Sports Income Analysis.ipynb

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      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import matplotlib.ticker as ticker"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Read the CSV file\n",
        "mlb = pd.read_csv('MLB Yearly Breakdown  Spotrac.csv')\n",
        "nhl = pd.read_csv('NHL Yearly Breakdown  Spotrac.csv')\n",
        "nba = pd.read_csv('NBA Yearly Breakdown  Spotrac.csv')\n",
        "nfl = pd.read_csv('NFL Yearly Breakdown  Spotrac.csv')"
      ],
      "metadata": {
        "id": "pwp49OUwc1MO"
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      "execution_count": null,
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      "source": [
        "# mlb.dropna(subset=['Average Income'], inplace=True)\n",
        "# mls.dropna(subset=['Average Income'], inplace=True)\n",
        "# #nba.dropna(subset=['AAV'], inplace=True)\n",
        "# nfl.dropna(subset=['Average Income'], inplace=True)"
      ],
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        "id": "zY7J2aO0fyLh"
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      "execution_count": null,
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    {
      "cell_type": "code",
      "source": [
        "mlb['AAV'] = mlb['AAV'].str.replace(',', '')\n",
        "mlb['AAV'] = mlb['AAV'].str.replace('.', '')\n",
        "mlb['AAV'] = mlb['AAV'].str.strip()"
      ],
      "metadata": {
        "id": "9-518-PsjOaD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9fcb8a05-4bd8-4fba-c54b-29b3992b2950"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
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          "text": [
            "<ipython-input-64-f19bfc3bbd0b>:2: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  mlb['AAV'] = mlb['AAV'].str.replace('.', '')\n"
          ]
        }
      ]
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      "source": [
        "nhl['AAV'] = nhl['AAV'].str.replace(',', '')\n",
        "nhl['AAV'] = nhl['AAV'].str.replace('.', '')\n",
        "nhl['AAV'] = nhl['AAV'].str.strip()"
      ],
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          "text": [
            "<ipython-input-65-a2aebd98e1db>:2: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  nhl['AAV'] = nhl['AAV'].str.replace('.', '')\n"
          ]
        }
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      "source": [
        "nba['AAV'] = nba['AAV'].str.replace(',', '')\n",
        "nba['AAV'] = nba['AAV'].str.replace('.', '')\n",
        "nba['AAV'] = nba['AAV'].str.strip()"
      ],
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          "text": [
            "<ipython-input-66-a71e08a3d071>:2: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  nba['AAV'] = nba['AAV'].str.replace('.', '')\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "nfl['AAV'] = nfl['AAV'].str.replace(',', '')\n",
        "nfl['AAV'] = nfl['AAV'].str.replace('.', '')\n",
        "nfl['AAV'] = nfl['AAV'].str.strip()"
      ],
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      },
      "execution_count": null,
      "outputs": [
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          "text": [
            "<ipython-input-67-8d05621e5ebc>:2: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  nfl['AAV'] = nfl['AAV'].str.replace('.', '')\n"
          ]
        }
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      "source": [
        "mlb['AAV'] = pd.to_numeric(mlb['AAV'].str.replace('$', ''), errors='coerce')\n",
        "nhl['AAV'] = pd.to_numeric(nhl['AAV'].str.replace('$', ''), errors='coerce')\n",
        "nba['AAV'] = pd.to_numeric(nba['AAV'].str.replace('$', ''), errors='coerce')\n",
        "nfl['AAV'] = pd.to_numeric(nfl['AAV'].str.replace('$', ''), errors='coerce')"
      ],
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        "outputId": "d96f361b-6898-46b0-8128-7cb9151999ca"
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      "execution_count": null,
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          "name": "stderr",
          "text": [
            "<ipython-input-68-3f3a97a3b023>:1: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  mlb['AAV'] = pd.to_numeric(mlb['AAV'].str.replace('$', ''), errors='coerce')\n",
            "<ipython-input-68-3f3a97a3b023>:2: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  nhl['AAV'] = pd.to_numeric(nhl['AAV'].str.replace('$', ''), errors='coerce')\n",
            "<ipython-input-68-3f3a97a3b023>:3: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  nba['AAV'] = pd.to_numeric(nba['AAV'].str.replace('$', ''), errors='coerce')\n",
            "<ipython-input-68-3f3a97a3b023>:4: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  nfl['AAV'] = pd.to_numeric(nfl['AAV'].str.replace('$', ''), errors='coerce')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# nba = nba[nba['AAV'] >= 16700]\n",
        "# # Reset the index of the filtered data\n",
        "# nba.reset_index(drop=True, inplace=True)"
      ],
      "metadata": {
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              "      Rank            Player  Year Pos Team       AAV  Unnamed: 6\n",
              "0      NaN      Max Scherzer  2023  SP  NYM  43333333         NaN\n",
              "1      NaN  Justin Verlander  2023  SP  NYM  43333333         NaN\n",
              "2      NaN       Aaron Judge  2023  CF  NYY  40000000         NaN\n",
              "3      NaN    Anthony Rendon  2023  3B  LAA  35000000         NaN\n",
              "4      NaN        Mike Trout  2023  CF  LAA  35541667         NaN\n",
              "...    ...               ...   ...  ..  ...       ...         ...\n",
              "1021   NaN     Mickey Moniak  2023  CF  LAA    720000         NaN\n",
              "1022   NaN         Zach Neto  2023  SS  LAA    720000         NaN\n",
              "1023   NaN       Kevin Padlo  2023  3B  LAA    720000         NaN\n",
              "1024   NaN      Jose Soriano  2023  RP  LAA    720000         NaN\n",
              "1025   NaN  Andrew Velazquez  2023  3B  LAA    720000         NaN\n",
              "\n",
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              "     Rank            Player  Year Pos Team       AAV  Unnamed: 6\n",
              "0     NaN  Nathan MacKinnon  2023   C  COL  12600000         NaN\n",
              "1     NaN    Connor McDavid  2023   C  EDM  12500000         NaN\n",
              "2     NaN    Artemi Panarin  2023  LW  NYR  11642857         NaN\n",
              "3     NaN   Auston Matthews  2023   C  TOR  11640250         NaN\n",
              "4     NaN     Erik Karlsson  2023   D  SJS  11500000         NaN\n",
              "..    ...               ...   ...  ..  ...       ...         ...\n",
              "551   NaN    Michael Amadio  2023   C  VGK    762500         NaN\n",
              "552   NaN   Jacob MacDonald  2023   D  SJS    762500         NaN\n",
              "553   NaN        A.J. Greer  2023  LW  BOS    762500         NaN\n",
              "554   NaN    Boris Katchouk  2023  LW  CHI    758333         NaN\n",
              "555   NaN    Taylor Raddysh  2023  RW  CHI    758333         NaN\n",
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              "      <td>EDM</td>\n",
              "      <td>12500000</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Artemi Panarin</td>\n",
              "      <td>2023</td>\n",
              "      <td>LW</td>\n",
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              "      <td>11642857</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Auston Matthews</td>\n",
              "      <td>2023</td>\n",
              "      <td>C</td>\n",
              "      <td>TOR</td>\n",
              "      <td>11640250</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Erik Karlsson</td>\n",
              "      <td>2023</td>\n",
              "      <td>D</td>\n",
              "      <td>SJS</td>\n",
              "      <td>11500000</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <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",
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              "    <tr>\n",
              "      <th>551</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Michael Amadio</td>\n",
              "      <td>2023</td>\n",
              "      <td>C</td>\n",
              "      <td>VGK</td>\n",
              "      <td>762500</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>552</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Jacob MacDonald</td>\n",
              "      <td>2023</td>\n",
              "      <td>D</td>\n",
              "      <td>SJS</td>\n",
              "      <td>762500</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>553</th>\n",
              "      <td>NaN</td>\n",
              "      <td>A.J. Greer</td>\n",
              "      <td>2023</td>\n",
              "      <td>LW</td>\n",
              "      <td>BOS</td>\n",
              "      <td>762500</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>554</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Boris Katchouk</td>\n",
              "      <td>2023</td>\n",
              "      <td>LW</td>\n",
              "      <td>CHI</td>\n",
              "      <td>758333</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>555</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Taylor Raddysh</td>\n",
              "      <td>2023</td>\n",
              "      <td>RW</td>\n",
              "      <td>CHI</td>\n",
              "      <td>758333</td>\n",
              "      <td>NaN</td>\n",
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              "<p>556 rows × 7 columns</p>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "     Rank              Player  Year Pos Team       AAV  Unnamed: 6\n",
              "0     NaN       Stephen Curry  2023  PG  GSW  53838416         NaN\n",
              "1     NaN        Kevin Durant  2023  SF  PHX  48554830         NaN\n",
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              "4     NaN         Joel Embiid  2023   C  PHI  53312000         NaN\n",
              "..    ...                 ...   ...  ..  ...       ...         ...\n",
              "322   NaN  Jeenathan Williams  2023  SG  POR    886254         NaN\n",
              "323   NaN        Max Christie  2023  SG  LAL   1368823         NaN\n",
              "324   NaN  Kenneth Lofton Jr.  2023  PF  MEM   1731754         NaN\n",
              "325   NaN      Bryce McGowens  2023  SG  CHA   1750504         NaN\n",
              "326   NaN    Christian Koloko  2023   C  TOR   1746521         NaN\n",
              "\n",
              "[327 rows x 7 columns]"
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            "text/plain": [
              "      Rank            Player  Year  Pos Team       AAV  Unnamed: 6\n",
              "0      NaN   Patrick Mahomes  2023   QB   KC  45000000         NaN\n",
              "1      NaN    Ryan Tannehill  2023   QB  TEN  29500000         NaN\n",
              "2      NaN  Leonard Williams  2023   DE  NYG  21000000         NaN\n",
              "3      NaN        Jared Goff  2023   QB  DET  33500000         NaN\n",
              "4      NaN         T.J. Watt  2023  OLB  PIT  28002750         NaN\n",
              "...    ...               ...   ...  ...  ...       ...         ...\n",
              "2848   NaN      Bradlee Anae  2023   DE  NYJ    905000         NaN\n",
              "2849   NaN     Zach Davidson  2023   TE  BUF         0         NaN\n",
              "2850   NaN      Andre Miller  2023   TE  NYG    750000         NaN\n",
              "2851   NaN      Jaylon Moore  2023   WR  JAC    750000         NaN\n",
              "2852   NaN       B.J. Baylor  2023   RB  ATL    750000         NaN\n",
              "\n",
              "[2853 rows x 7 columns]"
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              "      <td>NaN</td>\n",
              "      <td>Ryan Tannehill</td>\n",
              "      <td>2023</td>\n",
              "      <td>QB</td>\n",
              "      <td>TEN</td>\n",
              "      <td>29500000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Leonard Williams</td>\n",
              "      <td>2023</td>\n",
              "      <td>DE</td>\n",
              "      <td>NYG</td>\n",
              "      <td>21000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Jared Goff</td>\n",
              "      <td>2023</td>\n",
              "      <td>QB</td>\n",
              "      <td>DET</td>\n",
              "      <td>33500000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>T.J. Watt</td>\n",
              "      <td>2023</td>\n",
              "      <td>OLB</td>\n",
              "      <td>PIT</td>\n",
              "      <td>28002750</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2848</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Bradlee Anae</td>\n",
              "      <td>2023</td>\n",
              "      <td>DE</td>\n",
              "      <td>NYJ</td>\n",
              "      <td>905000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2849</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Zach Davidson</td>\n",
              "      <td>2023</td>\n",
              "      <td>TE</td>\n",
              "      <td>BUF</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2850</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Andre Miller</td>\n",
              "      <td>2023</td>\n",
              "      <td>TE</td>\n",
              "      <td>NYG</td>\n",
              "      <td>750000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2851</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Jaylon Moore</td>\n",
              "      <td>2023</td>\n",
              "      <td>WR</td>\n",
              "      <td>JAC</td>\n",
              "      <td>750000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2852</th>\n",
              "      <td>NaN</td>\n",
              "      <td>B.J. Baylor</td>\n",
              "      <td>2023</td>\n",
              "      <td>RB</td>\n",
              "      <td>ATL</td>\n",
              "      <td>750000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2853 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c8880535-1b80-4d26-b4b0-0e590e1d5f8c')\"\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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              "    .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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              "      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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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c8880535-1b80-4d26-b4b0-0e590e1d5f8c 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-c8880535-1b80-4d26-b4b0-0e590e1d5f8c');\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": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# mlb['Average Income'] = np.log(mlb['Average Income'])\n",
        "# mls['Average Income'] = np.log(mls['Average Income'])\n",
        "# nba['Average Income'] = np.log(nba['Average Income'])\n",
        "# nfl['Average Income'] = np.log(nfl['Average Income'])"
      ],
      "metadata": {
        "id": "c4d4Z6SSlsoP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mlb_average = mlb['AAV'].mean()\n",
        "nhl_average = nhl['AAV'].mean()\n",
        "nba_average = nba['AAV'].mean()\n",
        "nfl_average = nfl['AAV'].mean()\n",
        "\n",
        "print(mlb_average)\n",
        "print(nhl_average)\n",
        "print(nba_average)\n",
        "print(nfl_average)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9GUb8nVddAco",
        "outputId": "01ed91a5-2112-4285-e87f-ecd67bfc1c02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4715898.301169591\n",
            "4014713.2679856117\n",
            "11912965.905198777\n",
            "2879507.0599369085\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mlb_median = mlb['AAV'].median()\n",
        "nhl_median = nhl['AAV'].median()\n",
        "nba_median = nba['AAV'].median()\n",
        "nfl_median = nfl['AAV'].median()\n",
        "\n",
        "print(mlb_median)\n",
        "print(nhl_median)\n",
        "print(nba_median)\n",
        "print(nfl_median)"
      ],
      "metadata": {
        "id": "BoaktAsOdiGr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6097d436-4c6e-402f-9709-421c840f9629"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "987500.0\n",
            "3537500.0\n",
            "6876811.0\n",
            "1039114.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8HnWfu7dqOQB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Create the bar graph\n",
        "groups = ['NBA','NFL','MLB','NHL']\n",
        "mean_values = [nba_average, nfl_average, mlb_average, nhl_average]\n",
        "median_values = [nba_median, nfl_median, mlb_median, nhl_median]\n",
        "# Set the position of the bars\n",
        "bar_positions = np.arange(len(groups))\n",
        "bar_width = 0.35\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.barh(bar_positions, mean_values, bar_width, color='#008fd5', label='Mean')\n",
        "plt.barh(bar_positions + bar_width, median_values, bar_width, color='#fc4f30', label='Median')\n",
        "\n",
        "plt.xlabel('Salaries', fontsize=12)\n",
        "plt.ylabel('League', fontsize=12)\n",
        "#plt.title('Mean and Median Salaries by League 2023', fontsize=14)\n",
        "plt.xticks(fontsize=10)\n",
        "plt.yticks(bar_positions + bar_width / 2, groups, fontsize=10)\n",
        "plt.legend(fontsize=10)\n",
        "plt.grid(axis='x', linestyle='--', linewidth=0.5)\n",
        "plt.gca().spines['top'].set_visible(False)\n",
        "plt.gca().spines['right'].set_visible(False)\n",
        "plt.gca().spines['bottom'].set_linewidth(0.5)\n",
        "plt.gca().spines['left'].set_linewidth(0.5)\n",
        "plt.gca().set_facecolor('#f7f7f7')\n",
        "\n",
        "formatter = ticker.FuncFormatter(lambda x, pos: f'{x/1e6:.1f}M')\n",
        "plt.gca().xaxis.set_major_formatter(formatter)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "KO_hAPleli7m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "fa5727fb-1d86-4de7-e360-e78452157ffb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "from matplotlib.ticker import ScalarFormatter\n",
        "from matplotlib.ticker import FuncFormatter\n",
        "from matplotlib.lines import Line2D\n",
        "from scipy.stats import gaussian_kde\n"
      ],
      "metadata": {
        "id": "-bv2-2lrnbCO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "k7S5nHTesRkw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract the \"Average Income\" columns\n",
        "nba_income = nba['AAV']\n",
        "nfl_income = nfl['AAV']\n",
        "mlb_income = mlb['AAV']\n",
        "nhl_income = nhl['AAV']\n",
        "\n",
        "# Combine the income data into a single DataFrame\n",
        "combined_data = pd.DataFrame({'NBA': nba_income, 'NFL': nfl_income, 'MLB': mlb_income, 'NHL': nhl_income})\n",
        "\n",
        "# Create a violin plot\n",
        "sns.violinplot(data=combined_data)\n",
        "\n",
        "# Add labels to the plot\n",
        "plt.xlabel('League')\n",
        "plt.ylabel('Average Salaries')\n",
        "\n",
        "def millions_formatter(x, pos):\n",
        "    return f'{int(x/1e6)}M'\n",
        "\n",
        "# Apply the formatter to the y-axis tick labels\n",
        "plt.gca().yaxis.set_major_formatter(FuncFormatter(millions_formatter))\n",
        "plt.yticks([0, 5e6, 1e7, 1.5e7, 2e7, 2.5e7, 3e7, 3.5e7, 4e7, 4.5e7, 5e7, 5.5e7, 6e7, 6.5e7])\n",
        "plt.ylim(-10000000, 70000000)\n",
        "# plt.title('Density Distribution of Salaries by League 2023')\n",
        "\n",
        "mean_positions = [nba_average, nfl_average, mlb_average, nhl_average]\n",
        "labels = ['NBA', 'NFL', 'MLB', 'NHL']\n",
        "colors = ['white', 'white', 'white', 'white']\n",
        "icons = ['D', 'D', 'D', 'D']\n",
        "\n",
        "for x, label, color, icon in zip(mean_positions, labels, colors, icons):\n",
        "    if label == 'NBA':\n",
        "        position = 0\n",
        "    elif label == 'NFL':\n",
        "        position = 1\n",
        "    elif label == 'MLB':\n",
        "        position = 2\n",
        "    elif label == 'NHL':\n",
        "        position = 3\n",
        "    plt.plot(position, x, color=color, marker=icon, markersize=2.5, label=f'{label} Mean')  # Add a marker on top of the plot\n",
        "\n",
        "# Create a custom legend entry for the median\n",
        "median_legend_entry = Line2D([0], [0], marker='o', color='white', markersize=5, label='Median')\n",
        "mean_legend_entry = Line2D([0], [0], marker='D', color='white', markersize=5, label='Mean')\n",
        "\n",
        "# Display the legend with custom entries\n",
        "legend = plt.legend(handles=[median_legend_entry, mean_legend_entry], facecolor='darkgray')\n",
        "\n",
        "\n",
        "# Set the legend background color\n",
        "legend.get_frame().set_facecolor('darkgray')\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "M7W1u-9S4-jj",
        "outputId": "8040de5e-395a-417d-ab4a-815b78020c3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# Calculate the quartiles and interquartile range for each league\n",
        "nba_q1, nba_q3 = np.percentile(nba_income, [25, 75])\n",
        "nfl_q1, nfl_q3 = np.percentile(nfl_income, [25, 75])\n",
        "mlb_q1, mlb_q3 = np.percentile(mlb_income, [25, 75])\n",
        "nhl_q1, nhl_q3 = np.percentile(nhl_income, [25, 75])\n",
        "\n",
        "nba_iqr = nba_q3 - nba_q1\n",
        "nfl_iqr = nfl_q3 - nfl_q1\n",
        "mlb_iqr = mlb_q3 - mlb_q1\n",
        "nhl_iqr = nhl_q3 - nhl_q1\n",
        "\n",
        "# Define the outlier threshold (e.g., 1.5 times the IQR)\n",
        "outlier_threshold = 1.5\n",
        "\n",
        "# Calculate the upper and lower bounds for outliers for each league\n",
        "nba_lower_bound = nba_q1 - outlier_threshold * nba_iqr\n",
        "nba_upper_bound = nba_q3 + outlier_threshold * nba_iqr\n",
        "nfl_lower_bound = nfl_q1 - outlier_threshold * nfl_iqr\n",
        "nfl_upper_bound = nfl_q3 + outlier_threshold * nfl_iqr\n",
        "mlb_lower_bound = mlb_q1 - outlier_threshold * mlb_iqr\n",
        "mlb_upper_bound = mlb_q3 + outlier_threshold * mlb_iqr\n",
        "nhl_lower_bound = nhl_q1 - outlier_threshold * nhl_iqr\n",
        "nhl_upper_bound = nhl_q3 + outlier_threshold * nhl_iqr\n",
        "\n",
        "# Count the number of outliers for each league\n",
        "nba_outliers = np.sum((nba_income < nba_lower_bound) | (nba_income > nba_upper_bound))\n",
        "nfl_outliers = np.sum((nfl_income < nfl_lower_bound) | (nfl_income > nfl_upper_bound))\n",
        "mlb_outliers = np.sum((mlb_income < mlb_lower_bound) | (mlb_income > mlb_upper_bound))\n",
        "nhl_outliers = np.sum((nhl_income < nhl_lower_bound) | (nhl_income > nhl_upper_bound))\n",
        "\n",
        "# Print the number of outliers for each league\n",
        "print(\"Number of outliers:\")\n",
        "print(f\"NBA: {nba_outliers}\")\n",
        "print(f\"NFL: {nfl_outliers}\")\n",
        "print(f\"MLB: {mlb_outliers}\")\n",
        "print(f\"NHL: {nhl_outliers}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bY_lNk7utk9j",
        "outputId": "08999b08-633e-448a-b08a-828c8994f4b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of outliers:\n",
            "NBA: 20\n",
            "NFL: 453\n",
            "MLB: 114\n",
            "NHL: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # Calculate the kernel density estimates for each league's income data\n",
        "# nba_density = gaussian_kde(nba_income)\n",
        "# nfl_density = gaussian_kde(nfl_income)\n",
        "# mlb_density = gaussian_kde(mlb_income)\n",
        "# nhl_density = gaussian_kde(nhl_income)\n",
        "\n",
        "# # Calculate the densities at the median and mean\n",
        "# nba_density_at_median = nba_density.evaluate([nba_median])\n",
        "# nfl_density_at_median = nfl_density.evaluate([nfl_median])\n",
        "# mlb_density_at_median = mlb_density.evaluate([mlb_median])\n",
        "# nhl_density_at_median = nhl_density.evaluate([nhl_median])\n",
        "\n",
        "# nba_density_at_mean = nba_density.evaluate([nba_average])\n",
        "# nfl_density_at_mean = nfl_density.evaluate([nfl_average])\n",
        "# mlb_density_at_mean = mlb_density.evaluate([mlb_average])\n",
        "# nhl_density_at_mean = nhl_density.evaluate([nhl_average])"
      ],
      "metadata": {
        "id": "T5Iruuh1oliL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # Print the densities at the median and mean\n",
        "# print(\"Density at the median:\")\n",
        "# print(\"NBA:\", '{:.9f}'.format(nba_density_at_median[0]))\n",
        "# print(\"NFL:\", '{:.9f}'.format(nfl_density_at_median[0]))\n",
        "# print(\"MLB:\", '{:.9f}'.format(mlb_density_at_median[0]))\n",
        "# print(\"NHL:\", '{:.9f}'.format(nhl_density_at_median[0]))\n",
        "\n",
        "# print(\"\\nDensity at the mean:\")\n",
        "# print(\"NBA:\", '{:.9f}'.format(nba_density_at_mean[0]))\n",
        "# print(\"NFL:\", '{:.9f}'.format(nfl_density_at_mean[0]))\n",
        "# print(\"MLB:\", '{:.9f}'.format(mlb_density_at_mean[0]))\n",
        "# print(\"NHL:\", '{:.9f}'.format(nhl_density_at_mean[0]))\n"
      ],
      "metadata": {
        "id": "f-kzYlDLuWjV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # Calculate the kernel density estimates for each league's income data\n",
        "# nba_density = gaussian_kde(nba_income)\n",
        "# nfl_density = gaussian_kde(nfl_income)\n",
        "# mlb_density = gaussian_kde(mlb_income)\n",
        "# nhl_density = gaussian_kde(nhl_income)\n",
        "\n",
        "# # Evaluate the density over a range of values\n",
        "# x = np.linspace(0, 7e7, 1000)  # Adjust the range as needed\n",
        "\n",
        "# # Evaluate the densities at the given range\n",
        "# nba_density_values = nba_density(x)\n",
        "# nfl_density_values = nfl_density(x)\n",
        "# mlb_density_values = mlb_density(x)\n",
        "# nhl_density_values = nhl_density(x)\n",
        "\n",
        "# # Find the maximum density values and their corresponding range for each league\n",
        "# nba_max_density = np.max(nba_density_values)\n",
        "# nba_density_range = x[np.argmax(nba_density_values)]\n",
        "\n",
        "# nfl_max_density = np.max(nfl_density_values)\n",
        "# nfl_density_range = x[np.argmax(nfl_density_values)]\n",
        "\n",
        "# mlb_max_density = np.max(mlb_density_values)\n",
        "# mlb_density_range = x[np.argmax(mlb_density_values)]\n",
        "\n",
        "# nhl_max_density = np.max(nhl_density_values)\n",
        "# nhl_density_range = x[np.argmax(nhl_density_values)]\n",
        "\n",
        "# # Print the maximum density values and their corresponding range for each league\n",
        "# # Print the maximum density values and their corresponding range for each league\n",
        "# print(\"NBA - Maximum Density: {:.9f}\".format(nba_max_density))\n",
        "# print(\"NBA - Density Range: {:.0f}\".format(nba_density_range))\n",
        "\n",
        "# print(\"NFL - Maximum Density: {:.9f}\".format(nfl_max_density))\n",
        "# print(\"NFL - Density Range: {:.0f}\".format(nfl_density_range))\n",
        "\n",
        "# # print(\"ML - Maximum Density: {:.9f}\".format(mlb_max_density))\n",
        "# print(\"MLB - Density Range: {:.0f}\".format(mlb_density_range))\n",
        "\n",
        "# print(\"NHL - Maximum Density: {:.9f}\".format(nhl_max_density))\n",
        "# print(\"NHL - Density Range: {:.0f}\".format(nhl_density_range))\n"
      ],
      "metadata": {
        "id": "8SRAgr5xulZw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ct0xoG02MKWg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # Calculate the mean and standard deviation of MLB salaries\n",
        "# mlb_mean = mlb['AAV'].mean()\n",
        "# mlb_std = mlb['AAV'].std()\n",
        "\n",
        "# # Calculate the lower and upper bounds for one standard deviation\n",
        "# lower_bound = mlb_mean - mlb_std\n",
        "# upper_bound = mlb_mean + mlb_std\n",
        "\n",
        "# # Filter the players within one standard deviation\n",
        "# within_std = mlb[(mlb['AAV'] >= lower_bound) & (mlb['AAV'] <= upper_bound)]\n",
        "\n",
        "# # Count the number of players within one standard deviation\n",
        "# num_within_std = within_std.shape[0]\n",
        "\n",
        "# print(f\"Number of players within 1 standard deviation: {num_within_std}\")\n"
      ],
      "metadata": {
        "id": "6pn0nsliRb3A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # Calculate the mean salary of MLB players\n",
        "# mlb_mean = mlb['AAV'].mean()\n",
        "\n",
        "# # Define the range around the mean\n",
        "# range_width = 1000000  # Adjust this value as desired\n",
        "# lower_bound = mlb_mean - range_width/2\n",
        "# upper_bound = mlb_mean + range_width/2\n",
        "\n",
        "# Filter the players within the range around the mean\n",
        "around_mean = nfl[(nfl['AAV'] >= 0) & (nfl['AAV'] <= 5000000)]\n",
        "\n",
        "# Count the number of players around the mean\n",
        "num_around_mean = around_mean.shape[0]\n",
        "\n",
        "print(f\"Number of players around the mean: {num_around_mean}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0g_RhrwySxGq",
        "outputId": "da4d6205-25f5-4db6-dba8-0512ef0f6e93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of players around the mean: 2472\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the range around the median\n",
        "range_width = 1000000\n",
        "\n",
        "# Calculate the lower and upper bounds of the range\n",
        "lower_bound = mlb_median - range_width / 2\n",
        "upper_bound = mlb_median + range_width / 2\n",
        "\n",
        "# Count the number of people within the range\n",
        "num_people_around_median = ((mlb_income >= lower_bound) & (mlb_income <= upper_bound)).sum()\n",
        "\n",
        "print(f\"Number of people making around the median: {num_people_around_median}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ulPh47vZWuAp",
        "outputId": "3e381c68-4dc2-4f2b-cddb-34674bdf59d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of people making around the median: 562\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "salaries = nba['AAV'].values\n",
        "\n",
        "# Sort the salaries in descending order\n",
        "sorted_salaries = np.sort(salaries)[::-1]\n",
        "\n",
        "# Calculate the total number of players\n",
        "total_players = len(sorted_salaries)\n",
        "\n",
        "# Calculate the index position for the top 5% threshold\n",
        "top_5_percent_index = int(np.floor(0.03 * total_players))\n",
        "\n",
        "# Retrieve the salaries up to the top 5% index position\n",
        "top_5_percent_salaries = sorted_salaries[:top_5_percent_index]\n",
        "\n",
        "# Count the number of salaries within the top 5% range\n",
        "num_players_top_5_percent = len(top_5_percent_salaries)\n",
        "\n",
        "# Print the result\n",
        "print(\"Number of MLB players in the bottom 3% of salaries:\", num_players_top_5_percent)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PpDMf4CWYUfr",
        "outputId": "93433568-6af6-43c6-fc90-b7419d176e1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of MLB players in the bottom 3% of salaries: 9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming you have a DataFrame called 'nhl' with a column 'Salary' representing the salaries\n",
        "nhl_salary_data = nhl['AAV']\n",
        "\n",
        "# Count the number of players with salaries between 0 to 3 million\n",
        "num_players_0to3mil = sum((nhl_salary_data >= 0) & (nhl_salary_data <= 3000000))\n",
        "\n",
        "# Count the number of players with salaries between 3 to 6 million\n",
        "num_players_3to6mil = sum((nhl_salary_data > 3000000) & (nhl_salary_data <= 6000000))\n",
        "num_players_6mil = sum((nhl_salary_data > 6000000))\n",
        "# Print the number of players in each salary range\n",
        "print(\"Number of Players with salaries between 0 to 3 million: \", num_players_0to3mil)\n",
        "print(\"Number of Players with salaries between 3 to 6 million: \", num_players_3to6mil)\n",
        "print(\"Number of Players with salaries between 3 to 6 million: \", num_players_6mil)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WEtYToDE55zK",
        "outputId": "ace96e0d-e596-4b11-8c58-f94b7f0161e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Players with salaries between 0 to 3 million:  251\n",
            "Number of Players with salaries between 3 to 6 million:  180\n",
            "Number of Players with salaries between 3 to 6 million:  125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nba.sort_values('AAV', inplace=True)\n",
        "nfl.sort_values('AAV', inplace=True)\n",
        "mlb.sort_values('AAV', inplace=True)\n",
        "nhl.sort_values('AAV', inplace=True)"
      ],
      "metadata": {
        "id": "_FIMKvnIxdJz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mlb_cumulative_income = np.cumsum(mlb['AAV']) / np.sum(mlb['AAV'])\n",
        "nhl_cumulative_income = np.cumsum(nhl['AAV']) / np.sum(nhl['AAV'])\n",
        "nba_cumulative_income = np.cumsum(nba['AAV']) / np.sum(nba['AAV'])\n",
        "nfl_cumulative_income = np.cumsum(nfl['AAV']) / np.sum(nfl['AAV'])\n",
        "\n",
        "mlb_cumulative_income"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kOTBDvNcnEF-",
        "outputId": "bfac6bd7-35bc-4b11-a0e7-2bb9d5928f66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "904     0.000000\n",
              "1025    0.000149\n",
              "879     0.000298\n",
              "878     0.000446\n",
              "877     0.000595\n",
              "          ...   \n",
              "5       0.966174\n",
              "14      0.973821\n",
              "2       0.982088\n",
              "1       0.991044\n",
              "0       1.000000\n",
              "Name: AAV, Length: 1026, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Determine the minimum length for population_fraction\n",
        "population_fraction1 = np.linspace(0, 1, len(nba_cumulative_income))\n",
        "population_fraction2 = np.linspace(0, 1, len(mlb_cumulative_income))\n",
        "population_fraction3 = np.linspace(0, 1, len(nhl_cumulative_income))\n",
        "population_fraction4 = np.linspace(0, 1, len(nfl_cumulative_income))"
      ],
      "metadata": {
        "id": "4c7KUKqSoMJL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the Lorenz curves\n",
        "plt.plot(population_fraction2, mlb_cumulative_income, label='MLB')\n",
        "plt.plot(population_fraction3, nhl_cumulative_income, label='NHL')\n",
        "plt.plot(population_fraction1, nba_cumulative_income, label='NBA')\n",
        "plt.plot(population_fraction4, nfl_cumulative_income, label='NFL')\n",
        "\n",
        "plt.plot([0, 1], [0, 1], color='red', linestyle='--', label='Perfect Equality')\n",
        "# Add labels and legend\n",
        "plt.xlabel('Cumulative % Population')\n",
        "plt.ylabel('Cumulative % Income')\n",
        "# plt.title('Lorenz Curve')\n",
        "plt.legend()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "yuIHOX79oQQN",
        "outputId": "f1928537-fca4-4cd4-f68e-9a485211f9c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.offsetbox import OffsetImage, AnnotationBbox\n",
        "!pip install flags\n",
        "from flags import flags\n",
        "!pip install pycountry\n",
        "from pycountry import pycountry"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n5CmbJeWc_sb",
        "outputId": "100187ad-fbfe-4e03-fd06-fab0117eea4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: flags in /usr/local/lib/python3.10/dist-packages (0.0.1.2)\n",
            "Requirement already satisfied: pycountry in /usr/local/lib/python3.10/dist-packages (22.3.5)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from pycountry) (67.7.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate Gini index for each dataset\n",
        "gini_nba = 1 - np.trapz(nba_cumulative_income, population_fraction1)\n",
        "gini_mlb = 1 - np.trapz(mlb_cumulative_income, population_fraction2)\n",
        "gini_nhl = 1 - np.trapz(nhl_cumulative_income, population_fraction3)\n",
        "gini_nfl = 1 - np.trapz(nfl_cumulative_income, population_fraction4)\n",
        "\n",
        "# Create horizontal bar graph with lines and dots\n",
        "datasets = ['NBA', 'MLB', 'NHL', 'NFL']\n",
        "gini_indices = [gini_nba, gini_mlb, gini_nhl, gini_nfl]\n",
        "colors = ['green', 'blue', 'orange', 'red']  # Assign different colors to each bar\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "bar_plot = ax.barh(datasets, gini_indices, facecolor='none', edgecolor='none')\n",
        "\n",
        "# Add lines and dots at the end of each bar with corresponding colors\n",
        "for rect, color in zip(bar_plot, colors):\n",
        "    x = rect.get_width()\n",
        "    y = rect.get_y() + rect.get_height() / 2\n",
        "    ax.plot([0, x], [y, y], color=color, linestyle='-', linewidth=1)  # Line\n",
        "    ax.plot(x, y, color=color, marker='o', markersize=5)  # Dot\n",
        "    ax.text(x + 0.01, y - 0.000000000001, f'{x:.2f}', va='bottom', ha='right')\n",
        "\n",
        "ax.set_xlabel('Gini Index')\n",
        "ax.set_ylabel('Sports League')\n",
        "# ax.set_title('Gini Index by League')\n",
        "\n",
        "\n",
        "countries = ['South Africa', 'Finland'] #'Slovakia', 'Slovenia', 'Belarus']\n",
        "gini_indices = [0.6, 0.277]\n",
        "flag_paths = ['south-africa-flag-xs.png', 'finland-flag-xs.png'] #'slovenia-flag-xs.png', 'suriname-flag-xs.png']\n",
        "\n",
        "# Add flags to the plot\n",
        "for i, country in enumerate(countries):\n",
        "    flag_path = flag_paths[i]\n",
        "    img = OffsetImage(plt.imread(flag_path), zoom=0.1)  # Load the flag image\n",
        "    img.image.axes = ax\n",
        "    ab = AnnotationBbox(img, (gini_indices[i], 1.5), frameon=True, pad=0.2)  # Create the AnnotationBbox\n",
        "    ax.add_artist(ab)  # Add the AnnotationBbox to the plot\n",
        "    ax.text(gini_indices[i] - 0.05, 1.2, country, va='center')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "4xDGrMvfuV6x",
        "outputId": "a4fcbcfe-3b15-408d-dd64-c7b28d2c11fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plt.show()"
      ],
      "metadata": {
        "id": "7DaCpCze6RWP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import matplotlib.pyplot as plt\n",
        "# from matplotlib.offsetbox import OffsetImage, AnnotationBbox\n",
        "\n",
        "# # Existing code for calculating Gini indices and creating the bar graph\n",
        "\n",
        "# # Additional data for country flags and Gini indices\n",
        "# countries = ['South Africa', 'Namibia', 'Suriname', 'Slovakia', 'Slovenia', 'Belarus']\n",
        "# gini_indices = [0.6, 0.591, 0.579, 0.232, 0.244, 0.244]\n",
        "# flag_paths = ['south-africa-flag-xs.png', 'namibia-flag-xs.png', 'suriname-flag-xs.png', 'slovakia-flag-xs.png', 'slovenia-flag-xs.png', 'suriname-flag-xs.png']\n",
        "\n",
        "# # Add flags to the plot\n",
        "# for i, country in enumerate(countries):\n",
        "#     flag_path = flag_paths[i]\n",
        "#     img = OffsetImage(plt.imread(flag_path), zoom=0.15)  # Load the flag image\n",
        "#     img.image.axes = ax\n",
        "#     ab = AnnotationBbox(img, (gini_indices[i], i), frameon=False, pad=0.2)  # Create the AnnotationBbox\n",
        "#     ax.add_artist(ab)  # Add the AnnotationBbox to the plot\n",
        "\n",
        "# # Show the plot\n",
        "# plt.show()\n"
      ],
      "metadata": {
        "id": "RFmOBQz9fLQF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VUFnPrvMb0nu"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}