IPL_2022_Records.ipynb

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        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
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        "ipl = pd.read_csv('ipl_2022_dataset.csv')"
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              "   Unnamed: 0           Player  Base Price         TYPE  COST IN ₹ (CR.)  \\\n",
              "0           0      Rashid Khan  Draft Pick       BOWLER             15.0   \n",
              "1           1    Hardik Pandya  Draft Pick  ALL-ROUNDER             15.0   \n",
              "2           2  Lockie Ferguson        2 Cr       BOWLER             10.0   \n",
              "3           3    Rahul Tewatia     40 Lakh  ALL-ROUNDER              9.0   \n",
              "4           4     Shubman Gill  Draft Pick       BATTER              8.0   \n",
              "\n",
              "   Cost IN $ (000) 2021 Squad            Team 2022 Squad RCB player  \\\n",
              "0           1950.0        SRH  Gujarat Titans            NaN    NaN   \n",
              "1           1950.0         MI  Gujarat Titans            NaN    NaN   \n",
              "2           1300.0        KKR  Gujarat Titans            NaN    NaN   \n",
              "3           1170.0         RR  Gujarat Titans            NaN    NaN   \n",
              "4           1040.0        KKR  Gujarat Titans            NaN    NaN   \n",
              "\n",
              "   COST IN $ (000)  \n",
              "0              NaN  \n",
              "1              NaN  \n",
              "2              NaN  \n",
              "3              NaN  \n",
              "4              NaN  "
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              "      <td>Rashid Khan</td>\n",
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              "      <td>Hardik Pandya</td>\n",
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              "      <td>1950.0</td>\n",
              "      <td>MI</td>\n",
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              "      <td>Lockie Ferguson</td>\n",
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              "      <td>BOWLER</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1300.0</td>\n",
              "      <td>KKR</td>\n",
              "      <td>Gujarat Titans</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <th>3</th>\n",
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              "      <td>Rahul Tewatia</td>\n",
              "      <td>40 Lakh</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1170.0</td>\n",
              "      <td>RR</td>\n",
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              "      <td>Shubman Gill</td>\n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 633 entries, 0 to 632\n",
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        "outputId": "0f5bb3c4-1567-4e6c-9927-de5ac91def75"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'Player', 'Base Price', 'TYPE', 'COST IN ₹ (CR.)',\n",
              "       'Cost IN $ (000)', '2021 Squad', 'Team', '2022 Squad RCB', 'player',\n",
              "       'COST IN $ (000)'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ipl.drop('Unnamed: 0', axis=1, inplace= True)"
      ],
      "metadata": {
        "id": "8gFCIETV6age"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ipl.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "rtWZYTkm-Sln",
        "outputId": "17018c21-553c-47f6-f13c-d4d8214954c6"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Player  Base Price         TYPE  COST IN ₹ (CR.)  Cost IN $ (000)  \\\n",
              "0      Rashid Khan  Draft Pick       BOWLER             15.0           1950.0   \n",
              "1    Hardik Pandya  Draft Pick  ALL-ROUNDER             15.0           1950.0   \n",
              "2  Lockie Ferguson        2 Cr       BOWLER             10.0           1300.0   \n",
              "3    Rahul Tewatia     40 Lakh  ALL-ROUNDER              9.0           1170.0   \n",
              "4     Shubman Gill  Draft Pick       BATTER              8.0           1040.0   \n",
              "\n",
              "  2021 Squad            Team 2022 Squad RCB player  COST IN $ (000)  \n",
              "0        SRH  Gujarat Titans            NaN    NaN              NaN  \n",
              "1         MI  Gujarat Titans            NaN    NaN              NaN  \n",
              "2        KKR  Gujarat Titans            NaN    NaN              NaN  \n",
              "3         RR  Gujarat Titans            NaN    NaN              NaN  \n",
              "4        KKR  Gujarat Titans            NaN    NaN              NaN  "
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              "      <td>Rashid Khan</td>\n",
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              "      <td>Rahul Tewatia</td>\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ipl",
              "summary": "{\n  \"name\": \"ipl\",\n  \"rows\": 633,\n  \"fields\": [\n    {\n      \"column\": \"Player\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 584,\n        \"samples\": [\n          \"Prayas Barman\",\n          \"Thisara Perera\",\n          \"Rasikh Dar\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Base Price\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Retained\",\n          \"2 Cr\",\n          \"75 Lakh\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TYPE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"ALL-ROUNDER\",\n          \"WICKETKEEPER\",\n          \"BOWLER\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"COST IN \\u20b9 (CR.)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.238091881863017,\n        \"min\": 0.2,\n        \"max\": 17.0,\n        \"num_unique_values\": 65,\n        \"samples\": [\n          15.25,\n          3.4,\n          15.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cost IN $ (000)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 550.9519446421922,\n        \"min\": 26.0,\n        \"max\": 2210.0,\n        \"num_unique_values\": 65,\n        \"samples\": [\n          1982.5,\n          442.0,\n          1950.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2021 Squad\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"MI\",\n          \"CSK\",\n          \"SRH\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Team\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"Lucknow Super Giants\",\n          \"Gujarat Titans\",\n          \"Sunrisers Hyderabad\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2022 Squad RCB\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 22,\n        \"samples\": [\n          \"Virat Kohli\",\n          \"Finn Allen\",\n          \"Anuj Rawat\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"player\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 23,\n        \"samples\": [\n          \"Shreyas Gopal\",\n          \"Abdul Samad\",\n          \"Kane Williamson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"COST IN $ (000)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ipl[ipl['Cost IN $ (000)'].isnull()]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "zZlG4OTx2H5S",
        "outputId": "fb16c952-fa86-4edc-bed6-a68d1e582b02"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Player Base Price         TYPE  COST IN ₹ (CR.)  \\\n",
              "237            Suresh Raina       2 Cr       BATTER              NaN   \n",
              "238             Steve Smith       2 Cr       BATTER              NaN   \n",
              "239         Shakib Al Hasan       2 Cr  ALL-ROUNDER              NaN   \n",
              "240             Amit Mishra     1.5 Cr       BOWLER              NaN   \n",
              "241             Adil Rashid       2 Cr       BOWLER              NaN   \n",
              "..                      ...        ...          ...              ...   \n",
              "628            Sairaj Patil    20 Lakh       BATTER              NaN   \n",
              "629              Monu Singh    20 Lakh       BOWLER              NaN   \n",
              "630  Nivethan Radhakrishnan    20 Lakh       BOWLER              NaN   \n",
              "631            Lance Morris    20 Lakh       BOWLER              NaN   \n",
              "632            Aaron Hardie    20 Lakh  ALL-ROUNDER              NaN   \n",
              "\n",
              "     Cost IN $ (000) 2021 Squad    Team 2022 Squad RCB player  COST IN $ (000)  \n",
              "237              NaN        CSK  Unsold            NaN    NaN              NaN  \n",
              "238              NaN         DC  Unsold            NaN    NaN              NaN  \n",
              "239              NaN        KKR  Unsold            NaN    NaN              NaN  \n",
              "240              NaN         DC  Unsold            NaN    NaN              NaN  \n",
              "241              NaN       PBKS  Unsold            NaN    NaN              NaN  \n",
              "..               ...        ...     ...            ...    ...              ...  \n",
              "628              NaN        NaN  Unsold            NaN    NaN              NaN  \n",
              "629              NaN        NaN  Unsold            NaN    NaN              NaN  \n",
              "630              NaN        NaN  Unsold            NaN    NaN              NaN  \n",
              "631              NaN        NaN  Unsold            NaN    NaN              NaN  \n",
              "632              NaN        NaN  Unsold            NaN    NaN              NaN  \n",
              "\n",
              "[396 rows x 10 columns]"
            ],
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>237</th>\n",
              "      <td>Suresh Raina</td>\n",
              "      <td>2 Cr</td>\n",
              "      <td>BATTER</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CSK</td>\n",
              "      <td>Unsold</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>238</th>\n",
              "      <td>Steve Smith</td>\n",
              "      <td>2 Cr</td>\n",
              "      <td>BATTER</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>DC</td>\n",
              "      <td>Unsold</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>239</th>\n",
              "      <td>Shakib Al Hasan</td>\n",
              "      <td>2 Cr</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>KKR</td>\n",
              "      <td>Unsold</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>240</th>\n",
              "      <td>Amit Mishra</td>\n",
              "      <td>1.5 Cr</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>DC</td>\n",
              "      <td>Unsold</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>241</th>\n",
              "      <td>Adil Rashid</td>\n",
              "      <td>2 Cr</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PBKS</td>\n",
              "      <td>Unsold</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>628</th>\n",
              "      <td>Sairaj Patil</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>BATTER</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Unsold</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>629</th>\n",
              "      <td>Monu Singh</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Unsold</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>630</th>\n",
              "      <td>Nivethan Radhakrishnan</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Unsold</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>631</th>\n",
              "      <td>Lance Morris</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Unsold</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>632</th>\n",
              "      <td>Aaron Hardie</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Unsold</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>396 rows × 10 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ab93375c-2ea6-4137-ade8-59b1b74fbc84')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "      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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ab93375c-2ea6-4137-ade8-59b1b74fbc84 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-ab93375c-2ea6-4137-ade8-59b1b74fbc84');\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",
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              "\n",
              "\n",
              "<div id=\"df-f03ca069-d94e-419a-99cc-6f1f6ecc8bd2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f03ca069-d94e-419a-99cc-6f1f6ecc8bd2')\"\n",
              "            title=\"Suggest charts\"\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f03ca069-d94e-419a-99cc-6f1f6ecc8bd2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ipl['COST IN ₹ (CR.)'] = ipl['COST IN ₹ (CR.)'].fillna(0)\n",
        "ipl['Cost IN $ (000)'] = ipl['Cost IN $ (000)'].fillna(0)"
      ],
      "metadata": {
        "id": "b68oMV9P-gIJ"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ipl['2021 Squad'] = ipl['2021 Squad'].fillna('Not Participated')"
      ],
      "metadata": {
        "id": "w43MNsOT_Ycs"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ipl.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        },
        "id": "uduNo3dB_yk3",
        "outputId": "b3c20354-c7d6-4bd9-e14f-edd78054b0b1"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Player              45\n",
              "Base Price           0\n",
              "TYPE                 0\n",
              "COST IN ₹ (CR.)      0\n",
              "Cost IN $ (000)      0\n",
              "2021 Squad           0\n",
              "Team                 0\n",
              "2022 Squad RCB     611\n",
              "player             610\n",
              "COST IN $ (000)    633\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <td>0</td>\n",
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              "      <td>633</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 12
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    },
    {
      "cell_type": "code",
      "source": [
        "teams = ipl[ipl['COST IN ₹ (CR.)']>0]['Team'].unique()\n",
        "teams"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2DB6WyGL_8H6",
        "outputId": "6c05edb7-0342-4e0b-b33c-a5b6cf46efea"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Gujarat Titans', 'Chennai Super Kings', 'Delhi Capitals',\n",
              "       'Kolkata Knight Riders', 'Punjab Kings', 'Lucknow Super Giants',\n",
              "       'Mumbai Indians', 'Royal Challengers Bangalore ',\n",
              "       'Rajasthan Royals', 'Sunrisers Hyderabad'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ipl['status'] = ipl['Team'].replace(teams,'sold')"
      ],
      "metadata": {
        "id": "Jzt1hZ8pAjeX"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ipl.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "NG2mbGlwA776",
        "outputId": "b7a62309-ee37-4be7-860a-3e0b47610443"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Player  Base Price         TYPE  COST IN ₹ (CR.)  Cost IN $ (000)  \\\n",
              "0      Rashid Khan  Draft Pick       BOWLER             15.0           1950.0   \n",
              "1    Hardik Pandya  Draft Pick  ALL-ROUNDER             15.0           1950.0   \n",
              "2  Lockie Ferguson        2 Cr       BOWLER             10.0           1300.0   \n",
              "3    Rahul Tewatia     40 Lakh  ALL-ROUNDER              9.0           1170.0   \n",
              "4     Shubman Gill  Draft Pick       BATTER              8.0           1040.0   \n",
              "\n",
              "  2021 Squad            Team 2022 Squad RCB player  COST IN $ (000) status  \n",
              "0        SRH  Gujarat Titans            NaN    NaN              NaN   sold  \n",
              "1         MI  Gujarat Titans            NaN    NaN              NaN   sold  \n",
              "2        KKR  Gujarat Titans            NaN    NaN              NaN   sold  \n",
              "3         RR  Gujarat Titans            NaN    NaN              NaN   sold  \n",
              "4        KKR  Gujarat Titans            NaN    NaN              NaN   sold  "
            ],
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Player</th>\n",
              "      <th>Base Price</th>\n",
              "      <th>TYPE</th>\n",
              "      <th>COST IN ₹ (CR.)</th>\n",
              "      <th>Cost IN $ (000)</th>\n",
              "      <th>2021 Squad</th>\n",
              "      <th>Team</th>\n",
              "      <th>2022 Squad RCB</th>\n",
              "      <th>player</th>\n",
              "      <th>COST IN $ (000)</th>\n",
              "      <th>status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Rashid Khan</td>\n",
              "      <td>Draft Pick</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>15.0</td>\n",
              "      <td>1950.0</td>\n",
              "      <td>SRH</td>\n",
              "      <td>Gujarat Titans</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Hardik Pandya</td>\n",
              "      <td>Draft Pick</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>15.0</td>\n",
              "      <td>1950.0</td>\n",
              "      <td>MI</td>\n",
              "      <td>Gujarat Titans</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Lockie Ferguson</td>\n",
              "      <td>2 Cr</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1300.0</td>\n",
              "      <td>KKR</td>\n",
              "      <td>Gujarat Titans</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Rahul Tewatia</td>\n",
              "      <td>40 Lakh</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1170.0</td>\n",
              "      <td>RR</td>\n",
              "      <td>Gujarat Titans</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Shubman Gill</td>\n",
              "      <td>Draft Pick</td>\n",
              "      <td>BATTER</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1040.0</td>\n",
              "      <td>KKR</td>\n",
              "      <td>Gujarat Titans</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ipl",
              "summary": "{\n  \"name\": \"ipl\",\n  \"rows\": 633,\n  \"fields\": [\n    {\n      \"column\": \"Player\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 584,\n        \"samples\": [\n          \"Prayas Barman\",\n          \"Thisara Perera\",\n          \"Rasikh Dar\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Base Price\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Retained\",\n          \"2 Cr\",\n          \"75 Lakh\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TYPE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"ALL-ROUNDER\",\n          \"WICKETKEEPER\",\n          \"BOWLER\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"COST IN \\u20b9 (CR.)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.148242025337472,\n        \"min\": 0.0,\n        \"max\": 17.0,\n        \"num_unique_values\": 66,\n        \"samples\": [\n          1.6,\n          0.8,\n          15.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cost IN $ (000)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 409.2714632938713,\n        \"min\": 0.0,\n        \"max\": 2210.0,\n        \"num_unique_values\": 66,\n        \"samples\": [\n          208.0,\n          104.0,\n          1950.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2021 Squad\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"DC\",\n          \"MI\",\n          \"Not Participated\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Team\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"Lucknow Super Giants\",\n          \"Gujarat Titans\",\n          \"Sunrisers Hyderabad\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2022 Squad RCB\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 22,\n        \"samples\": [\n          \"Virat Kohli\",\n          \"Finn Allen\",\n          \"Anuj Rawat\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"player\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 23,\n        \"samples\": [\n          \"Shreyas Gopal\",\n          \"Abdul Samad\",\n          \"Kane Williamson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"COST IN $ (000)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ipl[ipl['Player'].duplicated(keep=False)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "o6PWDmdMBCVe",
        "outputId": "749e7f15-afa5-47ae-fe3f-7726d3bc7cb0"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Player Base Price          TYPE  COST IN ₹ (CR.)  Cost IN $ (000)  \\\n",
              "62     Lalit Yadav    20 Lakh   ALL-ROUNDER             0.65             84.5   \n",
              "168            NaN   Retained        BATTER            15.00           1950.0   \n",
              "169            NaN   Retained   ALL-ROUNDER            11.00           1430.0   \n",
              "170            NaN       1 Cr   ALL-ROUNDER            10.75           1397.5   \n",
              "171            NaN       2 Cr   ALL-ROUNDER            10.75           1397.5   \n",
              "172            NaN       2 Cr        BOWLER             7.75           1007.5   \n",
              "173            NaN       2 Cr        BATTER             7.00            910.0   \n",
              "174            NaN   Retained        BOWLER             7.00            910.0   \n",
              "175            NaN       2 Cr  WICKETKEEPER             5.50            715.0   \n",
              "176            NaN    20 Lakh  WICKETKEEPER             3.40            442.0   \n",
              "177            NaN    30 Lakh   ALL-ROUNDER             2.40            312.0   \n",
              "178            NaN       2 Cr   ALL-ROUNDER             2.00            260.0   \n",
              "179            NaN       1 Cr   ALL-ROUNDER             1.00            130.0   \n",
              "180            NaN    40 Lakh   ALL-ROUNDER             0.95            123.5   \n",
              "181            NaN    50 Lakh        BATTER             0.80            104.0   \n",
              "182            NaN    75 Lakh        BOWLER             0.75             97.5   \n",
              "183            NaN    75 Lakh        BOWLER             0.75             97.5   \n",
              "184            NaN    50 Lakh        BOWLER             0.50             65.0   \n",
              "185            NaN    20 Lakh   ALL-ROUNDER             0.30             39.0   \n",
              "186            NaN    20 Lakh        BOWLER             0.25             32.5   \n",
              "187            NaN    20 Lakh        BOWLER             0.20             26.0   \n",
              "188            NaN    20 Lakh   ALL-ROUNDER             0.20             26.0   \n",
              "189            NaN    20 Lakh  WICKETKEEPER             0.20             26.0   \n",
              "214            NaN   Retained        BATTER            14.00           1820.0   \n",
              "215            NaN     1.5 Cr  WICKETKEEPER            10.75           1397.5   \n",
              "216            NaN     1.5 Cr   ALL-ROUNDER             8.75           1137.5   \n",
              "217            NaN    40 Lakh        BATTER             8.50           1105.0   \n",
              "218            NaN    75 Lakh   ALL-ROUNDER             7.75           1007.5   \n",
              "219            NaN    20 Lakh   ALL-ROUNDER             6.50            845.0   \n",
              "220            NaN    50 Lakh   ALL-ROUNDER             4.20            546.0   \n",
              "221            NaN       2 Cr        BOWLER             4.20            546.0   \n",
              "222            NaN       1 Cr        BOWLER             4.00            520.0   \n",
              "223            NaN   Retained   ALL-ROUNDER             4.00            520.0   \n",
              "224            NaN   Retained        BOWLER             4.00            520.0   \n",
              "225            NaN    20 Lakh        BOWLER             4.00            520.0   \n",
              "226            NaN       1 Cr        BATTER             2.60            338.0   \n",
              "227            NaN    75 Lakh        BOWLER             2.40            312.0   \n",
              "228            NaN     1.5 Cr  WICKETKEEPER             1.50            195.0   \n",
              "229            NaN    20 Lakh        BOWLER             0.75             97.5   \n",
              "230            NaN    20 Lakh  WICKETKEEPER             0.50             65.0   \n",
              "231            NaN    50 Lakh        BOWLER             0.50             65.0   \n",
              "232            NaN    20 Lakh        BATTER             0.20             26.0   \n",
              "233            NaN    20 Lakh        BATTER             0.20             26.0   \n",
              "234            NaN    20 Lakh        BOWLER             0.20             26.0   \n",
              "235            NaN    20 Lakh   ALL-ROUNDER             0.20             26.0   \n",
              "236            NaN    20 Lakh        BOWLER             0.20             26.0   \n",
              "240    Amit Mishra     1.5 Cr        BOWLER             0.00              0.0   \n",
              "499    Amit Mishra    20 Lakh        BOWLER             0.00              0.0   \n",
              "518  Shivam Sharma    20 Lakh   ALL-ROUNDER             0.00              0.0   \n",
              "530    Lalit Yadav    20 Lakh        BOWLER             0.00              0.0   \n",
              "537  Shubham Singh    20 Lakh   ALL-ROUNDER             0.00              0.0   \n",
              "544  Shubham Singh    20 Lakh        BOWLER             0.00              0.0   \n",
              "569  Shivam Sharma    20 Lakh   ALL-ROUNDER             0.00              0.0   \n",
              "\n",
              "           2021 Squad                          Team       2022 Squad RCB  \\\n",
              "62                 DC                Delhi Capitals                  NaN   \n",
              "168               RCB  Royal Challengers Bangalore           Virat Kohli   \n",
              "169               RCB  Royal Challengers Bangalore         Glenn Maxwell   \n",
              "170               RCB  Royal Challengers Bangalore     Wanindu Hasaranga   \n",
              "171               RCB  Royal Challengers Bangalore         Harshal Patel   \n",
              "172               CSK  Royal Challengers Bangalore        Josh Hazlewood   \n",
              "173               CSK  Royal Challengers Bangalore        Faf Du Plessis   \n",
              "174               RCB  Royal Challengers Bangalore        Mohammed Siraj   \n",
              "175               KKR  Royal Challengers Bangalore        Dinesh Karthik   \n",
              "176                RR  Royal Challengers Bangalore            Anuj Rawat   \n",
              "177               RCB  Royal Challengers Bangalore        Shahbaz Ahamad   \n",
              "178  Not Participated  Royal Challengers Bangalore          David Willey   \n",
              "179  Not Participated  Royal Challengers Bangalore   Sherfane Rutherford   \n",
              "180                RR  Royal Challengers Bangalore        Mahipal Lomror   \n",
              "181               RCB  Royal Challengers Bangalore            Finn Allen   \n",
              "182               SRH  Royal Challengers Bangalore        Siddharth Kaul   \n",
              "183               CSK  Royal Challengers Bangalore     Jason Behrendorff   \n",
              "184               CSK  Royal Challengers Bangalore           Karn Sharma   \n",
              "185               RCB  Royal Challengers Bangalore   Suyash Prabhudessai   \n",
              "186  Not Participated  Royal Challengers Bangalore          Chama Milind   \n",
              "187               RCB  Royal Challengers Bangalore            Akash Deep   \n",
              "188  Not Participated  Royal Challengers Bangalore      Aneeshwar Gautam   \n",
              "189  Not Participated  Royal Challengers Bangalore       Luvnith Sisodia   \n",
              "214               SRH           Sunrisers Hyderabad                  NaN   \n",
              "215              PBKS           Sunrisers Hyderabad                  NaN   \n",
              "216               RCB           Sunrisers Hyderabad                  NaN   \n",
              "217               KKR           Sunrisers Hyderabad                  NaN   \n",
              "218  Not Participated           Sunrisers Hyderabad                  NaN   \n",
              "219               SRH           Sunrisers Hyderabad                  NaN   \n",
              "220                MI           Sunrisers Hyderabad                  NaN   \n",
              "221               SRH           Sunrisers Hyderabad                  NaN   \n",
              "222               SRH           Sunrisers Hyderabad                  NaN   \n",
              "223               SRH           Sunrisers Hyderabad                  NaN   \n",
              "224               SRH           Sunrisers Hyderabad                  NaN   \n",
              "225                RR           Sunrisers Hyderabad                  NaN   \n",
              "226              PBKS           Sunrisers Hyderabad                  NaN   \n",
              "227  Not Participated           Sunrisers Hyderabad                  NaN   \n",
              "228                RR           Sunrisers Hyderabad                  NaN   \n",
              "229                RR           Sunrisers Hyderabad                  NaN   \n",
              "230                DC           Sunrisers Hyderabad                  NaN   \n",
              "231  Not Participated           Sunrisers Hyderabad                  NaN   \n",
              "232               SRH           Sunrisers Hyderabad                  NaN   \n",
              "233  Not Participated           Sunrisers Hyderabad                  NaN   \n",
              "234               SRH           Sunrisers Hyderabad                  NaN   \n",
              "235  Not Participated           Sunrisers Hyderabad                  NaN   \n",
              "236  Not Participated           Sunrisers Hyderabad                  NaN   \n",
              "240                DC                        Unsold                  NaN   \n",
              "499  Not Participated                        Unsold                  NaN   \n",
              "518  Not Participated                        Unsold                  NaN   \n",
              "530  Not Participated                        Unsold                  NaN   \n",
              "537  Not Participated                        Unsold                  NaN   \n",
              "544  Not Participated                        Unsold                  NaN   \n",
              "569  Not Participated                        Unsold                  NaN   \n",
              "\n",
              "                 player  COST IN $ (000)  status  \n",
              "62                  NaN              NaN    sold  \n",
              "168                 NaN              NaN    sold  \n",
              "169                 NaN              NaN    sold  \n",
              "170                 NaN              NaN    sold  \n",
              "171                 NaN              NaN    sold  \n",
              "172                 NaN              NaN    sold  \n",
              "173                 NaN              NaN    sold  \n",
              "174                 NaN              NaN    sold  \n",
              "175                 NaN              NaN    sold  \n",
              "176                 NaN              NaN    sold  \n",
              "177                 NaN              NaN    sold  \n",
              "178                 NaN              NaN    sold  \n",
              "179                 NaN              NaN    sold  \n",
              "180                 NaN              NaN    sold  \n",
              "181                 NaN              NaN    sold  \n",
              "182                 NaN              NaN    sold  \n",
              "183                 NaN              NaN    sold  \n",
              "184                 NaN              NaN    sold  \n",
              "185                 NaN              NaN    sold  \n",
              "186                 NaN              NaN    sold  \n",
              "187                 NaN              NaN    sold  \n",
              "188                 NaN              NaN    sold  \n",
              "189                 NaN              NaN    sold  \n",
              "214     Kane Williamson              NaN    sold  \n",
              "215     Nicholas Pooran              NaN    sold  \n",
              "216   Washington Sundar              NaN    sold  \n",
              "217      Rahul Tripathi              NaN    sold  \n",
              "218    Romario Shepherd              NaN    sold  \n",
              "219     Abhishek Sharma              NaN    sold  \n",
              "220        Marco Jansen              NaN    sold  \n",
              "221   Bhuvneshwar Kumar              NaN    sold  \n",
              "222        T. Natarajan              NaN    sold  \n",
              "223         Abdul Samad              NaN    sold  \n",
              "224         Umran Malik              NaN    sold  \n",
              "225        Kartik Tyagi              NaN    sold  \n",
              "226       Aiden Markram              NaN    sold  \n",
              "227         Sean Abbott              NaN    sold  \n",
              "228      Glenn Phillips              NaN    sold  \n",
              "229       Shreyas Gopal              NaN    sold  \n",
              "230        Vishnu Vinod              NaN    sold  \n",
              "231    Fazalhaq Farooqi              NaN    sold  \n",
              "232         Priyam Garg              NaN    sold  \n",
              "233           R Samarth              NaN    sold  \n",
              "234  Jagadeesha Suchith              NaN    sold  \n",
              "235      Shashank Singh              NaN    sold  \n",
              "236       Saurabh Dubey              NaN    sold  \n",
              "240                 NaN              NaN  Unsold  \n",
              "499                 NaN              NaN  Unsold  \n",
              "518                 NaN              NaN  Unsold  \n",
              "530                 NaN              NaN  Unsold  \n",
              "537                 NaN              NaN  Unsold  \n",
              "544                 NaN              NaN  Unsold  \n",
              "569                 NaN              NaN  Unsold  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7e533b54-0a51-4c6e-95d2-4c3efa504719\" class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Player</th>\n",
              "      <th>Base Price</th>\n",
              "      <th>TYPE</th>\n",
              "      <th>COST IN ₹ (CR.)</th>\n",
              "      <th>Cost IN $ (000)</th>\n",
              "      <th>2021 Squad</th>\n",
              "      <th>Team</th>\n",
              "      <th>2022 Squad RCB</th>\n",
              "      <th>player</th>\n",
              "      <th>COST IN $ (000)</th>\n",
              "      <th>status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>Lalit Yadav</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>0.65</td>\n",
              "      <td>84.5</td>\n",
              "      <td>DC</td>\n",
              "      <td>Delhi Capitals</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Retained</td>\n",
              "      <td>BATTER</td>\n",
              "      <td>15.00</td>\n",
              "      <td>1950.0</td>\n",
              "      <td>RCB</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Virat Kohli</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Retained</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>11.00</td>\n",
              "      <td>1430.0</td>\n",
              "      <td>RCB</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Glenn Maxwell</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1 Cr</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>10.75</td>\n",
              "      <td>1397.5</td>\n",
              "      <td>RCB</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Wanindu Hasaranga</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2 Cr</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>10.75</td>\n",
              "      <td>1397.5</td>\n",
              "      <td>RCB</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Harshal Patel</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2 Cr</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>7.75</td>\n",
              "      <td>1007.5</td>\n",
              "      <td>CSK</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Josh Hazlewood</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2 Cr</td>\n",
              "      <td>BATTER</td>\n",
              "      <td>7.00</td>\n",
              "      <td>910.0</td>\n",
              "      <td>CSK</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Faf Du Plessis</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Retained</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>7.00</td>\n",
              "      <td>910.0</td>\n",
              "      <td>RCB</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Mohammed Siraj</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2 Cr</td>\n",
              "      <td>WICKETKEEPER</td>\n",
              "      <td>5.50</td>\n",
              "      <td>715.0</td>\n",
              "      <td>KKR</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Dinesh Karthik</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>NaN</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>WICKETKEEPER</td>\n",
              "      <td>3.40</td>\n",
              "      <td>442.0</td>\n",
              "      <td>RR</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Anuj Rawat</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>NaN</td>\n",
              "      <td>30 Lakh</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>2.40</td>\n",
              "      <td>312.0</td>\n",
              "      <td>RCB</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Shahbaz Ahamad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2 Cr</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>2.00</td>\n",
              "      <td>260.0</td>\n",
              "      <td>Not Participated</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>David Willey</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1 Cr</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>1.00</td>\n",
              "      <td>130.0</td>\n",
              "      <td>Not Participated</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Sherfane Rutherford</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>180</th>\n",
              "      <td>NaN</td>\n",
              "      <td>40 Lakh</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>0.95</td>\n",
              "      <td>123.5</td>\n",
              "      <td>RR</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Mahipal Lomror</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181</th>\n",
              "      <td>NaN</td>\n",
              "      <td>50 Lakh</td>\n",
              "      <td>BATTER</td>\n",
              "      <td>0.80</td>\n",
              "      <td>104.0</td>\n",
              "      <td>RCB</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Finn Allen</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>182</th>\n",
              "      <td>NaN</td>\n",
              "      <td>75 Lakh</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>0.75</td>\n",
              "      <td>97.5</td>\n",
              "      <td>SRH</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Siddharth Kaul</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>183</th>\n",
              "      <td>NaN</td>\n",
              "      <td>75 Lakh</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>0.75</td>\n",
              "      <td>97.5</td>\n",
              "      <td>CSK</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Jason Behrendorff</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>184</th>\n",
              "      <td>NaN</td>\n",
              "      <td>50 Lakh</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>0.50</td>\n",
              "      <td>65.0</td>\n",
              "      <td>CSK</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Karn Sharma</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>185</th>\n",
              "      <td>NaN</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>0.30</td>\n",
              "      <td>39.0</td>\n",
              "      <td>RCB</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Suyash Prabhudessai</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>186</th>\n",
              "      <td>NaN</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>0.25</td>\n",
              "      <td>32.5</td>\n",
              "      <td>Not Participated</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Chama Milind</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187</th>\n",
              "      <td>NaN</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>0.20</td>\n",
              "      <td>26.0</td>\n",
              "      <td>RCB</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Akash Deep</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188</th>\n",
              "      <td>NaN</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>0.20</td>\n",
              "      <td>26.0</td>\n",
              "      <td>Not Participated</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Aneeshwar Gautam</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>189</th>\n",
              "      <td>NaN</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>WICKETKEEPER</td>\n",
              "      <td>0.20</td>\n",
              "      <td>26.0</td>\n",
              "      <td>Not Participated</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Luvnith Sisodia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>214</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Retained</td>\n",
              "      <td>BATTER</td>\n",
              "      <td>14.00</td>\n",
              "      <td>1820.0</td>\n",
              "      <td>SRH</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Kane Williamson</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>215</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.5 Cr</td>\n",
              "      <td>WICKETKEEPER</td>\n",
              "      <td>10.75</td>\n",
              "      <td>1397.5</td>\n",
              "      <td>PBKS</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Nicholas Pooran</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>216</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.5 Cr</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>8.75</td>\n",
              "      <td>1137.5</td>\n",
              "      <td>RCB</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Washington Sundar</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>217</th>\n",
              "      <td>NaN</td>\n",
              "      <td>40 Lakh</td>\n",
              "      <td>BATTER</td>\n",
              "      <td>8.50</td>\n",
              "      <td>1105.0</td>\n",
              "      <td>KKR</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Rahul Tripathi</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>218</th>\n",
              "      <td>NaN</td>\n",
              "      <td>75 Lakh</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>7.75</td>\n",
              "      <td>1007.5</td>\n",
              "      <td>Not Participated</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Romario Shepherd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>219</th>\n",
              "      <td>NaN</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>6.50</td>\n",
              "      <td>845.0</td>\n",
              "      <td>SRH</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Abhishek Sharma</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>220</th>\n",
              "      <td>NaN</td>\n",
              "      <td>50 Lakh</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>4.20</td>\n",
              "      <td>546.0</td>\n",
              "      <td>MI</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Marco Jansen</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>221</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2 Cr</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>4.20</td>\n",
              "      <td>546.0</td>\n",
              "      <td>SRH</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Bhuvneshwar Kumar</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>222</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1 Cr</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>4.00</td>\n",
              "      <td>520.0</td>\n",
              "      <td>SRH</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>T. Natarajan</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>223</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Retained</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>4.00</td>\n",
              "      <td>520.0</td>\n",
              "      <td>SRH</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Abdul Samad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>224</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Retained</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>4.00</td>\n",
              "      <td>520.0</td>\n",
              "      <td>SRH</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Umran Malik</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>225</th>\n",
              "      <td>NaN</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>4.00</td>\n",
              "      <td>520.0</td>\n",
              "      <td>RR</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Kartik Tyagi</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>226</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1 Cr</td>\n",
              "      <td>BATTER</td>\n",
              "      <td>2.60</td>\n",
              "      <td>338.0</td>\n",
              "      <td>PBKS</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Aiden Markram</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>227</th>\n",
              "      <td>NaN</td>\n",
              "      <td>75 Lakh</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>2.40</td>\n",
              "      <td>312.0</td>\n",
              "      <td>Not Participated</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Sean Abbott</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>228</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.5 Cr</td>\n",
              "      <td>WICKETKEEPER</td>\n",
              "      <td>1.50</td>\n",
              "      <td>195.0</td>\n",
              "      <td>RR</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Glenn Phillips</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>229</th>\n",
              "      <td>NaN</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>0.75</td>\n",
              "      <td>97.5</td>\n",
              "      <td>RR</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Shreyas Gopal</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>230</th>\n",
              "      <td>NaN</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>WICKETKEEPER</td>\n",
              "      <td>0.50</td>\n",
              "      <td>65.0</td>\n",
              "      <td>DC</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Vishnu Vinod</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>231</th>\n",
              "      <td>NaN</td>\n",
              "      <td>50 Lakh</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>0.50</td>\n",
              "      <td>65.0</td>\n",
              "      <td>Not Participated</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Fazalhaq Farooqi</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>232</th>\n",
              "      <td>NaN</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>BATTER</td>\n",
              "      <td>0.20</td>\n",
              "      <td>26.0</td>\n",
              "      <td>SRH</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Priyam Garg</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>233</th>\n",
              "      <td>NaN</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>BATTER</td>\n",
              "      <td>0.20</td>\n",
              "      <td>26.0</td>\n",
              "      <td>Not Participated</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>R Samarth</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>234</th>\n",
              "      <td>NaN</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>0.20</td>\n",
              "      <td>26.0</td>\n",
              "      <td>SRH</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Jagadeesha Suchith</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>235</th>\n",
              "      <td>NaN</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>0.20</td>\n",
              "      <td>26.0</td>\n",
              "      <td>Not Participated</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Shashank Singh</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>236</th>\n",
              "      <td>NaN</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>0.20</td>\n",
              "      <td>26.0</td>\n",
              "      <td>Not Participated</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Saurabh Dubey</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>240</th>\n",
              "      <td>Amit Mishra</td>\n",
              "      <td>1.5 Cr</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>DC</td>\n",
              "      <td>Unsold</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Unsold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>Amit Mishra</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Not Participated</td>\n",
              "      <td>Unsold</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Unsold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>518</th>\n",
              "      <td>Shivam Sharma</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Not Participated</td>\n",
              "      <td>Unsold</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Unsold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>530</th>\n",
              "      <td>Lalit Yadav</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Not Participated</td>\n",
              "      <td>Unsold</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Unsold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>537</th>\n",
              "      <td>Shubham Singh</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Not Participated</td>\n",
              "      <td>Unsold</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Unsold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>544</th>\n",
              "      <td>Shubham Singh</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Not Participated</td>\n",
              "      <td>Unsold</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Unsold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>569</th>\n",
              "      <td>Shivam Sharma</td>\n",
              "      <td>20 Lakh</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Not Participated</td>\n",
              "      <td>Unsold</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Unsold</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7e533b54-0a51-4c6e-95d2-4c3efa504719')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "      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",
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              "        document.querySelector('#df-7e533b54-0a51-4c6e-95d2-4c3efa504719 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7e533b54-0a51-4c6e-95d2-4c3efa504719');\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",
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              "    0% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ed096d9a-7a44-48a5-8e60-4fa7742c3934 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"ipl[ipl['Player']\",\n  \"rows\": 53,\n  \"fields\": [\n    {\n      \"column\": \"Player\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Amit Mishra\",\n          \"Shubham Singh\",\n          \"Lalit Yadav\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Base Price\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"75 Lakh\",\n          \"Retained\",\n          \"40 Lakh\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TYPE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"BATTER\",\n          \"WICKETKEEPER\",\n          \"ALL-ROUNDER\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"COST IN \\u20b9 (CR.)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.078137843778606,\n        \"min\": 0.0,\n        \"max\": 15.0,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          2.4,\n          0.75,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cost IN $ (000)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 530.1579196912188,\n        \"min\": 0.0,\n        \"max\": 1950.0,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          312.0,\n          97.5,\n          260.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2021 Squad\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"PBKS\",\n          \"RCB\",\n          \"Not Participated\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Team\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Royal Challengers Bangalore \",\n          \"Unsold\",\n          \"Delhi Capitals\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2022 Squad RCB\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 22,\n        \"samples\": [\n          \"Virat Kohli\",\n          \"Finn Allen\",\n          \"Anuj Rawat\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"player\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 23,\n        \"samples\": [\n          \"Shreyas Gopal\",\n          \"Abdul Samad\",\n          \"Kane Williamson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"COST IN $ (000)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# How many players have participated in 2022 IPL Auction?\n",
        "\n",
        "ipl.shape[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YlLBO4c3BZGZ",
        "outputId": "f1126d90-3fe1-48d4-8200-dca7d9b2bfd5"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "633"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# How many types of players have participated?\n",
        "\n",
        "types = ipl['TYPE'].value_counts()\n",
        "types.reset_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "RWlGuCqnB4VV",
        "outputId": "ee5917df-924a-4f37-f28a-aeec4830a861"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           TYPE  count\n",
              "0   ALL-ROUNDER    242\n",
              "1        BOWLER    215\n",
              "2        BATTER    112\n",
              "3  WICKETKEEPER     64"
            ],
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-a1c2ea98-79a7-489e-8076-0484e88b4937');\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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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
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              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  }\n",
              "\n",
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              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d735ef07-9807-42b1-abc1-16950d6ff406 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"types\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"TYPE\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"BOWLER\",\n          \"WICKETKEEPER\",\n          \"ALL-ROUNDER\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 84,\n        \"min\": 64,\n        \"max\": 242,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          215,\n          64,\n          242\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.pie(types.values, labels=types.index,labeldistance=1.2,autopct='%1.2f%%', shadow=True, startangle=60)\n",
        "plt.title('Role of Players Participated', fontsize = 15)\n",
        "plt.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "yaU4tarDChlI",
        "outputId": "8de183a2-10b8-421a-b216-bcf27e87d343"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "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": [
        "# Players sold and unsold using a bar graph.\n",
        "\n",
        "plt.figure(figsize=(10,5))\n",
        "fig = sns.countplot(ipl['status'],palette=['orange','Pink'])\n",
        "plt.xlabel('Sold or Unsold') # Corrected line\n",
        "plt.ylabel('Number of Players') # Corrected line\n",
        "plt.title('Sold vs Unsold', fontsize=15)\n",
        "plt.plot()\n",
        "\n",
        "for p in fig.patches:\n",
        "  fig.annotate(format(p.get_height(), '.0f'), (p.get_x() +\n",
        "  p.get_width()/2., p.get_height()), ha = 'center', va = 'center',\n",
        "  xytext = (0,4), textcoords = 'offset points')"
      ],
      "metadata": {
        "id": "y936V9esELDr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "outputId": "e0221026-eca6-4a28-9c50-53cb0a502f07"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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wAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAI8PZ4cPH5bNZlNqauoNx6xfv142m02ZmZnFVhcAAAAAz+Lx4QwAAAAArIBwBgAAAAAWUGrC2dKlSxUdHa3AwEBVrFhR8fHxunDhgvLy8jRp0iTVqFFD/v7+atq0qVatWnXTfX399deqX7++AgMD1b59ex0+fLh4DgIAAACAxyoV4ez48eN68sknNWDAAKWlpWn9+vXq3r27jDGaPXu2ZsyYoenTp2v37t3q0KGDHn/8ce3fv7/AfR05ckTdu3dXly5dlJqaqueff15jx469ZQ2XL19Wdna2ww8AAAAAFJaPuwtwhePHj+vq1avq3r27IiMjJUnR0dGSpOnTp+vVV19Vnz59JElTp07VunXrNGvWLM2dOzffvubNm6c6depoxowZkqQGDRpoz549mjp16k1rmDx5st544w1XHhYAAAAAD1Iqrpw1adJEcXFxio6OVs+ePTV//nydPXtW2dnZOnbsmGJjYx3Gx8bGKi0trcB9paWlqVWrVg5tbdq0uWUN48aNU1ZWlv3nyJEjt39AAAAAADxOqQhn3t7eWrNmjb755htFRUVpzpw5atCggdLT04utBn9/fwUFBTn8AAAAAEBhlYpwJkk2m02xsbF64403tGvXLvn5+Wnt2rWqXr26Nm/e7DB28+bNioqKKnA/DRs21LZt2xzatm7dWmR1AwAAAIBUSp45S05O1tq1a/XII4+oSpUqSk5O1qlTp9SwYUONHj1aiYmJqlOnjpo2baqPPvpIqampWrRoUYH7Gjx4sGbMmKHRo0fr+eef186dO5WUlFS8BwQAAADA45SKcBYUFKSNGzdq1qxZys7OVmRkpGbMmKFOnTqpQ4cOysrK0qhRo3Ty5ElFRUXpiy++UL169QrcV0REhJYtW6aXX35Zc+bMUcuWLfX2229rwIABxXxUAAAAADyJzRhj3F1EaZSdna3g4GDtnhOh8oGl5u5RAAAkSTUHFt9z3QBQ0l3PBllZWTddm4LUAAAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACnA5nq1at0qZNm+yf586dq6ZNm+qpp57S2bNnXVocAAAAAHgKp8PZ6NGjlZ2dLUnas2ePRo0apc6dOys9PV0jR450eYEAAAAA4Al8nN0gPT1dUVFRkqRly5bpscce09tvv62UlBR17tzZ5QUCAAAAgCdw+sqZn5+fcnJyJEl///vf9cgjj0iSKlSoYL+iBgAAAABwjtNXzmJjYzVy5EjFxsZq27Zt+uSTTyRJ//73v1WjRg2XFwgAAAAAnsDpK2dz586Vr6+vli5dqnnz5umuu+6SJH3zzTfq2LGjywsEAAAAAE/g1JWzq1evav369Zo/f76qVavm0PfHP/7RpYUBAAAAgCdx6sqZj4+PBg8erMuXLxdVPQAAAADgkZy+rbFly5batWtXUdQCAAAAAB7L6QVBXnrpJY0aNUo///yzmjdvrrJlyzr0N27c2GXFAQAAAICncDqc9enTR5KUkJBgb7PZbDLGyGaz6dq1a66rDgAAAAA8xG29hBoAAAAA4FpOh7PIyMiiqAMAAAAAPJrTC4JI0l/+8hfFxsaqevXq+umnnyRJs2bN0ueff+7S4gAAAADAUzgdzubNm6eRI0eqc+fOyszMtD9jFhISolmzZrm6PgAAAADwCE6Hszlz5mj+/Pl6/fXX5e3tbW9v0aKF9uzZ49LiAAAAAMBTOB3O0tPT1axZs3zt/v7+unDhgkuKAgAAAABP43Q4q1WrllJTU/O1r1q1Sg0bNnRFTQAAAADgcZxerXHkyJEaOnSoLl26JGOMtm3bpsWLF2vy5Mn64IMPiqJGAAAAACj1nA5nzz//vAIDA/W73/1OOTk5euqpp1S9enXNnj3b/oJqAAAAAIBznA5nktS3b1/17dtXOTk5On/+vKpUqeLqugAAAADAozj9zFliYqL93WZlypQhmAEAAACACzgdzj7//HPVqVNHcXFx+tvf/qbLly8XRV0AAAAA4FGcDmepqanavn27GjVqpOHDh6tatWoaMmSItm/fXhT1AQAAAIBHcDqcSVKzZs30zjvv6NixY/rwww/1888/KzY2Vo0bN9bs2bOVlZXl6joBAAAAoFS7rXB2nTFGubm5unLliowxCg0N1bvvvqvw8HB98sknrqoRAAAAAEq92wpnO3fu1LBhwxQWFqaXX35ZzZo1U1pamjZs2KD9+/fr97//vRISElxdKwAAAACUWk6Hs+joaLVu3Vrp6en68MMPdeTIEU2ZMkV169a1j3nyySd16tQplxYKAAAAAKWZ0+8569WrlwYMGKC77rrrhmMqVaqkvLy8OyoMAAAAADyJ0+Fs/PjxRVEHAAAAAHg0p8OZJP3888/64osvlJGRoStXrjj0zZw50yWFAQAAAIAncTqcrV27Vo8//rhq166tH3/8Uffcc48OHz4sY4xiYmKKokYAAAAAKPWcXhBk3LhxeuWVV7Rnzx4FBARo2bJlOnLkiNq1a6eePXsWRY0AAAAAUOo5Hc7S0tL07LPPSpJ8fHx08eJFlStXTpMmTdLUqVNdXiAAAAAAeAKnw1nZsmXtz5mFhYXp4MGD9r7//Oc/rqsMAAAAADyI08+ctW7dWps2bVLDhg3VuXNnjRo1Snv27NHy5cvVunXroqgRAAAAAEo9p8PZzJkzdf78eUnSG2+8ofPnz+uTTz5RvXr1WKkRAAAAAG6T0+Gsdu3a9n+XLVtW77//vksLAgAAAABP5PQzZwAAAAAA1yvUlbPQ0FDZbLZC7fDMmTN3VBAAAAAAeKJChbNZs2YVcRkAAAAA4NkKFc769etX1HUAAAAAgEcr9DNneXl5mjp1qmJjY3Xvvfdq7NixunjxYlHWBgAAAAAeo9CrNf7+97/XxIkTFR8fr8DAQM2ePVsnT57UggULirK+Ei/y2T0KCgpydxkAAAAALK7QV87+/Oc/67333tPq1av12WefaeXKlVq0aJHy8vKKsj4AAAAA8AiFDmcZGRnq3Lmz/XN8fLxsNpuOHTtWJIUBAAAAgCcpdDi7evWqAgICHNp8fX2Vm5vr8qIAAAAAwNMU+pkzY4z69+8vf39/e9ulS5c0ePBglS1b1t62fPly11YIAAAAAB6g0OGsoOX0n376aZcWAwAAAACeqtDh7KOPPirKOgAAAADAoxX6mTMAAAAAQNEhnAEAAACABRDOAAAAAMACCGcAAAAAYAGFCmcxMTE6e/asJGnSpEnKyckp0qIAAAAAwNMUKpylpaXpwoULkqQ33nhD58+fL9KiAAAAAMDTFGop/aZNm+q5557T/fffL2OMpk+frnLlyhU4dsKECS4tEAAAAAA8gc0YY241aN++fUpMTNTBgweVkpKiqKgo+fjkz3U2m00pKSlFUmhJk52dreDgYGVlZSkoKMjd5QAAAABwk8Jmg0KFs1/z8vLSiRMnVKVKlTsusjQjnAEAAACQCp8NCnVb46/l5eXdUWEAAAAAgPycDmeSdPDgQc2aNUtpaWmSpKioKA0fPlx16tRxaXEAAAAA4Cmcfs/Z6tWrFRUVpW3btqlx48Zq3LixkpOT1ahRI61Zs6YoagQAAACAUs/pZ86aNWumDh06aMqUKQ7tY8eO1bfffsuCIP8Pz5wBAAAAkAqfDZy+cpaWlqaBAwfmax8wYID27t3r7O4AAAAAALqNcFa5cmWlpqbma09NTWUFRwAAAAC4TU4vCDJo0CC98MILOnTokO677z5J0ubNmzV16lSNHDnS5QUCAAAAgCdw+pkzY4xmzZqlGTNm6NixY5Kk6tWra/To0UpISJDNZiuSQksanjkDAAAAIBXhS6h/7dy5c5Kk8uXL3+4uSi3CGQAAAACpCF9C/WuEMgAAAABwDacXBAEAAAAAuB7hDAAAAAAsgHAGAAAAABbgVDjLzc1VXFyc9u/fX1T1AAAAAIBHciqc+fr6avfu3UVVCwAAAAB4LKdva3z66af14YcfFkUtAAAAAOCxnF5K/+rVq1qwYIH+/ve/q3nz5ipbtqxD/8yZM11WHAAAAAB4CqfD2ffff6+YmBhJ0r///W+HPpvN5pqqAAAAAMDDOB3O1q1bVxR1AAAAAIBHu+2l9A8cOKDVq1fr4sWLkiRjjMuKAgAAAABP43Q4O336tOLi4lS/fn117txZx48flyQNHDhQo0aNcnmBAAAAAOAJnA5nL7/8snx9fZWRkaEyZcrY23v37q1Vq1a5tDgAAAAA8BROP3P27bffavXq1apRo4ZDe7169fTTTz+5rDAAAAAA8CROh7MLFy44XDG77syZM/L393dJUaXJpR175Fe2nLvLAAAAADxGQKsm7i7htjh9W+MDDzygP//5z/bPNptNeXl5mjZtmtq3b+/S4gAAAADAUzh95WzatGmKi4vTjh07dOXKFY0ZM0Y//PCDzpw5o82bNxdFjQAAAABQ6jl95eyee+7Rv//9b91///3q2rWrLly4oO7du2vXrl2qU6dOUdQIAAAAAKWe01fOJCk4OFivv/66q2sBAAAAAI91W+Hs7Nmz+vDDD5WWliZJioqK0nPPPacKFSq4tDgAAAAA8BRO39a4ceNG1axZU++8847Onj2rs2fP6p133lGtWrW0cePGoqgRAAAAAEo9p6+cDR06VL1799a8efPk7e0tSbp27ZpeeuklDR06VHv27HF5kQAAAABQ2jl95ezAgQMaNWqUPZhJkre3t0aOHKkDBw64tDgAAAAA8BROh7OYmBj7s2a/lpaWpiZNSubL3gAAAADA3Qp1W+Pu3bvt/05ISNDw4cN14MABtW7dWpK0detWzZ07V1OmTCmaKgEAAACglLMZY8ytBnl5eclms+lWQ202m65du+ay4kqy7OxsBQcH65e1mxRUtpy7ywEAAAA8RkAra93Rdz0bZGVlKSgo6IbjCnXlLD093WWFAQAAAADyK1Q4i4yMLOo6AAAAAMCj3dZLqI8dO6ZNmzbp5MmTysvLc+hLSEhwSWEAAAAA4EmcDmdJSUl68cUX5efnp4oVK8pms9n7bDYb4QwAAAAAboPT4Wz8+PGaMGGCxo0bJy8vp1fiBwAAAAAUwOl0lZOToz59+hDMAAAAAMCFnE5YAwcO1JIlS4qiFgAAAADwWE7f1jh58mQ99thjWrVqlaKjo+Xr6+vQP3PmTJcVBwAAAACe4rbC2erVq9WgQQNJyrcgCAAAAADAeU6HsxkzZmjBggXq379/EZQDAAAAAJ7J6WfO/P39FRsbWxS1AAAAAIDHcjqcDR8+XHPmzCmKWgAAAADAYzl9W+O2bdv03Xff6csvv1SjRo3yLQiyfPlylxUHAAAAAJ7C6XAWEhKi7t27F0UtAAAAAOCxnA5nH330UVHUAQAAAAAezelnzgAAAAAAruf0lbNatWrd9H1mhw4duqOCAMAZm3bt1B//ulAp+9J04j+n9MnUmXq83UPuLgslFOcTAMCdnA5nI0aMcPicm5urXbt2adWqVRo9erSr6gKAQrlw8aKi69XXs126qc/Yke4uByUc5xMAwJ2cDmfDhw8vsH3u3LnasWPHHRcEAM7ocN/96nDf/e4uA6UE5xMAwJ1c9sxZp06dtGzZMlftDgAAAAA8isvC2dKlS1WhQgVX7Q4AAAAAPIrTtzU2a9bMYUEQY4xOnDihU6dO6b333nNpcQAAAADgKZwOZ926dXP47OXlpcqVK+vBBx/U3Xff7aq6AAAAAMCjOB3OEhMTi6IOAAAAAPBoToczALCS8zk5Ovhzhv3z4WNH9a9//6jQoGBFVAtzY2UoiTifAADuZDPGmMIM9PLyuunLpyXJZrPp6tWrLimspMvOzlZwcLB+WbtJQWXLubscoNTauHO7OgwdlK/96c5dNH/Cm26oCCUZ5xMAlA4BrZq4uwQH17NBVlaWgoKCbjiu0FfOVqxYccO+LVu26J133lFeXp5zVQLAHWrb/F5d3Jrq7jJQSnA+AQDcqdDhrGvXrvna9u3bp7Fjx2rlypXq27evJk2a5NLiAAAAAMBT3NZ7zo4dO6ZBgwYpOjpaV69eVWpqqhYuXKjIyEhX1wcAAAAAHsGpcJaVlaVXX31VdevW1Q8//KC1a9dq5cqVuueee4qqPgAAAADwCIW+rXHatGmaOnWqqlWrpsWLFxd4myMAAAAA4PY4tVpjYGCg4uPj5e3tfcNxy5cvd1lxJRmrNQIAAADuUepXa3z22WdvuZQ+AAAAAOD2FDqcJSUlFWEZAAAAAODZbmu1RgAAAACAaxHOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAEeH87Wr18vm82mzMzMG45JSkpSSEhIsdUEAAAAwPO4NZw9+OCDGjFiRL52whAAAAAAT+PxV84AAAAAwAosH8769++vbt26afr06QoLC1PFihU1dOhQ5ebm2se89957qlevngICAlS1alX95je/sfddvnxZCQkJqlKligICAnT//fdr+/btN/3OpKQkRUREqEyZMnriiSd0+vTpW9Z5+fJlZWdnO/wAAAAAQGFZPpxJ0rp163Tw4EGtW7dOCxcuVFJSkpKSkiRJO3bsUEJCgiZNmqR9+/Zp1apVatu2rX3bMWPGaNmyZVq4cKFSUlJUt25ddejQQWfOnCnwu5KTkzVw4EANGzZMqampat++vd56661b1jh58mQFBwfbf8LDw11y7AAAAAA8Q4kIZ6GhoXr33Xd1991367HHHtOjjz6qtWvXSpIyMjJUtmxZPfbYY4qMjFSzZs2UkJAgSbpw4YLmzZunP/zhD+rUqZOioqI0f/58BQYG6sMPPyzwu2bPnq2OHTtqzJgxql+/vhISEtShQ4db1jhu3DhlZWXZf44cOeK6CQAAAABQ6pWIcNaoUSN5e3vbP4eFhenkyZOSpIcffliRkZGqXbu2nnnmGS1atEg5OTmSpIMHDyo3N1exsbH2bX19fdWyZUulpaUV+F1paWlq1aqVQ1ubNm1uWaO/v7+CgoIcfgAAAACgsNwazoKCgpSVlZWvPTMzU8HBwfbPvr6+Dv02m015eXmSpPLlyyslJUWLFy9WWFiYJkyYoCZNmtx0aXwAAAAAsBq3hrMGDRooJSUlX3tKSorq169f6P34+PgoPj5e06ZN0+7du3X48GF99913qlOnjvz8/LR582b72NzcXG3fvl1RUVEF7qthw4ZKTk52aNu6dWuhawEAAACA2+Hjzi8fMmSI3n33XSUkJOj555+Xv7+/vvrqKy1evFgrV64s1D6+/PJLHTp0SG3btlVoaKi+/vpr5eXlqUGDBipbtqyGDBmi0aNHq0KFCoqIiNC0adOUk5OjgQMHFri/hIQExcbGavr06eratatWr16tVatWufKwAQAAACAft145q127tjZu3Kgff/xR8fHxatWqlT799FMtWbJEHTt2LNQ+QkJCtHz5cj300ENq2LCh3n//fS1evFiNGjWSJE2ZMkU9evTQM888o5iYGB04cECrV69WaGhogftr3bq15s+fr9mzZ6tJkyb69ttv9bvf/c5lxwwAAAAABbEZY4y7iyiNsrOzFRwcrF/WblJQ2XLuLgcAAADwGAGtmri7BAfXs0FWVtZNFw4sEas1AgAAAEBpRzgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAB93F1DaBbSIVkBQkLvLAAAAAGBxXDkDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgDAAAAAAsgnAEAAACABRDOAAAAAMACCGcAAAAAYAGEMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAW4OPuAkorY4wkKTs7282VAAAAAHCn65ngeka4EcJZETl9+rQkKTw83M2VAAAAALCCc+fOKTg4+Ib9hLMiUqFCBUlSRkbGTX8BcJ3s7GyFh4fryJEjCgoKcnc5HoE5L37MefFjzosfc+4ezHvxY86Ln7vm3Bijc+fOqXr16jcdRzgrIl5e/32cLzg4mP/YillQUBBzXsyY8+LHnBc/5rz4MefuwbwXP+a8+LljzgtzwYYFQQAAAADAAghnAAAAAGABhLMi4u/vr8TERPn7+7u7FI/BnBc/5rz4MefFjzkvfsy5ezDvxY85L35Wn3ObudV6jgAAAACAIseVMwAAAACwAMIZAAAAAFgA4QwAAAAALIBwBgAAAAAWQDgrAnPnzlXNmjUVEBCgVq1aadu2be4uqdSYOHGibDabw8/dd99t77906ZKGDh2qihUrqly5curRo4d++eUXN1Zc8mzcuFFdunRR9erVZbPZ9Nlnnzn0G2M0YcIEhYWFKTAwUPHx8dq/f7/DmDNnzqhv374KCgpSSEiIBg4cqPPnzxfjUZQst5rz/v375zvvO3bs6DCGOXfO5MmTde+996p8+fKqUqWKunXrpn379jmMKczfk4yMDD366KMqU6aMqlSpotGjR+vq1avFeSglRmHm/MEHH8x3rg8ePNhhDHPunHnz5qlx48b2F+62adNG33zzjb2f89z1bjXnnOdFb8qUKbLZbBoxYoS9raSc64QzF/vkk080cuRIJSYmKiUlRU2aNFGHDh108uRJd5dWajRq1EjHjx+3/2zatMne9/LLL2vlypVasmSJNmzYoGPHjql79+5urLbkuXDhgpo0aaK5c+cW2D9t2jS98847ev/995WcnKyyZcuqQ4cOunTpkn1M37599cMPP2jNmjX68ssvtXHjRr3wwgvFdQglzq3mXJI6duzocN4vXrzYoZ85d86GDRs0dOhQbd26VWvWrFFubq4eeeQRXbhwwT7mVn9Prl27pkcffVRXrlzRP//5Ty1cuFBJSUmaMGGCOw7J8goz55I0aNAgh3N92rRp9j7m3Hk1atTQlClTtHPnTu3YsUMPPfSQunbtqh9++EES53lRuNWcS5znRWn79u3605/+pMaNGzu0l5hz3cClWrZsaYYOHWr/fO3aNVO9enUzefJkN1ZVeiQmJpomTZoU2JeZmWl8fX3NkiVL7G1paWlGktmyZUsxVVi6SDIrVqywf87LyzPVqlUzf/jDH+xtmZmZxt/f3yxevNgYY8zevXuNJLN9+3b7mG+++cbYbDZz9OjRYqu9pPrfOTfGmH79+pmuXbvecBvm/M6dPHnSSDIbNmwwxhTu78nXX39tvLy8zIkTJ+xj5s2bZ4KCgszly5eL9wBKoP+dc2OMadeunRk+fPgNt2HOXSM0NNR88MEHnOfF6PqcG8N5XpTOnTtn6tWrZ9asWeMwzyXpXOfKmQtduXJFO3fuVHx8vL3Ny8tL8fHx2rJlixsrK13279+v6tWrq3bt2urbt68yMjIkSTt37lRubq7D/N99992KiIhg/l0kPT1dJ06ccJjj4OBgtWrVyj7HW7ZsUUhIiFq0aGEfEx8fLy8vLyUnJxd7zaXF+vXrVaVKFTVo0EBDhgzR6dOn7X3M+Z3LysqSJFWoUEFS4f6ebNmyRdHR0apatap9TIcOHZSdne3w/5CjYP8759ctWrRIlSpV0j333KNx48YpJyfH3sec35lr167p448/1oULF9SmTRvO82Lwv3N+Hed50Rg6dKgeffRRh3NaKll/032K7Zs8wH/+8x9du3bN4ZcqSVWrVtWPP/7opqpKl1atWikpKUkNGjTQ8ePH9cYbb+iBBx7Q999/rxMnTsjPz08hISEO21StWlUnTpxwT8GlzPV5LOgcv9534sQJValSxaHfx8dHFSpU4Pdwmzp27Kju3burVq1aOnjwoF577TV16tRJW7Zskbe3N3N+h/Ly8jRixAjFxsbqnnvukaRC/T05ceJEgf8tXO/DjRU055L01FNPKTIyUtWrV9fu3bv16quvat++fVq+fLkk5vx27dmzR23atNGlS5dUrlw5rVixQlFRUUpNTeU8LyI3mnOJ87yofPzxx0pJSdH27dvz9ZWkv+mEM5QonTp1sv+7cePGatWqlSIjI/Xpp58qMDDQjZUBRadPnz72f0dHR6tx48aqU6eO1q9fr7i4ODdWVjoMHTpU33//vcPzqyhaN5rzXz8nGR0drbCwMMXFxengwYOqU6dOcZdZajRo0ECpqanKysrS0qVL1a9fP23YsMHdZZVqN5rzqKgozvMicOTIEQ0fPlxr1qxRQECAu8u5I9zW6EKVKlWSt7d3vpVffvnlF1WrVs1NVZVuISEhql+/vg4cOKBq1arpypUryszMdBjD/LvO9Xm82TlerVq1fAvgXL16VWfOnOH34CK1a9dWpUqVdODAAUnM+Z0YNmyYvvzyS61bt041atSwtxfm70m1atUK/G/heh8KdqM5L0irVq0kyeFcZ86d5+fnp7p166p58+aaPHmymjRpotmzZ3OeF6EbzXlBOM/v3M6dO3Xy5EnFxMTIx8dHPj4+2rBhg9555x35+PioatWqJeZcJ5y5kJ+fn5o3b661a9fa2/Ly8rR27VqH+4zhOufPn9fBgwcVFham5s2by9fX12H+9+3bp4yMDObfRWrVqqVq1ao5zHF2draSk5Ptc9ymTRtlZmZq586d9jHfffed8vLy7P8DhDvz888/6/Tp0woLC5PEnN8OY4yGDRumFStW6LvvvlOtWrUc+gvz96RNmzbas2ePQzBes2aNgoKC7Lcv4f+71ZwXJDU1VZIcznXm/M7l5eXp8uXLnOfF6PqcF4Tz/M7FxcVpz549Sk1Ntf+0aNFCffv2tf+7xJzrxbb0iIf4+OOPjb+/v0lKSjJ79+41L7zwggkJCXFY+QW3b9SoUWb9+vUmPT3dbN682cTHx5tKlSqZkydPGmOMGTx4sImIiDDfffed2bFjh2nTpo1p06aNm6suWc6dO2d27dpldu3aZSSZmTNnml27dpmffvrJGGPMlClTTEhIiPn888/N7t27TdeuXU2tWrXMxYsX7fvo2LGjadasmUlOTjabNm0y9erVM08++aS7Dsnybjbn586dM6+88orZsmWLSU9PN3//+99NTEyMqVevnrl06ZJ9H8y5c4YMGWKCg4PN+vXrzfHjx+0/OTk59jG3+nty9epVc88995hHHnnEpKammlWrVpnKlSubcePGueOQLO9Wc37gwAEzadIks2PHDpOenm4+//xzU7t2bdO2bVv7Pphz540dO9Zs2LDBpKenm927d5uxY8cam81mvv32W2MM53lRuNmcc54Xn/9dFbOknOuEsyIwZ84cExERYfz8/EzLli3N1q1b3V1SqdG7d28TFhZm/Pz8zF133WV69+5tDhw4YO+/ePGieemll0xoaKgpU6aMeeKJJ8zx48fdWHHJs27dOiMp30+/fv2MMf9dTn/8+PGmatWqxt/f38TFxZl9+/Y57OP06dPmySefNOXKlTNBQUHmueeeM+fOnXPD0ZQMN5vznJwc88gjj5jKlSsbX19fExkZaQYNGpTv//Bhzp1T0HxLMh999JF9TGH+nhw+fNh06tTJBAYGmkqVKplRo0aZ3NzcYj6akuFWc56RkWHatm1rKlSoYPz9/U3dunXN6NGjTVZWlsN+mHPnDBgwwERGRho/Pz9TuXJlExcXZw9mxnCeF4WbzTnnefH533BWUs51mzHGFN91OgAAAABAQXjmDAAAAAAsgHAGAAAAABZAOAMAAAAACyCcAQAAAIAFEM4AAAAAwAIIZwAAAABgAYQzAAAAALAAwhkAAAAAWADhDABQqk2cOFFNmza96Zj+/furW7duxVKPVaxfv142m02ZmZk3HJOUlKSQkJBiqwkAPB3hDABgWadOndKQIUMUEREhf39/VatWTR06dNDmzZvdXdpte/DBBzVixIh87QQhAICPuwsAAOBGevTooStXrmjhwoWqXbu2fvnlF61du1anT592d2m3dOXKFfn5+bm7DABACcKVMwCAJWVmZuof//iHpk6dqvbt2ysyMlItW7bUuHHj9Pjjj9vHZWRkqGvXripXrpyCgoLUq1cv/fLLLzfc77Vr1zRy5EiFhISoYsWKGjNmjIwxt6xn2bJlatSokfz9/VWzZk3NmDHDob9mzZp688039eyzzyooKEgvvPDC7R+8/v+tltOnT1dYWJgqVqyooUOHKjc31z7mvffeU7169RQQEKCqVavqN7/5jb3v8uXLSkhIUJUqVRQQEKD7779f27dvv+l3JiUlKSIiQmXKlNETTzxRIkIwAJQmhDMAgCWVK1dO5cqV02effabLly8XOCYvL09du3bVmTNntGHDBq1Zs0aHDh1S7969b7jfGTNmKCkpSQsWLNCmTZt05swZrVix4qa17Ny5U7169VKfPn20Z88eTZw4UePHj1dSUpLDuOnTp6tJkybatWuXxo8f7/Qx/69169bp4MGDWrdunRYuXKikpCT7d+7YsUMJCQmaNGmS9u3bp1WrVqlt27b2bceMGaNly5Zp4cKFSklJUd26ddWhQwedOXOmwO9KTk7WwIEDNWzYMKWmpqp9+/Z666237vgYAABOMAAAWNTSpUtNaGioCQgIMPfdd58ZN26c+de//mXv//bbb423t7fJyMiwt/3www9Gktm2bZsxxpjExETTpEkTe39YWJiZNm2a/XNubq6pUaOG6dq16w3reOqpp8zDDz/s0DZ69GgTFRVl/xwZGWm6det2y2Nq166dGT58eL72jz76yAQHB9s/9+vXz0RGRpqrV6/a23r27Gl69+5tjDFm2bJlJigoyGRnZ+fb1/nz542vr69ZtGiRve3KlSumevXq9mNft26dkWTOnj1rjDHmySefNJ07d3bYT+/evR1qAgAULa6cAQAsq0ePHjp27Ji++OILdezYUevXr1dMTIz96lFaWprCw8MVHh5u3yYqKkohISFKS0vLt7+srCwdP35crVq1srf5+PioRYsWN60jLS1NsbGxDm2xsbHav3+/rl27Zm+71X6c1ahRI3l7e9s/h4WF6eTJk5Kkhx9+WJGRkapdu7aeeeYZLVq0SDk5OZKkgwcPKjc316FmX19ftWzZssB5kf57jL+eF0lq06aNS48HAHBzhDMAgKUFBATo4Ycf1vjx4/XPf/5T/fv3V2JiorvLKlDZsmVvOSYoKEhZWVn52jMzMxUcHOzQ5uvr6/DZZrMpLy9PklS+fHmlpKRo8eLFCgsL04QJE9SkSZObLo0PALA2whkAoESJiorShQsXJEkNGzbUkSNHdOTIEXv/3r17lZmZqaioqHzbBgcHKywsTMnJyfa2q1evaufOnTf9zoYNG+Zbvn/z5s2qX7++w5WtwmjQoIFSUlLytaekpKh+/fpO7cvHx0fx8fGaNm2adu/ercOHD+u7775TnTp15Ofn51Bzbm6utm/fXuC8SP89xl/PiyRt3brVqXoAAHeGpfQBAJZ0+vRp9ezZUwMGDFDjxo1Vvnx57dixQ9OmTVPXrl0lSfHx8YqOjlbfvn01a9YsXb16VS+99JLatWt3w1sMhw8frilTpqhevXq6++67NXPmzFtebRo1apTuvfdevfnmm+rdu7e2bNmid999V++9957TxzVkyBC9++67SkhI0PPPPy9/f3999dVXWrx4sVauXFno/Xz55Zc6dOiQ2rZtq9DQUH399dfKy8tTgwYNVLZsWQ0ZMkSjR49WhQoVFBERoWnTpiknJ0cDBw4scH8JCQmKjY3V9OnT1bVrV61evVqrVq1y+vgAALePcAYAsKRy5cqpVatW+uMf/2h/hio8PFyDBg3Sa6+9Jum/t/l9/vnn+u1vf6u2bdvKy8tLHTt21Jw5c26431GjRun48ePq16+fvLy8NGDAAD3xxBMF3mp4XUxMjD799FNNmDBBb775psLCwjRp0iT179/f6eOqXbu2Nm7cqNdff13x8fG6cuWK7r77bi1ZskQdO3Ys9H5CQkK0fPlyTZw4UZcuXVK9evW0ePFiNWrUSJI0ZcoU5eXl6ZlnntG5c+fUokULrV69WqGhoQXur3Xr1po/f74SExM1YcIExcfH63e/+53efPNNp48RAHB7bMYU4uUuAAAAAIAixTNnAAAAAGABhDMAAAAAsADCGQAAAABYAOEMAAAAACyAcAYAAAAAFkA4AwAAAAALIJwBAAAAgAUQzgAAAADAAghnAAAAAGABhDMAAAAAsADCGQAAAABYwP8FE9rsIqRJ8RoAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ipl.groupby('status')['status'].size()\n",
        "#or\n",
        "ipl.groupby('status')['Team'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "SSN4kSjDLh1O",
        "outputId": "bbe7240c-a9ba-4d94-eb62-0ed98f1b7721"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "status\n",
              "Unsold    396\n",
              "sold      237\n",
              "Name: Team, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Team</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>status</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Unsold</th>\n",
              "      <td>396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sold</th>\n",
              "      <td>237</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Total number of players bought by each team.\n",
        "\n",
        "plt.figure(figsize=(20,10))\n",
        "fig = sns.countplot(ipl[ipl['Team'] != 'Unsold']['Team']) # Corrected line\n",
        "plt.xlabel('Team Names')\n",
        "plt.ylabel('Number of Players')\n",
        "plt.title('Players Bought by Each Team', fontsize=12)\n",
        "plt.xticks(rotation=70)\n",
        "plt.plot()\n",
        "\n",
        "for p in fig.patches:\n",
        "    fig.annotate(format(p.get_height(), '.0f'), (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 4), textcoords='offset points') # Corrected line"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 732
        },
        "id": "2G6L5PWbN6I9",
        "outputId": "c7fd5456-f903-46e2-a894-80ea0badbd0f"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ipl['retention'] = ipl['Base Price']"
      ],
      "metadata": {
        "id": "oq_es_fiPv8W"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ipl['retention'].replace(['2 Cr','40 Lakh', '20 Lakh','1 Cr', '75 Lakh', '50 Lakh', '30 lakh','1.5 Cr'],'Form Auction', inplace = True)"
      ],
      "metadata": {
        "id": "yTGvTXq5P9nm"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Treating Base Price\n",
        "\n",
        "ipl['Base Price'].replace('Draft Pick',0, inplace = True)"
      ],
      "metadata": {
        "id": "LaOtCC4kRL1g"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ipl['base_price_unit'] = ipl['Base Price'].apply(lambda x: str(x).split(' ')[-1])\n",
        "ipl['base_price'] = ipl['Base Price'].apply(lambda x: str(x).split(' ')[0])"
      ],
      "metadata": {
        "id": "UL0VWr5GRrug"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ipl['Base Price'].replace('Retained',0, inplace = True)"
      ],
      "metadata": {
        "id": "Rs4kt9QMTKR_"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ipl.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "BuKWqVRZTgUQ",
        "outputId": "919cf756-1565-42da-e035-d4f0ca435886"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Player Base Price         TYPE  COST IN ₹ (CR.)  Cost IN $ (000)  \\\n",
              "0      Rashid Khan          0       BOWLER             15.0           1950.0   \n",
              "1    Hardik Pandya          0  ALL-ROUNDER             15.0           1950.0   \n",
              "2  Lockie Ferguson       2 Cr       BOWLER             10.0           1300.0   \n",
              "3    Rahul Tewatia    40 Lakh  ALL-ROUNDER              9.0           1170.0   \n",
              "4     Shubman Gill          0       BATTER              8.0           1040.0   \n",
              "\n",
              "  2021 Squad            Team 2022 Squad RCB player  COST IN $ (000) status  \\\n",
              "0        SRH  Gujarat Titans            NaN    NaN              NaN   sold   \n",
              "1         MI  Gujarat Titans            NaN    NaN              NaN   sold   \n",
              "2        KKR  Gujarat Titans            NaN    NaN              NaN   sold   \n",
              "3         RR  Gujarat Titans            NaN    NaN              NaN   sold   \n",
              "4        KKR  Gujarat Titans            NaN    NaN              NaN   sold   \n",
              "\n",
              "      retention base_price_unit base_price  \n",
              "0    Draft Pick               0          0  \n",
              "1    Draft Pick               0          0  \n",
              "2  Form Auction              Cr          2  \n",
              "3  Form Auction            Lakh         40  \n",
              "4    Draft Pick               0          0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ae3b596f-2713-4300-b25f-fb71f13a21aa\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Player</th>\n",
              "      <th>Base Price</th>\n",
              "      <th>TYPE</th>\n",
              "      <th>COST IN ₹ (CR.)</th>\n",
              "      <th>Cost IN $ (000)</th>\n",
              "      <th>2021 Squad</th>\n",
              "      <th>Team</th>\n",
              "      <th>2022 Squad RCB</th>\n",
              "      <th>player</th>\n",
              "      <th>COST IN $ (000)</th>\n",
              "      <th>status</th>\n",
              "      <th>retention</th>\n",
              "      <th>base_price_unit</th>\n",
              "      <th>base_price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Rashid Khan</td>\n",
              "      <td>0</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>15.0</td>\n",
              "      <td>1950.0</td>\n",
              "      <td>SRH</td>\n",
              "      <td>Gujarat Titans</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "      <td>Draft Pick</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Hardik Pandya</td>\n",
              "      <td>0</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>15.0</td>\n",
              "      <td>1950.0</td>\n",
              "      <td>MI</td>\n",
              "      <td>Gujarat Titans</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "      <td>Draft Pick</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Lockie Ferguson</td>\n",
              "      <td>2 Cr</td>\n",
              "      <td>BOWLER</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1300.0</td>\n",
              "      <td>KKR</td>\n",
              "      <td>Gujarat Titans</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "      <td>Form Auction</td>\n",
              "      <td>Cr</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Rahul Tewatia</td>\n",
              "      <td>40 Lakh</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1170.0</td>\n",
              "      <td>RR</td>\n",
              "      <td>Gujarat Titans</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "      <td>Form Auction</td>\n",
              "      <td>Lakh</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Shubman Gill</td>\n",
              "      <td>0</td>\n",
              "      <td>BATTER</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1040.0</td>\n",
              "      <td>KKR</td>\n",
              "      <td>Gujarat Titans</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sold</td>\n",
              "      <td>Draft Pick</td>\n",
              "      <td>0</td>\n",
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              "  }\n",
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              "  .colab-df-quickchart:hover {\n",
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              "    border: 2px solid var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      } catch (error) {\n",
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              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ipl",
              "summary": "{\n  \"name\": \"ipl\",\n  \"rows\": 633,\n  \"fields\": [\n    {\n      \"column\": \"Player\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 584,\n        \"samples\": [\n          \"Prayas Barman\",\n          \"Thisara Perera\",\n          \"Rasikh Dar\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Base Price\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"30 Lakh\",\n          \"2 Cr\",\n          \"75 Lakh\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TYPE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"ALL-ROUNDER\",\n          \"WICKETKEEPER\",\n          \"BOWLER\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"COST IN \\u20b9 (CR.)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.148242025337472,\n        \"min\": 0.0,\n        \"max\": 17.0,\n        \"num_unique_values\": 66,\n        \"samples\": [\n          1.6,\n          0.8,\n          15.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cost IN $ (000)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 409.2714632938713,\n        \"min\": 0.0,\n        \"max\": 2210.0,\n        \"num_unique_values\": 66,\n        \"samples\": [\n          208.0,\n          104.0,\n          1950.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2021 Squad\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"DC\",\n          \"MI\",\n          \"Not Participated\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Team\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"Lucknow Super Giants\",\n          \"Gujarat Titans\",\n          \"Sunrisers Hyderabad\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2022 Squad RCB\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 22,\n        \"samples\": [\n          \"Virat Kohli\",\n          \"Finn Allen\",\n          \"Anuj Rawat\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"player\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 23,\n        \"samples\": [\n          \"Shreyas Gopal\",\n          \"Abdul Samad\",\n          \"Kane Williamson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"COST IN $ (000)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"retention\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"base_price_unit\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"base_price\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Total players retained and bought\n",
        "ipl.groupby(['Team', 'retention'])['retention'].count()[:-1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 962
        },
        "id": "W_yyqZfoTvaT",
        "outputId": "4883c1ef-7cf5-40d8-e887-161ee2340a6a"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Team                          retention   \n",
              "Chennai Super Kings           30 Lakh          1\n",
              "                              Form Auction    20\n",
              "                              Retained         4\n",
              "Delhi Capitals                Form Auction    20\n",
              "                              Retained         4\n",
              "Gujarat Titans                30 Lakh          1\n",
              "                              Draft Pick       3\n",
              "                              Form Auction    18\n",
              "Kolkata Knight Riders         30 Lakh          1\n",
              "                              Form Auction    20\n",
              "                              Retained         4\n",
              "Lucknow Super Giants          Draft Pick       3\n",
              "                              Form Auction    19\n",
              "Mumbai Indians                30 Lakh          1\n",
              "                              Form Auction    20\n",
              "                              Retained         4\n",
              "Punjab Kings                  Form Auction    23\n",
              "                              Retained         2\n",
              "Rajasthan Royals              30 Lakh          1\n",
              "                              Form Auction    20\n",
              "                              Retained         3\n",
              "Royal Challengers Bangalore   30 Lakh          1\n",
              "                              Form Auction    18\n",
              "                              Retained         3\n",
              "Sunrisers Hyderabad           Form Auction    20\n",
              "                              Retained         3\n",
              "Unsold                        30 Lakh          3\n",
              "Name: retention, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>retention</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Team</th>\n",
              "      <th>retention</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">Chennai Super Kings</th>\n",
              "      <th>30 Lakh</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Form Auction</th>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Retained</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Delhi Capitals</th>\n",
              "      <th>Form Auction</th>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Retained</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">Gujarat Titans</th>\n",
              "      <th>30 Lakh</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Draft Pick</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Form Auction</th>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">Kolkata Knight Riders</th>\n",
              "      <th>30 Lakh</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Form Auction</th>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Retained</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Lucknow Super Giants</th>\n",
              "      <th>Draft Pick</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Form Auction</th>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">Mumbai Indians</th>\n",
              "      <th>30 Lakh</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Form Auction</th>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Retained</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Punjab Kings</th>\n",
              "      <th>Form Auction</th>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Retained</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">Rajasthan Royals</th>\n",
              "      <th>30 Lakh</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Form Auction</th>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Retained</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">Royal Challengers Bangalore</th>\n",
              "      <th>30 Lakh</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Form Auction</th>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Retained</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Sunrisers Hyderabad</th>\n",
              "      <th>Form Auction</th>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Retained</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Unsold</th>\n",
              "      <th>30 Lakh</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20,10))\n",
        "fig = sns.countplot(x='Team', hue='TYPE', data=ipl[ipl['Team'] != 'Unsold'])\n",
        "plt.title('Players in Each Team')\n",
        "plt.xlabel('Team Names')\n",
        "plt.ylabel('Number of Players')\n",
        "plt.xticks(rotation=50)\n",
        "plt.plot() # or plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 966
        },
        "id": "XRkPuhI1UzdJ",
        "outputId": "3ca434c0-8555-4a04-ca9c-0965dc7b25da"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Highest amount spent on a single player by each team\n",
        "\n",
        "ipl[ipl['retention']=='From Auction'].groupby(['Team'])['COST IN ₹ (CR.)'].max()[:1].sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        },
        "id": "rsPeiAAdVtlj",
        "outputId": "e330bbf5-9fd9-4cc7-e3ba-e0eb3acf16b3"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Series([], Name: COST IN ₹ (CR.), dtype: float64)"
            ],
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>COST IN ₹ (CR.)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Team</th>\n",
              "      <th></th>\n",
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              "</div><br><label><b>dtype:</b> float64</label>"
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          "metadata": {},
          "execution_count": 31
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    {
      "cell_type": "code",
      "source": [
        "# Player retained at maximum price.\n",
        "\n",
        "ipl[ipl['retention']=='Retained'].sort_values(by = 'COST IN ₹ (CR.)', ascending=False).head(1)"
      ],
      "metadata": {
        "id": "in85NQnAW-Z4",
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          "height": 115
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      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Player Base Price         TYPE  COST IN ₹ (CR.)  Cost IN $ (000)  \\\n",
              "22  Ravindra Jadeja          0  ALL-ROUNDER             16.0           2080.0   \n",
              "\n",
              "   2021 Squad                 Team 2022 Squad RCB player  COST IN $ (000)  \\\n",
              "22        CSK  Chennai Super Kings            NaN    NaN              NaN   \n",
              "\n",
              "   status retention base_price_unit base_price  \n",
              "22   sold  Retained        Retained   Retained  "
            ],
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              "      <th>TYPE</th>\n",
              "      <th>COST IN ₹ (CR.)</th>\n",
              "      <th>Cost IN $ (000)</th>\n",
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              "      <th>player</th>\n",
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              "      <th>base_price</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Ravindra Jadeja</td>\n",
              "      <td>0</td>\n",
              "      <td>ALL-ROUNDER</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2080.0</td>\n",
              "      <td>CSK</td>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>sold</td>\n",
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              "      <td>Retained</td>\n",
              "      <td>Retained</td>\n",
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              "      background-color: #434B5C;\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\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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              "\n",
              "\n",
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              "  </div>\n"
            ],
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          },
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      ]
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    {
      "cell_type": "code",
      "source": [
        "# Top 5 Bowlers\n",
        "\n",
        "ipl[(ipl['retention']=='From Auction') & (ipl['TYPE']=='Bowler')].sort_values(by = 'COST IN ₹ (CR.)', ascending=False).head(5)"
      ],
      "metadata": {
        "colab": {
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          "height": 139
        },
        "id": "rmvIF0PeaAef",
        "outputId": "ddd7f173-dbad-4d74-ec91-a668c6598eda"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Player, Base Price, TYPE, COST IN ₹ (CR.), Cost IN $ (000), 2021 Squad, Team, 2022 Squad RCB, player, COST IN $ (000), status, retention, base_price_unit, base_price]\n",
              "Index: []"
            ],
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              "  </style>\n",
              "\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",
              "          + ' 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",
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              "\n",
              "\n",
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    {
      "cell_type": "code",
      "source": [
        "# Top 5 All Rounder\n",
        "\n",
        "ipl[(ipl['retention']=='From Auction') & (ipl['TYPE']=='ALL-ROUNDER')].sort_values(by = 'COST IN ₹ (CR.)', ascending=False).head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "VAM-R8dWbd7o",
        "outputId": "cd221d1d-b410-4689-ff0b-462b74890f11"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Player, Base Price, TYPE, COST IN ₹ (CR.), Cost IN $ (000), 2021 Squad, Team, 2022 Squad RCB, player, COST IN $ (000), status, retention, base_price_unit, base_price]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-30f998aa-737a-4052-a33e-3f3fcbe76f2d\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>Player</th>\n",
              "      <th>Base Price</th>\n",
              "      <th>TYPE</th>\n",
              "      <th>COST IN ₹ (CR.)</th>\n",
              "      <th>Cost IN $ (000)</th>\n",
              "      <th>2021 Squad</th>\n",
              "      <th>Team</th>\n",
              "      <th>2022 Squad RCB</th>\n",
              "      <th>player</th>\n",
              "      <th>COST IN $ (000)</th>\n",
              "      <th>status</th>\n",
              "      <th>retention</th>\n",
              "      <th>base_price_unit</th>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-30f998aa-737a-4052-a33e-3f3fcbe76f2d 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-30f998aa-737a-4052-a33e-3f3fcbe76f2d');\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",
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              "\n",
              "\n",
              "    </div>\n",
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              "repr_error": "Out of range float values are not JSON compliant: nan"
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    },
    {
      "cell_type": "code",
      "source": [
        "ipl = ipl.rename(columns={'2021 Squad':'prev_team'})"
      ],
      "metadata": {
        "id": "oDSUUjB-cMr5"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unsold_players = ipl[(ipl.prev_team != 'Not Participated') & (ipl.Team == 'Unsold')][['Player','prev_team']]"
      ],
      "metadata": {
        "id": "c8UevNA_cpI0"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(unsold_players)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pac5r1dWd90R",
        "outputId": "d0407126-0706-4829-e4c6-c33568dacacf"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                      Player prev_team\n",
            "237             Suresh Raina       CSK\n",
            "238              Steve Smith        DC\n",
            "239          Shakib Al Hasan       KKR\n",
            "240              Amit Mishra        DC\n",
            "241              Adil Rashid      PBKS\n",
            "242              Imran Tahir       CSK\n",
            "243            Mujeeb Zadran       SRH\n",
            "244               Adam Zampa       RCB\n",
            "245            Rajat Patidar       RCB\n",
            "246     Mohammed Azharuddeen       RCB\n",
            "249             M. Siddharth        DC\n",
            "252              Dawid Malan      PBKS\n",
            "253              Eoin Morgan       KKR\n",
            "254        Cheteshwar Pujara       CSK\n",
            "255           Saurabh Tiwary        MI\n",
            "257            Ishant Sharma        DC\n",
            "259            Piyush Chawla        MI\n",
            "260           Tabraiz Shamsi        RR\n",
            "262              Sachin Baby       RCB\n",
            "267              Virat Singh       SRH\n",
            "269              Akash Singh        RR\n",
            "273               Chris Lynn        MI\n",
            "276            George Garton       RCB\n",
            "288               Andrew Tye        RR\n",
            "289          Sandeep Warrier       KKR\n",
            "311        Shreevats Goswami       SRH\n",
            "313              Aditya Tare        MI\n",
            "317  Lukman Hussain Meriwala        DC\n",
            "325             Jalaj Saxena      PBKS\n",
            "337              Ben Cutting       KKR\n",
            "339         Moises Henriques      PBKS\n",
            "342         Scott Kuggeleijn       RCB\n",
            "343               Pawan Negi       KKR\n",
            "346          Dhawal Kulkarni        MI\n",
            "348          Kane Richardson       RCB\n",
            "404             Kedar Jadhav       SRH\n",
            "429           Yudhvir Charak        MI\n",
            "447       Kulwant Khejroliya        DC\n",
            "535           Anirudha Joshi        DC\n",
            "623          Agnivesh Ayachi      PBKS\n"
          ]
        }
      ]
    }
  ]
}