predict.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "medals_data = pd.read_csv('data/medals.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21697, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medals_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>discipline_title</th>\n",
       "      <th>slug_game</th>\n",
       "      <th>event_title</th>\n",
       "      <th>event_gender</th>\n",
       "      <th>medal_type</th>\n",
       "      <th>participant_type</th>\n",
       "      <th>participant_title</th>\n",
       "      <th>athlete_url</th>\n",
       "      <th>athlete_full_name</th>\n",
       "      <th>country_name</th>\n",
       "      <th>country_code</th>\n",
       "      <th>country_3_letter_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Curling</td>\n",
       "      <td>beijing-2022</td>\n",
       "      <td>Mixed Doubles</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>GOLD</td>\n",
       "      <td>GameTeam</td>\n",
       "      <td>Italy</td>\n",
       "      <td>https://olympics.com/en/athletes/stefania-cons...</td>\n",
       "      <td>Stefania CONSTANTINI</td>\n",
       "      <td>Italy</td>\n",
       "      <td>IT</td>\n",
       "      <td>ITA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Curling</td>\n",
       "      <td>beijing-2022</td>\n",
       "      <td>Mixed Doubles</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>GOLD</td>\n",
       "      <td>GameTeam</td>\n",
       "      <td>Italy</td>\n",
       "      <td>https://olympics.com/en/athletes/amos-mosaner</td>\n",
       "      <td>Amos MOSANER</td>\n",
       "      <td>Italy</td>\n",
       "      <td>IT</td>\n",
       "      <td>ITA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Curling</td>\n",
       "      <td>beijing-2022</td>\n",
       "      <td>Mixed Doubles</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>SILVER</td>\n",
       "      <td>GameTeam</td>\n",
       "      <td>Norway</td>\n",
       "      <td>https://olympics.com/en/athletes/kristin-skaslien</td>\n",
       "      <td>Kristin SKASLIEN</td>\n",
       "      <td>Norway</td>\n",
       "      <td>NO</td>\n",
       "      <td>NOR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Curling</td>\n",
       "      <td>beijing-2022</td>\n",
       "      <td>Mixed Doubles</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>SILVER</td>\n",
       "      <td>GameTeam</td>\n",
       "      <td>Norway</td>\n",
       "      <td>https://olympics.com/en/athletes/magnus-nedreg...</td>\n",
       "      <td>Magnus NEDREGOTTEN</td>\n",
       "      <td>Norway</td>\n",
       "      <td>NO</td>\n",
       "      <td>NOR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Curling</td>\n",
       "      <td>beijing-2022</td>\n",
       "      <td>Mixed Doubles</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>BRONZE</td>\n",
       "      <td>GameTeam</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>https://olympics.com/en/athletes/almida-de-val</td>\n",
       "      <td>Almida DE VAL</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>SE</td>\n",
       "      <td>SWE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  discipline_title     slug_game    event_title event_gender medal_type  \\\n",
       "0          Curling  beijing-2022  Mixed Doubles        Mixed       GOLD   \n",
       "1          Curling  beijing-2022  Mixed Doubles        Mixed       GOLD   \n",
       "2          Curling  beijing-2022  Mixed Doubles        Mixed     SILVER   \n",
       "3          Curling  beijing-2022  Mixed Doubles        Mixed     SILVER   \n",
       "4          Curling  beijing-2022  Mixed Doubles        Mixed     BRONZE   \n",
       "\n",
       "  participant_type participant_title  \\\n",
       "0         GameTeam             Italy   \n",
       "1         GameTeam             Italy   \n",
       "2         GameTeam            Norway   \n",
       "3         GameTeam            Norway   \n",
       "4         GameTeam            Sweden   \n",
       "\n",
       "                                         athlete_url     athlete_full_name  \\\n",
       "0  https://olympics.com/en/athletes/stefania-cons...  Stefania CONSTANTINI   \n",
       "1      https://olympics.com/en/athletes/amos-mosaner          Amos MOSANER   \n",
       "2  https://olympics.com/en/athletes/kristin-skaslien      Kristin SKASLIEN   \n",
       "3  https://olympics.com/en/athletes/magnus-nedreg...    Magnus NEDREGOTTEN   \n",
       "4     https://olympics.com/en/athletes/almida-de-val         Almida DE VAL   \n",
       "\n",
       "  country_name country_code country_3_letter_code  \n",
       "0        Italy           IT                   ITA  \n",
       "1        Italy           IT                   ITA  \n",
       "2       Norway           NO                   NOR  \n",
       "3       Norway           NO                   NOR  \n",
       "4       Sweden           SE                   SWE  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medals_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "medals_data.drop(['athlete_url','athlete_full_name','participant_title','country_code'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>discipline_title</th>\n",
       "      <th>slug_game</th>\n",
       "      <th>event_title</th>\n",
       "      <th>event_gender</th>\n",
       "      <th>medal_type</th>\n",
       "      <th>participant_type</th>\n",
       "      <th>country_name</th>\n",
       "      <th>country_3_letter_code</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Curling</td>\n",
       "      <td>beijing-2022</td>\n",
       "      <td>Mixed Doubles</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>GOLD</td>\n",
       "      <td>GameTeam</td>\n",
       "      <td>Italy</td>\n",
       "      <td>ITA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Curling</td>\n",
       "      <td>beijing-2022</td>\n",
       "      <td>Mixed Doubles</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>GOLD</td>\n",
       "      <td>GameTeam</td>\n",
       "      <td>Italy</td>\n",
       "      <td>ITA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Curling</td>\n",
       "      <td>beijing-2022</td>\n",
       "      <td>Mixed Doubles</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>SILVER</td>\n",
       "      <td>GameTeam</td>\n",
       "      <td>Norway</td>\n",
       "      <td>NOR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Curling</td>\n",
       "      <td>beijing-2022</td>\n",
       "      <td>Mixed Doubles</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>SILVER</td>\n",
       "      <td>GameTeam</td>\n",
       "      <td>Norway</td>\n",
       "      <td>NOR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Curling</td>\n",
       "      <td>beijing-2022</td>\n",
       "      <td>Mixed Doubles</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>BRONZE</td>\n",
       "      <td>GameTeam</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>SWE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  discipline_title     slug_game    event_title event_gender medal_type  \\\n",
       "0          Curling  beijing-2022  Mixed Doubles        Mixed       GOLD   \n",
       "1          Curling  beijing-2022  Mixed Doubles        Mixed       GOLD   \n",
       "2          Curling  beijing-2022  Mixed Doubles        Mixed     SILVER   \n",
       "3          Curling  beijing-2022  Mixed Doubles        Mixed     SILVER   \n",
       "4          Curling  beijing-2022  Mixed Doubles        Mixed     BRONZE   \n",
       "\n",
       "  participant_type country_name country_3_letter_code  \n",
       "0         GameTeam        Italy                   ITA  \n",
       "1         GameTeam        Italy                   ITA  \n",
       "2         GameTeam       Norway                   NOR  \n",
       "3         GameTeam       Norway                   NOR  \n",
       "4         GameTeam       Sweden                   SWE  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medals_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing useless data\n",
    "medals_data.drop('country_3_letter_code',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "medals_data.drop('participant_type',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>discipline_title</th>\n",
       "      <th>slug_game</th>\n",
       "      <th>event_title</th>\n",
       "      <th>event_gender</th>\n",
       "      <th>medal_type</th>\n",
       "      <th>country_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Curling</td>\n",
       "      <td>beijing-2022</td>\n",
       "      <td>Mixed Doubles</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>GOLD</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Curling</td>\n",
       "      <td>beijing-2022</td>\n",
       "      <td>Mixed Doubles</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>GOLD</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Curling</td>\n",
       "      <td>beijing-2022</td>\n",
       "      <td>Mixed Doubles</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>SILVER</td>\n",
       "      <td>Norway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Curling</td>\n",
       "      <td>beijing-2022</td>\n",
       "      <td>Mixed Doubles</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>SILVER</td>\n",
       "      <td>Norway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Curling</td>\n",
       "      <td>beijing-2022</td>\n",
       "      <td>Mixed Doubles</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>BRONZE</td>\n",
       "      <td>Sweden</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  discipline_title     slug_game    event_title event_gender medal_type  \\\n",
       "0          Curling  beijing-2022  Mixed Doubles        Mixed       GOLD   \n",
       "1          Curling  beijing-2022  Mixed Doubles        Mixed       GOLD   \n",
       "2          Curling  beijing-2022  Mixed Doubles        Mixed     SILVER   \n",
       "3          Curling  beijing-2022  Mixed Doubles        Mixed     SILVER   \n",
       "4          Curling  beijing-2022  Mixed Doubles        Mixed     BRONZE   \n",
       "\n",
       "  country_name  \n",
       "0        Italy  \n",
       "1        Italy  \n",
       "2       Norway  \n",
       "3       Norway  \n",
       "4       Sweden  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medals_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#country wise grouping to get stats\n",
    "countryWise_grouped_data = medals_data.groupby(['country_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>discipline_title</th>\n",
       "      <th>slug_game</th>\n",
       "      <th>event_title</th>\n",
       "      <th>event_gender</th>\n",
       "      <th>medal_type</th>\n",
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       "    <tr>\n",
       "      <th>country_name</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>Taekwondo</td>\n",
       "      <td>london-2012</td>\n",
       "      <td>58 - 68 kg men</td>\n",
       "      <td>Men</td>\n",
       "      <td>BRONZE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>Athletics</td>\n",
       "      <td>rio-2016</td>\n",
       "      <td>800m men</td>\n",
       "      <td>Men</td>\n",
       "      <td>SILVER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>Rugby Sevens</td>\n",
       "      <td>tokyo-2020</td>\n",
       "      <td>Men</td>\n",
       "      <td>Men</td>\n",
       "      <td>BRONZE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>Boxing</td>\n",
       "      <td>tokyo-2020</td>\n",
       "      <td>Men's Light (57-63kg)</td>\n",
       "      <td>Men</td>\n",
       "      <td>BRONZE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australasia</th>\n",
       "      <td>Tennis</td>\n",
       "      <td>stockholm-1912</td>\n",
       "      <td>singles indoor men</td>\n",
       "      <td>Men</td>\n",
       "      <td>BRONZE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virgin Islands, US</th>\n",
       "      <td>Sailing</td>\n",
       "      <td>seoul-1988</td>\n",
       "      <td>Finn - One Person Dinghy (Heavyweight) men</td>\n",
       "      <td>Open</td>\n",
       "      <td>SILVER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Indies Federation</th>\n",
       "      <td>Athletics</td>\n",
       "      <td>rome-1960</td>\n",
       "      <td>4x400m relay men</td>\n",
       "      <td>Men</td>\n",
       "      <td>BRONZE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yugoslavia</th>\n",
       "      <td>Rowing</td>\n",
       "      <td>seoul-1988</td>\n",
       "      <td>pair without coxswain 2 men</td>\n",
       "      <td>Men</td>\n",
       "      <td>BRONZE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>Athletics</td>\n",
       "      <td>atlanta-1996</td>\n",
       "      <td>400m hurdles men</td>\n",
       "      <td>Men</td>\n",
       "      <td>SILVER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>Swimming</td>\n",
       "      <td>beijing-2008</td>\n",
       "      <td>100m backstroke women</td>\n",
       "      <td>Women</td>\n",
       "      <td>SILVER</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       discipline_title       slug_game  \\\n",
       "country_name                                              \n",
       "Afghanistan                   Taekwondo     london-2012   \n",
       "Algeria                       Athletics        rio-2016   \n",
       "Argentina                  Rugby Sevens      tokyo-2020   \n",
       "Armenia                          Boxing      tokyo-2020   \n",
       "Australasia                      Tennis  stockholm-1912   \n",
       "...                                 ...             ...   \n",
       "Virgin Islands, US              Sailing      seoul-1988   \n",
       "West Indies Federation        Athletics       rome-1960   \n",
       "Yugoslavia                       Rowing      seoul-1988   \n",
       "Zambia                        Athletics    atlanta-1996   \n",
       "Zimbabwe                       Swimming    beijing-2008   \n",
       "\n",
       "                                                       event_title  \\\n",
       "country_name                                                         \n",
       "Afghanistan                                         58 - 68 kg men   \n",
       "Algeria                                                   800m men   \n",
       "Argentina                                                      Men   \n",
       "Armenia                                      Men's Light (57-63kg)   \n",
       "Australasia                                     singles indoor men   \n",
       "...                                                            ...   \n",
       "Virgin Islands, US      Finn - One Person Dinghy (Heavyweight) men   \n",
       "West Indies Federation                            4x400m relay men   \n",
       "Yugoslavia                             pair without coxswain 2 men   \n",
       "Zambia                                            400m hurdles men   \n",
       "Zimbabwe                                     100m backstroke women   \n",
       "\n",
       "                       event_gender medal_type  \n",
       "country_name                                    \n",
       "Afghanistan                     Men     BRONZE  \n",
       "Algeria                         Men     SILVER  \n",
       "Argentina                       Men     BRONZE  \n",
       "Armenia                         Men     BRONZE  \n",
       "Australasia                     Men     BRONZE  \n",
       "...                             ...        ...  \n",
       "Virgin Islands, US             Open     SILVER  \n",
       "West Indies Federation          Men     BRONZE  \n",
       "Yugoslavia                      Men     BRONZE  \n",
       "Zambia                          Men     SILVER  \n",
       "Zimbabwe                      Women     SILVER  \n",
       "\n",
       "[154 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countryWise_grouped_data.first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Example \n",
    "countryWise_grouped_data.get_group('India').shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to obtain medal tally for every country \n",
    "country_list = []\n",
    "medal_tally_list = []\n",
    "def get_country_all_time_medal_tally(country_name):\n",
    "    country_list.append(country_name)\n",
    "    country_medal_tally = countryWise_grouped_data.get_group(country_name).shape[0]\n",
    "    medal_tally_list.append(country_medal_tally)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "for country in countryWise_grouped_data.country_name.first():\n",
    "    get_country_all_time_medal_tally(country)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#For representation in a dataframe - neater\n",
    "medal_tally_dataframe = pd.DataFrame(list(zip(country_list,medal_tally_list)),columns=['country','medal_tally'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Descending order of the most medals won \n",
    "country_output = medal_tally_dataframe.sort_values(by=['medal_tally'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualization\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.bar(country_output.country[:10],country_output.medal_tally[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.savefig('tally_plot.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py39-TF2.0",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}