Assignment3_Final(violin, seaborn).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Sharminrahi/Data-Visualization/blob/main/Assignment3_Final.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "82bb9968",
      "metadata": {
        "id": "82bb9968"
      },
      "source": [
        "# Title: Assignment 3\n",
        "### Name: Sharmin Akhter\n",
        "### ID: 201891112\n",
        "### Date 24-02-2023\n",
        "### Master of Data Science"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "15ba721f",
      "metadata": {
        "id": "15ba721f"
      },
      "source": [
        "# Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "38c7aa80",
      "metadata": {
        "id": "38c7aa80"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bc4920dc",
      "metadata": {
        "id": "bc4920dc"
      },
      "source": [
        "# Load Olympic Athletes Dataset \n",
        "The dataset contains information about all the athletes that have participated in the Olympics up until the 2016 games."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3d2cd78f",
      "metadata": {
        "id": "3d2cd78f",
        "outputId": "7ca67e7d-06b7-49c1-afe8-ee3840c435f0"
      },
      "outputs": [
        {
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              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271111</th>\n",
              "      <td>135569</td>\n",
              "      <td>Andrzej ya</td>\n",
              "      <td>M</td>\n",
              "      <td>29.0</td>\n",
              "      <td>179.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>Poland-1</td>\n",
              "      <td>POL</td>\n",
              "      <td>1976 Winter</td>\n",
              "      <td>1976</td>\n",
              "      <td>Winter</td>\n",
              "      <td>Innsbruck</td>\n",
              "      <td>Luge</td>\n",
              "      <td>Luge Mixed (Men)'s Doubles</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271112</th>\n",
              "      <td>135570</td>\n",
              "      <td>Piotr ya</td>\n",
              "      <td>M</td>\n",
              "      <td>27.0</td>\n",
              "      <td>176.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>Poland</td>\n",
              "      <td>POL</td>\n",
              "      <td>2014 Winter</td>\n",
              "      <td>2014</td>\n",
              "      <td>Winter</td>\n",
              "      <td>Sochi</td>\n",
              "      <td>Ski Jumping</td>\n",
              "      <td>Ski Jumping Men's Large Hill, Individual</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271113</th>\n",
              "      <td>135570</td>\n",
              "      <td>Piotr ya</td>\n",
              "      <td>M</td>\n",
              "      <td>27.0</td>\n",
              "      <td>176.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>Poland</td>\n",
              "      <td>POL</td>\n",
              "      <td>2014 Winter</td>\n",
              "      <td>2014</td>\n",
              "      <td>Winter</td>\n",
              "      <td>Sochi</td>\n",
              "      <td>Ski Jumping</td>\n",
              "      <td>Ski Jumping Men's Large Hill, Team</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271114</th>\n",
              "      <td>135571</td>\n",
              "      <td>Tomasz Ireneusz ya</td>\n",
              "      <td>M</td>\n",
              "      <td>30.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>Poland</td>\n",
              "      <td>POL</td>\n",
              "      <td>1998 Winter</td>\n",
              "      <td>1998</td>\n",
              "      <td>Winter</td>\n",
              "      <td>Nagano</td>\n",
              "      <td>Bobsleigh</td>\n",
              "      <td>Bobsleigh Men's Four</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271115</th>\n",
              "      <td>135571</td>\n",
              "      <td>Tomasz Ireneusz ya</td>\n",
              "      <td>M</td>\n",
              "      <td>34.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>Poland</td>\n",
              "      <td>POL</td>\n",
              "      <td>2002 Winter</td>\n",
              "      <td>2002</td>\n",
              "      <td>Winter</td>\n",
              "      <td>Salt Lake City</td>\n",
              "      <td>Bobsleigh</td>\n",
              "      <td>Bobsleigh Men's Four</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>271116 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            ID                      Name Sex   Age  Height  Weight  \\\n",
              "0            1                 A Dijiang   M  24.0   180.0    80.0   \n",
              "1            2                  A Lamusi   M  23.0   170.0    60.0   \n",
              "2            3       Gunnar Nielsen Aaby   M  24.0     NaN     NaN   \n",
              "3            4      Edgar Lindenau Aabye   M  34.0     NaN     NaN   \n",
              "4            5  Christine Jacoba Aaftink   F  21.0   185.0    82.0   \n",
              "...        ...                       ...  ..   ...     ...     ...   \n",
              "271111  135569                Andrzej ya   M  29.0   179.0    89.0   \n",
              "271112  135570                  Piotr ya   M  27.0   176.0    59.0   \n",
              "271113  135570                  Piotr ya   M  27.0   176.0    59.0   \n",
              "271114  135571        Tomasz Ireneusz ya   M  30.0   185.0    96.0   \n",
              "271115  135571        Tomasz Ireneusz ya   M  34.0   185.0    96.0   \n",
              "\n",
              "                  Team  NOC        Games  Year  Season            City  \\\n",
              "0                China  CHN  1992 Summer  1992  Summer       Barcelona   \n",
              "1                China  CHN  2012 Summer  2012  Summer          London   \n",
              "2              Denmark  DEN  1920 Summer  1920  Summer       Antwerpen   \n",
              "3       Denmark/Sweden  DEN  1900 Summer  1900  Summer           Paris   \n",
              "4          Netherlands  NED  1988 Winter  1988  Winter         Calgary   \n",
              "...                ...  ...          ...   ...     ...             ...   \n",
              "271111        Poland-1  POL  1976 Winter  1976  Winter       Innsbruck   \n",
              "271112          Poland  POL  2014 Winter  2014  Winter           Sochi   \n",
              "271113          Poland  POL  2014 Winter  2014  Winter           Sochi   \n",
              "271114          Poland  POL  1998 Winter  1998  Winter          Nagano   \n",
              "271115          Poland  POL  2002 Winter  2002  Winter  Salt Lake City   \n",
              "\n",
              "                Sport                                     Event Medal  \n",
              "0          Basketball               Basketball Men's Basketball   NaN  \n",
              "1                Judo              Judo Men's Extra-Lightweight   NaN  \n",
              "2            Football                   Football Men's Football   NaN  \n",
              "3          Tug-Of-War               Tug-Of-War Men's Tug-Of-War  Gold  \n",
              "4       Speed Skating          Speed Skating Women's 500 metres   NaN  \n",
              "...               ...                                       ...   ...  \n",
              "271111           Luge                Luge Mixed (Men)'s Doubles   NaN  \n",
              "271112    Ski Jumping  Ski Jumping Men's Large Hill, Individual   NaN  \n",
              "271113    Ski Jumping        Ski Jumping Men's Large Hill, Team   NaN  \n",
              "271114      Bobsleigh                      Bobsleigh Men's Four   NaN  \n",
              "271115      Bobsleigh                      Bobsleigh Men's Four   NaN  \n",
              "\n",
              "[271116 rows x 15 columns]"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Read in the olympics_athletes.csv file\n",
        "df = pd.read_csv(\"olympic_athletes.csv\")\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "27e669f0",
      "metadata": {
        "id": "27e669f0"
      },
      "source": [
        "# Data Exploration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c4b5e438",
      "metadata": {
        "id": "c4b5e438",
        "outputId": "7b4e3bad-1f8e-47bb-a4d9-03699fee579f"
      },
      "outputs": [
        {
          "data": {
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\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",
              "    <tr>\n",
              "      <th>5</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 1,000 metres</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5</td>\n",
              "      <td>Christine Jacoba Aaftink</td>\n",
              "      <td>F</td>\n",
              "      <td>25.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td>NED</td>\n",
              "      <td>1992 Winter</td>\n",
              "      <td>1992</td>\n",
              "      <td>Winter</td>\n",
              "      <td>Albertville</td>\n",
              "      <td>Speed Skating</td>\n",
              "      <td>Speed Skating Women's 500 metres</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5</td>\n",
              "      <td>Christine Jacoba Aaftink</td>\n",
              "      <td>F</td>\n",
              "      <td>25.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td>NED</td>\n",
              "      <td>1992 Winter</td>\n",
              "      <td>1992</td>\n",
              "      <td>Winter</td>\n",
              "      <td>Albertville</td>\n",
              "      <td>Speed Skating</td>\n",
              "      <td>Speed Skating Women's 1,000 metres</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>5</td>\n",
              "      <td>Christine Jacoba Aaftink</td>\n",
              "      <td>F</td>\n",
              "      <td>27.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td>NED</td>\n",
              "      <td>1994 Winter</td>\n",
              "      <td>1994</td>\n",
              "      <td>Winter</td>\n",
              "      <td>Lillehammer</td>\n",
              "      <td>Speed Skating</td>\n",
              "      <td>Speed Skating Women's 500 metres</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>5</td>\n",
              "      <td>Christine Jacoba Aaftink</td>\n",
              "      <td>F</td>\n",
              "      <td>27.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td>NED</td>\n",
              "      <td>1994 Winter</td>\n",
              "      <td>1994</td>\n",
              "      <td>Winter</td>\n",
              "      <td>Lillehammer</td>\n",
              "      <td>Speed Skating</td>\n",
              "      <td>Speed Skating Women's 1,000 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",
              "5   5  Christine Jacoba Aaftink   F  21.0   185.0    82.0     Netherlands   \n",
              "6   5  Christine Jacoba Aaftink   F  25.0   185.0    82.0     Netherlands   \n",
              "7   5  Christine Jacoba Aaftink   F  25.0   185.0    82.0     Netherlands   \n",
              "8   5  Christine Jacoba Aaftink   F  27.0   185.0    82.0     Netherlands   \n",
              "9   5  Christine Jacoba Aaftink   F  27.0   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",
              "5  NED  1988 Winter  1988  Winter      Calgary  Speed Skating   \n",
              "6  NED  1992 Winter  1992  Winter  Albertville  Speed Skating   \n",
              "7  NED  1992 Winter  1992  Winter  Albertville  Speed Skating   \n",
              "8  NED  1994 Winter  1994  Winter  Lillehammer  Speed Skating   \n",
              "9  NED  1994 Winter  1994  Winter  Lillehammer  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  \n",
              "5  Speed Skating Women's 1,000 metres   NaN  \n",
              "6    Speed Skating Women's 500 metres   NaN  \n",
              "7  Speed Skating Women's 1,000 metres   NaN  \n",
              "8    Speed Skating Women's 500 metres   NaN  \n",
              "9  Speed Skating Women's 1,000 metres   NaN  "
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2536d0f0",
      "metadata": {
        "id": "2536d0f0",
        "outputId": "db2ad275-c4c4-47fe-8296-86caefbbcd57"
      },
      "outputs": [
        {
          "data": {
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              "  <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>271106</th>\n",
              "      <td>135565</td>\n",
              "      <td>Fernando scar Zylberberg</td>\n",
              "      <td>M</td>\n",
              "      <td>27.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>ARG</td>\n",
              "      <td>2004 Summer</td>\n",
              "      <td>2004</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Athina</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Hockey Men's Hockey</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271107</th>\n",
              "      <td>135566</td>\n",
              "      <td>James Francis \"Jim\" Zylker</td>\n",
              "      <td>M</td>\n",
              "      <td>21.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA</td>\n",
              "      <td>1972 Summer</td>\n",
              "      <td>1972</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Munich</td>\n",
              "      <td>Football</td>\n",
              "      <td>Football Men's Football</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271108</th>\n",
              "      <td>135567</td>\n",
              "      <td>Aleksandr Viktorovich Zyuzin</td>\n",
              "      <td>M</td>\n",
              "      <td>24.0</td>\n",
              "      <td>183.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>Russia</td>\n",
              "      <td>RUS</td>\n",
              "      <td>2000 Summer</td>\n",
              "      <td>2000</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Sydney</td>\n",
              "      <td>Rowing</td>\n",
              "      <td>Rowing Men's Lightweight Coxless Fours</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271109</th>\n",
              "      <td>135567</td>\n",
              "      <td>Aleksandr Viktorovich Zyuzin</td>\n",
              "      <td>M</td>\n",
              "      <td>28.0</td>\n",
              "      <td>183.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>Russia</td>\n",
              "      <td>RUS</td>\n",
              "      <td>2004 Summer</td>\n",
              "      <td>2004</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Athina</td>\n",
              "      <td>Rowing</td>\n",
              "      <td>Rowing Men's Lightweight Coxless Fours</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271110</th>\n",
              "      <td>135568</td>\n",
              "      <td>Olga Igorevna Zyuzkova</td>\n",
              "      <td>F</td>\n",
              "      <td>33.0</td>\n",
              "      <td>171.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>Belarus</td>\n",
              "      <td>BLR</td>\n",
              "      <td>2016 Summer</td>\n",
              "      <td>2016</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Basketball Women's Basketball</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271111</th>\n",
              "      <td>135569</td>\n",
              "      <td>Andrzej ya</td>\n",
              "      <td>M</td>\n",
              "      <td>29.0</td>\n",
              "      <td>179.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>Poland-1</td>\n",
              "      <td>POL</td>\n",
              "      <td>1976 Winter</td>\n",
              "      <td>1976</td>\n",
              "      <td>Winter</td>\n",
              "      <td>Innsbruck</td>\n",
              "      <td>Luge</td>\n",
              "      <td>Luge Mixed (Men)'s Doubles</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271112</th>\n",
              "      <td>135570</td>\n",
              "      <td>Piotr ya</td>\n",
              "      <td>M</td>\n",
              "      <td>27.0</td>\n",
              "      <td>176.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>Poland</td>\n",
              "      <td>POL</td>\n",
              "      <td>2014 Winter</td>\n",
              "      <td>2014</td>\n",
              "      <td>Winter</td>\n",
              "      <td>Sochi</td>\n",
              "      <td>Ski Jumping</td>\n",
              "      <td>Ski Jumping Men's Large Hill, Individual</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271113</th>\n",
              "      <td>135570</td>\n",
              "      <td>Piotr ya</td>\n",
              "      <td>M</td>\n",
              "      <td>27.0</td>\n",
              "      <td>176.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>Poland</td>\n",
              "      <td>POL</td>\n",
              "      <td>2014 Winter</td>\n",
              "      <td>2014</td>\n",
              "      <td>Winter</td>\n",
              "      <td>Sochi</td>\n",
              "      <td>Ski Jumping</td>\n",
              "      <td>Ski Jumping Men's Large Hill, Team</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271114</th>\n",
              "      <td>135571</td>\n",
              "      <td>Tomasz Ireneusz ya</td>\n",
              "      <td>M</td>\n",
              "      <td>30.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>Poland</td>\n",
              "      <td>POL</td>\n",
              "      <td>1998 Winter</td>\n",
              "      <td>1998</td>\n",
              "      <td>Winter</td>\n",
              "      <td>Nagano</td>\n",
              "      <td>Bobsleigh</td>\n",
              "      <td>Bobsleigh Men's Four</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271115</th>\n",
              "      <td>135571</td>\n",
              "      <td>Tomasz Ireneusz ya</td>\n",
              "      <td>M</td>\n",
              "      <td>34.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>Poland</td>\n",
              "      <td>POL</td>\n",
              "      <td>2002 Winter</td>\n",
              "      <td>2002</td>\n",
              "      <td>Winter</td>\n",
              "      <td>Salt Lake City</td>\n",
              "      <td>Bobsleigh</td>\n",
              "      <td>Bobsleigh Men's Four</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            ID                          Name Sex   Age  Height  Weight  \\\n",
              "271106  135565      Fernando scar Zylberberg   M  27.0   168.0    76.0   \n",
              "271107  135566    James Francis \"Jim\" Zylker   M  21.0   175.0    75.0   \n",
              "271108  135567  Aleksandr Viktorovich Zyuzin   M  24.0   183.0    72.0   \n",
              "271109  135567  Aleksandr Viktorovich Zyuzin   M  28.0   183.0    72.0   \n",
              "271110  135568        Olga Igorevna Zyuzkova   F  33.0   171.0    69.0   \n",
              "271111  135569                    Andrzej ya   M  29.0   179.0    89.0   \n",
              "271112  135570                      Piotr ya   M  27.0   176.0    59.0   \n",
              "271113  135570                      Piotr ya   M  27.0   176.0    59.0   \n",
              "271114  135571            Tomasz Ireneusz ya   M  30.0   185.0    96.0   \n",
              "271115  135571            Tomasz Ireneusz ya   M  34.0   185.0    96.0   \n",
              "\n",
              "                 Team  NOC        Games  Year  Season            City  \\\n",
              "271106      Argentina  ARG  2004 Summer  2004  Summer          Athina   \n",
              "271107  United States  USA  1972 Summer  1972  Summer          Munich   \n",
              "271108         Russia  RUS  2000 Summer  2000  Summer          Sydney   \n",
              "271109         Russia  RUS  2004 Summer  2004  Summer          Athina   \n",
              "271110        Belarus  BLR  2016 Summer  2016  Summer  Rio de Janeiro   \n",
              "271111       Poland-1  POL  1976 Winter  1976  Winter       Innsbruck   \n",
              "271112         Poland  POL  2014 Winter  2014  Winter           Sochi   \n",
              "271113         Poland  POL  2014 Winter  2014  Winter           Sochi   \n",
              "271114         Poland  POL  1998 Winter  1998  Winter          Nagano   \n",
              "271115         Poland  POL  2002 Winter  2002  Winter  Salt Lake City   \n",
              "\n",
              "              Sport                                     Event Medal  \n",
              "271106       Hockey                       Hockey Men's Hockey   NaN  \n",
              "271107     Football                   Football Men's Football   NaN  \n",
              "271108       Rowing    Rowing Men's Lightweight Coxless Fours   NaN  \n",
              "271109       Rowing    Rowing Men's Lightweight Coxless Fours   NaN  \n",
              "271110   Basketball             Basketball Women's Basketball   NaN  \n",
              "271111         Luge                Luge Mixed (Men)'s Doubles   NaN  \n",
              "271112  Ski Jumping  Ski Jumping Men's Large Hill, Individual   NaN  \n",
              "271113  Ski Jumping        Ski Jumping Men's Large Hill, Team   NaN  \n",
              "271114    Bobsleigh                      Bobsleigh Men's Four   NaN  \n",
              "271115    Bobsleigh                      Bobsleigh Men's Four   NaN  "
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.tail(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7fbd1af8",
      "metadata": {
        "id": "7fbd1af8",
        "outputId": "dc46507e-ac96-4b75-c2e3-b1f167d29c24"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 271116 entries, 0 to 271115\n",
            "Data columns (total 15 columns):\n",
            " #   Column  Non-Null Count   Dtype  \n",
            "---  ------  --------------   -----  \n",
            " 0   ID      271116 non-null  int64  \n",
            " 1   Name    271116 non-null  object \n",
            " 2   Sex     271116 non-null  object \n",
            " 3   Age     261642 non-null  float64\n",
            " 4   Height  210945 non-null  float64\n",
            " 5   Weight  208241 non-null  float64\n",
            " 6   Team    271116 non-null  object \n",
            " 7   NOC     271116 non-null  object \n",
            " 8   Games   271116 non-null  object \n",
            " 9   Year    271116 non-null  int64  \n",
            " 10  Season  271116 non-null  object \n",
            " 11  City    271116 non-null  object \n",
            " 12  Sport   271116 non-null  object \n",
            " 13  Event   271116 non-null  object \n",
            " 14  Medal   39783 non-null   object \n",
            "dtypes: float64(3), int64(2), object(10)\n",
            "memory usage: 31.0+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bd913c92",
      "metadata": {
        "id": "bd913c92",
        "outputId": "e4875510-e1e3-4782-88df-2d293bdc4c73"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(271116, 15)"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a615bf95",
      "metadata": {
        "id": "a615bf95",
        "outputId": "3c4fdcb0-b141-4b3a-c801-b8ac424f0775"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "271116"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "54fd27cb",
      "metadata": {
        "id": "54fd27cb",
        "outputId": "97470746-50d2-4ba2-e9bf-1334b3ab591f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "ID          int64\n",
              "Name       object\n",
              "Sex        object\n",
              "Age       float64\n",
              "Height    float64\n",
              "Weight    float64\n",
              "Team       object\n",
              "NOC        object\n",
              "Games      object\n",
              "Year        int64\n",
              "Season     object\n",
              "City       object\n",
              "Sport      object\n",
              "Event      object\n",
              "Medal      object\n",
              "dtype: object"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6360dd26",
      "metadata": {
        "id": "6360dd26",
        "outputId": "ebe56b58-8236-45f7-8d0a-aebaa6cb0a3a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['ID', 'Name', 'Sex', 'Age', 'Height', 'Weight', 'Team', 'NOC', 'Games',\n",
              "       'Year', 'Season', 'City', 'Sport', 'Event', 'Medal'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3931c3f9",
      "metadata": {
        "id": "3931c3f9"
      },
      "source": [
        "# Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d4e3c4ed",
      "metadata": {
        "id": "d4e3c4ed",
        "outputId": "3b2083bd-bdbb-47f8-8d07-cdc475594235"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <td>271116.0</td>\n",
              "      <td>68248.954396</td>\n",
              "      <td>39022.286345</td>\n",
              "      <td>1.0</td>\n",
              "      <td>34643.0</td>\n",
              "      <td>68205.0</td>\n",
              "      <td>102097.25</td>\n",
              "      <td>135571.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>261642.0</td>\n",
              "      <td>25.556898</td>\n",
              "      <td>6.393561</td>\n",
              "      <td>10.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>28.00</td>\n",
              "      <td>97.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Height</th>\n",
              "      <td>210945.0</td>\n",
              "      <td>175.338970</td>\n",
              "      <td>10.518462</td>\n",
              "      <td>127.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>183.00</td>\n",
              "      <td>226.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weight</th>\n",
              "      <td>208241.0</td>\n",
              "      <td>70.702393</td>\n",
              "      <td>14.348020</td>\n",
              "      <td>25.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>79.00</td>\n",
              "      <td>214.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Year</th>\n",
              "      <td>271116.0</td>\n",
              "      <td>1978.378480</td>\n",
              "      <td>29.877632</td>\n",
              "      <td>1896.0</td>\n",
              "      <td>1960.0</td>\n",
              "      <td>1988.0</td>\n",
              "      <td>2002.00</td>\n",
              "      <td>2016.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           count          mean           std     min      25%      50%  \\\n",
              "ID      271116.0  68248.954396  39022.286345     1.0  34643.0  68205.0   \n",
              "Age     261642.0     25.556898      6.393561    10.0     21.0     24.0   \n",
              "Height  210945.0    175.338970     10.518462   127.0    168.0    175.0   \n",
              "Weight  208241.0     70.702393     14.348020    25.0     60.0     70.0   \n",
              "Year    271116.0   1978.378480     29.877632  1896.0   1960.0   1988.0   \n",
              "\n",
              "              75%       max  \n",
              "ID      102097.25  135571.0  \n",
              "Age         28.00      97.0  \n",
              "Height     183.00     226.0  \n",
              "Weight      79.00     214.0  \n",
              "Year      2002.00    2016.0  "
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe().T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "de02558c",
      "metadata": {
        "id": "de02558c",
        "outputId": "519b9ecf-d05c-4b52-9da1-4dc2e2498ae6"
      },
      "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": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "56b885cd",
      "metadata": {
        "id": "56b885cd"
      },
      "source": [
        "# Question 1\n",
        "The following violin plot shows the **height distributions of athletes** in the **Gymnastics, Cycling and Basketball** sports at the Olympic games between **2000 to 2016 (inclusive)**. The **line** inside each violin shows the location of the **median value.**\n",
        "\n",
        "Recreate violin plot using Matplotlib. The three violins should be **purple, green and blue** in colour, including their filled colour and the colour of the median lines (Hint: consider the return values of the violin plot method). Include the x-axis and y-axis labels, as well as the plot title.\n",
        "\n",
        "Since some athletes may compete in multiple events or multiple Olympics, make sure to include only one instance of an athlete **(no duplicates).**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b83238e9",
      "metadata": {
        "id": "b83238e9",
        "outputId": "cee3b0f4-b9d6-4bb8-f526-3e4f728d6309"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0         180.0\n",
              "1         170.0\n",
              "2           NaN\n",
              "3           NaN\n",
              "4         185.0\n",
              "          ...  \n",
              "271111    179.0\n",
              "271112    176.0\n",
              "271113    176.0\n",
              "271114    185.0\n",
              "271115    185.0\n",
              "Name: Height, Length: 271116, dtype: float64"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.Height"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7ef14ee9",
      "metadata": {
        "id": "7ef14ee9",
        "outputId": "e7cd0742-d33a-49d9-a344-dd8de54cc5f9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([180., 170.,  nan, 185., 188., 183., 168., 186., 182., 172., 159.,\n",
              "       171., 184., 175., 189., 176., 163., 187., 169., 174., 190., 194.,\n",
              "       177., 167., 164., 198., 165., 156., 179., 178., 166., 181., 155.,\n",
              "       160., 173., 192., 195., 191., 203., 158., 154., 153., 152., 193.,\n",
              "       162., 197., 157., 207., 147., 150., 202., 161., 210., 205., 200.,\n",
              "       148., 208., 140., 213., 204., 151., 196., 146., 201., 132., 149.,\n",
              "       199., 211., 215., 139., 206., 135., 142., 217., 209., 145., 221.,\n",
              "       144., 214., 143., 218., 127., 223., 216., 141., 212., 131., 136.,\n",
              "       137., 138., 220., 130., 219., 133., 128., 226.])"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.Height.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0d7ad5f3",
      "metadata": {
        "id": "0d7ad5f3",
        "outputId": "513f62ea-541e-4d8d-a11b-d0c3e4e2acd4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Basketball', 'Judo', 'Football', 'Tug-Of-War', 'Speed Skating',\n",
              "       'Cross Country Skiing', 'Athletics', 'Ice Hockey', 'Swimming',\n",
              "       'Badminton', 'Sailing', 'Biathlon', 'Gymnastics',\n",
              "       'Art Competitions', 'Alpine Skiing', 'Handball', 'Weightlifting',\n",
              "       'Wrestling', 'Luge', 'Water Polo', 'Hockey', 'Rowing', 'Bobsleigh',\n",
              "       'Fencing', 'Equestrianism', 'Shooting', 'Boxing', 'Taekwondo',\n",
              "       'Cycling', 'Diving', 'Canoeing', 'Tennis', 'Modern Pentathlon',\n",
              "       'Figure Skating', 'Golf', 'Softball', 'Archery', 'Volleyball',\n",
              "       'Synchronized Swimming', 'Table Tennis', 'Nordic Combined',\n",
              "       'Baseball', 'Rhythmic Gymnastics', 'Freestyle Skiing',\n",
              "       'Rugby Sevens', 'Trampolining', 'Beach Volleyball', 'Triathlon',\n",
              "       'Ski Jumping', 'Curling', 'Snowboarding', 'Rugby',\n",
              "       'Short Track Speed Skating', 'Skeleton', 'Lacrosse', 'Polo',\n",
              "       'Cricket', 'Racquets', 'Motorboating', 'Military Ski Patrol',\n",
              "       'Croquet', 'Jeu De Paume', 'Roque', 'Alpinism', 'Basque Pelota',\n",
              "       'Aeronautics'], dtype=object)"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.Sport.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c5c3632d",
      "metadata": {
        "id": "c5c3632d",
        "outputId": "5227e28a-7eb0-4a09-9135-20d78d3b236e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([1992, 2012, 1920, 1900, 1988, 1994, 1932, 2002, 1952, 1980, 2000,\n",
              "       1996, 1912, 1924, 2014, 1948, 1998, 2006, 2008, 2016, 2004, 1960,\n",
              "       1964, 1984, 1968, 1972, 1936, 1956, 1928, 1976, 2010, 1906, 1904,\n",
              "       1908, 1896])"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[\"Year\"].unique()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "87e62431",
      "metadata": {
        "id": "87e62431"
      },
      "source": [
        "### Sports we want to include"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "649d58ee",
      "metadata": {
        "id": "649d58ee"
      },
      "outputs": [],
      "source": [
        "sports = ['Gymnastics', 'Cycling', 'Basketball']"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4c053fa8",
      "metadata": {
        "id": "4c053fa8"
      },
      "source": [
        "### Filter the specified sports and time frame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9e9b8d5e",
      "metadata": {
        "id": "9e9b8d5e"
      },
      "outputs": [],
      "source": [
        "#df1 = df.loc[(df['Sport'].isin(sports)) & (df['Year'] >= 2000) & (df['Year'] <= 2016)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bd1cf590",
      "metadata": {
        "id": "bd1cf590"
      },
      "outputs": [],
      "source": [
        "df1 = df[(df['Sport'].isin(sports)) & (df['Year'].between(2000, 2016))]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5f932330",
      "metadata": {
        "id": "5f932330",
        "outputId": "428a9405-c342-4bd5-83b6-c97b5caaf1e6"
      },
      "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>139</th>\n",
              "      <td>51</td>\n",
              "      <td>Nstor Abad Sanjun</td>\n",
              "      <td>M</td>\n",
              "      <td>23.0</td>\n",
              "      <td>167.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>Spain</td>\n",
              "      <td>ESP</td>\n",
              "      <td>2016 Summer</td>\n",
              "      <td>2016</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>Gymnastics</td>\n",
              "      <td>Gymnastics Men's Individual All-Around</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>51</td>\n",
              "      <td>Nstor Abad Sanjun</td>\n",
              "      <td>M</td>\n",
              "      <td>23.0</td>\n",
              "      <td>167.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>Spain</td>\n",
              "      <td>ESP</td>\n",
              "      <td>2016 Summer</td>\n",
              "      <td>2016</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>Gymnastics</td>\n",
              "      <td>Gymnastics Men's Floor Exercise</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>51</td>\n",
              "      <td>Nstor Abad Sanjun</td>\n",
              "      <td>M</td>\n",
              "      <td>23.0</td>\n",
              "      <td>167.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>Spain</td>\n",
              "      <td>ESP</td>\n",
              "      <td>2016 Summer</td>\n",
              "      <td>2016</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>Gymnastics</td>\n",
              "      <td>Gymnastics Men's Parallel Bars</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>51</td>\n",
              "      <td>Nstor Abad Sanjun</td>\n",
              "      <td>M</td>\n",
              "      <td>23.0</td>\n",
              "      <td>167.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>Spain</td>\n",
              "      <td>ESP</td>\n",
              "      <td>2016 Summer</td>\n",
              "      <td>2016</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>Gymnastics</td>\n",
              "      <td>Gymnastics Men's Horizontal Bar</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>51</td>\n",
              "      <td>Nstor Abad Sanjun</td>\n",
              "      <td>M</td>\n",
              "      <td>23.0</td>\n",
              "      <td>167.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>Spain</td>\n",
              "      <td>ESP</td>\n",
              "      <td>2016 Summer</td>\n",
              "      <td>2016</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>Gymnastics</td>\n",
              "      <td>Gymnastics Men's Rings</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>270744</th>\n",
              "      <td>135402</td>\n",
              "      <td>Mindaugas ukauskas</td>\n",
              "      <td>M</td>\n",
              "      <td>28.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>Lithuania</td>\n",
              "      <td>LTU</td>\n",
              "      <td>2004 Summer</td>\n",
              "      <td>2004</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Athina</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Basketball Men's Basketball</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>270766</th>\n",
              "      <td>135411</td>\n",
              "      <td>Alex Zlle</td>\n",
              "      <td>M</td>\n",
              "      <td>32.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>Switzerland</td>\n",
              "      <td>SUI</td>\n",
              "      <td>2000 Summer</td>\n",
              "      <td>2000</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Sydney</td>\n",
              "      <td>Cycling</td>\n",
              "      <td>Cycling Men's Road Race, Individual</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>270767</th>\n",
              "      <td>135411</td>\n",
              "      <td>Alex Zlle</td>\n",
              "      <td>M</td>\n",
              "      <td>32.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>Switzerland</td>\n",
              "      <td>SUI</td>\n",
              "      <td>2000 Summer</td>\n",
              "      <td>2000</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Sydney</td>\n",
              "      <td>Cycling</td>\n",
              "      <td>Cycling Men's Individual Time Trial</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271026</th>\n",
              "      <td>135530</td>\n",
              "      <td>Philip Zwiener</td>\n",
              "      <td>M</td>\n",
              "      <td>23.0</td>\n",
              "      <td>202.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>Germany</td>\n",
              "      <td>GER</td>\n",
              "      <td>2008 Summer</td>\n",
              "      <td>2008</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Beijing</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Basketball Men's Basketball</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271110</th>\n",
              "      <td>135568</td>\n",
              "      <td>Olga Igorevna Zyuzkova</td>\n",
              "      <td>F</td>\n",
              "      <td>33.0</td>\n",
              "      <td>171.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>Belarus</td>\n",
              "      <td>BLR</td>\n",
              "      <td>2016 Summer</td>\n",
              "      <td>2016</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Basketball Women's Basketball</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9620 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            ID                    Name Sex   Age  Height  Weight         Team  \\\n",
              "139         51       Nstor Abad Sanjun   M  23.0   167.0    64.0        Spain   \n",
              "140         51       Nstor Abad Sanjun   M  23.0   167.0    64.0        Spain   \n",
              "141         51       Nstor Abad Sanjun   M  23.0   167.0    64.0        Spain   \n",
              "142         51       Nstor Abad Sanjun   M  23.0   167.0    64.0        Spain   \n",
              "143         51       Nstor Abad Sanjun   M  23.0   167.0    64.0        Spain   \n",
              "...        ...                     ...  ..   ...     ...     ...          ...   \n",
              "270744  135402      Mindaugas ukauskas   M  28.0   201.0   100.0    Lithuania   \n",
              "270766  135411               Alex Zlle   M  32.0   185.0    72.0  Switzerland   \n",
              "270767  135411               Alex Zlle   M  32.0   185.0    72.0  Switzerland   \n",
              "271026  135530          Philip Zwiener   M  23.0   202.0    98.0      Germany   \n",
              "271110  135568  Olga Igorevna Zyuzkova   F  33.0   171.0    69.0      Belarus   \n",
              "\n",
              "        NOC        Games  Year  Season            City       Sport  \\\n",
              "139     ESP  2016 Summer  2016  Summer  Rio de Janeiro  Gymnastics   \n",
              "140     ESP  2016 Summer  2016  Summer  Rio de Janeiro  Gymnastics   \n",
              "141     ESP  2016 Summer  2016  Summer  Rio de Janeiro  Gymnastics   \n",
              "142     ESP  2016 Summer  2016  Summer  Rio de Janeiro  Gymnastics   \n",
              "143     ESP  2016 Summer  2016  Summer  Rio de Janeiro  Gymnastics   \n",
              "...     ...          ...   ...     ...             ...         ...   \n",
              "270744  LTU  2004 Summer  2004  Summer          Athina  Basketball   \n",
              "270766  SUI  2000 Summer  2000  Summer          Sydney     Cycling   \n",
              "270767  SUI  2000 Summer  2000  Summer          Sydney     Cycling   \n",
              "271026  GER  2008 Summer  2008  Summer         Beijing  Basketball   \n",
              "271110  BLR  2016 Summer  2016  Summer  Rio de Janeiro  Basketball   \n",
              "\n",
              "                                         Event Medal  \n",
              "139     Gymnastics Men's Individual All-Around   NaN  \n",
              "140            Gymnastics Men's Floor Exercise   NaN  \n",
              "141             Gymnastics Men's Parallel Bars   NaN  \n",
              "142            Gymnastics Men's Horizontal Bar   NaN  \n",
              "143                     Gymnastics Men's Rings   NaN  \n",
              "...                                        ...   ...  \n",
              "270744             Basketball Men's Basketball   NaN  \n",
              "270766     Cycling Men's Road Race, Individual   NaN  \n",
              "270767     Cycling Men's Individual Time Trial   NaN  \n",
              "271026             Basketball Men's Basketball   NaN  \n",
              "271110           Basketball Women's Basketball   NaN  \n",
              "\n",
              "[9620 rows x 15 columns]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "301078e6",
      "metadata": {
        "id": "301078e6"
      },
      "source": [
        "### Since some athletes may compete in multiple events or multiple Olympics, make sure to include only one instance of an athlete (no duplicates). Drop duplicates based on the athlete's ID"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4bcaaefc",
      "metadata": {
        "id": "4bcaaefc",
        "outputId": "3a496f39-758d-470c-8584-50f4e9d8d629"
      },
      "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>139</th>\n",
              "      <td>51</td>\n",
              "      <td>Nstor Abad Sanjun</td>\n",
              "      <td>M</td>\n",
              "      <td>23.0</td>\n",
              "      <td>167.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>Spain</td>\n",
              "      <td>ESP</td>\n",
              "      <td>2016 Summer</td>\n",
              "      <td>2016</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>Gymnastics</td>\n",
              "      <td>Gymnastics Men's Individual All-Around</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>69</td>\n",
              "      <td>Tamara Abalde Daz</td>\n",
              "      <td>F</td>\n",
              "      <td>19.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>Spain</td>\n",
              "      <td>ESP</td>\n",
              "      <td>2008 Summer</td>\n",
              "      <td>2008</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Beijing</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Basketball Women's Basketball</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>90</td>\n",
              "      <td>Tamila Rashidovna Abasova</td>\n",
              "      <td>F</td>\n",
              "      <td>21.0</td>\n",
              "      <td>163.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>Russia</td>\n",
              "      <td>RUS</td>\n",
              "      <td>2004 Summer</td>\n",
              "      <td>2004</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Athina</td>\n",
              "      <td>Cycling</td>\n",
              "      <td>Cycling Women's Sprint</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>118</td>\n",
              "      <td>Mahmoud Abbas</td>\n",
              "      <td>M</td>\n",
              "      <td>22.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>Egypt</td>\n",
              "      <td>EGY</td>\n",
              "      <td>2000 Summer</td>\n",
              "      <td>2000</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Sydney</td>\n",
              "      <td>Cycling</td>\n",
              "      <td>Cycling Men's Road Race, Individual</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>264</th>\n",
              "      <td>136</td>\n",
              "      <td>Alessandro Abbio</td>\n",
              "      <td>M</td>\n",
              "      <td>29.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>Italy</td>\n",
              "      <td>ITA</td>\n",
              "      <td>2000 Summer</td>\n",
              "      <td>2000</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Sydney</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Basketball Men's Basketball</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>270741</th>\n",
              "      <td>135401</td>\n",
              "      <td>Eurelijus ukauskas</td>\n",
              "      <td>M</td>\n",
              "      <td>27.0</td>\n",
              "      <td>218.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>Lithuania</td>\n",
              "      <td>LTU</td>\n",
              "      <td>2000 Summer</td>\n",
              "      <td>2000</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Sydney</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Basketball Men's Basketball</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>270744</th>\n",
              "      <td>135402</td>\n",
              "      <td>Mindaugas ukauskas</td>\n",
              "      <td>M</td>\n",
              "      <td>28.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>Lithuania</td>\n",
              "      <td>LTU</td>\n",
              "      <td>2004 Summer</td>\n",
              "      <td>2004</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Athina</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Basketball Men's Basketball</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>270766</th>\n",
              "      <td>135411</td>\n",
              "      <td>Alex Zlle</td>\n",
              "      <td>M</td>\n",
              "      <td>32.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>Switzerland</td>\n",
              "      <td>SUI</td>\n",
              "      <td>2000 Summer</td>\n",
              "      <td>2000</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Sydney</td>\n",
              "      <td>Cycling</td>\n",
              "      <td>Cycling Men's Road Race, Individual</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271026</th>\n",
              "      <td>135530</td>\n",
              "      <td>Philip Zwiener</td>\n",
              "      <td>M</td>\n",
              "      <td>23.0</td>\n",
              "      <td>202.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>Germany</td>\n",
              "      <td>GER</td>\n",
              "      <td>2008 Summer</td>\n",
              "      <td>2008</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Beijing</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Basketball Men's Basketball</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271110</th>\n",
              "      <td>135568</td>\n",
              "      <td>Olga Igorevna Zyuzkova</td>\n",
              "      <td>F</td>\n",
              "      <td>33.0</td>\n",
              "      <td>171.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>Belarus</td>\n",
              "      <td>BLR</td>\n",
              "      <td>2016 Summer</td>\n",
              "      <td>2016</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Basketball Women's Basketball</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3521 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            ID                       Name Sex   Age  Height  Weight  \\\n",
              "139         51          Nstor Abad Sanjun   M  23.0   167.0    64.0   \n",
              "167         69          Tamara Abalde Daz   F  19.0   185.0    72.0   \n",
              "195         90  Tamila Rashidovna Abasova   F  21.0   163.0    60.0   \n",
              "242        118              Mahmoud Abbas   M  22.0   165.0    65.0   \n",
              "264        136           Alessandro Abbio   M  29.0   195.0    85.0   \n",
              "...        ...                        ...  ..   ...     ...     ...   \n",
              "270741  135401         Eurelijus ukauskas   M  27.0   218.0   115.0   \n",
              "270744  135402         Mindaugas ukauskas   M  28.0   201.0   100.0   \n",
              "270766  135411                  Alex Zlle   M  32.0   185.0    72.0   \n",
              "271026  135530             Philip Zwiener   M  23.0   202.0    98.0   \n",
              "271110  135568     Olga Igorevna Zyuzkova   F  33.0   171.0    69.0   \n",
              "\n",
              "               Team  NOC        Games  Year  Season            City  \\\n",
              "139           Spain  ESP  2016 Summer  2016  Summer  Rio de Janeiro   \n",
              "167           Spain  ESP  2008 Summer  2008  Summer         Beijing   \n",
              "195          Russia  RUS  2004 Summer  2004  Summer          Athina   \n",
              "242           Egypt  EGY  2000 Summer  2000  Summer          Sydney   \n",
              "264           Italy  ITA  2000 Summer  2000  Summer          Sydney   \n",
              "...             ...  ...          ...   ...     ...             ...   \n",
              "270741    Lithuania  LTU  2000 Summer  2000  Summer          Sydney   \n",
              "270744    Lithuania  LTU  2004 Summer  2004  Summer          Athina   \n",
              "270766  Switzerland  SUI  2000 Summer  2000  Summer          Sydney   \n",
              "271026      Germany  GER  2008 Summer  2008  Summer         Beijing   \n",
              "271110      Belarus  BLR  2016 Summer  2016  Summer  Rio de Janeiro   \n",
              "\n",
              "             Sport                                   Event   Medal  \n",
              "139     Gymnastics  Gymnastics Men's Individual All-Around     NaN  \n",
              "167     Basketball           Basketball Women's Basketball     NaN  \n",
              "195        Cycling                  Cycling Women's Sprint  Silver  \n",
              "242        Cycling     Cycling Men's Road Race, Individual     NaN  \n",
              "264     Basketball             Basketball Men's Basketball     NaN  \n",
              "...            ...                                     ...     ...  \n",
              "270741  Basketball             Basketball Men's Basketball  Bronze  \n",
              "270744  Basketball             Basketball Men's Basketball     NaN  \n",
              "270766     Cycling     Cycling Men's Road Race, Individual     NaN  \n",
              "271026  Basketball             Basketball Men's Basketball     NaN  \n",
              "271110  Basketball           Basketball Women's Basketball     NaN  \n",
              "\n",
              "[3521 rows x 15 columns]"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1 = df1.drop_duplicates(subset=['ID'])\n",
        "df1"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "da16c482",
      "metadata": {
        "id": "da16c482"
      },
      "source": [
        "### Remove rows where the height is missing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "af28eb84",
      "metadata": {
        "id": "af28eb84",
        "outputId": "704fb8d1-2d9c-4575-aed1-4da4c6c12ebe"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>139</th>\n",
              "      <td>51</td>\n",
              "      <td>Nstor Abad Sanjun</td>\n",
              "      <td>M</td>\n",
              "      <td>23.0</td>\n",
              "      <td>167.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>Spain</td>\n",
              "      <td>ESP</td>\n",
              "      <td>2016 Summer</td>\n",
              "      <td>2016</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>Gymnastics</td>\n",
              "      <td>Gymnastics Men's Individual All-Around</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>69</td>\n",
              "      <td>Tamara Abalde Daz</td>\n",
              "      <td>F</td>\n",
              "      <td>19.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>Spain</td>\n",
              "      <td>ESP</td>\n",
              "      <td>2008 Summer</td>\n",
              "      <td>2008</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Beijing</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Basketball Women's Basketball</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>90</td>\n",
              "      <td>Tamila Rashidovna Abasova</td>\n",
              "      <td>F</td>\n",
              "      <td>21.0</td>\n",
              "      <td>163.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>Russia</td>\n",
              "      <td>RUS</td>\n",
              "      <td>2004 Summer</td>\n",
              "      <td>2004</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Athina</td>\n",
              "      <td>Cycling</td>\n",
              "      <td>Cycling Women's Sprint</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>118</td>\n",
              "      <td>Mahmoud Abbas</td>\n",
              "      <td>M</td>\n",
              "      <td>22.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>Egypt</td>\n",
              "      <td>EGY</td>\n",
              "      <td>2000 Summer</td>\n",
              "      <td>2000</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Sydney</td>\n",
              "      <td>Cycling</td>\n",
              "      <td>Cycling Men's Road Race, Individual</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>264</th>\n",
              "      <td>136</td>\n",
              "      <td>Alessandro Abbio</td>\n",
              "      <td>M</td>\n",
              "      <td>29.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>Italy</td>\n",
              "      <td>ITA</td>\n",
              "      <td>2000 Summer</td>\n",
              "      <td>2000</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Sydney</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Basketball Men's Basketball</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>270741</th>\n",
              "      <td>135401</td>\n",
              "      <td>Eurelijus ukauskas</td>\n",
              "      <td>M</td>\n",
              "      <td>27.0</td>\n",
              "      <td>218.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>Lithuania</td>\n",
              "      <td>LTU</td>\n",
              "      <td>2000 Summer</td>\n",
              "      <td>2000</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Sydney</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Basketball Men's Basketball</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>270744</th>\n",
              "      <td>135402</td>\n",
              "      <td>Mindaugas ukauskas</td>\n",
              "      <td>M</td>\n",
              "      <td>28.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>Lithuania</td>\n",
              "      <td>LTU</td>\n",
              "      <td>2004 Summer</td>\n",
              "      <td>2004</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Athina</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Basketball Men's Basketball</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>270766</th>\n",
              "      <td>135411</td>\n",
              "      <td>Alex Zlle</td>\n",
              "      <td>M</td>\n",
              "      <td>32.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>Switzerland</td>\n",
              "      <td>SUI</td>\n",
              "      <td>2000 Summer</td>\n",
              "      <td>2000</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Sydney</td>\n",
              "      <td>Cycling</td>\n",
              "      <td>Cycling Men's Road Race, Individual</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271026</th>\n",
              "      <td>135530</td>\n",
              "      <td>Philip Zwiener</td>\n",
              "      <td>M</td>\n",
              "      <td>23.0</td>\n",
              "      <td>202.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>Germany</td>\n",
              "      <td>GER</td>\n",
              "      <td>2008 Summer</td>\n",
              "      <td>2008</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Beijing</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Basketball Men's Basketball</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271110</th>\n",
              "      <td>135568</td>\n",
              "      <td>Olga Igorevna Zyuzkova</td>\n",
              "      <td>F</td>\n",
              "      <td>33.0</td>\n",
              "      <td>171.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>Belarus</td>\n",
              "      <td>BLR</td>\n",
              "      <td>2016 Summer</td>\n",
              "      <td>2016</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Basketball Women's Basketball</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3498 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            ID                       Name Sex   Age  Height  Weight  \\\n",
              "139         51          Nstor Abad Sanjun   M  23.0   167.0    64.0   \n",
              "167         69          Tamara Abalde Daz   F  19.0   185.0    72.0   \n",
              "195         90  Tamila Rashidovna Abasova   F  21.0   163.0    60.0   \n",
              "242        118              Mahmoud Abbas   M  22.0   165.0    65.0   \n",
              "264        136           Alessandro Abbio   M  29.0   195.0    85.0   \n",
              "...        ...                        ...  ..   ...     ...     ...   \n",
              "270741  135401         Eurelijus ukauskas   M  27.0   218.0   115.0   \n",
              "270744  135402         Mindaugas ukauskas   M  28.0   201.0   100.0   \n",
              "270766  135411                  Alex Zlle   M  32.0   185.0    72.0   \n",
              "271026  135530             Philip Zwiener   M  23.0   202.0    98.0   \n",
              "271110  135568     Olga Igorevna Zyuzkova   F  33.0   171.0    69.0   \n",
              "\n",
              "               Team  NOC        Games  Year  Season            City  \\\n",
              "139           Spain  ESP  2016 Summer  2016  Summer  Rio de Janeiro   \n",
              "167           Spain  ESP  2008 Summer  2008  Summer         Beijing   \n",
              "195          Russia  RUS  2004 Summer  2004  Summer          Athina   \n",
              "242           Egypt  EGY  2000 Summer  2000  Summer          Sydney   \n",
              "264           Italy  ITA  2000 Summer  2000  Summer          Sydney   \n",
              "...             ...  ...          ...   ...     ...             ...   \n",
              "270741    Lithuania  LTU  2000 Summer  2000  Summer          Sydney   \n",
              "270744    Lithuania  LTU  2004 Summer  2004  Summer          Athina   \n",
              "270766  Switzerland  SUI  2000 Summer  2000  Summer          Sydney   \n",
              "271026      Germany  GER  2008 Summer  2008  Summer         Beijing   \n",
              "271110      Belarus  BLR  2016 Summer  2016  Summer  Rio de Janeiro   \n",
              "\n",
              "             Sport                                   Event   Medal  \n",
              "139     Gymnastics  Gymnastics Men's Individual All-Around     NaN  \n",
              "167     Basketball           Basketball Women's Basketball     NaN  \n",
              "195        Cycling                  Cycling Women's Sprint  Silver  \n",
              "242        Cycling     Cycling Men's Road Race, Individual     NaN  \n",
              "264     Basketball             Basketball Men's Basketball     NaN  \n",
              "...            ...                                     ...     ...  \n",
              "270741  Basketball             Basketball Men's Basketball  Bronze  \n",
              "270744  Basketball             Basketball Men's Basketball     NaN  \n",
              "270766     Cycling     Cycling Men's Road Race, Individual     NaN  \n",
              "271026  Basketball             Basketball Men's Basketball     NaN  \n",
              "271110  Basketball           Basketball Women's Basketball     NaN  \n",
              "\n",
              "[3498 rows x 15 columns]"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1 = df1[~df1['Height'].isnull()]\n",
        "df1"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "66fb7578",
      "metadata": {
        "id": "66fb7578"
      },
      "source": [
        "### New DataFrame with specific sports, year between 2000 to 2016 and heigth column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5ff6deec",
      "metadata": {
        "id": "5ff6deec",
        "outputId": "d1beef76-2f7e-4dca-c9fc-ecfaf7ced507"
      },
      "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>Sport</th>\n",
              "      <th>Height</th>\n",
              "      <th>Year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>51</td>\n",
              "      <td>Gymnastics</td>\n",
              "      <td>167.0</td>\n",
              "      <td>2016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>69</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>185.0</td>\n",
              "      <td>2008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>90</td>\n",
              "      <td>Cycling</td>\n",
              "      <td>163.0</td>\n",
              "      <td>2004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>118</td>\n",
              "      <td>Cycling</td>\n",
              "      <td>165.0</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>264</th>\n",
              "      <td>136</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>195.0</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>270741</th>\n",
              "      <td>135401</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>218.0</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>270744</th>\n",
              "      <td>135402</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>201.0</td>\n",
              "      <td>2004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>270766</th>\n",
              "      <td>135411</td>\n",
              "      <td>Cycling</td>\n",
              "      <td>185.0</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271026</th>\n",
              "      <td>135530</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>202.0</td>\n",
              "      <td>2008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271110</th>\n",
              "      <td>135568</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>171.0</td>\n",
              "      <td>2016</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3498 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            ID       Sport  Height  Year\n",
              "139         51  Gymnastics   167.0  2016\n",
              "167         69  Basketball   185.0  2008\n",
              "195         90     Cycling   163.0  2004\n",
              "242        118     Cycling   165.0  2000\n",
              "264        136  Basketball   195.0  2000\n",
              "...        ...         ...     ...   ...\n",
              "270741  135401  Basketball   218.0  2000\n",
              "270744  135402  Basketball   201.0  2004\n",
              "270766  135411     Cycling   185.0  2000\n",
              "271026  135530  Basketball   202.0  2008\n",
              "271110  135568  Basketball   171.0  2016\n",
              "\n",
              "[3498 rows x 4 columns]"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df2 = df1[['ID', 'Sport', 'Height', 'Year']]\n",
        "df2"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a5170aa4",
      "metadata": {
        "id": "a5170aa4"
      },
      "source": [
        "### Now recreate violin plot using Matplotlib which represents the distributions of athletes in the Gymnastics, Cycling and Basketball sports at the Olympic games between 2000 to 2016 (inclusive)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "40b4e99f",
      "metadata": {
        "id": "40b4e99f"
      },
      "outputs": [],
      "source": [
        "#Setup violin colours\n",
        "colors = ['purple', 'green', 'blue']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "19e2f76a",
      "metadata": {
        "id": "19e2f76a",
        "outputId": "d5a52ea8-85ad-41cf-d477-7ae0c87801d5"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#setup figure and axes objects\n",
        "fig, ax = plt.subplots(figsize = (8,6))\n",
        "\n",
        "#Create the violins\n",
        "violin = ax.violinplot([df2[df2['Sport']==sport]['Height'] for sport in sports],\n",
        "                            showmedians = True,\n",
        "                            showmeans = False,\n",
        "                            showextrema = False,\n",
        "                            vert = False,\n",
        "                            points = 50,\n",
        "                            widths = 0.8,\n",
        "                            positions = [1,2,3])\n",
        "\n",
        "bodies = violin['bodies']\n",
        "medians = violin['cmedians']\n",
        "\n",
        "for i, violin in enumerate(bodies):\n",
        "    violin.set_facecolor(colors[i])\n",
        "    violin.set_edgecolor('black')\n",
        "    violin.set_alpha(0.35)\n",
        "    medians.set_color(colors)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0307ddc1",
      "metadata": {
        "id": "0307ddc1"
      },
      "outputs": [],
      "source": [
        "#set the yaxis ticks and labels\n",
        "ax.set_yticks([1,2,3])\n",
        "ax.set_yticklabels(sports)\n",
        "\n",
        "# set the x-axis limits and ticks\n",
        "ax.set_xlim(120, 240)\n",
        "ax.set_xticks([120, 140, 160, 180, 200, 220, 240])\n",
        "xticklabels = [ '', '140', '160', '180', '200', '220', '' ]\n",
        "ax.set_xticklabels(xticklabels, fontsize = 10) # set empty labels for 120 and 240\n",
        "\n",
        "\n",
        "# add labels and title\n",
        "ax.set_xlabel('Height (cm)')\n",
        "ax.set_title('Height Distributions (2000 - 2016 Olympics)')\n",
        "\n",
        "# show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "48be2a80",
      "metadata": {
        "id": "48be2a80",
        "outputId": "db9b497f-2b3b-45c2-ef86-8aa2991193ca"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "fig"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "94a4a4e0",
      "metadata": {
        "id": "94a4a4e0"
      },
      "source": [
        "# Question 2\n",
        "This line plot shows the **average height of male (blue) and female (red)** Olympians over the **history** of the **Summer** Olympics. The **shaded region** represents the **standard deviation about the average**. Recreate the above line plot using Matplotlib. Include the axes labels, plot title, and legend in the lower left corner. The average male heights should be blue and average female heights red. \n",
        "\n",
        "**Each athlete should only be represented once in a given year.** Each line should include shading that has width equal to twice the standard deviation, that is, the **top of the shaded region is the average value plus the standard deviation, and the bottom of the shaded region is the average value minus the standard deviation (hint: try fill_between()). The shading uses a transparency value of 0.1.**"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5377e452",
      "metadata": {
        "id": "5377e452"
      },
      "source": [
        "### Filter only Summer olympics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "755065cb",
      "metadata": {
        "id": "755065cb",
        "outputId": "0729b192-5705-4c91-92c4-1b250cae7ac2"
      },
      "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>26</th>\n",
              "      <td>8</td>\n",
              "      <td>Cornelia \"Cor\" Aalten (-Strannood)</td>\n",
              "      <td>F</td>\n",
              "      <td>18.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td>NED</td>\n",
              "      <td>1932 Summer</td>\n",
              "      <td>1932</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>Athletics</td>\n",
              "      <td>Athletics Women's 100 metres</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>8</td>\n",
              "      <td>Cornelia \"Cor\" Aalten (-Strannood)</td>\n",
              "      <td>F</td>\n",
              "      <td>18.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td>NED</td>\n",
              "      <td>1932 Summer</td>\n",
              "      <td>1932</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>Athletics</td>\n",
              "      <td>Athletics Women's 4 x 100 metres Relay</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>10</td>\n",
              "      <td>Einar Ferdinand \"Einari\" Aalto</td>\n",
              "      <td>M</td>\n",
              "      <td>26.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>Swimming</td>\n",
              "      <td>Swimming Men's 400 metres Freestyle</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>12</td>\n",
              "      <td>Jyri Tapani Aalto</td>\n",
              "      <td>M</td>\n",
              "      <td>31.0</td>\n",
              "      <td>172.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>Finland</td>\n",
              "      <td>FIN</td>\n",
              "      <td>2000 Summer</td>\n",
              "      <td>2000</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Sydney</td>\n",
              "      <td>Badminton</td>\n",
              "      <td>Badminton Men's Singles</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>13</td>\n",
              "      <td>Minna Maarit Aalto</td>\n",
              "      <td>F</td>\n",
              "      <td>30.0</td>\n",
              "      <td>159.0</td>\n",
              "      <td>55.5</td>\n",
              "      <td>Finland</td>\n",
              "      <td>FIN</td>\n",
              "      <td>1996 Summer</td>\n",
              "      <td>1996</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Atlanta</td>\n",
              "      <td>Sailing</td>\n",
              "      <td>Sailing Women's Windsurfer</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>13</td>\n",
              "      <td>Minna Maarit Aalto</td>\n",
              "      <td>F</td>\n",
              "      <td>34.0</td>\n",
              "      <td>159.0</td>\n",
              "      <td>55.5</td>\n",
              "      <td>Finland</td>\n",
              "      <td>FIN</td>\n",
              "      <td>2000 Summer</td>\n",
              "      <td>2000</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Sydney</td>\n",
              "      <td>Sailing</td>\n",
              "      <td>Sailing Women's Windsurfer</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    ID                                Name Sex   Age  Height  Weight  \\\n",
              "0    1                           A Dijiang   M  24.0   180.0    80.0   \n",
              "1    2                            A Lamusi   M  23.0   170.0    60.0   \n",
              "2    3                 Gunnar Nielsen Aaby   M  24.0     NaN     NaN   \n",
              "3    4                Edgar Lindenau Aabye   M  34.0     NaN     NaN   \n",
              "26   8  Cornelia \"Cor\" Aalten (-Strannood)   F  18.0   168.0     NaN   \n",
              "27   8  Cornelia \"Cor\" Aalten (-Strannood)   F  18.0   168.0     NaN   \n",
              "29  10      Einar Ferdinand \"Einari\" Aalto   M  26.0     NaN     NaN   \n",
              "31  12                   Jyri Tapani Aalto   M  31.0   172.0    70.0   \n",
              "32  13                  Minna Maarit Aalto   F  30.0   159.0    55.5   \n",
              "33  13                  Minna Maarit Aalto   F  34.0   159.0    55.5   \n",
              "\n",
              "              Team  NOC        Games  Year  Season         City       Sport  \\\n",
              "0            China  CHN  1992 Summer  1992  Summer    Barcelona  Basketball   \n",
              "1            China  CHN  2012 Summer  2012  Summer       London        Judo   \n",
              "2          Denmark  DEN  1920 Summer  1920  Summer    Antwerpen    Football   \n",
              "3   Denmark/Sweden  DEN  1900 Summer  1900  Summer        Paris  Tug-Of-War   \n",
              "26     Netherlands  NED  1932 Summer  1932  Summer  Los Angeles   Athletics   \n",
              "27     Netherlands  NED  1932 Summer  1932  Summer  Los Angeles   Athletics   \n",
              "29         Finland  FIN  1952 Summer  1952  Summer     Helsinki    Swimming   \n",
              "31         Finland  FIN  2000 Summer  2000  Summer       Sydney   Badminton   \n",
              "32         Finland  FIN  1996 Summer  1996  Summer      Atlanta     Sailing   \n",
              "33         Finland  FIN  2000 Summer  2000  Summer       Sydney     Sailing   \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",
              "26            Athletics Women's 100 metres   NaN  \n",
              "27  Athletics Women's 4 x 100 metres Relay   NaN  \n",
              "29     Swimming Men's 400 metres Freestyle   NaN  \n",
              "31                 Badminton Men's Singles   NaN  \n",
              "32              Sailing Women's Windsurfer   NaN  \n",
              "33              Sailing Women's Windsurfer   NaN  "
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df3 = df[df.Season == \"Summer\"]\n",
        "df3.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2f2bc834",
      "metadata": {
        "id": "2f2bc834"
      },
      "source": [
        "### Drop Duplicates"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a4d119a4",
      "metadata": {
        "id": "a4d119a4",
        "outputId": "d6df7296-b341-415d-aa92-f51bc4d90443"
      },
      "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>26</th>\n",
              "      <td>8</td>\n",
              "      <td>Cornelia \"Cor\" Aalten (-Strannood)</td>\n",
              "      <td>F</td>\n",
              "      <td>18.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td>NED</td>\n",
              "      <td>1932 Summer</td>\n",
              "      <td>1932</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>Athletics</td>\n",
              "      <td>Athletics Women's 100 metres</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271106</th>\n",
              "      <td>135565</td>\n",
              "      <td>Fernando scar Zylberberg</td>\n",
              "      <td>M</td>\n",
              "      <td>27.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>ARG</td>\n",
              "      <td>2004 Summer</td>\n",
              "      <td>2004</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Athina</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Hockey Men's Hockey</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271107</th>\n",
              "      <td>135566</td>\n",
              "      <td>James Francis \"Jim\" Zylker</td>\n",
              "      <td>M</td>\n",
              "      <td>21.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA</td>\n",
              "      <td>1972 Summer</td>\n",
              "      <td>1972</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Munich</td>\n",
              "      <td>Football</td>\n",
              "      <td>Football Men's Football</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271108</th>\n",
              "      <td>135567</td>\n",
              "      <td>Aleksandr Viktorovich Zyuzin</td>\n",
              "      <td>M</td>\n",
              "      <td>24.0</td>\n",
              "      <td>183.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>Russia</td>\n",
              "      <td>RUS</td>\n",
              "      <td>2000 Summer</td>\n",
              "      <td>2000</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Sydney</td>\n",
              "      <td>Rowing</td>\n",
              "      <td>Rowing Men's Lightweight Coxless Fours</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271109</th>\n",
              "      <td>135567</td>\n",
              "      <td>Aleksandr Viktorovich Zyuzin</td>\n",
              "      <td>M</td>\n",
              "      <td>28.0</td>\n",
              "      <td>183.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>Russia</td>\n",
              "      <td>RUS</td>\n",
              "      <td>2004 Summer</td>\n",
              "      <td>2004</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Athina</td>\n",
              "      <td>Rowing</td>\n",
              "      <td>Rowing Men's Lightweight Coxless Fours</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271110</th>\n",
              "      <td>135568</td>\n",
              "      <td>Olga Igorevna Zyuzkova</td>\n",
              "      <td>F</td>\n",
              "      <td>33.0</td>\n",
              "      <td>171.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>Belarus</td>\n",
              "      <td>BLR</td>\n",
              "      <td>2016 Summer</td>\n",
              "      <td>2016</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Basketball Women's Basketball</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>158859 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            ID                                Name Sex   Age  Height  Weight  \\\n",
              "0            1                           A Dijiang   M  24.0   180.0    80.0   \n",
              "1            2                            A Lamusi   M  23.0   170.0    60.0   \n",
              "2            3                 Gunnar Nielsen Aaby   M  24.0     NaN     NaN   \n",
              "3            4                Edgar Lindenau Aabye   M  34.0     NaN     NaN   \n",
              "26           8  Cornelia \"Cor\" Aalten (-Strannood)   F  18.0   168.0     NaN   \n",
              "...        ...                                 ...  ..   ...     ...     ...   \n",
              "271106  135565            Fernando scar Zylberberg   M  27.0   168.0    76.0   \n",
              "271107  135566          James Francis \"Jim\" Zylker   M  21.0   175.0    75.0   \n",
              "271108  135567        Aleksandr Viktorovich Zyuzin   M  24.0   183.0    72.0   \n",
              "271109  135567        Aleksandr Viktorovich Zyuzin   M  28.0   183.0    72.0   \n",
              "271110  135568              Olga Igorevna Zyuzkova   F  33.0   171.0    69.0   \n",
              "\n",
              "                  Team  NOC        Games  Year  Season            City  \\\n",
              "0                China  CHN  1992 Summer  1992  Summer       Barcelona   \n",
              "1                China  CHN  2012 Summer  2012  Summer          London   \n",
              "2              Denmark  DEN  1920 Summer  1920  Summer       Antwerpen   \n",
              "3       Denmark/Sweden  DEN  1900 Summer  1900  Summer           Paris   \n",
              "26         Netherlands  NED  1932 Summer  1932  Summer     Los Angeles   \n",
              "...                ...  ...          ...   ...     ...             ...   \n",
              "271106       Argentina  ARG  2004 Summer  2004  Summer          Athina   \n",
              "271107   United States  USA  1972 Summer  1972  Summer          Munich   \n",
              "271108          Russia  RUS  2000 Summer  2000  Summer          Sydney   \n",
              "271109          Russia  RUS  2004 Summer  2004  Summer          Athina   \n",
              "271110         Belarus  BLR  2016 Summer  2016  Summer  Rio de Janeiro   \n",
              "\n",
              "             Sport                                   Event Medal  \n",
              "0       Basketball             Basketball Men's Basketball   NaN  \n",
              "1             Judo            Judo Men's Extra-Lightweight   NaN  \n",
              "2         Football                 Football Men's Football   NaN  \n",
              "3       Tug-Of-War             Tug-Of-War Men's Tug-Of-War  Gold  \n",
              "26       Athletics            Athletics Women's 100 metres   NaN  \n",
              "...            ...                                     ...   ...  \n",
              "271106      Hockey                     Hockey Men's Hockey   NaN  \n",
              "271107    Football                 Football Men's Football   NaN  \n",
              "271108      Rowing  Rowing Men's Lightweight Coxless Fours   NaN  \n",
              "271109      Rowing  Rowing Men's Lightweight Coxless Fours   NaN  \n",
              "271110  Basketball           Basketball Women's Basketball   NaN  \n",
              "\n",
              "[158859 rows x 15 columns]"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df3 = df3.drop_duplicates(subset=['ID', 'Year'])\n",
        "df3"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "26a84ea5",
      "metadata": {
        "id": "26a84ea5"
      },
      "source": [
        "### Remove the rows where heights is missing "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1e956558",
      "metadata": {
        "id": "1e956558",
        "outputId": "0d61f017-14a1-497e-e3e5-c7d5c76ea095"
      },
      "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>26</th>\n",
              "      <td>8</td>\n",
              "      <td>Cornelia \"Cor\" Aalten (-Strannood)</td>\n",
              "      <td>F</td>\n",
              "      <td>18.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td>NED</td>\n",
              "      <td>1932 Summer</td>\n",
              "      <td>1932</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>Athletics</td>\n",
              "      <td>Athletics Women's 100 metres</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>12</td>\n",
              "      <td>Jyri Tapani Aalto</td>\n",
              "      <td>M</td>\n",
              "      <td>31.0</td>\n",
              "      <td>172.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>Finland</td>\n",
              "      <td>FIN</td>\n",
              "      <td>2000 Summer</td>\n",
              "      <td>2000</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Sydney</td>\n",
              "      <td>Badminton</td>\n",
              "      <td>Badminton Men's Singles</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>13</td>\n",
              "      <td>Minna Maarit Aalto</td>\n",
              "      <td>F</td>\n",
              "      <td>30.0</td>\n",
              "      <td>159.0</td>\n",
              "      <td>55.5</td>\n",
              "      <td>Finland</td>\n",
              "      <td>FIN</td>\n",
              "      <td>1996 Summer</td>\n",
              "      <td>1996</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Atlanta</td>\n",
              "      <td>Sailing</td>\n",
              "      <td>Sailing Women's Windsurfer</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271106</th>\n",
              "      <td>135565</td>\n",
              "      <td>Fernando scar Zylberberg</td>\n",
              "      <td>M</td>\n",
              "      <td>27.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>ARG</td>\n",
              "      <td>2004 Summer</td>\n",
              "      <td>2004</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Athina</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Hockey Men's Hockey</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271107</th>\n",
              "      <td>135566</td>\n",
              "      <td>James Francis \"Jim\" Zylker</td>\n",
              "      <td>M</td>\n",
              "      <td>21.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA</td>\n",
              "      <td>1972 Summer</td>\n",
              "      <td>1972</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Munich</td>\n",
              "      <td>Football</td>\n",
              "      <td>Football Men's Football</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271108</th>\n",
              "      <td>135567</td>\n",
              "      <td>Aleksandr Viktorovich Zyuzin</td>\n",
              "      <td>M</td>\n",
              "      <td>24.0</td>\n",
              "      <td>183.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>Russia</td>\n",
              "      <td>RUS</td>\n",
              "      <td>2000 Summer</td>\n",
              "      <td>2000</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Sydney</td>\n",
              "      <td>Rowing</td>\n",
              "      <td>Rowing Men's Lightweight Coxless Fours</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271109</th>\n",
              "      <td>135567</td>\n",
              "      <td>Aleksandr Viktorovich Zyuzin</td>\n",
              "      <td>M</td>\n",
              "      <td>28.0</td>\n",
              "      <td>183.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>Russia</td>\n",
              "      <td>RUS</td>\n",
              "      <td>2004 Summer</td>\n",
              "      <td>2004</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Athina</td>\n",
              "      <td>Rowing</td>\n",
              "      <td>Rowing Men's Lightweight Coxless Fours</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271110</th>\n",
              "      <td>135568</td>\n",
              "      <td>Olga Igorevna Zyuzkova</td>\n",
              "      <td>F</td>\n",
              "      <td>33.0</td>\n",
              "      <td>171.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>Belarus</td>\n",
              "      <td>BLR</td>\n",
              "      <td>2016 Summer</td>\n",
              "      <td>2016</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Rio de Janeiro</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Basketball Women's Basketball</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>125190 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            ID                                Name Sex   Age  Height  Weight  \\\n",
              "0            1                           A Dijiang   M  24.0   180.0    80.0   \n",
              "1            2                            A Lamusi   M  23.0   170.0    60.0   \n",
              "26           8  Cornelia \"Cor\" Aalten (-Strannood)   F  18.0   168.0     NaN   \n",
              "31          12                   Jyri Tapani Aalto   M  31.0   172.0    70.0   \n",
              "32          13                  Minna Maarit Aalto   F  30.0   159.0    55.5   \n",
              "...        ...                                 ...  ..   ...     ...     ...   \n",
              "271106  135565            Fernando scar Zylberberg   M  27.0   168.0    76.0   \n",
              "271107  135566          James Francis \"Jim\" Zylker   M  21.0   175.0    75.0   \n",
              "271108  135567        Aleksandr Viktorovich Zyuzin   M  24.0   183.0    72.0   \n",
              "271109  135567        Aleksandr Viktorovich Zyuzin   M  28.0   183.0    72.0   \n",
              "271110  135568              Olga Igorevna Zyuzkova   F  33.0   171.0    69.0   \n",
              "\n",
              "                 Team  NOC        Games  Year  Season            City  \\\n",
              "0               China  CHN  1992 Summer  1992  Summer       Barcelona   \n",
              "1               China  CHN  2012 Summer  2012  Summer          London   \n",
              "26        Netherlands  NED  1932 Summer  1932  Summer     Los Angeles   \n",
              "31            Finland  FIN  2000 Summer  2000  Summer          Sydney   \n",
              "32            Finland  FIN  1996 Summer  1996  Summer         Atlanta   \n",
              "...               ...  ...          ...   ...     ...             ...   \n",
              "271106      Argentina  ARG  2004 Summer  2004  Summer          Athina   \n",
              "271107  United States  USA  1972 Summer  1972  Summer          Munich   \n",
              "271108         Russia  RUS  2000 Summer  2000  Summer          Sydney   \n",
              "271109         Russia  RUS  2004 Summer  2004  Summer          Athina   \n",
              "271110        Belarus  BLR  2016 Summer  2016  Summer  Rio de Janeiro   \n",
              "\n",
              "             Sport                                   Event Medal  \n",
              "0       Basketball             Basketball Men's Basketball   NaN  \n",
              "1             Judo            Judo Men's Extra-Lightweight   NaN  \n",
              "26       Athletics            Athletics Women's 100 metres   NaN  \n",
              "31       Badminton                 Badminton Men's Singles   NaN  \n",
              "32         Sailing              Sailing Women's Windsurfer   NaN  \n",
              "...            ...                                     ...   ...  \n",
              "271106      Hockey                     Hockey Men's Hockey   NaN  \n",
              "271107    Football                 Football Men's Football   NaN  \n",
              "271108      Rowing  Rowing Men's Lightweight Coxless Fours   NaN  \n",
              "271109      Rowing  Rowing Men's Lightweight Coxless Fours   NaN  \n",
              "271110  Basketball           Basketball Women's Basketball   NaN  \n",
              "\n",
              "[125190 rows x 15 columns]"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df4 = df3[~df3['Height'].isnull()]\n",
        "df4"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a0ca1510",
      "metadata": {
        "id": "a0ca1510"
      },
      "source": [
        "## Group by Sex, Year and find average height of Male and Female"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cfe6f1c7",
      "metadata": {
        "id": "cfe6f1c7",
        "outputId": "94267b26-e6f3-436e-cf14-0e756cb609a8"
      },
      "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>Sex</th>\n",
              "      <th>Year</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>F</td>\n",
              "      <td>1920</td>\n",
              "      <td>161.800000</td>\n",
              "      <td>8.470116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>F</td>\n",
              "      <td>1924</td>\n",
              "      <td>162.650000</td>\n",
              "      <td>8.462145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>F</td>\n",
              "      <td>1928</td>\n",
              "      <td>168.509434</td>\n",
              "      <td>6.485554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>F</td>\n",
              "      <td>1932</td>\n",
              "      <td>167.307692</td>\n",
              "      <td>6.049078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>F</td>\n",
              "      <td>1936</td>\n",
              "      <td>167.144737</td>\n",
              "      <td>6.898703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>F</td>\n",
              "      <td>1948</td>\n",
              "      <td>168.182927</td>\n",
              "      <td>5.784458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>F</td>\n",
              "      <td>1952</td>\n",
              "      <td>166.583333</td>\n",
              "      <td>6.386191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>F</td>\n",
              "      <td>1956</td>\n",
              "      <td>166.307317</td>\n",
              "      <td>6.692709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>F</td>\n",
              "      <td>1960</td>\n",
              "      <td>165.666667</td>\n",
              "      <td>7.001997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>F</td>\n",
              "      <td>1964</td>\n",
              "      <td>166.452774</td>\n",
              "      <td>7.154795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>F</td>\n",
              "      <td>1968</td>\n",
              "      <td>167.206675</td>\n",
              "      <td>7.300185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>F</td>\n",
              "      <td>1972</td>\n",
              "      <td>167.324561</td>\n",
              "      <td>6.917981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>F</td>\n",
              "      <td>1976</td>\n",
              "      <td>169.569145</td>\n",
              "      <td>8.018417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>F</td>\n",
              "      <td>1980</td>\n",
              "      <td>169.932666</td>\n",
              "      <td>9.249478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>F</td>\n",
              "      <td>1984</td>\n",
              "      <td>169.371521</td>\n",
              "      <td>8.623033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>F</td>\n",
              "      <td>1988</td>\n",
              "      <td>169.651292</td>\n",
              "      <td>8.937004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>F</td>\n",
              "      <td>1992</td>\n",
              "      <td>170.043458</td>\n",
              "      <td>8.803704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>F</td>\n",
              "      <td>1996</td>\n",
              "      <td>169.997340</td>\n",
              "      <td>8.883102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>F</td>\n",
              "      <td>2000</td>\n",
              "      <td>170.128904</td>\n",
              "      <td>8.797371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>F</td>\n",
              "      <td>2004</td>\n",
              "      <td>170.109248</td>\n",
              "      <td>8.916405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>F</td>\n",
              "      <td>2008</td>\n",
              "      <td>170.360929</td>\n",
              "      <td>8.859487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>F</td>\n",
              "      <td>2012</td>\n",
              "      <td>170.339922</td>\n",
              "      <td>8.811234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>F</td>\n",
              "      <td>2016</td>\n",
              "      <td>170.027152</td>\n",
              "      <td>8.729251</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>M</td>\n",
              "      <td>1896</td>\n",
              "      <td>174.857143</td>\n",
              "      <td>10.189415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>M</td>\n",
              "      <td>1900</td>\n",
              "      <td>175.115385</td>\n",
              "      <td>8.243010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>M</td>\n",
              "      <td>1904</td>\n",
              "      <td>175.825581</td>\n",
              "      <td>7.976719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>M</td>\n",
              "      <td>1906</td>\n",
              "      <td>177.362637</td>\n",
              "      <td>7.199562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>M</td>\n",
              "      <td>1908</td>\n",
              "      <td>176.619433</td>\n",
              "      <td>7.567643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>M</td>\n",
              "      <td>1912</td>\n",
              "      <td>177.305177</td>\n",
              "      <td>7.271439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>M</td>\n",
              "      <td>1920</td>\n",
              "      <td>176.353774</td>\n",
              "      <td>7.555458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>M</td>\n",
              "      <td>1924</td>\n",
              "      <td>176.370567</td>\n",
              "      <td>7.527516</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>M</td>\n",
              "      <td>1928</td>\n",
              "      <td>176.630909</td>\n",
              "      <td>8.253064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>M</td>\n",
              "      <td>1932</td>\n",
              "      <td>175.237003</td>\n",
              "      <td>8.567386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>M</td>\n",
              "      <td>1936</td>\n",
              "      <td>177.609687</td>\n",
              "      <td>8.270831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>M</td>\n",
              "      <td>1948</td>\n",
              "      <td>178.628225</td>\n",
              "      <td>8.059422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>M</td>\n",
              "      <td>1952</td>\n",
              "      <td>178.415371</td>\n",
              "      <td>8.361423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>M</td>\n",
              "      <td>1956</td>\n",
              "      <td>177.912157</td>\n",
              "      <td>8.656091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>M</td>\n",
              "      <td>1960</td>\n",
              "      <td>176.397197</td>\n",
              "      <td>8.428248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>M</td>\n",
              "      <td>1964</td>\n",
              "      <td>176.945782</td>\n",
              "      <td>8.944761</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>M</td>\n",
              "      <td>1968</td>\n",
              "      <td>177.482010</td>\n",
              "      <td>9.145459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>M</td>\n",
              "      <td>1972</td>\n",
              "      <td>178.207352</td>\n",
              "      <td>9.498561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>M</td>\n",
              "      <td>1976</td>\n",
              "      <td>178.852204</td>\n",
              "      <td>9.947223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>M</td>\n",
              "      <td>1980</td>\n",
              "      <td>179.273879</td>\n",
              "      <td>10.249608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>M</td>\n",
              "      <td>1984</td>\n",
              "      <td>179.339122</td>\n",
              "      <td>9.933587</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>M</td>\n",
              "      <td>1988</td>\n",
              "      <td>179.793775</td>\n",
              "      <td>10.152012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>M</td>\n",
              "      <td>1992</td>\n",
              "      <td>180.852842</td>\n",
              "      <td>10.015167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>M</td>\n",
              "      <td>1996</td>\n",
              "      <td>181.098399</td>\n",
              "      <td>10.020005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>M</td>\n",
              "      <td>2000</td>\n",
              "      <td>181.518308</td>\n",
              "      <td>9.826005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>M</td>\n",
              "      <td>2004</td>\n",
              "      <td>181.804718</td>\n",
              "      <td>9.987196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>M</td>\n",
              "      <td>2008</td>\n",
              "      <td>182.007587</td>\n",
              "      <td>10.052370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>M</td>\n",
              "      <td>2012</td>\n",
              "      <td>182.408649</td>\n",
              "      <td>10.127959</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>M</td>\n",
              "      <td>2016</td>\n",
              "      <td>182.152267</td>\n",
              "      <td>10.072226</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Sex  Year        mean        std\n",
              "0    F  1920  161.800000   8.470116\n",
              "1    F  1924  162.650000   8.462145\n",
              "2    F  1928  168.509434   6.485554\n",
              "3    F  1932  167.307692   6.049078\n",
              "4    F  1936  167.144737   6.898703\n",
              "5    F  1948  168.182927   5.784458\n",
              "6    F  1952  166.583333   6.386191\n",
              "7    F  1956  166.307317   6.692709\n",
              "8    F  1960  165.666667   7.001997\n",
              "9    F  1964  166.452774   7.154795\n",
              "10   F  1968  167.206675   7.300185\n",
              "11   F  1972  167.324561   6.917981\n",
              "12   F  1976  169.569145   8.018417\n",
              "13   F  1980  169.932666   9.249478\n",
              "14   F  1984  169.371521   8.623033\n",
              "15   F  1988  169.651292   8.937004\n",
              "16   F  1992  170.043458   8.803704\n",
              "17   F  1996  169.997340   8.883102\n",
              "18   F  2000  170.128904   8.797371\n",
              "19   F  2004  170.109248   8.916405\n",
              "20   F  2008  170.360929   8.859487\n",
              "21   F  2012  170.339922   8.811234\n",
              "22   F  2016  170.027152   8.729251\n",
              "23   M  1896  174.857143  10.189415\n",
              "24   M  1900  175.115385   8.243010\n",
              "25   M  1904  175.825581   7.976719\n",
              "26   M  1906  177.362637   7.199562\n",
              "27   M  1908  176.619433   7.567643\n",
              "28   M  1912  177.305177   7.271439\n",
              "29   M  1920  176.353774   7.555458\n",
              "30   M  1924  176.370567   7.527516\n",
              "31   M  1928  176.630909   8.253064\n",
              "32   M  1932  175.237003   8.567386\n",
              "33   M  1936  177.609687   8.270831\n",
              "34   M  1948  178.628225   8.059422\n",
              "35   M  1952  178.415371   8.361423\n",
              "36   M  1956  177.912157   8.656091\n",
              "37   M  1960  176.397197   8.428248\n",
              "38   M  1964  176.945782   8.944761\n",
              "39   M  1968  177.482010   9.145459\n",
              "40   M  1972  178.207352   9.498561\n",
              "41   M  1976  178.852204   9.947223\n",
              "42   M  1980  179.273879  10.249608\n",
              "43   M  1984  179.339122   9.933587\n",
              "44   M  1988  179.793775  10.152012\n",
              "45   M  1992  180.852842  10.015167\n",
              "46   M  1996  181.098399  10.020005\n",
              "47   M  2000  181.518308   9.826005\n",
              "48   M  2004  181.804718   9.987196\n",
              "49   M  2008  182.007587  10.052370\n",
              "50   M  2012  182.408649  10.127959\n",
              "51   M  2016  182.152267  10.072226"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df5 = df4.groupby(['Sex', 'Year'])['Height'].agg(['mean', 'std']).reset_index()\n",
        "df5"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5c2f1481",
      "metadata": {
        "id": "5c2f1481"
      },
      "source": [
        "### Extract the male and female data separately"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6bec7e15",
      "metadata": {
        "id": "6bec7e15"
      },
      "outputs": [],
      "source": [
        "male_data = df5[df5['Sex'] == 'M']\n",
        "female_data = df5[df5['Sex'] == 'F']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e3aa3fa6",
      "metadata": {
        "id": "e3aa3fa6",
        "outputId": "76a46edb-00cc-4628-e51c-6881d5335282"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   Sex  Year        mean        std\n",
            "23   M  1896  174.857143  10.189415\n",
            "24   M  1900  175.115385   8.243010\n",
            "25   M  1904  175.825581   7.976719\n",
            "26   M  1906  177.362637   7.199562\n",
            "27   M  1908  176.619433   7.567643\n",
            "28   M  1912  177.305177   7.271439\n",
            "29   M  1920  176.353774   7.555458\n",
            "30   M  1924  176.370567   7.527516\n",
            "31   M  1928  176.630909   8.253064\n",
            "32   M  1932  175.237003   8.567386\n",
            "33   M  1936  177.609687   8.270831\n",
            "34   M  1948  178.628225   8.059422\n",
            "35   M  1952  178.415371   8.361423\n",
            "36   M  1956  177.912157   8.656091\n",
            "37   M  1960  176.397197   8.428248\n",
            "38   M  1964  176.945782   8.944761\n",
            "39   M  1968  177.482010   9.145459\n",
            "40   M  1972  178.207352   9.498561\n",
            "41   M  1976  178.852204   9.947223\n",
            "42   M  1980  179.273879  10.249608\n",
            "43   M  1984  179.339122   9.933587\n",
            "44   M  1988  179.793775  10.152012\n",
            "45   M  1992  180.852842  10.015167\n",
            "46   M  1996  181.098399  10.020005\n",
            "47   M  2000  181.518308   9.826005\n",
            "48   M  2004  181.804718   9.987196\n",
            "49   M  2008  182.007587  10.052370\n",
            "50   M  2012  182.408649  10.127959\n",
            "51   M  2016  182.152267  10.072226\n"
          ]
        }
      ],
      "source": [
        "print(male_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "38c6a369",
      "metadata": {
        "id": "38c6a369",
        "outputId": "01d5a135-72cc-4d43-9e26-f636fd294b4c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   Sex  Year        mean       std\n",
            "0    F  1920  161.800000  8.470116\n",
            "1    F  1924  162.650000  8.462145\n",
            "2    F  1928  168.509434  6.485554\n",
            "3    F  1932  167.307692  6.049078\n",
            "4    F  1936  167.144737  6.898703\n",
            "5    F  1948  168.182927  5.784458\n",
            "6    F  1952  166.583333  6.386191\n",
            "7    F  1956  166.307317  6.692709\n",
            "8    F  1960  165.666667  7.001997\n",
            "9    F  1964  166.452774  7.154795\n",
            "10   F  1968  167.206675  7.300185\n",
            "11   F  1972  167.324561  6.917981\n",
            "12   F  1976  169.569145  8.018417\n",
            "13   F  1980  169.932666  9.249478\n",
            "14   F  1984  169.371521  8.623033\n",
            "15   F  1988  169.651292  8.937004\n",
            "16   F  1992  170.043458  8.803704\n",
            "17   F  1996  169.997340  8.883102\n",
            "18   F  2000  170.128904  8.797371\n",
            "19   F  2004  170.109248  8.916405\n",
            "20   F  2008  170.360929  8.859487\n",
            "21   F  2012  170.339922  8.811234\n",
            "22   F  2016  170.027152  8.729251\n"
          ]
        }
      ],
      "source": [
        "print(female_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "97bc5aad",
      "metadata": {
        "id": "97bc5aad"
      },
      "source": [
        "## Recreate the line plot using Matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "af8b67cd",
      "metadata": {
        "id": "af8b67cd",
        "outputId": "cabb2248-2dc2-49dc-c701-66abeac454c6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Create a figure and axis\n",
        "fig, ax = plt.subplots(figsize = (8,6))\n",
        "\n",
        "# plot the male and female lines with shading\n",
        "ax.plot(male_data['Year'], male_data['mean'], color = 'blue', label = 'Male')\n",
        "\n",
        "ax.fill_between(male_data['Year'],\n",
        "                male_data['mean']- male_data['std'],\n",
        "                male_data['mean'] + male_data['std'],\n",
        "                color = 'blue',\n",
        "                alpha = 0.1)\n",
        "\n",
        "ax.plot(female_data['Year'], female_data['mean'], color = 'red', label = 'Female')\n",
        "\n",
        "ax.fill_between(female_data['Year'],\n",
        "               female_data['mean']- female_data['std'],\n",
        "               female_data['mean'] + female_data['std'],\n",
        "               color = 'red',\n",
        "                alpha = 0.1);\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "843db73d",
      "metadata": {
        "id": "843db73d"
      },
      "outputs": [],
      "source": [
        "# Add labels, title, and legend\n",
        "ax.set_xlabel('Year')\n",
        "ax.set_ylim(140, 200)\n",
        "ax.set_yticks([140, 150, 160, 170, 180, 190, 200])\n",
        "yticklabels = ['140', '150', '160', '170', '180', '190', '200']\n",
        "ax.set_yticklabels(yticklabels)\n",
        "ax.set_ylabel('Height (cm)')\n",
        "ax.set_title('Average Olympian Height (+/- std dev) in the Summer Olympics')\n",
        "ax.legend(loc='lower left')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3d40dde2",
      "metadata": {
        "id": "3d40dde2",
        "outputId": "127ba5d7-9cfe-4bbb-e33d-b0ae7db35813"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "fig"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cfb09b98",
      "metadata": {
        "id": "cfb09b98"
      },
      "source": [
        "# Question 3\n",
        "The bar plot below shows the **top 10 most decorated Olympians.** That is, the Olympic athletes that have won the highest **total number of medals**. Each bar is colour coded according to the number of gold, silver and bronze medals won by that athlete. In this way, the total number of medals won is represented, along with the breakdown per medal type.\n",
        "\n",
        "Recreate the above bar plot using Matplotlib. The colours for the medal types are #FFD700, #C0C0C0, and #B08D57. Make sure to include the axes labels and plot title. The bars should be ordered as given in the figure."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8512e8c8",
      "metadata": {
        "id": "8512e8c8"
      },
      "source": [
        "## Top 10 most decorated Olympians based on their total number of medal"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4e9be84c",
      "metadata": {
        "id": "4e9be84c"
      },
      "source": [
        "### See more in Medal column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ecf01705",
      "metadata": {
        "id": "ecf01705",
        "outputId": "1f3bfbd4-e66c-485f-b60c-d638149cdcba"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Gold      13372\n",
              "Bronze    13295\n",
              "Silver    13116\n",
              "Name: Medal, dtype: int64"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.Medal.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9140baa7",
      "metadata": {
        "id": "9140baa7",
        "outputId": "9020b61e-ef06-4d27-933a-776bac4f457e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([nan, 'Gold', 'Bronze', 'Silver'], dtype=object)"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.Medal.unique()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4977f8a5",
      "metadata": {
        "id": "4977f8a5"
      },
      "source": [
        "### Drop missing value of Medal"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b49c6d3b",
      "metadata": {
        "id": "b49c6d3b",
        "outputId": "da011069-9897-4bee-c20a-11912c9cee07"
      },
      "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>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>37</th>\n",
              "      <td>15</td>\n",
              "      <td>Arvo Ossian Aaltonen</td>\n",
              "      <td>M</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Finland</td>\n",
              "      <td>FIN</td>\n",
              "      <td>1920 Summer</td>\n",
              "      <td>1920</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Antwerpen</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>Swimming Men's 200 metres Breaststroke</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>15</td>\n",
              "      <td>Arvo Ossian Aaltonen</td>\n",
              "      <td>M</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Finland</td>\n",
              "      <td>FIN</td>\n",
              "      <td>1920 Summer</td>\n",
              "      <td>1920</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Antwerpen</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>Swimming Men's 400 metres Breaststroke</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>16</td>\n",
              "      <td>Juhamatti Tapio Aaltonen</td>\n",
              "      <td>M</td>\n",
              "      <td>28.0</td>\n",
              "      <td>184.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>Finland</td>\n",
              "      <td>FIN</td>\n",
              "      <td>2014 Winter</td>\n",
              "      <td>2014</td>\n",
              "      <td>Winter</td>\n",
              "      <td>Sochi</td>\n",
              "      <td>Ice Hockey</td>\n",
              "      <td>Ice Hockey Men's Ice Hockey</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>17</td>\n",
              "      <td>Paavo Johannes Aaltonen</td>\n",
              "      <td>M</td>\n",
              "      <td>28.0</td>\n",
              "      <td>175.0</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>Bronze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271078</th>\n",
              "      <td>135553</td>\n",
              "      <td>Galina Ivanovna Zybina (-Fyodorova)</td>\n",
              "      <td>F</td>\n",
              "      <td>25.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>Soviet Union</td>\n",
              "      <td>URS</td>\n",
              "      <td>1956 Summer</td>\n",
              "      <td>1956</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Melbourne</td>\n",
              "      <td>Athletics</td>\n",
              "      <td>Athletics Women's Shot Put</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271080</th>\n",
              "      <td>135553</td>\n",
              "      <td>Galina Ivanovna Zybina (-Fyodorova)</td>\n",
              "      <td>F</td>\n",
              "      <td>33.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>Soviet Union</td>\n",
              "      <td>URS</td>\n",
              "      <td>1964 Summer</td>\n",
              "      <td>1964</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Tokyo</td>\n",
              "      <td>Athletics</td>\n",
              "      <td>Athletics Women's Shot Put</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271082</th>\n",
              "      <td>135554</td>\n",
              "      <td>Bogusaw Zych</td>\n",
              "      <td>M</td>\n",
              "      <td>28.0</td>\n",
              "      <td>182.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>Poland</td>\n",
              "      <td>POL</td>\n",
              "      <td>1980 Summer</td>\n",
              "      <td>1980</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Moskva</td>\n",
              "      <td>Fencing</td>\n",
              "      <td>Fencing Men's Foil, Team</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271102</th>\n",
              "      <td>135563</td>\n",
              "      <td>Olesya Nikolayevna Zykina</td>\n",
              "      <td>F</td>\n",
              "      <td>19.0</td>\n",
              "      <td>171.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>Russia</td>\n",
              "      <td>RUS</td>\n",
              "      <td>2000 Summer</td>\n",
              "      <td>2000</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Sydney</td>\n",
              "      <td>Athletics</td>\n",
              "      <td>Athletics Women's 4 x 400 metres Relay</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271103</th>\n",
              "      <td>135563</td>\n",
              "      <td>Olesya Nikolayevna Zykina</td>\n",
              "      <td>F</td>\n",
              "      <td>23.0</td>\n",
              "      <td>171.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>Russia</td>\n",
              "      <td>RUS</td>\n",
              "      <td>2004 Summer</td>\n",
              "      <td>2004</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Athina</td>\n",
              "      <td>Athletics</td>\n",
              "      <td>Athletics Women's 4 x 400 metres Relay</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>39783 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            ID                                 Name Sex   Age  Height  Weight  \\\n",
              "3            4                 Edgar Lindenau Aabye   M  34.0     NaN     NaN   \n",
              "37          15                 Arvo Ossian Aaltonen   M  30.0     NaN     NaN   \n",
              "38          15                 Arvo Ossian Aaltonen   M  30.0     NaN     NaN   \n",
              "40          16             Juhamatti Tapio Aaltonen   M  28.0   184.0    85.0   \n",
              "41          17              Paavo Johannes Aaltonen   M  28.0   175.0    64.0   \n",
              "...        ...                                  ...  ..   ...     ...     ...   \n",
              "271078  135553  Galina Ivanovna Zybina (-Fyodorova)   F  25.0   168.0    80.0   \n",
              "271080  135553  Galina Ivanovna Zybina (-Fyodorova)   F  33.0   168.0    80.0   \n",
              "271082  135554                         Bogusaw Zych   M  28.0   182.0    82.0   \n",
              "271102  135563            Olesya Nikolayevna Zykina   F  19.0   171.0    64.0   \n",
              "271103  135563            Olesya Nikolayevna Zykina   F  23.0   171.0    64.0   \n",
              "\n",
              "                  Team  NOC        Games  Year  Season       City       Sport  \\\n",
              "3       Denmark/Sweden  DEN  1900 Summer  1900  Summer      Paris  Tug-Of-War   \n",
              "37             Finland  FIN  1920 Summer  1920  Summer  Antwerpen    Swimming   \n",
              "38             Finland  FIN  1920 Summer  1920  Summer  Antwerpen    Swimming   \n",
              "40             Finland  FIN  2014 Winter  2014  Winter      Sochi  Ice Hockey   \n",
              "41             Finland  FIN  1948 Summer  1948  Summer     London  Gymnastics   \n",
              "...                ...  ...          ...   ...     ...        ...         ...   \n",
              "271078    Soviet Union  URS  1956 Summer  1956  Summer  Melbourne   Athletics   \n",
              "271080    Soviet Union  URS  1964 Summer  1964  Summer      Tokyo   Athletics   \n",
              "271082          Poland  POL  1980 Summer  1980  Summer     Moskva     Fencing   \n",
              "271102          Russia  RUS  2000 Summer  2000  Summer     Sydney   Athletics   \n",
              "271103          Russia  RUS  2004 Summer  2004  Summer     Athina   Athletics   \n",
              "\n",
              "                                         Event   Medal  \n",
              "3                  Tug-Of-War Men's Tug-Of-War    Gold  \n",
              "37      Swimming Men's 200 metres Breaststroke  Bronze  \n",
              "38      Swimming Men's 400 metres Breaststroke  Bronze  \n",
              "40                 Ice Hockey Men's Ice Hockey  Bronze  \n",
              "41      Gymnastics Men's Individual All-Around  Bronze  \n",
              "...                                        ...     ...  \n",
              "271078              Athletics Women's Shot Put  Silver  \n",
              "271080              Athletics Women's Shot Put  Bronze  \n",
              "271082                Fencing Men's Foil, Team  Bronze  \n",
              "271102  Athletics Women's 4 x 400 metres Relay  Bronze  \n",
              "271103  Athletics Women's 4 x 400 metres Relay  Silver  \n",
              "\n",
              "[39783 rows x 15 columns]"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df6 = df.dropna(subset = ['Medal'])\n",
        "df6"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0052896e",
      "metadata": {
        "id": "0052896e",
        "outputId": "c35ecf0d-505b-4c45-8822-14b7eba4e1b6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Robert Tait McKenzie            58\n",
              "Heikki Ilmari Savolainen        39\n",
              "Joseph \"Josy\" Stoffel           38\n",
              "Ioannis Theofilakis             36\n",
              "Takashi Ono                     33\n",
              "                                ..\n",
              "Tatyana Vasilyevna Kalmykova     1\n",
              "Mariya Lvovna Kalmykova          1\n",
              "Christine Kalmer                 1\n",
              "Joannis \"Jannis\" Kalmazidis      1\n",
              "Pierre-Georges LeClercq          1\n",
              "Name: Name, Length: 134732, dtype: int64"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.Name.value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a9c00cfa",
      "metadata": {
        "id": "a9c00cfa"
      },
      "source": [
        "### Groupby athletes name and count the number of Medals they won select top 20"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b59c1c1b",
      "metadata": {
        "id": "b59c1c1b",
        "outputId": "e78f978f-8b4b-4b72-8940-f2671e14f878"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Name\n",
              "Michael Fred Phelps, II                            28\n",
              "Larysa Semenivna Latynina (Diriy-)                 18\n",
              "Nikolay Yefimovich Andrianov                       15\n",
              "Borys Anfiyanovych Shakhlin                        13\n",
              "Takashi Ono                                        13\n",
              "Ole Einar Bjrndalen                                13\n",
              "Edoardo Mangiarotti                                13\n",
              "Ryan Steven Lochte                                 12\n",
              "Birgit Fischer-Schmidt                             12\n",
              "Paavo Johannes Nurmi                               12\n",
              "Natalie Anne Coughlin (-Hall)                      12\n",
              "Jennifer Elisabeth \"Jenny\" Thompson (-Cumpelik)    12\n",
              "Sawao Kato                                         12\n",
              "Dara Grace Torres (-Hoffman, -Minas)               12\n",
              "Aleksey Yuryevich Nemov                            12\n",
              "Aleksandr Vladimirovich Popov                      11\n",
              "Matthew Nicholas \"Matt\" Biondi                     11\n",
              "Mark Andrew Spitz                                  11\n",
              "Viktor Ivanovych Chukarin                          11\n",
              "Vra slavsk (-Odloilov)                             11\n",
              "Name: Medal, dtype: int64"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df7 = df6.groupby('Name')['Medal'].count().sort_values(ascending=False).head(20)\n",
        "df7"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9f8239f4",
      "metadata": {
        "id": "9f8239f4"
      },
      "outputs": [],
      "source": [
        "### Sort by medal count in descending order and select top 20"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5dce8141",
      "metadata": {
        "id": "5dce8141"
      },
      "outputs": [],
      "source": [
        "#top_20 = df7.sort_values(ascending=False).head(20)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e746b19a",
      "metadata": {
        "id": "e746b19a"
      },
      "source": [
        "### Filter the original DataFrame to include only rows where the athlete name is in the top 20 list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7775a8dd",
      "metadata": {
        "id": "7775a8dd",
        "outputId": "c0f484bc-75fb-4815-8228-8f11d1a3eba6"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        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>7511</th>\n",
              "      <td>4198</td>\n",
              "      <td>Nikolay Yefimovich Andrianov</td>\n",
              "      <td>M</td>\n",
              "      <td>19.0</td>\n",
              "      <td>166.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>Soviet Union</td>\n",
              "      <td>URS</td>\n",
              "      <td>1972 Summer</td>\n",
              "      <td>1972</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Munich</td>\n",
              "      <td>Gymnastics</td>\n",
              "      <td>Gymnastics Men's Team All-Around</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7512</th>\n",
              "      <td>4198</td>\n",
              "      <td>Nikolay Yefimovich Andrianov</td>\n",
              "      <td>M</td>\n",
              "      <td>19.0</td>\n",
              "      <td>166.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>Soviet Union</td>\n",
              "      <td>URS</td>\n",
              "      <td>1972 Summer</td>\n",
              "      <td>1972</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Munich</td>\n",
              "      <td>Gymnastics</td>\n",
              "      <td>Gymnastics Men's Floor Exercise</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7513</th>\n",
              "      <td>4198</td>\n",
              "      <td>Nikolay Yefimovich Andrianov</td>\n",
              "      <td>M</td>\n",
              "      <td>19.0</td>\n",
              "      <td>166.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>Soviet Union</td>\n",
              "      <td>URS</td>\n",
              "      <td>1972 Summer</td>\n",
              "      <td>1972</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Munich</td>\n",
              "      <td>Gymnastics</td>\n",
              "      <td>Gymnastics Men's Horse Vault</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7518</th>\n",
              "      <td>4198</td>\n",
              "      <td>Nikolay Yefimovich Andrianov</td>\n",
              "      <td>M</td>\n",
              "      <td>23.0</td>\n",
              "      <td>166.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>Soviet Union</td>\n",
              "      <td>URS</td>\n",
              "      <td>1976 Summer</td>\n",
              "      <td>1976</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Montreal</td>\n",
              "      <td>Gymnastics</td>\n",
              "      <td>Gymnastics Men's Individual All-Around</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7519</th>\n",
              "      <td>4198</td>\n",
              "      <td>Nikolay Yefimovich Andrianov</td>\n",
              "      <td>M</td>\n",
              "      <td>23.0</td>\n",
              "      <td>166.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>Soviet Union</td>\n",
              "      <td>URS</td>\n",
              "      <td>1976 Summer</td>\n",
              "      <td>1976</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Montreal</td>\n",
              "      <td>Gymnastics</td>\n",
              "      <td>Gymnastics Men's Team All-Around</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242082</th>\n",
              "      <td>121258</td>\n",
              "      <td>Dara Grace Torres (-Hoffman, -Minas)</td>\n",
              "      <td>F</td>\n",
              "      <td>33.0</td>\n",
              "      <td>183.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA</td>\n",
              "      <td>2000 Summer</td>\n",
              "      <td>2000</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Sydney</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>Swimming Women's 100 metres Butterfly</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242083</th>\n",
              "      <td>121258</td>\n",
              "      <td>Dara Grace Torres (-Hoffman, -Minas)</td>\n",
              "      <td>F</td>\n",
              "      <td>33.0</td>\n",
              "      <td>183.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA</td>\n",
              "      <td>2000 Summer</td>\n",
              "      <td>2000</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Sydney</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>Swimming Women's 4 x 100 metres Medley Relay</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242084</th>\n",
              "      <td>121258</td>\n",
              "      <td>Dara Grace Torres (-Hoffman, -Minas)</td>\n",
              "      <td>F</td>\n",
              "      <td>41.0</td>\n",
              "      <td>183.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA</td>\n",
              "      <td>2008 Summer</td>\n",
              "      <td>2008</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Beijing</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>Swimming Women's 50 metres Freestyle</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242085</th>\n",
              "      <td>121258</td>\n",
              "      <td>Dara Grace Torres (-Hoffman, -Minas)</td>\n",
              "      <td>F</td>\n",
              "      <td>41.0</td>\n",
              "      <td>183.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA</td>\n",
              "      <td>2008 Summer</td>\n",
              "      <td>2008</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Beijing</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>Swimming Women's 4 x 100 metres Freestyle Relay</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242086</th>\n",
              "      <td>121258</td>\n",
              "      <td>Dara Grace Torres (-Hoffman, -Minas)</td>\n",
              "      <td>F</td>\n",
              "      <td>41.0</td>\n",
              "      <td>183.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA</td>\n",
              "      <td>2008 Summer</td>\n",
              "      <td>2008</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Beijing</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>Swimming Women's 4 x 100 metres Medley Relay</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>264 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            ID                                  Name Sex   Age  Height  \\\n",
              "7511      4198          Nikolay Yefimovich Andrianov   M  19.0   166.0   \n",
              "7512      4198          Nikolay Yefimovich Andrianov   M  19.0   166.0   \n",
              "7513      4198          Nikolay Yefimovich Andrianov   M  19.0   166.0   \n",
              "7518      4198          Nikolay Yefimovich Andrianov   M  23.0   166.0   \n",
              "7519      4198          Nikolay Yefimovich Andrianov   M  23.0   166.0   \n",
              "...        ...                                   ...  ..   ...     ...   \n",
              "242082  121258  Dara Grace Torres (-Hoffman, -Minas)   F  33.0   183.0   \n",
              "242083  121258  Dara Grace Torres (-Hoffman, -Minas)   F  33.0   183.0   \n",
              "242084  121258  Dara Grace Torres (-Hoffman, -Minas)   F  41.0   183.0   \n",
              "242085  121258  Dara Grace Torres (-Hoffman, -Minas)   F  41.0   183.0   \n",
              "242086  121258  Dara Grace Torres (-Hoffman, -Minas)   F  41.0   183.0   \n",
              "\n",
              "        Weight           Team  NOC        Games  Year  Season      City  \\\n",
              "7511      60.0   Soviet Union  URS  1972 Summer  1972  Summer    Munich   \n",
              "7512      60.0   Soviet Union  URS  1972 Summer  1972  Summer    Munich   \n",
              "7513      60.0   Soviet Union  URS  1972 Summer  1972  Summer    Munich   \n",
              "7518      60.0   Soviet Union  URS  1976 Summer  1976  Summer  Montreal   \n",
              "7519      60.0   Soviet Union  URS  1976 Summer  1976  Summer  Montreal   \n",
              "...        ...            ...  ...          ...   ...     ...       ...   \n",
              "242082    68.0  United States  USA  2000 Summer  2000  Summer    Sydney   \n",
              "242083    68.0  United States  USA  2000 Summer  2000  Summer    Sydney   \n",
              "242084    68.0  United States  USA  2008 Summer  2008  Summer   Beijing   \n",
              "242085    68.0  United States  USA  2008 Summer  2008  Summer   Beijing   \n",
              "242086    68.0  United States  USA  2008 Summer  2008  Summer   Beijing   \n",
              "\n",
              "             Sport                                            Event   Medal  \n",
              "7511    Gymnastics                 Gymnastics Men's Team All-Around  Silver  \n",
              "7512    Gymnastics                  Gymnastics Men's Floor Exercise    Gold  \n",
              "7513    Gymnastics                     Gymnastics Men's Horse Vault  Bronze  \n",
              "7518    Gymnastics           Gymnastics Men's Individual All-Around    Gold  \n",
              "7519    Gymnastics                 Gymnastics Men's Team All-Around  Silver  \n",
              "...            ...                                              ...     ...  \n",
              "242082    Swimming            Swimming Women's 100 metres Butterfly  Bronze  \n",
              "242083    Swimming     Swimming Women's 4 x 100 metres Medley Relay    Gold  \n",
              "242084    Swimming             Swimming Women's 50 metres Freestyle  Silver  \n",
              "242085    Swimming  Swimming Women's 4 x 100 metres Freestyle Relay  Silver  \n",
              "242086    Swimming     Swimming Women's 4 x 100 metres Medley Relay  Silver  \n",
              "\n",
              "[264 rows x 15 columns]"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df8 = df6[df6['Name'].isin(df7.index)]\n",
        "df8"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "08b045e7",
      "metadata": {
        "id": "08b045e7",
        "outputId": "b7c942ec-1ac5-405a-82e0-c9fbd650027a"
      },
      "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>Medal</th>\n",
              "      <th>Bronze</th>\n",
              "      <th>Gold</th>\n",
              "      <th>Silver</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Aleksandr Vladimirovich Popov</th>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Aleksey Yuryevich Nemov</th>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Birgit Fischer-Schmidt</th>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Borys Anfiyanovych Shakhlin</th>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dara Grace Torres (-Hoffman, -Minas)</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Edoardo Mangiarotti</th>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jennifer Elisabeth \"Jenny\" Thompson (-Cumpelik)</th>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Larysa Semenivna Latynina (Diriy-)</th>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mark Andrew Spitz</th>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Matthew Nicholas \"Matt\" Biondi</th>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Michael Fred Phelps, II</th>\n",
              "      <td>2</td>\n",
              "      <td>23</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Natalie Anne Coughlin (-Hall)</th>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nikolay Yefimovich Andrianov</th>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ole Einar Bjrndalen</th>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Paavo Johannes Nurmi</th>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ryan Steven Lochte</th>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sawao Kato</th>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Takashi Ono</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Viktor Ivanovych Chukarin</th>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vra slavsk (-Odloilov)</th>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Medal                                            Bronze  Gold  Silver\n",
              "Name                                                                 \n",
              "Aleksandr Vladimirovich Popov                         0     5       6\n",
              "Aleksey Yuryevich Nemov                               6     4       2\n",
              "Birgit Fischer-Schmidt                                0     8       4\n",
              "Borys Anfiyanovych Shakhlin                           2     7       4\n",
              "Dara Grace Torres (-Hoffman, -Minas)                  4     4       4\n",
              "Edoardo Mangiarotti                                   2     6       5\n",
              "Jennifer Elisabeth \"Jenny\" Thompson (-Cumpelik)       1     8       3\n",
              "Larysa Semenivna Latynina (Diriy-)                    4     9       5\n",
              "Mark Andrew Spitz                                     1     9       1\n",
              "Matthew Nicholas \"Matt\" Biondi                        1     8       2\n",
              "Michael Fred Phelps, II                               2    23       3\n",
              "Natalie Anne Coughlin (-Hall)                         5     3       4\n",
              "Nikolay Yefimovich Andrianov                          3     7       5\n",
              "Ole Einar Bjrndalen                                   1     8       4\n",
              "Paavo Johannes Nurmi                                  0     9       3\n",
              "Ryan Steven Lochte                                    3     6       3\n",
              "Sawao Kato                                            1     8       3\n",
              "Takashi Ono                                           4     5       4\n",
              "Viktor Ivanovych Chukarin                             1     7       3\n",
              "Vra slavsk (-Odloilov)                                0     7       4"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Group by athlete name and medal type, and count the number of medals of each type\n",
        "medal_counts = df8.groupby(['Name', 'Medal']).size().unstack(fill_value=0)\n",
        "medal_counts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a6474ca5",
      "metadata": {
        "id": "a6474ca5"
      },
      "outputs": [],
      "source": [
        "# Calculate the total medal count for each athlete\n",
        "medal_counts['Total'] = medal_counts.sum(axis=1)\n",
        "\n",
        "# Sort by total medal count in descending order\n",
        "medal_counts = medal_counts.sort_values(['Total', 'Gold'], ascending=[False, False])\n",
        "top_10 = medal_counts.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6a1b1f9e",
      "metadata": {
        "id": "6a1b1f9e",
        "outputId": "2ffb2ced-7b28-4f92-8a54-c720a37c5cb5"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        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>Medal</th>\n",
              "      <th>Bronze</th>\n",
              "      <th>Gold</th>\n",
              "      <th>Silver</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Name</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Michael Fred Phelps, II</th>\n",
              "      <td>2</td>\n",
              "      <td>23</td>\n",
              "      <td>3</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Larysa Semenivna Latynina (Diriy-)</th>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nikolay Yefimovich Andrianov</th>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ole Einar Bjrndalen</th>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Borys Anfiyanovych Shakhlin</th>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Edoardo Mangiarotti</th>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Takashi Ono</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Paavo Johannes Nurmi</th>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Birgit Fischer-Schmidt</th>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jennifer Elisabeth \"Jenny\" Thompson (-Cumpelik)</th>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Medal                                            Bronze  Gold  Silver  Total\n",
              "Name                                                                        \n",
              "Michael Fred Phelps, II                               2    23       3     28\n",
              "Larysa Semenivna Latynina (Diriy-)                    4     9       5     18\n",
              "Nikolay Yefimovich Andrianov                          3     7       5     15\n",
              "Ole Einar Bjrndalen                                   1     8       4     13\n",
              "Borys Anfiyanovych Shakhlin                           2     7       4     13\n",
              "Edoardo Mangiarotti                                   2     6       5     13\n",
              "Takashi Ono                                           4     5       4     13\n",
              "Paavo Johannes Nurmi                                  0     9       3     12\n",
              "Birgit Fischer-Schmidt                                0     8       4     12\n",
              "Jennifer Elisabeth \"Jenny\" Thompson (-Cumpelik)       1     8       3     12"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "top_10"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ff28ff9d",
      "metadata": {
        "id": "ff28ff9d"
      },
      "source": [
        "# Recreate Bar Plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eeb905ea",
      "metadata": {
        "id": "eeb905ea",
        "outputId": "e2a15e07-950c-4d4b-9b53-80517e1fe8f2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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ISO59Vly//vorMjMz0blzZzx//hzPnz9Heno6OnfujAcPHhQ4pVTRSvL6AxDfA3m/E+vXry/3c/Hrr7/KTRemT8PVEYmI6D+tfPnyAIBly5YVuMpiSZYNd3FxgZWVFWbMmIGWLVsW+KE674NnWloaTExMZM79/fffYlwAUKtWLURGRkIQBFy5cgVhYWGYMWMGNDU1MWHChGLHVlyvXr3CkSNHUKNGDTG28uXLF7r4QF68FSpUQE5ODh4+fFjgsuUlaTuQ/+jQ0aNH8ffffyM2NlYc/QIgs+BCUcqXLw+JRIITJ06IH/w/lHcsL96HDx/KlXn48CHMzMwKvU/Lli0xceJE7N69G61bt863TN53vH24smZ+mjVrBnt7eyxfvhxSqRQXL178YqtuFtReNTW1Apdwv3btGi5fvoywsDD07t1bPH7nzp3PisXb2xve3t7Izs7GmTNnMHfuXHTv3h1mZmZo1KhRvtcYGhri3bt3ePLkiUwiJggCHj58KI6wldT69esBACNHjsTIkSPzPe/u7v5JdX8N8n7+fvvtt1Ib6SV5HAkjIqL/NBcXF+jr6yMxMRFOTk75bnn/UlxckyZNgpeXF8aMGVNgmbzpdR9/gI6Pj0dSUhKaN28ud41EIoGDgwMWL14MfX19me9CU1dXL9boT1FycnIwbNgwPH36FOPHjxePt23bFsnJyTA0NMy3j/ISkTZt2gB4/71LBfmUtn8sLzH7OHlavXq1XNmPR1Y+bJMgCPjrr7/ybVOtWrUAvP9qBA0NDbnvaDp9+nS+0/g+5uTkhFatWmH9+vU4deqU3PmTJ09iw4YNaN26NerVq1dkfSNGjMC+ffsQGBiISpUqoVOnTkVe8yl27twpM0L24sUL7N27Fz/++COUlZXzvaYkr8unUFdXh6urK+bPnw8Aha7Wl/c++vh9tmPHDmRlZRXrffaxpKQkxMXFoUOHDoiJiZHbmjdvjt9///2TR9m+Bu7u7lBRUUFycnKBvxPp83EkjIiI/tOkUimWLVuG3r17499//0XHjh1RsWJFPHnyBJcvX8aTJ08KTSjy06NHD/To0aPQMlZWVhgwYACWLVsmrjqXkpKCyZMnw9TUVFwdLioqCitWrICPjw/Mzc0hCAJ27tyJ58+fy4ya1KpVC7Gxsdi7dy+MjY2ho6MDKyurQmN49OgRzpw5A0EQ8OLFC/HLmi9fvoxRo0ahf//+YtmRI0dix44daNKkCUaNGoXatWsjNzcXqampOHToEMaMGYOGDRvixx9/RM+ePTFr1iw8evQIbdu2hbq6Oi5dugQtLS0MHz682G0vjLOzMwwMDDBo0CBMnToVqqqqiIiIwOXLl+XK5iVT8+fPR5s2baCsrIzatWvDxcUFAwYMQJ8+fXD+/Hk0adIE2traSEtLw8mTJ1GrVi0MHjwYBgYGCAgIwKxZs9CvXz906tQJDx48wLRp04o1HRF4/51SLVq0QKtWrTBixAgxATh69CiWLl0Ka2vrYi2LDrx/fwUGBuL48eOYNGlSif+RoLiUlZXRsmVLjB49Grm5uZg/fz4yMjIwffr0Aq+xtrZGjRo1MGHCBAiCgHLlymHv3r2fNUVvypQp+PPPP9G8eXOYmJjg+fPnWLp0qcwzgPlp2bIl3N3dMX78eGRkZMDFxQVXrlzB1KlTUadOHfTs2bPEseSNgo0bNw4NGjSQO//ixQtER0dj8+bN+Omnn0pc/9fAzMwMM2bMQFBQEO7evYvWrVvDwMAAjx49wrlz56CtrV3oe4CKqWzWAyEiIvp8xVkdcfv27TLH81YOCw0NlTl+7NgxwdPTUyhXrpygqqoqVKlSRfD09JS7/mMfro5YmPxW6MvJyRHmz58vWFpaCqqqqkL58uWFHj16CA8ePBDL3LhxQ+jWrZtQo0YNQVNTU9DT0xMaNGgghIWFydSVkJAguLi4CFpaWsVasQ+AuCkpKQm6urpCrVq1hAEDBogrR34sMzNTmDRpkmBlZSWoqakJenp6Qq1atYRRo0YJDx8+lGnX4sWLBXt7e7Fco0aNhL1795ao7YJQ+Kpwp0+fFho1aiRoaWkJFSpUEPr16ydcvHhR7vXNzs4W+vXrJ1SoUEGQSCRyq/lt2LBBaNiwoaCtrS1oamoKNWrUEHr16iWcP39eLJObmyvMnTtXMDU1FdTU1ITatWsLe/fulVuZrjCZmZnCnDlzBEdHR0FLS0vQ0tISateuLcyaNUvIzMyUK//x6ogf8vPzE1RUVIQ///xT7lzeioUfr2SZtyrfkydPZI5//HOU956eP3++MH36dMHExERQU1MT6tSpIxw8eDDfe33Yn4mJiULLli0FHR0dwcDAQOjUqZOQmpoqABCmTp1aZDwf1xkVFSW0adNGqFKliqCmpiZUrFhR8PDwEE6cOJFv33zo1atXwvjx44Vq1aoJqqqqgrGxsTB48GDh2bNnhfZBft68eSNUrFhRcHR0LLDMu3fvBBMTE6FWrVqCIJTt6ogf/04q6HdiQe+X3bt3C25uboKurq6grq4uVKtWTejYsaNw5MiREsVO+ZMIwkfLHxERERHRV+vNmzcwMzND48aNsW3btlKvPyUlBdWrV8fChQsREBBQ6vUTEacjEhEREX0Tnjx5gps3byI0NBSPHj36IouyEJFiMAkjIiIi+gbs27cPffr0gbGxMVasWJHvsvRE9G3gdEQiIiIiIiIF4hL1RERERERECsQkjIiIiIiISIGYhBERERERESkQF+YgIvrK5Obm4u+//4aOjg4kEklZh0NERETFIPz/L76vXLkylJQKH+tiEkZE9JX5+++/YWpqWtZhEBER0Sd48OABTExMCi3DJIyI6Cujo6MD4P0vcV1d3TKOhoiIiIojIyMDpqam4t/xwjAJIyL6yuRNQdTV1WUSRkRE9I0pzqMEXJiDiIiIiIhIgZiEERERERERKRCTMCIiIiIiIgViEkZERERERKRATMKIiIiIiIgUiEkYERERERGRAnGJeiKir9UtPUBa1kEQEVFZOvYotqxDKDXPL6ws6xDgPTqyrEMAwJEwIiIiIiIihWISRkREREREpEBMwoiIiIiIiBSISRjRZ2jatClGjhxZrLKxsbGQSCR4/vz5F40pLCwM+vr6X/QexZGSkgKJRIKEhIRPrqM02lIacRARERGVJiZhRB/w8/ODRCLBoEGD5M4NGTIEEokEfn5+4rGdO3di5syZCoywdEgkErmtcePGCo+jadOm4v3V1dVhaWmJOXPmICcnR+GxlKaPk8evJTEmIiKirwOTMKKPmJqaIjIyEq9evRKPvX79Glu3bkXVqlVlypYrVw46OjqKDrFUhIaGIi0tTdz27NmTb7m3b99+0Tj69++PtLQ03Lx5EyNGjMCkSZMQHBz8Re9JREREVJaYhBF9pG7duqhatSp27twpHtu5cydMTU1Rp04dmbIfT0fMzs7GuHHjYGpqCnV1ddSsWRPr16+XuebChQtwcnKClpYWnJ2dcfPmTfFccnIyvL29UalSJUilUtSvXx9HjhyRuf7NmzcYN24cqlSpAm1tbTRs2BCxsbElbqe+vj6MjIzErVy5cuLUvW3btqFp06bQ0NDA5s2bAbxP2mxsbKChoQFra2usWLFCpr5z586hTp060NDQgJOTEy5dulSsOLS0tGBkZAQzMzMMGzYMzZs3x+7du2XKHDx4EDY2NpBKpWjdujXS0tJkzhcV24fypoXu27cPDg4O0NDQQMOGDXH16lWxzP379+Hl5QUDAwNoa2vDzs4O+/fvL1Z7iIiIiIrCJIwoH3369EFoaKi4v2HDBvj7+xd5Xa9evRAZGYmff/4ZSUlJWLVqFaRS2S96CgoKQkhICM6fPw8VFRWZejMzM+Hh4YEjR47g0qVLcHd3h5eXF1JTU2ViO3XqFCIjI3HlyhV06tQJrVu3xu3bt0uh5e+NHz8eI0aMQFJSEtzd3bF27VoEBQVh9uzZSEpKwpw5czB58mSEh4cDALKystC2bVtYWVnhwoULmDZtGgICAj7p3pqamjKjby9fvkRwcDA2bdqE48ePIzU1VabuomIryNixYxEcHIz4+HhUrFgR//vf/8T7Dh06FNnZ2Th+/DiuXr2K+fPny72OpSk7OxsZGRkyGxEREX2/+GXNRPno2bMnAgMDxZGhvKSnsBGnW7duYdu2bTh8+DBatGgBADA3N5crN3v2bLi6ugIAJkyYAE9PT7x+/RoaGhpwcHCAg4ODWHbWrFnYtWsX9uzZg2HDhiE5ORlbt27Fn3/+icqVKwMAAgICcODAAYSGhmLOnDnFbmO3bt2grKws7m/evBmOjo4AgJEjR6J9+/biuZkzZyIkJEQ8Vr16dSQmJmL16tXo3bs3IiIikJOTgw0bNkBLSwt2dnb4888/MXjw4GLHk5ubi0OHDuHgwYMyo4tv377FqlWrUKNGDQDAsGHDMGPGjGLHVpCpU6eiZcuWAIDw8HCYmJhg165d6Ny5M1JTU9GhQwfUqlULQP6vY2maO3cupk+f/kXvQURERF8PJmFE+Shfvjw8PT0RHh4OQRDg6emJ8uXLF3pNQkIClJWVxQSrILVr1xb/39jYGADw+PFjVK1aFVlZWZg+fTqioqLw999/4927d3j16pU4Enbx4kUIggBLS0uZOrOzs2FoaFiiNi5evFhMFvNiefLkCQDAyclJPP7kyRM8ePAAffv2Rf/+/cXj7969g56eHgAgKSkJDg4O0NLSEs83atSoWHGsWLEC69atw5s3bwC8T4CnTp0qntfS0hITsLw4Hz9+XOzYCvJhfOXKlYOVlRWSkpIAACNGjMDgwYNx6NAhtGjRAh06dJB53UpbYGAgRo8eLe5nZGTA1NT0i92PiIiIyhaTMKIC+Pv7Y9iwYQCAX375pcjympqaxapXVVVV/H+JRALg/SgQ8H6K3MGDBxEcHAwLCwtoamqiY8eOYoKSm5sLZWVlXLhwQWYUC0CJp8sZGRnBwsJC5lheEqatrS0ey4tt7dq1aNiwoUz5vBgEQSjRvT/k6+uLoKAgqKuro3LlynLt+rC/gPd9lne/4sRWEnmvR79+/eDu7o59+/bh0KFDmDt3LkJCQjB8+PAS11kc6urqUFdX/yJ1ExER0deHSRhRAVq3bi0mP+7u7kWWr1WrFnJzc3Hs2DGZEaaSOHHiBPz8/NCuXTsA758RS0lJEc/XqVMHOTk5ePz4MX788cdPukdJVapUCVWqVMHdu3fh6+ubbxlbW1ts2rQJr169EpPRM2fOFKt+PT09uWSwNGMryJkzZ8TVLp89e4Zbt27B2tpaPG9qaopBgwZh0KBBCAwMxNq1a79YEkZERET/LUzCiAqgrKwsTk8rzqiKmZkZevfuDX9/f/z8889wcHDA/fv38fjxY3Tu3LlY97SwsMDOnTvh5eUFiUSCyZMni6M9AGBpaQlfX1/06tULISEhqFOnDv755x8cPXoUtWrVgoeHx6c1tgjTpk3DiBEjoKurizZt2iA7Oxvnz5/Hs2fPMHr0aHTv3h1BQUHo27cvJk2ahJSUFIUtM19UbAWZMWMGDA0NUalSJQQFBaF8+fLw8fEB8P6ZuDZt2sDS0hLPnj3D0aNHYWNjo5D2EBER0fePqyMSFUJXVxe6urrFLr9y5Up07NgRQ4YMgbW1Nfr374+srKxiX7948WIYGBjA2dkZXl5ecHd3R926dWXKhIaGolevXhgzZgysrKzwv//9D2fPnv2izxD169cP69atQ1hYGGrVqgVXV1eEhYWhevXqAN5Phdy7dy8SExNRp04dBAUFYf78+V8snpLEVpB58+bhp59+Qr169cTvSVNTUwMA5OTkYOjQobCxsUHr1q1hZWUls+y9mZkZpk2b9iWbRURERN8xifA5D3MQEX1jYmNj4ebmhmfPnkFfX7/E17969QrlypXD/v374ebmVvoB4v3CHHp6ekiPB3S/3Mr4RET0DTj2KLasQyg1zy+sLOsQ4D068ovVLf79Tk8v8h/xORJGRFQCx44dQ7Nmzb5YAkZERETfPz4TRkRUAq1bt0br1q3LOgwiIiL6hnE6IhHRV6Yk0xmIiIjo68DpiERERERERF8pJmFEREREREQKxCSMiIiIiIhIgZiEERERERERKRCTMCIiIiIiIgViEkZERERERKRATMKIiIiIiIgUiEkYERERERGRAjEJIyIiIiIiUiAmYURERERERArEJIyIiIiIiEiBmIQREREREREpEJMwIiIiIiIiBWISRkREREREpEBMwoiIiIiIiBSISRgREREREZECMQkjIiIiIiJSICZhRERERERECqRS1gEQEVEBbukB0rIOgr5Hxx7FlnUI35XnF1aWdQhfFe/RkWUdAtFXjyNhRERERERECsQkjIiIiIiISIGYhBERERERESkQkzCiYoqNjYVEIsHz58/LOpSv1rRp0+Do6KiQe928eRNGRkZ48eJFia/18/ODj49PkeUkEgl2795d8uD+v6tXr8LExARZWVmfXAcRERF9f5iEfQOK+4HxW7Bjxw40bNgQenp60NHRgZ2dHcaMGVPWYRWLs7Mz0tLSoKenV6ZxlEYy+LnJRUECAgIQHR1d6vXmJygoCEOHDoWOjg6A/+sXiUQCJSUl6OnpoU6dOhg3bhzS0tJkrl26dCnCwsKKvEdaWhratGnzyTHWqlULDRo0wOLFiz+5DiIiIvr+MAn7D8rJyUFubq7C73vkyBF07doVHTt2xLlz53DhwgXMnj0bb968UXgsn0JNTQ1GRkaQSCRlHcpXSyqVwtDQ8Ivf588//8SePXvQp08fuXM3b97E33//jfj4eIwfPx5HjhyBvb09rl69KpbR09ODvr5+gfXnvSeNjIygrq7+WbH26dMHK1euRE5OzmfVQ0RERN8PJmHfgUWLFqFWrVrQ1taGqakphgwZgszMTPF8WFgY9PX1ERUVBVtbW6irq+PEiRNQVVXFw4cPZeoaM2YMmjRpAgC4f/8+vLy8YGBgAG1tbdjZ2WH//v0A3idyffv2RfXq1aGpqQkrKyssXbq00DijoqLQuHFjjB07FlZWVrC0tISPjw+WLVsmU27v3r2oV68eNDQ0YG5ujunTp+Pdu3fieYlEgtWrV6Nt27bQ0tKCjY0N4uLicOfOHTRt2hTa2tpo1KgRkpOTS1zvunXr0K5dO2hpaaFmzZrYs2ePeP7DEaj09HRoamriwIEDMvfYuXMntLW1kZmZiZSUFEgkEuzcuRNubm7Q0tKCg4MD4uLixPJPnz5Ft27dYGJiAi0tLdSqVQtbt24ttB+LEh8fj5YtW6J8+fLQ09ODq6srLl68KJ43MzMDALRr1w4SiQRmZmZISUmBkpISzp8/L1PXsmXLUK1aNQiCILY/OjoaTk5O0NLSgrOzM27evCmW/3g6Yt4obnBwMIyNjWFoaIihQ4fi7du3YpnNmzfDyckJOjo6MDIyQvfu3fH48eNC27ht2zY4ODjAxMRE7lzFihVhZGQES0tLdO3aFadOnUKFChUwePBgubjyNG3aFMOGDcPo0aNRvnx5tGzZEoDsiGGzZs0wbNgwmXs9ffoU6urqOHr0aIGxuru74+nTpzh27FihbSIiIqL/DiZh3wElJSX8/PPPuHbtGsLDw3H06FGMGzdOpszLly8xd+5crFu3DtevX4eTkxPMzc2xadMmscy7d++wefNmcXRh6NChyM7OxvHjx3H16lXMnz8fUun7Ly3Kzc2FiYkJtm3bhsTEREyZMgUTJ07Etm3bCozTyMgI169fx7Vr1wosc/DgQfTo0QMjRoxAYmIiVq9ejbCwMMyePVum3MyZM9GrVy8kJCTA2toa3bt3x8CBAxEYGCgmEh9+YC5uvdOnT0fnzp1x5coVeHh4wNfXF//++69cnHp6evD09ERERITM8S1btsDb21vsJ+D9tLmAgAAkJCTA0tIS3bp1E5O/169fo169eoiKisK1a9cwYMAA9OzZE2fPni2wj4ry4sUL9O7dGydOnMCZM2dQs2ZNeHh4iM9OxcfHAwBCQ0ORlpaG+Ph4mJmZoUWLFggNDZWpKzQ0FH5+fjKjf0FBQQgJCcH58+ehoqICf3//QuOJiYlBcnIyYmJiEB4ejrCwMJmpgG/evMHMmTNx+fJl7N69G/fu3YOfn1+hdR4/fhxOTk7F6g9NTU0MGjQIp06dKjS5Cw8Ph4qKCk6dOoXVq1fLne/Xrx+2bNmC7Oxs8VhERAQqV64MNze3AutVU1ODg4MDTpw4UWCZ7OxsZGRkyGxERET0/WIS9h0YOXIk3NzcUL16dTRr1gwzZ86US4bevn2LFStWwNnZGVZWVtDW1kbfvn1lPnTv27cPL1++ROfOnQEAqampcHFxQa1atWBubo62bduKo2SqqqqYPn066tevj+rVq8PX1xd+fn6FJmHDhw9H/fr1UatWLZiZmaFr167YsGGDzIfa2bNnY8KECejduzfMzc3RsmVLzJw5U+5DcZ8+fdC5c2dYWlpi/PjxSElJga+vL9zd3WFjY4OffvoJsbGxJa7Xz88P3bp1g4WFBebMmYOsrCycO3cu3/b4+vpi9+7dePnyJQAgIyMD+/btQ48ePWTKBQQEwNPTE5aWlpg+fTru37+PO3fuAACqVKmCgIAAODo6wtzcHMOHD4e7uzu2b99eYD8WpVmzZujRowdsbGxgY2OD1atX4+XLl+JITIUKFQAA+vr6MDIyEvf79euHrVu3iq/H5cuXkZCQIDflb/bs2XB1dYWtrS0mTJiA06dP4/Xr1wXGY2BggOXLl8Pa2hpt27aFp6enzHNj/v7+aNOmDczNzfHDDz/g559/xh9//CEzmvuxlJQUVK5cudh9Ym1tLV5XEAsLCyxYsABWVlZi+Q916NABEokEv//+u3gsvyQ1P1WqVCn03nPnzoWenp64mZqaFt4gIiIi+qYxCfsOxMTEoGXLlqhSpQp0dHTQq1cvPH36VGZFNjU1NdSuXVvmOj8/P9y5cwdnzpwBAGzYsAGdO3eGtrY2AGDEiBGYNWsWXFxcMHXqVFy5ckXm+lWrVsHJyQkVKlSAVCrF2rVrkZqaWmCc2tra2LdvH+7cuYNJkyZBKpVizJgxaNCggZjIXLhwATNmzIBUKhW3/v37Iy0tTSwDQKYtlSpVAvB+EYQPj71+/VocUfiUerW1taGjo1Pg6ImnpydUVFTEKYs7duyAjo4OWrVqJVPuwzqNjY0BQKwzJycHs2fPRu3atWFoaAipVIpDhw4V2o9Fefz4MQYNGgRLS0vxQ31mZmaRdfr4+EBFRQW7du0C8P794ObmJk5fLE578mNnZwdlZWWZaz4sf+nSJXh7e6NatWrQ0dFB06ZNAaDQeF+9egUNDY1C2/MhQRAAoNBkqaiRNXV1dfTo0QMbNmwAACQkJODy5cviqF2bNm3E95adnZ3MtZqamjLvs48FBgYiPT1d3B48eFCcZhEREdE3iknYN+7+/fvw8PCAvb09duzYgQsXLuCXX34BAJnnbjQ1NeU+gFasWBFeXl4IDQ3F48ePsX//fpmpZf369cPdu3fRs2dPXL16FU5OTuLzW9u2bcOoUaPg7++PQ4cOiSMmxVlko0aNGujXrx/WrVuHixcvIjExEb/++iuA99Mcp0+fjoSEBHG7evUqbt++LfOhW1VVVfz/vHbldyxvAZJPqTevnoIWMVFTU0PHjh2xZcsWAO+nInbp0gUqKioy5QqLKyQkBIsXL8a4ceNw9OhRJCQkwN3d/bMWK/Hz88OFCxewZMkSnD59GgkJCTA0NCyyTjU1NfTs2ROhoaF48+YNtmzZku9Uw8Lak5/C+jQrKwutWrWCVCrF5s2bER8fLyaBhcVbvnx5PHv2rND2fCgpKQkA5BLKD+X940Nh+vXrh8OHD+PPP//Ehg0b0Lx5c1SrVg0AsG7dOvG9lffsZJ5///1XHHHMj7q6OnR1dWU2IiIi+n6pFF2Evmbnz5/Hu3fvEBISAiWl9zl1YVMCP9avXz907doVJiYmqFGjBlxcXGTOm5qaYtCgQRg0aBACAwOxdu1aDB8+HCdOnICzszOGDBkilv14IYziMDMzg5aWljhqV7duXdy8eRMWFhYlrqswX6peX19ftGrVCtevX0dMTAxmzpxZoutPnDgBb29vcQpjbm4ubt++DRsbm0+O6cSJE1ixYgU8PDwAAA8ePMA///wjU0ZVVTXf1fr69esHe3t7rFixAm/fvkX79u0/OY7iuHHjBv755x/MmzdPnIL38eIg+alTpw4SExOLdY9Xr15hzZo1aNKkSaGJUHHUqlULTk5OWLt2LbZs2SKzqEyVKlUKvO7atWvo2LHjZ92biIiIvh9Mwr4R6enpSEhIkDlWrlw51KhRA+/evcOyZcvg5eWFU6dOYdWqVcWu193dHXp6epg1axZmzJghc27kyJFo06YNLC0t8ezZMxw9elRMDiwsLLBx40YcPHgQ1atXx6ZNmxAfH4/q1asXeK9p06bh5cuX8PDwQLVq1fD8+XP8/PPPePv2rbga3ZQpU9C2bVuYmpqiU6dOUFJSwpUrV3D16lXMmjWr2O362Jeq19XVFZUqVYKvry/MzMzwww8/lOh6CwsL7NixA6dPn4aBgQEWLVqEhw8fFisJu3r1qvgdWXkcHR1hYWGBTZs2wcnJCRkZGRg7diw0NTVlypmZmSE6OhouLi5QV1eHgYEBAMDGxgY//PADxo8fD39/f7nrSlvVqlWhpqaGZcuWYdCgQbh27VqxEll3d3f069cPOTk5MlMdgfdTI1+/fo0XL17gwoULWLBgAf755x/s3LmzVGLu168fhg0bBi0tLbRr167I8ikpKfjrr7/QokWLUrk/ERERffs4HfEbERsbizp16shsU6ZMgaOjIxYtWoT58+fD3t4eERERmDt3brHrVVJSgp+fH3JyctCrVy+Zczk5ORg6dChsbGzQunVrWFlZYcWKFQCAQYMGoX379ujSpQsaNmyIp0+fyoyK5cfV1RV3795Fr169YG1tjTZt2uDhw4c4dOgQrKysALz/cB0VFYXDhw+jfv36+OGHH7Bo0SJxyten+lL1SiQSdOvWDZcvX4avr2+Jr588eTLq1q0Ld3d3NG3aFEZGRsX+Yu4mTZrIvSeA989yPXv2DHXq1EHPnj0xYsQIVKxYUebakJAQHD58GKampuJ1efr27Ys3b94UuephaahQoQLCwsKwfft22NraYt68eQgODi7yOg8PD6iqquLIkSNy56ysrFC5cmXUq1cP8+bNQ4sWLXDt2jXY2tqWSszdunWDiooKunfvXqzn0rZu3YpWrVp99nuNiIiIvh8SIe+JdfrP6t+/Px49eiTznVj03zV79mxERkbKfLnx12jFihX4/fffcfDgQYXe98GDBzAzM0N8fDzq1q1baNns7GzUrFkTW7dulZvqW5iMjAzo6ekhPR7QlRZdnqikjj2KLesQvivPL6ws6xC+Kt6jI8s6BKIyIf79Tk8v8vluTkf8D0tPT0d8fDwiIiJklt2m/6bMzEwkJSVh2bJlJX62rSwMGDAAz549w4sXL+SmZX4Jb9++RVpaGiZMmIAffvihyAQMeL9wTlBQUIkSMCIiIvr+MQn7D/P29sa5c+cwcOBA8Zks+u8aNmwYtm7dCh8fH4VMRfxcKioqCAoKUtj9Tp06BTc3N1haWuK3334r1jWWlpawtLT8wpERERHRt4bTEYmIvjIlmc5AREREX4eS/P3mwhxEREREREQKxCSMiIiIiIhIgZiEERERERERKRCTMCIiIiIiIgViEkZERERERKRATMKIiIiIiIgUiEkYERERERGRAjEJIyIiIiIiUiAmYURERERERArEJIyIiIiIiEiBmIQREREREREpEJMwIiIiIiIiBWISRkREREREpEBMwoiIiIiIiBSISRgREREREZECMQkjIiIiIiJSICZhRERERERECsQkjIiIiIiISIFUyjoAIiIqwC09QFrWQVBpOvYotqxD+O48v7CyrEMoFd6jI8s6BCJSII6EERERERERKRCTMCIiIiIiIgViEkZERERERKRATMJIYcLCwqCvry/uT5s2DY6OjqVW39fo1KlTqFWrFlRVVeHj44PY2FhIJBI8f/68TONKSUmBRCJBQkJCscr7+fnBx8fni8ZUEsXtx68tbiIiIiKASRiVAj8/P0gkEsybN0/m+O7duyGRSMT9Ll264NatW4oO75MdO3YMqqqqOHnypMzxrKwsmJubY9SoUUXWMXr0aDg6OuLevXsICwuDs7Mz0tLSoKen96XCLhZTU1OkpaXB3t7+i9/r1atXMDAwQLly5fDq1asvfr8PLV26FGFhYQq9JxEREVFRmIRRqdDQ0MD8+fPx7NmzAstoamqiYsWKCozq87i6umL48OHw8/NDVlaWeHzcuHFQV1fH3Llzi6wjOTkZzZo1g4mJCfT19aGmpgYjIyOZ5LQsKCsrw8jICCoqX36B1B07dsDe3h62trbYuXPnF78fAOTk5CA3Nxd6enpf/WgpERER/fcwCaNS0aJFCxgZGRWamBQ1ffDevXuwsLDA4MGDkZubi2fPnqFXr14wMDCAlpYW2rRpg9u3bxd4fXJyMry9vVGpUiVIpVLUr18fR44cEc/PmDEDtWrVkruuXr16mDJlSr51zpkzB2pqahg/fjwAICYmBmvXrsWmTZugrq6OBQsWwNzcHJqamnBwcMBvv/0G4P+m+z19+hT+/v6QSCQICwuTm0aX1ydRUVGwsrKClpYWOnbsiKysLISHh8PMzAwGBgYYPnw4cnJyxLgK65v09HRoamriwIEDMm3ZuXMntLW1kZmZme90xOvXr8PT0xO6urrQ0dHBjz/+iOTkZJk6goODYWxsDENDQwwdOhRv374t8PXIs379evTo0QM9evTA+vXr5c5LJBKsW7cO7dq1g5aWFmrWrIk9e/bIlNm/fz8sLS2hqakJNzc3pKSkyJz/sB9tbW2hrq6O+/fvy01HPHDgABo3bgx9fX0YGhqibdu2Mm3M65edO3fCzc0NWlpacHBwQFxcnMz9duzYATs7O6irq8PMzAwhISHiucDAQPzwww9y7axduzamTp1aZH8RERHR949JGJUKZWVlzJkzB8uWLcOff/5Z4uuvXbsGFxcXdOrUCStXroSSkhL8/Pxw/vx57NmzB3FxcRAEAR4eHgV+8M/MzISHhweOHDmCS5cuwd3dHV5eXkhNTQUA+Pv7IzExEfHx8eI1V65cwaVLl+Dn55dvnRoaGti4cSPWrFmD3bt3w9/fHxMnToSTkxMmTZqE0NBQrFy5EtevX8eoUaPQo0cPHDt2TJzup6uriyVLliAtLQ1dunTJ9x4vX77Ezz//jMjISBw4cACxsbFo37499u/fj/3792PTpk1Ys2aNmOABKLRv9PT04OnpiYiICJn7bNmyBd7e3pBK5b946q+//kKTJk2goaGBo0eP4sKFC/D398e7d+/EMjExMUhOTkZMTAzCw8MRFhZW5FS/5ORkxMXFoXPnzujcuTNOnz6Nu3fvypWbPn06OnfujCtXrsDDwwO+vr74999/AQAPHjxA+/bt4eHhgYSEBPTr1w8TJkzItx/nzp2LdevW4fr16/mOumZlZWH06NGIj49HdHQ0lJSU0K5dO+Tm5sqUCwoKQkBAABISEmBpaYlu3bqJfXHhwgV07twZXbt2xdWrVzFt2jRMnjxZ7AtfX1+cPXtWJrm7fv06rl69Cl9f33z7KTs7GxkZGTIbERERfb/4Zc1Uatq1awdHR0dMnTo13xGPgsTFxaFt27YIDAxEQEAAAOD27dvYs2cPTp06BWdnZwBAREQETE1NsXv3bnTq1EmuHgcHBzg4OIj7s2bNwq5du7Bnzx4MGzYMJiYmcHd3R2hoKOrXrw8ACA0NhaurK8zNzQuMz8nJCYGBgejQoQPq1KmDSZMmISsrC4sWLcLRo0fRqFEjAIC5uTlOnjyJ1atXw9XVVZx2qKenByMjowLrf/v2LVauXIkaNWoAADp27IhNmzbh0aNHkEqlsLW1hZubG2JiYtClS5di9Y2vry969eqFly9fQktLCxkZGdi3bx927NiRbwy//PIL9PT0EBkZCVVVVQCApaWlTBkDAwMsX74cysrKsLa2hqenJ6Kjo9G/f/8C27Zhwwa0adMGBgYGAIDWrVtjw4YNmDVrlkw5Pz8/dOvWDQDEZP7cuXNo3bo1Vq5cCXNzcyxevBgSiQRWVla4evUq5s+fL9ePK1askHkPfKxDhw4y++vXr0fFihWRmJgo83xcQEAAPD09AbxPEO3s7HDnzh1YW1tj0aJFaN68OSZPniz2U2JiIhYuXAg/Pz/Y29ujdu3a2LJli1gmIiIC9evXl+vTPHPnzsX06dMLjJuIiIi+LxwJo1I1f/58hIeHIzExsVjlU1NT0aJFC0yaNElMwAAgKSkJKioqaNiwoXjM0NAQVlZWSEpKyreurKwsjBs3Dra2ttDX14dUKsWNGzfEkTAA6N+/P7Zu3YrXr1/j7du3iIiIgL+/f5FxTpo0Cbm5uZgwYQJUVFSQmJiI169fo2XLlpBKpeK2ceNGuSl8RdHS0hITMACoVKkSzMzMZEasKlWqhMePHxe7bzw9PaGioiJO69uxYwd0dHTQqlWrfGNISEjAjz/+KCZg+bGzs4OysrK4b2xsLMaUn5ycHISHh6NHjx7isR49eiA8PFxmaiXwfqpeHm1tbejo6Mi094cffpB5ji4v8f2QmpqaTD35SU5ORvfu3WFubg5dXV1Ur14dAGTeIx/HY2xsDAAy8bi4uMiUd3Fxwe3bt8V2+fr6iiORgiBg69atBY6CAe+nMKanp4vbgwcPCm0HERERfds4EkalqkmTJnB3d8fEiRMLnOL3oQoVKqBy5cqIjIxE3759oaurC+D9B9f8CIJQ4KIWY8eOxcGDBxEcHAwLCwtoamqiY8eOePPmjVjGy8sL6urq2LVrF9TV1ZGdnS03OpKfvOQkbyGLvOlr+/btQ5UqVWTKqqurF1lffnXnkUgk+R7Lu2dx+kZNTQ0dO3bEli1b0LVrV2zZsgVdunQpcCEOTU3NT4rz42l8Hzp48CD++usvuWmYOTk5OHToENq0aVOsugtq78c0NTWLXPDEy8sLpqamWLt2LSpXrozc3FzY29vLvEc+jievzg/j+fg+H8fYvXt3TJgwARcvXsSrV6/w4MEDdO3atcC41NXVS/y+ISIiom8XkzAqdfPmzYOjo2OBU68+pKmpiaioKHh4eMDd3R2HDh2Cjo4ObG1t8e7dO5w9e1accvf06VPcunULNjY2+dZ14sQJ+Pn5oV27dgAgLkDxIRUVFfTu3RuhoaFQV1dH165doaWlVeI25i3+kJqaCldX1xJf/zmK2ze+vr5o1aoVrl+/jpiYGMycObPAOmvXro3w8HC8ffu20NGwkli/fj26du2KoKAgmePz5s3D+vXrZZKwwtja2mL37t0yx86cOVPieJ4+fYqkpCSsXr0aP/74IwDIff1AceP5+LrTp0/D0tJSHCk0MTFBkyZNEBERgVevXqFFixaoVKlSie9FRERE3ydOR6RSV6tWLfj6+mLZsmXFKq+trY19+/ZBRUUFbdq0QWZmJmrWrAlvb2/0798fJ0+exOXLl9GjRw9UqVIF3t7e+dZjYWGBnTt3IiEhAZcvX0b37t3zHanp168fjh49ij/++KNYUxHzo6Ojg4CAAIwaNQrh4eFITk7GpUuX8MsvvyA8PPyT6iyu4vaNq6srKlWqBF9fX5iZmeW7Yl+eYcOGISMjA127dsX58+dx+/ZtbNq0CTdv3vykGJ88eYK9e/eid+/esLe3l9l69+6NPXv24MmTJ8Wqa9CgQUhOTsbo0aNx8+ZNbNmy5ZO++8vAwACGhoZYs2YN7ty5g6NHj2L06NElrmfMmDGIjo7GzJkzcevWLYSHh2P58uUy02mB90lwZGQktm/fLjMlk4iIiIhJGH0RM2fOLPY0MgCQSqX4448/xFX+srKyEBoainr16qFt27Zo1KgRBEHA/v37CxypWbx4MQwMDODs7AwvLy+4u7ujbt26cuVq1qwJZ2dnWFlZyTxX9SltnDJlCubOnQsbGxu4u7tj79694nNGX1Jx+kYikaBbt264fPlyoc8jAe+fKTt69CgyMzPh6uqKevXqYe3atZ88KrZx40Zoa2ujefPmcufc3Nygo6ODTZs2FauuqlWrYseOHdi7dy8cHBywatUqzJkzp8QxKSkpITIyEhcuXIC9vT1GjRqFhQsXlrieunXrYtu2bYiMjIS9vT2mTJmCGTNmyE2/7dSpE54+fYqXL1/KLJNPREREJBFK8kmZ6DsgCAKsra0xcODATxoJIfrSMjIyoKenh/R4QFf+GwXoG3bsUWxZh/DdeX5hZVmHUCq8R0eWdQhE9JnEv9/p6eI6BwXhM2H0n/L48WNs2rQJf/31F/r06VPW4RARERHRfxCTMPpPqVSpEsqXL481a9aI311FRERERKRITMLoP4Wzb+mbYpkOFDGdgb4trtZlHcF3SMEr1BIRlQYuzEFERERERKRATMKIiIiIiIgUiEkYERERERGRAjEJIyIiIiIiUiAmYURERERERArEJIyIiIiIiEiBmIQREREREREpEJMwIiIiIiIiBWISRkREREREpEBMwoiIiIiIiBSISRgREREREZECMQkjIiIiIiJSICZhRERERERECsQkjIiIiIiISIGYhBERERERESkQkzAiIiIiIiIFYhJGRERERESkQEzCiIiIiIiIFEilrAMgIqIC3NIDpGUdBH1tjj2KLesQvirPL6z8rOu9R0eWUiRERMXHkTAiIiIiIiIFYhJGRERERESkQEzCiIiIiIiIFIhJGNFXwszMDEuWLPmi9wgLC4O+vv4Xvcfnio2NhUQiwfPnz0u97pSUFEgkEiQkJBT7Gj8/P/j4+JR6LERERPTfxSSM6At78OAB+vbti8qVK0NNTQ3VqlXDTz/9hKdPn5b6vSQSSb5bZOT7B8+7dOmCW7dulfp9i8vMzEyMSVlZGZUrV0bfvn3x7NkzsYyzszPS0tKgp6dXZnESERERfUlMwoi+oLt378LJyQm3bt3C1q1bcefOHaxatQrR0dFo1KgR/v3331K/Z2hoKNLS0mS2vJEcTU1NVKxYsdTv+bG3b98WeG7GjBlIS0tDamoqIiIicPz4cYwYMUI8r6amBiMjI0gkknyvz8nJQW5ubqnHTERERKQoTMKIvqChQ4dCTU0Nhw4dgqurK6pWrYo2bdrgyJEj+OuvvxAUFFTgtenp6RgwYAAqVqwIXV1dNGvWDJcvXy7ynvr6+jAyMpLZNDQ0AMhPR5w2bRocHR2xadMmmJmZQU9PD127dsWLFy/EMgcOHEDjxo2hr68PQ0NDtG3bFsnJyeL5vCl+27ZtQ9OmTaGhoYHNmzcXGJ+Ojg6MjIxQpUoVuLm5oVevXrh48aJ4/uPpiHkxR0VFwdbWFurq6rh//z7MzMwwZ84c+Pv7Q0dHB1WrVsWaNWtk7nXu3DnUqVMHGhoacHJywqVLl2TO5+TkoG/fvqhevTo0NTVhZWWFpUuXFtq/giBgwYIFMDc3h6amJhwcHPDbb7/JxR8dHQ0nJydoaWnB2dkZN2/eLLReIiIi+u9gEkb0hfz77784ePAghgwZAk1NTZlzRkZG8PX1xa+//gpBEOSuFQQBnp6eePjwIfbv348LFy6gbt26aN68eamPniUnJ2P37t2IiopCVFQUjh07hnnz5onns7KyMHr0aMTHxyM6OhpKSkpo166d3GjU+PHjMWLECCQlJcHd3b1Y9/7rr78QFRWFhg0bFlru5cuXmDt3LtatW4fr16+Lo3khISFicjVkyBAMHjwYN27cEONu27YtrKyscOHCBUybNg0BAQEy9ebm5sLExATbtm1DYmIipkyZgokTJ2Lbtm0FxjJp0iSEhoZi5cqVuH79OkaNGoUePXrg2LFjMuWCgoIQEhKC8+fPQ0VFBf7+/gXWmZ2djYyMDJmNiIiIvl/8smaiL+T27dsQBAE2Njb5nrexscGzZ8/w5MkTuSmCMTExuHr1Kh4/fgx1dXUAQHBwMHbv3o3ffvsNAwYMKPC+3bp1g7KyssyxK1euwNzcPN/yubm5CAsLg46ODgCgZ8+eiI6OxuzZswEAHTp0kCm/fv16VKxYEYmJibC3txePjxw5Eu3bty8wrjzjx4/HpEmTkJOTg9evX6Nhw4ZYtGhRode8ffsWK1asgIODg8xxDw8PDBkyRKx38eLFiI2NhbW1NSIiIpCTk4MNGzZAS0sLdnZ2+PPPPzF48GDxelVVVUyfPl3cr169Ok6fPo1t27ahc+fOcnFkZWVh0aJFOHr0KBo1agQAMDc3x8mTJ7F69Wq4urqKZWfPni3uT5gwAZ6ennj9+rU4KvmhuXPnysRBRERE3zeOhBGVkbwRsPyefbpw4QIyMzNhaGgIqVQqbvfu3ZOZCpifxYsXIyEhQWYzNTUtsLyZmZmYgAGAsbExHj9+LO4nJyeje/fuMDc3h66uLqpXrw4ASE1NlanHycmp6EYDGDt2LBISEnDlyhVER0cDADw9PZGTk1PgNWpqaqhdu7bc8Q+PSSQSGBkZibEnJSXBwcEBWlpaYpm8xOlDq1atgpOTEypUqACpVIq1a9fKtS1PYmIiXr9+jZYtW8q8Lhs3bpR7XT6MzdjYGABk+vVDgYGBSE9PF7cHDx4U1BVERET0HeBIGNEXYmFhAYlEgsTExHyXOL9x4wYMDAxQvnx5uXO5ubkwNjZGbGys3Lmilpg3MjKChYVFseNUVVWV2ZdIJDJTDb28vGBqaoq1a9eicuXKyM3Nhb29Pd68eSNznba2drHuV758eTG+mjVrYsmSJWjUqBFiYmLQokWLfK/R1NTMN1ktLPb8pnl+bNu2bRg1ahRCQkLQqFEj6OjoYOHChTh79my+5fPq3rdvH6pUqSJzLm/EMr/Y8mIvaEERdXV1ueuJiIjo+8UkjOgLMTQ0RMuWLbFixQqMGjVK5rmwhw8fIiIiAr169co3uahbty4ePnwIFRUVmJmZKTBqWU+fPkVSUhJWr16NH3/8EQBw8uTJUr1H3tTJV69elWq9tra22LRpE169eiX2/ZkzZ2TKnDhxAs7OzuKURgCFjjTmLQySmpoqM/WQiIiIqCQ4HZHoC1q+fDmys7Ph7u6O48eP48GDBzhw4ABatmyJKlWqiM9dfaxFixZo1KgRfHx8cPDgQaSkpOD06dOYNGkSzp8/X+g9nz9/jocPH8psWVlZnxS/gYEBDA0NsWbNGty5cwdHjx7F6NGjP6muPC9evMDDhw+RlpaGc+fOYezYsShfvjycnZ0/q96Pde/eHUpKSujbty8SExOxf/9+BAcHy5SxsLDA+fPncfDgQdy6dQuTJ09GfHx8gXXq6OggICAAo0aNQnh4OJKTk3Hp0iX88ssvCA8PL9X4iYiI6PvFJIzoC6pZsybOnz+PGjVqoEuXLqhRowYGDBgANzc3xMXFoVy5cvleJ5FIsH//fjRp0gT+/v6wtLRE165dkZKSgkqVKhV6zz59+sDY2FhmW7Zs2SfFr6SkhMjISFy4cAH29vYYNWoUFi5c+El15ZkyZQqMjY1RuXJltG3bFtra2jh8+DAMDQ0/q96PSaVS7N27F4mJiahTpw6CgoIwf/58mTKDBg1C+/bt0aVLFzRs2BBPnz6VGRXLz8yZMzFlyhTMnTsXNjY2cHd3x969e8Vn5YiIiIiKIhGK8+AEEZGCHDx4EG3atMHr16+hpqZW1uGUiYyMDOjp6SE9HtCVlnU09LU59ii2rEP4qjy/sPKzrvceHVlKkRDRf5349zs9Hbq6uoWW5UgYEX01Hj16hN9//x01a9b8zyZgRERE9P3jwhxE9NXw8PDAixcvsGLFirIOhYiIiOiL4XREIqKvTEmmMxAREdHXgdMRiYiIiIiIvlJMwoiIiIiIiBSISRgREREREZECMQkjIiIiIiJSICZhRERERERECsQkjIiIiIiISIGYhBERERERESkQkzAiIiIiIiIFYhJGRERERESkQEzCiIiIiIiIFIhJGBERERERkQIxCSMiIiIiIlIgJmFEREREREQKxCSMiIiIiIhIgZiEERERERERKRCTMCIiIiIiIgViEkZERERERKRATMKIiIiIiIgUSKWsAyAiogLc0gOkZR0ElaZjj2LLOoSvyvMLK8s6BHiPjizrEIjoP4gjYURERERERArEJIyIiIiIiEiBmIQREREREREpEJMwolLy8uVLdOjQAbq6upBIJHj+/DnMzMywZMmSsg5N4Zo2bYqRI0eWer2l0Z/Tpk2Do6NjgefDwsKgr69faB1+fn7w8fER979Ue4mIiOj7xCSMSp2fnx8kEom4GRoaonXr1rhy5UpZh1aoAQMGQFlZGZGRn/aQdnh4OE6cOIHTp08jLS0Nenp6iI+Px4ABA0o50u9TVlYWxo8fD3Nzc2hoaKBChQpo2rQpoqKiyjq0Iu3cuRMzZ84s6zCIiIjoG8EkjL6I1q1bIy0tDWlpaYiOjoaKigratm37WXW+efOmlKKT9/LlS/z6668YO3Ys1q9f/0l1JCcnw8bGBvb29jAyMoJEIkGFChWgpaVVytF+nwYNGoTdu3dj+fLluHHjBg4cOIAOHTrg6dOnZR1akcqVKwcdHZ2yDoOIiIi+EUzC6ItQV1eHkZERjIyM4OjoiPHjx+PBgwd48uSJWObq1ato1qwZNDU1YWhoiAEDBiAzM1M8nzfla+7cuahcuTIsLS0xY8YM1KpVS+5+9erVw5QpUwAAsbGxaNCgAbS1taGvrw8XFxfcv3+/0Hi3b98OW1tbBAYG4tSpU0hJSZE5nxdLcHAwjI2NYWhoiKFDh+Lt27cA3k9HCwkJwfHjxyGRSNC0aVMAstPn/P395RLRd+/ewcjICBs2bAAAHDhwAI0bN4a+vj4MDQ3Rtm1bJCcni+VTUlIgkUiwc+dOuLm5QUtLCw4ODoiLi5Opd8eOHbCzs4O6ujrMzMwQEhIingsMDMQPP/wg1we1a9fG1KlTcfz4caiqquLhw4cy58eMGYMmTZqI+6dOnYKrqyu0tLRgYGAAd3d3PHv2TDyfm5uLcePGoVy5cjAyMsK0adMKeQWAvXv3YuLEifDw8ICZmRnq1auH4cOHo3fv3jLlXr58CX9/f+jo6KBq1apYs2aNzPnx48fD0tISWlpaMDc3x+TJk8XXKT/37t2DhYUFBg8ejNzcXPH4wYMHYWNjA6lUKv6jQkE+no5oZmaGOXPmFBonERER/XcxCaMvLjMzExEREbCwsIChoSGA9x+kW7duDQMDA8THx2P79u04cuQIhg0bJnNtdHQ0kpKScPjwYURFRcHf3x+JiYmIj48Xy1y5cgWXLl2Cn58f3r17Bx8fH7i6uuLKlSuIi4vDgAEDIJFICo1x/fr16NGjB/T09ODh4YHQ0FC5MjExMUhOTkZMTAzCw8MRFhaGsLAwAO+no/Xv3x+NGjVCWloadu7cKXd9v379cODAAZkP8/v370dmZiY6d+4M4P2UvNGjRyM+Ph7R0dFQUlJCu3btZJIDAAgKCkJAQAASEhJgaWmJbt264d27dwCACxcuoHPnzujatSuuXr2KadOmYfLkyWKsvr6+OHv2rExyd/36dVy9ehW+vr5o0qQJzM3NsWnTJvH8u3fvsHnzZvTp0wcAkJCQgObNm8POzg5xcXE4efIkvLy8kJOTI14THh4ObW1tnD17FgsWLMCMGTNw+PDhAl8DIyMj7N+/Hy9evCiwDACEhITAyckJly5dwpAhQzB48GDcuHFDPK+jo4OwsDAkJiZi6dKlWLt2LRYvXpxvXdeuXYOLiws6deqElStXQknp/a/Ely9fIjg4GJs2bcLx48eRmpqKgICAQuMqaZwfys7ORkZGhsxGRERE3y8mYfRFREVFQSqVQiqVQkdHB3v27MGvv/4qfsiNiIjAq1evsHHjRtjb26NZs2ZYvnw5Nm3ahEePHon1aGtrY926dbCzs4O9vT1MTEzg7u4ukySFhobC1dUV5ubmyMjIQHp6Otq2bYsaNWrAxsYGvXv3RtWqVQuM9fbt2zhz5gy6dOkCAOjRowdCQ0PlEh8DAwMsX74c1tbWaNu2LTw9PREdHQ3g/XQ0LS0tqKmpwcjICOXKlZO7j7OzM6ysrGSSm9DQUHTq1AlS6ftv5O3QoQPat2+PmjVrwtHREevXr8fVq1eRmJgoU1dAQAA8PT1haWmJ6dOn4/79+7hz5w4AYNGiRWjevDkmT54MS0tL+Pn5YdiwYVi4cCEAwN7eHrVr18aWLVvE+iIiIlC/fn1YWloCAPr27SvTx/v27cPLly/FZHHBggVwcnLCihUr4ODgADs7OwwbNgzly5cXr8kbWatZsyZ69eoFJycnsb/ys2bNGpw+fRqGhoaoX78+Ro0ahVOnTsmV8/DwwJAhQ2BhYYHx48ejfPnyiI2NFc9PmjQJzs7OMDMzg5eXF8aMGYNt27bJ1RMXFwdXV1eMHj0ac+fOlTn39u1brFq1Ck5OTqhbty6GDRtWaOz5KSrOD82dOxd6enriZmpqWqJ7ERER0beFSRh9EW5ubkhISEBCQgLOnj2LVq1aoU2bNuK0wKSkJDg4OEBbW1u8xsXFBbm5ubh586Z4rFatWlBTU5Opu3///ti6dStev36Nt2/fIiIiAv7+/gDeJ0N+fn5wd3eHl5cXli5dWug0MuD9KJi7u7uYQHh4eCArKwtHjhyRKWdnZwdlZWVx39jYGI8fPy5Rv/Tr109Mbh4/fox9+/aJsQPvnyvr3r07zM3Noauri+rVqwMAUlNTZeqpXbu2TBx59QHv+9bFxUWmvIuLC27fvi2OVPn6+iIiIgIAIAgCtm7dCl9fX7G8n58f7ty5gzNnzgAANmzYgM6dO4uvV95IWGE+jDEvzsL6q0mTJrh79y6io6PRoUMHXL9+HT/++KPcghcf1iuRSGBkZCRT72+//YbGjRvDyMgIUqkUkydPluu/1NRUtGjRApMmTcp3hEtLSws1atQoduz5KSrODwUGBiI9PV3cHjx4UKJ7ERER0beFSRh9Edra2rCwsICFhQUaNGiA9evXIysrC2vXrgXw/oN/QVMEPzz+YZKWx8vLC+rq6ti1axf27t2L7OxsdOjQQTwfGhqKuLg4ODs749dff4WlpaWYTHwsJycHGzduxL59+6CiogIVFRVoaWnh33//lVugQ1VVVS7Oj0fLitKrVy/cvXsXcXFx2Lx5M8zMzPDjjz/KtO3p06dYu3Ytzp49i7NnzwKQX5Tkw1jy+isvlvz6VhAEmf3u3bvj1q1buHjxIk6fPo0HDx6ga9eu4vmKFSvCy8sLoaGhePz4Mfbv3y+TLGpqahbZ1k/pL1VVVfz444+YMGECDh06hBkzZmDmzJky7S+s3jNnzqBr165o06YNoqKicOnSJQQFBcn1X4UKFdCgQQNERkbmO/Uvv3t83IdFKUn71dXVoaurK7MRERHR90ulrAOg/waJRAIlJSW8evUKAGBra4vw8HBkZWWJidapU6egpKQkTokriIqKCnr37o3Q0FCoq6uja9eucisQ1qlTB3Xq1EFgYCAaNWqELVu25LsYRd4zSJcuXZIZ5bpx4wZ8fX3x9OlT8Tm20mBoaAgfHx8xUcx7xgoAnj59iqSkJKxevVpMzE6ePFnie9ja2spdd/r0aVhaWoptNDExQZMmTcRpoS1atEClSpVkrunXrx+6du0KExMT1KhRQ2Z0rXbt2oiOjsb06dNLHF9J2/Lu3Tu8fv1abkQ0P6dOnUK1atUQFBQkHstvURZNTU1ERUXBw8MD7u7uOHToEFc3JCIiIoXhSBh9EdnZ2Xj48CEePnyIpKQkDB8+HJmZmfDy8gLwfjqchoYGevfujWvXriEmJgbDhw9Hz5495ZKB/PTr1w9Hjx7FH3/8ITNCc+/ePQQGBiIuLg7379/HoUOHcOvWLdjY2ORbz/r16+Hp6QkHBwfY29uLW4cOHVChQgVs3ry5dDrko9jDw8ORlJQks/KfgYEBDA0NsWbNGty5cwdHjx7F6NGjS1z/mDFjEB0djZkzZ+LWrVsIDw/H8uXL5abd+fr6IjIyEtu3b0ePHj3k6nF3d4eenh5mzZolkywC76fPxcfHY8iQIbhy5Qpu3LiBlStX4p9//ilxvHmaNm2K1atX48KFC0hJScH+/fsxceJEuLm5FXtkyMLCAqmpqYiMjERycjJ+/vln7Nq1K9+y2tra4ghomzZtZFbmJCIiIvqSmITRF3HgwAEYGxvD2NgYDRs2FFdAzFu6XUtLCwcPHsS///6L+vXro2PHjmjevDmWL19erPpr1qwpLnTRsGFD8biWlhZu3LiBDh06wNLSEgMGDMCwYcMwcOBAuToePXqEffv2yUxlzCORSNC+fftP/s6wwrRo0QLGxsZwd3dH5cqVxeNKSkqIjIzEhQsXYG9vj1GjRomLaZRE3bp1sW3bNkRGRsLe3h5TpkzBjBkz4OfnJ1OuU6dOePr0KV6+fAkfHx+5epSUlODn54ecnBz06tVL5pylpSUOHTqEy5cvo0GDBmjUqBF+//13qKh8+uC6u7s7wsPD0apVK9jY2GD48OFwd3fPd1GNgnh7e2PUqFEYNmwYHB0dcfr0aUyePLnA8lKpFH/88QcEQRCfBSQiIiL60iRCSR90IPoKCIIAa2trDBw48JNGi8rSy5cvUblyZWzYsAHt27cv63AK1b9/fzx69Ah79uwp61D+UzIyMqCnp4f0eEBXWtbRUGk69ii2rEP4qjy/sLKsQ4D36MiyDoGIvhPi3+/09CJn8fCZMPrmPH78GJs2bcJff/0lN03ua5abm4uHDx8iJCQEenp6+N///lfWIRUoPT0d8fHxiIiIwO+//17W4RARERF9V5iE0TenUqVKKF++PNasWQMDA4OyDqfYUlNTUb16dZiYmCAsLOyzpu59ad7e3jh37hwGDhyIli1blnU4RERERN8VTkckIvrKlGQ6AxEREX0dSvL3mwtzEBERERERKRCTMCIiIiIiIgViEkZERERERKRATMKIiIiIiIgUiEkYERERERGRAjEJIyIiIiIiUiAmYURERERERArEJIyIiIiIiEiBmIQREREREREpEJMwIiIiIiIiBWISRkREREREpEBMwoiIiIiIiBSISRgREREREZECMQkjIiIiIiJSICZhRERERERECsQkjIiIiIiISIGYhBERERERESkQkzAiIiIiIiIFUinrAIiIqAC39ABpWQdBeY49ii3rEL47zy+sLOsQ4D06sqxDIKL/II6EERERERERKRCTMCIiIiIiIgViEkZERERERKRATMKIyohEIsHu3bvL5N7Tpk2Do6Njmdz7S/ta21aWrzcRERF9XZiEEX0mPz8/SCQSua1169ZlHdoXldfuQYMGyZ0bMmQIJBIJ/Pz8FB5XQEAAoqOjFXKv/BKrgpLAtLQ0tGnTRiFxERER0deNSRhRKWjdujXS0tJktq1bt5Z1WHj79u0Xrd/U1BSRkZF49eqVeOz169fYunUrqlat+kXvXRCpVApDQ8PPquNL9JuRkRHU1dVLvV4iIiL69jAJIyoF6urqMDIyktkMDAzE87dv30aTJk2goaEBW1tbHD58WK6Oq1evolmzZtDU1IShoSEGDBiAzMxM8Xx8fDxatmyJ8uXLQ09PD66urrh48aJMHRKJBKtWrYK3tze0tbUxa9YsAMC8efNQqVIl6OjooG/fvnj9+rXMdbm5uZgxYwZMTEygrq4OR0dHHDhwoMh2161bF1WrVsXOnTvFYzt37oSpqSnq1KkjU/bAgQNo3Lgx9PX1YWhoiLZt2yI5OVk8n5KSAolEgp07d8LNzQ1aWlpwcHBAXFycTD1r166FqakptLS00K5dOyxatAj6+vri+Y9Hoj6n31auXIkaNWpATU0NVlZW2LRpk3iNmZkZAKBdu3aQSCQwMzNDWFgYpk+fjsuXL4sjomFhYeI9OB2RiIiIACZhRF9cbm4u2rdvD2VlZZw5cwarVq3C+PHjZcq8fPkSrVu3hoGBAeLj47F9+3YcOXIEw4YNE8u8ePECvXv3xokTJ3DmzBnUrFkTHh4eePHihUxdU6dOhbe3N65evQp/f39s27YNU6dOxezZs3H+/HkYGxtjxYoVMtcsXboUISEhCA4OxpUrV+Du7o7//e9/uH37dpHt69OnD0JDQ8X9DRs2wN/fX65cVlYWRo8ejfj4eERHR0NJSQnt2rVDbm6uTLmgoCAEBAQgISEBlpaW6NatG969ewcAOHXqFAYNGoSffvoJCQkJaNmyJWbPnl1ofJ/ab7t27cJPP/2EMWPG4Nq1axg4cCD69OmDmJgYAO+TOwAIDQ1FWloa4uPj0aVLF4wZMwZ2dnbiiGiXLl2K7MPs7GxkZGTIbERERPT94pc1E5WCqKgoSKWy36o7fvx4TJ48GUeOHEFSUhJSUlJgYmICAJgzZ47M80ERERF49eoVNm7cCG1tbQDA8uXL4eXlhfnz56NSpUpo1qyZTP2rV6+GgYEBjh07hrZt24rHu3fvLpMEdevWDf7+/ujXrx8AYNasWThy5IjMaFhwcDDGjx+Prl27AgDmz5+PmJgYLFmyBL/88kuhbe/ZsycCAwPFkaxTp04hMjISsbGxMuU6dOggs79+/XpUrFgRiYmJsLe3F48HBATA09MTADB9+nTY2dnhzp07sLa2xrJly9CmTRsEBAQAACwtLXH69GlERUUVGN+n9lv37t3h5+eHIUOGAABGjx6NM2fOIDg4GG5ubqhQoQIAQF9fH0ZGRuJ1UqkUKioqMseKMnfuXEyfPr3Y5YmIiOjbxpEwolLg5uaGhIQEmW3o0KEAgKSkJFStWlVMwACgUaNGMtcnJSXBwcFBTMAAwMXFBbm5ubh58yYA4PHjxxg0aBAsLS2hp6cHPT09ZGZmIjU1VaYuJycnubo/vt+H+xkZGfj777/h4uIiU8bFxQVJSUlFtr18+fLw9PREeHg4QkND4enpifLly8uVS05ORvfu3WFubg5dXV1Ur14dAOTir127tvj/xsbGYtsB4ObNm2jQoIFM+Y/3P/Y5/fapfVJSgYGBSE9PF7cHDx6U+j2IiIjo68GRMKJSoK2tDQsLi3zPCYIgd0wikciV+fjYx2X9/Pzw5MkTLFmyBNWqVYO6ujoaNWqEN2/eyMXyKUoS08f8/f3FqZMFjZx5eXnB1NQUa9euReXKlZGbmwt7e3u5+FVVVeViypuymF9M+fXvhz6n3z6nT0pCXV2di3YQERH9h3AkjOgLs7W1RWpqKv7++2/x2MeLTdja2iIhIQFZWVnisVOnTkFJSQmWlpYAgBMnTmDEiBHw8PCAnZ0d1NXV8c8//xR5fxsbG5w5c0bm2If7urq6qFy5Mk6ePClT5vTp07CxsSlWG1u3bo03b97gzZs3cHd3lzv/9OlTJCUlYdKkSWjevDlsbGzw7NmzYtX9IWtra5w7d07m2Pnz5wu95nP6rag+UVVVRU5OjkwZNTU1uWNEREREH+JIGFEpyM7OxsOHD2WOqaiooHz58mjRogWsrKzQq1cvhISEICMjA0FBQTJlfX19MXXqVPTu3RvTpk3DkydPMHz4cPTs2ROVKlUCAFhYWGDTpk1wcnJCRkYGxo4dC01NzSJj++mnn9C7d284OTmhcePGiIiIwPXr12Fubi6WGTt2LKZOnYoaNWrA0dERoaGhSEhIQERERLHar6ysLE7TU1ZWljtvYGAAQ0NDrFmzBsbGxkhNTcWECROKVfeHhg8fjiZNmmDRokXw8vLC0aNH8ccffxQ6OvWp/TZ27Fh07twZdevWRfPmzbF3717s3LkTR44cEcuYmZkhOjoaLi4uUFdXh4GBAczMzHDv3j0kJCTAxMQEOjo6HOUiIiIiGRwJIyoFBw4cgLGxsczWuHFjAICSkhJ27dqF7OxsNGjQAP369ZNb0U9LSwsHDx7Ev//+i/r166Njx45o3rw5li9fLpbZsGEDnj17hjp16qBnz54YMWIEKlasWGRsXbp0wZQpUzB+/HjUq1cP9+/fx+DBg2XKjBgxAmPGjMGYMWNQq1YtHDhwAHv27EHNmjWL3Qe6urrQ1dXN95ySkhIiIyNx4cIF2NvbY9SoUVi4cGGx687j4uKCVatWYdGiRXBwcMCBAwcwatQoaGhoFHjNp/abj48Pli5dioULF8LOzg6rV69GaGgomjZtKpYJCQnB4cOHZZbk79ChA1q3bi0u3vE1fF8cERERfV0kQlEPVBARfcX69++PGzdu4MSJE2UdSqnJyMiAnp4e0uMBXWnR5Ukxjj2KLesQvjvPL6ws6xDgPTqyrEMgou+E+Pc7Pb3Af5jOw+mIRPRNCQ4ORsuWLaGtrY0//vgD4eHhct97RkRERPQ1YxJGRN+Uc+fOYcGCBXjx4gXMzc3x888/i9+BRkRERPQt4HREIqKvTEmmMxAREdHXoSR/v7kwBxERERERkQIxCSMiIiIiIlIgJmFEREREREQKxCSMiIiIiIhIgZiEERERERERKRCTMCIiIiIiIgViEkZERERERKRATMKIiIiIiIgUiEkYERERERGRAjEJIyIiIiIiUiAmYURERERERArEJIyIiIiIiEiBmIQREREREREpEJMwIiIiIiIiBWISRkREREREpEBMwoiIiIiIiBSISRgREREREZECMQkjIiIiIiJSIJWyDoCIiApwSw+QlnUQ34djj2LLOoSvyvMLK8s6hK+G9+jIsg6BiP6DOBJGRERERESkQEzCiIiIiIiIFIhJGBERERERkQIxCSOiL65p06YYOXLkF6k7JSUFEokECQkJBZYJCwuDvr7+F7k/ERERUUkxCSOiIkkkkkI3Pz+/sg6xUF26dMGtW7eKLBcVFYWmTZtCR0cHWlpaqF+/PsLCwr58gERERPSfwiSMiIqUlpYmbkuWLIGurq7MsaVLl5Z1iIXS1NRExYoVCy2zbNkyeHt7w9nZGWfPnsWVK1fQtWtXDBo0CAEBAQqKlIiIiP4LmIQRUZGMjIzETU9PDxKJRNxXVVXFoEGDYGJiAi0tLdSqVQtbt24ttL4DBw5AT08PGzduBABs3rwZTk5O0NHRgZGREbp3747Hjx+L5Z89ewZfX19UqFABmpqaqFmzJkJDQ2XqvHv3Ltzc3KClpQUHBwfExcWJ54qajvjgwQOMGTMGI0eOxJw5c2BrawsLCwuMGTMGCxcuREhICM6ePQsAiI2NhUQiQXR0NJycnKClpQVnZ2fcvHlTps6VK1eiRo0aUFNTg5WVFTZt2lSsviYiIqLvH5MwIvosr1+/Rr169RAVFYVr165hwIAB6Nmzp5i0fCwyMhKdO3fGxo0b0atXLwDAmzdvMHPmTFy+fBm7d+/GvXv3ZKY4Tp48GYmJifjjjz+QlJSElStXonz58jL1BgUFISAgAAkJCbC0tES3bt3w7t27YrXht99+w9u3b/Md8Ro4cCCkUqlcYhkUFISQkBCcP38eKioq8Pf3F8/t2rULP/30E8aMGYNr165h4MCB6NOnD2JiYvK9f3Z2NjIyMmQ2IiIi+n7xy5qJ6LNUqVJFJnkZPnw4Dhw4gO3bt6Nhw4YyZVesWIGJEyfi999/h5ubm3j8wwTG3NwcP//8Mxo0aIDMzExIpVKkpqaiTp06cHJyAgCYmZnJxREQEABPT08AwPTp02FnZ4c7d+7A2tq6yDbcunULenp6MDY2ljunpqYGc3NzuWfKZs+eDVdXVwDAhAkT4OnpidevX0NDQwPBwcHw8/PDkCFDAACjR4/GmTNnEBwcLNPuPHPnzsX06dOLjJOIiIi+DxwJI6LPkpOTg9mzZ6N27dowNDSEVCrFoUOHkJqaKlNux44dGDlyJA4dOiSXiFy6dAne3t6oVq0adHR00LRpUwAQ6xg8eDAiIyPh6OiIcePG4fTp03Jx1K5dW/z/vGTqwymNn0MQBEgkkmLfLykpCS4uLjLlXVxckJSUlG/9gYGBSE9PF7cHDx6UStxERET0dWISRkSfJSQkBIsXL8a4ceNw9OhRJCQkwN3dHW/evJEp5+joiAoVKiA0NBSCIIjHs7Ky0KpVK0ilUmzevBnx8fHYtWsXAIh1tGnTBvfv38fIkSPx999/o3nz5nJTB1VVVcX/z0uYcnNzi9UGS0tLpKen4++//5Y79+bNG9y9exc1a9Ys0f0+TtryS+TyqKurQ1dXV2YjIiKi7xeTMCL6LCdOnIC3tzd69OgBBwcHmJub4/bt23LlatSogZiYGPz+++8YPny4ePzGjRv4559/MG/ePPz444+wtrbOdwSrQoUK8PPzw+bNm7FkyRKsWbOm1NrQoUMHqKioICQkRO7cqlWrkJWVhW7duhW7PhsbG5w8eVLm2OnTp2FjY/PZsRIREdG3j8+EEdFnsbCwwI4dO3D69GkYGBhg0aJFePjwYb4Jh6WlJWJiYtC0aVOoqKhgyZIlqFq1KtTU1LBs2TIMGjQI165dw8yZM2WumzJlCurVqwc7OztkZ2cjKiqqVBOaqlWrYsGCBQgICICGhgZ69uwJVVVV/P7775g4cSLGjBkj93xbYcaOHYvOnTujbt26aN68Ofbu3YudO3fiyJEjpRYzERERfbs4EkZEn2Xy5MmoW7cu3N3d0bRpUxgZGcHHx6fA8lZWVjh69Ci2bt2KMWPGoEKFCggLC8P27dtha2uLefPmITg4WOYaNTU1BAYGonbt2mjSpAmUlZURGRlZqu0YNWoUdu3ahRMnTsDJyQn29vbYsmULVq5cKRdPUXx8fLB06VIsXLgQdnZ2WL16NUJDQ8Vn3YiIiOi/TSJ8+HAGERGVuYyMDOjp6SE9HtCVlnU034djj2LLOoSvyvMLK8s6hK+G9+jS/QcdIvrvEv9+p6cX+Xw3R8KIiIiIiIgUiEkYERERERGRAnFhDiKir5VlOsDl6kuFa9Hf2f3f8v+/aJyIiMoGR8KIiIiIiIgUiEkYERERERGRAjEJIyIiIiIiUiAmYURERERERArEJIyIiIiIiEiBmIQREREREREpEJMwIiIiIiIiBWISRkREREREpEBMwoiIiIiIiBSISRgREREREZECMQkjIiIiIiJSICZhRERERERECsQkjIiIiIiISIGYhBERERERESkQkzAiIiIiIiIFYhJGRERERESkQEzCiIiIiIiIFIhJGBERERERkQKplHUARERUgFt6gLSsg6Dv0bFHsWUdQqlxdXUt6xCIiEqMI2FEREREREQKxCSMiIiIiIhIgZiEERERERERKRCTMKL/IIlEgt27d5d1GP8p7HMiIiLKwySM6P/z8/ODRCKBRCKBqqoqzM3NERAQgKysrLIOrVBhYWHQ19cv6zC+GhKJBBoaGrh//77McR8fH/j5+ZVNUADS0tLQpk2bMrs/ERERfT2YhBF9oHXr1khLS8Pdu3cxa9YsrFixAgEBAWUdFpWQRCLBlClTSr3et2/ffvK1RkZGUFdXL8VoiIiI6FvFJIzoA+rq6jAyMoKpqSm6d+8OX19fcQrZ5s2b4eTkBB0dHRgZGaF79+54/PixeG1OTg769u2L6tWrQ1NTE1ZWVli6dKl4/uDBg9DQ0MDz589l7jlixAiZJZZ37NgBOzs7qKurw8zMDCEhISVux8qVK1GjRg2oqanBysoKmzZtkivzzz//oF27dtDS0kLNmjWxZ8+eYrcFeD9y6OPjg+DgYBgbG8PQ0BBDhw6VSVTMzMwwZ84c+Pv7Q0dHB1WrVsWaNWtk6vnrr7/QpUsXGBgYwNDQEN7e3khJSRHPx8bGokGDBtDW1oa+vj5cXFzkRrk+Nnz4cGzevBlXr14tsIyZmRmWLFkic8zR0RHTpk0T9yUSCVatWgVvb29oa2tj1qxZmDZtGhwdHbFhwwZUrVoVUqkUgwcPRk5ODhYsWAAjIyNUrFgRs2fPlqmb0xGJiIgoD5MwokJoamqKScWbN28wc+ZMXL58Gbt378a9e/dkprfl5ubCxMQE27ZtQ2JiIqZMmYKJEydi27ZtAIAWLVpAX18fO3bsEK/JycnBtm3b4OvrCwC4cOECOnfujK5du+Lq1auYNm0aJk+ejLCwsGLHvGvXLvz0008YM2YMrl27hoEDB6JPnz6IiYmRKTd9+nR07twZV65cgYeHB3x9ffHvv/8Wqy15YmJikJycjJiYGISHhyMsLEwu1pCQEDg5OeHSpUsYMmQIBg8ejBs3bgAAXr58CTc3N0ilUhw/fhwnT56EVCpF69at8ebNG7x79w4+Pj5wdXXFlStXEBcXhwEDBkAikRTaB87Ozmjbti0CAwOL3W8FmTp1Kry9vXH16lX4+/sDAJKTk/HHH3/gwIED2Lp1KzZs2ABPT0/8+eefOHbsGObPn49JkybhzJkzxbpHdnY2MjIyZDYiIiL6fvHLmokKcO7cOWzZsgXNmzcHAPEDOACYm5vj559/RoMGDZCZmQmpVApVVVVMnz5dLFO9enWcPn0a27ZtQ+fOnaGsrIwuXbpgy5Yt6Nu3LwAgOjoaz549Q6dOnQAAixYtQvPmzTF58mQAgKWlJRITE7Fw4cJiP88UHBwMPz8/DBkyBAAwevRonDlzBsHBwXBzcxPL+fn5oVu3bgCAOXPmYNmyZTh37hxat25dZFvyGBgYYPny5VBWVoa1tTU8PT0RHR2N/v37i2U8PDzEWMaPH4/FixcjNjYW1tbWiIyMhJKSEtatWycmVqGhodDX10dsbCycnJyQnp6Otm3bokaNGgAAGxubYvXD3LlzUbt2bZw4cQI//vhjsa7JT/fu3WVee+B9krphwwbo6OjA1tYWbm5uuHnzJvbv3w8lJSVYWVlh/vz5iI2NxQ8//FCsWD/sbyIiIvq+cSSM6ANRUVGQSqXQ0NBAo0aN0KRJEyxbtgwAcOnSJXh7e6NatWrQ0dFB06ZNAQCpqani9atWrYKTkxMqVKgAqVSKtWvXypz39fVFbGws/v77bwBAREQEPDw8YGBgAABISkqCi4uLTEwuLi64ffs2cnJyitWGgupISkqSOVa7dm3x/7W1taGjoyMzvbKotgCAnZ0dlJWVxX1jY2OZOj6+j0QigZGRkVjmwoULuHPnDnR0dCCVSiGVSlGuXDm8fv0aycnJKFeuHPz8/ODu7g4vLy8sXboUaWlpxeoHW1tb9OrVC+PHjy9W+YI4OTnJHTMzM4OOjo64X6lSJdja2kJJSUnm2Md9UZDAwECkp6eL24MHDz4rZiIiIvq6MQkj+oCbmxsSEhJw8+ZNvH79Gjt37kTFihWRlZWFVq1aQSqVYvPmzYiPj8euXbsAvJ+mCADbtm3DqFGj4O/vj0OHDiEhIQF9+vQRzwNAgwYNUKNGDURGRuLVq1fYtWsXevToIZ4XBEFuqp0gCCVuR351fHxMVVVV7prc3Nxit6WoOopTJjc3F/Xq1UNCQoLMduvWLXTv3h3A+5GxuLg4ODs749dff4WlpWWxp/lNnz4dly5dyvdZLCUlJbm+zW/hDW1tbblj+bWpOH1REHV1dejq6spsRERE9P3idESiD2hra8PCwkLu+I0bN/DPP/9g3rx5MDU1BQCcP39epsyJEyfg7OwsTr0D3j879LHu3bsjIiICJiYmUFJSgqenp3jO1tYWJ0+elCl/+vRpWFpayow4FcbGxgYnT55Er169ZOoo7jS+krTlc9WtWxe//vorKlasWGjiUadOHdSpUweBgYFo1KgRtmzZUqxpfqamphg2bBgmTpwoTmfMU6FCBZlRtYyMDNy7d+/TG0NERERUTBwJIyqGqlWrQk1NDcuWLcPdu3exZ88ezJw5U6aMhYUFzp8/j4MHD+LWrVuYPHky4uPj5ery9fXFxYsXMXv2bHTs2BEaGhriuTFjxiA6OhozZ87ErVu3EB4ejuXLl5domfyxY8ciLCwMq1atwu3bt7Fo0SLs3LmzRHUUty2fy9fXF+XLl4e3tzdOnDiBe/fu4dixY/jpp5/w559/4t69ewgMDERcXBzu37+PQ4cO4datWyVKKAMDA/H333/jyJEjMsebNWuGTZs24cSJE7h27Rp69+5d7ESXiIiI6HMwCSMqhgoVKiAsLAzbt2+Hra0t5s2bh+DgYJkygwYNQvv27dGlSxc0bNgQT58+lRlJylOzZk3Ur18fV65cEVdFzFO3bl1s27YNkZGRsLe3x5QpUzBjxoxCF+XIzc2Fisr/DWr7+Phg6dKlWLhwIezs7LB69WqEhoaKz7AVR3Hb8rm0tLRw/PhxVK1aFe3bt4eNjQ38/f3x6tUr6OrqQktLCzdu3ECHDh1gaWmJAQMGYNiwYRg4cGCx71GuXDmMHz8er1+/ljkeGBiIJk2aoG3btvDw8ICPj4/caBkRERHRlyARPuWBEyL6asybNw+bN2/GtWvXyjoUKiUZGRnQ09NDejygKy3raOh7dOxRbFmHUGo+/J5FIqKyJP79Tk8v8vluPhNG9I16+fIlbty4gdDQULRp06aswyEiIiKiYuJ0RKJv1Jo1a9CiRQs4ODhgypQpZR0OERERERUTpyMSEX1lSjKdgYiIiL4OJfn7zZEwIiIiIiIiBWISRkREREREpEBMwoiIiIiIiBSISRgREREREZECMQkjIiIiIiJSICZhRERERERECsQkjIiIiIiISIGYhBERERERESkQkzAiIiIiIiIFYhJGRERERESkQEzCiIiIiIiIFIhJGBERERERkQIxCSMiIiIiIlIgJmFEREREREQKxCSMiIiIiIhIgZiEERERERERKRCTMCIiIiIiIgViEkZERERERKRAKmUdABERFeCWHiAt6yDoa3PsUWxZh/BVcXV1LesQiIhKjCNhRERERERECsQkjIiIiIiISIGYhBERERERESkQkzCiz5CSkgKJRIKEhITPrsvMzAxLliz57HrylGZs06ZNg6Oj42fXowil2e6PFec1kkgk2L17d6nfm4iIiL4fTMKICuDn5weJRCJuhoaGaN26Na5cuSKWMTU1RVpaGuzt7T/7fvHx8RgwYIC4X9wP8x/GmLc1bty4VGNTpJycHMydOxfW1tbQ1NREuXLl8MMPPyA0NLSsQ5N7jYryJRNCIiIi+nZxdUSiQrRu3Vr88P/w4UNMmjQJbdu2RWpqKgBAWVkZRkZGBV4vCAJycnKgolL0j1qFChU+Oc7Q0FC0bt1a3FdTUysytrJWUN9MmzYNa9aswfLly+Hk5ISMjAycP38ez549K6NI/8/nvEZEREREeTgSRlQIdXV1GBkZwcjICI6Ojhg/fjwePHiAJ0+eAJAf6YiNjYVEIsHBgwfh5OQEdXV1nDhxAi9evICvry+0tbVhbGyMxYsXo2nTphg5cqR4rw+nupmZmQEA2rVrB4lEIu4XRF9fX4zTyMgI5cqVk4vt2bNn8PX1RYUKFaCpqYmaNWvKjC79+eef6Nq1K8qVKwdtbW04OTnh7NmzMvfZtGkTzMzMoKenh65du+LFixfiOUEQsGDBApibm0NTUxMODg747bffxPMF9c3H9u7diyFDhqBTp06oXr06HBwc0LdvX4wePVosk5ubi/nz58PCwgLq6uqoWrUqZs+eLVPP3bt34ebmBi0tLTg4OCAuLk48FxYWBn19fURFRcHKygpaWlro2LEjsrKyEB4eDjMzMxgYGGD48OHIycnJ9zUCgNu3b6NJkybQ0NCAra0tDh8+LBND9erVAQB16tSBRCJB06ZNC3oJiYiI6D+EI2FExZSZmYmIiAhYWFjA0NCw0LLjxo1DcHAwzM3Noa+vj9GjR+PUqVPYs2cPKlWqhClTpuDixYsFPmcVHx+PihUriiNcysrKnx3/5MmTkZiYiD/++APly5fHnTt38OrVK7Ftrq6uqFKlCvbs2QMjIyNcvHgRubm54vXJycnYvXs3oqKi8OzZM3Tu3Bnz5s0Tk59JkyZh586dWLlyJWrWrInjx4+jR48eqFChgsz3+HzcNx8zMjLC0aNHMWTIkAJHngIDA7F27VosXrwYjRs3RlpaGm7cuCFTJigoCMHBwahZsyaCgoLQrVs33LlzRxx5e/nyJX7++WdERkbixYsXaN++Pdq3bw99fX3s378fd+/eRYcOHdC4cWN06dJFLobc3Fy0b98e5cuXx5kzZ5CRkSGTVAPAuXPn0KBBAxw5cgR2dnZQU1PLtz3Z2dnIzs4W9zMyMvItR0RERN8HJmFEhYiKioJU+v7bcrOysmBsbIyoqCgoKRU+iDxjxgy0bNkSAPDixQuEh4djy5YtaN68OYD30wcrV65c4PV5yUfeCFdRunXrJpOobd68WS7BS01NRZ06deDk5AQAMqNrW7ZswZMnTxAfH49y5coBACwsLGSuz83NRVhYGHR0dAAAPXv2RHR0NGbPno2srCwsWrQIR48eRaNGjQAA5ubmOHnyJFavXi2ThH3YN/lZtGgROnbsCCMjI9jZ2cHZ2Rne3t5o06YNgPf9uXTpUixfvhy9e/cGANSoUQONGzeWqScgIACenp4AgOnTp8POzg537tyBtbU1AODt27dYuXIlatSoAQDo2LEjNm3ahEePHkEqlcLW1hZubm6IiYnJNwk7cuQIkpKSkJKSAhMTEwDAnDlzxDiB/3sdDQ0NC30d586di+nTpxd4noiIiL4vnI5IVAg3NzckJCQgISEBZ8+eRatWrdCmTRvcv3+/0OvyEh3g/bS4t2/fokGDBuIxPT09WFlZlVqcixcvFuNMSEjIN8kZPHgwIiMj4ejoiHHjxuH06dPiuYSEBNSpU0dMwPJjZmYmJmAAYGxsjMePHwMAEhMT8fr1a7Rs2RJSqVTcNm7ciOTkZJl6PuybD8sOGjQIAGBra4tr167hzJkz6NOnDx49egQvLy/069cPAJCUlITs7GwxoS1I7dq1ZWIFIMYLAFpaWmICBgCVKlWCmZmZmHTnHfvwmg8lJSWhatWqYgIGQExASyowMBDp6eni9uDBg0+qh4iIiL4NHAkjKoS2trbMiFC9evWgp6eHtWvXYtasWYVel0cQBADvVzH8UN7x0mBkZCQ3cpX33FqevORx3759OHLkCJo3b46hQ4ciODgYmpqaRd5DVVVVZl8ikYjTFfP+u2/fPlSpUkWmnLq6usz+h33z4aqBurq64v8rKSmhfv36qF+/PkaNGoXNmzejZ8+eCAoKKlasH8eb1/cfTq/Mrz2FtfFj+b1+H7/GxaWuri7XT0RERPT94kgYUQlIJBIoKSmJz1IVR40aNaCqqopz586JxzIyMnD79u1Cr1NVVZVZFKI0VKhQAX5+fti8eTOWLFmCNWvWAHg/apSQkIB///33k+q1tbWFuro6UlNTYWFhIbOZmpoWeN2H5SpWrFho/cD7KaE1a9aEpqYmoqOjPynW0mJra4vU1FT8/fff4rEPF/8AID4DVtqvIxEREX3bOBJGVIjs7Gw8fPgQwPvVBZcvX47MzEx4eXkVuw4dHR307t0bY8eORbly5VCxYkVMnToVSkpKhY6cmJmZITo6Gi4uLlBXV4eBgcFntWXKlCmoV68e7OzskJ2djaioKNjY2AB4/0zZnDlz4OPjg7lz58LY2BiXLl1C5cqVizXFTkdHBwEBARg1ahRyc3PRuHFjZGRk4PTp05BKpeKzW8XRsWNHuLi4wNnZGUZGRrh37x4CAwNhaWkJa2trqKioYPz48Rg3bhzU1NTg4uKCJ0+e4Pr16+jbt+8n909JtWjRAlZWVujVqxdCQkKQkZGBoKAgmTIVK1aEpqYmDhw4ABMTE2hoaEBPT09hMRIREdHXiSNhRIU4cOAAjI2NYWxsjIYNGyI+Ph7bt28v8VLjixYtQqNGjdC2bVu0aNECLi4usLGxgYaGRoHXhISE4PDhwzA1NUWdOnU+syXvR2UCAwNRu3ZtNGnSBMrKyoiMjBTPHTp0CBUrVoSHhwdq1aqFefPmlWhVxpkzZ2LKlCmYO3cubGxs4O7ujr1794rLtBdX3nVeXl6wtLRE7969YW1tjUOHDokrG06ePBljxozBlClTYGNjgy5duhT47NaXoqSkhF27diE7OxsNGjRAv3795JbJV1FRwc8//4zVq1ejcuXK8Pb2VmiMRERE9HWSCKX5YAoRFUtWVhaqVKmCkJAQhY7e0LchIyMDenp6SI8HdKVFl6f/lmOPYss6hK/Kh6uvEhGVJfHvd3q6zLPu+eF0RCIFuHTpEm7cuIEGDRogPT0dM2bMAACOjBARERH9BzEJI1KQ4OBg3Lx5E2pqaqhXrx5OnDiB8uXLl3VYRERERKRgnI5IRPSVKcl0BiIiIvo6lOTvNxfmICIiIiIiUiAmYURERERERArEJIyIiIiIiEiBmIQREREREREpEJMwIiIiIiIiBWISRkREREREpEBMwoiIiIiIiBSISRgREREREZECMQkjIiIiIiJSICZhRERERERECsQkjIiIiIiISIGYhBERERERESkQkzAiIiIiIiIFYhJGRERERESkQEzCiIiIiIiIFIhJGBERERERkQIxCSMiIiIiIlIgJmFEREREREQKpFLWARARUQFu6QHSsg6CvjbHHsWWdQil5vmFlZ9dh/foyFKIhIhIsTgSRkREREREpEBMwoiIiIiIiBSISRgREREREZECffVJWNOmTTFy5Ehx/+XLl+jQoQN0dXUhkUjw/PlzhcYzbdo0ODo6ivt+fn7w8fEptfolEgl2795davXlSUlJgUQiQUJCQqnX/T37+P1H7/Xs2RNz5swp6zC+uI9/vj9+P5iZmWHJkiUFXt+xY0csWrToywVIRERE36QSJWGlnXAUx86dOzFz5kxxPzw8HCdOnMDp06eRlpYGPT29UrtXXqKS33bmzJl8r1m6dCnCwsJKLYbSUJqvk5mZGWJjYxEbGwszMzPxeFhYGPT19UvlHqUp7zUE3ifMfn5++Zbz8/Mr8LXO2yh/V65cwb59+zB8+PAiy166dAmdOnVCpUqVoKGhAUtLS/Tv3x+3bt1SQKSl7+PfR0WZMmUKZs+ejYyMjC8YFREREX1rvvqRsHLlykFHR0fcT05Oho2NDezt7WFkZPRJH5ZzcnKQm5tb4PkjR44gLS1NZqtXr16+ZfX0/l97dx4U5X3GAfy7ICDnInIrAlYwoEBFDId3CoiYCCEFQjwwVFLbKKMGjYknGiXSYNNgcEysG6+Axwga0BCiYZVYkWgJRimjRLukA0U8ACGAyvYPhnd8BblcdsF+PzM7w77vy/s+++zzwj77ew9pv2xGqHN/+9vfRO8vAMhksnbTqL3t27cjPDxctF92JCsrCz4+PmhqasKBAwdQUlKCffv2QSqVYu3atWqKVrWe/HvUFXd3dzg4OODAgQN9GBURERENNL1uwpRKJZKSkjBy5Ejo6+vDw8MDR44cEebn5eVBIpHg1KlT8PLygoGBAfz8/FBaWios03Zo3759++Dg4ACpVIrXX38ddXV1wjKPH/4zbdo0JCcn48yZM5BIJJg2bRoAoLm5GStXrsSwYcNgaGgIb29v5OXlCetoG7XJysqCq6sr9PT08O9///upr23o0KGwtrYWPXR0dDpc9slRpyNHjsDNzQ36+voYOnQo/P39UV9fDwAoLCxEQEAAzM3NIZVKMXXqVFy6dKndOisqKjBz5kzo6+vD0dERhw8fFs3/z3/+g8jISAwZMgRDhw5FSEgIbt68KeR0z549OHbsmDCi83gufv75Z0yfPh0GBgbw8PDAP/7xj6fmoae++uorjB8/HoMHD8bIkSORkJCAhw8fCvMlEgl27dqFV199FQYGBnBycsLx48eF+V3VzM2bN6GlpYUffvhBtN2UlBTY29tDqVR2O1apVCp6fwHA1NS03TQAaGlpwcqVK2FmZgZra2ts2LBBtC6FQoGQkBAYGRnBxMQEERER+O9//yvMb6vz3bt3Y8SIETAyMsKf/vQnPHr0CElJSbC2toalpSU2b94sWq9EIsGOHTueWgvNzc1YvHgxbGxsMHjwYDg4OCAxMbHHcXW2/z2ppaUFhw8fxuzZszvNb0NDA958800EBwfj+PHj8Pf3h6OjI7y9vfHRRx9h586dADoeUc3MzBR9udJX+QM635c60tXhqTKZDFKpFLm5ucK02bNnIy0trdN8ERER0f+XXjdha9asgUwmw44dO3DlyhUsW7YMc+fOhVwuFy23evVqJCcn44cffsCgQYMQExMjml9WVobMzExkZWUhKysLcrkcH374YYfbPHr0KGJjY+Hr64uKigocPXoUAPDmm2/i+++/R3p6OoqLixEeHo6goCBcu3ZN+N2GhgYkJiZi165duHLlCiwtLXv70p+qoqICUVFRiImJQUlJCfLy8hAWFiY0B3V1dYiOjsbZs2dx/vx5ODk5ITg4uN2H3rVr1+K1117Djz/+iLlz5yIqKgolJSXC65g+fTqMjIxw5swZ5Ofnw8jICEFBQWhubkZ8fDwiIiIQFBQkjOj4+fkJ6169ejXi4+NRVFQEZ2dnREVFiRql3srJycHcuXMRFxeHq1evYufOnfjiiy/afTBOSEhAREQEiouLERwcjDlz5uDOnTuiZZ5WMw4ODvD394dMJhMtL5PJhMML+8KePXtgaGiIgoICJCUlYePGjcKHbKVSidDQUNy5cwdyuRy5ubkoKytDZGSkaB1lZWU4efIkvv76a6SlpWH37t2YNWsWfvnlF8jlcmzduhVr1qxpd9hrZ7XwySef4Pjx4zh06BBKS0uxf/9+4ZDRnsTV3f0PaD0U8d69e/Dy8uo0Zzk5OaiursbKlSs7nN/T0eO+yF9X+1JPffTRR4iPj0dOTg4CAgKE6S+++CIuXLiApqamp/5uU1MTamtrRQ8iIiJ6fvXqZs319fXYtm0bTp8+DV9fXwDAyJEjkZ+fj507d2Lq1KnCsps3bxaer1q1CrNmzUJjYyMGDx4MoPWb9S+++EI4xGfevHk4depUuw/vQOuhQAYGBtDV1RVGKsrKypCWloZffvkFtra2AID4+Hh8/fXXkMlkwsUDHjx4gNTUVHh4eHT5+vz8/KClJe5Pa2pqoK2t3envVVRU4OHDhwgLC4O9vT0AwM3NTZj/0ksviZbfuXMnhgwZArlcjpdfflmYHh4ejoULFwIANm3ahNzcXKSkpCA1NRXp6enQ0tLCrl27hKZDJpPB1NQUeXl5CAwMhL6+PpqamkSjOW3i4+Mxa9YsAK0N0ZgxY3D9+nW88MILHb6mx0cFOhsh2Lx5M1atWoXo6GgArfWwadMmrFy5EuvXrxeWW7BgAaKiogAAW7ZsQUpKCi5cuICgoCDRup5WMwsXLsSiRYuwbds26Onp4ccff0RRUZHQkDs4OAhN75MjVr3l7u4uvAYnJyds374dp06dQkBAAL799lsUFxfjxo0bsLOzAwDs27cPY8aMQWFhISZMmACgtc53794NY2NjuLq6Yvr06SgtLcWJEyegpaWF0aNHY+vWrcjLy4OPj4+w7c5qQaFQwMnJCZMmTYJEIhFqDkCP4uru/ge01oC2tnaXX2K0fQHytLrqqb7IX3f2pe567733sGfPHuTl5Yn2eQAYNmwYmpqaUFlZKXqPHpeYmIiEhISepoWIiIgGqF6NhF29ehWNjY0ICAiAkZGR8Ni7dy/KyspEy7q7uws/29jYAACqqqqEaQ4ODqJzLGxsbETzu3Lp0iUolUo4OzuLYpHL5aJYdHV1RbF05uDBgygqKhI9umrAAMDDwwO/+93v4ObmhvDwcHz++ee4e/euML+qqgqLFi2Cs7MzpFIppFIp7t+/D4VCIVpPW2P7+PO2b+8vXryI69evw9jYWHitZmZmaGxsbJf7jnT1fvTWxYsXsXHjRtF7EBsbi4qKCjQ0NHS4fUNDQxgbG7fbfmcxhoaGYtCgQcjIyAAA7N69G9OnTxddNETVnqybx2u0pKQEdnZ2QqMDAK6urjA1NRXeM6B9nVtZWcHV1VXU7FtZWbXLRWe1sGDBAhQVFWH06NGIi4vDN998IyzX27i62v9+/fVX6OnpiUYdt2zZInrfFQpFjw4N7Y6+yN+z7kttkpOTsXPnTuTn57drwABAX18fAET7wZPee+891NTUCI/y8vJub5+IiIgGnl6NhLVd1CI7OxvDhg0TzdPT0xM9f/xcqrYPbo9fFOPJc60kEkmnF83oKBZtbW1cvHixXaNkZGQk/Kyvr9/tw9Xs7OwwatSobsfQRltbG7m5uTh37hy++eYbpKSkYPXq1SgoKICjoyMWLFiAW7du4eOPP4a9vT309PTg6+vbrUOfHs/d+PHjOzzR38LCosv1dPV+9FZLSwsSEhIQFhbWbl7bqOeT22+L4cntdxajrq4u5s2bB5lMhrCwMHz55ZedXiJcFTqLWalUdlhXT07vaB29rf229Xp6euLGjRs4efIkvv32W0RERMDf3x9Hjhx5prg6i8Hc3BwNDQ1obm6Grq4uAGDRokWIiIgQlrG1tYWzszMA4F//+le7RuhxWlpa7Rq2Bw8etFuuL/L3rPtSm8mTJyM7OxuHDh3CqlWr2s1vO9y2s3Xq6em1+9tJREREz69eNWFtF7dQKBSiQw81Ydy4cXj06BGqqqowefJkjcYCtH7AmzhxIiZOnIh169bB3t4eGRkZWL58Oc6ePYvU1FQEBwcDAMrLy1FdXd1uHefPn8f8+fNFz8eNGweg9YP3wYMHYWlpCRMTkw5j0NXVxaNHj/rg1T2dp6cnSktLe9W89tTChQsxduxYpKam4sGDBx02furi6uoKhUKB8vJyYdTp6tWrqKmpgYuLyzOvv7NaAAATExNERkYiMjISv//97xEUFIQ7d+70WVxt98i7evWq8LOZmRnMzMxEywUGBsLc3BxJSUnCqOXj7t27B1NTU1hYWKCurg719fUwNDQEAJXey+5Z96XuePHFF7FkyRLMmDED2traWLFihWj+Tz/9hOHDh8Pc3LzX2yAiIqLnS6+aMGNjY8THx2PZsmVoaWnBpEmTUFtbi3PnzsHIyEg4L0gdnJ2dMWfOHMyfPx/JyckYN24cqqurcfr0abi5uQkNT0/cvn0blZWVommmpqaiEZ2OFBQU4NSpUwgMDISlpSUKCgpw69Yt4UPvqFGjsG/fPnh5eaG2thYrVqwQDlV63OHDh+Hl5YVJkybhwIEDuHDhAv7+978DAObMmYO//OUvCAkJwcaNGzF8+HAoFAocPXoUK1aswPDhw+Hg4ICcnByUlpZi6NChKr2X2tOsW7cOL7/8Muzs7BAeHg4tLS0UFxfj8uXL+OCDD1S6LRcXF/j4+ODdd99FTExMhzlUF39/f7i7u2POnDn4+OOP8fDhQ/z5z3/G1KlTu7x4RXd0Vgt//etfYWNjg9/+9rfQ0tLC4cOHYW1tDVNT0z6Ly8LCAp6ensjPzxfdtPxJhoaG2LVrF8LDwzF79mzExcVh1KhRqK6uxqFDh6BQKJCeng5vb28YGBjg/fffx5IlS3DhwgWV3nfvWfel7vL19cXJkycRFBSEQYMGYdmyZcK8s2fP9uj8MiIiInr+9eicsJaWFgwa1Nq3bdq0CevWrUNiYiJcXFwwY8YMfPXVV3B0dOyTQDsjk8kwf/58vPPOOxg9ejRmz56NgoIC0fkwPeHv7w8bGxvRIzMzs8vfMzExwZkzZxAcHAxnZ2esWbMGycnJmDlzJoDW85fu3r2LcePGYd68eYiLi+vwAgcJCQlIT0+Hu7s79uzZgwMHDsDV1RUAYGBggDNnzmDEiBEICwuDi4sLYmJi8Ouvvwrf5sfGxmL06NHw8vKChYUFvv/++17loTOP1wIAzJgxA1lZWcjNzcWECRPg4+ODbdu2PfVCBM/qD3/4A5qbm9tdbVPdJBIJMjMzMWTIEEyZMgX+/v4YOXIkDh48qJL1d1YLRkZG2Lp1K7y8vDBhwgTcvHlTuFBFX8b11ltvdeu+VyEhITh37hx0dHTwxhtv4IUXXkBUVBRqamqExtzMzAz79+/HiRMn4ObmhrS0NJVdUAV49n2pJyZOnIjs7GysXbsWn3zyCQCgsbERGRkZiI2NVdlrIiIiooFPouzBGfRBQUEYNWoUtm/f3pcx0QDw4YcfYv/+/fjpp580sv3NmzcjPT0dly9f1sj21UEikSAjI0N0H7r+oLGxEaNHj0Z6enqn53tpWn/I36effopjx46JLprSHbW1tZBKpagpBEyMul6e/r/I/5un6RBU5t7FHc+8jpDl6SqIhIjo2Qn/v2tquvxCt1sjYXfv3kV2djby8vLg7++vkiBpYGpoaMClS5cgk8k0Ugv3799HYWEhUlJSEBcXp/btU+uFVvbu3dvh+YwkpqOjg5SUFE2HQURERP1Mt84Ji4mJQWFhId555x2EhIT0dUzUj3322WfYuHEj/P39sW7dOrVvf/HixUhLS0NoaKjGD0X8f6bpC/IMFG+99ZamQyAiIqJ+qEeHIxIRUd/ryeEMRERE1D+o/HBEIiIiIiIiUg02YURERERERGrEJoyIiIiIiEiN2IQRERERERGpEZswIiIiIiIiNWITRkREREREpEZswoiIiIiIiNSoWzdrJiIi9Wm7fWNtba2GIyEiIqLuavu/3Z3bMLMJIyLqZ27fvg0AsLOz03AkRERE1FN1dXWQSqWdLsMmjIionzEzMwMAKBSKLv+IU9dqa2thZ2eH8vJymJiYaDqc5wJzqlrMp+oxp6rFfHaPUqlEXV0dbG1tu1yWTRgRUT+jpdV6uq5UKuU/OxUyMTFhPlWMOVUt5lP1mFPVYj671t0vT3lhDiIiIiIiIjViE0ZERERERKRGbMKIiPoZPT09rF+/Hnp6epoO5bnAfKoec6pazKfqMaeqxXyqnkTZnWsoEhERERERkUpwJIyIiIiIiEiN2IQRERERERGpEZswIiIiIiIiNWITRkREREREpEZswoiIiIiIiNSITRgRUT+TmpoKR0dHDB48GOPHj8fZs2c1HdKAtGHDBkgkEtHD2tpa02ENGGfOnMErr7wCW1tbSCQSZGZmiuYrlUps2LABtra20NfXx7Rp03DlyhXNBDtAdJXTBQsWtKtZHx8fzQQ7ACQmJmLChAkwNjaGpaUlQkNDUVpaKlqGddp93ckna1R12IQREfUjBw8exNKlS7F69Wr885//xOTJkzFz5kwoFApNhzYgjRkzBhUVFcLj8uXLmg5pwKivr4eHhwe2b9/e4fykpCRs27YN27dvR2FhIaytrREQEIC6ujo1RzpwdJVTAAgKChLV7IkTJ9QY4cAil8vx9ttv4/z588jNzcXDhw8RGBiI+vp6YRnWafd1J58Aa1RVeJ8wIqJ+xNvbG56entixY4cwzcXFBaGhoUhMTNRgZAPPhg0bkJmZiaKiIk2HMuBJJBJkZGQgNDQUQOvogq2tLZYuXYp3330XANDU1AQrKyts3boVf/zjHzUY7cDwZE6B1lGGe/futRsho+65desWLC0tIZfLMWXKFNbpM3oynwBrVJU4EkZE1E80Nzfj4sWLCAwMFE0PDAzEuXPnNBTVwHbt2jXY2trC0dERr7/+On7++WdNh/RcuHHjBiorK0W1qqenh6lTp7JWn1FeXh4sLS3h7OyM2NhYVFVVaTqkAaOmpgYAYGZmBoB1+qyezGcb1qhqsAkjIuonqqur8ejRI1hZWYmmW1lZobKyUkNRDVze3t7Yu3cvcnJy8Pnnn6OyshJ+fn64ffu2pkMb8NrqkbWqWjNnzsSBAwdw+vRpJCcno7CwEC+99BKampo0HVq/p1QqsXz5ckyaNAljx44FwDp9Fh3lE2CNqtIgTQdARERiEolE9FypVLabRl2bOXOm8LObmxt8fX3xm9/8Bnv27MHy5cs1GNnzg7WqWpGRkcLPY8eOhZeXF+zt7ZGdnY2wsDANRtb/LV68GMXFxcjPz283j3Xac0/LJ2tUdTgSRkTUT5ibm0NbW7vdN7RVVVXtvsmlnjM0NISbmxuuXbum6VAGvLarTLJW+5aNjQ3s7e1Zs11YsmQJjh8/ju+++w7Dhw8XprNOe+dp+ewIa7T32IQREfUTurq6GD9+PHJzc0XTc3Nz4efnp6Gonh9NTU0oKSmBjY2NpkMZ8BwdHWFtbS2q1ebmZsjlctaqCt2+fRvl5eWs2adQKpVYvHgxjh49itOnT8PR0VE0n3XaM13lsyOs0d7j4YhERP3I8uXLMW/ePHh5ecHX1xefffYZFAoFFi1apOnQBpz4+Hi88sorGDFiBKqqqvDBBx+gtrYW0dHRmg5tQLh//z6uX78uPL9x4waKiopgZmaGESNGYOnSpdiyZQucnJzg5OSELVu2wMDAAG+88YYGo+7fOsupmZkZNmzYgNdeew02Nja4efMm3n//fZibm+PVV1/VYNT919tvv40vv/wSx44dg7GxsTDiJZVKoa+vD4lEwjrtga7yef/+fdaoKimJiKhf+fTTT5X29vZKXV1dpaenp1Iul2s6pAEpMjJSaWNjo9TR0VHa2toqw8LClFeuXNF0WAPGd999pwTQ7hEdHa1UKpXKlpYW5fr165XW1tZKPT095ZQpU5SXL1/WbND9XGc5bWhoUAYGBiotLCyUOjo6yhEjRiijo6OVCoVC02H3Wx3lEoBSJpMJy7BOu6+rfLJGVYv3CSMiIiIiIlIjnhNGRERERESkRmzCiIiIiIiI1IhNGBERERERkRqxCSMiIiIiIlIjNmFERERERERqxCaMiIiIiIhIjdiEERERERERqRGbMCIiIiIiIjViE0ZERERERKRGbMKIiIiIiIjUiE0YERERERGRGv0P7llD9JpDbasAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Create the bar plot for the top 10 athletes\n",
        "fig, ax = plt.subplots()\n",
        "colors = ['#FFD700', '#C0C0C0', '#B08D57']\n",
        "top_10.loc[:, ['Gold', 'Silver', 'Bronze']].plot(kind='barh', stacked=True, color=colors, ax=ax)\n",
        "ax.set_title('The Most Decorated Olympians of All Time')\n",
        "ax.invert_yaxis()\n",
        "ax.legend().set_visible(False)\n",
        "ax.set_ylabel('')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7f82a1e8",
      "metadata": {
        "id": "7f82a1e8"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "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": "3.9.13"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}