data/country_flags_dataset.xlsx')\n",
    "country_flags.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3e2e78fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import master dataframe\n",
    "master_df = pd.read_csv('./data/FIFA_World_Rankings_Extracted_20210610.csv')\n",
    "\n",
    "# Rename countries of master dataframe\n",
    "master_df.replace('USA', 'United States', inplace=True)\n",
    "master_df.replace('Korea Republic', 'South Korea', inplace=True)\n",
    "master_df.replace('Korea DPR', 'North Korea', inplace=True)\n",
    "master_df.replace(\"Côte d'Ivoire\", 'Ivory Coast', inplace=True)\n",
    "master_df.replace('Congo DR', 'Democratic Republic of Congo', inplace=True)\n",
    "master_df.replace('Congo', 'Republic of Congo', inplace=True)\n",
    "master_df.replace('China PR', 'China', inplace=True)\n",
    "master_df.replace('IR Iran', 'Iran', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "39be583a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['North Macedonia', 'Cabo Verde', 'Curaçao', 'Kyrgyz Republic',\n",
       "       'Faroe Islands', 'St. Kitts and Nevis', 'Eswatini',\n",
       "       'New Caledonia', 'Tahiti', 'St. Vincent and the Grenadines',\n",
       "       'Bermuda', 'St. Lucia', 'Puerto Rico', 'Montserrat',\n",
       "       'São Tomé and Príncipe', 'American Samoa', 'Cayman Islands',\n",
       "       'Gibraltar', 'Timor-Leste', 'Guam', 'Aruba',\n",
       "       'Turks and Caicos Islands', 'US Virgin Islands',\n",
       "       'British Virgin Islands', 'Anguilla', 'Curacao',\n",
       "       'Sao Tome e Principe', 'FYR Macedonia', 'St. Vincent / Grenadines',\n",
       "       'São Tomé e Príncipe', 'Netherlands Antilles',\n",
       "       'Serbia and Montenegro', 'Yugoslavia', 'Zaire', 'Czechoslovakia'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge country flag URL with master dataframe\n",
    "master_df_flag = master_df.merge(country_flags, how = 'left', left_on='country', right_on='Country')\n",
    "\n",
    "# Drop extra Country column\n",
    "master_df_flag.drop(columns=['Country'], inplace=True)\n",
    "\n",
    "# List of countries without flag image\n",
    "master_df_flag[master_df_flag['ImageURL'].isnull()]['country'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1835dff7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>country</th>\n",
       "      <th>points</th>\n",
       "      <th>date</th>\n",
       "      <th>ImageURL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>1783.38</td>\n",
       "      <td>27 May 2021</td>\n",
       "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>France</td>\n",
       "      <td>1757.30</td>\n",
       "      <td>27 May 2021</td>\n",
       "      <td>https://upload.wikimedia.org/wikipedia/en/c/c3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>1742.65</td>\n",
       "      <td>27 May 2021</td>\n",
       "      <td>https://upload.wikimedia.org/wikipedia/en/0/05...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>England</td>\n",
       "      <td>1686.78</td>\n",
       "      <td>27 May 2021</td>\n",
       "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>1666.12</td>\n",
       "      <td>27 May 2021</td>\n",
       "      <td>https://upload.wikimedia.org/wikipedia/commons...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rank   country   points         date  \\\n",
       "0     1   Belgium  1783.38  27 May 2021   \n",
       "1     2    France  1757.30  27 May 2021   \n",
       "2     3    Brazil  1742.65  27 May 2021   \n",
       "3     4   England  1686.78  27 May 2021   \n",
       "4     5  Portugal  1666.12  27 May 2021   \n",
       "\n",
       "                                            ImageURL  \n",
       "0  https://upload.wikimedia.org/wikipedia/commons...  \n",
       "1  https://upload.wikimedia.org/wikipedia/en/c/c3...  \n",
       "2  https://upload.wikimedia.org/wikipedia/en/0/05...  \n",
       "3  https://upload.wikimedia.org/wikipedia/commons...  \n",
       "4  https://upload.wikimedia.org/wikipedia/commons...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df_flag.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47068991",
   "metadata": {},
   "source": [
    "___\n",
    "### Get country population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0e70f64f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Population</th>\n",
       "      <th>Yearly Change</th>\n",
       "      <th>Net Change</th>\n",
       "      <th>Density (P/KmÂ²)</th>\n",
       "      <th>Land Area (KmÂ²)</th>\n",
       "      <th>Migrants (net)</th>\n",
       "      <th>Fert. Rate</th>\n",
       "      <th>Med. Age</th>\n",
       "      <th>Urban Pop %</th>\n",
       "      <th>World Share</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>China</td>\n",
       "      <td>1440297825</td>\n",
       "      <td>0.0039</td>\n",
       "      <td>5540090.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>9388211.0</td>\n",
       "      <td>-348399.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>38</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.1847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>India</td>\n",
       "      <td>1382345085</td>\n",
       "      <td>0.0099</td>\n",
       "      <td>13586631.0</td>\n",
       "      <td>464.0</td>\n",
       "      <td>2973190.0</td>\n",
       "      <td>-532687.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>28</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.1770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>United States</td>\n",
       "      <td>331341050</td>\n",
       "      <td>0.0059</td>\n",
       "      <td>1937734.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>9147420.0</td>\n",
       "      <td>954806.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>38</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>274021604</td>\n",
       "      <td>0.0107</td>\n",
       "      <td>2898047.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>1811570.0</td>\n",
       "      <td>-98955.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>30</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.0351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>221612785</td>\n",
       "      <td>0.0200</td>\n",
       "      <td>4327022.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>770880.0</td>\n",
       "      <td>-233379.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>23</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.0283</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country  Population  Yearly Change  Net Change  Density (P/KmÂ²)  \\\n",
       "0          China  1440297825         0.0039   5540090.0             153.0   \n",
       "1          India  1382345085         0.0099  13586631.0             464.0   \n",
       "2  United States   331341050         0.0059   1937734.0              36.0   \n",
       "3      Indonesia   274021604         0.0107   2898047.0             151.0   \n",
       "4       Pakistan   221612785         0.0200   4327022.0             287.0   \n",
       "\n",
       "   Land Area (KmÂ²)  Migrants (net) Fert. Rate Med. Age Urban Pop %  \\\n",
       "0         9388211.0       -348399.0        1.7       38        0.61   \n",
       "1         2973190.0       -532687.0        2.2       28        0.35   \n",
       "2         9147420.0        954806.0        1.8       38        0.83   \n",
       "3         1811570.0        -98955.0        2.3       30        0.56   \n",
       "4          770880.0       -233379.0        3.6       23        0.35   \n",
       "\n",
       "   World Share  \n",
       "0       0.1847  \n",
       "1       0.1770  \n",
       "2       0.0425  \n",
       "3       0.0351  \n",
       "4       0.0283  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_pop = pd.read_excel('./data/country_population_2020.xlsx')\n",
    "country_pop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cdcfe007",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Curaçao', 'Kyrgyz Republic', 'Faroe Islands', 'Kosovo',\n",
       "       'St. Kitts and Nevis', 'Chinese Taipei', 'Tahiti',\n",
       "       'St. Vincent and the Grenadines', 'St. Lucia', 'Macau',\n",
       "       'São Tomé and Príncipe', 'Turks and Caicos Islands',\n",
       "       'US Virgin Islands', 'Curacao', 'Cape Verde Islands',\n",
       "       'Sao Tome e Principe', 'FYR Macedonia', 'Swaziland',\n",
       "       'St. Vincent / Grenadines', 'São Tomé e Príncipe',\n",
       "       'Netherlands Antilles', 'Serbia and Montenegro', 'Yugoslavia',\n",
       "       'Zaire', 'Czechoslovakia'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df_flag_pop = master_df_flag.merge(country_pop, how = 'left', left_on='country', right_on='Country')\n",
    "\n",
    "# Drop extra Country column\n",
    "master_df_flag_pop.drop(columns=['Country'], inplace=True)\n",
    "\n",
    "# List of countries without flag image\n",
    "master_df_flag_pop[master_df_flag_pop['Population'].isnull()]['country'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "40fd5ef6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create log population\n",
    "master_df_flag_pop['population_natural_log'] = np.log(master_df_flag_pop['Population'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b0046f02",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Work on most recent dataset\n",
    "latest_date = master_df_flag_pop.iloc[0]['date']\n",
    "master_df_flag_pop_latest = master_df_flag_pop[master_df_flag_pop['date'] == latest_date]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d1ddf763",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='population_natural_log', ylabel='points'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=master_df_flag_pop_latest, x=\"population_natural_log\", y=\"points\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a355ace",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new metric of points / natural log value (to see which country delivers the best out of their population)\n",
    "# Also do a scatter plot based against % urban"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}