Olympic EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This notebook is an exploration of an Olympics dataset, \"120 years of Olympic history: athletes and results\". First, we'll take a peek at the data and see what ideas we can come up with for possible tasks. Then, we'll perform Data Cleaning, EDA, and finally, some modeling. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# set the figure size for matplotlib plots \n",
    "plt.rcParams[\"figure.figsize\"] = (12, 8)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(271116, 15)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('120-years-of-olympic-history-athletes-and-results/athlete_events.csv')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>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",
       "    </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",
       "    </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",
       "    </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",
       "    </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/Sweden</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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID                      Name Sex   Age  Height  Weight            Team  \\\n",
       "0   1                 A Dijiang   M  24.0   180.0    80.0           China   \n",
       "1   2                  A Lamusi   M  23.0   170.0    60.0           China   \n",
       "2   3       Gunnar Nielsen Aaby   M  24.0     NaN     NaN         Denmark   \n",
       "3   4      Edgar Lindenau Aabye   M  34.0     NaN     NaN  Denmark/Sweden   \n",
       "4   5  Christine Jacoba Aaftink   F  21.0   185.0    82.0     Netherlands   \n",
       "\n",
       "   NOC        Games  Year  Season       City          Sport  \\\n",
       "0  CHN  1992 Summer  1992  Summer  Barcelona     Basketball   \n",
       "1  CHN  2012 Summer  2012  Summer     London           Judo   \n",
       "2  DEN  1920 Summer  1920  Summer  Antwerpen       Football   \n",
       "3  DEN  1900 Summer  1900  Summer      Paris     Tug-Of-War   \n",
       "4  NED  1988 Winter  1988  Winter    Calgary  Speed Skating   \n",
       "\n",
       "                              Event Medal  \n",
       "0       Basketball Men's Basketball   NaN  \n",
       "1      Judo Men's Extra-Lightweight   NaN  \n",
       "2           Football Men's Football   NaN  \n",
       "3       Tug-Of-War Men's Tug-Of-War  Gold  \n",
       "4  Speed Skating Women's 500 metres   NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Initial questions/ thoughts about the data:**\n",
    "\n",
    "\n",
    "General questions:\n",
    "* what's the distribution of male to female athletes? has this changed over time?\n",
    "* what about the distribution of athlete weights? how does this differ by sport?\n",
    "    * which sports have the highest/ lowest weights?\n",
    "* same as questions above, but with ages\n",
    "* do younger athletes win more than older ones?\n",
    "* what's the distribution of athlete age by country?\n",
    "    * do some countries have older athletes?\n",
    "    * how does this change over time for some specific countries?\n",
    "* is there a \"home country\" advantage?\n",
    "    * statistically significant difference in medals\n",
    "    * how would we measure this? lots of confounding variables\n",
    "    \n",
    "\n",
    "Data Cleaning:\n",
    "* how many missing values do we have in each column?\n",
    "* how will we parse out country names - \"Denmark/ Sweden\" should be 1 for Denmark, 1 for Sweden\n",
    "\n",
    "Modeling:\n",
    "* can we predict who will win certain sports?\n",
    "    * ex: if U.S. team has a much higher average weight for a sport\n",
    "      where weight is a factor\n",
    "* can we predict who will win a medal?\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Luckily, this dataset is quite clean. The dataset author on Kaggle did a great job making sure all the data is properly encoded and such. That being said, there's still a little bit of work the be done:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of missing entries by column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID             0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age         9474\n",
       "Height     60171\n",
       "Weight     62875\n",
       "Team           0\n",
       "NOC            0\n",
       "Games          0\n",
       "Year           0\n",
       "Season         0\n",
       "City           0\n",
       "Sport          0\n",
       "Event          0\n",
       "Medal     231333\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Share of total data that's missing by column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age       0.034944\n",
       "Height    0.221938\n",
       "Weight    0.231912\n",
       "Medal     0.853262\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = df.isna().sum()\n",
    "missing[missing!=0]/df.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're missing alot of the 'medals' column! Instead of missing data, we should instead have a category for \"No medal\", because the lack of a medal could be just as important."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Medal\n",
       "Bronze    13295\n",
       "Gold      13372\n",
       "Silver    13116\n",
       "Name: ID, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Medal')['ID'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>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",
       "    </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>No Medal</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>No Medal</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>No Medal</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/Sweden</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",
       "    </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>No Medal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID                      Name Sex   Age  Height  Weight            Team  \\\n",
       "0   1                 A Dijiang   M  24.0   180.0    80.0           China   \n",
       "1   2                  A Lamusi   M  23.0   170.0    60.0           China   \n",
       "2   3       Gunnar Nielsen Aaby   M  24.0     NaN     NaN         Denmark   \n",
       "3   4      Edgar Lindenau Aabye   M  34.0     NaN     NaN  Denmark/Sweden   \n",
       "4   5  Christine Jacoba Aaftink   F  21.0   185.0    82.0     Netherlands   \n",
       "\n",
       "   NOC        Games  Year  Season       City          Sport  \\\n",
       "0  CHN  1992 Summer  1992  Summer  Barcelona     Basketball   \n",
       "1  CHN  2012 Summer  2012  Summer     London           Judo   \n",
       "2  DEN  1920 Summer  1920  Summer  Antwerpen       Football   \n",
       "3  DEN  1900 Summer  1900  Summer      Paris     Tug-Of-War   \n",
       "4  NED  1988 Winter  1988  Winter    Calgary  Speed Skating   \n",
       "\n",
       "                              Event     Medal  \n",
       "0       Basketball Men's Basketball  No Medal  \n",
       "1      Judo Men's Extra-Lightweight  No Medal  \n",
       "2           Football Men's Football  No Medal  \n",
       "3       Tug-Of-War Men's Tug-Of-War      Gold  \n",
       "4  Speed Skating Women's 500 metres  No Medal  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Medal'].fillna('No Medal', inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see in the \"Medal\" column, the NaN's have been replaced. Success!\n",
    "\n",
    "Next, let's look at some summary statistics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>271116.000000</td>\n",
       "      <td>261642.000000</td>\n",
       "      <td>210945.000000</td>\n",
       "      <td>208241.000000</td>\n",
       "      <td>271116.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>68248.954396</td>\n",
       "      <td>25.556898</td>\n",
       "      <td>175.338970</td>\n",
       "      <td>70.702393</td>\n",
       "      <td>1978.378480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>39022.286345</td>\n",
       "      <td>6.393561</td>\n",
       "      <td>10.518462</td>\n",
       "      <td>14.348020</td>\n",
       "      <td>29.877632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1896.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>34643.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1960.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>68205.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1988.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>102097.250000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>183.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>135571.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>226.000000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ID            Age         Height         Weight  \\\n",
       "count  271116.000000  261642.000000  210945.000000  208241.000000   \n",
       "mean    68248.954396      25.556898     175.338970      70.702393   \n",
       "std     39022.286345       6.393561      10.518462      14.348020   \n",
       "min         1.000000      10.000000     127.000000      25.000000   \n",
       "25%     34643.000000      21.000000     168.000000      60.000000   \n",
       "50%     68205.000000      24.000000     175.000000      70.000000   \n",
       "75%    102097.250000      28.000000     183.000000      79.000000   \n",
       "max    135571.000000      97.000000     226.000000     214.000000   \n",
       "\n",
       "                Year  \n",
       "count  271116.000000  \n",
       "mean     1978.378480  \n",
       "std        29.877632  \n",
       "min      1896.000000  \n",
       "25%      1960.000000  \n",
       "50%      1988.000000  \n",
       "75%      2002.000000  \n",
       "max      2016.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this, we can see several interesting phenomena. For one, the minimum age is 10, and the maximum is 97 -   certainly intriguing. I'll look at this below, but first, some more observations: the maximum weight is **214 Kilos**, or **417 pounds**! Additionally, note that the maximum ID is 135,571, which is nowhere near the total number of rows (271,116). This implies a large number of repeat Olympians (as expected), which raises some more questions: \n",
    "* who went to the most Olympics? \n",
    "* how common is it for people to go to more than one Olympic games?\n",
    "\n",
    "And so on. \n",
    "    Now, let's explore these anomalies in more detail. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Olympic Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>142882</th>\n",
       "      <td>71691</td>\n",
       "      <td>Dimitrios Loundras</td>\n",
       "      <td>M</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ethnikos Gymnastikos Syllogos</td>\n",
       "      <td>GRE</td>\n",
       "      <td>1896 Summer</td>\n",
       "      <td>1896</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Athina</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Parallel Bars, Teams</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID                Name Sex   Age  Height  Weight  \\\n",
       "142882  71691  Dimitrios Loundras   M  10.0     NaN     NaN   \n",
       "\n",
       "                                 Team  NOC        Games  Year  Season    City  \\\n",
       "142882  Ethnikos Gymnastikos Syllogos  GRE  1896 Summer  1896  Summer  Athina   \n",
       "\n",
       "             Sport                                  Event   Medal  \n",
       "142882  Gymnastics  Gymnastics Men's Parallel Bars, Teams  Bronze  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Age'] ==10.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not only did a 10 year old compete in the Olympics - he was a Bronze medal recipient! The following is from his [Wikipedia page](https://en.wikipedia.org/wiki/Dimitrios_Loundras): \n",
    ">Loundras competed in the team parallel bars event.. at 10 years 218 days he remains the youngest medalist and competitor in Olympic history\n",
    "\n",
    "Also take a look at the picture below - he's the tiny one in the middle!\n"
   ]
  },
  {
   "attachments": {
    "olympic_youngest.jpeg": {
     "image/jpeg": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![olympic_youngest.jpeg](attachment:olympic_youngest.jpeg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>257054</th>\n",
       "      <td>128719</td>\n",
       "      <td>John Quincy Adams Ward</td>\n",
       "      <td>M</td>\n",
       "      <td>97.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>1928 Summer</td>\n",
       "      <td>1928</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Statues</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID                    Name Sex   Age  Height  Weight  \\\n",
       "257054  128719  John Quincy Adams Ward   M  97.0     NaN     NaN   \n",
       "\n",
       "                 Team  NOC        Games  Year  Season       City  \\\n",
       "257054  United States  USA  1928 Summer  1928  Summer  Amsterdam   \n",
       "\n",
       "                   Sport                                        Event  \\\n",
       "257054  Art Competitions  Art Competitions Mixed Sculpturing, Statues   \n",
       "\n",
       "           Medal  \n",
       "257054  No Medal  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Age'] == 97]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23155</th>\n",
       "      <td>12177</td>\n",
       "      <td>Ricardo Blas, Jr.</td>\n",
       "      <td>M</td>\n",
       "      <td>21.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>Guam</td>\n",
       "      <td>GUM</td>\n",
       "      <td>2008 Summer</td>\n",
       "      <td>2008</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>Judo</td>\n",
       "      <td>Judo Men's Heavyweight</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23156</th>\n",
       "      <td>12177</td>\n",
       "      <td>Ricardo Blas, Jr.</td>\n",
       "      <td>M</td>\n",
       "      <td>25.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>Guam</td>\n",
       "      <td>GUM</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 Heavyweight</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID               Name Sex   Age  Height  Weight  Team  NOC  \\\n",
       "23155  12177  Ricardo Blas, Jr.   M  21.0   183.0   214.0  Guam  GUM   \n",
       "23156  12177  Ricardo Blas, Jr.   M  25.0   183.0   214.0  Guam  GUM   \n",
       "\n",
       "             Games  Year  Season     City Sport                   Event  \\\n",
       "23155  2008 Summer  2008  Summer  Beijing  Judo  Judo Men's Heavyweight   \n",
       "23156  2012 Summer  2012  Summer   London  Judo  Judo Men's Heavyweight   \n",
       "\n",
       "          Medal  \n",
       "23155  No Medal  \n",
       "23156  No Medal  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Weight'] == 214] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID\n",
       "77710     58\n",
       "106296    39\n",
       "115354    38\n",
       "119591    36\n",
       "44875     32\n",
       "129196    32\n",
       "89187     32\n",
       "119590    32\n",
       "53240     32\n",
       "55047     31\n",
       "Name: Name, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "appearances = df.groupby('ID')['Name'].count().sort_values(ascending = False)\n",
    "appearances[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get an idea of how (un)likely it is that someone went to multiple Olympic games, I created a density plot below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df.groupby('ID')['Name'].count().values)\\\n",
    ".set_title('Distribution of # of Olympic games attended');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>154798</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1912 Summer</td>\n",
       "      <td>1912</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154799</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1928 Summer</td>\n",
       "      <td>1928</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Statues</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154800</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1928 Summer</td>\n",
       "      <td>1928</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Statues</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154801</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1928 Summer</td>\n",
       "      <td>1928</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Statues</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154802</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1928 Summer</td>\n",
       "      <td>1928</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Statues</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154803</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1928 Summer</td>\n",
       "      <td>1928</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Statues</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154804</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1928 Summer</td>\n",
       "      <td>1928</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Statues</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154805</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1928 Summer</td>\n",
       "      <td>1928</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Statues</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154806</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Medals And...</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154807</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154808</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154809</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154810</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154811</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154812</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154813</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154814</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154815</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154816</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154817</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154818</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154819</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154820</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154821</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154822</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154823</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154824</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154825</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154826</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154827</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154828</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154829</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154830</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154831</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154832</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154833</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154834</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154835</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154836</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154837</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154838</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154839</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154840</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154841</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154842</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154843</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154844</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154845</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154846</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154847</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154848</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154849</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154850</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1936 Summer</td>\n",
       "      <td>1936</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Medals</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154851</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1936 Summer</td>\n",
       "      <td>1936</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154852</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1936 Summer</td>\n",
       "      <td>1936</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154853</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1936 Summer</td>\n",
       "      <td>1936</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154854</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1936 Summer</td>\n",
       "      <td>1936</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154855</th>\n",
       "      <td>77710</td>\n",
       "      <td>Robert Tait McKenzie</td>\n",
       "      <td>M</td>\n",
       "      <td>81.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1948 Summer</td>\n",
       "      <td>1948</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Art Competitions</td>\n",
       "      <td>Art Competitions Mixed Sculpturing, Unknown Event</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID                  Name Sex   Age  Height  Weight    Team  NOC  \\\n",
       "154798  77710  Robert Tait McKenzie   M  44.0     NaN     NaN  Canada  CAN   \n",
       "154799  77710  Robert Tait McKenzie   M  60.0     NaN     NaN  Canada  CAN   \n",
       "154800  77710  Robert Tait McKenzie   M  60.0     NaN     NaN  Canada  CAN   \n",
       "154801  77710  Robert Tait McKenzie   M  60.0     NaN     NaN  Canada  CAN   \n",
       "154802  77710  Robert Tait McKenzie   M  60.0     NaN     NaN  Canada  CAN   \n",
       "154803  77710  Robert Tait McKenzie   M  60.0     NaN     NaN  Canada  CAN   \n",
       "154804  77710  Robert Tait McKenzie   M  60.0     NaN     NaN  Canada  CAN   \n",
       "154805  77710  Robert Tait McKenzie   M  60.0     NaN     NaN  Canada  CAN   \n",
       "154806  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154807  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154808  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154809  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154810  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154811  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154812  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154813  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154814  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154815  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154816  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154817  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154818  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154819  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154820  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154821  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154822  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154823  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154824  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154825  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154826  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154827  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154828  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154829  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154830  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154831  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154832  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154833  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154834  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154835  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154836  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154837  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154838  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154839  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154840  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154841  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154842  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154843  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154844  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154845  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154846  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154847  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154848  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154849  77710  Robert Tait McKenzie   M  65.0     NaN     NaN  Canada  CAN   \n",
       "154850  77710  Robert Tait McKenzie   M  69.0     NaN     NaN  Canada  CAN   \n",
       "154851  77710  Robert Tait McKenzie   M  69.0     NaN     NaN  Canada  CAN   \n",
       "154852  77710  Robert Tait McKenzie   M  69.0     NaN     NaN  Canada  CAN   \n",
       "154853  77710  Robert Tait McKenzie   M  69.0     NaN     NaN  Canada  CAN   \n",
       "154854  77710  Robert Tait McKenzie   M  69.0     NaN     NaN  Canada  CAN   \n",
       "154855  77710  Robert Tait McKenzie   M  81.0     NaN     NaN  Canada  CAN   \n",
       "\n",
       "              Games  Year  Season         City             Sport  \\\n",
       "154798  1912 Summer  1912  Summer    Stockholm  Art Competitions   \n",
       "154799  1928 Summer  1928  Summer    Amsterdam  Art Competitions   \n",
       "154800  1928 Summer  1928  Summer    Amsterdam  Art Competitions   \n",
       "154801  1928 Summer  1928  Summer    Amsterdam  Art Competitions   \n",
       "154802  1928 Summer  1928  Summer    Amsterdam  Art Competitions   \n",
       "154803  1928 Summer  1928  Summer    Amsterdam  Art Competitions   \n",
       "154804  1928 Summer  1928  Summer    Amsterdam  Art Competitions   \n",
       "154805  1928 Summer  1928  Summer    Amsterdam  Art Competitions   \n",
       "154806  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154807  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154808  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154809  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154810  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154811  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154812  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154813  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154814  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154815  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154816  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154817  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154818  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154819  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154820  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154821  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154822  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154823  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154824  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154825  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154826  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154827  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154828  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154829  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154830  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154831  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154832  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154833  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154834  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154835  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154836  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154837  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154838  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154839  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154840  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154841  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154842  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154843  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154844  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154845  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154846  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154847  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154848  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154849  1932 Summer  1932  Summer  Los Angeles  Art Competitions   \n",
       "154850  1936 Summer  1936  Summer       Berlin  Art Competitions   \n",
       "154851  1936 Summer  1936  Summer       Berlin  Art Competitions   \n",
       "154852  1936 Summer  1936  Summer       Berlin  Art Competitions   \n",
       "154853  1936 Summer  1936  Summer       Berlin  Art Competitions   \n",
       "154854  1936 Summer  1936  Summer       Berlin  Art Competitions   \n",
       "154855  1948 Summer  1948  Summer       London  Art Competitions   \n",
       "\n",
       "                                                    Event     Medal  \n",
       "154798                 Art Competitions Mixed Sculpturing  No Medal  \n",
       "154799        Art Competitions Mixed Sculpturing, Statues  No Medal  \n",
       "154800        Art Competitions Mixed Sculpturing, Statues  No Medal  \n",
       "154801        Art Competitions Mixed Sculpturing, Statues  No Medal  \n",
       "154802        Art Competitions Mixed Sculpturing, Statues  No Medal  \n",
       "154803        Art Competitions Mixed Sculpturing, Statues  No Medal  \n",
       "154804        Art Competitions Mixed Sculpturing, Statues  No Medal  \n",
       "154805        Art Competitions Mixed Sculpturing, Statues  No Medal  \n",
       "154806  Art Competitions Mixed Sculpturing, Medals And...    Bronze  \n",
       "154807  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154808  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154809  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154810  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154811  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154812  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154813  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154814  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154815  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154816  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154817  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154818  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154819  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154820  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154821  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154822  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154823  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154824  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154825  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154826  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154827  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154828  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154829  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154830  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154831  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154832  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154833  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154834  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154835  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154836  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154837  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154838  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154839  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154840  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154841  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154842  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154843  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154844  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154845  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154846  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154847  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154848  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154849  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154850         Art Competitions Mixed Sculpturing, Medals  No Medal  \n",
       "154851  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154852  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154853  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154854  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  \n",
       "154855  Art Competitions Mixed Sculpturing, Unknown Event  No Medal  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_appeared_id = appearances.index[0]\n",
    "df[df['ID'] == most_appeared_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>211668</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>20.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1928 Summer</td>\n",
       "      <td>1928</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Individual All-Around</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211669</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>20.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1928 Summer</td>\n",
       "      <td>1928</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Team All-Around</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211670</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>20.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1928 Summer</td>\n",
       "      <td>1928</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Horse Vault</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211671</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>20.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1928 Summer</td>\n",
       "      <td>1928</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Parallel Bars</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211672</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>20.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1928 Summer</td>\n",
       "      <td>1928</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Horizontal Bar</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211673</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>20.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1928 Summer</td>\n",
       "      <td>1928</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Rings</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211674</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>20.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1928 Summer</td>\n",
       "      <td>1928</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Pommelled Horse</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211675</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Individual All-Around</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211676</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Team All-Around</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211677</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Floor Exercise</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211678</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Horse Vault</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211679</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Parallel Bars</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211680</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Horizontal Bar</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211681</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Rings</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211682</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Pommelled Horse</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211683</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>28.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1936 Summer</td>\n",
       "      <td>1936</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Individual All-Around</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211684</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>28.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1936 Summer</td>\n",
       "      <td>1936</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Team All-Around</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211685</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>28.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1936 Summer</td>\n",
       "      <td>1936</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Floor Exercise</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211686</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>28.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1936 Summer</td>\n",
       "      <td>1936</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Horse Vault</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211687</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>28.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1936 Summer</td>\n",
       "      <td>1936</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Parallel Bars</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211688</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>28.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1936 Summer</td>\n",
       "      <td>1936</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Horizontal Bar</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211689</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>28.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1936 Summer</td>\n",
       "      <td>1936</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Rings</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211690</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>28.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1936 Summer</td>\n",
       "      <td>1936</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Pommelled Horse</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211691</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>40.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1948 Summer</td>\n",
       "      <td>1948</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Individual All-Around</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211692</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>40.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1948 Summer</td>\n",
       "      <td>1948</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Team All-Around</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211693</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>40.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1948 Summer</td>\n",
       "      <td>1948</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Floor Exercise</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211694</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>40.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1948 Summer</td>\n",
       "      <td>1948</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Horse Vault</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211695</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>40.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1948 Summer</td>\n",
       "      <td>1948</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Parallel Bars</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211696</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>40.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1948 Summer</td>\n",
       "      <td>1948</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Horizontal Bar</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211697</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>40.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1948 Summer</td>\n",
       "      <td>1948</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Rings</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211698</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>40.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1948 Summer</td>\n",
       "      <td>1948</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Pommelled Horse</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211699</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>44.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1952 Summer</td>\n",
       "      <td>1952</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Individual All-Around</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211700</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>44.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1952 Summer</td>\n",
       "      <td>1952</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Team All-Around</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211701</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>44.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1952 Summer</td>\n",
       "      <td>1952</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Floor Exercise</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211702</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>44.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1952 Summer</td>\n",
       "      <td>1952</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Horse Vault</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211703</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>44.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1952 Summer</td>\n",
       "      <td>1952</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Parallel Bars</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211704</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>44.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1952 Summer</td>\n",
       "      <td>1952</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Horizontal Bar</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211705</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>44.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1952 Summer</td>\n",
       "      <td>1952</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Rings</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211706</th>\n",
       "      <td>106296</td>\n",
       "      <td>Heikki Ilmari Savolainen</td>\n",
       "      <td>M</td>\n",
       "      <td>44.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1952 Summer</td>\n",
       "      <td>1952</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Pommelled Horse</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID                      Name Sex   Age  Height  Weight     Team  \\\n",
       "211668  106296  Heikki Ilmari Savolainen   M  20.0   172.0    64.0  Finland   \n",
       "211669  106296  Heikki Ilmari Savolainen   M  20.0   172.0    64.0  Finland   \n",
       "211670  106296  Heikki Ilmari Savolainen   M  20.0   172.0    64.0  Finland   \n",
       "211671  106296  Heikki Ilmari Savolainen   M  20.0   172.0    64.0  Finland   \n",
       "211672  106296  Heikki Ilmari Savolainen   M  20.0   172.0    64.0  Finland   \n",
       "211673  106296  Heikki Ilmari Savolainen   M  20.0   172.0    64.0  Finland   \n",
       "211674  106296  Heikki Ilmari Savolainen   M  20.0   172.0    64.0  Finland   \n",
       "211675  106296  Heikki Ilmari Savolainen   M  24.0   172.0    64.0  Finland   \n",
       "211676  106296  Heikki Ilmari Savolainen   M  24.0   172.0    64.0  Finland   \n",
       "211677  106296  Heikki Ilmari Savolainen   M  24.0   172.0    64.0  Finland   \n",
       "211678  106296  Heikki Ilmari Savolainen   M  24.0   172.0    64.0  Finland   \n",
       "211679  106296  Heikki Ilmari Savolainen   M  24.0   172.0    64.0  Finland   \n",
       "211680  106296  Heikki Ilmari Savolainen   M  24.0   172.0    64.0  Finland   \n",
       "211681  106296  Heikki Ilmari Savolainen   M  24.0   172.0    64.0  Finland   \n",
       "211682  106296  Heikki Ilmari Savolainen   M  24.0   172.0    64.0  Finland   \n",
       "211683  106296  Heikki Ilmari Savolainen   M  28.0   172.0    64.0  Finland   \n",
       "211684  106296  Heikki Ilmari Savolainen   M  28.0   172.0    64.0  Finland   \n",
       "211685  106296  Heikki Ilmari Savolainen   M  28.0   172.0    64.0  Finland   \n",
       "211686  106296  Heikki Ilmari Savolainen   M  28.0   172.0    64.0  Finland   \n",
       "211687  106296  Heikki Ilmari Savolainen   M  28.0   172.0    64.0  Finland   \n",
       "211688  106296  Heikki Ilmari Savolainen   M  28.0   172.0    64.0  Finland   \n",
       "211689  106296  Heikki Ilmari Savolainen   M  28.0   172.0    64.0  Finland   \n",
       "211690  106296  Heikki Ilmari Savolainen   M  28.0   172.0    64.0  Finland   \n",
       "211691  106296  Heikki Ilmari Savolainen   M  40.0   172.0    64.0  Finland   \n",
       "211692  106296  Heikki Ilmari Savolainen   M  40.0   172.0    64.0  Finland   \n",
       "211693  106296  Heikki Ilmari Savolainen   M  40.0   172.0    64.0  Finland   \n",
       "211694  106296  Heikki Ilmari Savolainen   M  40.0   172.0    64.0  Finland   \n",
       "211695  106296  Heikki Ilmari Savolainen   M  40.0   172.0    64.0  Finland   \n",
       "211696  106296  Heikki Ilmari Savolainen   M  40.0   172.0    64.0  Finland   \n",
       "211697  106296  Heikki Ilmari Savolainen   M  40.0   172.0    64.0  Finland   \n",
       "211698  106296  Heikki Ilmari Savolainen   M  40.0   172.0    64.0  Finland   \n",
       "211699  106296  Heikki Ilmari Savolainen   M  44.0   172.0    64.0  Finland   \n",
       "211700  106296  Heikki Ilmari Savolainen   M  44.0   172.0    64.0  Finland   \n",
       "211701  106296  Heikki Ilmari Savolainen   M  44.0   172.0    64.0  Finland   \n",
       "211702  106296  Heikki Ilmari Savolainen   M  44.0   172.0    64.0  Finland   \n",
       "211703  106296  Heikki Ilmari Savolainen   M  44.0   172.0    64.0  Finland   \n",
       "211704  106296  Heikki Ilmari Savolainen   M  44.0   172.0    64.0  Finland   \n",
       "211705  106296  Heikki Ilmari Savolainen   M  44.0   172.0    64.0  Finland   \n",
       "211706  106296  Heikki Ilmari Savolainen   M  44.0   172.0    64.0  Finland   \n",
       "\n",
       "        NOC        Games  Year  Season         City       Sport  \\\n",
       "211668  FIN  1928 Summer  1928  Summer    Amsterdam  Gymnastics   \n",
       "211669  FIN  1928 Summer  1928  Summer    Amsterdam  Gymnastics   \n",
       "211670  FIN  1928 Summer  1928  Summer    Amsterdam  Gymnastics   \n",
       "211671  FIN  1928 Summer  1928  Summer    Amsterdam  Gymnastics   \n",
       "211672  FIN  1928 Summer  1928  Summer    Amsterdam  Gymnastics   \n",
       "211673  FIN  1928 Summer  1928  Summer    Amsterdam  Gymnastics   \n",
       "211674  FIN  1928 Summer  1928  Summer    Amsterdam  Gymnastics   \n",
       "211675  FIN  1932 Summer  1932  Summer  Los Angeles  Gymnastics   \n",
       "211676  FIN  1932 Summer  1932  Summer  Los Angeles  Gymnastics   \n",
       "211677  FIN  1932 Summer  1932  Summer  Los Angeles  Gymnastics   \n",
       "211678  FIN  1932 Summer  1932  Summer  Los Angeles  Gymnastics   \n",
       "211679  FIN  1932 Summer  1932  Summer  Los Angeles  Gymnastics   \n",
       "211680  FIN  1932 Summer  1932  Summer  Los Angeles  Gymnastics   \n",
       "211681  FIN  1932 Summer  1932  Summer  Los Angeles  Gymnastics   \n",
       "211682  FIN  1932 Summer  1932  Summer  Los Angeles  Gymnastics   \n",
       "211683  FIN  1936 Summer  1936  Summer       Berlin  Gymnastics   \n",
       "211684  FIN  1936 Summer  1936  Summer       Berlin  Gymnastics   \n",
       "211685  FIN  1936 Summer  1936  Summer       Berlin  Gymnastics   \n",
       "211686  FIN  1936 Summer  1936  Summer       Berlin  Gymnastics   \n",
       "211687  FIN  1936 Summer  1936  Summer       Berlin  Gymnastics   \n",
       "211688  FIN  1936 Summer  1936  Summer       Berlin  Gymnastics   \n",
       "211689  FIN  1936 Summer  1936  Summer       Berlin  Gymnastics   \n",
       "211690  FIN  1936 Summer  1936  Summer       Berlin  Gymnastics   \n",
       "211691  FIN  1948 Summer  1948  Summer       London  Gymnastics   \n",
       "211692  FIN  1948 Summer  1948  Summer       London  Gymnastics   \n",
       "211693  FIN  1948 Summer  1948  Summer       London  Gymnastics   \n",
       "211694  FIN  1948 Summer  1948  Summer       London  Gymnastics   \n",
       "211695  FIN  1948 Summer  1948  Summer       London  Gymnastics   \n",
       "211696  FIN  1948 Summer  1948  Summer       London  Gymnastics   \n",
       "211697  FIN  1948 Summer  1948  Summer       London  Gymnastics   \n",
       "211698  FIN  1948 Summer  1948  Summer       London  Gymnastics   \n",
       "211699  FIN  1952 Summer  1952  Summer     Helsinki  Gymnastics   \n",
       "211700  FIN  1952 Summer  1952  Summer     Helsinki  Gymnastics   \n",
       "211701  FIN  1952 Summer  1952  Summer     Helsinki  Gymnastics   \n",
       "211702  FIN  1952 Summer  1952  Summer     Helsinki  Gymnastics   \n",
       "211703  FIN  1952 Summer  1952  Summer     Helsinki  Gymnastics   \n",
       "211704  FIN  1952 Summer  1952  Summer     Helsinki  Gymnastics   \n",
       "211705  FIN  1952 Summer  1952  Summer     Helsinki  Gymnastics   \n",
       "211706  FIN  1952 Summer  1952  Summer     Helsinki  Gymnastics   \n",
       "\n",
       "                                         Event     Medal  \n",
       "211668  Gymnastics Men's Individual All-Around  No Medal  \n",
       "211669        Gymnastics Men's Team All-Around  No Medal  \n",
       "211670            Gymnastics Men's Horse Vault  No Medal  \n",
       "211671          Gymnastics Men's Parallel Bars  No Medal  \n",
       "211672         Gymnastics Men's Horizontal Bar  No Medal  \n",
       "211673                  Gymnastics Men's Rings  No Medal  \n",
       "211674        Gymnastics Men's Pommelled Horse    Bronze  \n",
       "211675  Gymnastics Men's Individual All-Around    Bronze  \n",
       "211676        Gymnastics Men's Team All-Around    Bronze  \n",
       "211677         Gymnastics Men's Floor Exercise  No Medal  \n",
       "211678            Gymnastics Men's Horse Vault  No Medal  \n",
       "211679          Gymnastics Men's Parallel Bars    Bronze  \n",
       "211680         Gymnastics Men's Horizontal Bar    Silver  \n",
       "211681                  Gymnastics Men's Rings  No Medal  \n",
       "211682        Gymnastics Men's Pommelled Horse  No Medal  \n",
       "211683  Gymnastics Men's Individual All-Around  No Medal  \n",
       "211684        Gymnastics Men's Team All-Around    Bronze  \n",
       "211685         Gymnastics Men's Floor Exercise  No Medal  \n",
       "211686            Gymnastics Men's Horse Vault  No Medal  \n",
       "211687          Gymnastics Men's Parallel Bars  No Medal  \n",
       "211688         Gymnastics Men's Horizontal Bar  No Medal  \n",
       "211689                  Gymnastics Men's Rings  No Medal  \n",
       "211690        Gymnastics Men's Pommelled Horse  No Medal  \n",
       "211691  Gymnastics Men's Individual All-Around  No Medal  \n",
       "211692        Gymnastics Men's Team All-Around      Gold  \n",
       "211693         Gymnastics Men's Floor Exercise  No Medal  \n",
       "211694            Gymnastics Men's Horse Vault  No Medal  \n",
       "211695          Gymnastics Men's Parallel Bars  No Medal  \n",
       "211696         Gymnastics Men's Horizontal Bar  No Medal  \n",
       "211697                  Gymnastics Men's Rings  No Medal  \n",
       "211698        Gymnastics Men's Pommelled Horse      Gold  \n",
       "211699  Gymnastics Men's Individual All-Around  No Medal  \n",
       "211700        Gymnastics Men's Team All-Around    Bronze  \n",
       "211701         Gymnastics Men's Floor Exercise  No Medal  \n",
       "211702            Gymnastics Men's Horse Vault  No Medal  \n",
       "211703          Gymnastics Men's Parallel Bars  No Medal  \n",
       "211704         Gymnastics Men's Horizontal Bar  No Medal  \n",
       "211705                  Gymnastics Men's Rings  No Medal  \n",
       "211706        Gymnastics Men's Pommelled Horse  No Medal  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['ID'] == appearances.index[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145171</th>\n",
       "      <td>72863</td>\n",
       "      <td>Arthur Byron MacDonald</td>\n",
       "      <td>M</td>\n",
       "      <td>22.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1972 Summer</td>\n",
       "      <td>1972</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Swimming</td>\n",
       "      <td>Swimming Men's 100 metres Butterfly</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145172</th>\n",
       "      <td>72863</td>\n",
       "      <td>Arthur Byron MacDonald</td>\n",
       "      <td>M</td>\n",
       "      <td>22.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1972 Summer</td>\n",
       "      <td>1972</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Swimming</td>\n",
       "      <td>Swimming Men's 200 metres Butterfly</td>\n",
       "      <td>No Medal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID                    Name Sex   Age  Height  Weight    Team  NOC  \\\n",
       "145171  72863  Arthur Byron MacDonald   M  22.0   175.0    61.0  Canada  CAN   \n",
       "145172  72863  Arthur Byron MacDonald   M  22.0   175.0    61.0  Canada  CAN   \n",
       "\n",
       "              Games  Year  Season    City     Sport  \\\n",
       "145171  1972 Summer  1972  Summer  Munich  Swimming   \n",
       "145172  1972 Summer  1972  Summer  Munich  Swimming   \n",
       "\n",
       "                                      Event     Medal  \n",
       "145171  Swimming Men's 100 metres Butterfly  No Medal  \n",
       "145172  Swimming Men's 200 metres Butterfly  No Medal  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uncle = df[df['Name'] == 'Arthur Byron MacDonald']\n",
    "\n",
    "uncle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Season\n",
       "Summer    222552\n",
       "Winter     48564\n",
       "Name: ID, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Season').ID.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "summer_df = df[df['Season'] == 'Summer']\n",
    "winter_df = df[df['Season'] == 'Winter']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>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>Season</th>\n",
       "      <th>City</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Event</th>\n",
       "      <th>Medal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>13821</td>\n",
       "      <td>13821</td>\n",
       "      <td>13821</td>\n",
       "      <td>13820</td>\n",
       "      <td>13698</td>\n",
       "      <td>13695</td>\n",
       "      <td>13821</td>\n",
       "      <td>13821</td>\n",
       "      <td>13821</td>\n",
       "      <td>13821</td>\n",
       "      <td>13821</td>\n",
       "      <td>13821</td>\n",
       "      <td>13821</td>\n",
       "      <td>13821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>13443</td>\n",
       "      <td>13443</td>\n",
       "      <td>13443</td>\n",
       "      <td>13443</td>\n",
       "      <td>13407</td>\n",
       "      <td>13406</td>\n",
       "      <td>13443</td>\n",
       "      <td>13443</td>\n",
       "      <td>13443</td>\n",
       "      <td>13443</td>\n",
       "      <td>13443</td>\n",
       "      <td>13443</td>\n",
       "      <td>13443</td>\n",
       "      <td>13443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>13602</td>\n",
       "      <td>13602</td>\n",
       "      <td>13602</td>\n",
       "      <td>13600</td>\n",
       "      <td>13451</td>\n",
       "      <td>13443</td>\n",
       "      <td>13602</td>\n",
       "      <td>13602</td>\n",
       "      <td>13602</td>\n",
       "      <td>13602</td>\n",
       "      <td>13602</td>\n",
       "      <td>13602</td>\n",
       "      <td>13602</td>\n",
       "      <td>13602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>12920</td>\n",
       "      <td>12920</td>\n",
       "      <td>12920</td>\n",
       "      <td>12920</td>\n",
       "      <td>12752</td>\n",
       "      <td>12560</td>\n",
       "      <td>12920</td>\n",
       "      <td>12920</td>\n",
       "      <td>12920</td>\n",
       "      <td>12920</td>\n",
       "      <td>12920</td>\n",
       "      <td>12920</td>\n",
       "      <td>12920</td>\n",
       "      <td>12920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>13688</td>\n",
       "      <td>13688</td>\n",
       "      <td>13688</td>\n",
       "      <td>13688</td>\n",
       "      <td>13512</td>\n",
       "      <td>13465</td>\n",
       "      <td>13688</td>\n",
       "      <td>13688</td>\n",
       "      <td>13688</td>\n",
       "      <td>13688</td>\n",
       "      <td>13688</td>\n",
       "      <td>13688</td>\n",
       "      <td>13688</td>\n",
       "      <td>13688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID   Name    Sex    Age  Height  Weight   Team    NOC  Games  Season  \\\n",
       "Year                                                                            \n",
       "2000  13821  13821  13821  13820   13698   13695  13821  13821  13821   13821   \n",
       "2004  13443  13443  13443  13443   13407   13406  13443  13443  13443   13443   \n",
       "2008  13602  13602  13602  13600   13451   13443  13602  13602  13602   13602   \n",
       "2012  12920  12920  12920  12920   12752   12560  12920  12920  12920   12920   \n",
       "2016  13688  13688  13688  13688   13512   13465  13688  13688  13688   13688   \n",
       "\n",
       "       City  Sport  Event  Medal  \n",
       "Year                              \n",
       "2000  13821  13821  13821  13821  \n",
       "2004  13443  13443  13443  13443  \n",
       "2008  13602  13602  13602  13602  \n",
       "2012  12920  12920  12920  12920  \n",
       "2016  13688  13688  13688  13688  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summer_counts = summer_df.groupby('Year').count()\n",
    "summer_counts.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>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>Season</th>\n",
       "      <th>City</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Event</th>\n",
       "      <th>Medal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>3605</td>\n",
       "      <td>3605</td>\n",
       "      <td>3605</td>\n",
       "      <td>3603</td>\n",
       "      <td>3521</td>\n",
       "      <td>3519</td>\n",
       "      <td>3605</td>\n",
       "      <td>3605</td>\n",
       "      <td>3605</td>\n",
       "      <td>3605</td>\n",
       "      <td>3605</td>\n",
       "      <td>3605</td>\n",
       "      <td>3605</td>\n",
       "      <td>3605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>4109</td>\n",
       "      <td>4109</td>\n",
       "      <td>4109</td>\n",
       "      <td>4109</td>\n",
       "      <td>4080</td>\n",
       "      <td>4062</td>\n",
       "      <td>4109</td>\n",
       "      <td>4109</td>\n",
       "      <td>4109</td>\n",
       "      <td>4109</td>\n",
       "      <td>4109</td>\n",
       "      <td>4109</td>\n",
       "      <td>4109</td>\n",
       "      <td>4109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>4382</td>\n",
       "      <td>4382</td>\n",
       "      <td>4382</td>\n",
       "      <td>4382</td>\n",
       "      <td>4376</td>\n",
       "      <td>4366</td>\n",
       "      <td>4382</td>\n",
       "      <td>4382</td>\n",
       "      <td>4382</td>\n",
       "      <td>4382</td>\n",
       "      <td>4382</td>\n",
       "      <td>4382</td>\n",
       "      <td>4382</td>\n",
       "      <td>4382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>4402</td>\n",
       "      <td>4402</td>\n",
       "      <td>4402</td>\n",
       "      <td>4402</td>\n",
       "      <td>4400</td>\n",
       "      <td>4378</td>\n",
       "      <td>4402</td>\n",
       "      <td>4402</td>\n",
       "      <td>4402</td>\n",
       "      <td>4402</td>\n",
       "      <td>4402</td>\n",
       "      <td>4402</td>\n",
       "      <td>4402</td>\n",
       "      <td>4402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>4891</td>\n",
       "      <td>4891</td>\n",
       "      <td>4891</td>\n",
       "      <td>4891</td>\n",
       "      <td>4871</td>\n",
       "      <td>4673</td>\n",
       "      <td>4891</td>\n",
       "      <td>4891</td>\n",
       "      <td>4891</td>\n",
       "      <td>4891</td>\n",
       "      <td>4891</td>\n",
       "      <td>4891</td>\n",
       "      <td>4891</td>\n",
       "      <td>4891</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  Name   Sex   Age  Height  Weight  Team   NOC  Games  Season  City  \\\n",
       "Year                                                                            \n",
       "1998  3605  3605  3605  3603    3521    3519  3605  3605   3605    3605  3605   \n",
       "2002  4109  4109  4109  4109    4080    4062  4109  4109   4109    4109  4109   \n",
       "2006  4382  4382  4382  4382    4376    4366  4382  4382   4382    4382  4382   \n",
       "2010  4402  4402  4402  4402    4400    4378  4402  4402   4402    4402  4402   \n",
       "2014  4891  4891  4891  4891    4871    4673  4891  4891   4891    4891  4891   \n",
       "\n",
       "      Sport  Event  Medal  \n",
       "Year                       \n",
       "1998   3605   3605   3605  \n",
       "2002   4109   4109   4109  \n",
       "2006   4382   4382   4382  \n",
       "2010   4402   4402   4402  \n",
       "2014   4891   4891   4891  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "winter_counts = winter_df.groupby('Year').count()\n",
    "winter_counts.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "summer_medals = summer_df[summer_df['Medal'] != 'No Medal'][['Year', 'Medal']].groupby('Year').count()\n",
    "winter_medals = winter_df[winter_df['Medal'] != 'No Medal'][['Year', 'Medal']].groupby('Year').count()\n",
    "\n",
    "plt.plot(summer_medals);\n",
    "plt.plot(winter_medals);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How does the number of athletes change over time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df.groupby('Year').ID.count());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df[df['Sex'] == 'M'].groupby('Year').ID.count(), 'g', label = 'Male participants');\n",
    "plt.plot(df[df['Sex'] == 'F'].groupby('Year').ID.count(), 'm', label = 'Female participants');\n",
    "plt.legend(loc = 'upper left');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(summer_df.groupby('Year').ID.count());\n",
    "plt.plot(winter_df.groupby('Year').ID.count());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(summer_df[summer_df['Sex'] == 'M'].groupby('Year').ID.count(), 'r-', label = \"Male Summer participants\");\n",
    "plt.plot(summer_df[summer_df['Sex'] == 'F'].groupby('Year').ID.count(), 'r--', label = \"Female Summer participants\");\n",
    "plt.plot(winter_df[winter_df['Sex'] == 'M'].groupby('Year').ID.count(), 'b-', label = \"Male Winter participants\");\n",
    "plt.plot(winter_df[winter_df['Sex'] == 'F'].groupby('Year').ID.count(), 'b--', label = \"Female Winter participants\");\n",
    "plt.legend(loc = 'upper left');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# countries = pd.unique(df.NOC)\n",
    "# for i in countries:\n",
    "#     country_df = df[df['NOC'] == i]\n",
    "#     sns.countplot(x = 'NOC', data = country_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15064, 15)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "murica = df[(df['NOC'] == 'USA') & (df['Season'] == 'Summer')]\n",
    "murica.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Medal', data = murica);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (20, 8)\n",
    "sns.countplot(x = 'Year', hue = 'Medal', data = murica);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Year', hue = 'Sex', data = df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}