DataUnderstandingPart.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "2q00LaXfi7xM",
        "6F7CbKx28tq4",
        "0qAAagldSmdG",
        "dJs0r8bsDz0C"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Downloading Dataset"
      ],
      "metadata": {
        "id": "2q00LaXfi7xM"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 347,
      "metadata": {
        "id": "UKpMd-56Sc_4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5848afed-2bf4-404c-b961-01abee5f0798"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/drive/MyDrive/DataMiningProject/DataMiningProject.zip\n",
            "replace DataMiningProject/atp_2021.xlsx? [y]es, [n]o, [A]ll, [N]one, [r]ename: A\n",
            "  inflating: DataMiningProject/atp_2021.xlsx  \n",
            "  inflating: __MACOSX/DataMiningProject/._atp_2021.xlsx  \n",
            "  inflating: DataMiningProject/atp_2017.xlsx  \n",
            "  inflating: __MACOSX/DataMiningProject/._atp_2017.xlsx  \n",
            "  inflating: DataMiningProject/atp_2009.xls  \n",
            "  inflating: __MACOSX/DataMiningProject/._atp_2009.xls  \n",
            "  inflating: DataMiningProject/atp_2016.xlsx  \n",
            "  inflating: __MACOSX/DataMiningProject/._atp_2016.xlsx  \n",
            "  inflating: DataMiningProject/atp_2008.xls  \n",
            "  inflating: __MACOSX/DataMiningProject/._atp_2008.xls  \n",
            "  inflating: DataMiningProject/atp_2020.xlsx  \n",
            "  inflating: __MACOSX/DataMiningProject/._atp_2020.xlsx  \n",
            "  inflating: DataMiningProject/atp_2012.xls  \n",
            "  inflating: __MACOSX/DataMiningProject/._atp_2012.xls  \n",
            "  inflating: DataMiningProject/atp_2006.xls  \n",
            "  inflating: __MACOSX/DataMiningProject/._atp_2006.xls  \n",
            "  inflating: DataMiningProject/atp_2019.xlsx  \n",
            "  inflating: __MACOSX/DataMiningProject/._atp_2019.xlsx  \n",
            "  inflating: DataMiningProject/atp_2007.xls  \n",
            "  inflating: __MACOSX/DataMiningProject/._atp_2007.xls  \n",
            "  inflating: DataMiningProject/atp_2023.xlsx  \n",
            "  inflating: __MACOSX/DataMiningProject/._atp_2023.xlsx  \n",
            "  inflating: DataMiningProject/atp_2005.xls  \n",
            "  inflating: __MACOSX/DataMiningProject/._atp_2005.xls  \n",
            "  inflating: DataMiningProject/atp_2011.xls  \n",
            "  inflating: __MACOSX/DataMiningProject/._atp_2011.xls  \n",
            "  inflating: DataMiningProject/atp_2015.xlsx  \n",
            "  inflating: __MACOSX/DataMiningProject/._atp_2015.xlsx  \n",
            "  inflating: DataMiningProject/atp_2010.xls  \n",
            "  inflating: __MACOSX/DataMiningProject/._atp_2010.xls  \n",
            "  inflating: DataMiningProject/atp_2004.xls  \n",
            "  inflating: __MACOSX/DataMiningProject/._atp_2004.xls  \n",
            "  inflating: DataMiningProject/atp_2000.xls  \n",
            "  inflating: __MACOSX/DataMiningProject/._atp_2000.xls  \n",
            "  inflating: DataMiningProject/atp_2014.xlsx  \n",
            "  inflating: __MACOSX/DataMiningProject/._atp_2014.xlsx  \n",
            "  inflating: DataMiningProject/atp_2001.xls  \n",
            "  inflating: __MACOSX/DataMiningProject/._atp_2001.xls  \n",
            "  inflating: DataMiningProject/atp_2022.xlsx  \n",
            "  inflating: __MACOSX/DataMiningProject/._atp_2022.xlsx  \n",
            "  inflating: DataMiningProject/atp_2003.xls  \n",
            "  inflating: __MACOSX/DataMiningProject/._atp_2003.xls  \n",
            "  inflating: DataMiningProject/atp_2018.xlsx  \n",
            "  inflating: __MACOSX/DataMiningProject/._atp_2018.xlsx  \n",
            "  inflating: DataMiningProject/atp_2002.xls  \n",
            "  inflating: __MACOSX/DataMiningProject/._atp_2002.xls  \n",
            "  inflating: DataMiningProject/atp_2013.xlsx  \n",
            "  inflating: __MACOSX/DataMiningProject/._atp_2013.xlsx  \n",
            "  inflating: DataMiningProject/atp_players.csv  \n",
            "  inflating: __MACOSX/DataMiningProject/._atp_players.csv  \n"
          ]
        }
      ],
      "source": [
        "!unzip /content/drive/MyDrive/DataMiningProject/DataMiningProject.zip"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "pd.read_excel('/content/DataMiningProject/atp_2000.xls')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 765
        },
        "id": "3kD0hnQtVzCE",
        "outputId": "aee5e589-e1c8-4797-8450-3e821c191b0e"
      },
      "execution_count": 348,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      ATP  Location                          Tournament       Date  \\\n",
              "0       1  Adelaide  Australian Hardcourt Championships 2000-01-03   \n",
              "1       1  Adelaide  Australian Hardcourt Championships 2000-01-03   \n",
              "2       1  Adelaide  Australian Hardcourt Championships 2000-01-03   \n",
              "3       1  Adelaide  Australian Hardcourt Championships 2000-01-03   \n",
              "4       1  Adelaide  Australian Hardcourt Championships 2000-01-03   \n",
              "...   ...       ...                                 ...        ...   \n",
              "2958   69    Lisbon                         Masters Cup 2000-11-27   \n",
              "2959   69    Lisbon                         Masters Cup 2000-11-27   \n",
              "2960   69    Lisbon                         Masters Cup 2000-11-27   \n",
              "2961   69    Lisbon                         Masters Cup 2000-11-27   \n",
              "2962   69    Lisbon                         Masters Cup 2000-11-27   \n",
              "\n",
              "             Series    Court Surface        Round  Best of       Winner  ...  \\\n",
              "0     International  Outdoor    Hard    1st Round        3   Dosedel S.  ...   \n",
              "1     International  Outdoor    Hard    1st Round        3   Enqvist T.  ...   \n",
              "2     International  Outdoor    Hard    1st Round        3    Escude N.  ...   \n",
              "3     International  Outdoor    Hard    1st Round        3   Federer R.  ...   \n",
              "4     International  Outdoor    Hard    1st Round        3  Fromberg R.  ...   \n",
              "...             ...      ...     ...          ...      ...          ...  ...   \n",
              "2958    Masters Cup   Indoor    Hard  Round Robin        3   Sampras P.  ...   \n",
              "2959    Masters Cup   Indoor    Hard  Round Robin        3   Sampras P.  ...   \n",
              "2960    Masters Cup   Indoor    Hard   Semifinals        3    Agassi A.  ...   \n",
              "2961    Masters Cup   Indoor    Hard   Semifinals        3   Kuerten G.  ...   \n",
              "2962    Masters Cup   Indoor    Hard    The Final        5   Kuerten G.  ...   \n",
              "\n",
              "       L2   W3   L3  W4  L4  W5  L5  Wsets  Lsets    Comment  \n",
              "0     2.0  NaN  NaN NaN NaN NaN NaN      2      0  Completed  \n",
              "1     3.0  NaN  NaN NaN NaN NaN NaN      2      0  Completed  \n",
              "2     5.0  6.0  3.0 NaN NaN NaN NaN      2      1  Completed  \n",
              "3     4.0  NaN  NaN NaN NaN NaN NaN      2      0  Completed  \n",
              "4     7.0  6.0  4.0 NaN NaN NaN NaN      2      1  Completed  \n",
              "...   ...  ...  ...  ..  ..  ..  ..    ...    ...        ...  \n",
              "2958  5.0  NaN  NaN NaN NaN NaN NaN      2      0  Completed  \n",
              "2959  2.0  NaN  NaN NaN NaN NaN NaN      2      0  Completed  \n",
              "2960  3.0  NaN  NaN NaN NaN NaN NaN      2      0  Completed  \n",
              "2961  3.0  6.0  4.0 NaN NaN NaN NaN      2      1  Completed  \n",
              "2962  4.0  6.0  4.0 NaN NaN NaN NaN      3      0  Completed  \n",
              "\n",
              "[2963 rows x 26 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4a430b60-3e1c-431c-b8da-891cd01d335a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ATP</th>\n",
              "      <th>Location</th>\n",
              "      <th>Tournament</th>\n",
              "      <th>Date</th>\n",
              "      <th>Series</th>\n",
              "      <th>Court</th>\n",
              "      <th>Surface</th>\n",
              "      <th>Round</th>\n",
              "      <th>Best of</th>\n",
              "      <th>Winner</th>\n",
              "      <th>...</th>\n",
              "      <th>L2</th>\n",
              "      <th>W3</th>\n",
              "      <th>L3</th>\n",
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              "      <th>L4</th>\n",
              "      <th>W5</th>\n",
              "      <th>L5</th>\n",
              "      <th>Wsets</th>\n",
              "      <th>Lsets</th>\n",
              "      <th>Comment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Adelaide</td>\n",
              "      <td>Australian Hardcourt Championships</td>\n",
              "      <td>2000-01-03</td>\n",
              "      <td>International</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3</td>\n",
              "      <td>Dosedel S.</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>Completed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Adelaide</td>\n",
              "      <td>Australian Hardcourt Championships</td>\n",
              "      <td>2000-01-03</td>\n",
              "      <td>International</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3</td>\n",
              "      <td>Enqvist T.</td>\n",
              "      <td>...</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>Completed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Adelaide</td>\n",
              "      <td>Australian Hardcourt Championships</td>\n",
              "      <td>2000-01-03</td>\n",
              "      <td>International</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3</td>\n",
              "      <td>Escude N.</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Completed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>Adelaide</td>\n",
              "      <td>Australian Hardcourt Championships</td>\n",
              "      <td>2000-01-03</td>\n",
              "      <td>International</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3</td>\n",
              "      <td>Federer R.</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>0</td>\n",
              "      <td>Completed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>Adelaide</td>\n",
              "      <td>Australian Hardcourt Championships</td>\n",
              "      <td>2000-01-03</td>\n",
              "      <td>International</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3</td>\n",
              "      <td>Fromberg R.</td>\n",
              "      <td>...</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Completed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2958</th>\n",
              "      <td>69</td>\n",
              "      <td>Lisbon</td>\n",
              "      <td>Masters Cup</td>\n",
              "      <td>2000-11-27</td>\n",
              "      <td>Masters Cup</td>\n",
              "      <td>Indoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>Round Robin</td>\n",
              "      <td>3</td>\n",
              "      <td>Sampras P.</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>Completed</td>\n",
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              "    <tr>\n",
              "      <th>2959</th>\n",
              "      <td>69</td>\n",
              "      <td>Lisbon</td>\n",
              "      <td>Masters Cup</td>\n",
              "      <td>2000-11-27</td>\n",
              "      <td>Masters Cup</td>\n",
              "      <td>Indoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>Round Robin</td>\n",
              "      <td>3</td>\n",
              "      <td>Sampras P.</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>Completed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2960</th>\n",
              "      <td>69</td>\n",
              "      <td>Lisbon</td>\n",
              "      <td>Masters Cup</td>\n",
              "      <td>2000-11-27</td>\n",
              "      <td>Masters Cup</td>\n",
              "      <td>Indoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>Semifinals</td>\n",
              "      <td>3</td>\n",
              "      <td>Agassi A.</td>\n",
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              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>Completed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2961</th>\n",
              "      <td>69</td>\n",
              "      <td>Lisbon</td>\n",
              "      <td>Masters Cup</td>\n",
              "      <td>2000-11-27</td>\n",
              "      <td>Masters Cup</td>\n",
              "      <td>Indoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>Semifinals</td>\n",
              "      <td>3</td>\n",
              "      <td>Kuerten G.</td>\n",
              "      <td>...</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Completed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2962</th>\n",
              "      <td>69</td>\n",
              "      <td>Lisbon</td>\n",
              "      <td>Masters Cup</td>\n",
              "      <td>2000-11-27</td>\n",
              "      <td>Masters Cup</td>\n",
              "      <td>Indoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>The Final</td>\n",
              "      <td>5</td>\n",
              "      <td>Kuerten G.</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>Completed</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2963 rows × 26 columns</p>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4a430b60-3e1c-431c-b8da-891cd01d335a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4a430b60-3e1c-431c-b8da-891cd01d335a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 348
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preparing Data"
      ],
      "metadata": {
        "id": "fmOp_kokjAFE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Matches"
      ],
      "metadata": {
        "id": "gX7LxuS6SfDt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Combining years all together"
      ],
      "metadata": {
        "id": "TerzW-rps486"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "\n",
        "path_to_dir = '/content/DataMiningProject'\n",
        "dataframes_of_matches = []\n",
        "\n",
        "for file_path in os.listdir(path_to_dir):\n",
        "  direct_path_to_file = os.path.join(path_to_dir, file_path)\n",
        "  if file_path.endswith('.xls') or file_path.endswith('.xlsx'):\n",
        "    df = pd.read_excel(direct_path_to_file,)\n",
        "    if 'atp_players' not in file_path:\n",
        "      dataframes_of_matches.append(df)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L2wWGULSs-oz",
        "outputId": "d8ee403a-a8d2-4abf-fabd-08658e06561b"
      },
      "execution_count": 349,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/openpyxl/worksheet/_reader.py:312: UserWarning: Unknown extension is not supported and will be removed\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/openpyxl/worksheet/_reader.py:312: UserWarning: Unknown extension is not supported and will be removed\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/openpyxl/worksheet/_reader.py:312: UserWarning: Unknown extension is not supported and will be removed\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/openpyxl/worksheet/_reader.py:312: UserWarning: Unknown extension is not supported and will be removed\n",
            "  warn(msg)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "matches = pd.concat(dataframes_of_matches, ignore_index=True)"
      ],
      "metadata": {
        "id": "MlravPP_v9T_"
      },
      "execution_count": 350,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Cleaning"
      ],
      "metadata": {
        "id": "6F7CbKx28tq4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matches = matches.drop(columns=['ATP'])"
      ],
      "metadata": {
        "id": "7ant-W-R87UY"
      },
      "execution_count": 351,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "New feature country"
      ],
      "metadata": {
        "id": "s4mpFY_s9Dz_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import requests, json\n",
        "\n",
        "my_yandex_maps_api_key = '4405e07d-8fde-45f6-afb6-97a201b1a210'\n",
        "\n",
        "\n",
        "def get_country_name(town):\n",
        "  url = f'https://geocode-maps.yandex.ru/1.x/?apikey={my_yandex_maps_api_key}&format=json&geocode={town}&lang=en_US'\n",
        "  resp = requests.get(url)\n",
        "  answer = json.loads(resp.content)\n",
        "  tmp = answer['response']['GeoObjectCollection']['featureMember']\n",
        "  if len(tmp) == 0:\n",
        "    return 'YYY'\n",
        "  resp = answer['response']['GeoObjectCollection']['featureMember'][0]['GeoObject']['metaDataProperty']['GeocoderMetaData']['AddressDetails']['Country']['CountryName']\n",
        "  return resp"
      ],
      "metadata": {
        "id": "A1Lzald29LPg"
      },
      "execution_count": 352,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# city_to_country = {}\n",
        "# for city in matches[\"Location\"].unique():\n",
        "#   city_to_country[city] = get_country_name(city)\n",
        "# city_to_country"
      ],
      "metadata": {
        "id": "ACnAVsDp9wPR"
      },
      "execution_count": 353,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "city_to_country = {'Adelaide': 'Australia',\n",
        " 'Chennai': 'India',\n",
        " 'Doha': 'Qatar',\n",
        " 'Auckland': 'New Zealand',\n",
        " 'Sydney': 'Australia',\n",
        " 'Melbourne': 'Australia',\n",
        " 'Bogota': 'Colombia',\n",
        " 'Milan': 'Italy',\n",
        " 'Copenhagen': 'Denmark',\n",
        " 'Marseille': 'France',\n",
        " 'Vina del Mar': 'Chile',\n",
        " 'Buenos Aires': 'Argentina',\n",
        " 'Memphis': 'United States of America',\n",
        " 'Rotterdam': 'Netherlands',\n",
        " 'Acapulco': 'Mexico',\n",
        " 'Dubai': 'United Arab Emirates',\n",
        " 'San Jose': 'United States of America',\n",
        " 'Delray Beach': 'United States of America',\n",
        " 'Scottsdale': 'United States of America',\n",
        " 'Indian Wells': 'United States of America',\n",
        " 'Miami': 'United States of America',\n",
        " 'Casablanca': 'Morocco',\n",
        " 'Estoril': 'Portugal',\n",
        " 'Monte Carlo': 'Monaco',\n",
        " 'Atlanta': 'United States of America',\n",
        " 'Barcelona': 'Spain',\n",
        " 'Houston': 'United States of America',\n",
        " 'Mallorca': 'Spain',\n",
        " 'Munich': 'Germany',\n",
        " 'Rome': 'Italy',\n",
        " 'Hamburg': 'Germany',\n",
        " 'St. Polten': 'YYY',\n",
        " 'Paris': 'France',\n",
        " 'Halle': 'Germany',\n",
        " 'Queens Club': 'United Kingdom',\n",
        " 'Nottingham': 'United Kingdom',\n",
        " \"'s-Hertogenbosch\": 'Netherlands',\n",
        " 'London': 'United Kingdom',\n",
        " 'Bastad': 'Sweden',\n",
        " 'Gstaad': 'Switzerland',\n",
        " 'Newport': 'United Kingdom',\n",
        " 'Amsterdam': 'Netherlands',\n",
        " 'Stuttgart': 'Germany',\n",
        " 'Umag': 'Croatia',\n",
        " 'Kitzbuhel': 'Austria',\n",
        " 'Los Angeles': 'United States of America',\n",
        " 'Sopot': 'Poland',\n",
        " 'Montreal': 'Canada',\n",
        " 'Cincinnati': 'United States of America',\n",
        " 'Indianapolis': 'United States of America',\n",
        " 'Washington': 'United States of America',\n",
        " 'Long Island': 'Falkland Islands',\n",
        " 'New York': 'United States of America',\n",
        " 'Bucharest': 'Romania',\n",
        " 'Salvador': 'Brazil',\n",
        " 'Tashkent': 'Uzbekistan',\n",
        " 'Shanghai': 'China',\n",
        " 'Hong Kong': 'China',\n",
        " 'Palermo': 'Italy',\n",
        " 'Moscow': 'Russian Federation',\n",
        " 'Tokyo': 'Japan',\n",
        " 'Lyon': 'France',\n",
        " 'Vienna': 'Austria',\n",
        " 'Basel': 'Switzerland',\n",
        " 'St. Petersburg': 'Russian Federation',\n",
        " 'Stockholm': 'Sweden',\n",
        " 'Dubai ': 'United Arab Emirates',\n",
        " 'Estoril ': 'Portugal',\n",
        " 'Valencia': 'Spain',\n",
        " 'Amersfoort': 'Netherlands',\n",
        " 'Costa Do Sauipe': 'Brazil',\n",
        " 'Bangkok': 'Thailand',\n",
        " 'Metz': 'France',\n",
        " 'Vienna ': 'Austria',\n",
        " 'Madrid': 'Spain',\n",
        " 'New Haven': 'United States of America',\n",
        " 'Beijing': 'China',\n",
        " 'Ho Chi Min City': 'Vietnam',\n",
        " 'Brisbane': 'Australia',\n",
        " 'Montpellier': 'France',\n",
        " 'Zagreb': 'Croatia',\n",
        " 'Rio de Janeiro': 'Brazil',\n",
        " 'Sao Paulo': 'Brazil',\n",
        " 'Oeiras': 'Portugal',\n",
        " 'Dusseldorf': 'Germany',\n",
        " 'Nice': 'France',\n",
        " 'Eastbourne': 'United Kingdom',\n",
        " 'Toronto': 'Canada',\n",
        " 'Winston-Salem': 'United States of America',\n",
        " 'Kuala Lumpur': 'Malaysia',\n",
        " 'Shenzhen ': 'China',\n",
        " 'Pune': 'India',\n",
        " 'Cordoba': 'Spain',\n",
        " 'Dallas': 'United States of America',\n",
        " 'Santiago': 'Chile',\n",
        " 'Marrakech': 'Morocco',\n",
        " 'Banja Luka': 'Bosnia and Herzegovina',\n",
        " 'Sofia': 'Bulgaria',\n",
        " 'Budapest': 'Hungary',\n",
        " 'Geneva': 'Switzerland',\n",
        " 'Antalya': 'Türkiye',\n",
        " 'Los Cabos': 'Mexico',\n",
        " 'Chengdu': 'China',\n",
        " 'Zhuhai': 'China',\n",
        " 'Antwerp': 'Belgium',\n",
        " 'Johannesburg ': 'Republic of South Africa',\n",
        " 'Belgrade': 'Serbia',\n",
        " 'Singapore': 'Singapore',\n",
        " 'Cagliari': 'Italy',\n",
        " 'Marbella': 'Spain',\n",
        " 'Parma': 'Italy',\n",
        " 'Nur-Sultan': 'Kazakhstan',\n",
        " 'San Diego': 'United States of America',\n",
        " 'Turin': 'Italy',\n",
        " 'Mexico City': 'Mexico',\n",
        " 'Orlando': 'United States of America',\n",
        " 'San Marino': 'San Marino',\n",
        " 'Toulouse': 'France',\n",
        " 'Brighton': 'United Kingdom',\n",
        " 'Lisbon': 'Portugal',\n",
        " 'Las Vegas': 'United States of America',\n",
        " 'Portschach': 'Austria',\n",
        " 'Mumbai': 'India',\n",
        " 'Quito': 'Ecuador',\n",
        " 'Istanbul': 'Türkiye',\n",
        " 'Warsaw': 'Poland',\n",
        " 'Seoul': 'Republic of Korea',\n",
        " 'Tel Aviv': 'Israel',\n",
        " 'Florence': 'Italy',\n",
        " 'Gijon': 'Spain',\n",
        " 'Napoli': 'Italy',\n",
        " 'Cologne': 'Germany',\n",
        " 'Sardinia': 'Italy'}"
      ],
      "metadata": {
        "id": "sFqNsk0RnoJo"
      },
      "execution_count": 354,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "matches['Country'] = matches['Location'].apply(lambda x: city_to_country[x])"
      ],
      "metadata": {
        "id": "IKHRcZFy_A7y"
      },
      "execution_count": 355,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "matches['Winner'] = matches['Winner'].apply(lambda x: x.replace('.', ''))\n",
        "matches['Loser'] = matches['Loser'].apply(lambda x: x.replace('.', ''))\n",
        "matches"
      ],
      "metadata": {
        "id": "-FM2gi8u_l51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "4c8dffb2-d187-465f-dbb2-71f9061fda3e"
      },
      "execution_count": 356,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Location                Tournament       Date       Series    Court  \\\n",
              "0      Adelaide  Adelaide International 1 2023-01-01       ATP250  Outdoor   \n",
              "1      Adelaide  Adelaide International 1 2023-01-01       ATP250  Outdoor   \n",
              "2      Adelaide  Adelaide International 1 2023-01-02       ATP250  Outdoor   \n",
              "3      Adelaide  Adelaide International 1 2023-01-02       ATP250  Outdoor   \n",
              "4      Adelaide  Adelaide International 1 2023-01-02       ATP250  Outdoor   \n",
              "...         ...                       ...        ...          ...      ...   \n",
              "62297    London               Masters Cup 2015-11-20  Masters Cup   Indoor   \n",
              "62298    London               Masters Cup 2015-11-20  Masters Cup   Indoor   \n",
              "62299    London               Masters Cup 2015-11-21  Masters Cup   Indoor   \n",
              "62300    London               Masters Cup 2015-11-21  Masters Cup   Indoor   \n",
              "62301    London               Masters Cup 2015-11-22  Masters Cup   Indoor   \n",
              "\n",
              "      Surface        Round  Best of        Winner              Loser  ...  \\\n",
              "0        Hard    1st Round      3.0       Giron M          Gasquet R  ...   \n",
              "1        Hard    1st Round      3.0    Mcdonald M           Galan DE  ...   \n",
              "2        Hard    1st Round      3.0  Kecmanovic M        O Connell C  ...   \n",
              "3        Hard    1st Round      3.0    Nishioka Y             Rune H  ...   \n",
              "4        Hard    1st Round      3.0     Popyrin A  Auger-Aliassime F  ...   \n",
              "...       ...          ...      ...           ...                ...  ...   \n",
              "62297    Hard  Round Robin      3.0       Nadal R           Ferrer D  ...   \n",
              "62298    Hard  Round Robin      3.0    Wawrinka S           Murray A  ...   \n",
              "62299    Hard   Semifinals      3.0    Djokovic N            Nadal R  ...   \n",
              "62300    Hard   Semifinals      3.0     Federer R         Wawrinka S  ...   \n",
              "62301    Hard    The Final      3.0    Djokovic N          Federer R  ...   \n",
              "\n",
              "        EXL   LBW   LBL  SJW  SJL  B&WW B&WL UBW UBL         Country  \n",
              "0       NaN   NaN   NaN  NaN  NaN   NaN  NaN NaN NaN       Australia  \n",
              "1       NaN   NaN   NaN  NaN  NaN   NaN  NaN NaN NaN       Australia  \n",
              "2       NaN   NaN   NaN  NaN  NaN   NaN  NaN NaN NaN       Australia  \n",
              "3       NaN   NaN   NaN  NaN  NaN   NaN  NaN NaN NaN       Australia  \n",
              "4       NaN   NaN   NaN  NaN  NaN   NaN  NaN NaN NaN       Australia  \n",
              "...     ...   ...   ...  ...  ...   ...  ...  ..  ..             ...  \n",
              "62297  4.30  1.22  4.33  NaN  NaN   NaN  NaN NaN NaN  United Kingdom  \n",
              "62298  1.65  2.20  1.67  NaN  NaN   NaN  NaN NaN NaN  United Kingdom  \n",
              "62299  4.30  1.20  4.50  NaN  NaN   NaN  NaN NaN NaN  United Kingdom  \n",
              "62300  3.20  1.30  3.50  NaN  NaN   NaN  NaN NaN NaN  United Kingdom  \n",
              "62301  2.80  1.40  3.00  NaN  NaN   NaN  NaN NaN NaN  United Kingdom  \n",
              "\n",
              "[62302 rows x 54 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b5304d07-64f2-4918-91d0-8bef83ec9002\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Location</th>\n",
              "      <th>Tournament</th>\n",
              "      <th>Date</th>\n",
              "      <th>Series</th>\n",
              "      <th>Court</th>\n",
              "      <th>Surface</th>\n",
              "      <th>Round</th>\n",
              "      <th>Best of</th>\n",
              "      <th>Winner</th>\n",
              "      <th>Loser</th>\n",
              "      <th>...</th>\n",
              "      <th>EXL</th>\n",
              "      <th>LBW</th>\n",
              "      <th>LBL</th>\n",
              "      <th>SJW</th>\n",
              "      <th>SJL</th>\n",
              "      <th>B&amp;WW</th>\n",
              "      <th>B&amp;WL</th>\n",
              "      <th>UBW</th>\n",
              "      <th>UBL</th>\n",
              "      <th>Country</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Adelaide</td>\n",
              "      <td>Adelaide International 1</td>\n",
              "      <td>2023-01-01</td>\n",
              "      <td>ATP250</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Giron M</td>\n",
              "      <td>Gasquet R</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Australia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Adelaide</td>\n",
              "      <td>Adelaide International 1</td>\n",
              "      <td>2023-01-01</td>\n",
              "      <td>ATP250</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Mcdonald M</td>\n",
              "      <td>Galan DE</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Australia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Adelaide</td>\n",
              "      <td>Adelaide International 1</td>\n",
              "      <td>2023-01-02</td>\n",
              "      <td>ATP250</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Kecmanovic M</td>\n",
              "      <td>O Connell C</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Australia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Adelaide</td>\n",
              "      <td>Adelaide International 1</td>\n",
              "      <td>2023-01-02</td>\n",
              "      <td>ATP250</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Nishioka Y</td>\n",
              "      <td>Rune H</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Australia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Adelaide</td>\n",
              "      <td>Adelaide International 1</td>\n",
              "      <td>2023-01-02</td>\n",
              "      <td>ATP250</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Popyrin A</td>\n",
              "      <td>Auger-Aliassime F</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Australia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62297</th>\n",
              "      <td>London</td>\n",
              "      <td>Masters Cup</td>\n",
              "      <td>2015-11-20</td>\n",
              "      <td>Masters Cup</td>\n",
              "      <td>Indoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>Round Robin</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Nadal R</td>\n",
              "      <td>Ferrer D</td>\n",
              "      <td>...</td>\n",
              "      <td>4.30</td>\n",
              "      <td>1.22</td>\n",
              "      <td>4.33</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62298</th>\n",
              "      <td>London</td>\n",
              "      <td>Masters Cup</td>\n",
              "      <td>2015-11-20</td>\n",
              "      <td>Masters Cup</td>\n",
              "      <td>Indoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>Round Robin</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Wawrinka S</td>\n",
              "      <td>Murray A</td>\n",
              "      <td>...</td>\n",
              "      <td>1.65</td>\n",
              "      <td>2.20</td>\n",
              "      <td>1.67</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62299</th>\n",
              "      <td>London</td>\n",
              "      <td>Masters Cup</td>\n",
              "      <td>2015-11-21</td>\n",
              "      <td>Masters Cup</td>\n",
              "      <td>Indoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>Semifinals</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Djokovic N</td>\n",
              "      <td>Nadal R</td>\n",
              "      <td>...</td>\n",
              "      <td>4.30</td>\n",
              "      <td>1.20</td>\n",
              "      <td>4.50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62300</th>\n",
              "      <td>London</td>\n",
              "      <td>Masters Cup</td>\n",
              "      <td>2015-11-21</td>\n",
              "      <td>Masters Cup</td>\n",
              "      <td>Indoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>Semifinals</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Federer R</td>\n",
              "      <td>Wawrinka S</td>\n",
              "      <td>...</td>\n",
              "      <td>3.20</td>\n",
              "      <td>1.30</td>\n",
              "      <td>3.50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62301</th>\n",
              "      <td>London</td>\n",
              "      <td>Masters Cup</td>\n",
              "      <td>2015-11-22</td>\n",
              "      <td>Masters Cup</td>\n",
              "      <td>Indoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>The Final</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Djokovic N</td>\n",
              "      <td>Federer R</td>\n",
              "      <td>...</td>\n",
              "      <td>2.80</td>\n",
              "      <td>1.40</td>\n",
              "      <td>3.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>62302 rows × 54 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b5304d07-64f2-4918-91d0-8bef83ec9002')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b5304d07-64f2-4918-91d0-8bef83ec9002 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b5304d07-64f2-4918-91d0-8bef83ec9002');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 356
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "matches.columns"
      ],
      "metadata": {
        "id": "wzd1Idc9Ahak",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f37ec53-ef7c-4cbb-a29c-a67559ec26d9"
      },
      "execution_count": 357,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Location', 'Tournament', 'Date', 'Series', 'Court', 'Surface', 'Round',\n",
              "       'Best of', 'Winner', 'Loser', 'WRank', 'LRank', 'WPts', 'LPts', 'W1',\n",
              "       'L1', 'W2', 'L2', 'W3', 'L3', 'W4', 'L4', 'W5', 'L5', 'Wsets', 'Lsets',\n",
              "       'Comment', 'B365W', 'B365L', 'PSW', 'PSL', 'MaxW', 'MaxL', 'AvgW',\n",
              "       'AvgL', 'CBW', 'CBL', 'GBW', 'GBL', 'IWW', 'IWL', 'SBW', 'SBL', 'EXW',\n",
              "       'EXL', 'LBW', 'LBL', 'SJW', 'SJL', 'B&WW', 'B&WL', 'UBW', 'UBL',\n",
              "       'Country'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 357
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Instead of nan values let's put zeros"
      ],
      "metadata": {
        "id": "Wl9NnvVmAbsb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matches[['W1', 'L1', 'W2', 'L2', 'W3', 'L3', 'W4', 'L4', 'W5', 'L5']] = matches[['W1', 'L1', 'W2', 'L2', 'W3', 'L3', 'W4', 'L4', 'W5', 'L5']].fillna(0)"
      ],
      "metadata": {
        "id": "Pxq-MAM2Aehd"
      },
      "execution_count": 358,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "matches.head(5)"
      ],
      "metadata": {
        "id": "V9wMwI82T-RG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "79db3539-73c4-4210-90a0-ac903ac5c279"
      },
      "execution_count": 359,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Location                Tournament       Date  Series    Court Surface  \\\n",
              "0  Adelaide  Adelaide International 1 2023-01-01  ATP250  Outdoor    Hard   \n",
              "1  Adelaide  Adelaide International 1 2023-01-01  ATP250  Outdoor    Hard   \n",
              "2  Adelaide  Adelaide International 1 2023-01-02  ATP250  Outdoor    Hard   \n",
              "3  Adelaide  Adelaide International 1 2023-01-02  ATP250  Outdoor    Hard   \n",
              "4  Adelaide  Adelaide International 1 2023-01-02  ATP250  Outdoor    Hard   \n",
              "\n",
              "       Round  Best of        Winner              Loser  ... EXL LBW  LBL  SJW  \\\n",
              "0  1st Round      3.0       Giron M          Gasquet R  ... NaN NaN  NaN  NaN   \n",
              "1  1st Round      3.0    Mcdonald M           Galan DE  ... NaN NaN  NaN  NaN   \n",
              "2  1st Round      3.0  Kecmanovic M        O Connell C  ... NaN NaN  NaN  NaN   \n",
              "3  1st Round      3.0    Nishioka Y             Rune H  ... NaN NaN  NaN  NaN   \n",
              "4  1st Round      3.0     Popyrin A  Auger-Aliassime F  ... NaN NaN  NaN  NaN   \n",
              "\n",
              "   SJL  B&WW B&WL UBW UBL    Country  \n",
              "0  NaN   NaN  NaN NaN NaN  Australia  \n",
              "1  NaN   NaN  NaN NaN NaN  Australia  \n",
              "2  NaN   NaN  NaN NaN NaN  Australia  \n",
              "3  NaN   NaN  NaN NaN NaN  Australia  \n",
              "4  NaN   NaN  NaN NaN NaN  Australia  \n",
              "\n",
              "[5 rows x 54 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-49a123f0-563c-478e-8247-6431fbad891e\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Location</th>\n",
              "      <th>Tournament</th>\n",
              "      <th>Date</th>\n",
              "      <th>Series</th>\n",
              "      <th>Court</th>\n",
              "      <th>Surface</th>\n",
              "      <th>Round</th>\n",
              "      <th>Best of</th>\n",
              "      <th>Winner</th>\n",
              "      <th>Loser</th>\n",
              "      <th>...</th>\n",
              "      <th>EXL</th>\n",
              "      <th>LBW</th>\n",
              "      <th>LBL</th>\n",
              "      <th>SJW</th>\n",
              "      <th>SJL</th>\n",
              "      <th>B&amp;WW</th>\n",
              "      <th>B&amp;WL</th>\n",
              "      <th>UBW</th>\n",
              "      <th>UBL</th>\n",
              "      <th>Country</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Adelaide</td>\n",
              "      <td>Adelaide International 1</td>\n",
              "      <td>2023-01-01</td>\n",
              "      <td>ATP250</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Giron M</td>\n",
              "      <td>Gasquet R</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Australia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Adelaide</td>\n",
              "      <td>Adelaide International 1</td>\n",
              "      <td>2023-01-01</td>\n",
              "      <td>ATP250</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Mcdonald M</td>\n",
              "      <td>Galan DE</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Australia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Adelaide</td>\n",
              "      <td>Adelaide International 1</td>\n",
              "      <td>2023-01-02</td>\n",
              "      <td>ATP250</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Kecmanovic M</td>\n",
              "      <td>O Connell C</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Australia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Adelaide</td>\n",
              "      <td>Adelaide International 1</td>\n",
              "      <td>2023-01-02</td>\n",
              "      <td>ATP250</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Nishioka Y</td>\n",
              "      <td>Rune H</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Australia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Adelaide</td>\n",
              "      <td>Adelaide International 1</td>\n",
              "      <td>2023-01-02</td>\n",
              "      <td>ATP250</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Popyrin A</td>\n",
              "      <td>Auger-Aliassime F</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Australia</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 54 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-49a123f0-563c-478e-8247-6431fbad891e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "          if (!dataTable) return;\n",
              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 359
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "avg = np.nanmean(matches['Best of'])\n",
        "matches.loc[matches['Best of'].isna(), 'Best of'] = avg\n",
        "matches"
      ],
      "metadata": {
        "id": "5_XyNqa9EIhg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "c779326b-2793-4f56-8df3-3132d6ae9adf"
      },
      "execution_count": 360,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Location                Tournament       Date       Series    Court  \\\n",
              "0      Adelaide  Adelaide International 1 2023-01-01       ATP250  Outdoor   \n",
              "1      Adelaide  Adelaide International 1 2023-01-01       ATP250  Outdoor   \n",
              "2      Adelaide  Adelaide International 1 2023-01-02       ATP250  Outdoor   \n",
              "3      Adelaide  Adelaide International 1 2023-01-02       ATP250  Outdoor   \n",
              "4      Adelaide  Adelaide International 1 2023-01-02       ATP250  Outdoor   \n",
              "...         ...                       ...        ...          ...      ...   \n",
              "62297    London               Masters Cup 2015-11-20  Masters Cup   Indoor   \n",
              "62298    London               Masters Cup 2015-11-20  Masters Cup   Indoor   \n",
              "62299    London               Masters Cup 2015-11-21  Masters Cup   Indoor   \n",
              "62300    London               Masters Cup 2015-11-21  Masters Cup   Indoor   \n",
              "62301    London               Masters Cup 2015-11-22  Masters Cup   Indoor   \n",
              "\n",
              "      Surface        Round  Best of        Winner              Loser  ...  \\\n",
              "0        Hard    1st Round      3.0       Giron M          Gasquet R  ...   \n",
              "1        Hard    1st Round      3.0    Mcdonald M           Galan DE  ...   \n",
              "2        Hard    1st Round      3.0  Kecmanovic M        O Connell C  ...   \n",
              "3        Hard    1st Round      3.0    Nishioka Y             Rune H  ...   \n",
              "4        Hard    1st Round      3.0     Popyrin A  Auger-Aliassime F  ...   \n",
              "...       ...          ...      ...           ...                ...  ...   \n",
              "62297    Hard  Round Robin      3.0       Nadal R           Ferrer D  ...   \n",
              "62298    Hard  Round Robin      3.0    Wawrinka S           Murray A  ...   \n",
              "62299    Hard   Semifinals      3.0    Djokovic N            Nadal R  ...   \n",
              "62300    Hard   Semifinals      3.0     Federer R         Wawrinka S  ...   \n",
              "62301    Hard    The Final      3.0    Djokovic N          Federer R  ...   \n",
              "\n",
              "        EXL   LBW   LBL  SJW  SJL  B&WW B&WL UBW UBL         Country  \n",
              "0       NaN   NaN   NaN  NaN  NaN   NaN  NaN NaN NaN       Australia  \n",
              "1       NaN   NaN   NaN  NaN  NaN   NaN  NaN NaN NaN       Australia  \n",
              "2       NaN   NaN   NaN  NaN  NaN   NaN  NaN NaN NaN       Australia  \n",
              "3       NaN   NaN   NaN  NaN  NaN   NaN  NaN NaN NaN       Australia  \n",
              "4       NaN   NaN   NaN  NaN  NaN   NaN  NaN NaN NaN       Australia  \n",
              "...     ...   ...   ...  ...  ...   ...  ...  ..  ..             ...  \n",
              "62297  4.30  1.22  4.33  NaN  NaN   NaN  NaN NaN NaN  United Kingdom  \n",
              "62298  1.65  2.20  1.67  NaN  NaN   NaN  NaN NaN NaN  United Kingdom  \n",
              "62299  4.30  1.20  4.50  NaN  NaN   NaN  NaN NaN NaN  United Kingdom  \n",
              "62300  3.20  1.30  3.50  NaN  NaN   NaN  NaN NaN NaN  United Kingdom  \n",
              "62301  2.80  1.40  3.00  NaN  NaN   NaN  NaN NaN NaN  United Kingdom  \n",
              "\n",
              "[62302 rows x 54 columns]"
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              "      <th>Location</th>\n",
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              "      <th>0</th>\n",
              "      <td>Adelaide</td>\n",
              "      <td>Adelaide International 1</td>\n",
              "      <td>2023-01-01</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>2</th>\n",
              "      <td>Adelaide</td>\n",
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              "      <td>2023-01-02</td>\n",
              "      <td>ATP250</td>\n",
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              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Kecmanovic M</td>\n",
              "      <td>O Connell C</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>3</th>\n",
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              "      <td>2023-01-02</td>\n",
              "      <td>ATP250</td>\n",
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              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Nishioka Y</td>\n",
              "      <td>Rune H</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Australia</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Adelaide</td>\n",
              "      <td>Adelaide International 1</td>\n",
              "      <td>2023-01-02</td>\n",
              "      <td>ATP250</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>United Kingdom</td>\n",
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              "      <td>Djokovic N</td>\n",
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              "      <th>62301</th>\n",
              "      <td>London</td>\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 360
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "avg = np.nanmean(matches['Wsets'])\n",
        "matches.loc[matches['Wsets'].isna(), 'Wsets'] = avg"
      ],
      "metadata": {
        "id": "e-zueiYtEl3P"
      },
      "execution_count": 361,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "avg = np.nanmean(matches['Lsets'])\n",
        "matches.loc[matches['Lsets'].isna(), 'Lsets'] = avg"
      ],
      "metadata": {
        "id": "PEqvVU1vQ2Ut"
      },
      "execution_count": 362,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cols_to_drop = ['Tournament', 'Comment','CBW', 'CBL', 'GBW', 'GBL', 'IWW', 'IWL', 'SBW', 'SBL', 'B&WW', 'B&WL', 'WPts', 'LPts', 'EXW', 'EXL', 'PSW', 'PSL', 'LBW', 'LBL', 'SJW', 'SJL', 'MaxW', 'MaxL', 'AvgW', 'AvgL', 'UBW', 'UBL']\n",
        "\n",
        "matches.drop(columns=cols_to_drop, inplace=True)\n"
      ],
      "metadata": {
        "id": "KavIgtZ_ROpC"
      },
      "execution_count": 363,
      "outputs": []
    },
    {
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      "source": [
        "matches"
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          "height": 0
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        "id": "gUqTiF3eRSRw",
        "outputId": "3f54f57d-a8b0-4acf-d11e-169860bdcb5d"
      },
      "execution_count": 364,
      "outputs": [
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              "       Location       Date       Series    Court Surface        Round  \\\n",
              "0      Adelaide 2023-01-01       ATP250  Outdoor    Hard    1st Round   \n",
              "1      Adelaide 2023-01-01       ATP250  Outdoor    Hard    1st Round   \n",
              "2      Adelaide 2023-01-02       ATP250  Outdoor    Hard    1st Round   \n",
              "3      Adelaide 2023-01-02       ATP250  Outdoor    Hard    1st Round   \n",
              "4      Adelaide 2023-01-02       ATP250  Outdoor    Hard    1st Round   \n",
              "...         ...        ...          ...      ...     ...          ...   \n",
              "62297    London 2015-11-20  Masters Cup   Indoor    Hard  Round Robin   \n",
              "62298    London 2015-11-20  Masters Cup   Indoor    Hard  Round Robin   \n",
              "62299    London 2015-11-21  Masters Cup   Indoor    Hard   Semifinals   \n",
              "62300    London 2015-11-21  Masters Cup   Indoor    Hard   Semifinals   \n",
              "62301    London 2015-11-22  Masters Cup   Indoor    Hard    The Final   \n",
              "\n",
              "       Best of        Winner              Loser  WRank  ...   L3   W4   L4  \\\n",
              "0          3.0       Giron M          Gasquet R   61.0  ...  5.0  0.0  0.0   \n",
              "1          3.0    Mcdonald M           Galan DE   63.0  ...    0  0.0  0.0   \n",
              "2          3.0  Kecmanovic M        O Connell C   29.0  ...    0  0.0  0.0   \n",
              "3          3.0    Nishioka Y             Rune H   36.0  ...  4.0  0.0  0.0   \n",
              "4          3.0     Popyrin A  Auger-Aliassime F  120.0  ...    0  0.0  0.0   \n",
              "...        ...           ...                ...    ...  ...  ...  ...  ...   \n",
              "62297      3.0       Nadal R           Ferrer D    5.0  ...  4.0  0.0  0.0   \n",
              "62298      3.0    Wawrinka S           Murray A    4.0  ...    0  0.0  0.0   \n",
              "62299      3.0    Djokovic N            Nadal R    1.0  ...    0  0.0  0.0   \n",
              "62300      3.0     Federer R         Wawrinka S    3.0  ...    0  0.0  0.0   \n",
              "62301      3.0    Djokovic N          Federer R    1.0  ...    0  0.0  0.0   \n",
              "\n",
              "        W5   L5 Wsets Lsets  B365W  B365L         Country  \n",
              "0      0.0  0.0   2.0   1.0   1.91   1.91       Australia  \n",
              "1      0.0  0.0   1.0   0.0   1.36   3.20       Australia  \n",
              "2      0.0  0.0   2.0   0.0   1.57   2.38       Australia  \n",
              "3      0.0  0.0   2.0   1.0   3.75   1.29       Australia  \n",
              "4      0.0  0.0   2.0   0.0   6.50   1.11       Australia  \n",
              "...    ...  ...   ...   ...    ...    ...             ...  \n",
              "62297  0.0  0.0   2.0   1.0   1.20   4.50  United Kingdom  \n",
              "62298  0.0  0.0   2.0   0.0   2.30   1.61  United Kingdom  \n",
              "62299  0.0  0.0   2.0   0.0   1.20   4.50  United Kingdom  \n",
              "62300  0.0  0.0   2.0   0.0   1.30   3.50  United Kingdom  \n",
              "62301  0.0  0.0   2.0   0.0   1.44   2.75  United Kingdom  \n",
              "\n",
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              "      <th></th>\n",
              "      <th>Location</th>\n",
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              "      <td>Adelaide</td>\n",
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              "      <td>1.36</td>\n",
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              "      <th>2</th>\n",
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              "      <td>3.0</td>\n",
              "      <td>Kecmanovic M</td>\n",
              "      <td>O Connell C</td>\n",
              "      <td>29.0</td>\n",
              "      <td>...</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.57</td>\n",
              "      <td>2.38</td>\n",
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              "      <th>3</th>\n",
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              "      <td>3.0</td>\n",
              "      <td>Nishioka Y</td>\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-066e6abb-4fcc-42e4-ad4e-58ee2381e9b3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-066e6abb-4fcc-42e4-ad4e-58ee2381e9b3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 364
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "avg = np.nanmean(matches['B365W'])\n",
        "matches.loc[matches['B365W'].isna(), 'B365W'] = avg"
      ],
      "metadata": {
        "id": "nkqpeRhCRgfG"
      },
      "execution_count": 365,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "avg = np.nanmean(matches['B365L'])\n",
        "matches.loc[matches['B365L'].isna(), 'B365L'] = avg"
      ],
      "metadata": {
        "id": "J4QYOMJ8RtuX"
      },
      "execution_count": 366,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "matches['win'] = 1"
      ],
      "metadata": {
        "id": "PxiIP9KGSshF"
      },
      "execution_count": 367,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cols_to_rename = {\n",
        "    'Winner': 'first_name',\n",
        "    'Loser': 'second_name',\n",
        "    'WRank': 'first_rank',\n",
        "    'LRank': 'second_rank',\n",
        "    'W1': 'first_1',\n",
        "    'L1': 'second_1',\n",
        "    'W2': 'first_2',\n",
        "    'L2': 'second_2',\n",
        "    'W3': 'first_3',\n",
        "    'L3': 'second_3',\n",
        "    'W4': 'first_4',\n",
        "    'L4': 'second_4',\n",
        "    'W5': 'first_5',\n",
        "    'L5': 'second_5',\n",
        "    'Wsets': 'first_sets',\n",
        "    'Lsets': 'second_sets',\n",
        "    'B365W': 'first_6',\n",
        "    'B365L': 'second_6',\n",
        "}\n",
        "matches = matches.rename(columns=cols_to_rename)\n",
        "matches.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "HCVbXekBS9VO",
        "outputId": "58837b09-92a6-406e-c279-bf948b62fdea"
      },
      "execution_count": 368,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Location       Date  Series    Court Surface      Round  Best of  \\\n",
              "0  Adelaide 2023-01-01  ATP250  Outdoor    Hard  1st Round      3.0   \n",
              "1  Adelaide 2023-01-01  ATP250  Outdoor    Hard  1st Round      3.0   \n",
              "2  Adelaide 2023-01-02  ATP250  Outdoor    Hard  1st Round      3.0   \n",
              "3  Adelaide 2023-01-02  ATP250  Outdoor    Hard  1st Round      3.0   \n",
              "4  Adelaide 2023-01-02  ATP250  Outdoor    Hard  1st Round      3.0   \n",
              "\n",
              "     first_name        second_name first_rank  ... first_4  second_4  first_5  \\\n",
              "0       Giron M          Gasquet R       61.0  ...     0.0       0.0      0.0   \n",
              "1    Mcdonald M           Galan DE       63.0  ...     0.0       0.0      0.0   \n",
              "2  Kecmanovic M        O Connell C       29.0  ...     0.0       0.0      0.0   \n",
              "3    Nishioka Y             Rune H       36.0  ...     0.0       0.0      0.0   \n",
              "4     Popyrin A  Auger-Aliassime F      120.0  ...     0.0       0.0      0.0   \n",
              "\n",
              "  second_5 first_sets second_sets first_6  second_6    Country  win  \n",
              "0      0.0        2.0         1.0    1.91      1.91  Australia    1  \n",
              "1      0.0        1.0         0.0    1.36      3.20  Australia    1  \n",
              "2      0.0        2.0         0.0    1.57      2.38  Australia    1  \n",
              "3      0.0        2.0         1.0    3.75      1.29  Australia    1  \n",
              "4      0.0        2.0         0.0    6.50      1.11  Australia    1  \n",
              "\n",
              "[5 rows x 27 columns]"
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              "      <th></th>\n",
              "      <th>Location</th>\n",
              "      <th>Date</th>\n",
              "      <th>Series</th>\n",
              "      <th>Court</th>\n",
              "      <th>Surface</th>\n",
              "      <th>Round</th>\n",
              "      <th>Best of</th>\n",
              "      <th>first_name</th>\n",
              "      <th>second_name</th>\n",
              "      <th>first_rank</th>\n",
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              "      <th>first_6</th>\n",
              "      <th>second_6</th>\n",
              "      <th>Country</th>\n",
              "      <th>win</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Adelaide</td>\n",
              "      <td>2023-01-01</td>\n",
              "      <td>ATP250</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Giron M</td>\n",
              "      <td>Gasquet R</td>\n",
              "      <td>61.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.91</td>\n",
              "      <td>1.91</td>\n",
              "      <td>Australia</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Adelaide</td>\n",
              "      <td>2023-01-01</td>\n",
              "      <td>ATP250</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Mcdonald M</td>\n",
              "      <td>Galan DE</td>\n",
              "      <td>63.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.36</td>\n",
              "      <td>3.20</td>\n",
              "      <td>Australia</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Adelaide</td>\n",
              "      <td>2023-01-02</td>\n",
              "      <td>ATP250</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Kecmanovic M</td>\n",
              "      <td>O Connell C</td>\n",
              "      <td>29.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.57</td>\n",
              "      <td>2.38</td>\n",
              "      <td>Australia</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Adelaide</td>\n",
              "      <td>2023-01-02</td>\n",
              "      <td>ATP250</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Nishioka Y</td>\n",
              "      <td>Rune H</td>\n",
              "      <td>36.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.75</td>\n",
              "      <td>1.29</td>\n",
              "      <td>Australia</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Adelaide</td>\n",
              "      <td>2023-01-02</td>\n",
              "      <td>ATP250</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Popyrin A</td>\n",
              "      <td>Auger-Aliassime F</td>\n",
              "      <td>120.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.50</td>\n",
              "      <td>1.11</td>\n",
              "      <td>Australia</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 27 columns</p>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 368
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Players"
      ],
      "metadata": {
        "id": "0qAAagldSmdG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "players = pd.read_csv('/content/DataMiningProject/atp_players.csv')\n",
        "new_features = pd.read_csv('/content/new_feautures.csv')\n",
        "new_features = new_features[['full_name', ] + list(new_features.columns[10:-1])]\n",
        "players = players.merge(new_features, left_on='full_name', right_on='full_name', how='inner')\n",
        "players"
      ],
      "metadata": {
        "id": "yieawHn0SogH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "09ae56fc-ecff-460b-87b6-a1f02fe1c2eb"
      },
      "execution_count": 369,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      player_id first_name first_initial  last_name    full_name  \\\n",
              "0          a002    Ricardo             R      Acuna      Acuna R   \n",
              "1          a001      Sadiq             S  Abdullahi  Abdullahi S   \n",
              "2          a005     Nelson             N      Aerts      Aerts N   \n",
              "3          a004       Egan             E      Adams      Adams E   \n",
              "4          a006     Ronald             R     Agenor     Agenor R   \n",
              "...         ...        ...           ...        ...          ...   \n",
              "10878      z431    Nicolas             N      Zahar      Zahar N   \n",
              "10879      z435         A.             A      Zorab      Zorab A   \n",
              "10880      z436         G.             G       Zinn       Zinn G   \n",
              "10881      z440         C.             C     Zenger     Zenger C   \n",
              "10882      z442         A.             A      Ziffo      Ziffo A   \n",
              "\n",
              "                                              player_url flag_code  \\\n",
              "0      http://www.atpworldtour.com/en/players/ricardo...       CHI   \n",
              "1      http://www.atpworldtour.com/en/players/sadiq-a...       NGR   \n",
              "2      http://www.atpworldtour.com/en/players/nelson-...       BRA   \n",
              "3      http://www.atpworldtour.com/en/players/egan-ad...       USA   \n",
              "4      http://www.atpworldtour.com/en/players/ronald-...       USA   \n",
              "...                                                  ...       ...   \n",
              "10878  http://www.atpworldtour.com/en/players/nicolas...       EGY   \n",
              "10879  http://www.atpworldtour.com/en/players/a-zorab...       NaN   \n",
              "10880  http://www.atpworldtour.com/en/players/g-zinn/...       NaN   \n",
              "10881  http://www.atpworldtour.com/en/players/c-zenge...       GER   \n",
              "10882  http://www.atpworldtour.com/en/players/a-ziffo...       NaN   \n",
              "\n",
              "                            residence                birthplace birthdate  \\\n",
              "0                    Jupiter, FL, USA           Santiago, Chile  19580113   \n",
              "1                                 NaN                       NaN  19600202   \n",
              "2                                 NaN  Cachoeira Do Sul, Brazil  19630425   \n",
              "3                   Palmetto, FL, USA      Miami Beach, FL, USA  19590615   \n",
              "4      Beverly Hills, California, USA            Rabat, Morocco  19641113   \n",
              "...                               ...                       ...       ...   \n",
              "10878                             NaN                       NaN       NaN   \n",
              "10879                             NaN                       NaN       NaN   \n",
              "10880                             NaN                       NaN       NaN   \n",
              "10881                             NaN                       NaN       NaN   \n",
              "10882                             NaN                       NaN       NaN   \n",
              "\n",
              "       ...  backhand  service_games_won  total_service_points_won  \\\n",
              "0      ...       NaN               0.00                      0.00   \n",
              "1      ...       NaN               0.00                      0.00   \n",
              "2      ...       NaN               0.82                      0.63   \n",
              "3      ...       NaN               0.00                      0.00   \n",
              "4      ...       NaN               0.70                      0.59   \n",
              "...    ...       ...                ...                       ...   \n",
              "10878  ...       NaN               0.00                      0.00   \n",
              "10879  ...       NaN               0.00                      0.00   \n",
              "10880  ...       NaN               0.00                      0.00   \n",
              "10881  ...       NaN               0.00                      0.00   \n",
              "10882  ...       NaN               0.00                      0.00   \n",
              "\n",
              "       1st_serve_return_points_won  2nd_serve_return_points_won  \\\n",
              "0                             0.00                         0.00   \n",
              "1                             0.00                         0.00   \n",
              "2                             0.28                         0.45   \n",
              "3                             0.00                         0.00   \n",
              "4                             0.30                         0.51   \n",
              "...                            ...                          ...   \n",
              "10878                         0.00                         0.00   \n",
              "10879                         0.00                         0.00   \n",
              "10880                         0.00                         0.00   \n",
              "10881                         0.00                         0.00   \n",
              "10882                         0.00                         0.00   \n",
              "\n",
              "       break_points_opportunities break_points_converted  return_games_played  \\\n",
              "0                               0                   0.00                    0   \n",
              "1                               0                   0.00                    0   \n",
              "2                              11                   0.36                   29   \n",
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              "4                            1500                   0.40                 2466   \n",
              "...                           ...                    ...                  ...   \n",
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              "10880                           0                   0.00                    0   \n",
              "10881                           0                   0.00                    0   \n",
              "10882                           0                   0.00                    0   \n",
              "\n",
              "       return_games_won return_points_won  \n",
              "0                  0.00              0.00  \n",
              "1                  0.00              0.00  \n",
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              "3                  0.00              0.00  \n",
              "4                  0.25              0.38  \n",
              "...                 ...               ...  \n",
              "10878              0.00              0.00  \n",
              "10879              0.00              0.00  \n",
              "10880              0.00              0.00  \n",
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              "10882              0.00              0.00  \n",
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              "      <th></th>\n",
              "      <th>player_id</th>\n",
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              "      <th>first_initial</th>\n",
              "      <th>last_name</th>\n",
              "      <th>full_name</th>\n",
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              "      <th>residence</th>\n",
              "      <th>birthplace</th>\n",
              "      <th>birthdate</th>\n",
              "      <th>...</th>\n",
              "      <th>backhand</th>\n",
              "      <th>service_games_won</th>\n",
              "      <th>total_service_points_won</th>\n",
              "      <th>1st_serve_return_points_won</th>\n",
              "      <th>2nd_serve_return_points_won</th>\n",
              "      <th>break_points_opportunities</th>\n",
              "      <th>break_points_converted</th>\n",
              "      <th>return_games_played</th>\n",
              "      <th>return_games_won</th>\n",
              "      <th>return_points_won</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>a002</td>\n",
              "      <td>Ricardo</td>\n",
              "      <td>R</td>\n",
              "      <td>Acuna</td>\n",
              "      <td>Acuna R</td>\n",
              "      <td>http://www.atpworldtour.com/en/players/ricardo...</td>\n",
              "      <td>CHI</td>\n",
              "      <td>Jupiter, FL, USA</td>\n",
              "      <td>Santiago, Chile</td>\n",
              "      <td>19580113</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
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              "      <td>0.00</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>a001</td>\n",
              "      <td>Sadiq</td>\n",
              "      <td>S</td>\n",
              "      <td>Abdullahi</td>\n",
              "      <td>Abdullahi S</td>\n",
              "      <td>http://www.atpworldtour.com/en/players/sadiq-a...</td>\n",
              "      <td>NGR</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19600202</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>a005</td>\n",
              "      <td>Nelson</td>\n",
              "      <td>N</td>\n",
              "      <td>Aerts</td>\n",
              "      <td>Aerts N</td>\n",
              "      <td>http://www.atpworldtour.com/en/players/nelson-...</td>\n",
              "      <td>BRA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cachoeira Do Sul, Brazil</td>\n",
              "      <td>19630425</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.63</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.45</td>\n",
              "      <td>11</td>\n",
              "      <td>0.36</td>\n",
              "      <td>29</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>a004</td>\n",
              "      <td>Egan</td>\n",
              "      <td>E</td>\n",
              "      <td>Adams</td>\n",
              "      <td>Adams E</td>\n",
              "      <td>http://www.atpworldtour.com/en/players/egan-ad...</td>\n",
              "      <td>USA</td>\n",
              "      <td>Palmetto, FL, USA</td>\n",
              "      <td>Miami Beach, FL, USA</td>\n",
              "      <td>19590615</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>a006</td>\n",
              "      <td>Ronald</td>\n",
              "      <td>R</td>\n",
              "      <td>Agenor</td>\n",
              "      <td>Agenor R</td>\n",
              "      <td>http://www.atpworldtour.com/en/players/ronald-...</td>\n",
              "      <td>USA</td>\n",
              "      <td>Beverly Hills, California, USA</td>\n",
              "      <td>Rabat, Morocco</td>\n",
              "      <td>19641113</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.51</td>\n",
              "      <td>1500</td>\n",
              "      <td>0.40</td>\n",
              "      <td>2466</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>10878</th>\n",
              "      <td>z431</td>\n",
              "      <td>Nicolas</td>\n",
              "      <td>N</td>\n",
              "      <td>Zahar</td>\n",
              "      <td>Zahar N</td>\n",
              "      <td>http://www.atpworldtour.com/en/players/nicolas...</td>\n",
              "      <td>EGY</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
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              "    <tr>\n",
              "      <th>10879</th>\n",
              "      <td>z435</td>\n",
              "      <td>A.</td>\n",
              "      <td>A</td>\n",
              "      <td>Zorab</td>\n",
              "      <td>Zorab A</td>\n",
              "      <td>http://www.atpworldtour.com/en/players/a-zorab...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
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              "    <tr>\n",
              "      <th>10880</th>\n",
              "      <td>z436</td>\n",
              "      <td>G.</td>\n",
              "      <td>G</td>\n",
              "      <td>Zinn</td>\n",
              "      <td>Zinn G</td>\n",
              "      <td>http://www.atpworldtour.com/en/players/g-zinn/...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>10881</th>\n",
              "      <td>z440</td>\n",
              "      <td>C.</td>\n",
              "      <td>C</td>\n",
              "      <td>Zenger</td>\n",
              "      <td>Zenger C</td>\n",
              "      <td>http://www.atpworldtour.com/en/players/c-zenge...</td>\n",
              "      <td>GER</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10882</th>\n",
              "      <td>z442</td>\n",
              "      <td>A.</td>\n",
              "      <td>A</td>\n",
              "      <td>Ziffo</td>\n",
              "      <td>Ziffo A</td>\n",
              "      <td>http://www.atpworldtour.com/en/players/a-ziffo...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10883 rows × 30 columns</p>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
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              "\n",
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              "        async function convertToInteractive(key) {\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ]
          },
          "metadata": {},
          "execution_count": 369
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Cleaning"
      ],
      "metadata": {
        "id": "Pp8ujU5WX4Ri"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "players.columns"
      ],
      "metadata": {
        "id": "YmDJt62sX6GA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c6065da7-099b-4e9b-9f94-fc78f0454a91"
      },
      "execution_count": 370,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['player_id', 'first_name', 'first_initial', 'last_name', 'full_name',\n",
              "       'player_url', 'flag_code', 'residence', 'birthplace', 'birthdate',\n",
              "       'birth_year', 'birth_month', 'birth_day', 'turned_pro', 'weight_lbs',\n",
              "       'weight_kg', 'height_ft', 'height_inches', 'height_cm', 'handedness',\n",
              "       'backhand', 'service_games_won', 'total_service_points_won',\n",
              "       '1st_serve_return_points_won', '2nd_serve_return_points_won',\n",
              "       'break_points_opportunities', 'break_points_converted',\n",
              "       'return_games_played', 'return_games_won', 'return_points_won'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 370
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "players = players.drop(columns=[\n",
        "    'first_initial', 'player_url', 'height_ft',\n",
        "    'height_inches', 'birth_day', 'birth_month',\n",
        "    'birthdate', 'birthplace',\n",
        "    'weight_lbs', 'handedness', 'residence', 'backhand', 'first_name', 'last_name'])"
      ],
      "metadata": {
        "id": "EgLnsQR_q5JM"
      },
      "execution_count": 371,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "players"
      ],
      "metadata": {
        "id": "K8_kPNbF7hkg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "bea61230-d048-4dfa-ad68-56ec81809949"
      },
      "execution_count": 372,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      player_id    full_name flag_code  birth_year  turned_pro  weight_kg  \\\n",
              "0          a002      Acuna R       CHI      1958.0         0.0       68.0   \n",
              "1          a001  Abdullahi S       NGR      1960.0         0.0        0.0   \n",
              "2          a005      Aerts N       BRA      1963.0         0.0       75.0   \n",
              "3          a004      Adams E       USA      1959.0         0.0       73.0   \n",
              "4          a006     Agenor R       USA      1964.0      1983.0       82.0   \n",
              "...         ...          ...       ...         ...         ...        ...   \n",
              "10878      z431      Zahar N       EGY         NaN         NaN        NaN   \n",
              "10879      z435      Zorab A       NaN         NaN         NaN        NaN   \n",
              "10880      z436       Zinn G       NaN         NaN         NaN        NaN   \n",
              "10881      z440     Zenger C       GER         NaN         NaN        NaN   \n",
              "10882      z442      Ziffo A       NaN         NaN         NaN        NaN   \n",
              "\n",
              "       height_cm  service_games_won  total_service_points_won  \\\n",
              "0          175.0               0.00                      0.00   \n",
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              "2          188.0               0.82                      0.63   \n",
              "3          178.0               0.00                      0.00   \n",
              "4          180.0               0.70                      0.59   \n",
              "...          ...                ...                       ...   \n",
              "10878        NaN               0.00                      0.00   \n",
              "10879        NaN               0.00                      0.00   \n",
              "10880        NaN               0.00                      0.00   \n",
              "10881        NaN               0.00                      0.00   \n",
              "10882        NaN               0.00                      0.00   \n",
              "\n",
              "       1st_serve_return_points_won  2nd_serve_return_points_won  \\\n",
              "0                             0.00                         0.00   \n",
              "1                             0.00                         0.00   \n",
              "2                             0.28                         0.45   \n",
              "3                             0.00                         0.00   \n",
              "4                             0.30                         0.51   \n",
              "...                            ...                          ...   \n",
              "10878                         0.00                         0.00   \n",
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              "10880                         0.00                         0.00   \n",
              "10881                         0.00                         0.00   \n",
              "10882                         0.00                         0.00   \n",
              "\n",
              "       break_points_opportunities  break_points_converted  \\\n",
              "0                               0                    0.00   \n",
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              "      <th>0</th>\n",
              "      <td>a002</td>\n",
              "      <td>Acuna R</td>\n",
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              "      <td>1963.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>a004</td>\n",
              "      <td>Adams E</td>\n",
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              "      <td>1959.0</td>\n",
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              "      <td>a006</td>\n",
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              "      <td>USA</td>\n",
              "      <td>1964.0</td>\n",
              "      <td>1983.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>180.0</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>z442</td>\n",
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              "<p>10883 rows × 16 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 372
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "players.isna().sum()"
      ],
      "metadata": {
        "id": "U3JqUqgbB8KW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "11db4bea-d39e-40b2-b901-20a7d740bd3f"
      },
      "execution_count": 373,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "player_id                         0\n",
              "full_name                         0\n",
              "flag_code                      2161\n",
              "birth_year                     5771\n",
              "turned_pro                     9350\n",
              "weight_kg                      8178\n",
              "height_cm                      8226\n",
              "service_games_won                 0\n",
              "total_service_points_won          0\n",
              "1st_serve_return_points_won       0\n",
              "2nd_serve_return_points_won       0\n",
              "break_points_opportunities        0\n",
              "break_points_converted            0\n",
              "return_games_played               0\n",
              "return_games_won                  0\n",
              "return_points_won                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 373
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "players = players[~players.full_name.duplicated()]"
      ],
      "metadata": {
        "id": "w-X1esJVCHyA"
      },
      "execution_count": 374,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Some of players the country name absent"
      ],
      "metadata": {
        "id": "mMC7nev7CbyI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ioc = pd.read_html('https://en.wikipedia.org/wiki/List_of_IOC_country_codes')[0]\n",
        "ioc = ioc.assign(Code=ioc['Code'].str[-3:]).set_index('Code')['National Olympic Committee']\n",
        "ioc.loc['YUG'] = 'Yugoslavia'\n",
        "ioc.loc['LIB'] = 'Lebanon'\n",
        "ioc.loc['RHO'] = 'Zimbabwe'\n",
        "ioc.loc['SIN'] = 'Singapore'\n",
        "ioc.loc['AHO'] = 'Netherlands'\n"
      ],
      "metadata": {
        "id": "qlulcwfrChAY"
      },
      "execution_count": 375,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "players['Country'] = players['flag_code'].map(ioc)"
      ],
      "metadata": {
        "id": "jwNdDr3SCzr2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81b7e415-874c-4e2f-d678-b997f69b0a13"
      },
      "execution_count": 376,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-376-6bc6fa84e48f>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  players['Country'] = players['flag_code'].map(ioc)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "players"
      ],
      "metadata": {
        "id": "nywCdyLkC3OR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "c336edf2-12ac-41fa-c809-617fdc5d977a"
      },
      "execution_count": 377,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      player_id    full_name flag_code  birth_year  turned_pro  weight_kg  \\\n",
              "0          a002      Acuna R       CHI      1958.0         0.0       68.0   \n",
              "1          a001  Abdullahi S       NGR      1960.0         0.0        0.0   \n",
              "2          a005      Aerts N       BRA      1963.0         0.0       75.0   \n",
              "3          a004      Adams E       USA      1959.0         0.0       73.0   \n",
              "4          a006     Agenor R       USA      1964.0      1983.0       82.0   \n",
              "...         ...          ...       ...         ...         ...        ...   \n",
              "10878      z431      Zahar N       EGY         NaN         NaN        NaN   \n",
              "10879      z435      Zorab A       NaN         NaN         NaN        NaN   \n",
              "10880      z436       Zinn G       NaN         NaN         NaN        NaN   \n",
              "10881      z440     Zenger C       GER         NaN         NaN        NaN   \n",
              "10882      z442      Ziffo A       NaN         NaN         NaN        NaN   \n",
              "\n",
              "       height_cm  service_games_won  total_service_points_won  \\\n",
              "0          175.0               0.00                      0.00   \n",
              "1            0.0               0.00                      0.00   \n",
              "2          188.0               0.82                      0.63   \n",
              "3          178.0               0.00                      0.00   \n",
              "4          180.0               0.70                      0.59   \n",
              "...          ...                ...                       ...   \n",
              "10878        NaN               0.00                      0.00   \n",
              "10879        NaN               0.00                      0.00   \n",
              "10880        NaN               0.00                      0.00   \n",
              "10881        NaN               0.00                      0.00   \n",
              "10882        NaN               0.00                      0.00   \n",
              "\n",
              "       1st_serve_return_points_won  2nd_serve_return_points_won  \\\n",
              "0                             0.00                         0.00   \n",
              "1                             0.00                         0.00   \n",
              "2                             0.28                         0.45   \n",
              "3                             0.00                         0.00   \n",
              "4                             0.30                         0.51   \n",
              "...                            ...                          ...   \n",
              "10878                         0.00                         0.00   \n",
              "10879                         0.00                         0.00   \n",
              "10880                         0.00                         0.00   \n",
              "10881                         0.00                         0.00   \n",
              "10882                         0.00                         0.00   \n",
              "\n",
              "       break_points_opportunities  break_points_converted  \\\n",
              "0                               0                    0.00   \n",
              "1                               0                    0.00   \n",
              "2                              11                    0.36   \n",
              "3                               0                    0.00   \n",
              "4                            1500                    0.40   \n",
              "...                           ...                     ...   \n",
              "10878                           0                    0.00   \n",
              "10879                           0                    0.00   \n",
              "10880                           0                    0.00   \n",
              "10881                           0                    0.00   \n",
              "10882                           0                    0.00   \n",
              "\n",
              "       return_games_played  return_games_won  return_points_won        Country  \n",
              "0                        0              0.00               0.00          Chile  \n",
              "1                        0              0.00               0.00        Nigeria  \n",
              "2                       29              0.14               0.34         Brazil  \n",
              "3                        0              0.00               0.00  United States  \n",
              "4                     2466              0.25               0.38  United States  \n",
              "...                    ...               ...                ...            ...  \n",
              "10878                    0              0.00               0.00          Egypt  \n",
              "10879                    0              0.00               0.00            NaN  \n",
              "10880                    0              0.00               0.00            NaN  \n",
              "10881                    0              0.00               0.00        Germany  \n",
              "10882                    0              0.00               0.00            NaN  \n",
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              "      <td>a002</td>\n",
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              "      <td>a004</td>\n",
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              "      <td>z435</td>\n",
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              "    <tr>\n",
              "      <th>10881</th>\n",
              "      <td>z440</td>\n",
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              "      <td>GER</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "  </tbody>\n",
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              "<p>10448 rows × 17 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 377
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "players['Country'].unique()"
      ],
      "metadata": {
        "id": "q430yYxmDBlt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0d1d27a-b24e-4373-9e48-87e39df2b68d"
      },
      "execution_count": 378,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Chile', 'Nigeria', 'Brazil', 'United States', 'Spain',\n",
              "       'Netherlands', 'Italy', 'Australia', 'Great Britain', 'Sweden',\n",
              "       'India', 'Austria', 'Argentina', 'Peru', 'Venezuela',\n",
              "       'South Africa', 'Colombia', 'Bulgaria', 'Bermuda', 'Israel', nan,\n",
              "       'Iran', 'Mexico', 'Bahamas', 'Germany', 'Morocco', 'France',\n",
              "       'Niger', 'Denmark', 'Qatar', 'Norway', 'Switzerland', 'Azerbaijan',\n",
              "       'Croatia', 'Romania', 'South Korea', 'Lebanon',\n",
              "       'United Arab Emirates', 'Kuwait', 'Russia', 'Canada', 'Tunisia',\n",
              "       'Portugal', 'Oman', 'Greece', 'El Salvador', 'Turkey', 'Indonesia',\n",
              "       'Ukraine', 'Kazakhstan', 'Syria', 'Belgium', 'Moldova',\n",
              "       'Bosnia and Herzegovina', 'Monaco', 'Malaysia', 'Hungary',\n",
              "       'Philippines', 'Uruguay', 'Pakistan', 'Armenia', 'New Zealand',\n",
              "       'Japan', 'Bolivia', 'Czechia', 'Jamaica', 'Egypt', 'Finland',\n",
              "       'Zimbabwe', 'Slovenia', 'Slovakia', 'Cyprus', 'Poland', 'Serbia',\n",
              "       'Belarus', 'China', 'Lithuania', 'Thailand', 'Georgia', 'Ireland',\n",
              "       'Haiti', 'Yugoslavia', 'Paraguay', 'Puerto Rico', 'Ecuador',\n",
              "       'Chinese Taipei[8]', 'Latvia', 'Senegal', 'Costa Rica', 'Vietnam',\n",
              "       'Uzbekistan', 'Guatemala', 'Dominican Republic', 'Sri Lanka',\n",
              "       'Cuba', 'Hong Kong, China', 'Singapore', 'Guam', 'Estonia',\n",
              "       'Burkina Faso', 'North Macedonia', 'Sierra Leone', 'Togo',\n",
              "       'Barbados', 'Guyana', 'Kenya', 'Turkmenistan', 'Ivory Coast',\n",
              "       'Luxembourg', 'Benin', 'Andorra', 'Algeria'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 378
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "here are some contries that named differently but means one country\n"
      ],
      "metadata": {
        "id": "V0m-Fn9ODMm6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def rename_countries(cn):\n",
        "  country_mapping = {\n",
        "    'Chinese Taipei[8]': 'China',\n",
        "    'United States': 'United States of America',\n",
        "    'Great Britain': 'United Kingdom'\n",
        "  }\n",
        "  return country_mapping.get(cn, cn)\n",
        "\n",
        "players['Country'] = players['Country'].apply(rename_countries)\n",
        "unique_countries = players['Country'].unique()\n"
      ],
      "metadata": {
        "id": "bnLGNTzKDWn7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "deea9e92-e9be-499e-ec7a-61d74edddd59"
      },
      "execution_count": 379,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-379-7b21fb7d7bbf>:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  players['Country'] = players['Country'].apply(rename_countries)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "players"
      ],
      "metadata": {
        "id": "FW3YQASmDpHQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "a78a3cdf-b4c1-4430-f30d-ce481253a46c"
      },
      "execution_count": 380,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      player_id    full_name flag_code  birth_year  turned_pro  weight_kg  \\\n",
              "0          a002      Acuna R       CHI      1958.0         0.0       68.0   \n",
              "1          a001  Abdullahi S       NGR      1960.0         0.0        0.0   \n",
              "2          a005      Aerts N       BRA      1963.0         0.0       75.0   \n",
              "3          a004      Adams E       USA      1959.0         0.0       73.0   \n",
              "4          a006     Agenor R       USA      1964.0      1983.0       82.0   \n",
              "...         ...          ...       ...         ...         ...        ...   \n",
              "10878      z431      Zahar N       EGY         NaN         NaN        NaN   \n",
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              "10880      z436       Zinn G       NaN         NaN         NaN        NaN   \n",
              "10881      z440     Zenger C       GER         NaN         NaN        NaN   \n",
              "10882      z442      Ziffo A       NaN         NaN         NaN        NaN   \n",
              "\n",
              "       height_cm  service_games_won  total_service_points_won  \\\n",
              "0          175.0               0.00                      0.00   \n",
              "1            0.0               0.00                      0.00   \n",
              "2          188.0               0.82                      0.63   \n",
              "3          178.0               0.00                      0.00   \n",
              "4          180.0               0.70                      0.59   \n",
              "...          ...                ...                       ...   \n",
              "10878        NaN               0.00                      0.00   \n",
              "10879        NaN               0.00                      0.00   \n",
              "10880        NaN               0.00                      0.00   \n",
              "10881        NaN               0.00                      0.00   \n",
              "10882        NaN               0.00                      0.00   \n",
              "\n",
              "       1st_serve_return_points_won  2nd_serve_return_points_won  \\\n",
              "0                             0.00                         0.00   \n",
              "1                             0.00                         0.00   \n",
              "2                             0.28                         0.45   \n",
              "3                             0.00                         0.00   \n",
              "4                             0.30                         0.51   \n",
              "...                            ...                          ...   \n",
              "10878                         0.00                         0.00   \n",
              "10879                         0.00                         0.00   \n",
              "10880                         0.00                         0.00   \n",
              "10881                         0.00                         0.00   \n",
              "10882                         0.00                         0.00   \n",
              "\n",
              "       break_points_opportunities  break_points_converted  \\\n",
              "0                               0                    0.00   \n",
              "1                               0                    0.00   \n",
              "2                              11                    0.36   \n",
              "3                               0                    0.00   \n",
              "4                            1500                    0.40   \n",
              "...                           ...                     ...   \n",
              "10878                           0                    0.00   \n",
              "10879                           0                    0.00   \n",
              "10880                           0                    0.00   \n",
              "10881                           0                    0.00   \n",
              "10882                           0                    0.00   \n",
              "\n",
              "       return_games_played  return_games_won  return_points_won  \\\n",
              "0                        0              0.00               0.00   \n",
              "1                        0              0.00               0.00   \n",
              "2                       29              0.14               0.34   \n",
              "3                        0              0.00               0.00   \n",
              "4                     2466              0.25               0.38   \n",
              "...                    ...               ...                ...   \n",
              "10878                    0              0.00               0.00   \n",
              "10879                    0              0.00               0.00   \n",
              "10880                    0              0.00               0.00   \n",
              "10881                    0              0.00               0.00   \n",
              "10882                    0              0.00               0.00   \n",
              "\n",
              "                        Country  \n",
              "0                         Chile  \n",
              "1                       Nigeria  \n",
              "2                        Brazil  \n",
              "3      United States of America  \n",
              "4      United States of America  \n",
              "...                         ...  \n",
              "10878                     Egypt  \n",
              "10879                       NaN  \n",
              "10880                       NaN  \n",
              "10881                   Germany  \n",
              "10882                       NaN  \n",
              "\n",
              "[10448 rows x 17 columns]"
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>a002</td>\n",
              "      <td>Acuna R</td>\n",
              "      <td>CHI</td>\n",
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              "      <th>1</th>\n",
              "      <td>a001</td>\n",
              "      <td>Abdullahi S</td>\n",
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              "      <td>1960.0</td>\n",
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              "      <td>Nigeria</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>a005</td>\n",
              "      <td>Aerts N</td>\n",
              "      <td>BRA</td>\n",
              "      <td>1963.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>188.0</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.63</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.45</td>\n",
              "      <td>11</td>\n",
              "      <td>0.36</td>\n",
              "      <td>29</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.34</td>\n",
              "      <td>Brazil</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>a004</td>\n",
              "      <td>Adams E</td>\n",
              "      <td>USA</td>\n",
              "      <td>1959.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>178.0</td>\n",
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              "      <td>United States of America</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>a006</td>\n",
              "      <td>Agenor R</td>\n",
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              "      <td>1964.0</td>\n",
              "      <td>1983.0</td>\n",
              "      <td>82.0</td>\n",
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              "      <td>NaN</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 380
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "all_valid_pro_years = players[(players['turned_pro'] != 0) & (~players['turned_pro'].isna())][['birth_year','turned_pro']]\n",
        "mean_diff = int((all_valid_pro_years['turned_pro'] - all_valid_pro_years['birth_year']).mean())\n",
        "players.loc[(players['turned_pro'].isna()) | (players['turned_pro'] == 0), 'turned_pro'] = players['birth_year'] + mean_diff\n",
        "players"
      ],
      "metadata": {
        "id": "SjiMr5SByFru",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "1d382d88-90e6-485a-e5d2-9d23e023cd64"
      },
      "execution_count": 381,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      player_id    full_name flag_code  birth_year  turned_pro  weight_kg  \\\n",
              "0          a002      Acuna R       CHI      1958.0      1977.0       68.0   \n",
              "1          a001  Abdullahi S       NGR      1960.0      1979.0        0.0   \n",
              "2          a005      Aerts N       BRA      1963.0      1982.0       75.0   \n",
              "3          a004      Adams E       USA      1959.0      1978.0       73.0   \n",
              "4          a006     Agenor R       USA      1964.0      1983.0       82.0   \n",
              "...         ...          ...       ...         ...         ...        ...   \n",
              "10878      z431      Zahar N       EGY         NaN         NaN        NaN   \n",
              "10879      z435      Zorab A       NaN         NaN         NaN        NaN   \n",
              "10880      z436       Zinn G       NaN         NaN         NaN        NaN   \n",
              "10881      z440     Zenger C       GER         NaN         NaN        NaN   \n",
              "10882      z442      Ziffo A       NaN         NaN         NaN        NaN   \n",
              "\n",
              "       height_cm  service_games_won  total_service_points_won  \\\n",
              "0          175.0               0.00                      0.00   \n",
              "1            0.0               0.00                      0.00   \n",
              "2          188.0               0.82                      0.63   \n",
              "3          178.0               0.00                      0.00   \n",
              "4          180.0               0.70                      0.59   \n",
              "...          ...                ...                       ...   \n",
              "10878        NaN               0.00                      0.00   \n",
              "10879        NaN               0.00                      0.00   \n",
              "10880        NaN               0.00                      0.00   \n",
              "10881        NaN               0.00                      0.00   \n",
              "10882        NaN               0.00                      0.00   \n",
              "\n",
              "       1st_serve_return_points_won  2nd_serve_return_points_won  \\\n",
              "0                             0.00                         0.00   \n",
              "1                             0.00                         0.00   \n",
              "2                             0.28                         0.45   \n",
              "3                             0.00                         0.00   \n",
              "4                             0.30                         0.51   \n",
              "...                            ...                          ...   \n",
              "10878                         0.00                         0.00   \n",
              "10879                         0.00                         0.00   \n",
              "10880                         0.00                         0.00   \n",
              "10881                         0.00                         0.00   \n",
              "10882                         0.00                         0.00   \n",
              "\n",
              "       break_points_opportunities  break_points_converted  \\\n",
              "0                               0                    0.00   \n",
              "1                               0                    0.00   \n",
              "2                              11                    0.36   \n",
              "3                               0                    0.00   \n",
              "4                            1500                    0.40   \n",
              "...                           ...                     ...   \n",
              "10878                           0                    0.00   \n",
              "10879                           0                    0.00   \n",
              "10880                           0                    0.00   \n",
              "10881                           0                    0.00   \n",
              "10882                           0                    0.00   \n",
              "\n",
              "       return_games_played  return_games_won  return_points_won  \\\n",
              "0                        0              0.00               0.00   \n",
              "1                        0              0.00               0.00   \n",
              "2                       29              0.14               0.34   \n",
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              "10882                    0              0.00               0.00   \n",
              "\n",
              "                        Country  \n",
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              "      <td>a005</td>\n",
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              "      <td>a004</td>\n",
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              "      <td>a006</td>\n",
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          },
          "metadata": {},
          "execution_count": 381
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "avg_height = np.nanmean(players['height_cm'])\n",
        "players.loc[players['height_cm'].isna(), 'height_cm'] = avg_height\n",
        "players"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "cMs6tz_LB6qd",
        "outputId": "3b18bad6-9067-49ee-cee7-f0f3ba611618"
      },
      "execution_count": 382,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      player_id    full_name flag_code  birth_year  turned_pro  weight_kg  \\\n",
              "0          a002      Acuna R       CHI      1958.0      1977.0       68.0   \n",
              "1          a001  Abdullahi S       NGR      1960.0      1979.0        0.0   \n",
              "2          a005      Aerts N       BRA      1963.0      1982.0       75.0   \n",
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              "...         ...          ...       ...         ...         ...        ...   \n",
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              "10882      z442      Ziffo A       NaN         NaN         NaN        NaN   \n",
              "\n",
              "        height_cm  service_games_won  total_service_points_won  \\\n",
              "0      175.000000               0.00                      0.00   \n",
              "1        0.000000               0.00                      0.00   \n",
              "2      188.000000               0.82                      0.63   \n",
              "3      178.000000               0.00                      0.00   \n",
              "4      180.000000               0.70                      0.59   \n",
              "...           ...                ...                       ...   \n",
              "10878  176.310889               0.00                      0.00   \n",
              "10879  176.310889               0.00                      0.00   \n",
              "10880  176.310889               0.00                      0.00   \n",
              "10881  176.310889               0.00                      0.00   \n",
              "10882  176.310889               0.00                      0.00   \n",
              "\n",
              "       1st_serve_return_points_won  2nd_serve_return_points_won  \\\n",
              "0                             0.00                         0.00   \n",
              "1                             0.00                         0.00   \n",
              "2                             0.28                         0.45   \n",
              "3                             0.00                         0.00   \n",
              "4                             0.30                         0.51   \n",
              "...                            ...                          ...   \n",
              "10878                         0.00                         0.00   \n",
              "10879                         0.00                         0.00   \n",
              "10880                         0.00                         0.00   \n",
              "10881                         0.00                         0.00   \n",
              "10882                         0.00                         0.00   \n",
              "\n",
              "       break_points_opportunities  break_points_converted  \\\n",
              "0                               0                    0.00   \n",
              "1                               0                    0.00   \n",
              "2                              11                    0.36   \n",
              "3                               0                    0.00   \n",
              "4                            1500                    0.40   \n",
              "...                           ...                     ...   \n",
              "10878                           0                    0.00   \n",
              "10879                           0                    0.00   \n",
              "10880                           0                    0.00   \n",
              "10881                           0                    0.00   \n",
              "10882                           0                    0.00   \n",
              "\n",
              "       return_games_played  return_games_won  return_points_won  \\\n",
              "0                        0              0.00               0.00   \n",
              "1                        0              0.00               0.00   \n",
              "2                       29              0.14               0.34   \n",
              "3                        0              0.00               0.00   \n",
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              "10880                    0              0.00               0.00   \n",
              "10881                    0              0.00               0.00   \n",
              "10882                    0              0.00               0.00   \n",
              "\n",
              "                        Country  \n",
              "0                         Chile  \n",
              "1                       Nigeria  \n",
              "2                        Brazil  \n",
              "3      United States of America  \n",
              "4      United States of America  \n",
              "...                         ...  \n",
              "10878                     Egypt  \n",
              "10879                       NaN  \n",
              "10880                       NaN  \n",
              "10881                   Germany  \n",
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              "\n",
              "[10448 rows x 17 columns]"
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              "      <th>0</th>\n",
              "      <td>a002</td>\n",
              "      <td>Acuna R</td>\n",
              "      <td>CHI</td>\n",
              "      <td>1958.0</td>\n",
              "      <td>1977.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>175.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Chile</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>a001</td>\n",
              "      <td>Abdullahi S</td>\n",
              "      <td>NGR</td>\n",
              "      <td>1960.0</td>\n",
              "      <td>1979.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Nigeria</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>a005</td>\n",
              "      <td>Aerts N</td>\n",
              "      <td>BRA</td>\n",
              "      <td>1963.0</td>\n",
              "      <td>1982.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>188.000000</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.63</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.45</td>\n",
              "      <td>11</td>\n",
              "      <td>0.36</td>\n",
              "      <td>29</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.34</td>\n",
              "      <td>Brazil</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>a004</td>\n",
              "      <td>Adams E</td>\n",
              "      <td>USA</td>\n",
              "      <td>1959.0</td>\n",
              "      <td>1978.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>United States of America</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>a006</td>\n",
              "      <td>Agenor R</td>\n",
              "      <td>USA</td>\n",
              "      <td>1964.0</td>\n",
              "      <td>1983.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.51</td>\n",
              "      <td>1500</td>\n",
              "      <td>0.40</td>\n",
              "      <td>2466</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.38</td>\n",
              "      <td>United States of America</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10878</th>\n",
              "      <td>z431</td>\n",
              "      <td>Zahar N</td>\n",
              "      <td>EGY</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>176.310889</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Egypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10879</th>\n",
              "      <td>z435</td>\n",
              "      <td>Zorab A</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>176.310889</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10880</th>\n",
              "      <td>z436</td>\n",
              "      <td>Zinn G</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>176.310889</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10881</th>\n",
              "      <td>z440</td>\n",
              "      <td>Zenger C</td>\n",
              "      <td>GER</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>176.310889</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Germany</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10882</th>\n",
              "      <td>z442</td>\n",
              "      <td>Ziffo A</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>176.310889</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10448 rows × 17 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d4ba3b5-00c9-4467-921e-32716eb0d684')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 382
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "avg = np.nanmean(players['weight_kg'])\n",
        "players.loc[players['weight_kg'].isna(), 'weight_kg'] = avg"
      ],
      "metadata": {
        "id": "qemxgXZAQ_nU"
      },
      "execution_count": 383,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "players.columns\n"
      ],
      "metadata": {
        "id": "h5eYdeB-QSkg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0623b183-efbb-437f-b520-c14ec9045279"
      },
      "execution_count": 384,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['player_id', 'full_name', 'flag_code', 'birth_year', 'turned_pro',\n",
              "       'weight_kg', 'height_cm', 'service_games_won',\n",
              "       'total_service_points_won', '1st_serve_return_points_won',\n",
              "       '2nd_serve_return_points_won', 'break_points_opportunities',\n",
              "       'break_points_converted', 'return_games_played', 'return_games_won',\n",
              "       'return_points_won', 'Country'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 384
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Merging"
      ],
      "metadata": {
        "id": "UsYxNlBFT2Rz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matches"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "75I-gO5qUMPa",
        "outputId": "ee3f3288-86a8-4538-a767-4440fc3504cd"
      },
      "execution_count": 385,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Location       Date       Series    Court Surface        Round  \\\n",
              "0      Adelaide 2023-01-01       ATP250  Outdoor    Hard    1st Round   \n",
              "1      Adelaide 2023-01-01       ATP250  Outdoor    Hard    1st Round   \n",
              "2      Adelaide 2023-01-02       ATP250  Outdoor    Hard    1st Round   \n",
              "3      Adelaide 2023-01-02       ATP250  Outdoor    Hard    1st Round   \n",
              "4      Adelaide 2023-01-02       ATP250  Outdoor    Hard    1st Round   \n",
              "...         ...        ...          ...      ...     ...          ...   \n",
              "62297    London 2015-11-20  Masters Cup   Indoor    Hard  Round Robin   \n",
              "62298    London 2015-11-20  Masters Cup   Indoor    Hard  Round Robin   \n",
              "62299    London 2015-11-21  Masters Cup   Indoor    Hard   Semifinals   \n",
              "62300    London 2015-11-21  Masters Cup   Indoor    Hard   Semifinals   \n",
              "62301    London 2015-11-22  Masters Cup   Indoor    Hard    The Final   \n",
              "\n",
              "       Best of    first_name        second_name first_rank  ... first_4  \\\n",
              "0          3.0       Giron M          Gasquet R       61.0  ...     0.0   \n",
              "1          3.0    Mcdonald M           Galan DE       63.0  ...     0.0   \n",
              "2          3.0  Kecmanovic M        O Connell C       29.0  ...     0.0   \n",
              "3          3.0    Nishioka Y             Rune H       36.0  ...     0.0   \n",
              "4          3.0     Popyrin A  Auger-Aliassime F      120.0  ...     0.0   \n",
              "...        ...           ...                ...        ...  ...     ...   \n",
              "62297      3.0       Nadal R           Ferrer D        5.0  ...     0.0   \n",
              "62298      3.0    Wawrinka S           Murray A        4.0  ...     0.0   \n",
              "62299      3.0    Djokovic N            Nadal R        1.0  ...     0.0   \n",
              "62300      3.0     Federer R         Wawrinka S        3.0  ...     0.0   \n",
              "62301      3.0    Djokovic N          Federer R        1.0  ...     0.0   \n",
              "\n",
              "       second_4  first_5 second_5 first_sets second_sets first_6  second_6  \\\n",
              "0           0.0      0.0      0.0        2.0         1.0    1.91      1.91   \n",
              "1           0.0      0.0      0.0        1.0         0.0    1.36      3.20   \n",
              "2           0.0      0.0      0.0        2.0         0.0    1.57      2.38   \n",
              "3           0.0      0.0      0.0        2.0         1.0    3.75      1.29   \n",
              "4           0.0      0.0      0.0        2.0         0.0    6.50      1.11   \n",
              "...         ...      ...      ...        ...         ...     ...       ...   \n",
              "62297       0.0      0.0      0.0        2.0         1.0    1.20      4.50   \n",
              "62298       0.0      0.0      0.0        2.0         0.0    2.30      1.61   \n",
              "62299       0.0      0.0      0.0        2.0         0.0    1.20      4.50   \n",
              "62300       0.0      0.0      0.0        2.0         0.0    1.30      3.50   \n",
              "62301       0.0      0.0      0.0        2.0         0.0    1.44      2.75   \n",
              "\n",
              "              Country  win  \n",
              "0           Australia    1  \n",
              "1           Australia    1  \n",
              "2           Australia    1  \n",
              "3           Australia    1  \n",
              "4           Australia    1  \n",
              "...               ...  ...  \n",
              "62297  United Kingdom    1  \n",
              "62298  United Kingdom    1  \n",
              "62299  United Kingdom    1  \n",
              "62300  United Kingdom    1  \n",
              "62301  United Kingdom    1  \n",
              "\n",
              "[62302 rows x 27 columns]"
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              "      <th></th>\n",
              "      <th>Location</th>\n",
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              "      <th>0</th>\n",
              "      <td>Adelaide</td>\n",
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              "      <td>ATP250</td>\n",
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              "      <td>3.0</td>\n",
              "      <td>Giron M</td>\n",
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              "      <td>1.91</td>\n",
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              "      <th>1</th>\n",
              "      <td>Adelaide</td>\n",
              "      <td>2023-01-01</td>\n",
              "      <td>ATP250</td>\n",
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              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Mcdonald M</td>\n",
              "      <td>Galan DE</td>\n",
              "      <td>63.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.36</td>\n",
              "      <td>3.20</td>\n",
              "      <td>Australia</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Adelaide</td>\n",
              "      <td>2023-01-02</td>\n",
              "      <td>ATP250</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Kecmanovic M</td>\n",
              "      <td>O Connell C</td>\n",
              "      <td>29.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.57</td>\n",
              "      <td>2.38</td>\n",
              "      <td>Australia</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Adelaide</td>\n",
              "      <td>2023-01-02</td>\n",
              "      <td>ATP250</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Nishioka Y</td>\n",
              "      <td>Rune H</td>\n",
              "      <td>36.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.75</td>\n",
              "      <td>1.29</td>\n",
              "      <td>Australia</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Adelaide</td>\n",
              "      <td>2023-01-02</td>\n",
              "      <td>ATP250</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Popyrin A</td>\n",
              "      <td>Auger-Aliassime F</td>\n",
              "      <td>120.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.50</td>\n",
              "      <td>1.11</td>\n",
              "      <td>Australia</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62297</th>\n",
              "      <td>London</td>\n",
              "      <td>2015-11-20</td>\n",
              "      <td>Masters Cup</td>\n",
              "      <td>Indoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>Round Robin</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Nadal R</td>\n",
              "      <td>Ferrer D</td>\n",
              "      <td>5.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.20</td>\n",
              "      <td>4.50</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62298</th>\n",
              "      <td>London</td>\n",
              "      <td>2015-11-20</td>\n",
              "      <td>Masters Cup</td>\n",
              "      <td>Indoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>Round Robin</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Wawrinka S</td>\n",
              "      <td>Murray A</td>\n",
              "      <td>4.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.30</td>\n",
              "      <td>1.61</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62299</th>\n",
              "      <td>London</td>\n",
              "      <td>2015-11-21</td>\n",
              "      <td>Masters Cup</td>\n",
              "      <td>Indoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>Semifinals</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Djokovic N</td>\n",
              "      <td>Nadal R</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.20</td>\n",
              "      <td>4.50</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62300</th>\n",
              "      <td>London</td>\n",
              "      <td>2015-11-21</td>\n",
              "      <td>Masters Cup</td>\n",
              "      <td>Indoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>Semifinals</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Federer R</td>\n",
              "      <td>Wawrinka S</td>\n",
              "      <td>3.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.30</td>\n",
              "      <td>3.50</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62301</th>\n",
              "      <td>London</td>\n",
              "      <td>2015-11-22</td>\n",
              "      <td>Masters Cup</td>\n",
              "      <td>Indoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>The Final</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Djokovic N</td>\n",
              "      <td>Federer R</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.44</td>\n",
              "      <td>2.75</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>62302 rows × 27 columns</p>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "players"
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      "metadata": {
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          "height": 0
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        "id": "a6omZzI1UOsl",
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      },
      "execution_count": 386,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      player_id    full_name flag_code  birth_year  turned_pro  weight_kg  \\\n",
              "0          a002      Acuna R       CHI      1958.0      1977.0  68.000000   \n",
              "1          a001  Abdullahi S       NGR      1960.0      1979.0   0.000000   \n",
              "2          a005      Aerts N       BRA      1963.0      1982.0  75.000000   \n",
              "3          a004      Adams E       USA      1959.0      1978.0  73.000000   \n",
              "4          a006     Agenor R       USA      1964.0      1983.0  82.000000   \n",
              "...         ...          ...       ...         ...         ...        ...   \n",
              "10878      z431      Zahar N       EGY         NaN         NaN  75.032124   \n",
              "10879      z435      Zorab A       NaN         NaN         NaN  75.032124   \n",
              "10880      z436       Zinn G       NaN         NaN         NaN  75.032124   \n",
              "10881      z440     Zenger C       GER         NaN         NaN  75.032124   \n",
              "10882      z442      Ziffo A       NaN         NaN         NaN  75.032124   \n",
              "\n",
              "        height_cm  service_games_won  total_service_points_won  \\\n",
              "0      175.000000               0.00                      0.00   \n",
              "1        0.000000               0.00                      0.00   \n",
              "2      188.000000               0.82                      0.63   \n",
              "3      178.000000               0.00                      0.00   \n",
              "4      180.000000               0.70                      0.59   \n",
              "...           ...                ...                       ...   \n",
              "10878  176.310889               0.00                      0.00   \n",
              "10879  176.310889               0.00                      0.00   \n",
              "10880  176.310889               0.00                      0.00   \n",
              "10881  176.310889               0.00                      0.00   \n",
              "10882  176.310889               0.00                      0.00   \n",
              "\n",
              "       1st_serve_return_points_won  2nd_serve_return_points_won  \\\n",
              "0                             0.00                         0.00   \n",
              "1                             0.00                         0.00   \n",
              "2                             0.28                         0.45   \n",
              "3                             0.00                         0.00   \n",
              "4                             0.30                         0.51   \n",
              "...                            ...                          ...   \n",
              "10878                         0.00                         0.00   \n",
              "10879                         0.00                         0.00   \n",
              "10880                         0.00                         0.00   \n",
              "10881                         0.00                         0.00   \n",
              "10882                         0.00                         0.00   \n",
              "\n",
              "       break_points_opportunities  break_points_converted  \\\n",
              "0                               0                    0.00   \n",
              "1                               0                    0.00   \n",
              "2                              11                    0.36   \n",
              "3                               0                    0.00   \n",
              "4                            1500                    0.40   \n",
              "...                           ...                     ...   \n",
              "10878                           0                    0.00   \n",
              "10879                           0                    0.00   \n",
              "10880                           0                    0.00   \n",
              "10881                           0                    0.00   \n",
              "10882                           0                    0.00   \n",
              "\n",
              "       return_games_played  return_games_won  return_points_won  \\\n",
              "0                        0              0.00               0.00   \n",
              "1                        0              0.00               0.00   \n",
              "2                       29              0.14               0.34   \n",
              "3                        0              0.00               0.00   \n",
              "4                     2466              0.25               0.38   \n",
              "...                    ...               ...                ...   \n",
              "10878                    0              0.00               0.00   \n",
              "10879                    0              0.00               0.00   \n",
              "10880                    0              0.00               0.00   \n",
              "10881                    0              0.00               0.00   \n",
              "10882                    0              0.00               0.00   \n",
              "\n",
              "                        Country  \n",
              "0                         Chile  \n",
              "1                       Nigeria  \n",
              "2                        Brazil  \n",
              "3      United States of America  \n",
              "4      United States of America  \n",
              "...                         ...  \n",
              "10878                     Egypt  \n",
              "10879                       NaN  \n",
              "10880                       NaN  \n",
              "10881                   Germany  \n",
              "10882                       NaN  \n",
              "\n",
              "[10448 rows x 17 columns]"
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              "      <th></th>\n",
              "      <th>player_id</th>\n",
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              "      <th>Country</th>\n",
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              "      <th>0</th>\n",
              "      <td>a002</td>\n",
              "      <td>Acuna R</td>\n",
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              "      <th>1</th>\n",
              "      <td>a001</td>\n",
              "      <td>Abdullahi S</td>\n",
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              "      <td>1960.0</td>\n",
              "      <td>1979.0</td>\n",
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              "      <td>a005</td>\n",
              "      <td>Aerts N</td>\n",
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              "      <td>1963.0</td>\n",
              "      <td>1982.0</td>\n",
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              "      <td>0.82</td>\n",
              "      <td>0.63</td>\n",
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              "      <td>0.36</td>\n",
              "      <td>29</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.34</td>\n",
              "      <td>Brazil</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>a004</td>\n",
              "      <td>Adams E</td>\n",
              "      <td>USA</td>\n",
              "      <td>1959.0</td>\n",
              "      <td>1978.0</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>178.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>United States of America</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>a006</td>\n",
              "      <td>Agenor R</td>\n",
              "      <td>USA</td>\n",
              "      <td>1964.0</td>\n",
              "      <td>1983.0</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.51</td>\n",
              "      <td>1500</td>\n",
              "      <td>0.40</td>\n",
              "      <td>2466</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.38</td>\n",
              "      <td>United States of America</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10878</th>\n",
              "      <td>z431</td>\n",
              "      <td>Zahar N</td>\n",
              "      <td>EGY</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>75.032124</td>\n",
              "      <td>176.310889</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Egypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10879</th>\n",
              "      <td>z435</td>\n",
              "      <td>Zorab A</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>75.032124</td>\n",
              "      <td>176.310889</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10880</th>\n",
              "      <td>z436</td>\n",
              "      <td>Zinn G</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>75.032124</td>\n",
              "      <td>176.310889</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10881</th>\n",
              "      <td>z440</td>\n",
              "      <td>Zenger C</td>\n",
              "      <td>GER</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>75.032124</td>\n",
              "      <td>176.310889</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>Germany</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10882</th>\n",
              "      <td>z442</td>\n",
              "      <td>Ziffo A</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>75.032124</td>\n",
              "      <td>176.310889</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10448 rows × 17 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0ad51e98-72af-4e38-bd78-5d1a6eb5d3ff')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          document.querySelector('#df-0ad51e98-72af-4e38-bd78-5d1a6eb5d3ff button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0ad51e98-72af-4e38-bd78-5d1a6eb5d3ff');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 386
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = matches.merge(players, left_on='first_name', right_on='full_name', how='inner').merge(players, left_on='second_name', right_on='full_name', how='inner')"
      ],
      "metadata": {
        "id": "MyjVLm2TSsON"
      },
      "execution_count": 387,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "New column if match played in winner's country"
      ],
      "metadata": {
        "id": "aMP_9fxq9vmH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['first_home_country'] = np.where(df['Country'] == df['Country_x'], 1, 0)\n",
        "df['second_home_country'] = np.where(df['Country'] == df['Country_y'], 1, 0)"
      ],
      "metadata": {
        "id": "mod2Cq9F9vI9"
      },
      "execution_count": 388,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yT79po2nbgQ2"
      },
      "execution_count": 388,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "corr_matrix = df.corr()\n",
        "corr_matrix\n",
        "\n",
        "sns.heatmap(corr_matrix, cmap='coolwarm', cbar=True)\n",
        "\n",
        "# Set plot title\n",
        "plt.title('Correlation Matrix')\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "hN4K3c07_OPW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "b6cc3d69-e883-4814-fff1-54126bf9733f"
      },
      "execution_count": 389,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-389-581c17eedd15>:4: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  corr_matrix = df.corr()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ii_8DS7BU_4_",
        "outputId": "a17308a9-9449-4f74-f256-b64607ed93a9"
      },
      "execution_count": 390,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Location', 'Date', 'Series', 'Court', 'Surface', 'Round', 'Best of',\n",
              "       'first_name', 'second_name', 'first_rank', 'second_rank', 'first_1',\n",
              "       'second_1', 'first_2', 'second_2', 'first_3', 'second_3', 'first_4',\n",
              "       'second_4', 'first_5', 'second_5', 'first_sets', 'second_sets',\n",
              "       'first_6', 'second_6', 'Country_x', 'win', 'player_id_x', 'full_name_x',\n",
              "       'flag_code_x', 'birth_year_x', 'turned_pro_x', 'weight_kg_x',\n",
              "       'height_cm_x', 'service_games_won_x', 'total_service_points_won_x',\n",
              "       '1st_serve_return_points_won_x', '2nd_serve_return_points_won_x',\n",
              "       'break_points_opportunities_x', 'break_points_converted_x',\n",
              "       'return_games_played_x', 'return_games_won_x', 'return_points_won_x',\n",
              "       'Country_y', 'player_id_y', 'full_name_y', 'flag_code_y',\n",
              "       'birth_year_y', 'turned_pro_y', 'weight_kg_y', 'height_cm_y',\n",
              "       'service_games_won_y', 'total_service_points_won_y',\n",
              "       '1st_serve_return_points_won_y', '2nd_serve_return_points_won_y',\n",
              "       'break_points_opportunities_y', 'break_points_converted_y',\n",
              "       'return_games_played_y', 'return_games_won_y', 'return_points_won_y',\n",
              "       'Country', 'first_home_country', 'second_home_country'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 390
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cols_keep = [\n",
        "  'Series', 'Court', 'Surface', 'Round', 'Best of', 'Date',\n",
        "  'first_rank', 'second_rank',\n",
        "  'weight_kg_x', 'height_cm_x',\n",
        "  'weight_kg_y', 'height_cm_y', \n",
        "  'birth_year_x', 'turned_pro_x',  'birth_year_y', 'first_home_country','second_home_country', 'first_1',\n",
        "  'second_1', 'first_2', 'second_2', 'first_3', 'second_3', 'first_4',\n",
        "  'second_4', 'first_5', 'second_5', 'first_6', 'second_6',\n",
        "  'win', 'service_games_won_x', 'total_service_points_won_x',\n",
        "  '1st_serve_return_points_won_x', '2nd_serve_return_points_won_x',\n",
        "  'break_points_opportunities_x', 'break_points_converted_x',\n",
        "  'return_games_played_x', 'return_games_won_x', 'return_points_won_x',\n",
        "  'service_games_won_y', 'total_service_points_won_y',\n",
        "  '1st_serve_return_points_won_y', '2nd_serve_return_points_won_y',\n",
        "  'break_points_opportunities_y', 'break_points_converted_y',\n",
        "  'return_games_played_y', 'return_games_won_y', 'return_points_won_y',\n",
        "]\n",
        "\n",
        "prepared_df = df[cols_keep]\n",
        "prepared_df\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "PXNOW9NSUtRE",
        "outputId": "b30de3e7-327f-4621-f192-492ce813e0cf"
      },
      "execution_count": 391,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Series    Court Surface          Round  Best of       Date  \\\n",
              "0             ATP250  Outdoor    Hard      1st Round      3.0 2023-01-01   \n",
              "1       Masters 1000  Outdoor    Hard      1st Round      3.0 2022-03-24   \n",
              "2         Grand Slam  Outdoor    Hard      1st Round      5.0 2021-08-31   \n",
              "3             ATP250   Indoor    Hard      1st Round      3.0 2020-10-14   \n",
              "4             ATP250  Outdoor    Clay  Quarterfinals      3.0 2021-05-21   \n",
              "...              ...      ...     ...            ...      ...        ...   \n",
              "55204         ATP500  Outdoor    Clay      1st Round      3.0 2013-04-22   \n",
              "55205         ATP250  Outdoor    Hard      1st Round      3.0 2015-09-29   \n",
              "55206         ATP250   Indoor    Hard      1st Round      3.0 2012-02-14   \n",
              "55207         ATP250   Indoor    Hard      2nd Round      3.0 2012-02-16   \n",
              "55208  International  Outdoor    Clay      1st Round      3.0 2008-07-08   \n",
              "\n",
              "      first_rank second_rank  weight_kg_x  height_cm_x  ...  \\\n",
              "0           61.0        68.0         77.0        180.0  ...   \n",
              "1             97        81.0         82.0        196.0  ...   \n",
              "2              2        79.0         81.0        198.0  ...   \n",
              "3            6.0        53.0         81.0        198.0  ...   \n",
              "4             26        52.0         88.0        198.0  ...   \n",
              "...          ...         ...          ...          ...  ...   \n",
              "55204      309.0       280.0         74.0        175.0  ...   \n",
              "55205      266.0       340.0         80.0        185.0  ...   \n",
              "55206      438.0       719.0         73.0        175.0  ...   \n",
              "55207      438.0       393.0         73.0        175.0  ...   \n",
              "55208      137.0       588.0         82.0        185.0  ...   \n",
              "\n",
              "       return_points_won_x  service_games_won_y  total_service_points_won_y  \\\n",
              "0                     0.35                 0.82                        0.65   \n",
              "1                     0.34                 0.82                        0.65   \n",
              "2                     0.39                 0.82                        0.65   \n",
              "3                     0.39                 0.82                        0.65   \n",
              "4                     0.37                 0.82                        0.65   \n",
              "...                    ...                  ...                         ...   \n",
              "55204                 0.34                 0.51                        0.51   \n",
              "55205                 0.34                 0.56                        0.54   \n",
              "55206                 0.38                 0.69                        0.59   \n",
              "55207                 0.38                 0.60                        0.59   \n",
              "55208                 0.31                 0.57                        0.54   \n",
              "\n",
              "       1st_serve_return_points_won_y  2nd_serve_return_points_won_y  \\\n",
              "0                               0.30                           0.51   \n",
              "1                               0.30                           0.51   \n",
              "2                               0.30                           0.51   \n",
              "3                               0.30                           0.51   \n",
              "4                               0.30                           0.51   \n",
              "...                              ...                            ...   \n",
              "55204                           0.33                           0.49   \n",
              "55205                           0.21                           0.53   \n",
              "55206                           0.32                           0.48   \n",
              "55207                           0.29                           0.47   \n",
              "55208                           0.17                           0.52   \n",
              "\n",
              "       break_points_opportunities_y  break_points_converted_y  \\\n",
              "0                              6479                      0.41   \n",
              "1                              6479                      0.41   \n",
              "2                              6479                      0.41   \n",
              "3                              6479                      0.41   \n",
              "4                              6479                      0.41   \n",
              "...                             ...                       ...   \n",
              "55204                            27                      0.41   \n",
              "55205                             2                      0.50   \n",
              "55206                             6                      0.67   \n",
              "55207                             5                      0.40   \n",
              "55208                             8                      0.50   \n",
              "\n",
              "       return_games_played_y  return_games_won_y return_points_won_y  \n",
              "0                      10739                0.24                0.38  \n",
              "1                      10739                0.24                0.38  \n",
              "2                      10739                0.24                0.38  \n",
              "3                      10739                0.24                0.38  \n",
              "4                      10739                0.24                0.38  \n",
              "...                      ...                 ...                 ...  \n",
              "55204                     44                0.25                0.40  \n",
              "55205                     10                0.10                0.32  \n",
              "55206                     17                0.24                0.38  \n",
              "55207                     11                0.18                0.37  \n",
              "55208                     22                0.18                0.32  \n",
              "\n",
              "[55209 rows x 48 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4cd05723-a247-4c7a-ab37-e08f68408caf\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Series</th>\n",
              "      <th>Court</th>\n",
              "      <th>Surface</th>\n",
              "      <th>Round</th>\n",
              "      <th>Best of</th>\n",
              "      <th>Date</th>\n",
              "      <th>first_rank</th>\n",
              "      <th>second_rank</th>\n",
              "      <th>weight_kg_x</th>\n",
              "      <th>height_cm_x</th>\n",
              "      <th>...</th>\n",
              "      <th>return_points_won_x</th>\n",
              "      <th>service_games_won_y</th>\n",
              "      <th>total_service_points_won_y</th>\n",
              "      <th>1st_serve_return_points_won_y</th>\n",
              "      <th>2nd_serve_return_points_won_y</th>\n",
              "      <th>break_points_opportunities_y</th>\n",
              "      <th>break_points_converted_y</th>\n",
              "      <th>return_games_played_y</th>\n",
              "      <th>return_games_won_y</th>\n",
              "      <th>return_points_won_y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ATP250</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2023-01-01</td>\n",
              "      <td>61.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.51</td>\n",
              "      <td>6479</td>\n",
              "      <td>0.41</td>\n",
              "      <td>10739</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Masters 1000</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2022-03-24</td>\n",
              "      <td>97</td>\n",
              "      <td>81.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>196.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.51</td>\n",
              "      <td>6479</td>\n",
              "      <td>0.41</td>\n",
              "      <td>10739</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Grand Slam</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2021-08-31</td>\n",
              "      <td>2</td>\n",
              "      <td>79.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.51</td>\n",
              "      <td>6479</td>\n",
              "      <td>0.41</td>\n",
              "      <td>10739</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ATP250</td>\n",
              "      <td>Indoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2020-10-14</td>\n",
              "      <td>6.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.51</td>\n",
              "      <td>6479</td>\n",
              "      <td>0.41</td>\n",
              "      <td>10739</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ATP250</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Clay</td>\n",
              "      <td>Quarterfinals</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2021-05-21</td>\n",
              "      <td>26</td>\n",
              "      <td>52.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.51</td>\n",
              "      <td>6479</td>\n",
              "      <td>0.41</td>\n",
              "      <td>10739</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55204</th>\n",
              "      <td>ATP500</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Clay</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2013-04-22</td>\n",
              "      <td>309.0</td>\n",
              "      <td>280.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.51</td>\n",
              "      <td>0.51</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.49</td>\n",
              "      <td>27</td>\n",
              "      <td>0.41</td>\n",
              "      <td>44</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55205</th>\n",
              "      <td>ATP250</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2015-09-29</td>\n",
              "      <td>266.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.53</td>\n",
              "      <td>2</td>\n",
              "      <td>0.50</td>\n",
              "      <td>10</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55206</th>\n",
              "      <td>ATP250</td>\n",
              "      <td>Indoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2012-02-14</td>\n",
              "      <td>438.0</td>\n",
              "      <td>719.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.48</td>\n",
              "      <td>6</td>\n",
              "      <td>0.67</td>\n",
              "      <td>17</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55207</th>\n",
              "      <td>ATP250</td>\n",
              "      <td>Indoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>2nd Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2012-02-16</td>\n",
              "      <td>438.0</td>\n",
              "      <td>393.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.47</td>\n",
              "      <td>5</td>\n",
              "      <td>0.40</td>\n",
              "      <td>11</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55208</th>\n",
              "      <td>International</td>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Clay</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2008-07-08</td>\n",
              "      <td>137.0</td>\n",
              "      <td>588.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.52</td>\n",
              "      <td>8</td>\n",
              "      <td>0.50</td>\n",
              "      <td>22</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.32</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>55209 rows × 48 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4cd05723-a247-4c7a-ab37-e08f68408caf')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4cd05723-a247-4c7a-ab37-e08f68408caf button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4cd05723-a247-4c7a-ab37-e08f68408caf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 391
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "a_dciT_bcuEq"
      },
      "execution_count": 391,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Encoding"
      ],
      "metadata": {
        "id": "EYinya5aZYGf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prepared_df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KJY2WyNyZZ84",
        "outputId": "3efbd926-3f52-45a3-a9b3-e0d4298267e3"
      },
      "execution_count": 392,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Series                                   object\n",
              "Court                                    object\n",
              "Surface                                  object\n",
              "Round                                    object\n",
              "Best of                                 float64\n",
              "Date                             datetime64[ns]\n",
              "first_rank                               object\n",
              "second_rank                              object\n",
              "weight_kg_x                             float64\n",
              "height_cm_x                             float64\n",
              "weight_kg_y                             float64\n",
              "height_cm_y                             float64\n",
              "birth_year_x                            float64\n",
              "turned_pro_x                            float64\n",
              "birth_year_y                            float64\n",
              "first_home_country                        int64\n",
              "second_home_country                       int64\n",
              "first_1                                 float64\n",
              "second_1                                float64\n",
              "first_2                                  object\n",
              "second_2                                 object\n",
              "first_3                                  object\n",
              "second_3                                 object\n",
              "first_4                                 float64\n",
              "second_4                                float64\n",
              "first_5                                 float64\n",
              "second_5                                float64\n",
              "first_6                                 float64\n",
              "second_6                                float64\n",
              "win                                       int64\n",
              "service_games_won_x                     float64\n",
              "total_service_points_won_x              float64\n",
              "1st_serve_return_points_won_x           float64\n",
              "2nd_serve_return_points_won_x           float64\n",
              "break_points_opportunities_x              int64\n",
              "break_points_converted_x                float64\n",
              "return_games_played_x                     int64\n",
              "return_games_won_x                      float64\n",
              "return_points_won_x                     float64\n",
              "service_games_won_y                     float64\n",
              "total_service_points_won_y              float64\n",
              "1st_serve_return_points_won_y           float64\n",
              "2nd_serve_return_points_won_y           float64\n",
              "break_points_opportunities_y              int64\n",
              "break_points_converted_y                float64\n",
              "return_games_played_y                     int64\n",
              "return_games_won_y                      float64\n",
              "return_points_won_y                     float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 392
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prepared_df['Series'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_em8k6nWZxPu",
        "outputId": "06cb993f-98b0-4cab-b389-c26641e93783"
      },
      "execution_count": 393,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['ATP250', 'Masters 1000', 'Grand Slam', 'International', 'Masters',\n",
              "       'ATP500', 'Masters Cup', 'International Gold'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 393
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "\n",
        "encoded_df = pd.get_dummies(prepared_df['Series'])\n",
        "prepared_df = pd.concat([prepared_df, encoded_df], axis=1)\n",
        "prepared_df = prepared_df.drop('Series', axis=1)\n",
        "\n",
        "prepared_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "uHKeLJFJaOlr",
        "outputId": "cde13448-64cb-4d9f-eaa2-9c84b081ff5d"
      },
      "execution_count": 394,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Court Surface          Round  Best of       Date first_rank  \\\n",
              "0      Outdoor    Hard      1st Round      3.0 2023-01-01       61.0   \n",
              "1      Outdoor    Hard      1st Round      3.0 2022-03-24         97   \n",
              "2      Outdoor    Hard      1st Round      5.0 2021-08-31          2   \n",
              "3       Indoor    Hard      1st Round      3.0 2020-10-14        6.0   \n",
              "4      Outdoor    Clay  Quarterfinals      3.0 2021-05-21         26   \n",
              "...        ...     ...            ...      ...        ...        ...   \n",
              "55204  Outdoor    Clay      1st Round      3.0 2013-04-22      309.0   \n",
              "55205  Outdoor    Hard      1st Round      3.0 2015-09-29      266.0   \n",
              "55206   Indoor    Hard      1st Round      3.0 2012-02-14      438.0   \n",
              "55207   Indoor    Hard      2nd Round      3.0 2012-02-16      438.0   \n",
              "55208  Outdoor    Clay      1st Round      3.0 2008-07-08      137.0   \n",
              "\n",
              "      second_rank  weight_kg_x  height_cm_x  weight_kg_y  ...  \\\n",
              "0            68.0         77.0        180.0    75.000000  ...   \n",
              "1            81.0         82.0        196.0    75.000000  ...   \n",
              "2            79.0         81.0        198.0    75.000000  ...   \n",
              "3            53.0         81.0        198.0    75.000000  ...   \n",
              "4            52.0         88.0        198.0    75.000000  ...   \n",
              "...           ...          ...          ...          ...  ...   \n",
              "55204       280.0         74.0        175.0    75.032124  ...   \n",
              "55205       340.0         80.0        185.0    73.000000  ...   \n",
              "55206       719.0         73.0        175.0    75.032124  ...   \n",
              "55207       393.0         73.0        175.0    84.000000  ...   \n",
              "55208       588.0         82.0        185.0    75.032124  ...   \n",
              "\n",
              "       return_games_won_y  return_points_won_y  ATP250  ATP500  Grand Slam  \\\n",
              "0                    0.24                 0.38       1       0           0   \n",
              "1                    0.24                 0.38       0       0           0   \n",
              "2                    0.24                 0.38       0       0           1   \n",
              "3                    0.24                 0.38       1       0           0   \n",
              "4                    0.24                 0.38       1       0           0   \n",
              "...                   ...                  ...     ...     ...         ...   \n",
              "55204                0.25                 0.40       0       1           0   \n",
              "55205                0.10                 0.32       1       0           0   \n",
              "55206                0.24                 0.38       1       0           0   \n",
              "55207                0.18                 0.37       1       0           0   \n",
              "55208                0.18                 0.32       0       0           0   \n",
              "\n",
              "       International  International Gold  Masters Masters 1000 Masters Cup  \n",
              "0                  0                   0        0            0           0  \n",
              "1                  0                   0        0            1           0  \n",
              "2                  0                   0        0            0           0  \n",
              "3                  0                   0        0            0           0  \n",
              "4                  0                   0        0            0           0  \n",
              "...              ...                 ...      ...          ...         ...  \n",
              "55204              0                   0        0            0           0  \n",
              "55205              0                   0        0            0           0  \n",
              "55206              0                   0        0            0           0  \n",
              "55207              0                   0        0            0           0  \n",
              "55208              1                   0        0            0           0  \n",
              "\n",
              "[55209 rows x 55 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Court</th>\n",
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              "      <td>2023-01-01</td>\n",
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              "      <td>68.0</td>\n",
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              "      <td>2022-03-24</td>\n",
              "      <td>97</td>\n",
              "      <td>81.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>196.0</td>\n",
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              "      <td>5.0</td>\n",
              "      <td>2021-08-31</td>\n",
              "      <td>2</td>\n",
              "      <td>79.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.38</td>\n",
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              "      <td>1</td>\n",
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              "      <td>3.0</td>\n",
              "      <td>2020-10-14</td>\n",
              "      <td>6.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>81.0</td>\n",
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              "      <td>3.0</td>\n",
              "      <td>2021-05-21</td>\n",
              "      <td>26</td>\n",
              "      <td>52.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>198.0</td>\n",
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              "      <th>55207</th>\n",
              "      <td>Indoor</td>\n",
              "      <td>Hard</td>\n",
              "      <td>2nd Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2012-02-16</td>\n",
              "      <td>438.0</td>\n",
              "      <td>393.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.37</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55208</th>\n",
              "      <td>Outdoor</td>\n",
              "      <td>Clay</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2008-07-08</td>\n",
              "      <td>137.0</td>\n",
              "      <td>588.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>75.032124</td>\n",
              "      <td>...</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>55209 rows × 55 columns</p>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 394
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prepared_df['Court'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vizfn1xobHTf",
        "outputId": "c848051d-d909-4014-ceb8-a8b0d3db1fc1"
      },
      "execution_count": 395,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Outdoor', 'Indoor'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 395
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "label_encoder = LabelEncoder()\n",
        "prepared_df['encoded_court'] = label_encoder.fit_transform(prepared_df['Court'])\n",
        "prepared_df = prepared_df.drop('Court', axis=1)\n",
        "prepared_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5aWGGR1rbTPU",
        "outputId": "2f8e295f-f99f-455c-8843-04884e6b0f48"
      },
      "execution_count": 396,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Surface          Round  Best of       Date first_rank second_rank  \\\n",
              "0        Hard      1st Round      3.0 2023-01-01       61.0        68.0   \n",
              "1        Hard      1st Round      3.0 2022-03-24         97        81.0   \n",
              "2        Hard      1st Round      5.0 2021-08-31          2        79.0   \n",
              "3        Hard      1st Round      3.0 2020-10-14        6.0        53.0   \n",
              "4        Clay  Quarterfinals      3.0 2021-05-21         26        52.0   \n",
              "...       ...            ...      ...        ...        ...         ...   \n",
              "55204    Clay      1st Round      3.0 2013-04-22      309.0       280.0   \n",
              "55205    Hard      1st Round      3.0 2015-09-29      266.0       340.0   \n",
              "55206    Hard      1st Round      3.0 2012-02-14      438.0       719.0   \n",
              "55207    Hard      2nd Round      3.0 2012-02-16      438.0       393.0   \n",
              "55208    Clay      1st Round      3.0 2008-07-08      137.0       588.0   \n",
              "\n",
              "       weight_kg_x  height_cm_x  weight_kg_y  height_cm_y  ...  \\\n",
              "0             77.0        180.0    75.000000   185.000000  ...   \n",
              "1             82.0        196.0    75.000000   185.000000  ...   \n",
              "2             81.0        198.0    75.000000   185.000000  ...   \n",
              "3             81.0        198.0    75.000000   185.000000  ...   \n",
              "4             88.0        198.0    75.000000   185.000000  ...   \n",
              "...            ...          ...          ...          ...  ...   \n",
              "55204         74.0        175.0    75.032124   176.310889  ...   \n",
              "55205         80.0        185.0    73.000000   183.000000  ...   \n",
              "55206         73.0        175.0    75.032124   176.310889  ...   \n",
              "55207         73.0        175.0    84.000000   188.000000  ...   \n",
              "55208         82.0        185.0    75.032124   176.310889  ...   \n",
              "\n",
              "       return_points_won_y  ATP250  ATP500  Grand Slam  International  \\\n",
              "0                     0.38       1       0           0              0   \n",
              "1                     0.38       0       0           0              0   \n",
              "2                     0.38       0       0           1              0   \n",
              "3                     0.38       1       0           0              0   \n",
              "4                     0.38       1       0           0              0   \n",
              "...                    ...     ...     ...         ...            ...   \n",
              "55204                 0.40       0       1           0              0   \n",
              "55205                 0.32       1       0           0              0   \n",
              "55206                 0.38       1       0           0              0   \n",
              "55207                 0.37       1       0           0              0   \n",
              "55208                 0.32       0       0           0              1   \n",
              "\n",
              "       International Gold  Masters Masters 1000 Masters Cup encoded_court  \n",
              "0                       0        0            0           0             1  \n",
              "1                       0        0            1           0             1  \n",
              "2                       0        0            0           0             1  \n",
              "3                       0        0            0           0             0  \n",
              "4                       0        0            0           0             1  \n",
              "...                   ...      ...          ...         ...           ...  \n",
              "55204                   0        0            0           0             1  \n",
              "55205                   0        0            0           0             1  \n",
              "55206                   0        0            0           0             0  \n",
              "55207                   0        0            0           0             0  \n",
              "55208                   0        0            0           0             1  \n",
              "\n",
              "[55209 rows x 55 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Surface</th>\n",
              "      <th>Round</th>\n",
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              "      <th>0</th>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2023-01-01</td>\n",
              "      <td>61.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>180.0</td>\n",
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              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2022-03-24</td>\n",
              "      <td>97</td>\n",
              "      <td>81.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>196.0</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.38</td>\n",
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              "      <th>2</th>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2021-08-31</td>\n",
              "      <td>2</td>\n",
              "      <td>79.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Hard</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2020-10-14</td>\n",
              "      <td>6.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>185.000000</td>\n",
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              "      <th>4</th>\n",
              "      <td>Clay</td>\n",
              "      <td>Quarterfinals</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2021-05-21</td>\n",
              "      <td>26</td>\n",
              "      <td>52.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>185.000000</td>\n",
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              "      <td>0.38</td>\n",
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              "      <th>55208</th>\n",
              "      <td>Clay</td>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2008-07-08</td>\n",
              "      <td>137.0</td>\n",
              "      <td>588.0</td>\n",
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              "      <td>185.0</td>\n",
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              "      <td>0.32</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>55209 rows × 55 columns</p>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 396
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prepared_df['Surface'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DUlU8mMDbcr3",
        "outputId": "9042b23f-8742-417b-daee-5dc9392de733"
      },
      "execution_count": 397,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Hard', 'Clay', 'Grass', 'Carpet'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 397
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "\n",
        "label_encoder = LabelEncoder()\n",
        "prepared_df['encoded_surface'] = label_encoder.fit_transform(prepared_df['Surface'])\n",
        "prepared_df = prepared_df.drop('Surface', axis=1)\n",
        "prepared_df\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "z8sNux0abz96",
        "outputId": "f568f431-11aa-4d4f-ce00-44994c0e7793"
      },
      "execution_count": 398,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Round  Best of       Date first_rank second_rank  weight_kg_x  \\\n",
              "0          1st Round      3.0 2023-01-01       61.0        68.0         77.0   \n",
              "1          1st Round      3.0 2022-03-24         97        81.0         82.0   \n",
              "2          1st Round      5.0 2021-08-31          2        79.0         81.0   \n",
              "3          1st Round      3.0 2020-10-14        6.0        53.0         81.0   \n",
              "4      Quarterfinals      3.0 2021-05-21         26        52.0         88.0   \n",
              "...              ...      ...        ...        ...         ...          ...   \n",
              "55204      1st Round      3.0 2013-04-22      309.0       280.0         74.0   \n",
              "55205      1st Round      3.0 2015-09-29      266.0       340.0         80.0   \n",
              "55206      1st Round      3.0 2012-02-14      438.0       719.0         73.0   \n",
              "55207      2nd Round      3.0 2012-02-16      438.0       393.0         73.0   \n",
              "55208      1st Round      3.0 2008-07-08      137.0       588.0         82.0   \n",
              "\n",
              "       height_cm_x  weight_kg_y  height_cm_y  birth_year_x  ...  ATP250  \\\n",
              "0            180.0    75.000000   185.000000        1993.0  ...       1   \n",
              "1            196.0    75.000000   185.000000        1996.0  ...       0   \n",
              "2            198.0    75.000000   185.000000        1996.0  ...       0   \n",
              "3            198.0    75.000000   185.000000        1996.0  ...       1   \n",
              "4            198.0    75.000000   185.000000        1996.0  ...       1   \n",
              "...            ...          ...          ...           ...  ...     ...   \n",
              "55204        175.0    75.032124   176.310889        1988.0  ...       0   \n",
              "55205        185.0    73.000000   183.000000        1989.0  ...       1   \n",
              "55206        175.0    75.032124   176.310889        1987.0  ...       1   \n",
              "55207        175.0    84.000000   188.000000        1987.0  ...       1   \n",
              "55208        185.0    75.032124   176.310889        1983.0  ...       0   \n",
              "\n",
              "       ATP500  Grand Slam  International  International Gold  Masters  \\\n",
              "0           0           0              0                   0        0   \n",
              "1           0           0              0                   0        0   \n",
              "2           0           1              0                   0        0   \n",
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              "4           0           0              0                   0        0   \n",
              "...       ...         ...            ...                 ...      ...   \n",
              "55204       1           0              0                   0        0   \n",
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              "55206       0           0              0                   0        0   \n",
              "55207       0           0              0                   0        0   \n",
              "55208       0           0              1                   0        0   \n",
              "\n",
              "      Masters 1000 Masters Cup encoded_court encoded_surface  \n",
              "0                0           0             1               3  \n",
              "1                1           0             1               3  \n",
              "2                0           0             1               3  \n",
              "3                0           0             0               3  \n",
              "4                0           0             1               1  \n",
              "...            ...         ...           ...             ...  \n",
              "55204            0           0             1               1  \n",
              "55205            0           0             1               3  \n",
              "55206            0           0             0               3  \n",
              "55207            0           0             0               3  \n",
              "55208            0           0             1               1  \n",
              "\n",
              "[55209 rows x 55 columns]"
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              "      <th></th>\n",
              "      <th>Round</th>\n",
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              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2023-01-01</td>\n",
              "      <td>61.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>180.0</td>\n",
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              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2022-03-24</td>\n",
              "      <td>97</td>\n",
              "      <td>81.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>196.0</td>\n",
              "      <td>75.000000</td>\n",
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              "      <td>1996.0</td>\n",
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              "      <td>1st Round</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2021-08-31</td>\n",
              "      <td>2</td>\n",
              "      <td>79.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>1996.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1st Round</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2020-10-14</td>\n",
              "      <td>6.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>1996.0</td>\n",
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              "      <th>4</th>\n",
              "      <td>Quarterfinals</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2021-05-21</td>\n",
              "      <td>26</td>\n",
              "      <td>52.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>185.000000</td>\n",
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              "      <td>3.0</td>\n",
              "      <td>2013-04-22</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>55209 rows × 55 columns</p>\n",
              "</div>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 398
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prepared_df['Round'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jglxVveyb-ZH",
        "outputId": "040a5840-3182-43d0-c644-460ac1387493"
      },
      "execution_count": 399,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['1st Round', 'Quarterfinals', '3rd Round', '4th Round',\n",
              "       'The Final', '2nd Round', 'Semifinals', 'Round Robin'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 399
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "\n",
        "label_encoder = LabelEncoder()\n",
        "prepared_df['encoded_round'] = label_encoder.fit_transform(prepared_df['Round'])\n",
        "prepared_df = prepared_df.drop('Round', axis=1)\n",
        "prepared_df\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "mfz_VRh8cCag",
        "outputId": "8826a2e7-0a74-4af3-e12e-a0a5401f956a"
      },
      "execution_count": 400,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Best of       Date first_rank second_rank  weight_kg_x  height_cm_x  \\\n",
              "0          3.0 2023-01-01       61.0        68.0         77.0        180.0   \n",
              "1          3.0 2022-03-24         97        81.0         82.0        196.0   \n",
              "2          5.0 2021-08-31          2        79.0         81.0        198.0   \n",
              "3          3.0 2020-10-14        6.0        53.0         81.0        198.0   \n",
              "4          3.0 2021-05-21         26        52.0         88.0        198.0   \n",
              "...        ...        ...        ...         ...          ...          ...   \n",
              "55204      3.0 2013-04-22      309.0       280.0         74.0        175.0   \n",
              "55205      3.0 2015-09-29      266.0       340.0         80.0        185.0   \n",
              "55206      3.0 2012-02-14      438.0       719.0         73.0        175.0   \n",
              "55207      3.0 2012-02-16      438.0       393.0         73.0        175.0   \n",
              "55208      3.0 2008-07-08      137.0       588.0         82.0        185.0   \n",
              "\n",
              "       weight_kg_y  height_cm_y  birth_year_x  turned_pro_x  ...  ATP500  \\\n",
              "0        75.000000   185.000000        1993.0        2014.0  ...       0   \n",
              "1        75.000000   185.000000        1996.0        2013.0  ...       0   \n",
              "2        75.000000   185.000000        1996.0        2014.0  ...       0   \n",
              "3        75.000000   185.000000        1996.0        2014.0  ...       0   \n",
              "4        75.000000   185.000000        1996.0        2013.0  ...       0   \n",
              "...            ...          ...           ...           ...  ...     ...   \n",
              "55204    75.032124   176.310889        1988.0        2006.0  ...       1   \n",
              "55205    73.000000   183.000000        1989.0        2008.0  ...       0   \n",
              "55206    75.032124   176.310889        1987.0        2006.0  ...       0   \n",
              "55207    84.000000   188.000000        1987.0        2006.0  ...       0   \n",
              "55208    75.032124   176.310889        1983.0        2002.0  ...       0   \n",
              "\n",
              "       Grand Slam  International  International Gold  Masters Masters 1000  \\\n",
              "0               0              0                   0        0            0   \n",
              "1               0              0                   0        0            1   \n",
              "2               1              0                   0        0            0   \n",
              "3               0              0                   0        0            0   \n",
              "4               0              0                   0        0            0   \n",
              "...           ...            ...                 ...      ...          ...   \n",
              "55204           0              0                   0        0            0   \n",
              "55205           0              0                   0        0            0   \n",
              "55206           0              0                   0        0            0   \n",
              "55207           0              0                   0        0            0   \n",
              "55208           0              1                   0        0            0   \n",
              "\n",
              "      Masters Cup encoded_court encoded_surface  encoded_round  \n",
              "0               0             1               3              0  \n",
              "1               0             1               3              0  \n",
              "2               0             1               3              0  \n",
              "3               0             0               3              0  \n",
              "4               0             1               1              4  \n",
              "...           ...           ...             ...            ...  \n",
              "55204           0             1               1              0  \n",
              "55205           0             1               3              0  \n",
              "55206           0             0               3              0  \n",
              "55207           0             0               3              1  \n",
              "55208           0             1               1              0  \n",
              "\n",
              "[55209 rows x 55 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Best of</th>\n",
              "      <th>Date</th>\n",
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              "      <td>3.0</td>\n",
              "      <td>2023-01-01</td>\n",
              "      <td>61.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>180.0</td>\n",
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              "      <td>2022-03-24</td>\n",
              "      <td>97</td>\n",
              "      <td>81.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>196.0</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>1996.0</td>\n",
              "      <td>2013.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.0</td>\n",
              "      <td>2021-08-31</td>\n",
              "      <td>2</td>\n",
              "      <td>79.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>1996.0</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2020-10-14</td>\n",
              "      <td>6.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>1996.0</td>\n",
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              "      <th>4</th>\n",
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              "      <td>2021-05-21</td>\n",
              "      <td>26</td>\n",
              "      <td>52.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>198.0</td>\n",
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-59dbe89b-e130-44e3-b220-c8b861303dad')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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          "metadata": {},
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      "source": [
        "prepared_df.dtypes"
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      "metadata": {
        "colab": {
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      "execution_count": 401,
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          "output_type": "execute_result",
          "data": {
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              "Best of                                 float64\n",
              "Date                             datetime64[ns]\n",
              "first_rank                               object\n",
              "second_rank                              object\n",
              "weight_kg_x                             float64\n",
              "height_cm_x                             float64\n",
              "weight_kg_y                             float64\n",
              "height_cm_y                             float64\n",
              "birth_year_x                            float64\n",
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              "second_home_country                       int64\n",
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              "second_1                                float64\n",
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              "first_4                                 float64\n",
              "second_4                                float64\n",
              "first_5                                 float64\n",
              "second_5                                float64\n",
              "first_6                                 float64\n",
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              "win                                       int64\n",
              "service_games_won_x                     float64\n",
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              "return_points_won_y                     float64\n",
              "ATP250                                    uint8\n",
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              "encoded_surface                           int64\n",
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              "dtype: object"
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        "avg = prepared_df['first_2'].unique()[:-1].mean()\n",
        "prepared_df.loc[prepared_df['first_2'] == ' ', 'first_2'] = avg\n",
        "prepared_df['first_2'] = prepared_df['first_2'].astype(float)\n",
        "prepared_df['first_2'].unique()\n"
      ],
      "metadata": {
        "colab": {
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        "id": "vZZVmwWDdGdP",
        "outputId": "06985358-381d-4d45-f3c8-9e69917ec832"
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          "data": {
            "text/plain": [
              "array([6. , 7. , 0. , 1. , 4. , 3. , 5. , 2. , 3.5])"
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          "metadata": {},
          "execution_count": 402
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      "source": [
        "avg = prepared_df['second_2'].unique()[:-1].mean()\n",
        "prepared_df.loc[prepared_df['second_2'] == ' ', 'second_2'] = avg\n",
        "prepared_df['second_2'] = prepared_df['second_2'].astype(float)\n",
        "prepared_df['second_2'].unique()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KIlnpQPpd9e8",
        "outputId": "258bba63-0166-4687-9e56-98d5f8831fea"
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      "execution_count": 403,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([7. , 2. , 3. , 6. , 4. , 1. , 5. , 0. , 3.5])"
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          "metadata": {},
          "execution_count": 403
        }
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    {
      "cell_type": "code",
      "source": [
        "avg = (prepared_df['first_3'].unique()[:-2] + prepared_df['first_3'].unique()[-1:]).mean()\n",
        "prepared_df.loc[prepared_df['first_3'] == ' ', 'first_3'] = avg\n",
        "prepared_df['first_3'] = prepared_df['first_3'].astype(float)\n",
        "prepared_df['first_3'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y1rWb3zBnUbz",
        "outputId": "3297cdf9-8267-4c6b-beab-c27167008168"
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      "execution_count": 404,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([7.        , 0.        , 6.        , 2.        , 5.        ,\n",
              "       4.        , 3.        , 4.85714286, 1.        ])"
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          "metadata": {},
          "execution_count": 404
        }
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      "cell_type": "code",
      "source": [
        "avg = prepared_df['second_3'].unique()[:-1].mean()\n",
        "prepared_df.loc[prepared_df['second_3'] == ' ', 'second_3'] = avg\n",
        "prepared_df['second_3'] = prepared_df['second_3'].astype(float)\n",
        "prepared_df['second_3'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "pcC-iXe7oUuf",
        "outputId": "0cff8acf-7bdb-4291-b1f9-b78c2657d182"
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      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5. , 0. , 1. , 3. , 2. , 4. , 6. , 7. , 3.5])"
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          "metadata": {},
          "execution_count": 405
        }
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      "cell_type": "code",
      "source": [
        "prepared_df['first_rank'] = pd.to_numeric(prepared_df['first_rank'], errors='coerce')\n",
        "\n",
        "mean_value = np.mean(prepared_df['first_rank'].dropna())\n",
        "\n",
        "prepared_df.loc[prepared_df['first_rank'].isnull(), 'first_rank'] = mean_value"
      ],
      "metadata": {
        "id": "dZK0kUhMofwt"
      },
      "execution_count": 406,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "prepared_df['second_rank'] = pd.to_numeric(prepared_df['second_rank'], errors='coerce')\n",
        "\n",
        "mean_value = np.mean(prepared_df['second_rank'].dropna())\n",
        "\n",
        "prepared_df.loc[prepared_df['second_rank'].isnull(), 'second_rank'] = mean_value"
      ],
      "metadata": {
        "id": "-bJActHNpSiV"
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      "outputs": []
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      "cell_type": "code",
      "source": [
        "prepared_df.isna().sum()\n"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "b4lhWcN2E1oq",
        "outputId": "1f036f40-d89e-4e45-bfca-c08b5c8131e1"
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      "execution_count": 408,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
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              "second_4                           0\n",
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              "encoded_court                      0\n",
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      "source": [
        "prepared_df = prepared_df.dropna()\n",
        "\n"
      ],
      "metadata": {
        "id": "8L-PldTMGU9M"
      },
      "execution_count": 409,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "end_mapping  = {\n",
        "    '_x': 'first_',\n",
        "    '_y': 'second_'\n",
        "}\n",
        "\n",
        "cols_to_rename = {\n",
        "    old_name: end_mapping[old_name[-2:]] + old_name[:-2] for old_name in prepared_df.columns if old_name[-2:] in end_mapping\n",
        "}\n",
        "prepared_df = prepared_df.rename(columns=cols_to_rename)\n",
        "prepared_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "MwVD8FrUHbaB",
        "outputId": "b9177c24-835a-421d-f24e-ede2c1553df8"
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      "execution_count": 410,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Best of       Date  first_rank  second_rank  first_weight_kg  \\\n",
              "0          3.0 2023-01-01        61.0         68.0             77.0   \n",
              "1          3.0 2022-03-24        97.0         81.0             82.0   \n",
              "2          5.0 2021-08-31         2.0         79.0             81.0   \n",
              "3          3.0 2020-10-14         6.0         53.0             81.0   \n",
              "4          3.0 2021-05-21        26.0         52.0             88.0   \n",
              "...        ...        ...         ...          ...              ...   \n",
              "55204      3.0 2013-04-22       309.0        280.0             74.0   \n",
              "55205      3.0 2015-09-29       266.0        340.0             80.0   \n",
              "55206      3.0 2012-02-14       438.0        719.0             73.0   \n",
              "55207      3.0 2012-02-16       438.0        393.0             73.0   \n",
              "55208      3.0 2008-07-08       137.0        588.0             82.0   \n",
              "\n",
              "       first_height_cm  second_weight_kg  second_height_cm  first_birth_year  \\\n",
              "0                180.0         75.000000        185.000000            1993.0   \n",
              "1                196.0         75.000000        185.000000            1996.0   \n",
              "2                198.0         75.000000        185.000000            1996.0   \n",
              "3                198.0         75.000000        185.000000            1996.0   \n",
              "4                198.0         75.000000        185.000000            1996.0   \n",
              "...                ...               ...               ...               ...   \n",
              "55204            175.0         75.032124        176.310889            1988.0   \n",
              "55205            185.0         73.000000        183.000000            1989.0   \n",
              "55206            175.0         75.032124        176.310889            1987.0   \n",
              "55207            175.0         84.000000        188.000000            1987.0   \n",
              "55208            185.0         75.032124        176.310889            1983.0   \n",
              "\n",
              "       first_turned_pro  ...  ATP500  Grand Slam  International  \\\n",
              "0                2014.0  ...       0           0              0   \n",
              "1                2013.0  ...       0           0              0   \n",
              "2                2014.0  ...       0           1              0   \n",
              "3                2014.0  ...       0           0              0   \n",
              "4                2013.0  ...       0           0              0   \n",
              "...                 ...  ...     ...         ...            ...   \n",
              "55204            2006.0  ...       1           0              0   \n",
              "55205            2008.0  ...       0           0              0   \n",
              "55206            2006.0  ...       0           0              0   \n",
              "55207            2006.0  ...       0           0              0   \n",
              "55208            2002.0  ...       0           0              1   \n",
              "\n",
              "       International Gold  Masters  Masters 1000  Masters Cup  encoded_court  \\\n",
              "0                       0        0             0            0              1   \n",
              "1                       0        0             1            0              1   \n",
              "2                       0        0             0            0              1   \n",
              "3                       0        0             0            0              0   \n",
              "4                       0        0             0            0              1   \n",
              "...                   ...      ...           ...          ...            ...   \n",
              "55204                   0        0             0            0              1   \n",
              "55205                   0        0             0            0              1   \n",
              "55206                   0        0             0            0              0   \n",
              "55207                   0        0             0            0              0   \n",
              "55208                   0        0             0            0              1   \n",
              "\n",
              "       encoded_surface  encoded_round  \n",
              "0                    3              0  \n",
              "1                    3              0  \n",
              "2                    3              0  \n",
              "3                    3              0  \n",
              "4                    1              4  \n",
              "...                ...            ...  \n",
              "55204                1              0  \n",
              "55205                3              0  \n",
              "55206                3              0  \n",
              "55207                3              1  \n",
              "55208                1              0  \n",
              "\n",
              "[54931 rows x 55 columns]"
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              "      <th>Best of</th>\n",
              "      <th>Date</th>\n",
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              "      <th>1</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2022-03-24</td>\n",
              "      <td>97.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>196.0</td>\n",
              "      <td>75.000000</td>\n",
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              "      <td>1996.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.0</td>\n",
              "      <td>2021-08-31</td>\n",
              "      <td>2.0</td>\n",
              "      <td>79.0</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2020-10-14</td>\n",
              "      <td>6.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>1996.0</td>\n",
              "      <td>2014.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2021-05-21</td>\n",
              "      <td>26.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>1996.0</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>55204</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2013-04-22</td>\n",
              "      <td>309.0</td>\n",
              "      <td>280.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>75.032124</td>\n",
              "      <td>176.310889</td>\n",
              "      <td>1988.0</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55205</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2015-09-29</td>\n",
              "      <td>266.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>183.000000</td>\n",
              "      <td>1989.0</td>\n",
              "      <td>2008.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>55206</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2012-02-14</td>\n",
              "      <td>438.0</td>\n",
              "      <td>719.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>75.032124</td>\n",
              "      <td>176.310889</td>\n",
              "      <td>1987.0</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55207</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2012-02-16</td>\n",
              "      <td>438.0</td>\n",
              "      <td>393.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>188.000000</td>\n",
              "      <td>1987.0</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55208</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2008-07-08</td>\n",
              "      <td>137.0</td>\n",
              "      <td>588.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>75.032124</td>\n",
              "      <td>176.310889</td>\n",
              "      <td>1983.0</td>\n",
              "      <td>2002.0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>54931 rows × 55 columns</p>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 410
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prepared_df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A8KPhfj1eky2",
        "outputId": "21ec6471-73e7-4ed1-c100-e96a90a4098a"
      },
      "execution_count": 411,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Best of', 'Date', 'first_rank', 'second_rank', 'first_weight_kg',\n",
              "       'first_height_cm', 'second_weight_kg', 'second_height_cm',\n",
              "       'first_birth_year', 'first_turned_pro', 'second_birth_year',\n",
              "       'first_home_country', 'second_home_country', 'first_1', 'second_1',\n",
              "       'first_2', 'second_2', 'first_3', 'second_3', 'first_4', 'second_4',\n",
              "       'first_5', 'second_5', 'first_6', 'second_6', 'win',\n",
              "       'first_service_games_won', 'first_total_service_points_won',\n",
              "       'first_1st_serve_return_points_won',\n",
              "       'first_2nd_serve_return_points_won', 'first_break_points_opportunities',\n",
              "       'first_break_points_converted', 'first_return_games_played',\n",
              "       'first_return_games_won', 'first_return_points_won',\n",
              "       'second_service_games_won', 'second_total_service_points_won',\n",
              "       'second_1st_serve_return_points_won',\n",
              "       'second_2nd_serve_return_points_won',\n",
              "       'second_break_points_opportunities', 'second_break_points_converted',\n",
              "       'second_return_games_played', 'second_return_games_won',\n",
              "       'second_return_points_won', 'ATP250', 'ATP500', 'Grand Slam',\n",
              "       'International', 'International Gold', 'Masters', 'Masters 1000',\n",
              "       'Masters Cup', 'encoded_court', 'encoded_surface', 'encoded_round'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 411
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Adding rows with 0 win column"
      ],
      "metadata": {
        "id": "NYiuUk6yGuT9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prepared_df = prepared_df[[col for col in prepared_df.columns if col not in ['first_turned_pro']]]\n",
        "prepared_df"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "4Dl58VURKMcE",
        "outputId": "e34ad186-d2dc-44ae-82d2-9ec77d18fd84"
      },
      "execution_count": 412,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Best of       Date  first_rank  second_rank  first_weight_kg  \\\n",
              "0          3.0 2023-01-01        61.0         68.0             77.0   \n",
              "1          3.0 2022-03-24        97.0         81.0             82.0   \n",
              "2          5.0 2021-08-31         2.0         79.0             81.0   \n",
              "3          3.0 2020-10-14         6.0         53.0             81.0   \n",
              "4          3.0 2021-05-21        26.0         52.0             88.0   \n",
              "...        ...        ...         ...          ...              ...   \n",
              "55204      3.0 2013-04-22       309.0        280.0             74.0   \n",
              "55205      3.0 2015-09-29       266.0        340.0             80.0   \n",
              "55206      3.0 2012-02-14       438.0        719.0             73.0   \n",
              "55207      3.0 2012-02-16       438.0        393.0             73.0   \n",
              "55208      3.0 2008-07-08       137.0        588.0             82.0   \n",
              "\n",
              "       first_height_cm  second_weight_kg  second_height_cm  first_birth_year  \\\n",
              "0                180.0         75.000000        185.000000            1993.0   \n",
              "1                196.0         75.000000        185.000000            1996.0   \n",
              "2                198.0         75.000000        185.000000            1996.0   \n",
              "3                198.0         75.000000        185.000000            1996.0   \n",
              "4                198.0         75.000000        185.000000            1996.0   \n",
              "...                ...               ...               ...               ...   \n",
              "55204            175.0         75.032124        176.310889            1988.0   \n",
              "55205            185.0         73.000000        183.000000            1989.0   \n",
              "55206            175.0         75.032124        176.310889            1987.0   \n",
              "55207            175.0         84.000000        188.000000            1987.0   \n",
              "55208            185.0         75.032124        176.310889            1983.0   \n",
              "\n",
              "       second_birth_year  ...  ATP500  Grand Slam  International  \\\n",
              "0                 1986.0  ...       0           0              0   \n",
              "1                 1986.0  ...       0           0              0   \n",
              "2                 1986.0  ...       0           1              0   \n",
              "3                 1986.0  ...       0           0              0   \n",
              "4                 1986.0  ...       0           0              0   \n",
              "...                  ...  ...     ...         ...            ...   \n",
              "55204             1989.0  ...       1           0              0   \n",
              "55205             1987.0  ...       0           0              0   \n",
              "55206             1989.0  ...       0           0              0   \n",
              "55207             1987.0  ...       0           0              0   \n",
              "55208             1988.0  ...       0           0              1   \n",
              "\n",
              "       International Gold  Masters  Masters 1000  Masters Cup  encoded_court  \\\n",
              "0                       0        0             0            0              1   \n",
              "1                       0        0             1            0              1   \n",
              "2                       0        0             0            0              1   \n",
              "3                       0        0             0            0              0   \n",
              "4                       0        0             0            0              1   \n",
              "...                   ...      ...           ...          ...            ...   \n",
              "55204                   0        0             0            0              1   \n",
              "55205                   0        0             0            0              1   \n",
              "55206                   0        0             0            0              0   \n",
              "55207                   0        0             0            0              0   \n",
              "55208                   0        0             0            0              1   \n",
              "\n",
              "       encoded_surface  encoded_round  \n",
              "0                    3              0  \n",
              "1                    3              0  \n",
              "2                    3              0  \n",
              "3                    3              0  \n",
              "4                    1              4  \n",
              "...                ...            ...  \n",
              "55204                1              0  \n",
              "55205                3              0  \n",
              "55206                3              0  \n",
              "55207                3              1  \n",
              "55208                1              0  \n",
              "\n",
              "[54931 rows x 54 columns]"
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              "      <th></th>\n",
              "      <th>Best of</th>\n",
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              "      <td>3.0</td>\n",
              "      <td>2023-01-01</td>\n",
              "      <td>61.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>1993.0</td>\n",
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              "      <td>3.0</td>\n",
              "      <td>2022-03-24</td>\n",
              "      <td>97.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>196.0</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>185.000000</td>\n",
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              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.0</td>\n",
              "      <td>2021-08-31</td>\n",
              "      <td>2.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>1996.0</td>\n",
              "      <td>1986.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "      <th>3</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2020-10-14</td>\n",
              "      <td>6.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>1996.0</td>\n",
              "      <td>1986.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2021-05-21</td>\n",
              "      <td>26.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>1996.0</td>\n",
              "      <td>1986.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55204</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2013-04-22</td>\n",
              "      <td>309.0</td>\n",
              "      <td>280.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>75.032124</td>\n",
              "      <td>176.310889</td>\n",
              "      <td>1988.0</td>\n",
              "      <td>1989.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55205</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2015-09-29</td>\n",
              "      <td>266.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>183.000000</td>\n",
              "      <td>1989.0</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>55206</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2012-02-14</td>\n",
              "      <td>438.0</td>\n",
              "      <td>719.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>175.0</td>\n",
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              "      <td>176.310889</td>\n",
              "      <td>1987.0</td>\n",
              "      <td>1989.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>55207</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2012-02-16</td>\n",
              "      <td>438.0</td>\n",
              "      <td>393.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>188.000000</td>\n",
              "      <td>1987.0</td>\n",
              "      <td>1987.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55208</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2008-07-08</td>\n",
              "      <td>137.0</td>\n",
              "      <td>588.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>75.032124</td>\n",
              "      <td>176.310889</td>\n",
              "      <td>1983.0</td>\n",
              "      <td>1988.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>54931 rows × 54 columns</p>\n",
              "</div>\n",
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          },
          "metadata": {},
          "execution_count": 412
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_rows = len(prepared_df)\n",
        "num_rows_to_extract = int(num_rows * 0.5)  # 50% of the rows\n",
        "df_extracted = prepared_df.sample(n=num_rows_to_extract, random_state=42)\n",
        "df_extracted"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "KDCKoCqqYkqb",
        "outputId": "43b2733a-208a-44e1-e63c-2525e2930ecc"
      },
      "execution_count": 413,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Best of       Date  first_rank  second_rank  first_weight_kg  \\\n",
              "20005      5.0 2012-01-17       181.0         83.0             77.0   \n",
              "11514      3.0 2017-08-04        47.0         25.0             70.0   \n",
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              "51910      3.0 2012-09-25        64.0       1066.0             80.0   \n",
              "3277       3.0 2018-05-10        22.0         10.0             83.0   \n",
              "...        ...        ...         ...          ...              ...   \n",
              "42342      3.0 2015-06-21        59.0         48.0             82.0   \n",
              "36240      3.0 2009-04-30        13.0         31.0             88.0   \n",
              "9017       3.0 2015-10-07        45.0         15.0             74.0   \n",
              "52883      5.0 2005-05-24       105.0        125.0             74.0   \n",
              "12865      3.0 2022-05-04         3.0         24.0             86.0   \n",
              "\n",
              "       first_height_cm  second_weight_kg  second_height_cm  first_birth_year  \\\n",
              "20005            178.0              70.0             183.0            1981.0   \n",
              "11514            178.0              74.0             178.0            1983.0   \n",
              "18784            178.0              93.0             203.0            1989.0   \n",
              "51910            188.0              79.0             183.0            1987.0   \n",
              "3277             188.0              68.0             180.0            1995.0   \n",
              "...                ...               ...               ...               ...   \n",
              "42342            178.0              77.0             170.0            1985.0   \n",
              "36240            193.0              81.0             183.0            1955.0   \n",
              "9017             185.0              73.0             185.0            1989.0   \n",
              "52883            173.0              85.0             201.0            1977.0   \n",
              "12865            198.0              89.0             198.0            1997.0   \n",
              "\n",
              "       second_birth_year  ...  ATP500  Grand Slam  International  \\\n",
              "20005             1990.0  ...       0           1              0   \n",
              "11514             1987.0  ...       0           0              0   \n",
              "18784             1986.0  ...       0           0              0   \n",
              "51910             1992.0  ...       0           0              0   \n",
              "3277              1990.0  ...       0           0              0   \n",
              "...                  ...  ...     ...         ...            ...   \n",
              "42342             1980.0  ...       1           0              0   \n",
              "36240             1981.0  ...       0           0              0   \n",
              "9017              1981.0  ...       1           0              0   \n",
              "52883             1976.0  ...       0           1              0   \n",
              "12865             1988.0  ...       0           0              0   \n",
              "\n",
              "       International Gold  Masters  Masters 1000  Masters Cup  encoded_court  \\\n",
              "20005                   0        0             0            0              1   \n",
              "11514                   0        0             0            0              1   \n",
              "18784                   0        0             1            0              1   \n",
              "51910                   0        0             0            0              0   \n",
              "3277                    0        0             1            0              1   \n",
              "...                   ...      ...           ...          ...            ...   \n",
              "42342                   0        0             0            0              1   \n",
              "36240                   0        0             1            0              1   \n",
              "9017                    0        0             0            0              1   \n",
              "52883                   0        0             0            0              1   \n",
              "12865                   0        0             1            0              1   \n",
              "\n",
              "       encoded_surface  encoded_round  \n",
              "20005                3              0  \n",
              "11514                1              6  \n",
              "18784                3              1  \n",
              "51910                3              0  \n",
              "3277                 1              2  \n",
              "...                ...            ...  \n",
              "42342                2              0  \n",
              "36240                1              2  \n",
              "9017                 3              0  \n",
              "52883                1              0  \n",
              "12865                1              1  \n",
              "\n",
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              "      <th>11514</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2017-08-04</td>\n",
              "      <td>47.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>178.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>178.0</td>\n",
              "      <td>1983.0</td>\n",
              "      <td>1987.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>18784</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2017-03-24</td>\n",
              "      <td>4.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>178.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>203.0</td>\n",
              "      <td>1989.0</td>\n",
              "      <td>1986.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>51910</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2012-09-25</td>\n",
              "      <td>64.0</td>\n",
              "      <td>1066.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>188.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>183.0</td>\n",
              "      <td>1987.0</td>\n",
              "      <td>1992.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3277</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2018-05-10</td>\n",
              "      <td>22.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>188.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>1995.0</td>\n",
              "      <td>1990.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>42342</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2015-06-21</td>\n",
              "      <td>59.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>178.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>1985.0</td>\n",
              "      <td>1980.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36240</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2009-04-30</td>\n",
              "      <td>13.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>193.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>183.0</td>\n",
              "      <td>1955.0</td>\n",
              "      <td>1981.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>9017</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2015-10-07</td>\n",
              "      <td>45.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>1989.0</td>\n",
              "      <td>1981.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "      <th>52883</th>\n",
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              "      <td>2005-05-24</td>\n",
              "      <td>105.0</td>\n",
              "      <td>125.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>173.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>1977.0</td>\n",
              "      <td>1976.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12865</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2022-05-04</td>\n",
              "      <td>3.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>198.0</td>\n",
              "      <td>1997.0</td>\n",
              "      <td>1988.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>27465 rows × 54 columns</p>\n",
              "</div>\n",
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          "metadata": {},
          "execution_count": 413
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    {
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        "df_extracted.index"
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      "execution_count": 414,
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              "Int64Index([20005, 11514, 18784, 51910,  3277, 22510, 10263, 39010, 40372,\n",
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          "metadata": {},
          "execution_count": 414
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    {
      "cell_type": "code",
      "source": [
        "prepared_df = prepared_df.drop(df_extracted.index)\n",
        "prepared_df"
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          "height": 0
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      "execution_count": 415,
      "outputs": [
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          "data": {
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              "       Best of       Date  first_rank  second_rank  first_weight_kg  \\\n",
              "2          5.0 2021-08-31         2.0         79.0             81.0   \n",
              "5          5.0 2018-09-02         6.0         25.0             77.0   \n",
              "8          3.0 2007-05-06         5.0         15.0             77.0   \n",
              "9          3.0 2010-10-14         2.0         31.0             77.0   \n",
              "10         3.0 2006-10-18        17.0         27.0             77.0   \n",
              "...        ...        ...         ...          ...              ...   \n",
              "55203      3.0 2012-09-17        81.0        785.0             74.0   \n",
              "55204      3.0 2013-04-22       309.0        280.0             74.0   \n",
              "55205      3.0 2015-09-29       266.0        340.0             80.0   \n",
              "55206      3.0 2012-02-14       438.0        719.0             73.0   \n",
              "55208      3.0 2008-07-08       137.0        588.0             82.0   \n",
              "\n",
              "       first_height_cm  second_weight_kg  second_height_cm  first_birth_year  \\\n",
              "2                198.0         75.000000        185.000000            1996.0   \n",
              "5                188.0         75.000000        185.000000            1987.0   \n",
              "8                188.0         75.000000        185.000000            1987.0   \n",
              "9                188.0         75.000000        185.000000            1987.0   \n",
              "10               188.0         75.000000        185.000000            1987.0   \n",
              "...                ...               ...               ...               ...   \n",
              "55203            173.0         80.000000        185.000000            1983.0   \n",
              "55204            175.0         75.032124        176.310889            1988.0   \n",
              "55205            185.0         73.000000        183.000000            1989.0   \n",
              "55206            175.0         75.032124        176.310889            1987.0   \n",
              "55208            185.0         75.032124        176.310889            1983.0   \n",
              "\n",
              "       second_birth_year  ...  ATP500  Grand Slam  International  \\\n",
              "2                 1986.0  ...       0           1              0   \n",
              "5                 1986.0  ...       0           1              0   \n",
              "8                 1986.0  ...       0           0              1   \n",
              "9                 1986.0  ...       0           0              0   \n",
              "10                1986.0  ...       0           0              0   \n",
              "...                  ...  ...     ...         ...            ...   \n",
              "55203             1991.0  ...       0           0              0   \n",
              "55204             1989.0  ...       1           0              0   \n",
              "55205             1987.0  ...       0           0              0   \n",
              "55206             1989.0  ...       0           0              0   \n",
              "55208             1988.0  ...       0           0              1   \n",
              "\n",
              "       International Gold  Masters  Masters 1000  Masters Cup  encoded_court  \\\n",
              "2                       0        0             0            0              1   \n",
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              "55206                   0        0             0            0              0   \n",
              "55208                   0        0             0            0              1   \n",
              "\n",
              "       encoded_surface  encoded_round  \n",
              "2                    3              0  \n",
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              "8                    1              7  \n",
              "9                    3              2  \n",
              "10                   3              1  \n",
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              "55204                1              0  \n",
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              "55206                3              0  \n",
              "55208                1              0  \n",
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              "      <td>75.000000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>1987.0</td>\n",
              "      <td>1986.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2007-05-06</td>\n",
              "      <td>5.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>188.0</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>1987.0</td>\n",
              "      <td>1986.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2010-10-14</td>\n",
              "      <td>2.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>188.0</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>1987.0</td>\n",
              "      <td>1986.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2006-10-18</td>\n",
              "      <td>17.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>188.0</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>1987.0</td>\n",
              "      <td>1986.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55203</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2012-09-17</td>\n",
              "      <td>81.0</td>\n",
              "      <td>785.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>173.0</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>1983.0</td>\n",
              "      <td>1991.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55204</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2013-04-22</td>\n",
              "      <td>309.0</td>\n",
              "      <td>280.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>75.032124</td>\n",
              "      <td>176.310889</td>\n",
              "      <td>1988.0</td>\n",
              "      <td>1989.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55205</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2015-09-29</td>\n",
              "      <td>266.0</td>\n",
              "      <td>340.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>183.000000</td>\n",
              "      <td>1989.0</td>\n",
              "      <td>1987.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55206</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2012-02-14</td>\n",
              "      <td>438.0</td>\n",
              "      <td>719.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>75.032124</td>\n",
              "      <td>176.310889</td>\n",
              "      <td>1987.0</td>\n",
              "      <td>1989.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55208</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2008-07-08</td>\n",
              "      <td>137.0</td>\n",
              "      <td>588.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>75.032124</td>\n",
              "      <td>176.310889</td>\n",
              "      <td>1983.0</td>\n",
              "      <td>1988.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>27466 rows × 54 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-828ec224-6562-4143-ba10-f1ef45c15b62')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-828ec224-6562-4143-ba10-f1ef45c15b62 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-828ec224-6562-4143-ba10-f1ef45c15b62');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 415
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import random\n",
        "\n",
        "def swap_columns(data):\n",
        "    df_copy = data.copy()\n",
        "    df_swapped = data.copy() \n",
        "    for column in data.columns:\n",
        "        if column.startswith('first_'):\n",
        "            suffix = column[6:]\n",
        "            df_swapped[['first_'+suffix, 'second_'+suffix]] = df_swapped[['second_'+suffix, 'first_'+suffix]]\n",
        "    df_swapped['win'] = 0\n",
        "    return df_swapped\n",
        "df_extracted = swap_columns(df_extracted).convert_dtypes()\n",
        "dff = pd.concat([df_extracted,prepared_df])\n",
        "# shuffled_df\n"
      ],
      "metadata": {
        "id": "3nr25Q-4Gb6l"
      },
      "execution_count": 416,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dff['win'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LnQ-I1h8exTy",
        "outputId": "4c2592d4-d3e6-4abf-b7c1-c958840c6164"
      },
      "execution_count": 417,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IntegerArray>\n",
              "[0, 1]\n",
              "Length: 2, dtype: Int64"
            ]
          },
          "metadata": {},
          "execution_count": 417
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "\n",
        "# Calculate the correlation matrix\n",
        "corr_matrix = dff.corr()\n",
        "\n",
        "# Create a heatmap using Seaborn\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(corr_matrix, annot=False, cmap='coolwarm', fmt=\".2f\", linewidths=0.5)\n",
        "plt.title('Correlation Matrix')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "fHY-Tw_YSqxo",
        "outputId": "2afbdc0f-66eb-4063-92d5-be24b61fa439"
      },
      "execution_count": 418,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-418-a4f601125d43>:8: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  corr_matrix = dff.corr()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dff.to_csv('/content/drive/MyDrive/DataMiningProject/prepared_df.csv')"
      ],
      "metadata": {
        "id": "HeTgkoV2pfdo"
      },
      "execution_count": 419,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dff.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8N1E-atsvhrW",
        "outputId": "77e52fd2-0465-418a-b2c5-4aafd37852ef"
      },
      "execution_count": 420,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Best of', 'Date', 'first_rank', 'second_rank', 'first_weight_kg',\n",
              "       'first_height_cm', 'second_weight_kg', 'second_height_cm',\n",
              "       'first_birth_year', 'second_birth_year', 'first_home_country',\n",
              "       'second_home_country', 'first_1', 'second_1', 'first_2', 'second_2',\n",
              "       'first_3', 'second_3', 'first_4', 'second_4', 'first_5', 'second_5',\n",
              "       'first_6', 'second_6', 'win', 'first_service_games_won',\n",
              "       'first_total_service_points_won', 'first_1st_serve_return_points_won',\n",
              "       'first_2nd_serve_return_points_won', 'first_break_points_opportunities',\n",
              "       'first_break_points_converted', 'first_return_games_played',\n",
              "       'first_return_games_won', 'first_return_points_won',\n",
              "       'second_service_games_won', 'second_total_service_points_won',\n",
              "       'second_1st_serve_return_points_won',\n",
              "       'second_2nd_serve_return_points_won',\n",
              "       'second_break_points_opportunities', 'second_break_points_converted',\n",
              "       'second_return_games_played', 'second_return_games_won',\n",
              "       'second_return_points_won', 'ATP250', 'ATP500', 'Grand Slam',\n",
              "       'International', 'International Gold', 'Masters', 'Masters 1000',\n",
              "       'Masters Cup', 'encoded_court', 'encoded_surface', 'encoded_round'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 420
        }
      ]
    }
  ]
}