Olympics_Data_Analysis.ipynb

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      "name": "Olympics Data Analysis.ipynb",
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        "<a href=\"https://colab.research.google.com/github/preethi4848/ETG_Olympics-Data-Analysis/blob/main/Olympics_Data_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
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        "id": "nFJWAnzK-h0W"
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      "source": [
        "# **AN EXPLORATORY DATA ANALYSIS ON OLYMPICS GAMES FROM 1986 - 2012**\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "This is a historical dataset on the modern Olympic Games, including all the Games from Athens 1896 to Rio 2012. I have fetched the dataset from this link: [Kaggle](https://www.kaggle.com/the-guardian/olympic-games)\n",
        "\n",
        "**Submitted By :** [Preethi G](https://preethi4848.github.io/My-Porfolio/)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "#Importing libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "#visualization\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": 247,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RF57EDJB8fHW"
      },
      "source": [
        "#Reading the three datasets\n",
        "dict_df = pd.read_csv(\"/content/dictionary.csv\")\n",
        "summer_df = pd.read_csv(\"/content/summer.csv\")\n",
        "winter_df = pd.read_csv(\"/content/winter.csv\")"
      ],
      "execution_count": 248,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T0vDIoYO-5Bo"
      },
      "source": [
        "### **DATA CLEANING & UNDERSTANDING**"
      ]
    },
    {
      "cell_type": "code",
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        "id": "dxKny3xC81PN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
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        "outputId": "e57f74a0-fccd-4ee0-dd2c-2dd5d8a64628"
      },
      "source": [
        "dict_df.head()"
      ],
      "execution_count": 249,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Country</th>\n",
              "      <th>Code</th>\n",
              "      <th>Population</th>\n",
              "      <th>GDP per Capita</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>32526562.0</td>\n",
              "      <td>594.323081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Albania</td>\n",
              "      <td>ALB</td>\n",
              "      <td>2889167.0</td>\n",
              "      <td>3945.217582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>ALG</td>\n",
              "      <td>39666519.0</td>\n",
              "      <td>4206.031232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>American Samoa*</td>\n",
              "      <td>ASA</td>\n",
              "      <td>55538.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Andorra</td>\n",
              "      <td>AND</td>\n",
              "      <td>70473.0</td>\n",
              "      <td>NaN</td>\n",
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              "           Country Code  Population  GDP per Capita\n",
              "0      Afghanistan  AFG  32526562.0      594.323081\n",
              "1          Albania  ALB   2889167.0     3945.217582\n",
              "2          Algeria  ALG  39666519.0     4206.031232\n",
              "3  American Samoa*  ASA     55538.0             NaN\n",
              "4          Andorra  AND     70473.0             NaN"
            ]
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          "metadata": {},
          "execution_count": 249
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        "id": "4CAxBvixOl0c",
        "outputId": "0fde0ff3-84a0-4d97-ffc8-b8b444e98faf"
      },
      "source": [
        "summer_df"
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      "execution_count": 250,
      "outputs": [
        {
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              "      <td>1896</td>\n",
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              "      <td>Swimming</td>\n",
              "      <td>HAJOS, Alfred</td>\n",
              "      <td>HUN</td>\n",
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              "      <td>DRIVAS, Dimitrios</td>\n",
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              "      <th>3</th>\n",
              "      <td>1896</td>\n",
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              "      <td>MALOKINIS, Ioannis</td>\n",
              "      <td>GRE</td>\n",
              "      <td>Men</td>\n",
              "      <td>100M Freestyle For Sailors</td>\n",
              "      <td>Gold</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1896</td>\n",
              "      <td>Athens</td>\n",
              "      <td>Aquatics</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>CHASAPIS, Spiridon</td>\n",
              "      <td>GRE</td>\n",
              "      <td>Men</td>\n",
              "      <td>100M Freestyle For Sailors</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",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31160</th>\n",
              "      <td>2012</td>\n",
              "      <td>London</td>\n",
              "      <td>Wrestling</td>\n",
              "      <td>Wrestling Freestyle</td>\n",
              "      <td>JANIKOWSKI, Damian</td>\n",
              "      <td>POL</td>\n",
              "      <td>Men</td>\n",
              "      <td>Wg 84 KG</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31161</th>\n",
              "      <td>2012</td>\n",
              "      <td>London</td>\n",
              "      <td>Wrestling</td>\n",
              "      <td>Wrestling Freestyle</td>\n",
              "      <td>REZAEI, Ghasem Gholamreza</td>\n",
              "      <td>IRI</td>\n",
              "      <td>Men</td>\n",
              "      <td>Wg 96 KG</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31162</th>\n",
              "      <td>2012</td>\n",
              "      <td>London</td>\n",
              "      <td>Wrestling</td>\n",
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              "      <td>TOTROV, Rustam</td>\n",
              "      <td>RUS</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>31163</th>\n",
              "      <td>2012</td>\n",
              "      <td>London</td>\n",
              "      <td>Wrestling</td>\n",
              "      <td>Wrestling Freestyle</td>\n",
              "      <td>ALEKSANYAN, Artur</td>\n",
              "      <td>ARM</td>\n",
              "      <td>Men</td>\n",
              "      <td>Wg 96 KG</td>\n",
              "      <td>Bronze</td>\n",
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              "    <tr>\n",
              "      <th>31164</th>\n",
              "      <td>2012</td>\n",
              "      <td>London</td>\n",
              "      <td>Wrestling</td>\n",
              "      <td>Wrestling Freestyle</td>\n",
              "      <td>LIDBERG, Jimmy</td>\n",
              "      <td>SWE</td>\n",
              "      <td>Men</td>\n",
              "      <td>Wg 96 KG</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>31165 rows × 9 columns</p>\n",
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              "       Year    City      Sport  ... Gender                       Event   Medal\n",
              "0      1896  Athens   Aquatics  ...    Men              100M Freestyle    Gold\n",
              "1      1896  Athens   Aquatics  ...    Men              100M Freestyle  Silver\n",
              "2      1896  Athens   Aquatics  ...    Men  100M Freestyle For Sailors  Bronze\n",
              "3      1896  Athens   Aquatics  ...    Men  100M Freestyle For Sailors    Gold\n",
              "4      1896  Athens   Aquatics  ...    Men  100M Freestyle For Sailors  Silver\n",
              "...     ...     ...        ...  ...    ...                         ...     ...\n",
              "31160  2012  London  Wrestling  ...    Men                    Wg 84 KG  Bronze\n",
              "31161  2012  London  Wrestling  ...    Men                    Wg 96 KG    Gold\n",
              "31162  2012  London  Wrestling  ...    Men                    Wg 96 KG  Silver\n",
              "31163  2012  London  Wrestling  ...    Men                    Wg 96 KG  Bronze\n",
              "31164  2012  London  Wrestling  ...    Men                    Wg 96 KG  Bronze\n",
              "\n",
              "[31165 rows x 9 columns]"
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      "source": [
        "summer_df.head()"
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      "outputs": [
        {
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              "      <td>1896</td>\n",
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              "      <td>100M Freestyle For Sailors</td>\n",
              "      <td>Bronze</td>\n",
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              "      <th>3</th>\n",
              "      <td>1896</td>\n",
              "      <td>Athens</td>\n",
              "      <td>Aquatics</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>MALOKINIS, Ioannis</td>\n",
              "      <td>GRE</td>\n",
              "      <td>Men</td>\n",
              "      <td>100M Freestyle For Sailors</td>\n",
              "      <td>Gold</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1896</td>\n",
              "      <td>Athens</td>\n",
              "      <td>Aquatics</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>CHASAPIS, Spiridon</td>\n",
              "      <td>GRE</td>\n",
              "      <td>Men</td>\n",
              "      <td>100M Freestyle For Sailors</td>\n",
              "      <td>Silver</td>\n",
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            "text/plain": [
              "   Year    City     Sport  ... Gender                       Event   Medal\n",
              "0  1896  Athens  Aquatics  ...    Men              100M Freestyle    Gold\n",
              "1  1896  Athens  Aquatics  ...    Men              100M Freestyle  Silver\n",
              "2  1896  Athens  Aquatics  ...    Men  100M Freestyle For Sailors  Bronze\n",
              "3  1896  Athens  Aquatics  ...    Men  100M Freestyle For Sailors    Gold\n",
              "4  1896  Athens  Aquatics  ...    Men  100M Freestyle For Sailors  Silver\n",
              "\n",
              "[5 rows x 9 columns]"
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        "outputId": "4bdd41c8-7454-4f02-e4f8-ec3ecd95a90b"
      },
      "source": [
        "winter_df.head()"
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      "execution_count": 252,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>1924</td>\n",
              "      <td>Chamonix</td>\n",
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              "      <td>BERTHET, G.</td>\n",
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              "      <td>1924</td>\n",
              "      <td>Chamonix</td>\n",
              "      <td>Biathlon</td>\n",
              "      <td>Biathlon</td>\n",
              "      <td>MANDRILLON, Maurice</td>\n",
              "      <td>FRA</td>\n",
              "      <td>Men</td>\n",
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              "      <td>Bronze</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1924</td>\n",
              "      <td>Chamonix</td>\n",
              "      <td>Biathlon</td>\n",
              "      <td>Biathlon</td>\n",
              "      <td>VANDELLE, André</td>\n",
              "      <td>FRA</td>\n",
              "      <td>Men</td>\n",
              "      <td>Military Patrol</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1924</td>\n",
              "      <td>Chamonix</td>\n",
              "      <td>Biathlon</td>\n",
              "      <td>Biathlon</td>\n",
              "      <td>AUFDENBLATTEN, Adolf</td>\n",
              "      <td>SUI</td>\n",
              "      <td>Men</td>\n",
              "      <td>Military Patrol</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year      City     Sport Discipline  ... Country Gender            Event   Medal\n",
              "0  1924  Chamonix  Biathlon   Biathlon  ...     FRA    Men  Military Patrol  Bronze\n",
              "1  1924  Chamonix  Biathlon   Biathlon  ...     FRA    Men  Military Patrol  Bronze\n",
              "2  1924  Chamonix  Biathlon   Biathlon  ...     FRA    Men  Military Patrol  Bronze\n",
              "3  1924  Chamonix  Biathlon   Biathlon  ...     FRA    Men  Military Patrol  Bronze\n",
              "4  1924  Chamonix  Biathlon   Biathlon  ...     SUI    Men  Military Patrol    Gold\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 252
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tdTaCAdr_OTs"
      },
      "source": [
        "### **OBSERVATIONS**\n",
        "Here are my observations from the three datasets:\n",
        "\n",
        "I'm going to grouped the datasets into two, i.e \n",
        "1. Summer Olympics\n",
        "2. Winter Olympics\n",
        "\n",
        "I'II merge Summer and dict dataframe to have Summer Olympics\n",
        "\n",
        "Also, I'II merge Winter and dict dataframe to have Winter Olympics\n",
        "\n",
        "Looking at the three datasets, I noticed column \"Code\" from dict dataframe and column \"Country\" from winter and summer dataframe are kind of similar. Now, Since I want to merge the dataframe, the best approach is to change the column \"Country\" in Winter nad Summer to \"Code\" so that i can merge them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uu2ODBSW9Jl0"
      },
      "source": [
        "#To change the feature country to code\n",
        "summer_df.rename(columns={\"Country\": \"Code\"}, inplace =True)\n",
        "winter_df.rename(columns={\"Country\": \"Code\"}, inplace =True)"
      ],
      "execution_count": 253,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OCGVaNNY9JoJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "24aba281-ad9a-42ab-f33f-bc543e6ffa10"
      },
      "source": [
        "summer_df.head() #To see  the changes"
      ],
      "execution_count": 254,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Year</th>\n",
              "      <th>City</th>\n",
              "      <th>Sport</th>\n",
              "      <th>Discipline</th>\n",
              "      <th>Athlete</th>\n",
              "      <th>Code</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Event</th>\n",
              "      <th>Medal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1896</td>\n",
              "      <td>Athens</td>\n",
              "      <td>Aquatics</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>HAJOS, Alfred</td>\n",
              "      <td>HUN</td>\n",
              "      <td>Men</td>\n",
              "      <td>100M Freestyle</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1896</td>\n",
              "      <td>Athens</td>\n",
              "      <td>Aquatics</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>HERSCHMANN, Otto</td>\n",
              "      <td>AUT</td>\n",
              "      <td>Men</td>\n",
              "      <td>100M Freestyle</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1896</td>\n",
              "      <td>Athens</td>\n",
              "      <td>Aquatics</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>DRIVAS, Dimitrios</td>\n",
              "      <td>GRE</td>\n",
              "      <td>Men</td>\n",
              "      <td>100M Freestyle For Sailors</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1896</td>\n",
              "      <td>Athens</td>\n",
              "      <td>Aquatics</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>MALOKINIS, Ioannis</td>\n",
              "      <td>GRE</td>\n",
              "      <td>Men</td>\n",
              "      <td>100M Freestyle For Sailors</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1896</td>\n",
              "      <td>Athens</td>\n",
              "      <td>Aquatics</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>CHASAPIS, Spiridon</td>\n",
              "      <td>GRE</td>\n",
              "      <td>Men</td>\n",
              "      <td>100M Freestyle For Sailors</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year    City     Sport  ... Gender                       Event   Medal\n",
              "0  1896  Athens  Aquatics  ...    Men              100M Freestyle    Gold\n",
              "1  1896  Athens  Aquatics  ...    Men              100M Freestyle  Silver\n",
              "2  1896  Athens  Aquatics  ...    Men  100M Freestyle For Sailors  Bronze\n",
              "3  1896  Athens  Aquatics  ...    Men  100M Freestyle For Sailors    Gold\n",
              "4  1896  Athens  Aquatics  ...    Men  100M Freestyle For Sailors  Silver\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 254
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M-wYDkT3_rxW"
      },
      "source": [
        "\n",
        "From the code above, we now have two datasets, Winter Olympics and Summer Olympics. First, I'II be working on the-\n",
        "### **SUMMER OLYMPICS** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S15dWtwo9Jsp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "fe771d91-5454-4f8d-fe85-30b0061f4b64"
      },
      "source": [
        "#Check the describtion of the numeric features\n",
        "summer_df.describe()"
      ],
      "execution_count": 255,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>31165.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1970.482785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>33.158416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1896.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1948.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1980.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2012.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Year\n",
              "count  31165.000000\n",
              "mean    1970.482785\n",
              "std       33.158416\n",
              "min     1896.000000\n",
              "25%     1948.000000\n",
              "50%     1980.000000\n",
              "75%     2000.000000\n",
              "max     2012.000000"
            ]
          },
          "metadata": {},
          "execution_count": 255
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HuosBNT_DuDO"
      },
      "source": [
        "From the code above, the dataset goes from 1896 t0 2012."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9eWg_Q62EDkr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "outputId": "625b29ae-af16-4ceb-ce81-3980f5c4eac5"
      },
      "source": [
        "#Check the describtion of the categorical  features\n",
        "summer_df.describe(include=['O'])"
      ],
      "execution_count": 256,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>City</th>\n",
              "      <th>Sport</th>\n",
              "      <th>Discipline</th>\n",
              "      <th>Athlete</th>\n",
              "      <th>Code</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Event</th>\n",
              "      <th>Medal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>31165</td>\n",
              "      <td>31165</td>\n",
              "      <td>31165</td>\n",
              "      <td>31165</td>\n",
              "      <td>31161</td>\n",
              "      <td>31165</td>\n",
              "      <td>31165</td>\n",
              "      <td>31165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>22</td>\n",
              "      <td>43</td>\n",
              "      <td>67</td>\n",
              "      <td>22762</td>\n",
              "      <td>147</td>\n",
              "      <td>2</td>\n",
              "      <td>666</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>London</td>\n",
              "      <td>Aquatics</td>\n",
              "      <td>Athletics</td>\n",
              "      <td>PHELPS, Michael</td>\n",
              "      <td>USA</td>\n",
              "      <td>Men</td>\n",
              "      <td>Football</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>3567</td>\n",
              "      <td>4170</td>\n",
              "      <td>3638</td>\n",
              "      <td>22</td>\n",
              "      <td>4585</td>\n",
              "      <td>22746</td>\n",
              "      <td>1497</td>\n",
              "      <td>10486</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          City     Sport Discipline  ... Gender     Event  Medal\n",
              "count    31165     31165      31165  ...  31165     31165  31165\n",
              "unique      22        43         67  ...      2       666      3\n",
              "top     London  Aquatics  Athletics  ...    Men  Football   Gold\n",
              "freq      3567      4170       3638  ...  22746      1497  10486\n",
              "\n",
              "[4 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 256
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZAXZIIJiEEpz"
      },
      "source": [
        "**Observations**\n",
        "\n",
        "* USA has the highest number of medal\n",
        "* London has the majority of the medal where the game was hosted \n",
        "* The sport Aquatics has the majority of the medals\n",
        "  \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZoKmdjb9JvX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b00f6514-8d14-46a9-a450-1d40fca8a559"
      },
      "source": [
        "#To see the summary of the datatypes\n",
        "summer_df.info()"
      ],
      "execution_count": 257,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 31165 entries, 0 to 31164\n",
            "Data columns (total 9 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   Year        31165 non-null  int64 \n",
            " 1   City        31165 non-null  object\n",
            " 2   Sport       31165 non-null  object\n",
            " 3   Discipline  31165 non-null  object\n",
            " 4   Athlete     31165 non-null  object\n",
            " 5   Code        31161 non-null  object\n",
            " 6   Gender      31165 non-null  object\n",
            " 7   Event       31165 non-null  object\n",
            " 8   Medal       31165 non-null  object\n",
            "dtypes: int64(1), object(8)\n",
            "memory usage: 2.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qTn_19FBB7DV"
      },
      "source": [
        "### **SUMMER DATASET DESCRIPTION**\n",
        "***Features and Description:***\n",
        "\n",
        "1. **Country** : The country that participated in the Olympics code\n",
        "\n",
        "2. **Code** : The abbreviation of the country\n",
        "\n",
        "3. **Population** : Numbers of population that was present for the olympics\n",
        "\n",
        "4. **GDP per Capita**\n",
        "\n",
        "5. **Year** : The year of the olympics games\n",
        "\n",
        "6. **City** : The City that host the games\n",
        "\n",
        "7. **Sport** : The kind of sport that happened in the games\n",
        "\n",
        "8. **Discpline** : Specialization of the type of sport that Atlete contetsted for.\n",
        "\n",
        "9. **Gender** : Contestant that was present 1.e Male or Female\n",
        "\n",
        "10. **Event** : Events in the Olympics\n",
        "\n",
        "11. **Medal** : Awards presented to the Winner. Gold, Silver or Bronze."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ghbkg-lRBTdX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "2da035ba-6a73-4d0a-a9cf-0d8b018130ae"
      },
      "source": [
        "#To see first few rows\n",
        "summer_df.head()"
      ],
      "execution_count": 258,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Year</th>\n",
              "      <th>City</th>\n",
              "      <th>Sport</th>\n",
              "      <th>Discipline</th>\n",
              "      <th>Athlete</th>\n",
              "      <th>Code</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Event</th>\n",
              "      <th>Medal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1896</td>\n",
              "      <td>Athens</td>\n",
              "      <td>Aquatics</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>HAJOS, Alfred</td>\n",
              "      <td>HUN</td>\n",
              "      <td>Men</td>\n",
              "      <td>100M Freestyle</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1896</td>\n",
              "      <td>Athens</td>\n",
              "      <td>Aquatics</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>HERSCHMANN, Otto</td>\n",
              "      <td>AUT</td>\n",
              "      <td>Men</td>\n",
              "      <td>100M Freestyle</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1896</td>\n",
              "      <td>Athens</td>\n",
              "      <td>Aquatics</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>DRIVAS, Dimitrios</td>\n",
              "      <td>GRE</td>\n",
              "      <td>Men</td>\n",
              "      <td>100M Freestyle For Sailors</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1896</td>\n",
              "      <td>Athens</td>\n",
              "      <td>Aquatics</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>MALOKINIS, Ioannis</td>\n",
              "      <td>GRE</td>\n",
              "      <td>Men</td>\n",
              "      <td>100M Freestyle For Sailors</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1896</td>\n",
              "      <td>Athens</td>\n",
              "      <td>Aquatics</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>CHASAPIS, Spiridon</td>\n",
              "      <td>GRE</td>\n",
              "      <td>Men</td>\n",
              "      <td>100M Freestyle For Sailors</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year    City     Sport  ... Gender                       Event   Medal\n",
              "0  1896  Athens  Aquatics  ...    Men              100M Freestyle    Gold\n",
              "1  1896  Athens  Aquatics  ...    Men              100M Freestyle  Silver\n",
              "2  1896  Athens  Aquatics  ...    Men  100M Freestyle For Sailors  Bronze\n",
              "3  1896  Athens  Aquatics  ...    Men  100M Freestyle For Sailors    Gold\n",
              "4  1896  Athens  Aquatics  ...    Men  100M Freestyle For Sailors  Silver\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 258
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RSd1h2tLBTgd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "6526704a-ec63-447e-a42c-0f650d215366"
      },
      "source": [
        "#To see the last few rows\n",
        "summer_df.tail()"
      ],
      "execution_count": 259,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Year</th>\n",
              "      <th>City</th>\n",
              "      <th>Sport</th>\n",
              "      <th>Discipline</th>\n",
              "      <th>Athlete</th>\n",
              "      <th>Code</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Event</th>\n",
              "      <th>Medal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>31160</th>\n",
              "      <td>2012</td>\n",
              "      <td>London</td>\n",
              "      <td>Wrestling</td>\n",
              "      <td>Wrestling Freestyle</td>\n",
              "      <td>JANIKOWSKI, Damian</td>\n",
              "      <td>POL</td>\n",
              "      <td>Men</td>\n",
              "      <td>Wg 84 KG</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31161</th>\n",
              "      <td>2012</td>\n",
              "      <td>London</td>\n",
              "      <td>Wrestling</td>\n",
              "      <td>Wrestling Freestyle</td>\n",
              "      <td>REZAEI, Ghasem Gholamreza</td>\n",
              "      <td>IRI</td>\n",
              "      <td>Men</td>\n",
              "      <td>Wg 96 KG</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31162</th>\n",
              "      <td>2012</td>\n",
              "      <td>London</td>\n",
              "      <td>Wrestling</td>\n",
              "      <td>Wrestling Freestyle</td>\n",
              "      <td>TOTROV, Rustam</td>\n",
              "      <td>RUS</td>\n",
              "      <td>Men</td>\n",
              "      <td>Wg 96 KG</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31163</th>\n",
              "      <td>2012</td>\n",
              "      <td>London</td>\n",
              "      <td>Wrestling</td>\n",
              "      <td>Wrestling Freestyle</td>\n",
              "      <td>ALEKSANYAN, Artur</td>\n",
              "      <td>ARM</td>\n",
              "      <td>Men</td>\n",
              "      <td>Wg 96 KG</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31164</th>\n",
              "      <td>2012</td>\n",
              "      <td>London</td>\n",
              "      <td>Wrestling</td>\n",
              "      <td>Wrestling Freestyle</td>\n",
              "      <td>LIDBERG, Jimmy</td>\n",
              "      <td>SWE</td>\n",
              "      <td>Men</td>\n",
              "      <td>Wg 96 KG</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Year    City      Sport  ... Gender     Event   Medal\n",
              "31160  2012  London  Wrestling  ...    Men  Wg 84 KG  Bronze\n",
              "31161  2012  London  Wrestling  ...    Men  Wg 96 KG    Gold\n",
              "31162  2012  London  Wrestling  ...    Men  Wg 96 KG  Silver\n",
              "31163  2012  London  Wrestling  ...    Men  Wg 96 KG  Bronze\n",
              "31164  2012  London  Wrestling  ...    Men  Wg 96 KG  Bronze\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 259
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q-iKkZCcDHY0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96fb43a1-19e3-4339-8ad1-2b8cd0192d0c"
      },
      "source": [
        "summer_df.shape"
      ],
      "execution_count": 260,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(31165, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 260
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wypZ7yDxDPNq"
      },
      "source": [
        "From the above code, we have 31165 rows and 9 columns(features)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VTcdaWbxDQ2f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c0b7d14-5579-4cdf-baba-63cd566bdb42"
      },
      "source": [
        "#To check if the headers is well written\n",
        "summer_df.columns"
      ],
      "execution_count": 261,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Year', 'City', 'Sport', 'Discipline', 'Athlete', 'Code', 'Gender',\n",
              "       'Event', 'Medal'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 261
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m_5IPpmtDRF5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f255253-edab-4bb8-a0f3-03a76929bdce"
      },
      "source": [
        "#Checking for the number of each data type----\n",
        "df_numeric = summer_df.select_dtypes(include='number') # extract columns with number\n",
        "num = len(df_numeric.columns) # sum the columns extracted\n",
        "print(f'We have {num} numeric columns') #format string\n",
        "\n",
        "df_obj = summer_df.select_dtypes(include='object')\n",
        "num_2 = len(df_obj.columns)\n",
        "print(f'we have {num_2} object columns')\n",
        "\n",
        "df_bool = summer_df.select_dtypes(include='bool')\n",
        "num_3 = len(df_bool.columns)\n",
        "print(f'we have {num_3} boolean columns')\n",
        "\n",
        "print(f'With a total number of {num + num_2 + num_3} columns')"
      ],
      "execution_count": 262,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "We have 1 numeric columns\n",
            "we have 8 object columns\n",
            "we have 0 boolean columns\n",
            "With a total number of 9 columns\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zz5clxsRDn7X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ead98f53-966c-406f-e234-4da45f94fdf1"
      },
      "source": [
        "#To check if there is missing data in the datframe\n",
        "summer_df.isnull().values.any()"
      ],
      "execution_count": 263,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 263
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WL2eN-RaExFw"
      },
      "source": [
        "\n",
        "From the above code we can see there are missing data in the dataframe. We need to find percentage of missing data and drop all neccesary rows or columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jac1ydIXE0nh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "outputId": "860399f6-ce2f-4437-a858-2ba19313c3fb"
      },
      "source": [
        "#Checking for the percentage of the missing data\n",
        "\n",
        "#creating a dataframe for percebtage of missing data\n",
        "percentage_of_missing_data = pd.DataFrame({'Percentage of Missing of Data':round((summer_df.isnull().sum()/len(summer_df.index)) * 100,2)}) \n",
        "percentage_of_missing_data = percentage_of_missing_data.sort_values(by='Percentage of Missing of Data',ascending=False)\n",
        "percentage_of_missing_data"
      ],
      "execution_count": 264,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Percentage of Missing of Data</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Code</th>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Year</th>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>City</th>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sport</th>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Discipline</th>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Athlete</th>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender</th>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Event</th>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Medal</th>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Percentage of Missing of Data\n",
              "Code                                 0.01\n",
              "Year                                 0.00\n",
              "City                                 0.00\n",
              "Sport                                0.00\n",
              "Discipline                           0.00\n",
              "Athlete                              0.00\n",
              "Gender                               0.00\n",
              "Event                                0.00\n",
              "Medal                                0.00"
            ]
          },
          "metadata": {},
          "execution_count": 264
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "103kW-3hE3Rf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "2dae7695-5958-40b0-b8cf-9b3881408122"
      },
      "source": [
        "#We have small percentage of missing value (2.33%), we will drop rows with missing data.\n",
        "summer_df = summer_df.dropna()\n",
        "summer_df.head()"
      ],
      "execution_count": 265,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Year</th>\n",
              "      <th>City</th>\n",
              "      <th>Sport</th>\n",
              "      <th>Discipline</th>\n",
              "      <th>Athlete</th>\n",
              "      <th>Code</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Event</th>\n",
              "      <th>Medal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1896</td>\n",
              "      <td>Athens</td>\n",
              "      <td>Aquatics</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>HAJOS, Alfred</td>\n",
              "      <td>HUN</td>\n",
              "      <td>Men</td>\n",
              "      <td>100M Freestyle</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1896</td>\n",
              "      <td>Athens</td>\n",
              "      <td>Aquatics</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>HERSCHMANN, Otto</td>\n",
              "      <td>AUT</td>\n",
              "      <td>Men</td>\n",
              "      <td>100M Freestyle</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1896</td>\n",
              "      <td>Athens</td>\n",
              "      <td>Aquatics</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>DRIVAS, Dimitrios</td>\n",
              "      <td>GRE</td>\n",
              "      <td>Men</td>\n",
              "      <td>100M Freestyle For Sailors</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1896</td>\n",
              "      <td>Athens</td>\n",
              "      <td>Aquatics</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>MALOKINIS, Ioannis</td>\n",
              "      <td>GRE</td>\n",
              "      <td>Men</td>\n",
              "      <td>100M Freestyle For Sailors</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1896</td>\n",
              "      <td>Athens</td>\n",
              "      <td>Aquatics</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>CHASAPIS, Spiridon</td>\n",
              "      <td>GRE</td>\n",
              "      <td>Men</td>\n",
              "      <td>100M Freestyle For Sailors</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year    City     Sport  ... Gender                       Event   Medal\n",
              "0  1896  Athens  Aquatics  ...    Men              100M Freestyle    Gold\n",
              "1  1896  Athens  Aquatics  ...    Men              100M Freestyle  Silver\n",
              "2  1896  Athens  Aquatics  ...    Men  100M Freestyle For Sailors  Bronze\n",
              "3  1896  Athens  Aquatics  ...    Men  100M Freestyle For Sailors    Gold\n",
              "4  1896  Athens  Aquatics  ...    Men  100M Freestyle For Sailors  Silver\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 265
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xXCX9fx-E9UE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d6919fd-869a-4c43-8e69-c6a370da18f0"
      },
      "source": [
        "#To check if there is still missing data\n",
        "summer_df.isnull().sum().any()"
      ],
      "execution_count": 266,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 266
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l6K5GJrIFBf-"
      },
      "source": [
        "Now, we don't have any mising data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lneJCWqqFHIs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04febf2b-26ba-495a-d745-3eead3d9ebe2"
      },
      "source": [
        "#To check for unique items in our categorical Features\n",
        "summer_df.nunique() "
      ],
      "execution_count": 267,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year             27\n",
              "City             22\n",
              "Sport            43\n",
              "Discipline       67\n",
              "Athlete       22761\n",
              "Code            147\n",
              "Gender            2\n",
              "Event           666\n",
              "Medal             3\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 267
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q6abdaZ2FUrY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a4e936b-c211-4849-d079-78d65c78290c"
      },
      "source": [
        "#Checking to see the unique Country\n",
        "print(summer_df.Code.unique())\n",
        "print(f'Total number of Country that participated in the Olympic games from 1896 10 2012: {len(summer_df.Code.unique())}')"
      ],
      "execution_count": 268,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['HUN' 'AUT' 'GRE' 'USA' 'GER' 'GBR' 'FRA' 'AUS' 'DEN' 'SUI' 'ZZX' 'NED'\n",
            " 'BEL' 'IND' 'CAN' 'BOH' 'SWE' 'NOR' 'ESP' 'ITA' 'CUB' 'ANZ' 'RSA' 'FIN'\n",
            " 'RU1' 'EST' 'TCH' 'NZL' 'BRA' 'JPN' 'LUX' 'ARG' 'POL' 'POR' 'URU' 'YUG'\n",
            " 'ROU' 'HAI' 'EGY' 'PHI' 'IRL' 'CHI' 'LAT' 'MEX' 'TUR' 'PAN' 'JAM' 'SRI'\n",
            " 'KOR' 'PUR' 'PER' 'IRI' 'TRI' 'URS' 'VEN' 'BUL' 'LIB' 'EUA' 'ISL' 'PAK'\n",
            " 'BAH' 'BWI' 'TPE' 'ETH' 'MAR' 'GHA' 'IRQ' 'SIN' 'TUN' 'KEN' 'NGR' 'GDR'\n",
            " 'FRG' 'UGA' 'CMR' 'MGL' 'PRK' 'COL' 'NIG' 'THA' 'BER' 'TAN' 'GUY' 'ZIM'\n",
            " 'CHN' 'CIV' 'ZAM' 'DOM' 'ALG' 'SYR' 'SUR' 'CRC' 'INA' 'SEN' 'DJI' 'AHO'\n",
            " 'ISV' 'EUN' 'NAM' 'QAT' 'LTU' 'MAS' 'CRO' 'ISR' 'SLO' 'IOP' 'RUS' 'UKR'\n",
            " 'ECU' 'BDI' 'MOZ' 'CZE' 'BLR' 'TGA' 'KAZ' 'UZB' 'SVK' 'MDA' 'GEO' 'HKG'\n",
            " 'ARM' 'AZE' 'BAR' 'KSA' 'KGZ' 'KUW' 'VIE' 'MKD' 'SCG' 'ERI' 'PAR' 'UAE'\n",
            " 'SRB' 'SUD' 'MRI' 'TOG' 'TJK' 'AFG' 'BRN' 'GUA' 'GRN' 'TTO' 'BOT' 'MNE'\n",
            " 'CYP' 'SGP' 'GAB']\n",
            "Total number of Country that participated in the Olympic games from 1896 10 2012: 147\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OSdpTDhjFW3s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e19d377d-1dca-46c7-ae8a-1900fdcff294"
      },
      "source": [
        "#checking to see the  uniques year\n",
        "print(summer_df.Year.unique())\n",
        "print(f\"The total number of years: {len(summer_df.Year.unique())}\")"
      ],
      "execution_count": 269,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1896 1900 1904 1908 1912 1920 1924 1928 1932 1936 1948 1952 1956 1960\n",
            " 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012]\n",
            "The total number of years: 27\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vi30LMjMFaSs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "917e84de-7b31-4946-e146-d003b9cad4fb"
      },
      "source": [
        "#checking to see the  host city\n",
        "print(summer_df.City.unique())\n",
        "print(f\"The total number of host City: {len(summer_df.City.unique())}\")"
      ],
      "execution_count": 270,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Athens' 'Paris' 'St Louis' 'London' 'Stockholm' 'Antwerp' 'Amsterdam'\n",
            " 'Los Angeles' 'Berlin' 'Helsinki' 'Melbourne / Stockholm' 'Rome' 'Tokyo'\n",
            " 'Mexico' 'Munich' 'Montreal' 'Moscow' 'Seoul' 'Barcelona' 'Atlanta'\n",
            " 'Sydney' 'Beijing']\n",
            "The total number of host City: 22\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xAJaecWOFaHc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d2e1a02-3305-4e19-e6e3-113b3fdb59ef"
      },
      "source": [
        "#checking to see the  unique Sport\n",
        "print(summer_df.Sport.unique())\n",
        "print(f\"The total number of Sport: {len(summer_df.Sport.unique())}\")"
      ],
      "execution_count": 271,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Aquatics' 'Athletics' 'Cycling' 'Fencing' 'Gymnastics' 'Shooting'\n",
            " 'Tennis' 'Weightlifting' 'Wrestling' 'Archery' 'Basque Pelota' 'Cricket'\n",
            " 'Croquet' 'Equestrian' 'Football' 'Golf' 'Polo' 'Rowing' 'Rugby'\n",
            " 'Sailing' 'Tug of War' 'Boxing' 'Lacrosse' 'Roque' 'Hockey'\n",
            " 'Jeu de paume' 'Rackets' 'Skating' 'Water Motorsports'\n",
            " 'Modern Pentathlon' 'Ice Hockey' 'Basketball' 'Canoe / Kayak' 'Handball'\n",
            " 'Judo' 'Volleyball' 'Table Tennis' 'Badminton' 'Baseball' 'Softball'\n",
            " 'Taekwondo' 'Triathlon' 'Canoe']\n",
            "The total number of Sport: 43\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x8aoPZN0FaAL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e247c843-a913-430b-c2e9-f84977618891"
      },
      "source": [
        "#checking to see the  unique Event\n",
        "# print(summer_df.Event.unique())\n",
        "print(f\"The total number of Event: {len(summer_df.Event.unique())}\")"
      ],
      "execution_count": 272,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The total number of Event: 666\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tjagO1GOFjB4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b1ac9504-1f75-463d-8876-7fa99c33c2f1"
      },
      "source": [
        "#checking to see the  unique Discipline\n",
        "print(summer_df.Discipline.unique())\n",
        "print(f\"The total number of Discipline: {len(summer_df.Discipline.unique())}\")"
      ],
      "execution_count": 273,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Swimming' 'Athletics' 'Cycling Road' 'Cycling Track' 'Fencing'\n",
            " 'Artistic G.' 'Shooting' 'Tennis' 'Weightlifting' 'Wrestling Gre-R'\n",
            " 'Water polo' 'Archery' 'Basque Pelota' 'Cricket' 'Croquet' 'Jumping'\n",
            " 'Football' 'Golf' 'Polo' 'Rowing' 'Rugby' 'Sailing' 'Tug of War' 'Diving'\n",
            " 'Boxing' 'Lacrosse' 'Roque' 'Wrestling Free.' 'Hockey' 'Jeu de Paume'\n",
            " 'Rackets' 'Figure skating' 'Water Motorspor' 'Dressage' 'Eventing'\n",
            " 'Modern Pentath.' 'Vaulting' 'Ice Hockey' 'Basketball' 'Canoe / Kayak F'\n",
            " 'Handball' 'Judo' 'Volleyball' 'Canoe / Kayak S' 'Synchronized S.'\n",
            " 'Rhythmic G.' 'Table Tennis' 'Badminton' 'Baseball' 'Mountain Bike'\n",
            " 'Softball' 'Beach volley.' 'Trampoline' 'Taekwondo' 'Triathlon' 'BMX'\n",
            " 'Marathon swimming' 'Synchronized Swimming' 'Water Polo' 'Canoe Slalom'\n",
            " 'Canoe Sprint' 'Cycling BMX' 'Gymnastics Artistic' 'Gymnastics Rhythmic'\n",
            " 'Modern Pentathlon' 'Beach Volleyball' 'Wrestling Freestyle']\n",
            "The total number of Discipline: 67\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WGDXEoH6FoyE"
      },
      "source": [
        "As per the above data we have 43 sports, 67 discipline, and 666 events in summer Olympics.\n",
        "\n",
        "We can see Sport is differnet from Disclipline. We can say column \"Discipline\" is a subset of column \"Sport\"."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pwuUn7DUFi-z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35aee8be-ac98-4df3-9616-10a4ccf1846b"
      },
      "source": [
        "#checking to see the  uniques Athletes\n",
        "print(summer_df.Athlete.unique())\n",
        "print(f\"The total number of Athlete: {len(summer_df.Athlete.unique())}\")"
      ],
      "execution_count": 274,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['HAJOS, Alfred' 'HERSCHMANN, Otto' 'DRIVAS, Dimitrios' ...\n",
            " 'TOTROV, Rustam' 'ALEKSANYAN, Artur' 'LIDBERG, Jimmy']\n",
            "The total number of Athlete: 22761\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-4U8uTGFFi8Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "761b42fa-1530-4df5-9ced-a10186fdb9a1"
      },
      "source": [
        "#checking to see the  unique Medal\n",
        "print(summer_df.Medal.unique())\n",
        "print(f\"The total number of Medal: {len(summer_df.Medal.unique())}\")"
      ],
      "execution_count": 275,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Gold' 'Silver' 'Bronze']\n",
            "The total number of Medal: 3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i-KR87ZVkkYt"
      },
      "source": [
        "### **TOP COUNTRIES PARTICIPATION:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "l2a6EzLai1K4",
        "outputId": "c233351d-ba45-44fb-8061-615c51dc689d"
      },
      "source": [
        "#Top Countries Participating in Summer Olympics\n",
        "Top_Countries =summer_df.value_counts([\"Code\"]).reset_index(name= \"Counts\")[:10]\n",
        "Top_Countries"
      ],
      "execution_count": 276,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Code</th>\n",
              "      <th>Counts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>USA</td>\n",
              "      <td>4585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>URS</td>\n",
              "      <td>2049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GBR</td>\n",
              "      <td>1720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>FRA</td>\n",
              "      <td>1396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GER</td>\n",
              "      <td>1305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>ITA</td>\n",
              "      <td>1296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>AUS</td>\n",
              "      <td>1189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>HUN</td>\n",
              "      <td>1079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>SWE</td>\n",
              "      <td>1044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>NED</td>\n",
              "      <td>851</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Code  Counts\n",
              "0  USA    4585\n",
              "1  URS    2049\n",
              "2  GBR    1720\n",
              "3  FRA    1396\n",
              "4  GER    1305\n",
              "5  ITA    1296\n",
              "6  AUS    1189\n",
              "7  HUN    1079\n",
              "8  SWE    1044\n",
              "9  NED     851"
            ]
          },
          "metadata": {},
          "execution_count": 276
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        },
        "id": "EMBLd6pGkFAc",
        "outputId": "40bb3e8f-a0ac-4081-9ec6-ae1e31dd7770"
      },
      "source": [
        "sns.set_style('darkgrid')\n",
        "matplotlib.rcParams['figure.figsize'] = (12, 10)\n",
        "\n",
        "plt.title(\"Overall Participation Country-Wise\")\n",
        "barplot = sns.barplot(data=Top_Countries, x=\"Code\", y=\"Counts\", hue=\"Code\", palette=\"CMRmap\", dodge=False)\n",
        "barplot.set_xticklabels(rotation=50, labels=Top_Countries.Code)\n",
        "plt.ylabel(\"Counts\")\n",
        "plt.show()"
      ],
      "execution_count": 277,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hh6Jnv9lkt1S"
      },
      "source": [
        "As USA has historically won maximum no of medals it would make sense the participation is highest from USA."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HR8gt2YnkyMq"
      },
      "source": [
        "### **GENDER PARTICIPATION:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "id": "LRsByzslk2YY",
        "outputId": "b091b9a9-818d-45f4-9b72-b14cef1c3ecb"
      },
      "source": [
        "Gender_Counts =summer_df.value_counts([\"Gender\"]).reset_index(name= \"Counts\")[:10]\n",
        "\n",
        "#To get the percentage\n",
        "Gender_Counts['Percentage %'] = round(Gender_Counts.Counts / Gender_Counts.Counts.sum() * 100, 2)\n",
        "\n",
        "Gender_Counts"
      ],
      "execution_count": 278,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Gender</th>\n",
              "      <th>Counts</th>\n",
              "      <th>Percentage %</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Men</td>\n",
              "      <td>22744</td>\n",
              "      <td>72.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Women</td>\n",
              "      <td>8417</td>\n",
              "      <td>27.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Gender  Counts  Percentage %\n",
              "0    Men   22744         72.99\n",
              "1  Women    8417         27.01"
            ]
          },
          "metadata": {},
          "execution_count": 278
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 603
        },
        "id": "fGw2xEtGk62g",
        "outputId": "46bc1871-37f1-40e9-a05a-ffd8638007d9"
      },
      "source": [
        "Gender_percentage = Gender_Counts[\"Percentage %\"].to_list()\n",
        "plt.title('Gender Distribution', fontsize=30, loc='Right')\n",
        "plt.pie(Gender_percentage, colors=['#009047','#E23651'], labels= [\"Men\", \"Women\"], radius=1.2, autopct= \"%0.1f%%\", pctdistance = 0.77,\n",
        "        textprops = {\"size\" : 17, \"color\" : \"#000000\"}, explode= [0.00, 0.00])\n",
        "\n",
        "centre_circle = plt.Circle((0,0), 0.8, fc = \"w\")\n",
        "fig = plt.gcf()\n",
        "fig.gca().add_artist(centre_circle)\n",
        "plt.show()"
      ],
      "execution_count": 279,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5nEyBYOrk_p1"
      },
      "source": [
        "Male seems to be dominating in terms of participation. I used colors from Olympic logo for the doungnurt chart\n",
        "\n",
        "I'm curious to know the female participants across the years."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "id": "0MmJPwwDk8x3",
        "outputId": "73bcfc0e-0f70-4b13-ff06-21aa03fb2b01"
      },
      "source": [
        "#To filter gender that are  female and Year\n",
        "female_participants = summer_df[(summer_df[\"Gender\"] == \"Women\")][[\"Gender\", \"Year\"]]\n",
        "\n",
        "#To group by year\n",
        "female_participants = female_participants.groupby('Year').count().reset_index()\n",
        "female_participants"
      ],
      "execution_count": 280,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Year</th>\n",
              "      <th>Gender</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1900</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1904</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1908</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1912</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1920</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1924</td>\n",
              "      <td>51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1928</td>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1932</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1936</td>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1948</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1952</td>\n",
              "      <td>139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1956</td>\n",
              "      <td>146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1960</td>\n",
              "      <td>145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1964</td>\n",
              "      <td>190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1968</td>\n",
              "      <td>212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1972</td>\n",
              "      <td>238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1976</td>\n",
              "      <td>381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1980</td>\n",
              "      <td>429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1984</td>\n",
              "      <td>486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1988</td>\n",
              "      <td>535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1992</td>\n",
              "      <td>600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1996</td>\n",
              "      <td>777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2000</td>\n",
              "      <td>889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2004</td>\n",
              "      <td>899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2008</td>\n",
              "      <td>932</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2012</td>\n",
              "      <td>922</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Year  Gender\n",
              "0   1900      11\n",
              "1   1904      12\n",
              "2   1908      15\n",
              "3   1912      30\n",
              "4   1920      43\n",
              "5   1924      51\n",
              "6   1928      95\n",
              "7   1932      60\n",
              "8   1936      84\n",
              "9   1948      96\n",
              "10  1952     139\n",
              "11  1956     146\n",
              "12  1960     145\n",
              "13  1964     190\n",
              "14  1968     212\n",
              "15  1972     238\n",
              "16  1976     381\n",
              "17  1980     429\n",
              "18  1984     486\n",
              "19  1988     535\n",
              "20  1992     600\n",
              "21  1996     777\n",
              "22  2000     889\n",
              "23  2004     899\n",
              "24  2008     932\n",
              "25  2012     922"
            ]
          },
          "metadata": {},
          "execution_count": 280
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "HHTCg7wZlSWV",
        "outputId": "6db0d25f-a0cb-49a9-ff46-b997ebd96e87"
      },
      "source": [
        "sns.set_style('darkgrid')\n",
        "# matplotlib.rcParams['figure.figsize'] = (12, 10)\n",
        "plt.figure(figsize=(10,6))\n",
        "sns.lineplot(data=female_participants, x='Year', y='Gender', color= \"red\")\n",
        "plt.title(\"Female Participation over the Years \")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 281,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5QnxdP8dlWay"
      },
      "source": [
        "From the chart above, we can see the Women has increased signifiacntly across the years."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "p8JcGiUXle3s",
        "outputId": "ffc50e52-d4a9-4480-db8b-92638fe4a9d9"
      },
      "source": [
        "Gender_by_Medals = pd.crosstab(summer_df[\"Gender\"],summer_df[\"Medal\"])\n",
        "Gender_by_Medals"
      ],
      "execution_count": 282,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Gender</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Men</th>\n",
              "      <td>7539</td>\n",
              "      <td>7698</td>\n",
              "      <td>7507</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Women</th>\n",
              "      <td>2830</td>\n",
              "      <td>2786</td>\n",
              "      <td>2801</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Medal   Bronze  Gold  Silver\n",
              "Gender                      \n",
              "Men       7539  7698    7507\n",
              "Women     2830  2786    2801"
            ]
          },
          "metadata": {},
          "execution_count": 282
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CHjvTm87li_U"
      },
      "source": [
        "I'II be using stack barplot to visualize the Gender by Male.\n",
        "\n",
        "Stacking basciaclly means moving inner coulumns to beacome the inner rows index."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "UfpFwHlnlmZP",
        "outputId": "885d7e96-b9d9-490d-d67e-a7df54b00976"
      },
      "source": [
        "sns.set_style('darkgrid')\n",
        "matplotlib.rcParams['figure.figsize'] = (12, 10)\n",
        "plt.title(\"Overall Participation CountryWise\")\n",
        "\n",
        "#To moved the Medal to become the inner row index\n",
        "stacked = Gender_by_Medals.stack().reset_index().rename(columns={0:'value'})\n",
        "sns.barplot(x=stacked.Medal, y=stacked.value, hue=stacked.Gender, palette=\"CMRmap\")\n",
        "plt.ylabel(\"Counts of Medal\")\n",
        "plt.show()"
      ],
      "execution_count": 283,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z-S8CxLNF3wW"
      },
      "source": [
        "# **SUMMER OLYMPICS DATA ANALYSIS ASSIGNMENT**\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YYXz6nVSGBoV"
      },
      "source": [
        "## **1. In how many cities Summer Olympics is held so far?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MrwkSqUQFi6W",
        "outputId": "a43cbd61-9e70-4d6d-d063-dc930a0aa672"
      },
      "source": [
        "#checking to see the  host city\n",
        "print(summer_df.City.unique())\n",
        "print(f\"\\nThe total number of cities Summer Olympics is held so far: {len(summer_df.City.unique())}\")"
      ],
      "execution_count": 228,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Athens' 'Paris' 'St Louis' 'London' 'Stockholm' 'Antwerp' 'Amsterdam'\n",
            " 'Los Angeles' 'Berlin' 'Helsinki' 'Melbourne / Stockholm' 'Rome' 'Tokyo'\n",
            " 'Mexico' 'Munich' 'Montreal' 'Moscow' 'Seoul' 'Barcelona' 'Atlanta'\n",
            " 'Sydney' 'Beijing']\n",
            "\n",
            "The total number of cities Summer Olympics is held so far: 22\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EZ3lLgtsJn9E"
      },
      "source": [
        "\n",
        "## **2. Which sport is having most number of Gold Medals so far? (Top 5)**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J0i0RieKPzuv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "0f8e0c0f-3983-4efe-882b-5a08ada51bab"
      },
      "source": [
        "gold = []\n",
        "gold_data = summer_df[summer_df['Medal']=='Gold']\n",
        "\n",
        "for sport in gold_data['Sport'].unique():\n",
        "    gold.append([sport,len(gold_data[gold_data['Sport']==sport])])\n",
        "\n",
        "print(f\"SPORTS HAVING MOST NUMBER OF GOLD MEDALS:\\n\")\n",
        "\n",
        "gold = pd.DataFrame(gold,columns=['Sport','Count_of_Gold_Medals'])\n",
        "c=gold.sort_values(by='Count_of_Gold_Medals',ascending=False).head(5)\n",
        "c"
      ],
      "execution_count": 229,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SPORTS HAVING MOST NUMBER OF GOLD MEDALS:\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "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>Sport</th>\n",
              "      <th>Count_of_Gold_Medals</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Aquatics</td>\n",
              "      <td>1421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Athletics</td>\n",
              "      <td>1214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Rowing</td>\n",
              "      <td>890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Gymnastics</td>\n",
              "      <td>820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fencing</td>\n",
              "      <td>552</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Sport  Count_of_Gold_Medals\n",
              "0     Aquatics                  1421\n",
              "1    Athletics                  1214\n",
              "17      Rowing                   890\n",
              "4   Gymnastics                   820\n",
              "3      Fencing                   552"
            ]
          },
          "metadata": {},
          "execution_count": 229
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "Z-VLzHXHnvaX",
        "outputId": "38e5737d-e5ff-4816-8ee9-37fa0384eb72"
      },
      "source": [
        "sns.set_style('whitegrid')\n",
        "matplotlib.rcParams['figure.figsize'] = (12, 10)\n",
        "\n",
        "plt.title(\"Sport with the Maximum Gold Medals\")\n",
        "barplot = sns.barplot(data=c, x=\"Sport\", y=\"Count_of_Gold_Medals\", hue=\"Sport\", palette=\"CMRmap\", dodge=False)\n",
        "barplot.set_xticklabels(rotation=50, labels=c.Sport)\n",
        "plt.ylabel(\"Count_of_Gold_Medals\")\n",
        "plt.show()"
      ],
      "execution_count": 230,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "VuYW7xeecFZ0",
        "outputId": "56880fd8-9275-40c0-88f3-6e7bf6b20f98"
      },
      "source": [
        "gold_data['Medal'].unique()\n",
        "gold=gold_data[gold_data['Medal'] == 'Gold']\n",
        "gold['Sport'].value_counts().head().plot.pie(figsize = (8,8))"
      ],
      "execution_count": 231,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd8a7af1e50>"
            ]
          },
          "metadata": {},
          "execution_count": 231
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n4Ndp6V8PxJK"
      },
      "source": [
        "## **3. Which sport is having most number of medals so far? (Top 5)** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YDwFo-9CQmak",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "97e2c995-cbfe-46ae-dd58-580d79006f61"
      },
      "source": [
        "Most_number_medal = summer_df[[\"Sport\",\"Medal\"]]\n",
        "Most_number_medal = Most_number_medal.value_counts(\"Sport\").reset_index(name=\"Counts_of_Medals\")[:5]\n",
        "print(f\"SPORTS HAVING MOST NUMBER OF MEDALS:\\n\")\n",
        "Most_number_medal"
      ],
      "execution_count": 232,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SPORTS HAVING MOST NUMBER OF MEDALS:\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "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>Sport</th>\n",
              "      <th>Counts_of_Medals</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Aquatics</td>\n",
              "      <td>4170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Athletics</td>\n",
              "      <td>3637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Rowing</td>\n",
              "      <td>2667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Gymnastics</td>\n",
              "      <td>2307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Fencing</td>\n",
              "      <td>1613</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Sport  Counts_of_Medals\n",
              "0    Aquatics              4170\n",
              "1   Athletics              3637\n",
              "2      Rowing              2667\n",
              "3  Gymnastics              2307\n",
              "4     Fencing              1613"
            ]
          },
          "metadata": {},
          "execution_count": 232
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "UOAEsR0vr683",
        "outputId": "cce811a7-8ab0-4cb5-f75a-6e87da605ca9"
      },
      "source": [
        "sns.set_style('whitegrid')\n",
        "matplotlib.rcParams['figure.figsize'] = (12, 10)\n",
        "\n",
        "plt.title(\"Sport with the Maximum Medals\")\n",
        "barplot = sns.barplot(data=Most_number_medal, x=\"Sport\", y=\"Counts_of_Medals\", hue=\"Sport\", palette=\"CMRmap\", dodge=False)\n",
        "barplot.set_xticklabels(rotation=50, labels=Most_number_medal.Sport)\n",
        "plt.ylabel(\"Counts_of_Medals\")\n",
        "plt.show()"
      ],
      "execution_count": 233,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vbG4-BDIs22C"
      },
      "source": [
        "## **4. Which player has won most number of medals? (Top 5)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "cGdnYuQctAJp",
        "outputId": "7d1a2460-acf9-426d-9c54-bb4e8f329fe8"
      },
      "source": [
        "Individual_medal = summer_df[[\"Athlete\",\"Medal\"]]\n",
        "Individual_medal['Athlete'].unique()\n",
        "Individual_medal = Individual_medal.value_counts(\"Athlete\").reset_index(name=\"Counts_of_Medals\")[:5]\n",
        "print(f\"PLAYERS WHO WON MOST NUMBER OF MEDALS:\\n\")\n",
        "Individual_medal"
      ],
      "execution_count": 234,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PLAYERS WHO WON MOST NUMBER OF MEDALS:\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "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>Athlete</th>\n",
              "      <th>Counts_of_Medals</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>PHELPS, Michael</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LATYNINA, Larisa</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ANDRIANOV, Nikolay</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ONO, Takashi</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>MANGIAROTTI, Edoardo</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Athlete  Counts_of_Medals\n",
              "0       PHELPS, Michael                22\n",
              "1      LATYNINA, Larisa                18\n",
              "2    ANDRIANOV, Nikolay                15\n",
              "3          ONO, Takashi                13\n",
              "4  MANGIAROTTI, Edoardo                13"
            ]
          },
          "metadata": {},
          "execution_count": 234
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "W1Wslz2FeJge",
        "outputId": "3023c154-ec05-4b91-91d3-54028dfc3771"
      },
      "source": [
        "sns.set_style('whitegrid')\n",
        "matplotlib.rcParams['figure.figsize'] = (12, 10)\n",
        "\n",
        "plt.title(\"Players with the Maximum Medals\")\n",
        "barplot = sns.barplot(data=Individual_medal, x=\"Athlete\", y=\"Counts_of_Medals\", hue=\"Athlete\", palette=\"CMRmap\", dodge=False)\n",
        "barplot.set_xticklabels(rotation=50, labels=Individual_medal.Athlete)\n",
        "plt.ylabel(\"Counts_of_Medals\")\n",
        "plt.show()"
      ],
      "execution_count": 235,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lr19XMD86RfB"
      },
      "source": [
        "## **5. Which player has won most number Gold Medals of medals? (Top 5)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "1HMOBYub6WZh",
        "outputId": "f91fcc02-0113-489c-d426-f00487d21142"
      },
      "source": [
        "Gold_medal = summer_df[(summer_df[\"Medal\"] == \"Gold\")][[\"Athlete\", \"Medal\"]]\n",
        "Gold_medal = Gold_medal.value_counts([\"Athlete\"]).reset_index(name = \"Counts_of_Gold_Medal\")[:5]\n",
        "print(f\"PLAYERS WHO WON MOST NUMBER OF GOLD MEDALS:\\n\")\n",
        "Gold_medal"
      ],
      "execution_count": 236,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PLAYERS WHO WON MOST NUMBER OF GOLD MEDALS:\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "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>Athlete</th>\n",
              "      <th>Counts_of_Gold_Medal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>PHELPS, Michael</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LATYNINA, Larisa</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SPITZ, Mark</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LEWIS, Carl</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NURMI, Paavo</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Athlete  Counts_of_Gold_Medal\n",
              "0   PHELPS, Michael                    18\n",
              "1  LATYNINA, Larisa                     9\n",
              "2       SPITZ, Mark                     9\n",
              "3       LEWIS, Carl                     9\n",
              "4      NURMI, Paavo                     9"
            ]
          },
          "metadata": {},
          "execution_count": 236
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 683
        },
        "id": "bCRAdWEKerP8",
        "outputId": "31cab553-f5eb-40c5-8b2c-1dffbd415344"
      },
      "source": [
        "sns.set_style('whitegrid')\n",
        "matplotlib.rcParams['figure.figsize'] = (12, 10)\n",
        "\n",
        "plt.title(\"Players with the Maximum Gold Medals\")\n",
        "barplot = sns.barplot(data=Gold_medal, x=\"Athlete\", y=\"Counts_of_Gold_Medal\", hue=\"Athlete\", palette=\"CMRmap\", dodge=False)\n",
        "barplot.set_xticklabels(rotation=50, labels=Gold_medal.Athlete)\n",
        "plt.ylabel(\"Counts_of_Gold_Medal\")\n",
        "plt.show()"
      ],
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-eOUaG7v7lqb"
      },
      "source": [
        "## **6. In which year India won first Gold Medal in Summer Olympics?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "c9vKQCO6CPez",
        "outputId": "75ec6ef4-2811-424a-c12f-54a06dec34a9"
      },
      "source": [
        "# Making a new dataset which contains only the sports in which atleast one gold medal is won.\n",
        "gold_dataset= summer_df[summer_df[\"Medal\"]==\"Gold\"]\n",
        "gold_dataset.head()"
      ],
      "execution_count": 238,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Year</th>\n",
              "      <th>City</th>\n",
              "      <th>Sport</th>\n",
              "      <th>Discipline</th>\n",
              "      <th>Athlete</th>\n",
              "      <th>Code</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Event</th>\n",
              "      <th>Medal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1896</td>\n",
              "      <td>Athens</td>\n",
              "      <td>Aquatics</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>HAJOS, Alfred</td>\n",
              "      <td>HUN</td>\n",
              "      <td>Men</td>\n",
              "      <td>100M Freestyle</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1896</td>\n",
              "      <td>Athens</td>\n",
              "      <td>Aquatics</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>MALOKINIS, Ioannis</td>\n",
              "      <td>GRE</td>\n",
              "      <td>Men</td>\n",
              "      <td>100M Freestyle For Sailors</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1896</td>\n",
              "      <td>Athens</td>\n",
              "      <td>Aquatics</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>HAJOS, Alfred</td>\n",
              "      <td>HUN</td>\n",
              "      <td>Men</td>\n",
              "      <td>1200M Freestyle</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1896</td>\n",
              "      <td>Athens</td>\n",
              "      <td>Aquatics</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>NEUMANN, Paul</td>\n",
              "      <td>AUT</td>\n",
              "      <td>Men</td>\n",
              "      <td>400M Freestyle</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1896</td>\n",
              "      <td>Athens</td>\n",
              "      <td>Athletics</td>\n",
              "      <td>Athletics</td>\n",
              "      <td>BURKE, Thomas</td>\n",
              "      <td>USA</td>\n",
              "      <td>Men</td>\n",
              "      <td>100M</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Year    City      Sport  ... Gender                       Event Medal\n",
              "0   1896  Athens   Aquatics  ...    Men              100M Freestyle  Gold\n",
              "3   1896  Athens   Aquatics  ...    Men  100M Freestyle For Sailors  Gold\n",
              "6   1896  Athens   Aquatics  ...    Men             1200M Freestyle  Gold\n",
              "9   1896  Athens   Aquatics  ...    Men              400M Freestyle  Gold\n",
              "13  1896  Athens  Athletics  ...    Men                        100M  Gold\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 238
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ee5Wirhlc2X7",
        "outputId": "b0e6f30e-68a3-439d-b2d6-17e3158ca725"
      },
      "source": [
        "First_Medal = gold_data[gold_data['Medal'] == 'Gold']\n",
        "Firstmedal_India = First_Medal[First_Medal['Code'] == 'IND']\n",
        " \n",
        "lstt = []\n",
        "for i in Firstmedal_India['Year'].unique():\n",
        "     lstt.append(i)\n",
        "min(lstt)\n",
        "print(f\"INDIA WON FIRST GOLD MEDAL IN SUMMER OLYMPICS: {min(lstt)}\")"
      ],
      "execution_count": 239,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INDIA WON FIRST GOLD MEDAL IN SUMMER OLYMPICS: 1928\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 114
        },
        "id": "FNgQYaywC7D7",
        "outputId": "5e209e8b-be2d-42cb-c9ae-0d0fa3dffbc9"
      },
      "source": [
        "India_goldmedals=gold_dataset[gold_dataset[\"Code\"]==\"IND\"]\n",
        "India_goldmedals=India_goldmedals.sort_values(by=\"Year\", ascending=True)\n",
        "print(f\"INDIA WON FIRST GOLD MEDAL IN SUMMER OLYMPICS:\\n\")\n",
        "India_goldmedals.head(1)"
      ],
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INDIA WON FIRST GOLD MEDAL IN SUMMER OLYMPICS:\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "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>Year</th>\n",
              "      <th>City</th>\n",
              "      <th>Sport</th>\n",
              "      <th>Discipline</th>\n",
              "      <th>Athlete</th>\n",
              "      <th>Code</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Event</th>\n",
              "      <th>Medal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5512</th>\n",
              "      <td>1928</td>\n",
              "      <td>Amsterdam</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>ALLEN, Richard James</td>\n",
              "      <td>IND</td>\n",
              "      <td>Men</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Year       City   Sport Discipline  ... Code Gender   Event Medal\n",
              "5512  1928  Amsterdam  Hockey     Hockey  ...  IND    Men  Hockey  Gold\n",
              "\n",
              "[1 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 240
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "yi49yi267qRB",
        "outputId": "9481a050-4876-4ef0-acf7-d34631266428"
      },
      "source": [
        "Country_by_medal = summer_df[(summer_df[\"Code\"] == \"IND\")][[\"Sport\", \"Medal\"]]\n",
        "Country_by_medal = Country_by_medal.value_counts([\"Sport\"]).reset_index(name = \"Counts_of_Medal\")[:10]\n",
        "print(f\"TOTAL MEDALS WON BY INDIA IN SUMMER OLYMPICS:\\n\")\n",
        "Country_by_medal"
      ],
      "execution_count": 241,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TOTAL MEDALS WON BY INDIA IN SUMMER OLYMPICS:\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "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>Sport</th>\n",
              "      <th>Counts_of_Medal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Hockey</td>\n",
              "      <td>169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Wrestling</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Shooting</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Boxing</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Athletics</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Weightlifting</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Tennis</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Badminton</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Sport  Counts_of_Medal\n",
              "0         Hockey              169\n",
              "1      Wrestling                4\n",
              "2       Shooting                4\n",
              "3         Boxing                2\n",
              "4      Athletics                2\n",
              "5  Weightlifting                1\n",
              "6         Tennis                1\n",
              "7      Badminton                1"
            ]
          },
          "metadata": {},
          "execution_count": 241
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        },
        "id": "8pn1rv-XfM4K",
        "outputId": "2aafd3d5-1ef1-4def-f09b-1559a6b1bd2c"
      },
      "source": [
        "sns.set_style('whitegrid')\n",
        "matplotlib.rcParams['figure.figsize'] = (12, 10)\n",
        "\n",
        "plt.title(\"Total Medals won by India\")\n",
        "barplot = sns.barplot(data=Country_by_medal, y=\"Sport\", x=\"Counts_of_Medal\", hue=\"Sport\", palette=\"CMRmap\", dodge=False)\n",
        "barplot.set_xticklabels(rotation=0, labels=Country_by_medal.Sport)\n",
        "plt.xlabel(\"Counts_of_Medal\")\n",
        "plt.show()"
      ],
      "execution_count": 242,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zzyoyMlK33367vv76a40ZM0b9+vXT7NmzXeud6zWyePFi/fLLL4qOjlZUVJSkk18M2O12hYeH6/nnny/X1uzZsxUREaEhQ4boyJEjtdpHaCAMKq1jx44mIiLC9a93795m6tSpxhhjHnjgAZOYmGiMMea9994zDz30kDHGmHHjxpm33nrLGGNMSUmJyc3NNT/++KMZMGCASU9PNw6Hw+zdu9cYY8zIkSPN/v37jTHG7Nq1y0RFRRljjJk0aZL5+OOPjTHGLF++3MyYMaOuDrlS+vTpYzIyMszbb79t/vGPf5jZs2ebTz/91Gzfvt0MHz7cjBgxwjz99NPGGGOKiorM0KFDTVZWljHGmKSkJDNp0iRjjDG33HKLKSwsNMYYc/ToUWOMMXPmzDFvvPGGa1+nLj/22GNm3bp1rhoWL15sjDFm6dKl5vHHHzfGGDN16lTz6quvGmOM2bx5swkICHDtu77k5+ebiIgI069fP/P000+bbdu2GWPOfgzTpk0zc+fONcYY88UXX5iIiIgKH6+oz0aMGOF6/Xz66acmOjraGGPMG2+8YZ588kljjDH/+c9/TGBgoPn6669rrQ+qq+y9eNttt5ng4GCTmppqjDFm/fr1JiYmxpSUlJjDhw+b3r17m8zMTHPs2DETFhZmvvzyS9OvXz/z3//+1xjz++ybVatWmcmTJxtjjBk6dKhJTU01K1ascI1Vxpw87qFDh5rCwkKTlZVlevbsaYqKiowxxnTr1s0YY8yWLVvM3/72N1NaWmqcTqe5//77zVdffeUav8qcurxs2TLz8MMPm+LiYmOMMTk5OSY7O9v069fPlJaWGmP+996ubRWNS0OGDDnrOFQ2rpw4ccL88Y9/NAcOHDDGGDN+/Hhz//33G2PO3n8V9c3WrVtd25/6szH/6/PfLm/dutUEBwebn3/+2TidTjNkyBCTnJxcYW31ac6cOeb1118/a9+e7T22YsUKExISYnJzc82JEyfMn/70J/PTTz8ZY07+HrOyssz69evNE0884dpXbm5uHR5Zxc42HhUXF5u8vDxjjDFZWVkmNDTUlJaWmtTUVBMeHm6OHTtm8vLyTGhoaLlxaObMmcYYYxISEswtt9xiMjMzTWFhoenVq5fJzs42xpz7NWLM//rOGGMOHTpkevfubbKyskxxcbGJiopyfa4ICAgwn3zyiTHGmPj4ePPyyy/XRbehnjEFqApOPc0nnfzW4ptvvpF08pTy3LlzJUkOh8OVrrdu3er6NsPd3V3NmzfX0aNHlZ2drdGjR2vevHm6+uqrVVBQoJSUlHKnn8vmTw4ePFhvvPGGQkNDlZiYqGeeeaZOjreygoKClJKSopSUFI0aNUqZmZnauXOnmjdvruDgYO3evVthYWGSpP379ystLU2jRo2SJJWWluqSSy6RJF1zzTX6y1/+or59+yo0NLTKdfTr10+S1LlzZ3388ceSpB07dri++f3jH/+oiy66qNrHW13e3t5KTEzU9u3btW3bNo0fP971zd/ZjqHstXXTTTfp119/VX5+/lkfP5dbb71VktSpUydlZGS49jFy5EhJUkBAgK655poaPOKad+p7MSUlRY899pjWrFmjHTt2aMCAAXJ3d9fFF1+sHj16KDU1VX379tUzzzyjESNGaPLkybriiivO2O7voW+SkpJc9YaFhSkpKUn+/v6nrde7d295eXnJz89Pfn5+ysrKUps2bVzPf/755/r88881cOBASdKxY8f0ww8/6NJLLz3rvr/88ksNGzbMNc3Px8dHJSUlatSokR5//HH16dNHf/rTn2rwaM+uonGpdevW+uyzz844DpX5/vvv1a5dO7Vr106SNGDAAL377ruu58/Uf7Wha9eurt9Lx44dlZGRIW9v7wprq09FRUVnHeOlM7/HpJNjWPPmzSVJV111lTIyMsq91gICAhQfH6/nn39effr0Uffu3evicCrlbOORMUYvvviikpOTZbPZlJmZqSNHjmj79u0KDQ11Tc0JCQkp117ZckBAgPz9/dWqVStJUrt27XTo0CH5+vqWW/9Mr5Hf9k9qaqp69uwpPz8/SZLdbldycrJCQ0Pl6empPn36SDr5f8/nn39ek92DBooAUE+aN2+uyy67TDt27NDVV18tY4xatGhRLmCUuf766zV16lRt27ZNTqdTAQEB9VDx2QUHByslJUVpaWny9/dXmzZt9Oabb6pZs2aKjIzU7t27XQOdMUb+/v565513TmvntddeU3JysjZt2qRXX31Vq1evrlIdnp6ekiSbzSan01n9A6tF7u7uuuGGG3TDDTcoICDAdSFqXRxD2dSjC6GfKiMoKEg5OTnnnNqVlpYmHx8f13SEM7nQ++bXX3/V1q1blZaWJjc3NzmdTrm5uenqq68+bd2yY5VOvh5LSkrKPW+M0f3333/axYYHDx6sUk0eHh56//339eWXX2r9+vVaunTpWacc1aSKxqWePXsqMzPzjONQZZ2r/2rKb/fT0F+XFY3x0tnfY+c6zvbt2ysxMVGbN2/WSy+9pBtvvFFjxoyphSOonlPHo82bNys7O1uJiYny9PRUSEiICgsLz9nGqX10ar/YbLYzvs6q+xrx9PSUm5ubax8N/TWGmsE1ADUkKChISUlJkqTVq1e70vdNN92kf/zjH5JOzo/Ny8uTdPINN2/ePK1cuVKrV69Ws2bN1LZtW61bt07SyUG0bK63JA0cOFATJkxQZGRkXR5WpQQHB2vTpk266KKL5O7uLh8fH+Xl5WnXrl2n3VGkffv2ys7OVkpKiiSpuLhY+/btU2lpqX7++WfdeOON+stf/qK8vDwdO3ZM3t7eKigoqFZtZX362Wef6ejRo+d/oDXk+++/1w8//OBa3rt3ry677LKzrt+9e3d9+OGHkk7O9/T19VWzZs3O+vj59Nmp/fTdd98pLS2tikdVf9LT0+V0OuXj46Pu3btr3bp1cjqdys7O1vbt29W1a1dlZGTorbfe0gcffKB//ev4FK6aAAAJkUlEQVRf2r17d6Xbv5D65qOPPpLD4dCmTZu0ceNGbd68WW3bttXPP/9c5dfEH/7wB61YscK1XWZmprKysip8fd1888165513XB9Sfv31VxUUFCgvL0+9e/fW448/rv/85z/VO8hKqmhcCg8PP+M4dKr27dvrxx9/dAWeysw5P9/xysPDo0rXdZ1PbXWl7MYGFfXt+cjMzFSTJk3kcDgUGxvrupC6oTl1PMrLy1PLli3l6emprVu3us549OjRQxs2bNCJEyeUn5+vTZs21Uotp74eu3btquTkZGVnZ8vpdCopKUk9evSolf3iwsAZgBry5JNPavLkyVq4cKH8/Pw0Y8YMSdITTzyhJ598UitWrJDNZtOUKVNcp0ObNm2qBQsWaNSoUWratKmef/55TZkyRfPnz1dJSYnCwsLUsWNHSSdP17300ksN8vahAQEBysnJKVdbQECACgoKXKcby3h5eWnOnDmKi4tTXl6enE6noqOjdeWVV2rixInKz8+XMUYjR45UixYt1KdPH40dO1affPKJnnzyySrXNmbMGD366KP68MMP1a1bN11yySVq1qxZtY+5Oo4dO6a4uDjl5ubK3d1d//d//6dp06bp008/PeP6Y8aM0eOPPy673a4mTZroueeeq/Dx8+mzu+66S5MmTVJYWJg6dOigq6++2nU6viEqu2hS+t+Fv+7u7rr11luVkpIih8MhNzc3TZw4URdffLFGjRqlv/71r2rdurWmT5+uyZMn6/3336/Uvi6kvlmzZo3uu+++co/169dP6enp+u677+RwOCp9segf/vAHpaenu84AlI1RV1xxhYKDgxUeHq5evXqVu+PNnXfeqR9++EERERHy8PDQkCFD1K9fP40ePdr1zeekSZNq6GgrVtG41LJlyzOOQ6dOlWrcuLGefvpp3XvvvWratKk6d+58zn36+vqetW8qMmTIEEVEROjaa6/VCy+8cM71z6e2umKz2c7Zt+cjLS1NM2fOlM1mk4eHh6ZMmVIzBdeAs41HdrtdDz30kOx2uzp37qwOHTpIOjn9KSwsTA6HQ35+fq67Q9W0IUOG6N5771WrVq20ZMkSTZgwQdHR0TLGqHfv3uc11Ra/H27G/P9bCqBBW79+vT755JNyV+7j3IqKilz/YaSkpGjKlClnnGZldU6n0zVX+8CBA4qJidH69evLnVq2KvrGugoKCuTt7S1jjKZOnaorr7xSMTEx9V2WpIZdG4CGjzMAF4BnnnlG//rXv/Taa6/VdykXnJ9++kmPPPKISktL5enp2eAuoG4ojh8/rpEjR6qkpETGGD399NN8wP3/6Bvreu+99/TBBx+ouLhYgYGBGjp0aH2X5NKQawPQ8HEGAAAAALAQLgIGAAAALIQAAAAAAFgIAQAAAACwEAIAAAAAYCEEAACwuMOHD2v8+PEKDQ1VZGSk7rvvPu3fv7/G2t+2bZt27txZY+1J0qOPPiq73a6EhIQzPj9p0iRdd911ys/Pdz02ffp0XXPNNef8q9GnSkxM1LRp06q9DgA0JNwGFAAszBijMWPGaODAgZo9e7Yk6dtvv1VWVpbat29fI/v46quv1LRpUwUHB9dIe4cPH1Zqaqo+/vjjCte74oor9Mknn8jhcKi0tFRbt25V69ata6QGALiQcQYAACxs69at8vDw0PDhw12PdezYUddff73i4+MVHh4uu92utWvXSjr5bf6pf0142rRpSkxMlCSFhIRozpw5GjRokOx2u9LT03Xw4EEtX75cCQkJcjgc2r59u9atW6fw8HBFRERU+NdyCwsLNXnyZNntdg0cOFBbt26VJN1zzz3KzMx0tXc2AwYM0Lp161x1BwcHy8Pjf997rVq1SoMHD5bD4dBTTz0lp9MpSVqxYoVuu+02DR48uNyZi40bN+rOO+/UwIEDFRMToyNHjlS6nwGgIeEMAABY2L59+9SpU6fTHv/nP/+pb7/9VqtWrVJOTo4GDx6s7t27n7M9X19fffDBB1q2bJnefPNNTZ8+XcOGDVPTpk0VGxsrSbLb7Vq4cKFat26t3Nzcs7a1bNkySdLq1auVnp6u2NhYffTRR5o/f74efPDBc/5V7yuvvFIbN27U0aNHlZSUpIiICG3ZskWSlJ6ernXr1untt9+Wp6enpkyZotWrV+vmm2/W3LlzlZiYqGbNmmnkyJG69tprJUnXX3+93n33Xbm5uem9997TG2+8oUmTJp2zTwCgoSEAAABOs2PHDg0YMEDu7u66+OKL1aNHD6WmpqpZs2YVbtevXz9JUufOnc86RScoKEiTJk3S7bffrltvvbXCGkaMGCFJuuqqq3TZZZdp//7956zhVLfeequSkpK0e/fucvP0v/zyS33zzTcaPHiwJOnEiRNq2bKlvv76a/Xs2VN+fn6SpLCwMP3www+SpEOHDmn8+PE6fPiwioqK1LZt20rXAQANCQEAACzM399fH330UaXXd3d3V2lpqWu5sLCw3POenp6SJJvN5ppS81vTpk3T7t279emnn+qOO+7QihUr5Ovrex7Vn1tYWJgiIyM1aNAg2Wz/m/VqjNGgQYM0YcKEcutv2LDhrG3FxcUpJiZGffv21bZt2zRv3rxaqRkAahvXAACAhd14440qKirSO++843rs22+/VYsWLbRu3To5nU5lZ2dr+/bt6tq1qy6//HKlp6erqKhIubm5+vLLL8+5D29vbxUUFLiWDxw4oOuuu07jxo2Tr6+vDh06dMbtunfvrtWrV0uS9u/fr59//lkdOnSo0vFdfvnlGj9+vO66665yj99000366KOPlJWVJUn69ddflZGRoa5duyo5OVk5OTkqLi7W+vXrXdvk5eW5LiJeuXJlleoAgIaEMwAAYGFubm6aN2+enn32Wb3++utq1KiRLr/8cj3++OMqKCiQw+GQm5ubJk6cqEsuuUSS1L9/f4WHh6tt27au+fEV6dOnj8aOHatPPvlETz75pBISEvTf//5XxhjdeOON6tix4xm3u+uuuzRlyhTZ7Xa5u7trxowZ8vLyqvIxDhs27LTHrr76aj3yyCO65557VFpaKk9PTz311FPq1q2bxowZo2HDhql58+YKDAx0bTNmzBiNGzdOF110kW644QYdPHiwyrUAQEPgZowx9V0EAAAAgLrBFCAAAADAQpgCBACoV1u2bNGsWbPKPda2bVu9/PLL59x26tSpp/2V4ZEjR+qOO+6o0RoB4PeEKUAAAACAhTAFCAAAALAQAgAAAABgIQQAAAAAwEIIAAAAAICF/D+nQMvuDIozCAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WJ_j5wN-DUDg"
      },
      "source": [
        "## **7. Which event is most popular in terms on number of players? (Top 5)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "H_uG176RDZFa",
        "outputId": "d09807b7-659f-46d9-8a3b-aed742431be0"
      },
      "source": [
        "Number_of_players = summer_df[[\"Event\",\"Athlete\"]]\n",
        "Number_of_players = Number_of_players.value_counts(\"Event\").reset_index(name=\"No_of_players\")[:5]\n",
        "print(f\"MOST POPULAR EVENT IN TERMS ON NUMBER OF PLAYERS:\\n\")\n",
        "Number_of_players"
      ],
      "execution_count": 243,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MOST POPULAR EVENT IN TERMS ON NUMBER OF PLAYERS:\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "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>Event</th>\n",
              "      <th>No_of_players</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Football</td>\n",
              "      <td>1497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Hockey</td>\n",
              "      <td>1422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Team Competition</td>\n",
              "      <td>1147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Basketball</td>\n",
              "      <td>1012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Handball</td>\n",
              "      <td>973</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Event  No_of_players\n",
              "0          Football           1497\n",
              "1            Hockey           1422\n",
              "2  Team Competition           1147\n",
              "3        Basketball           1012\n",
              "4          Handball            973"
            ]
          },
          "metadata": {},
          "execution_count": 243
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 687
        },
        "id": "fgWEDHZGg3hH",
        "outputId": "7722f107-13f3-4c1c-da9b-7abd780b8ab4"
      },
      "source": [
        "sns.set_style('whitegrid')\n",
        "matplotlib.rcParams['figure.figsize'] = (12, 10)\n",
        "\n",
        "plt.title(\"Most Popular Events in terms of No of Players\")\n",
        "barplot = sns.barplot(data=Number_of_players, x=\"Event\", y=\"No_of_players\", hue=\"Event\", palette=\"CMRmap\", dodge=False)\n",
        "barplot.set_xticklabels(rotation=50, labels=Number_of_players.Event)\n",
        "plt.ylabel(\"No_of_players\")\n",
        "plt.show()"
      ],
      "execution_count": 244,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qC-fnSDgFHjg"
      },
      "source": [
        "## **8. Which sport is having most female Gold Medalists? (Top 5)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "FVa8MFxiFLpn",
        "outputId": "2ab392ad-3294-448c-91a0-01442bf843e9"
      },
      "source": [
        "Female_gold_medalists = summer_df[(summer_df[\"Medal\"] == \"Gold\") & (summer_df[\"Gender\"] == \"Women\")][[\"Sport\", \"Medal\"]]\n",
        "Female_gold_medalists = Female_gold_medalists.value_counts([\"Sport\"]).reset_index(name = \"Female_gold_medalists\")[:5]\n",
        "print(f\"\\nFEMALE GOLD MEDALISTS IN SUMMER OLYMPICS:\\n\")\n",
        "Female_gold_medalists"
      ],
      "execution_count": 245,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "FEMALE GOLD MEDALISTS IN SUMMER OLYMPICS:\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "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>Sport</th>\n",
              "      <th>Female_gold_medalists</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Aquatics</td>\n",
              "      <td>589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Athletics</td>\n",
              "      <td>388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Gymnastics</td>\n",
              "      <td>268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Rowing</td>\n",
              "      <td>217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Volleyball</td>\n",
              "      <td>166</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Sport  Female_gold_medalists\n",
              "0    Aquatics                    589\n",
              "1   Athletics                    388\n",
              "2  Gymnastics                    268\n",
              "3      Rowing                    217\n",
              "4  Volleyball                    166"
            ]
          },
          "metadata": {},
          "execution_count": 245
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "mmx9vJvdhjRr",
        "outputId": "b4c3384c-fbd7-4d58-c97d-ebe4d83a83cb"
      },
      "source": [
        "sns.set_style('whitegrid')\n",
        "matplotlib.rcParams['figure.figsize'] = (12, 10)\n",
        "\n",
        "plt.title(\"Female Gold Medalists in Summer Olympics\")\n",
        "barplot = sns.barplot(data=Female_gold_medalists, x=\"Sport\", y=\"Female_gold_medalists\", hue=\"Sport\", palette=\"CMRmap\", dodge=False)\n",
        "barplot.set_xticklabels(rotation=50, labels=Female_gold_medalists.Sport)\n",
        "plt.ylabel(\"Female_gold_medalists\")\n",
        "plt.show()"
      ],
      "execution_count": 246,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}