Athlete Data for Capstone.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data = pd.read_csv('/Users/jaredcahners/Desktop/Studying/Coursera/Learn_SQL_Basics_for_Data_Science_Specialization/Capstone/Data/athlete_events.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_populations = pd.read_csv('/Users/jaredcahners/Desktop/Studying/Coursera/Learn_SQL_Basics_for_Data_Science_Specialization/Capstone/Data/population_by_country_2020.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "GDP_per_cap = pd.read_csv('/Users/jaredcahners/Desktop/Studying/Coursera/Learn_SQL_Basics_for_Data_Science_Specialization/Capstone/Data/gdp.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'USA', 'Team'] = 'United States'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'GRE', 'Team'] = 'Greece'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'GER', 'Team'] = 'Germany'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'SWE', 'Team'] = 'Sweden'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'ITA', 'Team'] = 'Italy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'MEX', 'Team'] = 'Mexico'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'FRA', 'Team'] = 'France'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'AUT', 'Team'] = 'Austria'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'BEL', 'Team'] = 'Belgium'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'DEN', 'Team'] = 'Denmark'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'FIN', 'Team'] = 'Finland'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'NZL', 'Team'] = 'New Zealand'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'NOR', 'Team'] = 'Norway'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'GBR', 'Team'] = 'United Kingdom'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'NED', 'Team'] = 'Netherlands'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'ARG', 'Team'] = 'Argentina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'IRL', 'Team'] = 'Ireland'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'SUI', 'Team'] = 'Switzerland'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'POR', 'Team'] = 'Portugal'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'AUS', 'Team'] = 'Australia'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'EST', 'Team'] = 'Estonia'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'TUR', 'Team'] = 'Turkey'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'JPN', 'Team'] = 'Japan'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'CAN', 'Team'] = 'Canada'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'URU', 'Team'] = 'Uruguay'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'HUN', 'Team'] = 'Hungary'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 271116 entries, 0 to 271115\n",
      "Data columns (total 15 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   ID      271116 non-null  int64  \n",
      " 1   Name    271116 non-null  object \n",
      " 2   Sex     271116 non-null  object \n",
      " 3   Age     261642 non-null  float64\n",
      " 4   Height  210945 non-null  float64\n",
      " 5   Weight  208241 non-null  float64\n",
      " 6   Team    271116 non-null  object \n",
      " 7   NOC     271116 non-null  object \n",
      " 8   Games   271116 non-null  object \n",
      " 9   Year    271116 non-null  int64  \n",
      " 10  Season  271116 non-null  object \n",
      " 11  City    271116 non-null  object \n",
      " 12  Sport   271116 non-null  object \n",
      " 13  Event   271116 non-null  object \n",
      " 14  Medal   39783 non-null   object \n",
      "dtypes: float64(3), int64(2), object(10)\n",
      "memory usage: 31.0+ MB\n"
     ]
    }
   ],
   "source": [
    "#athlete_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Team</th>\n",
       "      <th>NOC</th>\n",
       "      <th>Games</th>\n",
       "      <th>Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>City</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Event</th>\n",
       "      <th>Medal</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A Dijiang</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>1992 Summer</td>\n",
       "      <td>1992</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>Basketball Men's Basketball</td>\n",
       "      <td>NaN</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A Lamusi</td>\n",
       "      <td>M</td>\n",
       "      <td>23.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>2012 Summer</td>\n",
       "      <td>2012</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Judo</td>\n",
       "      <td>Judo Men's Extra-Lightweight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Gunnar Nielsen Aaby</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1920 Summer</td>\n",
       "      <td>1920</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>Football</td>\n",
       "      <td>Football Men's Football</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Edgar Lindenau Aabye</td>\n",
       "      <td>M</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1900 Summer</td>\n",
       "      <td>1900</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Paris</td>\n",
       "      <td>Tug-Of-War</td>\n",
       "      <td>Tug-Of-War Men's Tug-Of-War</td>\n",
       "      <td>Gold</td>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Christine Jacoba Aaftink</td>\n",
       "      <td>F</td>\n",
       "      <td>21.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NED</td>\n",
       "      <td>1988 Winter</td>\n",
       "      <td>1988</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Calgary</td>\n",
       "      <td>Speed Skating</td>\n",
       "      <td>Speed Skating Women's 500 metres</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID                      Name Sex   Age  Height  Weight         Team  NOC  \\\n",
       "0   1                 A Dijiang   M  24.0   180.0    80.0        China  CHN   \n",
       "1   2                  A Lamusi   M  23.0   170.0    60.0        China  CHN   \n",
       "2   3       Gunnar Nielsen Aaby   M  24.0     NaN     NaN      Denmark  DEN   \n",
       "3   4      Edgar Lindenau Aabye   M  34.0     NaN     NaN      Denmark  DEN   \n",
       "4   5  Christine Jacoba Aaftink   F  21.0   185.0    82.0  Netherlands  NED   \n",
       "\n",
       "         Games  Year  Season       City          Sport  \\\n",
       "0  1992 Summer  1992  Summer  Barcelona     Basketball   \n",
       "1  2012 Summer  2012  Summer     London           Judo   \n",
       "2  1920 Summer  1920  Summer  Antwerpen       Football   \n",
       "3  1900 Summer  1900  Summer      Paris     Tug-Of-War   \n",
       "4  1988 Winter  1988  Winter    Calgary  Speed Skating   \n",
       "\n",
       "                              Event Medal      Country  \n",
       "0       Basketball Men's Basketball   NaN        China  \n",
       "1      Judo Men's Extra-Lightweight   NaN        China  \n",
       "2           Football Men's Football   NaN      Denmark  \n",
       "3       Tug-Of-War Men's Tug-Of-War  Gold      Denmark  \n",
       "4  Speed Skating Women's 500 metres   NaN  Netherlands  "
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#athlete_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_populations.rename(columns = {'Country (or dependency)' : 'Country_or_Dependency', 'Population (2020)' : 'Population_2020', 'Urban Pop %' : 'Urban_Pop_Pct'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_populations['Country_or_Dependency'] = country_populations['Country_or_Dependency'].replace(['DR Congo', 'Congo'], ['Congo (Kinshasa)', 'Congo (Brazzaville)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_populations['Country_or_Dependency'] = country_populations['Country_or_Dependency'].replace([\"Côte d'Ivoire\"], \"Cote d'Ivoire\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_populations['Country_or_Dependency'] = country_populations['Country_or_Dependency'].replace([\"Czech Republic (Czechia)\"], \"Czech Republic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_populations.loc[country_populations['Country_or_Dependency'] == 'Venezuela', 'Urban_Pop_Pct'] = '88.3%'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_populations.loc[country_populations['Country_or_Dependency'] == 'Hong Kong', 'Urban_Pop_Pct'] = '100%'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_populations.loc[country_populations['Country_or_Dependency'] == 'Singapore', 'Urban_Pop_Pct'] = '100%'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_populations.loc[country_populations['Country_or_Dependency'] == 'Kuwait', 'Urban_Pop_Pct'] = '100%'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_populations.loc[country_populations['Country_or_Dependency'] == 'Puerto Rico', 'Urban_Pop_Pct'] = '93.6%'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_populations.loc[country_populations['Country_or_Dependency'] == 'Macao', 'Urban_Pop_Pct'] = '100%'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_populations.loc[country_populations['Country_or_Dependency'] == 'Monaco', 'Urban_Pop_Pct'] = '100%'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_populations.loc[country_populations['Country_or_Dependency'] == 'Gibraltar', 'Urban_Pop_Pct'] = '100%'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_populations.loc[country_populations['Country_or_Dependency'] == 'Palau', 'Urban_Pop_Pct'] = '81%'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_populations.loc[country_populations['Country_or_Dependency'] == 'Anguilla', 'Urban_Pop_Pct'] = '100%'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_populations.loc[country_populations['Country_or_Dependency'] == 'Nauru', 'Urban_Pop_Pct'] = '100%'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_populations.loc[country_populations['Country_or_Dependency'] == 'Holy See', 'Urban_Pop_Pct'] = '100%'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_populations.loc[country_populations['Country_or_Dependency'] == 'Guadeloupe', 'Urban_Pop_Pct'] = '100%'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_populations['Urban_Pop_Pct'] = country_populations['Urban_Pop_Pct'].str[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_populations['Urban_Pop_Pct'] = country_populations['Urban_Pop_Pct'].astype(float).div(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 235 entries, 0 to 234\n",
      "Data columns (total 11 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   Country_or_Dependency  235 non-null    object \n",
      " 1   Population_2020        235 non-null    int64  \n",
      " 2   Yearly Change          235 non-null    object \n",
      " 3   Net Change             235 non-null    int64  \n",
      " 4   Density (P/Km²)        235 non-null    int64  \n",
      " 5   Land Area (Km²)        235 non-null    int64  \n",
      " 6   Migrants (net)         201 non-null    float64\n",
      " 7   Fert. Rate             235 non-null    object \n",
      " 8   Med. Age               235 non-null    object \n",
      " 9   Urban_Pop_Pct          235 non-null    float64\n",
      " 10  World Share            235 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 20.3+ KB\n"
     ]
    }
   ],
   "source": [
    "country_populations.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "GDP_per_cap.rename(columns = {'Country Name' : 'Country_Name', 'Country Code' : 'Country_Code'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "GDP_per_cap['Country_Name'] = GDP_per_cap['Country_Name'].replace(['Congo, Dem. Rep.', 'Congo, Rep.'], ['Congo (Kinshasa)', 'Congo (Brazzaville)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "GDP_per_cap['Country_Name'] = GDP_per_cap['Country_Name'].replace(['Korea, Rep.'], 'South Korea')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "GDP_per_cap['Country_Name'] = GDP_per_cap['Country_Name'].replace(['Russian Federation'], 'Russia')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11508 entries, 0 to 11507\n",
      "Data columns (total 4 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Country_Name  11508 non-null  object \n",
      " 1   Country_Code  11508 non-null  object \n",
      " 2   Year          11508 non-null  int64  \n",
      " 3   Value         11508 non-null  float64\n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 359.8+ KB\n"
     ]
    }
   ],
   "source": [
    "GDP_per_cap.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!conda install -c anaconda pandasql -y\n",
    "from pandasql import sqldf\n",
    "sqlit = lambda q: sqldf(q, globals())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit('SELECT * FROM athlete_data ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit(\"SELECT Name, Team, Event, Medal, Year FROM athlete_data WHERE Team = 'China' and Medal = 'Silver'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit(\"SELECT * FROM athlete_data WHERE Team = 'Cambodia'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit(\"SELECT * FROM country_populations\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit('SELECT Team, COUNT(*) as Athletes FROM athlete_data GROUP BY Team ORDER BY Athletes asc LIMIT 10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit(\"SELECT `Country Name` FROM GDP_per_cap GROUP BY `Country Name` LIMIT 30\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit(\"SELECT c.Country_or_Dependency, COUNT(*) as Athletes, c.population_2020 FROM country_populations c JOIN athlete_data a ON c.Country_or_Dependency = a.Team GROUP BY c.Country_or_Dependency ORDER BY Athletes DESC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit(\"SELECT * FROM athlete_data LIMIT 10\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit(\"SELECT Team, COUNT(*) as Athletes FROM athlete_data GROUP BY Team ORDER BY Athletes DESC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#sqlit(\"SELECT Team, COUNT(*) as Athletes FROM athlete_data GROUP BY Team ORDER BY Athletes ASC LIMIT 20\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit(\"SELECT * FROM GDP_per_cap ORDER BY Value DESC LIMIT 20 \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit(\"SELECT * FROM GDP_per_cap LIMIT 20\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit(\"SELECT g.Country_Code as Code, c.Country_or_Dependency as Country, c.Population_2020 as Population, c.Urban_Pop_Pct as Urban_Pop FROM country_populations c JOIN GDP_per_cap g ON c.Country_or_Dependency = g.Country_Name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit(\"SELECT Country_or_Dependency, Urban_Pop_Pct FROM country_populations WHERE Urban_Pop_Pct = 'N.A.' GROUP BY country_or_dependency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit(\"SELECT Country_Name, Country_Code, AVG(Value) as GDP_Per_Cap FROM GDP_per_cap GROUP BY Country_Name, Country_Code\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit(\"SELECT g.Country_Code as Code, c.Country_or_Dependency as Country, c.Population_2020 as Population, c.Urban_Pop_Pct as Urban_Pop, g.GDP_Per_Cap FROM country_populations c JOIN (SELECT Country_Name, Country_Code, AVG(Value) as GDP_Per_Cap FROM GDP_per_cap GROUP BY Country_Name, Country_Code) g ON c.Country_or_Dependency = g.Country_Name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "Countries = sqlit(\"SELECT g.Country_Code as Code, c.Country_or_Dependency as Country, c.Population_2020 as Population, c.Urban_Pop_Pct as Urban_Pop, g.GDP_Per_Cap FROM country_populations c JOIN (SELECT Country_Name, Country_Code, AVG(Value) as GDP_Per_Cap FROM GDP_per_cap GROUP BY Country_Name, Country_Code) g ON c.Country_or_Dependency = g.Country_Name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Countries.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "#athletes table w/o join#sqlit(\"SELECT ID, Name, Sex, Team, Games, Sport, Event, Medal FROM athlete_data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#athlete_data['Team'].str.contains('.*[0-9].*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#athlete_data[athlete_data['Team'].str.contains('.*[0-9].*')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#athlete_data['Team'].str.split('-|/', expand = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "country_split = athlete_data['Team'].str.split('-|/', expand = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "#country_split[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data['Country'] = country_split[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit('SELECT Name, Country FROM athlete_data Limit 10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "#athlete_data[athlete_data['Country'].str.contains(\".*[^A-Za-z ()'].*\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#athlete_data[athlete_data['Country'].str.contains('Congo')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Congo_athletes = athlete_data[athlete_data['Country'].str.contains('Congo')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit('SELECT Country, COUNT(*) FROM Congo_athletes GROUP BY Country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Countries[Countries['Country'].str.contains('Congo')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "#country_populations[country_populations['Country_or_Dependency'].str.contains('Congo')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "#GDP_per_cap[GDP_per_cap['Country_Name'].str.contains('Congo')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit('SELECT Country, COUNT(*) FROM athlete_data WHERE Country LIKE \"%)%\" GROUP BY Country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit('SELECT Country, COUNT(*) FROM athlete_data WHERE Country LIKE \"%\\'%\" GROUP BY Country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "#GDP_per_cap[GDP_per_cap['Country_Name'].str.contains('Cote')].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#country_populations[country_populations['Country_or_Dependency'].str.contains('v')].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Countries[Countries['Country'].str.contains('Cote')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Countries[Countries['Country'].str.contains('Kingdom')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit('SELECT Country, COUNT(*) FROM athlete_data WHERE Country = \"Great Britain\" GROUP BY Country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Countries[Countries['Country'].str.contains('Netherlands')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Athletes after JOIN #sqlit(\"SELECT a.ID, a.Name, a.Sex, a.Country, a.Games, a.Sport, a.Event, a.Medal FROM athlete_data a JOIN Countries c ON a.Country = c.Country\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Countries[Countries['Country'].str.contains('Korea')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "#country_populations[country_populations['Country_or_Dependency'].str.contains('Korea')].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "#GDP_per_cap[GDP_per_cap['Country_Name'].str.contains('Korea')].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "#athlete_data[athlete_data['Country'].str.contains('Korea')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit('SELECT Country_Name FROM GDP_per_cap WHERE Country_Name LIKE \"%Korea%\" GROUP BY Country_Name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit(\"SELECT ID, Name, Sex, NOC, Team, Country, Games, Sport, Event, Medal FROM athlete_data WHERE Country = 'East Germany' or Country = 'West Germany'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "#GDP_per_cap[GDP_per_cap['Country_Name'].str.contains('Tai Wan')].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'GDR', 'Country'] = 'Germany'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'FRG', 'Country'] = 'Germany'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Countries[Countries['Country'].str.contains('Soviet')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "#GDP_per_cap[GDP_per_cap['Country_Name'].str.contains('Russia')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "#country_populations[country_populations['Country_or_Dependency'].str.contains('Soviet')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'URS', 'Country'] = 'Russia'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "athlete_data.loc[athlete_data['NOC'] == 'TCH', 'Country'] = 'Czech Republic'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit(\"SELECT a.ID, a.Name, a.Sex, a.Country, a.Games, a.Sport, a.Event, a.Medal FROM athlete_data a JOIN Countries c ON a.Country = c.Country\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit(\"SELECT Country, COUNT(*) FROM athlete_data GROUP BY Country ORDER BY COUNT(*) ASC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit(\"SELECT ID, Name, Sex, NOC, Team, Country, Games, Sport, Event, Medal FROM athlete_data WHERE NOC = 'YUG'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "#GDP_per_cap[GDP_per_cap['Country_Name'].str.contains('Yugo')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "#country_populations[country_populations['Country_or_Dependency'].str.contains('Yugo')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "#GDP_per_cap[GDP_per_cap['Country_Name'].str.contains('Czecho')].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "#country_populations[country_populations['Country_or_Dependency'].str.contains('Czech')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "#athlete_data[athlete_data['Country'].str.contains('Czech')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Countries[Countries['Country'].str.contains('Czech')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "Athletes = sqlit(\"SELECT a.ID, a.Name, a.Sex, a.Country, a.Games, a.Sport, a.Event, a.Medal FROM athlete_data a JOIN Countries c ON a.Country = c.Country\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 257728 entries, 0 to 257727\n",
      "Data columns (total 8 columns):\n",
      " #   Column   Non-Null Count   Dtype \n",
      "---  ------   --------------   ----- \n",
      " 0   ID       257728 non-null  int64 \n",
      " 1   Name     257728 non-null  object\n",
      " 2   Sex      257728 non-null  object\n",
      " 3   Country  257728 non-null  object\n",
      " 4   Games    257728 non-null  object\n",
      " 5   Sport    257728 non-null  object\n",
      " 6   Event    257728 non-null  object\n",
      " 7   Medal    38659 non-null   object\n",
      "dtypes: int64(1), object(7)\n",
      "memory usage: 15.7+ MB\n"
     ]
    }
   ],
   "source": [
    "Athletes.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "athletes_per_country = sqlit(\"SELECT Country, COUNT(*) as total_athletes FROM Athletes GROUP BY Country\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Countries.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit('SELECT games, year, season, city FROM athlete_data GROUP BY games')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "Events = sqlit('SELECT games, year, season, city FROM athlete_data GROUP BY games')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Events.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit(\"SELECT * FROM Urban_Pct WHERE Country = 'Ukraine'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit(\"SELECT * FROM Countries LIMIT 20\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Gold Medals by Country#sqlit(\"SELECT Country, Count(*) as Gold_Medals FROM Athletes WHERE Medal = 'Gold' GROUP BY Country ORDER BY Count(*) DESC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit(\"SELECT Country, Count(*) as All_Medals FROM Athletes WHERE Medal != 'None' GROUP BY Country ORDER BY Count(*) DESC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "Gold_By_Country = sqlit(\"SELECT Country, Count(*) as Gold_Medals FROM Athletes WHERE Medal = 'Gold' GROUP BY Country ORDER BY Count(*) DESC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "Gold_By_Country['Quartile_Rank'] = pd.qcut(Gold_By_Country['Gold_Medals'], q = 4, labels = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit('SELECT * FROM Gold_By_Country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit('SELECT Quartile_Rank, SUM(Gold_Medals) as Golds FROM Gold_By_Country GROUP BY Quartile_Rank')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "Gold_By_Quartile = sqlit('SELECT Quartile_Rank, SUM(Gold_Medals) as Golds FROM Gold_By_Country GROUP BY Quartile_Rank')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Gold_By_Quartile.plot(kind = 'bar', x = 'Quartile_Rank', y = 'Golds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "Medals_By_Country = sqlit(\"SELECT Country, Count(*) as All_Medals FROM Athletes WHERE Medal != 'None' GROUP BY Country ORDER BY Count(*) DESC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "Medals_By_Country['Quartile_Rank'] = pd.qcut(Medals_By_Country['All_Medals'], q = 4, labels = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit('SELECT * FROM Medals_By_Country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "Medals_By_Quartile = sqlit('SELECT Quartile_Rank, SUM(All_Medals) as Medals FROM Medals_By_Country GROUP BY Quartile_Rank')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit('SELECT * FROM Medals_By_Quartile')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Medals_By_Quartile.plot(kind = 'bar', x = 'Quartile_Rank', y = 'Medals')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 186 entries, 0 to 185\n",
      "Data columns (total 5 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Code         186 non-null    object \n",
      " 1   Country      186 non-null    object \n",
      " 2   Population   186 non-null    int64  \n",
      " 3   Urban_Pop    186 non-null    float64\n",
      " 4   GDP_Per_Cap  186 non-null    float64\n",
      "dtypes: float64(2), int64(1), object(2)\n",
      "memory usage: 7.4+ KB\n"
     ]
    }
   ],
   "source": [
    "Countries.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit('SELECT c.Country, c.Population, c.Urban_Pop, c.GDP_Per_Cap, g.Gold_Medals, g.Quartile_Rank as Gold_Quartile, m.All_Medals, m.Quartile_Rank as Medals_Quartile FROM Countries c JOIN Gold_By_Country g on c.Country = g.Country JOIN Medals_By_Country m on c.Country = m.Country ORDER BY Population DESC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "Indicators_and_Medals = sqlit('SELECT c.Country, c.Population, c.Urban_Pop, c.GDP_Per_Cap, g.Gold_Medals, g.Quartile_Rank as Gold_Quartile, m.All_Medals, m.Quartile_Rank as Medals_Quartile FROM Countries c JOIN Gold_By_Country g on c.Country = g.Country JOIN Medals_By_Country m on c.Country = m.Country ORDER BY Population DESC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Indicators_and_Medals.plot(kind = 'scatter', x = 'Population', y = 'Gold_Medals')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.scatter(Indicators_and_Medals['Population'],Indicators_and_Medals['Gold_Medals'])\n",
    "#plt.grid()\n",
    "#plt.xscale(\"log\")\n",
    "#plt.yscale(\"log\")\n",
    "#plt.title(\"Gold Medals and Population\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "Indicators_and_Medals['Population_Quartile'] = pd.qcut(Indicators_and_Medals['Population'], q = 4, labels = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit('SELECT * FROM Indicators_and_Medals')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "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>Population_Quartile</th>\n",
       "      <th>Total_Golds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>8479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Population_Quartile  Total_Golds\n",
       "0                    0          678\n",
       "1                    1         2133\n",
       "2                    2         1713\n",
       "3                    3         8479"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sqlit(\"SELECT Population_Quartile, SUM(Gold_Medals) as Total_Golds FROM Indicators_and_Medals GROUP BY Population_Quartile\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'All Medals and Population')"
      ]
     },
     "execution_count": 155,
     "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": [
    "plt.scatter(Indicators_and_Medals['Population'],Indicators_and_Medals['All_Medals'])\n",
    "plt.grid()\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "plt.title(\"All Medals and Population\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "Medals_by_Pop_Quartile = sqlit(\"SELECT Population_Quartile, SUM(Gold_Medals) as Gold_Medals, SUM(All_Medals) as Total_Medals FROM Indicators_and_Medals GROUP BY Population_Quartile\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "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": [
    "plt.bar(Medals_by_Pop_Quartile['Population_Quartile'],Medals_by_Pop_Quartile['Gold_Medals'])\n",
    "plt.title(\"Gold Medals and Population Quartiles\")\n",
    "plt.tick_params(bottom=False, labelbottom= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "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": [
    "plt.bar(Medals_by_Pop_Quartile['Population_Quartile'],Medals_by_Pop_Quartile['Total_Medals'])\n",
    "plt.title(\"Total Medals and Population Quartiles\")\n",
    "plt.tick_params(bottom=False, labelbottom= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.scatter(Indicators_and_Medals['GDP_Per_Cap'],Indicators_and_Medals['Gold_Medals'])\n",
    "#plt.grid()\n",
    "#plt.xscale(\"log\")\n",
    "#plt.yscale(\"log\")\n",
    "#plt.title(\"Gold Medals and GDP \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "Indicators_and_Medals['GDP_Quartile'] = pd.qcut(Indicators_and_Medals['GDP_Per_Cap'], q = 4, labels = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit('SELECT * FROM Indicators_and_Medals')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "Indicators_and_Medals['Golds_per_cap'] = Indicators_and_Medals['Gold_Medals']/Indicators_and_Medals['Population']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "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>Golds_per_cap*1000000</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69.631289</td>\n",
       "      <td>Norway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52.428762</td>\n",
       "      <td>Liechtenstein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47.377741</td>\n",
       "      <td>Sweden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44.739101</td>\n",
       "      <td>Hungary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.725647</td>\n",
       "      <td>Finland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>0.037755</td>\n",
       "      <td>Cote d'Ivoire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>0.034217</td>\n",
       "      <td>Nepal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>0.031848</td>\n",
       "      <td>Mozambique</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>0.030257</td>\n",
       "      <td>Peru</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>0.010257</td>\n",
       "      <td>Vietnam</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Golds_per_cap*1000000        Country\n",
       "0               69.631289         Norway\n",
       "1               52.428762  Liechtenstein\n",
       "2               47.377741         Sweden\n",
       "3               44.739101        Hungary\n",
       "4               35.725647        Finland\n",
       "..                    ...            ...\n",
       "84               0.037755  Cote d'Ivoire\n",
       "85               0.034217          Nepal\n",
       "86               0.031848     Mozambique\n",
       "87               0.030257           Peru\n",
       "88               0.010257        Vietnam\n",
       "\n",
       "[89 rows x 2 columns]"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sqlit('SELECT Golds_per_cap*1000000, Country FROM Indicators_and_Medals Order BY Golds_per_cap DESC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "Indicators_and_Medals['Medals_per_cap'] = Indicators_and_Medals['All_Medals']/Indicators_and_Medals['Population']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "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>Medals_per_cap*1000000</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>235.929431</td>\n",
       "      <td>Liechtenstein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>190.288682</td>\n",
       "      <td>Norway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162.389302</td>\n",
       "      <td>Finland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.925282</td>\n",
       "      <td>Sweden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>117.543703</td>\n",
       "      <td>Hungary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>0.113067</td>\n",
       "      <td>Ecuador</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>0.097842</td>\n",
       "      <td>Jordan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>0.063697</td>\n",
       "      <td>Mozambique</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>0.041030</td>\n",
       "      <td>Vietnam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>0.034217</td>\n",
       "      <td>Nepal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Medals_per_cap*1000000        Country\n",
       "0               235.929431  Liechtenstein\n",
       "1               190.288682         Norway\n",
       "2               162.389302        Finland\n",
       "3               151.925282         Sweden\n",
       "4               117.543703        Hungary\n",
       "..                     ...            ...\n",
       "84                0.113067        Ecuador\n",
       "85                0.097842         Jordan\n",
       "86                0.063697     Mozambique\n",
       "87                0.041030        Vietnam\n",
       "88                0.034217          Nepal\n",
       "\n",
       "[89 rows x 2 columns]"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sqlit('SELECT Medals_per_cap*1000000, Country FROM Indicators_and_Medals Order BY Medals_per_cap DESC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.scatter(Indicators_and_Medals['GDP_Per_Cap'],Indicators_and_Medals['All_Medals'])\n",
    "#plt.grid()\n",
    "#plt.xscale(\"log\")\n",
    "#plt.yscale(\"log\")\n",
    "#plt.title(\"All Medals and GDP \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "Medals_by_GDP_Quartile = sqlit(\"SELECT GDP_Quartile, SUM(Gold_Medals) as Gold_Medals, SUM(All_Medals) as All_Medals FROM Indicators_and_Medals GROUP BY GDP_Quartile\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Total Medals and GDP Quartiles\n",
    "#plt.bar(Medals_by_GDP_Quartile['GDP_Quartile'],Medals_by_GDP_Quartile['All_Medals'])\n",
    "#plt.title(\"Total Medals and GDP Quartiles\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Urban Pop and Gold Medals\n",
    "#plt.scatter(Indicators_and_Medals['Urban_Pop'],Indicators_and_Medals['Gold_Medals'])\n",
    "#plt.grid()\n",
    "#plt.xscale(\"log\")\n",
    "#plt.yscale(\"log\")\n",
    "#plt.title(\"Gold Medals and Urbanization\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "Indicators_and_Medals['Urb_Pop_Quartile'] = pd.qcut(Indicators_and_Medals['Urban_Pop'], q = 4, labels = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit('SELECT * FROM Indicators_and_Medals')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "Medals_by_Urb_Quartile = sqlit(\"SELECT Urb_Pop_Quartile, SUM(Gold_Medals) as Gold_Medals, SUM(All_Medals) as Total_Medals FROM Indicators_and_Medals GROUP BY Urb_Pop_Quartile\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Gold Medals and Urb Quartiles\n",
    "#plt.bar(Golds_by_Urb_Quartile['Urb_Pop_Quartile'],Golds_by_Urb_Quartile['Gold_Medals'])\n",
    "#plt.title(\"Gold Medals and Urbanization Quartiles\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'All Medals and Urbanization')"
      ]
     },
     "execution_count": 174,
     "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": [
    "#Urban Pop and All Medals\n",
    "plt.scatter(Indicators_and_Medals['Urban_Pop'],Indicators_and_Medals['All_Medals'])\n",
    "plt.grid()\n",
    "#plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "plt.title(\"All Medals and Urbanization\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAD9CAYAAAC85wBuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAW00lEQVR4nO3dfZBldZ3f8fcHUGSVZwaEGWCIjGaBLTCMI5YaSdiSB90Au1A76DJoMGNY2GjiZgO4iWYDFlgqLlkhiwvFgKwwogIRSGRhV2OKBweKiAOyjjDAMAMzAsL4ADLwzR/n13K7ud19u+ehG+b9qrrV937P+Z37O7dvn889v3Pu6VQVkiRtMdUdkCRNDwaCJAkwECRJjYEgSQIMBElSYyBIkgADYbOSpJLsu4mfc3mS3x1w3k3evw0hyaeTfGUjLfvGJCdthOUuTXLohl7udJHkzCR/0+7Pbu+traa6X9OdgTANJPl5z+3FJL/qefzBUdocmmTFBuzDP7Q/mgNH1K9p9UM31HNtSEk+lOSFntfrgSSnTHJZfV/T9tp8ZP17O3FVdWRVLVqfZSS5NMlZI5a7f1X9w3p1bvTne3+SO5L8IskTSb6SZObGeK72fC/7vVXVZ6pqSn5nr2QGwjRQVW8YugEPA7/XU7tiE3blH4EFQw+S7AwcAqzZhH2YjFt7Xr/jgM8meeumeGI/dQ6X5Djgb4G/BHYB9gd+DfyfJDtshOfz9d+ADIRpLMnWSb6YZGW7fbHVXg/cCOzR88l4jyTzktya5GdJViX5qySvncBTXgH8YZIt2+MTgG/S/UEP9WmLJKcn+Un79Lc4yU49009M8lCb9skR6zNw/5IcleTeJGuTPJrkTwdZgaq6C7gP+O22nOuT/MmIZf8gyTGDLK9Pvz6d5Or2qfcZ4ENt0uuSXNX6e1fvnlbP67W2rdOxPdM+lOR7ST6X5KkkDyY5smf6b/ZOkvy/EXuTv9lzS/K1JI8leTrJd5Ps3+oLgQ8Cf9ba/M9W/81Q3mjvszbt0CQrknwiyer2e/vwKK9NgM8DZ1XVFVX1q6p6DPgI8EvgYz2v4Vd62g0b0kny4ST3tdfrgSQf7Zl3qD//KcljwFfp/7cw6jBeku2TXNzW5dEkZw2955Psm+Q77XX8aZKrxng7vOoYCNPbJ+k+oR8EHAjMA/68qn4BHAms7NmTWAm8APx7uk9m7wAOA/54As+3ErgXeG97vAC4bMQ8/w44BngPsAfwFPAlgCT7ARcCJ7ZpOwOzetpOpH8XAx+tqm2BA4BbBlmBJG8D3gwsaaVFwB/1TD8QmAncMMjyRnE0cDWwA12IDtW+BuxE9wn5miSvadN+Arwb2B74r8BXkuzes7y3A/fTvS6fBS5uG9dhqurAnj2h/9Da3NUm3wjMAXZttStam4va/c+2tr/XZ336vs96pr+x9X0mcDLwpSQ79lnOW4C92uvQ2+8Xga/z0vtqPKuB9wPbAR8Gzkvyz0b0Zydgb7r3aL+/hbEsAtYB+wJvbf0aGl76b8C3gR3p3rv/fcA+vyoYCNPbB4G/qKrVVbWGbmNy4mgzV9WdVXVbVa2rquXAX9NtuCfiMmBBkrcAO1TVrSOmfxT4ZFWtqKrngE8Dx7VPd8cB36qq77Zp/xl4cZL9ex7YL8l2VfVU++Q/mkPaXsfPgTuAy4Eft2nXAnOSzGmPTwSuqqpf91nOoG6tqmuq6sWq+lWr3VlVV1fV88AXgNfRbWSpqq9V1co2/1Wtb/N6lvdQVX25ql6g21jtDuw22pMneRdwFvCvquqZ9hyXVNXant/JgUm2H3B9xnufPd+mP19VNwA/p9v4j7RL+7mqz7RVwIxBOlNV11fVT6rzHboN9Lt7ZnkR+FRVPdfz+g8kyW50AfLxqvpFVa0GzgPmt1mepwuaParq2ar63kSW/0pnIExvewAP9Tx+qNX6SvLmJN9qQwfPAJ/hpT/SQX0D+JfAn9BtWEfaG/hm2wD/jG545gW6DdgewCNDM7Y9mScm2b8/AI4CHmq78O8Yo8+3VdUO7ZPzG+nGrT/T+vAcsBj4oyRb0A2D9Vsv6D41vqZP/TV0G4ohj/SZp3e9XwRW0H5XSRYkubvnNTuA4ev9WE/bX7a7b+jXwSR7tvU5qar+sdW2THJOG5Z6BljeZh/0dz/e++yJqlrX8/iXo/Tvp+3n7n2m7c6Ax6KSHJnktiRPttfrKIavy5qqenaQZfWxN93vc1XP7+Ov6fasAP4MCHBHujOx/vUkn+cVyUCY3lbSvYGH7NVqAP0uU3sh8CNgTlVtB5xJ9+YeWNsg3QicQv8N5yPAkW0DPHR7XVU9SvcpcM+hGZP8Ft2w0YT7V1Xfr6qj6f5Qr6HbCA7S/8fphid6h0YW0X0KPgz4ZZ+9niEPA7sk+c3Grg3d7M3wDWa/1753vbegG25YmWRv4MvAacDOVbUD8EMm+Htpy92G7rX4YlXd2DPpA3RDVr9LN7Qze6jJGP3tNdb7bCLupwvC40f0ewu6gP9OK/0C+K2eWd7YM+/WdL+/zwG7tdfrBoa/XiPXZyKXbH4EeA7Ypef9u11V7Q9QVY9V1b+pqj3o9oYvyCvwVOjJMhCmt68Cf55kRpJdgP8CDB0oexzYecSwwLbAM8DPk/xTuo36ZJwJvKcN64z0P4Cz24aO1rej27SrgfcneVe6g8V/wfD32ED9S/LaJB9Msn0bgnmGbi9kXOnOjDoWWDpUawHwIt0Bz9H2Dqiqh4HbgXOTvKFtnP4j3Z7DbeM89cFJfr8NnX2cbqNzG/B6ug3Wmta/D9PtIUzGJcCPquqzI+rbtud7gm5D+5kR0x8H/skYyx3rfTaw6q6l/6dtWR9Isk2SNwJ/Q/cJf2g8/m7gnyfZq71/z+hZzGuBreler3XpDrCPd+yh39/CaH1cRTcE9fkk26U7SeJNSd4DkOT4JEPHvZ6i+90N9N57NTAQprez6A6O/gC4h+5g4VkAVfUjuj/kB9qu7x50f4wfANbSfSqd1BkSbbx7tLHTvwSuA76dZC3dRu/trd1S4FS6g6qr6P6ges8Pn0j/TgSWtyGQf0vPgeE+3jF0hgndENYauiGvXpcBv8P4G7o/pNsrWQY8SrdXcdQAQxTXtrZPtb7/fhtzv5cuiG6l23D9DvB/x1nWaOYDx2b4mUbvplu3h1p/7+Xl4XUx3fGYnyW5ps9yR32fTVQ7RnIi3ckDT9C9D95G9wFjVZvnJrrf/Q+AO4Fv9bRfS3fiwmK61/IDdO+3sZ6z39/CWBbQBc+97Tmu5qVhrrcBt7f30nXAx6rqwYFW/lUg5T/I0WYgyQJgYVW9a6r7sjlJ8l66jfVhVXX3FHdH43APQa967VjGHwMXTXVfNjdV9W2672ocMsVd0QDcQ9CrWpLD6c6c+jvgD0acLSOph4EgSQIGGDJKsmeSv0/3VfKlSXq/fv5oO7/67iRH9bQ5I8myJPe3T2hD9YOT3NOmnT/0bcx0X52/qtVvTzJ7I6yrJGkMg1wYah3wiaq6K8m2wJ1JbmrTzquqz/XOnO7yBfPpvhy0B/B3Sd7cvoV5IbCQ7iyIG4Aj6M55Pxl4qqr2TTIfOJfujI1R7bLLLjV79uwBV1OSBHDnnXf+tKr6fmt83EBop4oNnS62Nsl9dNc0Gc3RwJXtG6IPJlkGzEuyHNhu6EtBSS6juybOja3Np1v7q4G/SpIaYzxr9uzZLFmyZLTJkqQ+kjw02rQJnWXUhnLeSvflHYDT0l058pK8dLGrmQz/av+KVpvJ8HPSh+rD2rSDfk8z/BuuQ8+/MMmSJEvWrJnuV2SWpFeWgQOhfZ3/63QXhXqGbvjnTXRXSFxF9+Ub6P+V/BqjPlab4YWqi6pqblXNnTFjoOtkSZIGNFAgpLuM79eBK6rqG9BdM6aqXmgX8voyL129cQU913WhXdOl1Wf1qQ9r0776vz3w5GRWSJI0OYOcZRS6r77fV1Vf6Kn3XtHwWLoLdkH3de/57cyhfeiu0X5HOxaxNskhbZkL6L7uP9Rm6P/GHgfcMtbxA0nShjfIWUbvpLs2yT1J7m61M4ETkhxEN7SznO7KgFTV0iSL6a4Tsg44tZ1hBN3FzC4FtqE7mDx0xcaLgcvbAegneena5JKkTeQV+8W0uXPnlmcZSdLEJLmzqub2m+a1jCRJgIEgSWoMBEkSMNhBZUkb0OzTr5/qLky55ee8b6q7oD7cQ5AkAQaCJKkxECRJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWoMBEkSYCBIkppxAyHJnkn+Psl9SZYm+Vir75TkpiQ/bj937GlzRpJlSe5PcnhP/eAk97Rp5ydJq2+d5KpWvz3J7I2wrpKkMQyyh7AO+ERV/TZwCHBqkv2A04Gbq2oOcHN7TJs2H9gfOAK4IMmWbVkXAguBOe12RKufDDxVVfsC5wHnboB1kyRNwLiBUFWrququdn8tcB8wEzgaWNRmWwQc0+4fDVxZVc9V1YPAMmBekt2B7arq1qoq4LIRbYaWdTVw2NDegyRp05jQMYQ2lPNW4HZgt6paBV1oALu22WYCj/Q0W9FqM9v9kfVhbapqHfA0sHOf51+YZEmSJWvWrJlI1yVJ4xg4EJK8Afg68PGqemasWfvUaoz6WG2GF6ouqqq5VTV3xowZ43VZkjQBAwVCktfQhcEVVfWNVn68DQPRfq5u9RXAnj3NZwErW31Wn/qwNkm2ArYHnpzoykiSJm+Qs4wCXAzcV1Vf6Jl0HXBSu38ScG1PfX47c2gfuoPHd7RhpbVJDmnLXDCizdCyjgNuaccZJEmbyFYDzPNO4ETgniR3t9qZwDnA4iQnAw8DxwNU1dIki4F76c5QOrWqXmjtTgEuBbYBbmw36ALn8iTL6PYM5q/fakmSJmrcQKiq79F/jB/gsFHanA2c3ae+BDigT/1ZWqBIkqaG31SWJAEGgiSpMRAkSYCBIElqDARJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkZNxCSXJJkdZIf9tQ+neTRJHe321E9085IsizJ/UkO76kfnOSeNu38JGn1rZNc1eq3J5m9gddRkjSAQfYQLgWO6FM/r6oOarcbAJLsB8wH9m9tLkiyZZv/QmAhMKfdhpZ5MvBUVe0LnAecO8l1kSSth3EDoaq+Czw54PKOBq6squeq6kFgGTAvye7AdlV1a1UVcBlwTE+bRe3+1cBhQ3sPkqRNZ32OIZyW5AdtSGnHVpsJPNIzz4pWm9nuj6wPa1NV64CngZ37PWGShUmWJFmyZs2a9ei6JGmkyQbChcCbgIOAVcDnW73fJ/saoz5Wm5cXqy6qqrlVNXfGjBkT6rAkaWyTCoSqeryqXqiqF4EvA/PapBXAnj2zzgJWtvqsPvVhbZJsBWzP4ENUkqQNZFKB0I4JDDkWGDoD6TpgfjtzaB+6g8d3VNUqYG2SQ9rxgQXAtT1tTmr3jwNuaccZJEmb0FbjzZDkq8ChwC5JVgCfAg5NchDd0M5y4KMAVbU0yWLgXmAdcGpVvdAWdQrdGUvbADe2G8DFwOVJltHtGczfAOslSZqgcQOhqk7oU754jPnPBs7uU18CHNCn/ixw/Hj9kCRtXH5TWZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEmAgSJIaA0GSBBgIkqTGQJAkAQaCJKkxECRJgIEgSWoMBEkSYCBIkhoDQZIEGAiSpMZAkCQBBoIkqTEQJEmAgSBJagwESRJgIEiSGgNBkgQYCJKkZtxASHJJktVJfthT2ynJTUl+3H7u2DPtjCTLktyf5PCe+sFJ7mnTzk+SVt86yVWtfnuS2Rt4HSVJAxhkD+FS4IgRtdOBm6tqDnBze0yS/YD5wP6tzQVJtmxtLgQWAnPabWiZJwNPVdW+wHnAuZNdGUnS5I0bCFX1XeDJEeWjgUXt/iLgmJ76lVX1XFU9CCwD5iXZHdiuqm6tqgIuG9FmaFlXA4cN7T1IkjadyR5D2K2qVgG0n7u2+kzgkZ75VrTazHZ/ZH1Ym6paBzwN7NzvSZMsTLIkyZI1a9ZMsuuSpH429EHlfp/sa4z6WG1eXqy6qKrmVtXcGTNmTLKLkqR+JhsIj7dhINrP1a2+AtizZ75ZwMpWn9WnPqxNkq2A7Xn5EJUkaSObbCBcB5zU7p8EXNtTn9/OHNqH7uDxHW1YaW2SQ9rxgQUj2gwt6zjglnacQZK0CW013gxJvgocCuySZAXwKeAcYHGSk4GHgeMBqmppksXAvcA64NSqeqEt6hS6M5a2AW5sN4CLgcuTLKPbM5i/QdZMkjQh4wZCVZ0wyqTDRpn/bODsPvUlwAF96s/SAkWSNHX8prIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSc24l66QpOlm9unXT3UXptTyc963UZbrHoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkCDARJUmMgSJIAA0GS1BgIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJGA9AyHJ8iT3JLk7yZJW2ynJTUl+3H7u2DP/GUmWJbk/yeE99YPbcpYlOT9J1qdfkqSJ2xB7CP+iqg6qqrnt8enAzVU1B7i5PSbJfsB8YH/gCOCCJFu2NhcCC4E57XbEBuiXJGkCNsaQ0dHAonZ/EXBMT/3Kqnquqh4ElgHzkuwObFdVt1ZVAZf1tJEkbSLrGwgFfDvJnUkWttpuVbUKoP3ctdVnAo/0tF3RajPb/ZF1SdImtNV6tn9nVa1MsitwU5IfjTFvv+MCNUb95QvoQmchwF577TXRvkqSxrBeewhVtbL9XA18E5gHPN6GgWg/V7fZVwB79jSfBaxs9Vl96v2e76KqmltVc2fMmLE+XZckjTDpQEjy+iTbDt0H3gv8ELgOOKnNdhJwbbt/HTA/ydZJ9qE7eHxHG1Zam+SQdnbRgp42kqRNZH2GjHYDvtnOEN0K+Nuq+l9Jvg8sTnIy8DBwPEBVLU2yGLgXWAecWlUvtGWdAlwKbAPc2G6SpE1o0oFQVQ8AB/apPwEcNkqbs4Gz+9SXAAdMti/atGaffv1Ud2FKLT/nfVPdBWmj8JvKkiTAQJAkNQaCJAkwECRJjYEgSQIMBElSYyBIkgADQZLUGAiSJMBAkCQ1BoIkCTAQJEmNgSBJAgwESVJjIEiSAANBktQYCJIkwECQJDUGgiQJMBAkSY2BIEkCDARJUrPVVHdgKsw+/fqp7sKUWn7O+6a6C5KmIfcQJEmAgSBJagwESRJgIEiSGgNBkgQYCJKkxkCQJAEGgiSpMRAkSYCBIElqDARJEmAgSJKaaRMISY5Icn+SZUlOn+r+SNLmZloEQpItgS8BRwL7ASck2W9qeyVJm5dpEQjAPGBZVT1QVb8GrgSOnuI+SdJmJVU11X0gyXHAEVX1kfb4RODtVXXaiPkWAgvbw7cA92/SjkrSK9/eVTWj34Tp8g9y0qf2sqSqqouAizZ+dyRp8zNdhoxWAHv2PJ4FrJyivkjSZmm6BML3gTlJ9knyWmA+cN0U90mSNivTYsioqtYlOQ3438CWwCVVtXSKuyVJm5VpcVBZkjT1psuQkSRpihkIkiTAQJAkNQaCJAkwECRJjYEgSQIMBElS8/8B1hQtY8DZwU0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Total Medals and Urb Quartiles\n",
    "plt.bar(Medals_by_Urb_Quartile['Urb_Pop_Quartile'],Medals_by_Urb_Quartile['Total_Medals'])\n",
    "plt.title(\"Total Medals By Urbanization Quartiles\")\n",
    "plt.tick_params(bottom=False, labelbottom= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Indicators_and_Medals.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "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": [
    "plt.bar(Indicators_and_Medals['Urb_Pop_Quartile'],Indicators_and_Medals['Gold_Medals'])\n",
    "plt.title(\"Total Golds By Urbanization Quartiles\")\n",
    "plt.tick_params(bottom=False, labelbottom= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "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>Country</th>\n",
       "      <th>pop_millions</th>\n",
       "      <th>Gold_Medals</th>\n",
       "      <th>All_Medals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>331</td>\n",
       "      <td>2638</td>\n",
       "      <td>5637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Russia</td>\n",
       "      <td>145</td>\n",
       "      <td>1472</td>\n",
       "      <td>3661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Germany</td>\n",
       "      <td>83</td>\n",
       "      <td>1301</td>\n",
       "      <td>3756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>67</td>\n",
       "      <td>678</td>\n",
       "      <td>2068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>65</td>\n",
       "      <td>501</td>\n",
       "      <td>1777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Canada</td>\n",
       "      <td>37</td>\n",
       "      <td>463</td>\n",
       "      <td>1352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Hungary</td>\n",
       "      <td>9</td>\n",
       "      <td>432</td>\n",
       "      <td>1135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Norway</td>\n",
       "      <td>5</td>\n",
       "      <td>378</td>\n",
       "      <td>1033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>South Korea</td>\n",
       "      <td>51</td>\n",
       "      <td>221</td>\n",
       "      <td>638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>8</td>\n",
       "      <td>175</td>\n",
       "      <td>691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Cuba</td>\n",
       "      <td>11</td>\n",
       "      <td>164</td>\n",
       "      <td>407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>10</td>\n",
       "      <td>123</td>\n",
       "      <td>632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Spain</td>\n",
       "      <td>46</td>\n",
       "      <td>110</td>\n",
       "      <td>489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>6</td>\n",
       "      <td>54</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>84</td>\n",
       "      <td>40</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Lithuania</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>43</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Peru</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country  pop_millions  Gold_Medals  All_Medals\n",
       "0    United States           331         2638        5637\n",
       "1           Russia           145         1472        3661\n",
       "2          Germany            83         1301        3756\n",
       "3   United Kingdom            67          678        2068\n",
       "4           France            65          501        1777\n",
       "5           Canada            37          463        1352\n",
       "6          Hungary             9          432        1135\n",
       "7           Norway             5          378        1033\n",
       "8      South Korea            51          221         638\n",
       "9      Switzerland             8          175         691\n",
       "10            Cuba            11          164         407\n",
       "11  Czech Republic            10          123         632\n",
       "12           Spain            46          110         489\n",
       "13        Bulgaria             6           54         342\n",
       "14          Turkey            84           40          95\n",
       "15         Belarus             9           24         139\n",
       "16       Lithuania             2            6          61\n",
       "17        Colombia            50            5          27\n",
       "18         Algeria            43            5          17\n",
       "19         Tunisia            11            3          13\n",
       "20            Peru            33            1          15\n",
       "21      Costa Rica             5            1           4"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sqlit('SELECT Country, Population/1000000 as pop_millions, Gold_Medals, All_Medals FROM Indicators_and_Medals WHERE Urb_Pop_Quartile = 2 ORDER BY Gold_Medals DESC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "Indicators_and_Medals = sqlit(\"SELECT i.*, a.total_athletes FROM Indicators_and_Medals i JOIN athletes_per_country a ON a.Country = i.Country\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Indicators_and_Medals.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "Indicators_and_Medals['Athletes_per_cap'] = Indicators_and_Medals['total_athletes']/Indicators_and_Medals['Population']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sqlit(\"SELECT Country, Athletes_per_cap*1000000 FROM Indicators_and_Medals ORDER BY Athletes_per_cap DESC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>gold_per_100_athletes</th>\n",
       "      <th>medals_per_100_athletes</th>\n",
       "      <th>Athletes_per_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>0.000057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Russia</td>\n",
       "      <td>13</td>\n",
       "      <td>33</td>\n",
       "      <td>0.000074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Germany</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>0.000188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Slovenia</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Latvia</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Liechtenstein</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.009673</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country  gold_per_100_athletes  medals_per_100_athletes  \\\n",
       "0   United States                     13                       29   \n",
       "1          Russia                     13                       33   \n",
       "2           India                      9                       13   \n",
       "3         Germany                      8                       23   \n",
       "4        Pakistan                      7                       21   \n",
       "..            ...                    ...                      ...   \n",
       "84       Slovenia                      0                        4   \n",
       "85         Latvia                      0                        3   \n",
       "86        Bahrain                      0                        2   \n",
       "87     Luxembourg                      0                        0   \n",
       "88  Liechtenstein                      0                        2   \n",
       "\n",
       "    Athletes_per_cap  \n",
       "0           0.000057  \n",
       "1           0.000074  \n",
       "2           0.000001  \n",
       "3           0.000188  \n",
       "4           0.000003  \n",
       "..               ...  \n",
       "84          0.000534  \n",
       "85          0.000505  \n",
       "86          0.000074  \n",
       "87          0.001587  \n",
       "88          0.009673  \n",
       "\n",
       "[89 rows x 4 columns]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sqlit(\"SELECT Country, 100*Gold_Medals/total_athletes as gold_per_100_athletes, 100*All_Medals/total_athletes as medals_per_100_athletes, Athletes_per_cap FROM Indicators_and_Medals ORDER BY gold_per_100_athletes DESC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>golds_per_100_athletes</th>\n",
       "      <th>medals_per_100_athletes</th>\n",
       "      <th>Athletes_per_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>13</td>\n",
       "      <td>29</td>\n",
       "      <td>0.000057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Russia</td>\n",
       "      <td>13</td>\n",
       "      <td>33</td>\n",
       "      <td>0.000074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Germany</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>0.000188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Norway</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>0.000914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>China</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0.000012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Cuba</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>0.000218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Hungary</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>0.000684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>0.000214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>0.000180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Italy</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>0.000177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>0.000021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>0.000825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>0.000616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Fiji</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>0.000014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country  golds_per_100_athletes  medals_per_100_athletes  \\\n",
       "0    United States                      13                       29   \n",
       "1           Russia                      13                       33   \n",
       "2            India                       9                       13   \n",
       "3          Germany                       8                       23   \n",
       "4         Pakistan                       7                       21   \n",
       "5           Norway                       7                       20   \n",
       "6            China                       6                       19   \n",
       "7         Cameroon                       6                        7   \n",
       "8             Cuba                       6                       16   \n",
       "9          Hungary                       6                       17   \n",
       "10         Croatia                       6                       17   \n",
       "11        Ethiopia                       5                       14   \n",
       "12  United Kingdom                       5                       16   \n",
       "13           Italy                       5                       15   \n",
       "14        Zimbabwe                       5                        7   \n",
       "15          Sweden                       5                       18   \n",
       "16         Denmark                       5                       16   \n",
       "17         Uruguay                       5                       10   \n",
       "18            Fiji                       5                        5   \n",
       "19           Kenya                       4                       13   \n",
       "\n",
       "    Athletes_per_cap  \n",
       "0           0.000057  \n",
       "1           0.000074  \n",
       "2           0.000001  \n",
       "3           0.000188  \n",
       "4           0.000003  \n",
       "5           0.000914  \n",
       "6           0.000004  \n",
       "7           0.000012  \n",
       "8           0.000218  \n",
       "9           0.000684  \n",
       "10          0.000214  \n",
       "11          0.000003  \n",
       "12          0.000180  \n",
       "13          0.000177  \n",
       "14          0.000021  \n",
       "15          0.000825  \n",
       "16          0.000616  \n",
       "17          0.000166  \n",
       "18          0.000258  \n",
       "19          0.000014  "
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sqlit(\"SELECT Country, 100*Gold_Medals/total_athletes as golds_per_100_athletes, 100*All_Medals/total_athletes as medals_per_100_athletes, Athletes_per_cap FROM Indicators_and_Medals ORDER BY golds_per_100_athletes DESC LIMIT 20\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Medals_per_cap and Urbanization Quartiles')"
      ]
     },
     "execution_count": 185,
     "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": [
    "plt.bar(Indicators_and_Medals['Urb_Pop_Quartile'],Indicators_and_Medals['Medals_per_cap'])\n",
    "plt.title(\"Medals_per_cap and Urbanization Quartiles\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Golds_per_cap and Urbanization Quartiles')"
      ]
     },
     "execution_count": 186,
     "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": [
    "plt.bar(Indicators_and_Medals['Urb_Pop_Quartile'],Indicators_and_Medals['Golds_per_cap'])\n",
    "plt.title(\"Golds_per_cap and Urbanization Quartiles\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "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": [
    "plt.bar(Indicators_and_Medals['GDP_Quartile'],Indicators_and_Medals['Golds_per_cap'])\n",
    "plt.title(\"Golds_per_cap and GDP Quartiles\")\n",
    "plt.tick_params(bottom=False, labelbottom= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "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": [
    "plt.bar(Indicators_and_Medals['GDP_Quartile'],Indicators_and_Medals['Medals_per_cap'])\n",
    "plt.title(\"Medals_per_cap and GDP Quartiles\")\n",
    "plt.tick_params(bottom=False, labelbottom= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Gold Medals per Pop and GDP')"
      ]
     },
     "execution_count": 189,
     "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": [
    "#GDP and Gold Medals\n",
    "plt.scatter(Indicators_and_Medals['GDP_Per_Cap'],Indicators_and_Medals['Gold_Medals'])\n",
    "plt.grid()\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "plt.title(\"Gold Medals per Pop and GDP\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Gold Medals per Pop and GDP')"
      ]
     },
     "execution_count": 190,
     "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": [
    "#GDP and Gold Medals\n",
    "plt.scatter(Indicators_and_Medals['GDP_Per_Cap'],Indicators_and_Medals['Gold_Medals'], s = (Indicators_and_Medals['Population_Quartile']+1)*20)\n",
    "plt.grid()\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "plt.title(\"Gold Medals per Pop and GDP\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Total Medals per Pop and GDP')"
      ]
     },
     "execution_count": 191,
     "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": [
    "#GDP and All Medals\n",
    "plt.scatter(Indicators_and_Medals['GDP_Per_Cap'],Indicators_and_Medals['All_Medals'], s = (Indicators_and_Medals['Population_Quartile']+1)*20)\n",
    "plt.grid()\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "plt.title(\"Total Medals per Pop and GDP\")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Total Medals per Pop, GDP per Capita, Urbanization Quartile (color) and Population Quartile (size)')"
      ]
     },
     "execution_count": 192,
     "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": [
    "#GDP and All Medals\n",
    "GDPAM_scatter = plt.scatter(Indicators_and_Medals['GDP_Per_Cap'],Indicators_and_Medals['All_Medals'], s = (Indicators_and_Medals['Population_Quartile']+1)*40, c = (Indicators_and_Medals[\"Urb_Pop_Quartile\"]))\n",
    "plt.grid()\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "handles, labels = GDPAM_scatter.legend_elements(prop = \"colors\")\n",
    "labels = ['0', '1', '2', '3']\n",
    "plt.xlabel('GDP Per Capita')\n",
    "plt.ylabel('Total Medals Per Capita')\n",
    "plt.legend(handles, labels, title = 'Urban Pop Quartile')\n",
    "plt.title(\"Total Medals per Pop, GDP per Capita, Urbanization Quartile (color) and Population Quartile (size)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "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": [
    "plt.bar(Indicators_and_Medals['GDP_Quartile'],Indicators_and_Medals['All_Medals'])\n",
    "plt.title(\"Total Medals By GDP per capita Quartiles\")\n",
    "plt.tick_params(bottom=False, labelbottom= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "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": [
    "plt.bar(Indicators_and_Medals['GDP_Quartile'],Indicators_and_Medals['Gold_Medals'])\n",
    "plt.title(\"Gold Medals By GDP per capita Quartiles\")\n",
    "plt.tick_params(bottom=False, labelbottom= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 89 entries, 0 to 88\n",
      "Data columns (total 15 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Country              89 non-null     object \n",
      " 1   Population           89 non-null     int64  \n",
      " 2   Urban_Pop            89 non-null     float64\n",
      " 3   GDP_Per_Cap          89 non-null     float64\n",
      " 4   Gold_Medals          89 non-null     int64  \n",
      " 5   Gold_Quartile        89 non-null     int64  \n",
      " 6   All_Medals           89 non-null     int64  \n",
      " 7   Medals_Quartile      89 non-null     int64  \n",
      " 8   Population_Quartile  89 non-null     int64  \n",
      " 9   GDP_Quartile         89 non-null     int64  \n",
      " 10  Golds_per_cap        89 non-null     float64\n",
      " 11  Medals_per_cap       89 non-null     float64\n",
      " 12  Urb_Pop_Quartile     89 non-null     int64  \n",
      " 13  total_athletes       89 non-null     int64  \n",
      " 14  Athletes_per_cap     89 non-null     float64\n",
      "dtypes: float64(5), int64(9), object(1)\n",
      "memory usage: 10.6+ KB\n"
     ]
    }
   ],
   "source": [
    "Indicators_and_Medals.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "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": [
    "plt.bar(Indicators_and_Medals['Population_Quartile'],Indicators_and_Medals['Golds_per_cap'])\n",
    "plt.title(\"Gold Medals Per Capita By Population Quartiles\")\n",
    "plt.tick_params(bottom=False, labelbottom= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "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": [
    "plt.bar(Indicators_and_Medals['Population_Quartile'],Indicators_and_Medals['Medals_per_cap'])\n",
    "plt.title(\"Total Medals Per Capita By Population Quartiles\")\n",
    "plt.tick_params(bottom=False, labelbottom= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "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>Country</th>\n",
       "      <th>Golds per Million Pop</th>\n",
       "      <th>Medals per Million Pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>47.377741</td>\n",
       "      <td>151.925282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Finland</td>\n",
       "      <td>35.725647</td>\n",
       "      <td>162.389302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>30.884540</td>\n",
       "      <td>103.005980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>18.637318</td>\n",
       "      <td>47.214539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>16.742949</td>\n",
       "      <td>60.671314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Australia</td>\n",
       "      <td>13.619988</td>\n",
       "      <td>51.662024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>8.918702</td>\n",
       "      <td>18.125105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>8.449404</td>\n",
       "      <td>40.350216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>6.372430</td>\n",
       "      <td>12.744861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Greece</td>\n",
       "      <td>5.953561</td>\n",
       "      <td>24.486419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2.010275</td>\n",
       "      <td>6.052915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Japan</td>\n",
       "      <td>1.953999</td>\n",
       "      <td>7.222677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>0.584434</td>\n",
       "      <td>1.753302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>0.512165</td>\n",
       "      <td>2.231912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>0.351264</td>\n",
       "      <td>3.161378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>0.276074</td>\n",
       "      <td>0.644172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>0.232259</td>\n",
       "      <td>0.851617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>0.170697</td>\n",
       "      <td>1.536276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Chile</td>\n",
       "      <td>0.156702</td>\n",
       "      <td>1.671489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Israel</td>\n",
       "      <td>0.115227</td>\n",
       "      <td>1.037044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>0.100899</td>\n",
       "      <td>0.201798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Jordan</td>\n",
       "      <td>0.097842</td>\n",
       "      <td>0.097842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Country  Golds per Million Pop  Medals per Million Pop\n",
       "0                 Sweden              47.377741              151.925282\n",
       "1                Finland              35.725647              162.389302\n",
       "2                Denmark              30.884540              103.005980\n",
       "3            New Zealand              18.637318               47.214539\n",
       "4            Netherlands              16.742949               60.671314\n",
       "5              Australia              13.619988               51.662024\n",
       "6                Uruguay               8.918702               18.125105\n",
       "7                Belgium               8.449404               40.350216\n",
       "8             Luxembourg               6.372430               12.744861\n",
       "9                 Greece               5.953561               24.486419\n",
       "10             Argentina               2.010275                6.052915\n",
       "11                 Japan               1.953999                7.222677\n",
       "12               Bahrain               0.584434                1.753302\n",
       "13                Brazil               0.512165                2.231912\n",
       "14           Puerto Rico               0.351264                3.161378\n",
       "15    Dominican Republic               0.276074                0.644172\n",
       "16                Mexico               0.232259                0.851617\n",
       "17             Singapore               0.170697                1.536276\n",
       "18                 Chile               0.156702                1.671489\n",
       "19                Israel               0.115227                1.037044\n",
       "20  United Arab Emirates               0.100899                0.201798\n",
       "21                Jordan               0.097842                0.097842"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sqlit(\"SELECT Country, Golds_per_cap*1000000 as 'Golds per Million Pop', Medals_per_cap*1000000 as 'Medals per Million Pop' FROM Indicators_and_Medals WHERE Urb_Pop_Quartile = 3 ORDER BY Golds_per_cap*1000000 DESC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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