Python_Pandas2).ipynb

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    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/MohammedNayaz/Machine-learning-Internship-2/blob/master/Python_Pandas2).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WQf4rV_qK21T",
        "colab_type": "text"
      },
      "source": [
        "#Pandas Package"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KkYniP8dK6_Q",
        "colab_type": "text"
      },
      "source": [
        "##Pandas.Series"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9aYL-dNbK9Kr",
        "colab_type": "code",
        "colab": {
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          "height": 87
        },
        "outputId": "c59f7f9f-5b30-449c-e9a2-2270d3eb607a"
      },
      "source": [
        "#Create empty series\n",
        "import pandas as pd\n",
        "s = pd.Series()\n",
        "print (s)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Series([], dtype: float64)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ingRjE58LEIG",
        "colab_type": "code",
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          "height": 101
        },
        "outputId": "ae6a8fc0-c0fb-4cd4-8188-ce1390710220"
      },
      "source": [
        "#Create a Series from ndarray\n",
        "import pandas as pd\n",
        "import numpy as np \n",
        "data = np.array(['a','b','c','d']) \n",
        "s = pd.Series(data) \n",
        "print (s)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0    a\n",
            "1    b\n",
            "2    c\n",
            "3    d\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dABixgQTLEKq",
        "colab_type": "code",
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          "height": 101
        },
        "outputId": "c9cdd706-5d39-4592-8dcf-e8d0bb1485ac"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "data = np.array(['a','b','c','d'])\n",
        "s = pd.Series(data,index=[100,101,102,103]) \n",
        "print (s)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100    a\n",
            "101    b\n",
            "102    c\n",
            "103    d\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J7N8s2Z2LEM1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "5e536741-85b4-4ba3-cb29-7b0f868e48db"
      },
      "source": [
        "#Create a Series from dict\n",
        "import numpy as np\n",
        "data = {'a' : 0., 'b' : 1., 'c' : 2.}\n",
        "s = pd.Series(data)\n",
        "print (s)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "a    0.0\n",
            "b    1.0\n",
            "c    2.0\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
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    {
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        "id": "KZ0fJEERLZsv",
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          "height": 101
        },
        "outputId": "e4579f36-8e0e-4e56-d142-9e4d029e3b55"
      },
      "source": [
        "#example 2\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "data = {'a' : 0., 'b' : 1., 'c' : 2.}\n",
        "s = pd.Series(data,index=['b','c','d','a'])\n",
        "print (s)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "b    1.0\n",
            "c    2.0\n",
            "d    NaN\n",
            "a    0.0\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vlNq4BKeLZu9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "89768448-fcb9-439a-9b10-9608da22b2cb"
      },
      "source": [
        "#Create a Series from Scalar\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "s = pd.Series(5, index=[0, 1, 2, 3])\n",
        "print (s)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0    5\n",
            "1    5\n",
            "2    5\n",
            "3    5\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0is-S3aKLZw6",
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          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "ee62ae6a-f167-43d0-fd87-11a0082de59c"
      },
      "source": [
        "#Create a Series from Scalar\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "s = pd.Series(5, index=['a','b','c','d','e'])\n",
        "print (s)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "a    5\n",
            "b    5\n",
            "c    5\n",
            "d    5\n",
            "e    5\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
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    {
      "cell_type": "code",
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          "height": 34
        },
        "outputId": "dee761dc-140b-498e-cf80-b5fe0d4e58fc"
      },
      "source": [
        "#Accessing Data from Series with Position\n",
        "import pandas as pd\n",
        "s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e']) \n",
        "#retrieve the first element\n",
        "print (s[0])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SfF9qrhkL6dg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "ae6778e1-e015-4909-e507-aed7427a61d3"
      },
      "source": [
        "s[:]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    1\n",
              "b    2\n",
              "c    3\n",
              "d    4\n",
              "e    5\n",
              "dtype: int64"
            ]
          },
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            "tags": []
          },
          "execution_count": 10
        }
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        "outputId": "7c80363a-e91c-4f4a-d898-5eb63ac3a2df"
      },
      "source": [
        "s[:2]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    1\n",
              "b    2\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eOckO_hUL6iB",
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          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "d1829298-e80c-4e89-e340-46cedafe9ae7"
      },
      "source": [
        "#example 2\n",
        "import pandas as pd\n",
        "s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e'])\n",
        "#retrieve the first three element\n",
        "print (s[:3])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "a    1\n",
            "b    2\n",
            "c    3\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zd9MzDTlL6kh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "6f48eae1-4e67-4424-b934-a653c6927674"
      },
      "source": [
        "#Example 3\n",
        "import pandas as pd \n",
        "s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e']) \n",
        "#retrieve the last three element\n",
        "print (s[-3:])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "c    3\n",
            "d    4\n",
            "e    5\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8401jKlLERi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "b71047bb-a428-431b-ccea-98d3e5c8ccb2"
      },
      "source": [
        "#Retrieve Data Using Label (Index)\n",
        "import pandas as pd\n",
        "s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e']) #retrieve multiple elements \n",
        "print (s[['a','c','d']])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "a    1\n",
            "c    3\n",
            "d    4\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PSAZTwNrMJEX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "397a337a-e06f-48a8-e852-02291bb4da6b"
      },
      "source": [
        "print([['a','b','d']])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[['a', 'b', 'd']]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r34PYnWRMPhc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "ab37afaf-aa07-4487-b59f-e269e46c4efb"
      },
      "source": [
        "print(s[['a','b','d']])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "a    1\n",
            "b    2\n",
            "d    4\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C4AOcyyGMS89",
        "colab_type": "text"
      },
      "source": [
        "##Pandas.Dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PpOKFp9hMXPS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "4f93ab1c-0786-4e64-880a-d3dbaaf8afed"
      },
      "source": [
        "#Create an Empty DataFrame\n",
        "import pandas as pd\n",
        "df = pd.DataFrame()\n",
        "print (df)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Empty DataFrame\n",
            "Columns: []\n",
            "Index: []\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pjZzppvLMXRs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "da2000fa-a8c2-423e-f4ab-8d8db8b569b9"
      },
      "source": [
        "#Create a DataFrame from Lists\n",
        "import pandas as pd\n",
        "data = [1,2,3,4,5] \n",
        "df = pd.DataFrame(data) \n",
        "print (df)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   0\n",
            "0  1\n",
            "1  2\n",
            "2  3\n",
            "3  4\n",
            "4  5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7oxP8MvFMXUR",
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          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "ab767f23-6020-4fe6-f9af-6f9215f864f7"
      },
      "source": [
        "#Example 2\n",
        "import pandas as pd\n",
        "data = [['Alex',10],['Bob',12],['Clarke',13]]\n",
        "df=pd.DataFrame(data,columns=['Name','Age'])\n",
        "print (df)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     Name  Age\n",
            "0    Alex   10\n",
            "1     Bob   12\n",
            "2  Clarke   13\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9sxSkDFoMXWp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "72349e98-7ebc-4f3c-e24b-250f8054592b"
      },
      "source": [
        "#Example 3\n",
        "import pandas as pd\n",
        "data = [['Alex',10],['Bob',12.5],['Clarke',13]]\n",
        "df=pd.DataFrame(data,columns=['Name','Age'],dtype=float)\n",
        "print (df)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     Name   Age\n",
            "0    Alex  10.0\n",
            "1     Bob  12.5\n",
            "2  Clarke  13.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2GpF7I0MXY1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "ea7c4b69-70d1-4435-ac0b-9efd0296a301"
      },
      "source": [
        "#Create a DataFrame from Dict of ndarrays / Lists\n",
        "import pandas as pd\n",
        "data={'Name':['Tom','Jack','Steve','Ricky'],'Age':[28,34,29,42]} \n",
        "df = pd.DataFrame(data) \n",
        "print (df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    Name  Age\n",
            "0    Tom   28\n",
            "1   Jack   34\n",
            "2  Steve   29\n",
            "3  Ricky   42\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r9_cuRV5MXa8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "da0cf539-7858-4899-d9c2-722665732a18"
      },
      "source": [
        "#Ex-2\n",
        "import pandas as pd\n",
        "data = {'Name':['Tom', 'Jack', 'Steve', 'Ricky'],'Age':[28,34,29,42]}\n",
        "df=pd.DataFrame(data,index=['rank1','rank2','rank3','rank4']) \n",
        "print (df)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "        Name  Age\n",
            "rank1    Tom   28\n",
            "rank2   Jack   34\n",
            "rank3  Steve   29\n",
            "rank4  Ricky   42\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RJLPr8roMXdS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "e6f14000-cc8e-4c5a-c2db-d15ccada455e"
      },
      "source": [
        "#Create a DataFrame from Dict of Series\n",
        "import pandas as pd\n",
        "d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']), 'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])} \n",
        "df = pd.DataFrame(d) \n",
        "print (df)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   one  two\n",
            "a  1.0    1\n",
            "b  2.0    2\n",
            "c  3.0    3\n",
            "d  NaN    4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OCWxQMJgUO1k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "ee24cb71-2ee2-4257-fc2f-5002b9cbbf84"
      },
      "source": [
        "#Create a DataFrame from Dict of Series\n",
        "import pandas as pd\n",
        "d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']), 'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])} \n",
        "df = pd.DataFrame(d) \n",
        "print (df)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   one  two\n",
            "a  1.0    1\n",
            "b  2.0    2\n",
            "c  3.0    3\n",
            "d  NaN    4\n"
          ],
          "name": "stdout"
        }
      ]
    },
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        },
        "outputId": "cba34fbb-2640-4567-82f8-bafa55b42c3b"
      },
      "source": [
        "#Create a DataFrame from Dict of Series\n",
        "import pandas as pd\n",
        "x = {'one' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd']), 'two' : pd.Series([5, 6, 7, 8, 9, 10], index=['a', 'b', 'c', 'd'])} \n",
        "df = pd.DataFrame(x) \n",
        "print (df)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   one  two\n",
            "a    1    1\n",
            "b    2    2\n",
            "c    3    3\n",
            "d    4    4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NziZcKj8cpTo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "b8c4fdb4-b8d5-44d2-b7e5-9eabbaca7447"
      },
      "source": [
        "#Create a DataFrame from Dict of Series\n",
        "import pandas as pd\n",
        "d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']), 'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd']), 'three' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])} \n",
        "df = pd.DataFrame(d) \n",
        "print (df)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   one  two  three\n",
            "a  1.0    1      1\n",
            "b  2.0    2      2\n",
            "c  3.0    3      3\n",
            "d  NaN    4      4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LTAXDA1GTaC4",
        "colab_type": "text"
      },
      "source": [
        "##Pandas Series.to_numpy()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "33SBcH5vTd4o",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "outputId": "b01920c4-433b-42d8-ca89-0692c92b0c41"
      },
      "source": [
        "import pandas as pd\n",
        "# reading the csv   \n",
        "data = pd.read_csv(\"teams.csv\")\n",
        "data.dropna(inplace = True) \n",
        "data"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>team1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Pune Warriors</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Rajasthan Royals</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Kochi Tuskers Kerala</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Gujarat Lions</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Chennai Super Kings</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Rising Pune Supergiants</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Delhi Daredevils</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Deccan Chargers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Delhi Capitals</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Mumbai Indians</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Rising Pune Supergiant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Kings XI Punjab</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          team1\n",
              "0                 Pune Warriors\n",
              "1         Kolkata Knight Riders\n",
              "2              Rajasthan Royals\n",
              "3          Kochi Tuskers Kerala\n",
              "4                 Gujarat Lions\n",
              "5           Chennai Super Kings\n",
              "6       Rising Pune Supergiants\n",
              "7              Delhi Daredevils\n",
              "8               Deccan Chargers\n",
              "9                Delhi Capitals\n",
              "10               Mumbai Indians\n",
              "11          Sunrisers Hyderabad\n",
              "12       Rising Pune Supergiant\n",
              "13  Royal Challengers Bangalore\n",
              "14              Kings XI Punjab"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9wtfgZ3fUgB6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 825
        },
        "outputId": "6a294007-bfb9-48c7-8180-0a7f12667ca2"
      },
      "source": [
        "import pandas as pd\n",
        "# reading the csv   \n",
        "data = pd.read_csv(\"matches.csv\")\n",
        "data.dropna(inplace = True) \n",
        "data"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>Season</th>\n",
              "      <th>city</th>\n",
              "      <th>date</th>\n",
              "      <th>team1</th>\n",
              "      <th>team2</th>\n",
              "      <th>toss_winner</th>\n",
              "      <th>toss_decision</th>\n",
              "      <th>result</th>\n",
              "      <th>dl_applied</th>\n",
              "      <th>winner</th>\n",
              "      <th>win_by_runs</th>\n",
              "      <th>win_by_wickets</th>\n",
              "      <th>player_of_match</th>\n",
              "      <th>venue</th>\n",
              "      <th>umpire1</th>\n",
              "      <th>umpire2</th>\n",
              "      <th>umpire3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>636</th>\n",
              "      <td>7894</td>\n",
              "      <td>IPL-2018</td>\n",
              "      <td>Mumbai</td>\n",
              "      <td>07-04-2018</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>field</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>DJ Bravo</td>\n",
              "      <td>Wankhede Stadium</td>\n",
              "      <td>Chris Gaffaney</td>\n",
              "      <td>A Nanda Kishore</td>\n",
              "      <td>Anil Chaudhary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>637</th>\n",
              "      <td>7895</td>\n",
              "      <td>IPL-2018</td>\n",
              "      <td>Mohali</td>\n",
              "      <td>08-04-2018</td>\n",
              "      <td>Delhi Daredevils</td>\n",
              "      <td>Kings XI Punjab</td>\n",
              "      <td>Kings XI Punjab</td>\n",
              "      <td>field</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Kings XI Punjab</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>KL Rahul</td>\n",
              "      <td>Punjab Cricket Association IS Bindra Stadium, ...</td>\n",
              "      <td>Rod Tucker</td>\n",
              "      <td>K Ananthapadmanabhan</td>\n",
              "      <td>Nitin Menon</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>638</th>\n",
              "      <td>7896</td>\n",
              "      <td>IPL-2018</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>08-04-2018</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>field</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>SP Narine</td>\n",
              "      <td>Eden Gardens</td>\n",
              "      <td>C Shamshuddin</td>\n",
              "      <td>A.D Deshmukh</td>\n",
              "      <td>S Ravi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>639</th>\n",
              "      <td>7897</td>\n",
              "      <td>IPL-2018</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>09-04-2018</td>\n",
              "      <td>Rajasthan Royals</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>field</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>S Dhawan</td>\n",
              "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
              "      <td>Nigel Llong</td>\n",
              "      <td>Vineet Kulkarni</td>\n",
              "      <td>O Nandan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>640</th>\n",
              "      <td>7898</td>\n",
              "      <td>IPL-2018</td>\n",
              "      <td>Chennai</td>\n",
              "      <td>10-04-2018</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>field</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>SW Billings</td>\n",
              "      <td>MA Chidambaram Stadium, Chepauk</td>\n",
              "      <td>Anil Chaudhary</td>\n",
              "      <td>Chris Gaffaney</td>\n",
              "      <td>A Nanda Kishore</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>750</th>\n",
              "      <td>11346</td>\n",
              "      <td>IPL-2019</td>\n",
              "      <td>Mohali</td>\n",
              "      <td>05-05-2019</td>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>Kings XI Punjab</td>\n",
              "      <td>Kings XI Punjab</td>\n",
              "      <td>field</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Kings XI Punjab</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>KL Rahul</td>\n",
              "      <td>IS Bindra Stadium</td>\n",
              "      <td>KN Ananthapadmanabhan</td>\n",
              "      <td>C Shamshuddin</td>\n",
              "      <td>Bruce Oxenford</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>751</th>\n",
              "      <td>11347</td>\n",
              "      <td>IPL-2019</td>\n",
              "      <td>Mumbai</td>\n",
              "      <td>05-05-2019</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>field</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>HH Pandya</td>\n",
              "      <td>Wankhede Stadium</td>\n",
              "      <td>Nanda Kishore</td>\n",
              "      <td>O Nandan</td>\n",
              "      <td>S Ravi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>752</th>\n",
              "      <td>11412</td>\n",
              "      <td>IPL-2019</td>\n",
              "      <td>Chennai</td>\n",
              "      <td>07-05-2019</td>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>bat</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>AS Yadav</td>\n",
              "      <td>M. A. Chidambaram Stadium</td>\n",
              "      <td>Nigel Llong</td>\n",
              "      <td>Nitin Menon</td>\n",
              "      <td>Ian Gould</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>754</th>\n",
              "      <td>11414</td>\n",
              "      <td>IPL-2019</td>\n",
              "      <td>Visakhapatnam</td>\n",
              "      <td>10-05-2019</td>\n",
              "      <td>Delhi Capitals</td>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>field</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>F du Plessis</td>\n",
              "      <td>ACA-VDCA Stadium</td>\n",
              "      <td>Sundaram Ravi</td>\n",
              "      <td>Bruce Oxenford</td>\n",
              "      <td>Chettithody Shamshuddin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>755</th>\n",
              "      <td>11415</td>\n",
              "      <td>IPL-2019</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>12-05-2019</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>bat</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>JJ Bumrah</td>\n",
              "      <td>Rajiv Gandhi Intl. Cricket Stadium</td>\n",
              "      <td>Nitin Menon</td>\n",
              "      <td>Ian Gould</td>\n",
              "      <td>Nigel Llong</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>118 rows × 18 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        id    Season  ...               umpire2                  umpire3\n",
              "636   7894  IPL-2018  ...       A Nanda Kishore           Anil Chaudhary\n",
              "637   7895  IPL-2018  ...  K Ananthapadmanabhan              Nitin Menon\n",
              "638   7896  IPL-2018  ...          A.D Deshmukh                   S Ravi\n",
              "639   7897  IPL-2018  ...       Vineet Kulkarni                 O Nandan\n",
              "640   7898  IPL-2018  ...        Chris Gaffaney          A Nanda Kishore\n",
              "..     ...       ...  ...                   ...                      ...\n",
              "750  11346  IPL-2019  ...         C Shamshuddin           Bruce Oxenford\n",
              "751  11347  IPL-2019  ...              O Nandan                   S Ravi\n",
              "752  11412  IPL-2019  ...           Nitin Menon                Ian Gould\n",
              "754  11414  IPL-2019  ...        Bruce Oxenford  Chettithody Shamshuddin\n",
              "755  11415  IPL-2019  ...             Ian Gould              Nigel Llong\n",
              "\n",
              "[118 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wkli9CU5TiZK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "a6cb4a8e-56bb-40af-a9de-196817c005e6"
      },
      "source": [
        "# creating series form weight column \n",
        "df = pd.Series(data['toss_winner'].head()) \n",
        "df"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "636      Chennai Super Kings\n",
              "637          Kings XI Punjab\n",
              "638    Kolkata Knight Riders\n",
              "639      Sunrisers Hyderabad\n",
              "640      Chennai Super Kings\n",
              "Name: toss_winner, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g7g_VHslTivA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        },
        "outputId": "154ca825-17b1-4112-e3e7-1e2ade3793b6"
      },
      "source": [
        "# using to_numpy() function \n",
        "print(df.to_numpy())\n",
        "print(df)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Chennai Super Kings' 'Kings XI Punjab' 'Kolkata Knight Riders'\n",
            " 'Sunrisers Hyderabad' 'Chennai Super Kings']\n",
            "636      Chennai Super Kings\n",
            "637          Kings XI Punjab\n",
            "638    Kolkata Knight Riders\n",
            "639      Sunrisers Hyderabad\n",
            "640      Chennai Super Kings\n",
            "Name: toss_winner, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IOpRmJalVVKo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "5ca1f1a6-4893-4d2f-f8fe-b4564db476ef"
      },
      "source": [
        "# creating series form weight column \n",
        "df = pd.Series(data['win_by_wickets'].head()) \n",
        "df"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "636    1\n",
              "637    6\n",
              "638    4\n",
              "639    9\n",
              "640    5\n",
              "Name: win_by_wickets, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gS1BjnVqVf8K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "b6e394c8-cb01-4b8c-a1d4-20d451d71bbc"
      },
      "source": [
        "# using to_numpy() function \n",
        "print(df.to_numpy())\n",
        "print(df)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 6 4 9 5]\n",
            "636    1\n",
            "637    6\n",
            "638    4\n",
            "639    9\n",
            "640    5\n",
            "Name: win_by_wickets, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XqO7U_02opBr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "8db5c446-7b93-4f81-c063-83ce6f320e52"
      },
      "source": [
        "import pandas as pd\n",
        "ser = pd.Series(pd.date_range('2000', periods=2, tz=\"CET\"))\n",
        "ser\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0   2000-01-01 00:00:00+01:00\n",
              "1   2000-01-02 00:00:00+01:00\n",
              "dtype: datetime64[ns, CET]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ZI4Bdv2Tixe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "243acec7-c1f6-4b16-eaf1-fbd5a3f04b2a"
      },
      "source": [
        "ser.to_numpy(dtype=object)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([Timestamp('2000-01-01 00:00:00+0100', tz='CET', freq='D'),\n",
              "       Timestamp('2000-01-02 00:00:00+0100', tz='CET', freq='D')],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "clVLj-Qkor-3",
        "colab_type": "text"
      },
      "source": [
        "##Pandas Dataframe.to_numpy()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q8XpqdtoTiz9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "05cc5606-411c-404b-9517-07df6cff3abc"
      },
      "source": [
        "import pandas as pd\n",
        "# reading the csv   \n",
        "data = pd.read_csv(\"matches.csv\")\n",
        "data.dropna(inplace = True) \n",
        "# creating DataFrame form weight column \n",
        "df = pd.DataFrame(data['player_of_match'].head()) \n",
        "df\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>player_of_match</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>636</th>\n",
              "      <td>DJ Bravo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>637</th>\n",
              "      <td>KL Rahul</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>638</th>\n",
              "      <td>SP Narine</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>639</th>\n",
              "      <td>S Dhawan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>640</th>\n",
              "      <td>SW Billings</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    player_of_match\n",
              "636        DJ Bravo\n",
              "637        KL Rahul\n",
              "638       SP Narine\n",
              "639        S Dhawan\n",
              "640     SW Billings"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k7YB3KUNWS1V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "outputId": "931db7c4-2e88-46bd-9b9d-3222e0acd5b5"
      },
      "source": [
        "import pandas as pd\n",
        "# reading the csv   \n",
        "data = pd.read_csv(\"f500.csv\")\n",
        "data.dropna(inplace = True) \n",
        "data"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>company</th>\n",
              "      <th>rank</th>\n",
              "      <th>revenues</th>\n",
              "      <th>revenue_change</th>\n",
              "      <th>profits</th>\n",
              "      <th>assets</th>\n",
              "      <th>profit_change</th>\n",
              "      <th>ceo</th>\n",
              "      <th>industry</th>\n",
              "      <th>sector</th>\n",
              "      <th>previous_rank</th>\n",
              "      <th>country</th>\n",
              "      <th>hq_location</th>\n",
              "      <th>website</th>\n",
              "      <th>years_on_global_500_list</th>\n",
              "      <th>employees</th>\n",
              "      <th>total_stockholder_equity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Walmart</td>\n",
              "      <td>1</td>\n",
              "      <td>485873</td>\n",
              "      <td>0.8</td>\n",
              "      <td>13643.0</td>\n",
              "      <td>198825</td>\n",
              "      <td>-7.2</td>\n",
              "      <td>C. Douglas McMillon</td>\n",
              "      <td>General Merchandisers</td>\n",
              "      <td>Retailing</td>\n",
              "      <td>1</td>\n",
              "      <td>USA</td>\n",
              "      <td>Bentonville, AR</td>\n",
              "      <td>http://www.walmart.com</td>\n",
              "      <td>23</td>\n",
              "      <td>2300000</td>\n",
              "      <td>77798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>State Grid</td>\n",
              "      <td>2</td>\n",
              "      <td>315199</td>\n",
              "      <td>-4.4</td>\n",
              "      <td>9571.3</td>\n",
              "      <td>489838</td>\n",
              "      <td>-6.2</td>\n",
              "      <td>Kou Wei</td>\n",
              "      <td>Utilities</td>\n",
              "      <td>Energy</td>\n",
              "      <td>2</td>\n",
              "      <td>China</td>\n",
              "      <td>Beijing, China</td>\n",
              "      <td>http://www.sgcc.com.cn</td>\n",
              "      <td>17</td>\n",
              "      <td>926067</td>\n",
              "      <td>209456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sinopec Group</td>\n",
              "      <td>3</td>\n",
              "      <td>267518</td>\n",
              "      <td>-9.1</td>\n",
              "      <td>1257.9</td>\n",
              "      <td>310726</td>\n",
              "      <td>-65.0</td>\n",
              "      <td>Wang Yupu</td>\n",
              "      <td>Petroleum Refining</td>\n",
              "      <td>Energy</td>\n",
              "      <td>4</td>\n",
              "      <td>China</td>\n",
              "      <td>Beijing, China</td>\n",
              "      <td>http://www.sinopec.com</td>\n",
              "      <td>19</td>\n",
              "      <td>713288</td>\n",
              "      <td>106523</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>China National Petroleum</td>\n",
              "      <td>4</td>\n",
              "      <td>262573</td>\n",
              "      <td>-12.3</td>\n",
              "      <td>1867.5</td>\n",
              "      <td>585619</td>\n",
              "      <td>-73.7</td>\n",
              "      <td>Zhang Jianhua</td>\n",
              "      <td>Petroleum Refining</td>\n",
              "      <td>Energy</td>\n",
              "      <td>3</td>\n",
              "      <td>China</td>\n",
              "      <td>Beijing, China</td>\n",
              "      <td>http://www.cnpc.com.cn</td>\n",
              "      <td>17</td>\n",
              "      <td>1512048</td>\n",
              "      <td>301893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Toyota Motor</td>\n",
              "      <td>5</td>\n",
              "      <td>254694</td>\n",
              "      <td>7.7</td>\n",
              "      <td>16899.3</td>\n",
              "      <td>437575</td>\n",
              "      <td>-12.3</td>\n",
              "      <td>Akio Toyoda</td>\n",
              "      <td>Motor Vehicles and Parts</td>\n",
              "      <td>Motor Vehicles &amp; Parts</td>\n",
              "      <td>8</td>\n",
              "      <td>Japan</td>\n",
              "      <td>Toyota, Japan</td>\n",
              "      <td>http://www.toyota-global.com</td>\n",
              "      <td>23</td>\n",
              "      <td>364445</td>\n",
              "      <td>157210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>Teva Pharmaceutical Industries</td>\n",
              "      <td>496</td>\n",
              "      <td>21903</td>\n",
              "      <td>11.5</td>\n",
              "      <td>329.0</td>\n",
              "      <td>92890</td>\n",
              "      <td>-79.3</td>\n",
              "      <td>Yitzhak Peterburg</td>\n",
              "      <td>Pharmaceuticals</td>\n",
              "      <td>Health Care</td>\n",
              "      <td>0</td>\n",
              "      <td>Israel</td>\n",
              "      <td>Petach Tikva, Israel</td>\n",
              "      <td>http://www.tevapharm.com</td>\n",
              "      <td>1</td>\n",
              "      <td>56960</td>\n",
              "      <td>33337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>New China Life Insurance</td>\n",
              "      <td>497</td>\n",
              "      <td>21796</td>\n",
              "      <td>-13.3</td>\n",
              "      <td>743.9</td>\n",
              "      <td>100609</td>\n",
              "      <td>-45.6</td>\n",
              "      <td>Wan Feng</td>\n",
              "      <td>Insurance: Life, Health (stock)</td>\n",
              "      <td>Financials</td>\n",
              "      <td>427</td>\n",
              "      <td>China</td>\n",
              "      <td>Beijing, China</td>\n",
              "      <td>http://www.newchinalife.com</td>\n",
              "      <td>2</td>\n",
              "      <td>54378</td>\n",
              "      <td>8507</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>Wm. Morrison Supermarkets</td>\n",
              "      <td>498</td>\n",
              "      <td>21741</td>\n",
              "      <td>-11.3</td>\n",
              "      <td>406.4</td>\n",
              "      <td>11630</td>\n",
              "      <td>20.4</td>\n",
              "      <td>David T. Potts</td>\n",
              "      <td>Food and Drug Stores</td>\n",
              "      <td>Food &amp; Drug Stores</td>\n",
              "      <td>437</td>\n",
              "      <td>Britain</td>\n",
              "      <td>Bradford, Britain</td>\n",
              "      <td>http://www.morrisons.com</td>\n",
              "      <td>13</td>\n",
              "      <td>77210</td>\n",
              "      <td>5111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>TUI</td>\n",
              "      <td>499</td>\n",
              "      <td>21655</td>\n",
              "      <td>-5.5</td>\n",
              "      <td>1151.7</td>\n",
              "      <td>16247</td>\n",
              "      <td>195.5</td>\n",
              "      <td>Friedrich Joussen</td>\n",
              "      <td>Travel Services</td>\n",
              "      <td>Business Services</td>\n",
              "      <td>467</td>\n",
              "      <td>Germany</td>\n",
              "      <td>Hanover, Germany</td>\n",
              "      <td>http://www.tuigroup.com</td>\n",
              "      <td>23</td>\n",
              "      <td>66779</td>\n",
              "      <td>3006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>AutoNation</td>\n",
              "      <td>500</td>\n",
              "      <td>21609</td>\n",
              "      <td>3.6</td>\n",
              "      <td>430.5</td>\n",
              "      <td>10060</td>\n",
              "      <td>-2.7</td>\n",
              "      <td>Michael J. Jackson</td>\n",
              "      <td>Specialty Retailers</td>\n",
              "      <td>Retailing</td>\n",
              "      <td>0</td>\n",
              "      <td>USA</td>\n",
              "      <td>Fort Lauderdale, FL</td>\n",
              "      <td>http://www.autonation.com</td>\n",
              "      <td>12</td>\n",
              "      <td>26000</td>\n",
              "      <td>2310</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>436 rows × 17 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                            company  rank  ...  employees  total_stockholder_equity\n",
              "0                           Walmart     1  ...    2300000                     77798\n",
              "1                        State Grid     2  ...     926067                    209456\n",
              "2                     Sinopec Group     3  ...     713288                    106523\n",
              "3          China National Petroleum     4  ...    1512048                    301893\n",
              "4                      Toyota Motor     5  ...     364445                    157210\n",
              "..                              ...   ...  ...        ...                       ...\n",
              "495  Teva Pharmaceutical Industries   496  ...      56960                     33337\n",
              "496        New China Life Insurance   497  ...      54378                      8507\n",
              "497       Wm. Morrison Supermarkets   498  ...      77210                      5111\n",
              "498                             TUI   499  ...      66779                      3006\n",
              "499                      AutoNation   500  ...      26000                      2310\n",
              "\n",
              "[436 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dG8RQeElWHxs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "b455855f-eebb-4c36-9988-f6a0c0bb88da"
      },
      "source": [
        "import pandas as pd\n",
        "# reading the csv   \n",
        "data = pd.read_csv(\"f500.csv\")\n",
        "data.dropna(inplace = True) \n",
        "# creating DataFrame form weight column \n",
        "df = pd.DataFrame(data['revenues'].head()) \n",
        "df\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>revenues</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>485873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>315199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>267518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>262573</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>254694</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   revenues\n",
              "0    485873\n",
              "1    315199\n",
              "2    267518\n",
              "3    262573\n",
              "4    254694"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mMtaiv0wTi2T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "48ceb1b3-28f8-4dcb-9b09-f4f8ce390b0b"
      },
      "source": [
        "# using to_numpy() function \n",
        "print(df.to_numpy()) "
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[485873]\n",
            " [315199]\n",
            " [267518]\n",
            " [262573]\n",
            " [254694]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pj6qE6JWowLo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "1d175079-c971-46b2-ff00-5c3f99089639"
      },
      "source": [
        "#ex-2\n",
        "\n",
        "import pandas as pd\n",
        "# read csv file   \n",
        "data = pd.read_csv(\"f500.csv\")\n",
        "data.dropna(inplace = True)\n",
        "# creating DataFrame form weight column \n",
        "df = pd.DataFrame(data['assets'].head())\n",
        "# providing dtype \n",
        "print(df.to_numpy(dtype ='float32'))\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[198825.]\n",
            " [489838.]\n",
            " [310726.]\n",
            " [585619.]\n",
            " [437575.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "36wfJqr4owOU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "outputId": "d00a9045-f1fb-440e-bbf2-1f66c32a5057"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "np_array = np.random.rand(12,3)\n",
        "print(\"Original Numpy array:\")\n",
        "print(np_array)\n",
        "print(\"Type: \",type(np_array))\n",
        "df = pd.DataFrame(np.random.rand(12,3),columns=['A','B','C'])\n",
        "print(\"\\nPanda's DataFrame: \")\n",
        "print(df)\n",
        "print(\"Type: \",type(df))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Original Numpy array:\n",
            "[[0.42993883 0.88212169 0.83942244]\n",
            " [0.29441756 0.43159656 0.15675084]\n",
            " [0.38615385 0.95550975 0.43730514]\n",
            " [0.47082204 0.19819627 0.41568184]\n",
            " [0.5171153  0.49607392 0.81784267]\n",
            " [0.4874217  0.21228144 0.35641828]\n",
            " [0.47730661 0.81247561 0.84439812]\n",
            " [0.47428863 0.5462064  0.98471136]\n",
            " [0.60570902 0.01511992 0.48280053]\n",
            " [0.74917778 0.9813467  0.43395103]\n",
            " [0.61589292 0.97316028 0.56322247]\n",
            " [0.85561398 0.75546231 0.00694593]]\n",
            "Type:  <class 'numpy.ndarray'>\n",
            "\n",
            "Panda's DataFrame: \n",
            "           A         B         C\n",
            "0   0.554112  0.086379  0.130244\n",
            "1   0.344524  0.532194  0.176493\n",
            "2   0.230424  0.929775  0.170659\n",
            "3   0.515756  0.653112  0.212376\n",
            "4   0.038430  0.641063  0.652271\n",
            "5   0.245228  0.589363  0.358070\n",
            "6   0.139335  0.692967  0.839033\n",
            "7   0.700788  0.947613  0.217729\n",
            "8   0.941159  0.774276  0.082665\n",
            "9   0.853139  0.288719  0.691460\n",
            "10  0.450981  0.278842  0.888218\n",
            "11  0.373264  0.813735  0.582122\n",
            "Type:  <class 'pandas.core.frame.DataFrame'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "78B0ymvGowQu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "0a8e8052-f8fc-4fef-e26b-6ec6e08a78cd"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "#initialize a dataframe\n",
        "df = pd.DataFrame(\n",
        "\t[[21, 72, 67],\n",
        "\t[23, 78, 69],\n",
        "\t[32, 74, 56],\n",
        "\t[52, 54, 76]],\n",
        "\tcolumns=['a', 'b', 'c'])\n",
        "\n",
        "print('DataFrame\\n----------\\n', df)\n",
        "\n",
        "#convert dataframe to numpy array\n",
        "arr = df.to_numpy()\n",
        "\n",
        "print('\\nNumpy Array\\n----------\\n', arr)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DataFrame\n",
            "----------\n",
            "     a   b   c\n",
            "0  21  72  67\n",
            "1  23  78  69\n",
            "2  32  74  56\n",
            "3  52  54  76\n",
            "\n",
            "Numpy Array\n",
            "----------\n",
            " [[21 72 67]\n",
            " [23 78 69]\n",
            " [32 74 56]\n",
            " [52 54 76]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VoLUyCzdowTF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "outputId": "c6d07542-0698-42e7-efb8-2b559c2ab392"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "#initialize a dataframe\n",
        "df = pd.DataFrame(\n",
        "\t[[21, 72, 67.1],\n",
        "\t[23, 78, 69.5],\n",
        "\t[32, 74, 56.6],\n",
        "\t[52, 54, 76.2]],\n",
        "\tcolumns=['a', 'b', 'c'])\n",
        "\n",
        "print('DataFrame\\n----------\\n', df)\n",
        "print('\\nDataFrame datatypes :\\n', df.dtypes)\n",
        "\n",
        "#convert pandas dataframe to numpy array\n",
        "arr = df.to_numpy()\n",
        "\n",
        "print('\\nNumpy Array\\n----------\\n', arr)\n",
        "print('\\nNumpy Array Datatype :', arr.dtype)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DataFrame\n",
            "----------\n",
            "     a   b     c\n",
            "0  21  72  67.1\n",
            "1  23  78  69.5\n",
            "2  32  74  56.6\n",
            "3  52  54  76.2\n",
            "\n",
            "DataFrame datatypes :\n",
            " a      int64\n",
            "b      int64\n",
            "c    float64\n",
            "dtype: object\n",
            "\n",
            "Numpy Array\n",
            "----------\n",
            " [[21.  72.  67.1]\n",
            " [23.  78.  69.5]\n",
            " [32.  74.  56.6]\n",
            " [52.  54.  76.2]]\n",
            "\n",
            "Numpy Array Datatype : float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ILiT4bh5XPTP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "caa85d06-72c8-415b-befe-95390de834f7"
      },
      "source": [
        "df"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>21</td>\n",
              "      <td>72</td>\n",
              "      <td>67.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>23</td>\n",
              "      <td>78</td>\n",
              "      <td>69.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>32</td>\n",
              "      <td>74</td>\n",
              "      <td>56.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>52</td>\n",
              "      <td>54</td>\n",
              "      <td>76.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    a   b     c\n",
              "0  21  72  67.1\n",
              "1  23  78  69.5\n",
              "2  32  74  56.6\n",
              "3  52  54  76.2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EPHzhLaSXS_q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "ff39708d-1d12-4c98-b9e4-9a1f6dfbec57"
      },
      "source": [
        "arr"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[21. , 72. , 67.1],\n",
              "       [23. , 78. , 69.5],\n",
              "       [32. , 74. , 56.6],\n",
              "       [52. , 54. , 76.2]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BTgYG4WAo-bC",
        "colab_type": "text"
      },
      "source": [
        "##Reading and Writing CSV Files in Python with Pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GoO_jyP8owVo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "outputId": "b9b82af5-2df6-442f-bd6a-19961776af55"
      },
      "source": [
        "import pandas as pd\n",
        "t20_data = pd.read_csv('matches.csv')\n",
        "t20_data.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>Season</th>\n",
              "      <th>city</th>\n",
              "      <th>date</th>\n",
              "      <th>team1</th>\n",
              "      <th>team2</th>\n",
              "      <th>toss_winner</th>\n",
              "      <th>toss_decision</th>\n",
              "      <th>result</th>\n",
              "      <th>dl_applied</th>\n",
              "      <th>winner</th>\n",
              "      <th>win_by_runs</th>\n",
              "      <th>win_by_wickets</th>\n",
              "      <th>player_of_match</th>\n",
              "      <th>venue</th>\n",
              "      <th>umpire1</th>\n",
              "      <th>umpire2</th>\n",
              "      <th>umpire3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>IPL-2017</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>05-04-2017</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>field</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>Yuvraj Singh</td>\n",
              "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
              "      <td>AY Dandekar</td>\n",
              "      <td>NJ Llong</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>IPL-2017</td>\n",
              "      <td>Pune</td>\n",
              "      <td>06-04-2017</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>Rising Pune Supergiant</td>\n",
              "      <td>Rising Pune Supergiant</td>\n",
              "      <td>field</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Rising Pune Supergiant</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>SPD Smith</td>\n",
              "      <td>Maharashtra Cricket Association Stadium</td>\n",
              "      <td>A Nand Kishore</td>\n",
              "      <td>S Ravi</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>IPL-2017</td>\n",
              "      <td>Rajkot</td>\n",
              "      <td>07-04-2017</td>\n",
              "      <td>Gujarat Lions</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>field</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>CA Lynn</td>\n",
              "      <td>Saurashtra Cricket Association Stadium</td>\n",
              "      <td>Nitin Menon</td>\n",
              "      <td>CK Nandan</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>IPL-2017</td>\n",
              "      <td>Indore</td>\n",
              "      <td>08-04-2017</td>\n",
              "      <td>Rising Pune Supergiant</td>\n",
              "      <td>Kings XI Punjab</td>\n",
              "      <td>Kings XI Punjab</td>\n",
              "      <td>field</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Kings XI Punjab</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>GJ Maxwell</td>\n",
              "      <td>Holkar Cricket Stadium</td>\n",
              "      <td>AK Chaudhary</td>\n",
              "      <td>C Shamshuddin</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>IPL-2017</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>08-04-2017</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Delhi Daredevils</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>bat</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>KM Jadhav</td>\n",
              "      <td>M Chinnaswamy Stadium</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id    Season       city  ...         umpire1        umpire2 umpire3\n",
              "0   1  IPL-2017  Hyderabad  ...     AY Dandekar       NJ Llong     NaN\n",
              "1   2  IPL-2017       Pune  ...  A Nand Kishore         S Ravi     NaN\n",
              "2   3  IPL-2017     Rajkot  ...     Nitin Menon      CK Nandan     NaN\n",
              "3   4  IPL-2017     Indore  ...    AK Chaudhary  C Shamshuddin     NaN\n",
              "4   5  IPL-2017  Bangalore  ...             NaN            NaN     NaN\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ehWkBVs9YRQ9",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iNNwrKu7Xed0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 750
        },
        "outputId": "105cf58b-a9cb-428b-a0a6-0bb41709771f"
      },
      "source": [
        "#Add Specific column name in data\n",
        "import pandas as pd\n",
        "col_names=['id','city','team1','team2','toss_winner']\n",
        "t20_data = pd.read_csv('matches.csv', names=col_names, header=None)\n",
        "t20_data.head(10)\n"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>city</th>\n",
              "      <th>team1</th>\n",
              "      <th>team2</th>\n",
              "      <th>toss_winner</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th>Season</th>\n",
              "      <th>city</th>\n",
              "      <th>date</th>\n",
              "      <th>team1</th>\n",
              "      <th>team2</th>\n",
              "      <th>toss_winner</th>\n",
              "      <th>toss_decision</th>\n",
              "      <th>result</th>\n",
              "      <th>dl_applied</th>\n",
              "      <th>winner</th>\n",
              "      <th>win_by_runs</th>\n",
              "      <th>win_by_wickets</th>\n",
              "      <td>player_of_match</td>\n",
              "      <td>venue</td>\n",
              "      <td>umpire1</td>\n",
              "      <td>umpire2</td>\n",
              "      <td>umpire3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Hyderabad</th>\n",
              "      <th>05-04-2017</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>35</th>\n",
              "      <th>0</th>\n",
              "      <td>Yuvraj Singh</td>\n",
              "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
              "      <td>AY Dandekar</td>\n",
              "      <td>NJ Llong</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Pune</th>\n",
              "      <th>06-04-2017</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <th>0</th>\n",
              "      <th>7</th>\n",
              "      <td>SPD Smith</td>\n",
              "      <td>Maharashtra Cricket Association Stadium</td>\n",
              "      <td>A Nand Kishore</td>\n",
              "      <td>S Ravi</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Rajkot</th>\n",
              "      <th>07-04-2017</th>\n",
              "      <th>Gujarat Lions</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>0</th>\n",
              "      <th>10</th>\n",
              "      <td>CA Lynn</td>\n",
              "      <td>Saurashtra Cricket Association Stadium</td>\n",
              "      <td>Nitin Menon</td>\n",
              "      <td>CK Nandan</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Indore</th>\n",
              "      <th>08-04-2017</th>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>0</th>\n",
              "      <th>6</th>\n",
              "      <td>GJ Maxwell</td>\n",
              "      <td>Holkar Cricket Stadium</td>\n",
              "      <td>AK Chaudhary</td>\n",
              "      <td>C Shamshuddin</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Bangalore</th>\n",
              "      <th>08-04-2017</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>Delhi Daredevils</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>bat</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>15</th>\n",
              "      <th>0</th>\n",
              "      <td>KM Jadhav</td>\n",
              "      <td>M Chinnaswamy Stadium</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Hyderabad</th>\n",
              "      <th>09-04-2017</th>\n",
              "      <th>Gujarat Lions</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>0</th>\n",
              "      <th>9</th>\n",
              "      <td>Rashid Khan</td>\n",
              "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
              "      <td>A Deshmukh</td>\n",
              "      <td>NJ Llong</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Mumbai</th>\n",
              "      <th>09-04-2017</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>0</th>\n",
              "      <th>4</th>\n",
              "      <td>N Rana</td>\n",
              "      <td>Wankhede Stadium</td>\n",
              "      <td>Nitin Menon</td>\n",
              "      <td>CK Nandan</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Indore</th>\n",
              "      <th>10-04-2017</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>bat</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>0</th>\n",
              "      <th>8</th>\n",
              "      <td>AR Patel</td>\n",
              "      <td>Holkar Cricket Stadium</td>\n",
              "      <td>AK Chaudhary</td>\n",
              "      <td>C Shamshuddin</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Pune</th>\n",
              "      <th>11-04-2017</th>\n",
              "      <th>Delhi Daredevils</th>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Delhi Daredevils</th>\n",
              "      <th>97</th>\n",
              "      <th>0</th>\n",
              "      <td>SV Samson</td>\n",
              "      <td>Maharashtra Cricket Association Stadium</td>\n",
              "      <td>AY Dandekar</td>\n",
              "      <td>S Ravi</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                                                                                                                                                                          id  ... toss_winner\n",
              "id Season   city      date       team1                       team2                       toss_winner                 toss_decision result dl_applied winner                      win_by_runs win_by_wickets  player_of_match  ...     umpire3\n",
              "1  IPL-2017 Hyderabad 05-04-2017 Sunrisers Hyderabad         Royal Challengers Bangalore Royal Challengers Bangalore field         normal 0          Sunrisers Hyderabad         35          0                  Yuvraj Singh  ...         NaN\n",
              "2  IPL-2017 Pune      06-04-2017 Mumbai Indians              Rising Pune Supergiant      Rising Pune Supergiant      field         normal 0          Rising Pune Supergiant      0           7                     SPD Smith  ...         NaN\n",
              "3  IPL-2017 Rajkot    07-04-2017 Gujarat Lions               Kolkata Knight Riders       Kolkata Knight Riders       field         normal 0          Kolkata Knight Riders       0           10                      CA Lynn  ...         NaN\n",
              "4  IPL-2017 Indore    08-04-2017 Rising Pune Supergiant      Kings XI Punjab             Kings XI Punjab             field         normal 0          Kings XI Punjab             0           6                    GJ Maxwell  ...         NaN\n",
              "5  IPL-2017 Bangalore 08-04-2017 Royal Challengers Bangalore Delhi Daredevils            Royal Challengers Bangalore bat           normal 0          Royal Challengers Bangalore 15          0                     KM Jadhav  ...         NaN\n",
              "6  IPL-2017 Hyderabad 09-04-2017 Gujarat Lions               Sunrisers Hyderabad         Sunrisers Hyderabad         field         normal 0          Sunrisers Hyderabad         0           9                   Rashid Khan  ...         NaN\n",
              "7  IPL-2017 Mumbai    09-04-2017 Kolkata Knight Riders       Mumbai Indians              Mumbai Indians              field         normal 0          Mumbai Indians              0           4                        N Rana  ...         NaN\n",
              "8  IPL-2017 Indore    10-04-2017 Royal Challengers Bangalore Kings XI Punjab             Royal Challengers Bangalore bat           normal 0          Kings XI Punjab             0           8                      AR Patel  ...         NaN\n",
              "9  IPL-2017 Pune      11-04-2017 Delhi Daredevils            Rising Pune Supergiant      Rising Pune Supergiant      field         normal 0          Delhi Daredevils            97          0                     SV Samson  ...         NaN\n",
              "\n",
              "[10 rows x 5 columns]"
            ]
          },
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              "      <th>11347</th>\n",
              "      <th>IPL-2019</th>\n",
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              "      <th>05-05-2019</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
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              "      <td>S Ravi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11412</th>\n",
              "      <th>IPL-2019</th>\n",
              "      <th>Chennai</th>\n",
              "      <th>07-05-2019</th>\n",
              "      <th>Chennai Super Kings</th>\n",
              "      <th>Mumbai Indians</th>\n",
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              "      <th>0</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>0</th>\n",
              "      <th>6</th>\n",
              "      <td>AS Yadav</td>\n",
              "      <td>M. A. Chidambaram Stadium</td>\n",
              "      <td>Nigel Llong</td>\n",
              "      <td>Nitin Menon</td>\n",
              "      <td>Ian Gould</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11413</th>\n",
              "      <th>IPL-2019</th>\n",
              "      <th>Visakhapatnam</th>\n",
              "      <th>08-05-2019</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>Delhi Capitals</th>\n",
              "      <th>Delhi Capitals</th>\n",
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              "      <th>0</th>\n",
              "      <th>2</th>\n",
              "      <td>RR Pant</td>\n",
              "      <td>ACA-VDCA Stadium</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>11414</th>\n",
              "      <th>IPL-2019</th>\n",
              "      <th>Visakhapatnam</th>\n",
              "      <th>10-05-2019</th>\n",
              "      <th>Delhi Capitals</th>\n",
              "      <th>Chennai Super Kings</th>\n",
              "      <th>Chennai Super Kings</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Chennai Super Kings</th>\n",
              "      <th>0</th>\n",
              "      <th>6</th>\n",
              "      <td>F du Plessis</td>\n",
              "      <td>ACA-VDCA Stadium</td>\n",
              "      <td>Sundaram Ravi</td>\n",
              "      <td>Bruce Oxenford</td>\n",
              "      <td>Chettithody Shamshuddin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11415</th>\n",
              "      <th>IPL-2019</th>\n",
              "      <th>Hyderabad</th>\n",
              "      <th>12-05-2019</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>Chennai Super Kings</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>bat</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>1</th>\n",
              "      <th>0</th>\n",
              "      <td>JJ Bumrah</td>\n",
              "      <td>Rajiv Gandhi Intl. Cricket Stadium</td>\n",
              "      <td>Nitin Menon</td>\n",
              "      <td>Ian Gould</td>\n",
              "      <td>Nigel Llong</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>"
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            "text/plain": [
              "                                                                                                                                                        id  ...              toss_winner\n",
              "11347 IPL-2019 Mumbai        05-05-2019 Kolkata Knight Riders Mumbai Indians      Mumbai Indians      field normal 0 Mumbai Indians      0 9     HH Pandya  ...                   S Ravi\n",
              "11412 IPL-2019 Chennai       07-05-2019 Chennai Super Kings   Mumbai Indians      Chennai Super Kings bat   normal 0 Mumbai Indians      0 6      AS Yadav  ...                Ian Gould\n",
              "11413 IPL-2019 Visakhapatnam 08-05-2019 Sunrisers Hyderabad   Delhi Capitals      Delhi Capitals      field normal 0 Delhi Capitals      0 2       RR Pant  ...                      NaN\n",
              "11414 IPL-2019 Visakhapatnam 10-05-2019 Delhi Capitals        Chennai Super Kings Chennai Super Kings field normal 0 Chennai Super Kings 0 6  F du Plessis  ...  Chettithody Shamshuddin\n",
              "11415 IPL-2019 Hyderabad     12-05-2019 Mumbai Indians        Chennai Super Kings Mumbai Indians      bat   normal 0 Mumbai Indians      1 0     JJ Bumrah  ...              Nigel Llong\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11332</th>\n",
              "      <th>IPL-2019</th>\n",
              "      <th>Chennai</th>\n",
              "      <th>23-04-2019</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
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              "      <th>0</th>\n",
              "      <th>6</th>\n",
              "      <td>SR Watson</td>\n",
              "      <td>M. A. Chidambaram Stadium</td>\n",
              "      <td>Nigel Llong</td>\n",
              "      <td>Anil Chaudhary</td>\n",
              "      <td>Vineet Kulkarni</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11333</th>\n",
              "      <th>IPL-2019</th>\n",
              "      <th>Bengaluru</th>\n",
              "      <th>24-04-2019</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>17</th>\n",
              "      <th>0</th>\n",
              "      <td>AB de Villiers</td>\n",
              "      <td>M. Chinnaswamy Stadium</td>\n",
              "      <td>Bruce Oxenford</td>\n",
              "      <td>C Shamshuddin</td>\n",
              "      <td>Rod Tucker</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11334</th>\n",
              "      <th>IPL-2019</th>\n",
              "      <th>Kolkata</th>\n",
              "      <th>25-04-2019</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>Rajasthan Royals</th>\n",
              "      <th>Rajasthan Royals</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Rajasthan Royals</th>\n",
              "      <th>0</th>\n",
              "      <th>3</th>\n",
              "      <td>VR Aaron</td>\n",
              "      <td>Eden Gardens</td>\n",
              "      <td>Ian Gould</td>\n",
              "      <td>Anil Dandekar</td>\n",
              "      <td>Nitin Menon</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11335</th>\n",
              "      <th>IPL-2019</th>\n",
              "      <th>Chennai</th>\n",
              "      <th>26-04-2019</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>Chennai Super Kings</th>\n",
              "      <th>Chennai Super Kings</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>46</th>\n",
              "      <th>0</th>\n",
              "      <td>RG Sharma</td>\n",
              "      <td>M. A. Chidambaram Stadium</td>\n",
              "      <td>Nigel Llong</td>\n",
              "      <td>Anil Chaudhary</td>\n",
              "      <td>Vineet Kulkarni</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11336</th>\n",
              "      <th>IPL-2019</th>\n",
              "      <th>Jaipur</th>\n",
              "      <th>27-04-2019</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>Rajasthan Royals</th>\n",
              "      <th>Rajasthan Royals</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Rajasthan Royals</th>\n",
              "      <th>0</th>\n",
              "      <th>7</th>\n",
              "      <td>JD Unadkat</td>\n",
              "      <td>Sawai Mansingh Stadium</td>\n",
              "      <td>Yeshwant Barde</td>\n",
              "      <td>Nand Kishore</td>\n",
              "      <td>Sundaram Ravi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11337</th>\n",
              "      <th>IPL-2019</th>\n",
              "      <th>Delhi</th>\n",
              "      <th>28-04-2019</th>\n",
              "      <th>Delhi Capitals</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>Delhi Capitals</th>\n",
              "      <th>bat</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Delhi Capitals</th>\n",
              "      <th>16</th>\n",
              "      <th>0</th>\n",
              "      <td>S Dhawan</td>\n",
              "      <td>Feroz Shah Kotla Ground</td>\n",
              "      <td>Bruce Oxenford</td>\n",
              "      <td>KN Ananthapadmanabhan</td>\n",
              "      <td>C Shamshuddin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11338</th>\n",
              "      <th>IPL-2019</th>\n",
              "      <th>Kolkata</th>\n",
              "      <th>28-04-2019</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>34</th>\n",
              "      <th>0</th>\n",
              "      <td>AD Russell</td>\n",
              "      <td>Eden Gardens</td>\n",
              "      <td>Ian Gould</td>\n",
              "      <td>Nitin Menon</td>\n",
              "      <td>Anil Dandekar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11339</th>\n",
              "      <th>IPL-2019</th>\n",
              "      <th>Hyderabad</th>\n",
              "      <th>29-04-2019</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>45</th>\n",
              "      <th>0</th>\n",
              "      <td>DA Warner</td>\n",
              "      <td>Rajiv Gandhi Intl. Cricket Stadium</td>\n",
              "      <td>S Ravi</td>\n",
              "      <td>O Nandan</td>\n",
              "      <td>Nanda Kishore</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11340</th>\n",
              "      <th>IPL-2019</th>\n",
              "      <th>Bengaluru</th>\n",
              "      <th>30-04-2019</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>Rajasthan Royals</th>\n",
              "      <th>Rajasthan Royals</th>\n",
              "      <th>field</th>\n",
              "      <th>no result</th>\n",
              "      <th>0</th>\n",
              "      <th>NaN</th>\n",
              "      <th>0</th>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>M. Chinnaswamy Stadium</td>\n",
              "      <td>Nigel Llong</td>\n",
              "      <td>Ulhas Gandhe</td>\n",
              "      <td>Anil Chaudhary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11341</th>\n",
              "      <th>IPL-2019</th>\n",
              "      <th>Chennai</th>\n",
              "      <th>01-05-2019</th>\n",
              "      <th>Chennai Super Kings</th>\n",
              "      <th>Delhi Capitals</th>\n",
              "      <th>Delhi Capitals</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Chennai Super Kings</th>\n",
              "      <th>80</th>\n",
              "      <th>0</th>\n",
              "      <td>MS Dhoni</td>\n",
              "      <td>M. A. Chidambaram Stadium</td>\n",
              "      <td>Anil Dandekar</td>\n",
              "      <td>Nitin Menon</td>\n",
              "      <td>Ian Gould</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11342</th>\n",
              "      <th>IPL-2019</th>\n",
              "      <th>Mumbai</th>\n",
              "      <th>02-05-2019</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>bat</th>\n",
              "      <th>tie</th>\n",
              "      <th>0</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>0</th>\n",
              "      <th>0</th>\n",
              "      <td>JJ Bumrah</td>\n",
              "      <td>Wankhede Stadium</td>\n",
              "      <td>S Ravi</td>\n",
              "      <td>O Nandan</td>\n",
              "      <td>Nanda Kishore</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11343</th>\n",
              "      <th>IPL-2019</th>\n",
              "      <th>Mohali</th>\n",
              "      <th>03-05-2019</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>0</th>\n",
              "      <th>7</th>\n",
              "      <td>S Gill</td>\n",
              "      <td>IS Bindra Stadium</td>\n",
              "      <td>Bruce Oxenford</td>\n",
              "      <td>C Shamshuddin</td>\n",
              "      <td>KN Ananthapadmanabhan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11344</th>\n",
              "      <th>IPL-2019</th>\n",
              "      <th>Delhi</th>\n",
              "      <th>04-05-2019</th>\n",
              "      <th>Rajasthan Royals</th>\n",
              "      <th>Delhi Capitals</th>\n",
              "      <th>Rajasthan Royals</th>\n",
              "      <th>bat</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Delhi Capitals</th>\n",
              "      <th>0</th>\n",
              "      <th>5</th>\n",
              "      <td>A Mishra</td>\n",
              "      <td>Feroz Shah Kotla Ground</td>\n",
              "      <td>Ian Gould</td>\n",
              "      <td>Anil Dandekar</td>\n",
              "      <td>Nitin Menon</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11345</th>\n",
              "      <th>IPL-2019</th>\n",
              "      <th>Bengaluru</th>\n",
              "      <th>04-05-2019</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>0</th>\n",
              "      <th>4</th>\n",
              "      <td>S Hetmyer</td>\n",
              "      <td>M. Chinnaswamy Stadium</td>\n",
              "      <td>Nigel Llong</td>\n",
              "      <td>Anil Chaudhary</td>\n",
              "      <td>Ulhas Gandhe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11346</th>\n",
              "      <th>IPL-2019</th>\n",
              "      <th>Mohali</th>\n",
              "      <th>05-05-2019</th>\n",
              "      <th>Chennai Super Kings</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>0</th>\n",
              "      <th>6</th>\n",
              "      <td>KL Rahul</td>\n",
              "      <td>IS Bindra Stadium</td>\n",
              "      <td>KN Ananthapadmanabhan</td>\n",
              "      <td>C Shamshuddin</td>\n",
              "      <td>Bruce Oxenford</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11347</th>\n",
              "      <th>IPL-2019</th>\n",
              "      <th>Mumbai</th>\n",
              "      <th>05-05-2019</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>0</th>\n",
              "      <th>9</th>\n",
              "      <td>HH Pandya</td>\n",
              "      <td>Wankhede Stadium</td>\n",
              "      <td>Nanda Kishore</td>\n",
              "      <td>O Nandan</td>\n",
              "      <td>S Ravi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11412</th>\n",
              "      <th>IPL-2019</th>\n",
              "      <th>Chennai</th>\n",
              "      <th>07-05-2019</th>\n",
              "      <th>Chennai Super Kings</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>Chennai Super Kings</th>\n",
              "      <th>bat</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>0</th>\n",
              "      <th>6</th>\n",
              "      <td>AS Yadav</td>\n",
              "      <td>M. A. Chidambaram Stadium</td>\n",
              "      <td>Nigel Llong</td>\n",
              "      <td>Nitin Menon</td>\n",
              "      <td>Ian Gould</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11413</th>\n",
              "      <th>IPL-2019</th>\n",
              "      <th>Visakhapatnam</th>\n",
              "      <th>08-05-2019</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>Delhi Capitals</th>\n",
              "      <th>Delhi Capitals</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Delhi Capitals</th>\n",
              "      <th>0</th>\n",
              "      <th>2</th>\n",
              "      <td>RR Pant</td>\n",
              "      <td>ACA-VDCA Stadium</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11414</th>\n",
              "      <th>IPL-2019</th>\n",
              "      <th>Visakhapatnam</th>\n",
              "      <th>10-05-2019</th>\n",
              "      <th>Delhi Capitals</th>\n",
              "      <th>Chennai Super Kings</th>\n",
              "      <th>Chennai Super Kings</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Chennai Super Kings</th>\n",
              "      <th>0</th>\n",
              "      <th>6</th>\n",
              "      <td>F du Plessis</td>\n",
              "      <td>ACA-VDCA Stadium</td>\n",
              "      <td>Sundaram Ravi</td>\n",
              "      <td>Bruce Oxenford</td>\n",
              "      <td>Chettithody Shamshuddin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11415</th>\n",
              "      <th>IPL-2019</th>\n",
              "      <th>Hyderabad</th>\n",
              "      <th>12-05-2019</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>Chennai Super Kings</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>bat</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>1</th>\n",
              "      <th>0</th>\n",
              "      <td>JJ Bumrah</td>\n",
              "      <td>Rajiv Gandhi Intl. Cricket Stadium</td>\n",
              "      <td>Nitin Menon</td>\n",
              "      <td>Ian Gould</td>\n",
              "      <td>Nigel Llong</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                                                                                                                                            id  ...              toss_winner\n",
              "11332 IPL-2019 Chennai       23-04-2019 Sunrisers Hyderabad         Chennai Super Kings         Chennai Super Kings         field normal    0 Chennai Super Kings         0  6       SR Watson  ...          Vineet Kulkarni\n",
              "11333 IPL-2019 Bengaluru     24-04-2019 Royal Challengers Bangalore Kings XI Punjab             Kings XI Punjab             field normal    0 Royal Challengers Bangalore 17 0  AB de Villiers  ...               Rod Tucker\n",
              "11334 IPL-2019 Kolkata       25-04-2019 Kolkata Knight Riders       Rajasthan Royals            Rajasthan Royals            field normal    0 Rajasthan Royals            0  3        VR Aaron  ...              Nitin Menon\n",
              "11335 IPL-2019 Chennai       26-04-2019 Mumbai Indians              Chennai Super Kings         Chennai Super Kings         field normal    0 Mumbai Indians              46 0       RG Sharma  ...          Vineet Kulkarni\n",
              "11336 IPL-2019 Jaipur        27-04-2019 Sunrisers Hyderabad         Rajasthan Royals            Rajasthan Royals            field normal    0 Rajasthan Royals            0  7      JD Unadkat  ...            Sundaram Ravi\n",
              "11337 IPL-2019 Delhi         28-04-2019 Delhi Capitals              Royal Challengers Bangalore Delhi Capitals              bat   normal    0 Delhi Capitals              16 0        S Dhawan  ...            C Shamshuddin\n",
              "11338 IPL-2019 Kolkata       28-04-2019 Kolkata Knight Riders       Mumbai Indians              Mumbai Indians              field normal    0 Kolkata Knight Riders       34 0      AD Russell  ...            Anil Dandekar\n",
              "11339 IPL-2019 Hyderabad     29-04-2019 Sunrisers Hyderabad         Kings XI Punjab             Kings XI Punjab             field normal    0 Sunrisers Hyderabad         45 0       DA Warner  ...            Nanda Kishore\n",
              "11340 IPL-2019 Bengaluru     30-04-2019 Royal Challengers Bangalore Rajasthan Royals            Rajasthan Royals            field no result 0 NaN                         0  0             NaN  ...           Anil Chaudhary\n",
              "11341 IPL-2019 Chennai       01-05-2019 Chennai Super Kings         Delhi Capitals              Delhi Capitals              field normal    0 Chennai Super Kings         80 0        MS Dhoni  ...                Ian Gould\n",
              "11342 IPL-2019 Mumbai        02-05-2019 Mumbai Indians              Sunrisers Hyderabad         Mumbai Indians              bat   tie       0 Mumbai Indians              0  0       JJ Bumrah  ...            Nanda Kishore\n",
              "11343 IPL-2019 Mohali        03-05-2019 Kings XI Punjab             Kolkata Knight Riders       Kolkata Knight Riders       field normal    0 Kolkata Knight Riders       0  7          S Gill  ...    KN Ananthapadmanabhan\n",
              "11344 IPL-2019 Delhi         04-05-2019 Rajasthan Royals            Delhi Capitals              Rajasthan Royals            bat   normal    0 Delhi Capitals              0  5        A Mishra  ...              Nitin Menon\n",
              "11345 IPL-2019 Bengaluru     04-05-2019 Sunrisers Hyderabad         Royal Challengers Bangalore Royal Challengers Bangalore field normal    0 Royal Challengers Bangalore 0  4       S Hetmyer  ...             Ulhas Gandhe\n",
              "11346 IPL-2019 Mohali        05-05-2019 Chennai Super Kings         Kings XI Punjab             Kings XI Punjab             field normal    0 Kings XI Punjab             0  6        KL Rahul  ...           Bruce Oxenford\n",
              "11347 IPL-2019 Mumbai        05-05-2019 Kolkata Knight Riders       Mumbai Indians              Mumbai Indians              field normal    0 Mumbai Indians              0  9       HH Pandya  ...                   S Ravi\n",
              "11412 IPL-2019 Chennai       07-05-2019 Chennai Super Kings         Mumbai Indians              Chennai Super Kings         bat   normal    0 Mumbai Indians              0  6        AS Yadav  ...                Ian Gould\n",
              "11413 IPL-2019 Visakhapatnam 08-05-2019 Sunrisers Hyderabad         Delhi Capitals              Delhi Capitals              field normal    0 Delhi Capitals              0  2         RR Pant  ...                      NaN\n",
              "11414 IPL-2019 Visakhapatnam 10-05-2019 Delhi Capitals              Chennai Super Kings         Chennai Super Kings         field normal    0 Chennai Super Kings         0  6    F du Plessis  ...  Chettithody Shamshuddin\n",
              "11415 IPL-2019 Hyderabad     12-05-2019 Mumbai Indians              Chennai Super Kings         Mumbai Indians              bat   normal    0 Mumbai Indians              1  0       JJ Bumrah  ...              Nigel Llong\n",
              "\n",
              "[20 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FOo6zAHmXe5H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "66bd0884-be6d-452e-bbb9-036102d0ee7b"
      },
      "source": [
        "t20_data.head()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>city</th>\n",
              "      <th>team1</th>\n",
              "      <th>team2</th>\n",
              "      <th>toss_winner</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th>Season</th>\n",
              "      <th>city</th>\n",
              "      <th>date</th>\n",
              "      <th>team1</th>\n",
              "      <th>team2</th>\n",
              "      <th>toss_winner</th>\n",
              "      <th>toss_decision</th>\n",
              "      <th>result</th>\n",
              "      <th>dl_applied</th>\n",
              "      <th>winner</th>\n",
              "      <th>win_by_runs</th>\n",
              "      <th>win_by_wickets</th>\n",
              "      <td>player_of_match</td>\n",
              "      <td>venue</td>\n",
              "      <td>umpire1</td>\n",
              "      <td>umpire2</td>\n",
              "      <td>umpire3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Hyderabad</th>\n",
              "      <th>05-04-2017</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>35</th>\n",
              "      <th>0</th>\n",
              "      <td>Yuvraj Singh</td>\n",
              "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
              "      <td>AY Dandekar</td>\n",
              "      <td>NJ Llong</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Pune</th>\n",
              "      <th>06-04-2017</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <th>0</th>\n",
              "      <th>7</th>\n",
              "      <td>SPD Smith</td>\n",
              "      <td>Maharashtra Cricket Association Stadium</td>\n",
              "      <td>A Nand Kishore</td>\n",
              "      <td>S Ravi</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Rajkot</th>\n",
              "      <th>07-04-2017</th>\n",
              "      <th>Gujarat Lions</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>0</th>\n",
              "      <th>10</th>\n",
              "      <td>CA Lynn</td>\n",
              "      <td>Saurashtra Cricket Association Stadium</td>\n",
              "      <td>Nitin Menon</td>\n",
              "      <td>CK Nandan</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Indore</th>\n",
              "      <th>08-04-2017</th>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>0</th>\n",
              "      <th>6</th>\n",
              "      <td>GJ Maxwell</td>\n",
              "      <td>Holkar Cricket Stadium</td>\n",
              "      <td>AK Chaudhary</td>\n",
              "      <td>C Shamshuddin</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                                                                                                                                                                id  ... toss_winner\n",
              "id Season   city      date       team1                  team2                       toss_winner                 toss_decision result dl_applied winner                 win_by_runs win_by_wickets  player_of_match  ...     umpire3\n",
              "1  IPL-2017 Hyderabad 05-04-2017 Sunrisers Hyderabad    Royal Challengers Bangalore Royal Challengers Bangalore field         normal 0          Sunrisers Hyderabad    35          0                  Yuvraj Singh  ...         NaN\n",
              "2  IPL-2017 Pune      06-04-2017 Mumbai Indians         Rising Pune Supergiant      Rising Pune Supergiant      field         normal 0          Rising Pune Supergiant 0           7                     SPD Smith  ...         NaN\n",
              "3  IPL-2017 Rajkot    07-04-2017 Gujarat Lions          Kolkata Knight Riders       Kolkata Knight Riders       field         normal 0          Kolkata Knight Riders  0           10                      CA Lynn  ...         NaN\n",
              "4  IPL-2017 Indore    08-04-2017 Rising Pune Supergiant Kings XI Punjab             Kings XI Punjab             field         normal 0          Kings XI Punjab        0           6                    GJ Maxwell  ...         NaN\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
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          "execution_count": 31
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    {
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
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              "      <th>toss_winner</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th>Season</th>\n",
              "      <th>city</th>\n",
              "      <th>date</th>\n",
              "      <th>team1</th>\n",
              "      <th>team2</th>\n",
              "      <th>toss_winner</th>\n",
              "      <th>toss_decision</th>\n",
              "      <th>result</th>\n",
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              "      <td>umpire1</td>\n",
              "      <td>umpire2</td>\n",
              "      <td>umpire3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Hyderabad</th>\n",
              "      <th>05-04-2017</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>35</th>\n",
              "      <th>0</th>\n",
              "      <td>Yuvraj Singh</td>\n",
              "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
              "      <td>AY Dandekar</td>\n",
              "      <td>NJ Llong</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Pune</th>\n",
              "      <th>06-04-2017</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <th>0</th>\n",
              "      <th>7</th>\n",
              "      <td>SPD Smith</td>\n",
              "      <td>Maharashtra Cricket Association Stadium</td>\n",
              "      <td>A Nand Kishore</td>\n",
              "      <td>S Ravi</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Rajkot</th>\n",
              "      <th>07-04-2017</th>\n",
              "      <th>Gujarat Lions</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>0</th>\n",
              "      <th>10</th>\n",
              "      <td>CA Lynn</td>\n",
              "      <td>Saurashtra Cricket Association Stadium</td>\n",
              "      <td>Nitin Menon</td>\n",
              "      <td>CK Nandan</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Indore</th>\n",
              "      <th>08-04-2017</th>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>0</th>\n",
              "      <th>6</th>\n",
              "      <td>GJ Maxwell</td>\n",
              "      <td>Holkar Cricket Stadium</td>\n",
              "      <td>AK Chaudhary</td>\n",
              "      <td>C Shamshuddin</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Bangalore</th>\n",
              "      <th>08-04-2017</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>Delhi Daredevils</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>bat</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>15</th>\n",
              "      <th>0</th>\n",
              "      <td>KM Jadhav</td>\n",
              "      <td>M Chinnaswamy Stadium</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Hyderabad</th>\n",
              "      <th>09-04-2017</th>\n",
              "      <th>Gujarat Lions</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>0</th>\n",
              "      <th>9</th>\n",
              "      <td>Rashid Khan</td>\n",
              "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
              "      <td>A Deshmukh</td>\n",
              "      <td>NJ Llong</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Mumbai</th>\n",
              "      <th>09-04-2017</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>0</th>\n",
              "      <th>4</th>\n",
              "      <td>N Rana</td>\n",
              "      <td>Wankhede Stadium</td>\n",
              "      <td>Nitin Menon</td>\n",
              "      <td>CK Nandan</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Indore</th>\n",
              "      <th>10-04-2017</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>bat</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>0</th>\n",
              "      <th>8</th>\n",
              "      <td>AR Patel</td>\n",
              "      <td>Holkar Cricket Stadium</td>\n",
              "      <td>AK Chaudhary</td>\n",
              "      <td>C Shamshuddin</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Pune</th>\n",
              "      <th>11-04-2017</th>\n",
              "      <th>Delhi Daredevils</th>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Delhi Daredevils</th>\n",
              "      <th>97</th>\n",
              "      <th>0</th>\n",
              "      <td>SV Samson</td>\n",
              "      <td>Maharashtra Cricket Association Stadium</td>\n",
              "      <td>AY Dandekar</td>\n",
              "      <td>S Ravi</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Mumbai</th>\n",
              "      <th>12-04-2017</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>0</th>\n",
              "      <th>4</th>\n",
              "      <td>JJ Bumrah</td>\n",
              "      <td>Wankhede Stadium</td>\n",
              "      <td>Nitin Menon</td>\n",
              "      <td>CK Nandan</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Kolkata</th>\n",
              "      <th>13-04-2017</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>0</th>\n",
              "      <th>8</th>\n",
              "      <td>SP Narine</td>\n",
              "      <td>Eden Gardens</td>\n",
              "      <td>A Deshmukh</td>\n",
              "      <td>NJ Llong</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Bangalore</th>\n",
              "      <th>14-04-2017</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>0</th>\n",
              "      <th>4</th>\n",
              "      <td>KA Pollard</td>\n",
              "      <td>M Chinnaswamy Stadium</td>\n",
              "      <td>KN Ananthapadmanabhan</td>\n",
              "      <td>AK Chaudhary</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Rajkot</th>\n",
              "      <th>14-04-2017</th>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <th>Gujarat Lions</th>\n",
              "      <th>Gujarat Lions</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Gujarat Lions</th>\n",
              "      <th>0</th>\n",
              "      <th>7</th>\n",
              "      <td>AJ Tye</td>\n",
              "      <td>Saurashtra Cricket Association Stadium</td>\n",
              "      <td>A Nand Kishore</td>\n",
              "      <td>S Ravi</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Kolkata</th>\n",
              "      <th>15-04-2017</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>17</th>\n",
              "      <th>0</th>\n",
              "      <td>RV Uthappa</td>\n",
              "      <td>Eden Gardens</td>\n",
              "      <td>AY Dandekar</td>\n",
              "      <td>NJ Llong</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                                                                                                                                                                          id  ... toss_winner\n",
              "id Season   city      date       team1                       team2                       toss_winner                 toss_decision result dl_applied winner                      win_by_runs win_by_wickets  player_of_match  ...     umpire3\n",
              "1  IPL-2017 Hyderabad 05-04-2017 Sunrisers Hyderabad         Royal Challengers Bangalore Royal Challengers Bangalore field         normal 0          Sunrisers Hyderabad         35          0                  Yuvraj Singh  ...         NaN\n",
              "2  IPL-2017 Pune      06-04-2017 Mumbai Indians              Rising Pune Supergiant      Rising Pune Supergiant      field         normal 0          Rising Pune Supergiant      0           7                     SPD Smith  ...         NaN\n",
              "3  IPL-2017 Rajkot    07-04-2017 Gujarat Lions               Kolkata Knight Riders       Kolkata Knight Riders       field         normal 0          Kolkata Knight Riders       0           10                      CA Lynn  ...         NaN\n",
              "4  IPL-2017 Indore    08-04-2017 Rising Pune Supergiant      Kings XI Punjab             Kings XI Punjab             field         normal 0          Kings XI Punjab             0           6                    GJ Maxwell  ...         NaN\n",
              "5  IPL-2017 Bangalore 08-04-2017 Royal Challengers Bangalore Delhi Daredevils            Royal Challengers Bangalore bat           normal 0          Royal Challengers Bangalore 15          0                     KM Jadhav  ...         NaN\n",
              "6  IPL-2017 Hyderabad 09-04-2017 Gujarat Lions               Sunrisers Hyderabad         Sunrisers Hyderabad         field         normal 0          Sunrisers Hyderabad         0           9                   Rashid Khan  ...         NaN\n",
              "7  IPL-2017 Mumbai    09-04-2017 Kolkata Knight Riders       Mumbai Indians              Mumbai Indians              field         normal 0          Mumbai Indians              0           4                        N Rana  ...         NaN\n",
              "8  IPL-2017 Indore    10-04-2017 Royal Challengers Bangalore Kings XI Punjab             Royal Challengers Bangalore bat           normal 0          Kings XI Punjab             0           8                      AR Patel  ...         NaN\n",
              "9  IPL-2017 Pune      11-04-2017 Delhi Daredevils            Rising Pune Supergiant      Rising Pune Supergiant      field         normal 0          Delhi Daredevils            97          0                     SV Samson  ...         NaN\n",
              "10 IPL-2017 Mumbai    12-04-2017 Sunrisers Hyderabad         Mumbai Indians              Mumbai Indians              field         normal 0          Mumbai Indians              0           4                     JJ Bumrah  ...         NaN\n",
              "11 IPL-2017 Kolkata   13-04-2017 Kings XI Punjab             Kolkata Knight Riders       Kolkata Knight Riders       field         normal 0          Kolkata Knight Riders       0           8                     SP Narine  ...         NaN\n",
              "12 IPL-2017 Bangalore 14-04-2017 Royal Challengers Bangalore Mumbai Indians              Mumbai Indians              field         normal 0          Mumbai Indians              0           4                    KA Pollard  ...         NaN\n",
              "13 IPL-2017 Rajkot    14-04-2017 Rising Pune Supergiant      Gujarat Lions               Gujarat Lions               field         normal 0          Gujarat Lions               0           7                        AJ Tye  ...         NaN\n",
              "14 IPL-2017 Kolkata   15-04-2017 Kolkata Knight Riders       Sunrisers Hyderabad         Sunrisers Hyderabad         field         normal 0          Kolkata Knight Riders       17          0                    RV Uthappa  ...         NaN\n",
              "\n",
              "[15 rows x 5 columns]"
            ]
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      "source": [
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              "      <th>42</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Bangalore</th>\n",
              "      <th>05-05-2017</th>\n",
              "      <th>Kings XI Punjab</th>\n",
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              "      <td>Sandeep Sharma</td>\n",
              "      <td>M Chinnaswamy Stadium</td>\n",
              "      <td>CB Gaffaney</td>\n",
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              "      <td>NaN</td>\n",
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            ],
            "text/plain": [
              "                                                                                                                                                          id  ... toss_winner\n",
              "42 IPL-2017 Bangalore 05-05-2017 Kings XI Punjab Royal Challengers Bangalore Royal Challengers Bangalore field normal 0 Kings XI Punjab 19 0  Sandeep Sharma  ...         NaN\n",
              "\n",
              "[1 rows x 5 columns]"
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      "source": [
        "t20_data.sample(5)"
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      "outputs": [
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              "        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></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>city</th>\n",
              "      <th>team1</th>\n",
              "      <th>team2</th>\n",
              "      <th>toss_winner</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11139</th>\n",
              "      <th>IPL-2019</th>\n",
              "      <th>Mumbai</th>\n",
              "      <th>24-03-2019</th>\n",
              "      <th>Delhi Capitals</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Delhi Capitals</th>\n",
              "      <th>37</th>\n",
              "      <th>0</th>\n",
              "      <td>RR Pant</td>\n",
              "      <td>Wankhede Stadium</td>\n",
              "      <td>S Ravi</td>\n",
              "      <td>Yeshwant Barde</td>\n",
              "      <td>O Nandan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>553</th>\n",
              "      <th>IPL-2015</th>\n",
              "      <th>Mumbai</th>\n",
              "      <th>03-05-2015</th>\n",
              "      <th>Rajasthan Royals</th>\n",
              "      <th>Delhi Daredevils</th>\n",
              "      <th>Delhi Daredevils</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Rajasthan Royals</th>\n",
              "      <th>14</th>\n",
              "      <th>0</th>\n",
              "      <td>AM Rahane</td>\n",
              "      <td>Brabourne Stadium</td>\n",
              "      <td>HDPK Dharmasena</td>\n",
              "      <td>CB Gaffaney</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>556</th>\n",
              "      <th>IPL-2015</th>\n",
              "      <th>Mumbai</th>\n",
              "      <th>05-05-2015</th>\n",
              "      <th>Delhi Daredevils</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>Delhi Daredevils</th>\n",
              "      <th>bat</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>0</th>\n",
              "      <th>5</th>\n",
              "      <td>Harbhajan Singh</td>\n",
              "      <td>Wankhede Stadium</td>\n",
              "      <td>HDPK Dharmasena</td>\n",
              "      <td>CB Gaffaney</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>461</th>\n",
              "      <th>IPL-2014</th>\n",
              "      <th>Abu Dhabi</th>\n",
              "      <th>18-04-2014</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>Rajasthan Royals</th>\n",
              "      <th>Rajasthan Royals</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Rajasthan Royals</th>\n",
              "      <th>0</th>\n",
              "      <th>4</th>\n",
              "      <td>AM Rahane</td>\n",
              "      <td>Sheikh Zayed Stadium</td>\n",
              "      <td>BF Bowden</td>\n",
              "      <td>RK Illingworth</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11151</th>\n",
              "      <th>IPL-2019</th>\n",
              "      <th>Mumbai</th>\n",
              "      <th>03-04-2019</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>Chennai Super Kings</th>\n",
              "      <th>Chennai Super Kings</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>37</th>\n",
              "      <th>0</th>\n",
              "      <td>HH Pandya</td>\n",
              "      <td>Wankhede Stadium</td>\n",
              "      <td>Bruce Oxenford</td>\n",
              "      <td>Rod Tucker</td>\n",
              "      <td>Yeshwant Barde</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                                                                                                   id  ...     toss_winner\n",
              "11139 IPL-2019 Mumbai    24-03-2019 Delhi Capitals      Mumbai Indians      Mumbai Indians      field normal 0 Delhi Capitals   37 0          RR Pant  ...        O Nandan\n",
              "553   IPL-2015 Mumbai    03-05-2015 Rajasthan Royals    Delhi Daredevils    Delhi Daredevils    field normal 0 Rajasthan Royals 14 0        AM Rahane  ...             NaN\n",
              "556   IPL-2015 Mumbai    05-05-2015 Delhi Daredevils    Mumbai Indians      Delhi Daredevils    bat   normal 0 Mumbai Indians   0  5  Harbhajan Singh  ...             NaN\n",
              "461   IPL-2014 Abu Dhabi 18-04-2014 Sunrisers Hyderabad Rajasthan Royals    Rajasthan Royals    field normal 0 Rajasthan Royals 0  4        AM Rahane  ...             NaN\n",
              "11151 IPL-2019 Mumbai    03-04-2019 Mumbai Indians      Chennai Super Kings Chennai Super Kings field normal 0 Mumbai Indians   37 0        HH Pandya  ...  Yeshwant Barde\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BY80WQ2IZqIq",
        "colab_type": "code",
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          "height": 417
        },
        "outputId": "ed7df9a7-23b5-4877-a4b0-0db5c16e6d66"
      },
      "source": [
        "t20_data.head()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>id</th>\n",
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              "      <th>toss_winner</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th>Season</th>\n",
              "      <th>city</th>\n",
              "      <th>date</th>\n",
              "      <th>team1</th>\n",
              "      <th>team2</th>\n",
              "      <th>toss_winner</th>\n",
              "      <th>toss_decision</th>\n",
              "      <th>result</th>\n",
              "      <th>dl_applied</th>\n",
              "      <th>winner</th>\n",
              "      <th>win_by_runs</th>\n",
              "      <th>win_by_wickets</th>\n",
              "      <td>player_of_match</td>\n",
              "      <td>venue</td>\n",
              "      <td>umpire1</td>\n",
              "      <td>umpire2</td>\n",
              "      <td>umpire3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Hyderabad</th>\n",
              "      <th>05-04-2017</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>35</th>\n",
              "      <th>0</th>\n",
              "      <td>Yuvraj Singh</td>\n",
              "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
              "      <td>AY Dandekar</td>\n",
              "      <td>NJ Llong</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Pune</th>\n",
              "      <th>06-04-2017</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <th>0</th>\n",
              "      <th>7</th>\n",
              "      <td>SPD Smith</td>\n",
              "      <td>Maharashtra Cricket Association Stadium</td>\n",
              "      <td>A Nand Kishore</td>\n",
              "      <td>S Ravi</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Rajkot</th>\n",
              "      <th>07-04-2017</th>\n",
              "      <th>Gujarat Lions</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>0</th>\n",
              "      <th>10</th>\n",
              "      <td>CA Lynn</td>\n",
              "      <td>Saurashtra Cricket Association Stadium</td>\n",
              "      <td>Nitin Menon</td>\n",
              "      <td>CK Nandan</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Indore</th>\n",
              "      <th>08-04-2017</th>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>0</th>\n",
              "      <th>6</th>\n",
              "      <td>GJ Maxwell</td>\n",
              "      <td>Holkar Cricket Stadium</td>\n",
              "      <td>AK Chaudhary</td>\n",
              "      <td>C Shamshuddin</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                                                                                                                                                                id  ... toss_winner\n",
              "id Season   city      date       team1                  team2                       toss_winner                 toss_decision result dl_applied winner                 win_by_runs win_by_wickets  player_of_match  ...     umpire3\n",
              "1  IPL-2017 Hyderabad 05-04-2017 Sunrisers Hyderabad    Royal Challengers Bangalore Royal Challengers Bangalore field         normal 0          Sunrisers Hyderabad    35          0                  Yuvraj Singh  ...         NaN\n",
              "2  IPL-2017 Pune      06-04-2017 Mumbai Indians         Rising Pune Supergiant      Rising Pune Supergiant      field         normal 0          Rising Pune Supergiant 0           7                     SPD Smith  ...         NaN\n",
              "3  IPL-2017 Rajkot    07-04-2017 Gujarat Lions          Kolkata Knight Riders       Kolkata Knight Riders       field         normal 0          Kolkata Knight Riders  0           10                      CA Lynn  ...         NaN\n",
              "4  IPL-2017 Indore    08-04-2017 Rising Pune Supergiant Kings XI Punjab             Kings XI Punjab             field         normal 0          Kings XI Punjab        0           6                    GJ Maxwell  ...         NaN\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sTf92GmAXfCu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "4936dd6f-2017-4adf-b422-d08dea5eb6b7"
      },
      "source": [
        "# Select specific columns of your dataframe\n",
        "\n",
        "t20_data[['id','city']].head(5)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>city</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <th>Season</th>\n",
              "      <th>city</th>\n",
              "      <th>date</th>\n",
              "      <th>team1</th>\n",
              "      <th>team2</th>\n",
              "      <th>toss_winner</th>\n",
              "      <th>toss_decision</th>\n",
              "      <th>result</th>\n",
              "      <th>dl_applied</th>\n",
              "      <th>winner</th>\n",
              "      <th>win_by_runs</th>\n",
              "      <th>win_by_wickets</th>\n",
              "      <td>player_of_match</td>\n",
              "      <td>venue</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Hyderabad</th>\n",
              "      <th>05-04-2017</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>Royal Challengers Bangalore</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Sunrisers Hyderabad</th>\n",
              "      <th>35</th>\n",
              "      <th>0</th>\n",
              "      <td>Yuvraj Singh</td>\n",
              "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Pune</th>\n",
              "      <th>06-04-2017</th>\n",
              "      <th>Mumbai Indians</th>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <th>0</th>\n",
              "      <th>7</th>\n",
              "      <td>SPD Smith</td>\n",
              "      <td>Maharashtra Cricket Association Stadium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Rajkot</th>\n",
              "      <th>07-04-2017</th>\n",
              "      <th>Gujarat Lions</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Kolkata Knight Riders</th>\n",
              "      <th>0</th>\n",
              "      <th>10</th>\n",
              "      <td>CA Lynn</td>\n",
              "      <td>Saurashtra Cricket Association Stadium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <th>IPL-2017</th>\n",
              "      <th>Indore</th>\n",
              "      <th>08-04-2017</th>\n",
              "      <th>Rising Pune Supergiant</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>field</th>\n",
              "      <th>normal</th>\n",
              "      <th>0</th>\n",
              "      <th>Kings XI Punjab</th>\n",
              "      <th>0</th>\n",
              "      <th>6</th>\n",
              "      <td>GJ Maxwell</td>\n",
              "      <td>Holkar Cricket Stadium</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                                                                                                                                                                id                                       city\n",
              "id Season   city      date       team1                  team2                       toss_winner                 toss_decision result dl_applied winner                 win_by_runs win_by_wickets  player_of_match                                      venue\n",
              "1  IPL-2017 Hyderabad 05-04-2017 Sunrisers Hyderabad    Royal Challengers Bangalore Royal Challengers Bangalore field         normal 0          Sunrisers Hyderabad    35          0                  Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal\n",
              "2  IPL-2017 Pune      06-04-2017 Mumbai Indians         Rising Pune Supergiant      Rising Pune Supergiant      field         normal 0          Rising Pune Supergiant 0           7                     SPD Smith    Maharashtra Cricket Association Stadium\n",
              "3  IPL-2017 Rajkot    07-04-2017 Gujarat Lions          Kolkata Knight Riders       Kolkata Knight Riders       field         normal 0          Kolkata Knight Riders  0           10                      CA Lynn     Saurashtra Cricket Association Stadium\n",
              "4  IPL-2017 Indore    08-04-2017 Rising Pune Supergiant Kings XI Punjab             Kings XI Punjab             field         normal 0          Kings XI Punjab        0           6                    GJ Maxwell                     Holkar Cricket Stadium"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WqCiIxfdaW2C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "outputId": "b9a4ed68-cfef-4820-cfa7-0fab39f1ab7e"
      },
      "source": [
        "import pandas as pd\n",
        "t20_data = pd.read_csv('matches.csv')\n",
        "t20_data.head()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>Season</th>\n",
              "      <th>city</th>\n",
              "      <th>date</th>\n",
              "      <th>team1</th>\n",
              "      <th>team2</th>\n",
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              "      <th>toss_decision</th>\n",
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              "      <th>win_by_wickets</th>\n",
              "      <th>player_of_match</th>\n",
              "      <th>venue</th>\n",
              "      <th>umpire1</th>\n",
              "      <th>umpire2</th>\n",
              "      <th>umpire3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>IPL-2017</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>05-04-2017</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>field</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>Yuvraj Singh</td>\n",
              "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
              "      <td>AY Dandekar</td>\n",
              "      <td>NJ Llong</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>IPL-2017</td>\n",
              "      <td>Pune</td>\n",
              "      <td>06-04-2017</td>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>Rising Pune Supergiant</td>\n",
              "      <td>Rising Pune Supergiant</td>\n",
              "      <td>field</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Rising Pune Supergiant</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>SPD Smith</td>\n",
              "      <td>Maharashtra Cricket Association Stadium</td>\n",
              "      <td>A Nand Kishore</td>\n",
              "      <td>S Ravi</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>IPL-2017</td>\n",
              "      <td>Rajkot</td>\n",
              "      <td>07-04-2017</td>\n",
              "      <td>Gujarat Lions</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>field</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>CA Lynn</td>\n",
              "      <td>Saurashtra Cricket Association Stadium</td>\n",
              "      <td>Nitin Menon</td>\n",
              "      <td>CK Nandan</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>IPL-2017</td>\n",
              "      <td>Indore</td>\n",
              "      <td>08-04-2017</td>\n",
              "      <td>Rising Pune Supergiant</td>\n",
              "      <td>Kings XI Punjab</td>\n",
              "      <td>Kings XI Punjab</td>\n",
              "      <td>field</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Kings XI Punjab</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>GJ Maxwell</td>\n",
              "      <td>Holkar Cricket Stadium</td>\n",
              "      <td>AK Chaudhary</td>\n",
              "      <td>C Shamshuddin</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>IPL-2017</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>08-04-2017</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>Delhi Daredevils</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>bat</td>\n",
              "      <td>normal</td>\n",
              "      <td>0</td>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>KM Jadhav</td>\n",
              "      <td>M Chinnaswamy Stadium</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id    Season       city  ...         umpire1        umpire2 umpire3\n",
              "0   1  IPL-2017  Hyderabad  ...     AY Dandekar       NJ Llong     NaN\n",
              "1   2  IPL-2017       Pune  ...  A Nand Kishore         S Ravi     NaN\n",
              "2   3  IPL-2017     Rajkot  ...     Nitin Menon      CK Nandan     NaN\n",
              "3   4  IPL-2017     Indore  ...    AK Chaudhary  C Shamshuddin     NaN\n",
              "4   5  IPL-2017  Bangalore  ...             NaN            NaN     NaN\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uk8q19kiXfFg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "dcf7a4ce-aa16-47e8-d9af-5ace41d5a822"
      },
      "source": [
        "#Filter for specific values in your dataframe\n",
        "t20_data['toss_winner'] == 'Royal Challengers Bangalore'"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       True\n",
              "1      False\n",
              "2      False\n",
              "3      False\n",
              "4       True\n",
              "       ...  \n",
              "751    False\n",
              "752    False\n",
              "753    False\n",
              "754    False\n",
              "755    False\n",
              "Name: toss_winner, Length: 756, dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A4YLA6ABXfH1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "ca9b90ef-deb9-4e4b-bae9-0d6e47170791"
      },
      "source": [
        "#Filter for specific values in your dataframe\n",
        "t20_data['winner'] == 'Royal Challengers Bangalore'"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      False\n",
              "1      False\n",
              "2      False\n",
              "3      False\n",
              "4       True\n",
              "       ...  \n",
              "751    False\n",
              "752    False\n",
              "753    False\n",
              "754    False\n",
              "755    False\n",
              "Name: winner, Length: 756, dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q2vpeD6oXfKE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "359ee188-f23c-45b8-fb87-d428dd0bc9d1"
      },
      "source": [
        "df = pd.read_csv(\"f500.csv\")\n",
        "df.head() "
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>company</th>\n",
              "      <th>rank</th>\n",
              "      <th>revenues</th>\n",
              "      <th>revenue_change</th>\n",
              "      <th>profits</th>\n",
              "      <th>assets</th>\n",
              "      <th>profit_change</th>\n",
              "      <th>ceo</th>\n",
              "      <th>industry</th>\n",
              "      <th>sector</th>\n",
              "      <th>previous_rank</th>\n",
              "      <th>country</th>\n",
              "      <th>hq_location</th>\n",
              "      <th>website</th>\n",
              "      <th>years_on_global_500_list</th>\n",
              "      <th>employees</th>\n",
              "      <th>total_stockholder_equity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Walmart</td>\n",
              "      <td>1</td>\n",
              "      <td>485873</td>\n",
              "      <td>0.8</td>\n",
              "      <td>13643.0</td>\n",
              "      <td>198825</td>\n",
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              "      <td>C. Douglas McMillon</td>\n",
              "      <td>General Merchandisers</td>\n",
              "      <td>Retailing</td>\n",
              "      <td>1</td>\n",
              "      <td>USA</td>\n",
              "      <td>Bentonville, AR</td>\n",
              "      <td>http://www.walmart.com</td>\n",
              "      <td>23</td>\n",
              "      <td>2300000</td>\n",
              "      <td>77798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>State Grid</td>\n",
              "      <td>2</td>\n",
              "      <td>315199</td>\n",
              "      <td>-4.4</td>\n",
              "      <td>9571.3</td>\n",
              "      <td>489838</td>\n",
              "      <td>-6.2</td>\n",
              "      <td>Kou Wei</td>\n",
              "      <td>Utilities</td>\n",
              "      <td>Energy</td>\n",
              "      <td>2</td>\n",
              "      <td>China</td>\n",
              "      <td>Beijing, China</td>\n",
              "      <td>http://www.sgcc.com.cn</td>\n",
              "      <td>17</td>\n",
              "      <td>926067</td>\n",
              "      <td>209456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sinopec Group</td>\n",
              "      <td>3</td>\n",
              "      <td>267518</td>\n",
              "      <td>-9.1</td>\n",
              "      <td>1257.9</td>\n",
              "      <td>310726</td>\n",
              "      <td>-65.0</td>\n",
              "      <td>Wang Yupu</td>\n",
              "      <td>Petroleum Refining</td>\n",
              "      <td>Energy</td>\n",
              "      <td>4</td>\n",
              "      <td>China</td>\n",
              "      <td>Beijing, China</td>\n",
              "      <td>http://www.sinopec.com</td>\n",
              "      <td>19</td>\n",
              "      <td>713288</td>\n",
              "      <td>106523</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>China National Petroleum</td>\n",
              "      <td>4</td>\n",
              "      <td>262573</td>\n",
              "      <td>-12.3</td>\n",
              "      <td>1867.5</td>\n",
              "      <td>585619</td>\n",
              "      <td>-73.7</td>\n",
              "      <td>Zhang Jianhua</td>\n",
              "      <td>Petroleum Refining</td>\n",
              "      <td>Energy</td>\n",
              "      <td>3</td>\n",
              "      <td>China</td>\n",
              "      <td>Beijing, China</td>\n",
              "      <td>http://www.cnpc.com.cn</td>\n",
              "      <td>17</td>\n",
              "      <td>1512048</td>\n",
              "      <td>301893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Toyota Motor</td>\n",
              "      <td>5</td>\n",
              "      <td>254694</td>\n",
              "      <td>7.7</td>\n",
              "      <td>16899.3</td>\n",
              "      <td>437575</td>\n",
              "      <td>-12.3</td>\n",
              "      <td>Akio Toyoda</td>\n",
              "      <td>Motor Vehicles and Parts</td>\n",
              "      <td>Motor Vehicles &amp; Parts</td>\n",
              "      <td>8</td>\n",
              "      <td>Japan</td>\n",
              "      <td>Toyota, Japan</td>\n",
              "      <td>http://www.toyota-global.com</td>\n",
              "      <td>23</td>\n",
              "      <td>364445</td>\n",
              "      <td>157210</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    company  rank  ...  employees  total_stockholder_equity\n",
              "0                   Walmart     1  ...    2300000                     77798\n",
              "1                State Grid     2  ...     926067                    209456\n",
              "2             Sinopec Group     3  ...     713288                    106523\n",
              "3  China National Petroleum     4  ...    1512048                    301893\n",
              "4              Toyota Motor     5  ...     364445                    157210\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gvDgrFn0XfOB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "fe68303c-fa9a-4258-f355-6522e0d6860d"
      },
      "source": [
        "df = pd.read_csv(\"f500.csv\", header = 0)\n",
        "df.head()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>company</th>\n",
              "      <th>rank</th>\n",
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              "      <th>revenue_change</th>\n",
              "      <th>profits</th>\n",
              "      <th>assets</th>\n",
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              "      <th>ceo</th>\n",
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              "      <th>hq_location</th>\n",
              "      <th>website</th>\n",
              "      <th>years_on_global_500_list</th>\n",
              "      <th>employees</th>\n",
              "      <th>total_stockholder_equity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Walmart</td>\n",
              "      <td>1</td>\n",
              "      <td>485873</td>\n",
              "      <td>0.8</td>\n",
              "      <td>13643.0</td>\n",
              "      <td>198825</td>\n",
              "      <td>-7.2</td>\n",
              "      <td>C. Douglas McMillon</td>\n",
              "      <td>General Merchandisers</td>\n",
              "      <td>Retailing</td>\n",
              "      <td>1</td>\n",
              "      <td>USA</td>\n",
              "      <td>Bentonville, AR</td>\n",
              "      <td>http://www.walmart.com</td>\n",
              "      <td>23</td>\n",
              "      <td>2300000</td>\n",
              "      <td>77798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>State Grid</td>\n",
              "      <td>2</td>\n",
              "      <td>315199</td>\n",
              "      <td>-4.4</td>\n",
              "      <td>9571.3</td>\n",
              "      <td>489838</td>\n",
              "      <td>-6.2</td>\n",
              "      <td>Kou Wei</td>\n",
              "      <td>Utilities</td>\n",
              "      <td>Energy</td>\n",
              "      <td>2</td>\n",
              "      <td>China</td>\n",
              "      <td>Beijing, China</td>\n",
              "      <td>http://www.sgcc.com.cn</td>\n",
              "      <td>17</td>\n",
              "      <td>926067</td>\n",
              "      <td>209456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sinopec Group</td>\n",
              "      <td>3</td>\n",
              "      <td>267518</td>\n",
              "      <td>-9.1</td>\n",
              "      <td>1257.9</td>\n",
              "      <td>310726</td>\n",
              "      <td>-65.0</td>\n",
              "      <td>Wang Yupu</td>\n",
              "      <td>Petroleum Refining</td>\n",
              "      <td>Energy</td>\n",
              "      <td>4</td>\n",
              "      <td>China</td>\n",
              "      <td>Beijing, China</td>\n",
              "      <td>http://www.sinopec.com</td>\n",
              "      <td>19</td>\n",
              "      <td>713288</td>\n",
              "      <td>106523</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>China National Petroleum</td>\n",
              "      <td>4</td>\n",
              "      <td>262573</td>\n",
              "      <td>-12.3</td>\n",
              "      <td>1867.5</td>\n",
              "      <td>585619</td>\n",
              "      <td>-73.7</td>\n",
              "      <td>Zhang Jianhua</td>\n",
              "      <td>Petroleum Refining</td>\n",
              "      <td>Energy</td>\n",
              "      <td>3</td>\n",
              "      <td>China</td>\n",
              "      <td>Beijing, China</td>\n",
              "      <td>http://www.cnpc.com.cn</td>\n",
              "      <td>17</td>\n",
              "      <td>1512048</td>\n",
              "      <td>301893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Toyota Motor</td>\n",
              "      <td>5</td>\n",
              "      <td>254694</td>\n",
              "      <td>7.7</td>\n",
              "      <td>16899.3</td>\n",
              "      <td>437575</td>\n",
              "      <td>-12.3</td>\n",
              "      <td>Akio Toyoda</td>\n",
              "      <td>Motor Vehicles and Parts</td>\n",
              "      <td>Motor Vehicles &amp; Parts</td>\n",
              "      <td>8</td>\n",
              "      <td>Japan</td>\n",
              "      <td>Toyota, Japan</td>\n",
              "      <td>http://www.toyota-global.com</td>\n",
              "      <td>23</td>\n",
              "      <td>364445</td>\n",
              "      <td>157210</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    company  rank  ...  employees  total_stockholder_equity\n",
              "0                   Walmart     1  ...    2300000                     77798\n",
              "1                State Grid     2  ...     926067                    209456\n",
              "2             Sinopec Group     3  ...     713288                    106523\n",
              "3  China National Petroleum     4  ...    1512048                    301893\n",
              "4              Toyota Motor     5  ...     364445                    157210\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zHQ5oGMaXfMN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "outputId": "00b3cd40-2dbf-4ad9-88cf-db25d7798243"
      },
      "source": [
        "df = pd.read_csv(\"f500.csv\", header = 10)\n",
        "df.head()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "    }\n",
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Exxon Mobil</th>\n",
              "      <th>10</th>\n",
              "      <th>205004</th>\n",
              "      <th>-16.7</th>\n",
              "      <th>7840.0</th>\n",
              "      <th>330314</th>\n",
              "      <th>-51.5</th>\n",
              "      <th>Darren W. Woods</th>\n",
              "      <th>Petroleum Refining</th>\n",
              "      <th>Energy</th>\n",
              "      <th>6</th>\n",
              "      <th>USA</th>\n",
              "      <th>Irving, TX</th>\n",
              "      <th>http://www.exxonmobil.com</th>\n",
              "      <th>23</th>\n",
              "      <th>72700</th>\n",
              "      <th>167325</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>McKesson</td>\n",
              "      <td>11</td>\n",
              "      <td>198533</td>\n",
              "      <td>3.1</td>\n",
              "      <td>5070.0</td>\n",
              "      <td>60969</td>\n",
              "      <td>124.5</td>\n",
              "      <td>John H. Hammergren</td>\n",
              "      <td>Wholesalers: Health Care</td>\n",
              "      <td>Wholesalers</td>\n",
              "      <td>12</td>\n",
              "      <td>USA</td>\n",
              "      <td>San Francisco, CA</td>\n",
              "      <td>http://www.mckesson.com</td>\n",
              "      <td>23</td>\n",
              "      <td>64500</td>\n",
              "      <td>11095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BP</td>\n",
              "      <td>12</td>\n",
              "      <td>186606</td>\n",
              "      <td>-17.4</td>\n",
              "      <td>115.0</td>\n",
              "      <td>263316</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Robert W. Dudley</td>\n",
              "      <td>Petroleum Refining</td>\n",
              "      <td>Energy</td>\n",
              "      <td>10</td>\n",
              "      <td>Britain</td>\n",
              "      <td>London, Britain</td>\n",
              "      <td>http://www.bp.com</td>\n",
              "      <td>23</td>\n",
              "      <td>74500</td>\n",
              "      <td>95286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>UnitedHealth Group</td>\n",
              "      <td>13</td>\n",
              "      <td>184840</td>\n",
              "      <td>17.7</td>\n",
              "      <td>7017.0</td>\n",
              "      <td>122810</td>\n",
              "      <td>20.7</td>\n",
              "      <td>Stephen J. Hemsley</td>\n",
              "      <td>Health Care: Insurance and Managed Care</td>\n",
              "      <td>Health Care</td>\n",
              "      <td>17</td>\n",
              "      <td>USA</td>\n",
              "      <td>Minnetonka, MN</td>\n",
              "      <td>http://www.unitedhealthgroup.com</td>\n",
              "      <td>21</td>\n",
              "      <td>230000</td>\n",
              "      <td>38274</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CVS Health</td>\n",
              "      <td>14</td>\n",
              "      <td>177526</td>\n",
              "      <td>15.8</td>\n",
              "      <td>5317.0</td>\n",
              "      <td>94462</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Larry J. Merlo</td>\n",
              "      <td>Health Care: Pharmacy and Other Services</td>\n",
              "      <td>Health Care</td>\n",
              "      <td>18</td>\n",
              "      <td>USA</td>\n",
              "      <td>Woonsocket, RI</td>\n",
              "      <td>http://www.cvshealth.com</td>\n",
              "      <td>22</td>\n",
              "      <td>204000</td>\n",
              "      <td>36830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Samsung Electronics</td>\n",
              "      <td>15</td>\n",
              "      <td>173957</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>19316.5</td>\n",
              "      <td>217104</td>\n",
              "      <td>16.8</td>\n",
              "      <td>Oh-Hyun Kwon</td>\n",
              "      <td>Electronics, Electrical Equip.</td>\n",
              "      <td>Technology</td>\n",
              "      <td>13</td>\n",
              "      <td>South Korea</td>\n",
              "      <td>Suwon, South Korea</td>\n",
              "      <td>http://www.samsung.com</td>\n",
              "      <td>23</td>\n",
              "      <td>325000</td>\n",
              "      <td>154376</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Exxon Mobil  10  205004  ...  23   72700  167325\n",
              "0             McKesson  11  198533  ...  23   64500   11095\n",
              "1                   BP  12  186606  ...  23   74500   95286\n",
              "2   UnitedHealth Group  13  184840  ...  21  230000   38274\n",
              "3           CVS Health  14  177526  ...  22  204000   36830\n",
              "4  Samsung Electronics  15  173957  ...  23  325000  154376\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E4-IpWJfbHWq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "6511c8be-101f-4550-d645-5f1ef28c46c2"
      },
      "source": [
        "df = pd.read_csv(\"f500.csv\", header = 0)\n",
        "df.head()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>company</th>\n",
              "      <th>rank</th>\n",
              "      <th>revenues</th>\n",
              "      <th>revenue_change</th>\n",
              "      <th>profits</th>\n",
              "      <th>assets</th>\n",
              "      <th>profit_change</th>\n",
              "      <th>ceo</th>\n",
              "      <th>industry</th>\n",
              "      <th>sector</th>\n",
              "      <th>previous_rank</th>\n",
              "      <th>country</th>\n",
              "      <th>hq_location</th>\n",
              "      <th>website</th>\n",
              "      <th>years_on_global_500_list</th>\n",
              "      <th>employees</th>\n",
              "      <th>total_stockholder_equity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Walmart</td>\n",
              "      <td>1</td>\n",
              "      <td>485873</td>\n",
              "      <td>0.8</td>\n",
              "      <td>13643.0</td>\n",
              "      <td>198825</td>\n",
              "      <td>-7.2</td>\n",
              "      <td>C. Douglas McMillon</td>\n",
              "      <td>General Merchandisers</td>\n",
              "      <td>Retailing</td>\n",
              "      <td>1</td>\n",
              "      <td>USA</td>\n",
              "      <td>Bentonville, AR</td>\n",
              "      <td>http://www.walmart.com</td>\n",
              "      <td>23</td>\n",
              "      <td>2300000</td>\n",
              "      <td>77798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>State Grid</td>\n",
              "      <td>2</td>\n",
              "      <td>315199</td>\n",
              "      <td>-4.4</td>\n",
              "      <td>9571.3</td>\n",
              "      <td>489838</td>\n",
              "      <td>-6.2</td>\n",
              "      <td>Kou Wei</td>\n",
              "      <td>Utilities</td>\n",
              "      <td>Energy</td>\n",
              "      <td>2</td>\n",
              "      <td>China</td>\n",
              "      <td>Beijing, China</td>\n",
              "      <td>http://www.sgcc.com.cn</td>\n",
              "      <td>17</td>\n",
              "      <td>926067</td>\n",
              "      <td>209456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sinopec Group</td>\n",
              "      <td>3</td>\n",
              "      <td>267518</td>\n",
              "      <td>-9.1</td>\n",
              "      <td>1257.9</td>\n",
              "      <td>310726</td>\n",
              "      <td>-65.0</td>\n",
              "      <td>Wang Yupu</td>\n",
              "      <td>Petroleum Refining</td>\n",
              "      <td>Energy</td>\n",
              "      <td>4</td>\n",
              "      <td>China</td>\n",
              "      <td>Beijing, China</td>\n",
              "      <td>http://www.sinopec.com</td>\n",
              "      <td>19</td>\n",
              "      <td>713288</td>\n",
              "      <td>106523</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>China National Petroleum</td>\n",
              "      <td>4</td>\n",
              "      <td>262573</td>\n",
              "      <td>-12.3</td>\n",
              "      <td>1867.5</td>\n",
              "      <td>585619</td>\n",
              "      <td>-73.7</td>\n",
              "      <td>Zhang Jianhua</td>\n",
              "      <td>Petroleum Refining</td>\n",
              "      <td>Energy</td>\n",
              "      <td>3</td>\n",
              "      <td>China</td>\n",
              "      <td>Beijing, China</td>\n",
              "      <td>http://www.cnpc.com.cn</td>\n",
              "      <td>17</td>\n",
              "      <td>1512048</td>\n",
              "      <td>301893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Toyota Motor</td>\n",
              "      <td>5</td>\n",
              "      <td>254694</td>\n",
              "      <td>7.7</td>\n",
              "      <td>16899.3</td>\n",
              "      <td>437575</td>\n",
              "      <td>-12.3</td>\n",
              "      <td>Akio Toyoda</td>\n",
              "      <td>Motor Vehicles and Parts</td>\n",
              "      <td>Motor Vehicles &amp; Parts</td>\n",
              "      <td>8</td>\n",
              "      <td>Japan</td>\n",
              "      <td>Toyota, Japan</td>\n",
              "      <td>http://www.toyota-global.com</td>\n",
              "      <td>23</td>\n",
              "      <td>364445</td>\n",
              "      <td>157210</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    company  rank  ...  employees  total_stockholder_equity\n",
              "0                   Walmart     1  ...    2300000                     77798\n",
              "1                State Grid     2  ...     926067                    209456\n",
              "2             Sinopec Group     3  ...     713288                    106523\n",
              "3  China National Petroleum     4  ...    1512048                    301893\n",
              "4              Toyota Motor     5  ...     364445                    157210\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wlaTCukJbC4h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "outputId": "b4da1bb2-bbd8-4396-8560-8f45b62e7fe4"
      },
      "source": [
        "#If you set index_col to 0, then the first column of the dataframe will become the row label. Notice how the row labels change.\n",
        "df = pd.read_csv(\"f500.csv\", index_col = 0)\n",
        "df.head(5)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rank</th>\n",
              "      <th>revenues</th>\n",
              "      <th>revenue_change</th>\n",
              "      <th>profits</th>\n",
              "      <th>assets</th>\n",
              "      <th>profit_change</th>\n",
              "      <th>ceo</th>\n",
              "      <th>industry</th>\n",
              "      <th>sector</th>\n",
              "      <th>previous_rank</th>\n",
              "      <th>country</th>\n",
              "      <th>hq_location</th>\n",
              "      <th>website</th>\n",
              "      <th>years_on_global_500_list</th>\n",
              "      <th>employees</th>\n",
              "      <th>total_stockholder_equity</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>company</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Walmart</th>\n",
              "      <td>1</td>\n",
              "      <td>485873</td>\n",
              "      <td>0.8</td>\n",
              "      <td>13643.0</td>\n",
              "      <td>198825</td>\n",
              "      <td>-7.2</td>\n",
              "      <td>C. Douglas McMillon</td>\n",
              "      <td>General Merchandisers</td>\n",
              "      <td>Retailing</td>\n",
              "      <td>1</td>\n",
              "      <td>USA</td>\n",
              "      <td>Bentonville, AR</td>\n",
              "      <td>http://www.walmart.com</td>\n",
              "      <td>23</td>\n",
              "      <td>2300000</td>\n",
              "      <td>77798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>State Grid</th>\n",
              "      <td>2</td>\n",
              "      <td>315199</td>\n",
              "      <td>-4.4</td>\n",
              "      <td>9571.3</td>\n",
              "      <td>489838</td>\n",
              "      <td>-6.2</td>\n",
              "      <td>Kou Wei</td>\n",
              "      <td>Utilities</td>\n",
              "      <td>Energy</td>\n",
              "      <td>2</td>\n",
              "      <td>China</td>\n",
              "      <td>Beijing, China</td>\n",
              "      <td>http://www.sgcc.com.cn</td>\n",
              "      <td>17</td>\n",
              "      <td>926067</td>\n",
              "      <td>209456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sinopec Group</th>\n",
              "      <td>3</td>\n",
              "      <td>267518</td>\n",
              "      <td>-9.1</td>\n",
              "      <td>1257.9</td>\n",
              "      <td>310726</td>\n",
              "      <td>-65.0</td>\n",
              "      <td>Wang Yupu</td>\n",
              "      <td>Petroleum Refining</td>\n",
              "      <td>Energy</td>\n",
              "      <td>4</td>\n",
              "      <td>China</td>\n",
              "      <td>Beijing, China</td>\n",
              "      <td>http://www.sinopec.com</td>\n",
              "      <td>19</td>\n",
              "      <td>713288</td>\n",
              "      <td>106523</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>China National Petroleum</th>\n",
              "      <td>4</td>\n",
              "      <td>262573</td>\n",
              "      <td>-12.3</td>\n",
              "      <td>1867.5</td>\n",
              "      <td>585619</td>\n",
              "      <td>-73.7</td>\n",
              "      <td>Zhang Jianhua</td>\n",
              "      <td>Petroleum Refining</td>\n",
              "      <td>Energy</td>\n",
              "      <td>3</td>\n",
              "      <td>China</td>\n",
              "      <td>Beijing, China</td>\n",
              "      <td>http://www.cnpc.com.cn</td>\n",
              "      <td>17</td>\n",
              "      <td>1512048</td>\n",
              "      <td>301893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Toyota Motor</th>\n",
              "      <td>5</td>\n",
              "      <td>254694</td>\n",
              "      <td>7.7</td>\n",
              "      <td>16899.3</td>\n",
              "      <td>437575</td>\n",
              "      <td>-12.3</td>\n",
              "      <td>Akio Toyoda</td>\n",
              "      <td>Motor Vehicles and Parts</td>\n",
              "      <td>Motor Vehicles &amp; Parts</td>\n",
              "      <td>8</td>\n",
              "      <td>Japan</td>\n",
              "      <td>Toyota, Japan</td>\n",
              "      <td>http://www.toyota-global.com</td>\n",
              "      <td>23</td>\n",
              "      <td>364445</td>\n",
              "      <td>157210</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          rank  revenues  ...  employees  total_stockholder_equity\n",
              "company                                   ...                                     \n",
              "Walmart                      1    485873  ...    2300000                     77798\n",
              "State Grid                   2    315199  ...     926067                    209456\n",
              "Sinopec Group                3    267518  ...     713288                    106523\n",
              "China National Petroleum     4    262573  ...    1512048                    301893\n",
              "Toyota Motor                 5    254694  ...     364445                    157210\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YQzjKB2DbDJY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "f2033a73-8a9b-4053-cc26-360e1a346b27"
      },
      "source": [
        "#load specific column \n",
        "df = pd.read_csv(\"f500.csv\", usecols = [\"company\", \"rank\", \"revenues\"])\n",
        "df.head()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>company</th>\n",
              "      <th>rank</th>\n",
              "      <th>revenues</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Walmart</td>\n",
              "      <td>1</td>\n",
              "      <td>485873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>State Grid</td>\n",
              "      <td>2</td>\n",
              "      <td>315199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sinopec Group</td>\n",
              "      <td>3</td>\n",
              "      <td>267518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>China National Petroleum</td>\n",
              "      <td>4</td>\n",
              "      <td>262573</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Toyota Motor</td>\n",
              "      <td>5</td>\n",
              "      <td>254694</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    company  rank  revenues\n",
              "0                   Walmart     1    485873\n",
              "1                State Grid     2    315199\n",
              "2             Sinopec Group     3    267518\n",
              "3  China National Petroleum     4    262573\n",
              "4              Toyota Motor     5    254694"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IDmjvg3-bPBu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "outputId": "aeda0722-7b34-4640-bc7e-83bead89c137"
      },
      "source": [
        "#Skip first 4 rows\n",
        "df = pd.read_csv(\"f500.csv\", skiprows = 4)\n",
        "df.head(5)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>China National Petroleum</th>\n",
              "      <th>4</th>\n",
              "      <th>262573</th>\n",
              "      <th>-12.3</th>\n",
              "      <th>1867.5</th>\n",
              "      <th>585619</th>\n",
              "      <th>-73.7</th>\n",
              "      <th>Zhang Jianhua</th>\n",
              "      <th>Petroleum Refining</th>\n",
              "      <th>Energy</th>\n",
              "      <th>3</th>\n",
              "      <th>China</th>\n",
              "      <th>Beijing, China</th>\n",
              "      <th>http://www.cnpc.com.cn</th>\n",
              "      <th>17</th>\n",
              "      <th>1512048</th>\n",
              "      <th>301893</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Toyota Motor</td>\n",
              "      <td>5</td>\n",
              "      <td>254694</td>\n",
              "      <td>7.7</td>\n",
              "      <td>16899.3</td>\n",
              "      <td>437575</td>\n",
              "      <td>-12.3</td>\n",
              "      <td>Akio Toyoda</td>\n",
              "      <td>Motor Vehicles and Parts</td>\n",
              "      <td>Motor Vehicles &amp; Parts</td>\n",
              "      <td>8</td>\n",
              "      <td>Japan</td>\n",
              "      <td>Toyota, Japan</td>\n",
              "      <td>http://www.toyota-global.com</td>\n",
              "      <td>23</td>\n",
              "      <td>364445</td>\n",
              "      <td>157210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Volkswagen</td>\n",
              "      <td>6</td>\n",
              "      <td>240264</td>\n",
              "      <td>1.5</td>\n",
              "      <td>5937.3</td>\n",
              "      <td>432116</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Matthias Muller</td>\n",
              "      <td>Motor Vehicles and Parts</td>\n",
              "      <td>Motor Vehicles &amp; Parts</td>\n",
              "      <td>7</td>\n",
              "      <td>Germany</td>\n",
              "      <td>Wolfsburg, Germany</td>\n",
              "      <td>http://www.volkswagen.com</td>\n",
              "      <td>23</td>\n",
              "      <td>626715</td>\n",
              "      <td>97753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Royal Dutch Shell</td>\n",
              "      <td>7</td>\n",
              "      <td>240033</td>\n",
              "      <td>-11.8</td>\n",
              "      <td>4575.0</td>\n",
              "      <td>411275</td>\n",
              "      <td>135.9</td>\n",
              "      <td>Ben van Beurden</td>\n",
              "      <td>Petroleum Refining</td>\n",
              "      <td>Energy</td>\n",
              "      <td>5</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td>The Hague, Netherlands</td>\n",
              "      <td>http://www.shell.com</td>\n",
              "      <td>23</td>\n",
              "      <td>89000</td>\n",
              "      <td>186646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Berkshire Hathaway</td>\n",
              "      <td>8</td>\n",
              "      <td>223604</td>\n",
              "      <td>6.1</td>\n",
              "      <td>24074.0</td>\n",
              "      <td>620854</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Warren E. Buffett</td>\n",
              "      <td>Insurance: Property and Casualty (Stock)</td>\n",
              "      <td>Financials</td>\n",
              "      <td>11</td>\n",
              "      <td>USA</td>\n",
              "      <td>Omaha, NE</td>\n",
              "      <td>http://www.berkshirehathaway.com</td>\n",
              "      <td>21</td>\n",
              "      <td>367700</td>\n",
              "      <td>283001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Apple</td>\n",
              "      <td>9</td>\n",
              "      <td>215639</td>\n",
              "      <td>-7.7</td>\n",
              "      <td>45687.0</td>\n",
              "      <td>321686</td>\n",
              "      <td>-14.4</td>\n",
              "      <td>Timothy D. Cook</td>\n",
              "      <td>Computers, Office Equipment</td>\n",
              "      <td>Technology</td>\n",
              "      <td>9</td>\n",
              "      <td>USA</td>\n",
              "      <td>Cupertino, CA</td>\n",
              "      <td>http://www.apple.com</td>\n",
              "      <td>15</td>\n",
              "      <td>116000</td>\n",
              "      <td>128249</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  China National Petroleum  4  262573  ...  17  1512048  301893\n",
              "0             Toyota Motor  5  254694  ...  23   364445  157210\n",
              "1               Volkswagen  6  240264  ...  23   626715   97753\n",
              "2        Royal Dutch Shell  7  240033  ...  23    89000  186646\n",
              "3       Berkshire Hathaway  8  223604  ...  21   367700  283001\n",
              "4                    Apple  9  215639  ...  15   116000  128249\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VJJeELlgbPEQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "d443ddae-f803-4705-d079-3e74ed13d930"
      },
      "source": [
        "#If you want to read a limited number of rows, instead of all the rows in a dataset, use nrows.\n",
        "df = pd.read_csv(\"f500.csv\", nrows = 4)\n",
        "df.head(5)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>company</th>\n",
              "      <th>rank</th>\n",
              "      <th>revenues</th>\n",
              "      <th>revenue_change</th>\n",
              "      <th>profits</th>\n",
              "      <th>assets</th>\n",
              "      <th>profit_change</th>\n",
              "      <th>ceo</th>\n",
              "      <th>industry</th>\n",
              "      <th>sector</th>\n",
              "      <th>previous_rank</th>\n",
              "      <th>country</th>\n",
              "      <th>hq_location</th>\n",
              "      <th>website</th>\n",
              "      <th>years_on_global_500_list</th>\n",
              "      <th>employees</th>\n",
              "      <th>total_stockholder_equity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Walmart</td>\n",
              "      <td>1</td>\n",
              "      <td>485873</td>\n",
              "      <td>0.8</td>\n",
              "      <td>13643.0</td>\n",
              "      <td>198825</td>\n",
              "      <td>-7.2</td>\n",
              "      <td>C. Douglas McMillon</td>\n",
              "      <td>General Merchandisers</td>\n",
              "      <td>Retailing</td>\n",
              "      <td>1</td>\n",
              "      <td>USA</td>\n",
              "      <td>Bentonville, AR</td>\n",
              "      <td>http://www.walmart.com</td>\n",
              "      <td>23</td>\n",
              "      <td>2300000</td>\n",
              "      <td>77798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>State Grid</td>\n",
              "      <td>2</td>\n",
              "      <td>315199</td>\n",
              "      <td>-4.4</td>\n",
              "      <td>9571.3</td>\n",
              "      <td>489838</td>\n",
              "      <td>-6.2</td>\n",
              "      <td>Kou Wei</td>\n",
              "      <td>Utilities</td>\n",
              "      <td>Energy</td>\n",
              "      <td>2</td>\n",
              "      <td>China</td>\n",
              "      <td>Beijing, China</td>\n",
              "      <td>http://www.sgcc.com.cn</td>\n",
              "      <td>17</td>\n",
              "      <td>926067</td>\n",
              "      <td>209456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sinopec Group</td>\n",
              "      <td>3</td>\n",
              "      <td>267518</td>\n",
              "      <td>-9.1</td>\n",
              "      <td>1257.9</td>\n",
              "      <td>310726</td>\n",
              "      <td>-65.0</td>\n",
              "      <td>Wang Yupu</td>\n",
              "      <td>Petroleum Refining</td>\n",
              "      <td>Energy</td>\n",
              "      <td>4</td>\n",
              "      <td>China</td>\n",
              "      <td>Beijing, China</td>\n",
              "      <td>http://www.sinopec.com</td>\n",
              "      <td>19</td>\n",
              "      <td>713288</td>\n",
              "      <td>106523</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>China National Petroleum</td>\n",
              "      <td>4</td>\n",
              "      <td>262573</td>\n",
              "      <td>-12.3</td>\n",
              "      <td>1867.5</td>\n",
              "      <td>585619</td>\n",
              "      <td>-73.7</td>\n",
              "      <td>Zhang Jianhua</td>\n",
              "      <td>Petroleum Refining</td>\n",
              "      <td>Energy</td>\n",
              "      <td>3</td>\n",
              "      <td>China</td>\n",
              "      <td>Beijing, China</td>\n",
              "      <td>http://www.cnpc.com.cn</td>\n",
              "      <td>17</td>\n",
              "      <td>1512048</td>\n",
              "      <td>301893</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    company  rank  ...  employees  total_stockholder_equity\n",
              "0                   Walmart     1  ...    2300000                     77798\n",
              "1                State Grid     2  ...     926067                    209456\n",
              "2             Sinopec Group     3  ...     713288                    106523\n",
              "3  China National Petroleum     4  ...    1512048                    301893\n",
              "\n",
              "[4 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PAbYnTp6bPGp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177
        },
        "outputId": "2ba6d47a-3a26-41a6-f324-7d8921799346"
      },
      "source": [
        "#If you want to read a limited number of rows, instead of all the rows in a dataset, use nrows.\n",
        "df = pd.read_csv(\"f500.csv\", nrows = 2)\n",
        "df.head(5)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>company</th>\n",
              "      <th>rank</th>\n",
              "      <th>revenues</th>\n",
              "      <th>revenue_change</th>\n",
              "      <th>profits</th>\n",
              "      <th>assets</th>\n",
              "      <th>profit_change</th>\n",
              "      <th>ceo</th>\n",
              "      <th>industry</th>\n",
              "      <th>sector</th>\n",
              "      <th>previous_rank</th>\n",
              "      <th>country</th>\n",
              "      <th>hq_location</th>\n",
              "      <th>website</th>\n",
              "      <th>years_on_global_500_list</th>\n",
              "      <th>employees</th>\n",
              "      <th>total_stockholder_equity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Walmart</td>\n",
              "      <td>1</td>\n",
              "      <td>485873</td>\n",
              "      <td>0.8</td>\n",
              "      <td>13643.0</td>\n",
              "      <td>198825</td>\n",
              "      <td>-7.2</td>\n",
              "      <td>C. Douglas McMillon</td>\n",
              "      <td>General Merchandisers</td>\n",
              "      <td>Retailing</td>\n",
              "      <td>1</td>\n",
              "      <td>USA</td>\n",
              "      <td>Bentonville, AR</td>\n",
              "      <td>http://www.walmart.com</td>\n",
              "      <td>23</td>\n",
              "      <td>2300000</td>\n",
              "      <td>77798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>State Grid</td>\n",
              "      <td>2</td>\n",
              "      <td>315199</td>\n",
              "      <td>-4.4</td>\n",
              "      <td>9571.3</td>\n",
              "      <td>489838</td>\n",
              "      <td>-6.2</td>\n",
              "      <td>Kou Wei</td>\n",
              "      <td>Utilities</td>\n",
              "      <td>Energy</td>\n",
              "      <td>2</td>\n",
              "      <td>China</td>\n",
              "      <td>Beijing, China</td>\n",
              "      <td>http://www.sgcc.com.cn</td>\n",
              "      <td>17</td>\n",
              "      <td>926067</td>\n",
              "      <td>209456</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      company  rank  ...  employees  total_stockholder_equity\n",
              "0     Walmart     1  ...    2300000                     77798\n",
              "1  State Grid     2  ...     926067                    209456\n",
              "\n",
              "[2 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oknmPv93bPIv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "NewYork=pd.read_csv('NYC.csv')"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OUq-7HFpbPLk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "outputId": "e76362c6-771b-40d0-8d5a-09fa5f9bec95"
      },
      "source": [
        "NewYork.head()\n"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>name</th>\n",
              "      <th>host_id</th>\n",
              "      <th>host_name</th>\n",
              "      <th>neighbourhood_group</th>\n",
              "      <th>neighbourhood</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>room_type</th>\n",
              "      <th>price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>last_review</th>\n",
              "      <th>reviews_per_month</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2539</td>\n",
              "      <td>Clean &amp; quiet apt home by the park</td>\n",
              "      <td>2787.0</td>\n",
              "      <td>John</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Kensington</td>\n",
              "      <td>40.64749</td>\n",
              "      <td>-73.97237</td>\n",
              "      <td>Private room</td>\n",
              "      <td>149.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>19-10-2018</td>\n",
              "      <td>0.21</td>\n",
              "      <td>6.0</td>\n",
              "      <td>365.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2595</td>\n",
              "      <td>Skylit Midtown Castle</td>\n",
              "      <td>2845.0</td>\n",
              "      <td>Jennifer</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Midtown</td>\n",
              "      <td>40.75362</td>\n",
              "      <td>-73.98377</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>225.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>21-05-2019</td>\n",
              "      <td>0.38</td>\n",
              "      <td>2.0</td>\n",
              "      <td>355.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3647</td>\n",
              "      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
              "      <td>4632.0</td>\n",
              "      <td>Elisabeth</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Harlem</td>\n",
              "      <td>40.80902</td>\n",
              "      <td>-73.94190</td>\n",
              "      <td>Private room</td>\n",
              "      <td>150.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>365.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3831</td>\n",
              "      <td>Cozy Entire Floor of Brownstone</td>\n",
              "      <td>4869.0</td>\n",
              "      <td>LisaRoxanne</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Clinton Hill</td>\n",
              "      <td>40.68514</td>\n",
              "      <td>-73.95976</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>89.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>270.0</td>\n",
              "      <td>05-07-2019</td>\n",
              "      <td>4.64</td>\n",
              "      <td>1.0</td>\n",
              "      <td>194.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5022</td>\n",
              "      <td>Entire Apt: Spacious Studio/Loft by central park</td>\n",
              "      <td>7192.0</td>\n",
              "      <td>Laura</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>East Harlem</td>\n",
              "      <td>40.79851</td>\n",
              "      <td>-73.94399</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>80.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>19-11-2018</td>\n",
              "      <td>0.10</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     id  ... availability_365\n",
              "0  2539  ...            365.0\n",
              "1  2595  ...            355.0\n",
              "2  3647  ...            365.0\n",
              "3  3831  ...            194.0\n",
              "4  5022  ...              0.0\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "euX-kClCbDLz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "8f2934f8-819e-4c3e-e06a-e28a23c2e8b8"
      },
      "source": [
        "NewYork.tail()"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>name</th>\n",
              "      <th>host_id</th>\n",
              "      <th>host_name</th>\n",
              "      <th>neighbourhood_group</th>\n",
              "      <th>neighbourhood</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>room_type</th>\n",
              "      <th>price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>last_review</th>\n",
              "      <th>reviews_per_month</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>21918</th>\n",
              "      <td>17625485</td>\n",
              "      <td>Bahia Brazil Vibe - Entire apt</td>\n",
              "      <td>7824337.0</td>\n",
              "      <td>Miralva</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Flatbush</td>\n",
              "      <td>40.65218</td>\n",
              "      <td>-73.96193</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>140.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>01-07-2019</td>\n",
              "      <td>1.09</td>\n",
              "      <td>2.0</td>\n",
              "      <td>161.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21919</th>\n",
              "      <td>17625707</td>\n",
              "      <td>Prospect Park Apt 20 min to Soho 5 min from Tr...</td>\n",
              "      <td>8314232.0</td>\n",
              "      <td>Mo</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Prospect-Lefferts Gardens</td>\n",
              "      <td>40.65802</td>\n",
              "      <td>-73.95595</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>100.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>17-06-2018</td>\n",
              "      <td>1.34</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21920</th>\n",
              "      <td>17625824</td>\n",
              "      <td>Private Room in Brooklyn Brownstone</td>\n",
              "      <td>79882333.0</td>\n",
              "      <td>Hallie</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>40.68119</td>\n",
              "      <td>-73.93722</td>\n",
              "      <td>Private room</td>\n",
              "      <td>39.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>29-03-2017</td>\n",
              "      <td>0.07</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21921</th>\n",
              "      <td>17626236</td>\n",
              "      <td>Cozy 2 Bedroom (Train L or M)</td>\n",
              "      <td>54637605.0</td>\n",
              "      <td>Albertina</td>\n",
              "      <td>Queens</td>\n",
              "      <td>Ridgewood</td>\n",
              "      <td>40.70200</td>\n",
              "      <td>-73.89768</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>110.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>28-05-2019</td>\n",
              "      <td>0.37</td>\n",
              "      <td>1.0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21922</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             id  ... availability_365\n",
              "21918  17625485  ...            161.0\n",
              "21919  17625707  ...              0.0\n",
              "21920  17625824  ...              0.0\n",
              "21921  17626236  ...             16.0\n",
              "21922         1  ...              NaN\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8lkcP_yybDOB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "6051408b-3efe-4a87-b7d8-b8cb8fcf0a9f"
      },
      "source": [
        "NewYork.dtypes"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                  int64\n",
              "name                               object\n",
              "host_id                           float64\n",
              "host_name                          object\n",
              "neighbourhood_group                object\n",
              "neighbourhood                      object\n",
              "latitude                          float64\n",
              "longitude                         float64\n",
              "room_type                          object\n",
              "price                             float64\n",
              "minimum_nights                    float64\n",
              "number_of_reviews                 float64\n",
              "last_review                        object\n",
              "reviews_per_month                 float64\n",
              "calculated_host_listings_count    float64\n",
              "availability_365                  float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0hNv3aTMd6sX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "outputId": "f7ec1643-d512-48c6-ff37-875ad082970e"
      },
      "source": [
        "NewYork.drop(columns='name')\n",
        "#delete column continent"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>host_id</th>\n",
              "      <th>host_name</th>\n",
              "      <th>neighbourhood_group</th>\n",
              "      <th>neighbourhood</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>room_type</th>\n",
              "      <th>price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>last_review</th>\n",
              "      <th>reviews_per_month</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2539</td>\n",
              "      <td>2787.0</td>\n",
              "      <td>John</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Kensington</td>\n",
              "      <td>40.64749</td>\n",
              "      <td>-73.97237</td>\n",
              "      <td>Private room</td>\n",
              "      <td>149.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>19-10-2018</td>\n",
              "      <td>0.21</td>\n",
              "      <td>6.0</td>\n",
              "      <td>365.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2595</td>\n",
              "      <td>2845.0</td>\n",
              "      <td>Jennifer</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Midtown</td>\n",
              "      <td>40.75362</td>\n",
              "      <td>-73.98377</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>225.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>21-05-2019</td>\n",
              "      <td>0.38</td>\n",
              "      <td>2.0</td>\n",
              "      <td>355.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3647</td>\n",
              "      <td>4632.0</td>\n",
              "      <td>Elisabeth</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Harlem</td>\n",
              "      <td>40.80902</td>\n",
              "      <td>-73.94190</td>\n",
              "      <td>Private room</td>\n",
              "      <td>150.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>365.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3831</td>\n",
              "      <td>4869.0</td>\n",
              "      <td>LisaRoxanne</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Clinton Hill</td>\n",
              "      <td>40.68514</td>\n",
              "      <td>-73.95976</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>89.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>270.0</td>\n",
              "      <td>05-07-2019</td>\n",
              "      <td>4.64</td>\n",
              "      <td>1.0</td>\n",
              "      <td>194.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5022</td>\n",
              "      <td>7192.0</td>\n",
              "      <td>Laura</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>East Harlem</td>\n",
              "      <td>40.79851</td>\n",
              "      <td>-73.94399</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>80.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>19-11-2018</td>\n",
              "      <td>0.10</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21918</th>\n",
              "      <td>17625485</td>\n",
              "      <td>7824337.0</td>\n",
              "      <td>Miralva</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Flatbush</td>\n",
              "      <td>40.65218</td>\n",
              "      <td>-73.96193</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>140.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>01-07-2019</td>\n",
              "      <td>1.09</td>\n",
              "      <td>2.0</td>\n",
              "      <td>161.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21919</th>\n",
              "      <td>17625707</td>\n",
              "      <td>8314232.0</td>\n",
              "      <td>Mo</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Prospect-Lefferts Gardens</td>\n",
              "      <td>40.65802</td>\n",
              "      <td>-73.95595</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>100.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>17-06-2018</td>\n",
              "      <td>1.34</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21920</th>\n",
              "      <td>17625824</td>\n",
              "      <td>79882333.0</td>\n",
              "      <td>Hallie</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>40.68119</td>\n",
              "      <td>-73.93722</td>\n",
              "      <td>Private room</td>\n",
              "      <td>39.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>29-03-2017</td>\n",
              "      <td>0.07</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21921</th>\n",
              "      <td>17626236</td>\n",
              "      <td>54637605.0</td>\n",
              "      <td>Albertina</td>\n",
              "      <td>Queens</td>\n",
              "      <td>Ridgewood</td>\n",
              "      <td>40.70200</td>\n",
              "      <td>-73.89768</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>110.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>28-05-2019</td>\n",
              "      <td>0.37</td>\n",
              "      <td>1.0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21922</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>21923 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id     host_id  ... calculated_host_listings_count availability_365\n",
              "0          2539      2787.0  ...                            6.0            365.0\n",
              "1          2595      2845.0  ...                            2.0            355.0\n",
              "2          3647      4632.0  ...                            1.0            365.0\n",
              "3          3831      4869.0  ...                            1.0            194.0\n",
              "4          5022      7192.0  ...                            1.0              0.0\n",
              "...         ...         ...  ...                            ...              ...\n",
              "21918  17625485   7824337.0  ...                            2.0            161.0\n",
              "21919  17625707   8314232.0  ...                            1.0              0.0\n",
              "21920  17625824  79882333.0  ...                            1.0              0.0\n",
              "21921  17626236  54637605.0  ...                            1.0             16.0\n",
              "21922         1         NaN  ...                            NaN              NaN\n",
              "\n",
              "[21923 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-p7WE1hmeEUc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 758
        },
        "outputId": "2f255cdf-c734-402a-c43e-b686494c4593"
      },
      "source": [
        "NewYork.drop([0,1,2,3,4])\n",
        "#delete top 5 rows"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>name</th>\n",
              "      <th>host_id</th>\n",
              "      <th>host_name</th>\n",
              "      <th>neighbourhood_group</th>\n",
              "      <th>neighbourhood</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>room_type</th>\n",
              "      <th>price</th>\n",
              "      <th>minimum_nights</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>last_review</th>\n",
              "      <th>reviews_per_month</th>\n",
              "      <th>calculated_host_listings_count</th>\n",
              "      <th>availability_365</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5099</td>\n",
              "      <td>Large Cozy 1 BR Apartment In Midtown East</td>\n",
              "      <td>7322.0</td>\n",
              "      <td>Chris</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Murray Hill</td>\n",
              "      <td>40.74767</td>\n",
              "      <td>-73.97500</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>200.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>22-06-2019</td>\n",
              "      <td>0.59</td>\n",
              "      <td>1.0</td>\n",
              "      <td>129.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5121</td>\n",
              "      <td>BlissArtsSpace!</td>\n",
              "      <td>7356.0</td>\n",
              "      <td>Garon</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>40.68688</td>\n",
              "      <td>-73.95596</td>\n",
              "      <td>Private room</td>\n",
              "      <td>60.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>05-10-2017</td>\n",
              "      <td>0.40</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5178</td>\n",
              "      <td>Large Furnished Room Near B'way</td>\n",
              "      <td>8967.0</td>\n",
              "      <td>Shunichi</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Hell's Kitchen</td>\n",
              "      <td>40.76489</td>\n",
              "      <td>-73.98493</td>\n",
              "      <td>Private room</td>\n",
              "      <td>79.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>430.0</td>\n",
              "      <td>24-06-2019</td>\n",
              "      <td>3.47</td>\n",
              "      <td>1.0</td>\n",
              "      <td>220.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>5203</td>\n",
              "      <td>Cozy Clean Guest Room - Family Apt</td>\n",
              "      <td>7490.0</td>\n",
              "      <td>MaryEllen</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Upper West Side</td>\n",
              "      <td>40.80178</td>\n",
              "      <td>-73.96723</td>\n",
              "      <td>Private room</td>\n",
              "      <td>79.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>21-07-2017</td>\n",
              "      <td>0.99</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>5238</td>\n",
              "      <td>Cute &amp; Cozy Lower East Side 1 bdrm</td>\n",
              "      <td>7549.0</td>\n",
              "      <td>Ben</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>Chinatown</td>\n",
              "      <td>40.71344</td>\n",
              "      <td>-73.99037</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>150.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>09-06-2019</td>\n",
              "      <td>1.33</td>\n",
              "      <td>4.0</td>\n",
              "      <td>188.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21918</th>\n",
              "      <td>17625485</td>\n",
              "      <td>Bahia Brazil Vibe - Entire apt</td>\n",
              "      <td>7824337.0</td>\n",
              "      <td>Miralva</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Flatbush</td>\n",
              "      <td>40.65218</td>\n",
              "      <td>-73.96193</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>140.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>01-07-2019</td>\n",
              "      <td>1.09</td>\n",
              "      <td>2.0</td>\n",
              "      <td>161.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21919</th>\n",
              "      <td>17625707</td>\n",
              "      <td>Prospect Park Apt 20 min to Soho 5 min from Tr...</td>\n",
              "      <td>8314232.0</td>\n",
              "      <td>Mo</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Prospect-Lefferts Gardens</td>\n",
              "      <td>40.65802</td>\n",
              "      <td>-73.95595</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>100.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>17-06-2018</td>\n",
              "      <td>1.34</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21920</th>\n",
              "      <td>17625824</td>\n",
              "      <td>Private Room in Brooklyn Brownstone</td>\n",
              "      <td>79882333.0</td>\n",
              "      <td>Hallie</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Bedford-Stuyvesant</td>\n",
              "      <td>40.68119</td>\n",
              "      <td>-73.93722</td>\n",
              "      <td>Private room</td>\n",
              "      <td>39.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>29-03-2017</td>\n",
              "      <td>0.07</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21921</th>\n",
              "      <td>17626236</td>\n",
              "      <td>Cozy 2 Bedroom (Train L or M)</td>\n",
              "      <td>54637605.0</td>\n",
              "      <td>Albertina</td>\n",
              "      <td>Queens</td>\n",
              "      <td>Ridgewood</td>\n",
              "      <td>40.70200</td>\n",
              "      <td>-73.89768</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>110.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>28-05-2019</td>\n",
              "      <td>0.37</td>\n",
              "      <td>1.0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21922</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>21918 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id  ... availability_365\n",
              "5          5099  ...            129.0\n",
              "6          5121  ...              0.0\n",
              "7          5178  ...            220.0\n",
              "8          5203  ...              0.0\n",
              "9          5238  ...            188.0\n",
              "...         ...  ...              ...\n",
              "21918  17625485  ...            161.0\n",
              "21919  17625707  ...              0.0\n",
              "21920  17625824  ...              0.0\n",
              "21921  17626236  ...             16.0\n",
              "21922         1  ...              NaN\n",
              "\n",
              "[21918 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e_cOxwMxeEma",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "8c929f40-8556-487d-8fbf-154210693818"
      },
      "source": [
        "NewYork.mean()"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                8.453310e+06\n",
              "host_id                           2.426045e+07\n",
              "latitude                          4.072959e+01\n",
              "longitude                        -7.395696e+01\n",
              "price                             1.513750e+02\n",
              "minimum_nights                    7.416522e+00\n",
              "number_of_reviews                 3.579870e+01\n",
              "reviews_per_month                 9.413612e-01\n",
              "calculated_host_listings_count    3.732643e+00\n",
              "availability_365                  1.035321e+02\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XN4UhD8meEo7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "925d5338-6894-43c7-e56a-77cd81514ab2"
      },
      "source": [
        "NewYork.mean().shape"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LZM9HLyneErq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "0dc3146f-8beb-4d1d-ba50-7550d55d4d8c"
      },
      "source": [
        "NewYork.mean(axis=1)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        5.822885e+02\n",
              "1        6.035150e+02\n",
              "2        9.738741e+02\n",
              "3        9.226365e+02\n",
              "4        1.228095e+03\n",
              "             ...     \n",
              "21918    2.545012e+06\n",
              "21919    2.594005e+06\n",
              "21920    9.750817e+06\n",
              "21921    7.226395e+06\n",
              "21922    1.000000e+00\n",
              "Length: 21923, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dAS4ORIDeEwz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "93b9bb18-78f7-414a-f3f0-5836754705c6"
      },
      "source": [
        "NewYork.mean(axis=0)"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                8.453310e+06\n",
              "host_id                           2.426045e+07\n",
              "latitude                          4.072959e+01\n",
              "longitude                        -7.395696e+01\n",
              "price                             1.513750e+02\n",
              "minimum_nights                    7.416522e+00\n",
              "number_of_reviews                 3.579870e+01\n",
              "reviews_per_month                 9.413612e-01\n",
              "calculated_host_listings_count    3.732643e+00\n",
              "availability_365                  1.035321e+02\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zA86Rq9yeE6N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "runs=pd.read_csv('runrate.csv')"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dbAS3IWJeEvL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "3aa2a0a1-a6cb-4982-858b-7bf1839211f4"
      },
      "source": [
        "runs.head()\n"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>team</th>\n",
              "      <th>home_wins</th>\n",
              "      <th>away_wins</th>\n",
              "      <th>home_matches</th>\n",
              "      <th>away_matches</th>\n",
              "      <th>home_win_percentage</th>\n",
              "      <th>away_win_percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Rising Pune Supergiant</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>62.500000</td>\n",
              "      <td>62.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>58</td>\n",
              "      <td>51</td>\n",
              "      <td>101</td>\n",
              "      <td>86</td>\n",
              "      <td>57.425743</td>\n",
              "      <td>59.302326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>51</td>\n",
              "      <td>49</td>\n",
              "      <td>89</td>\n",
              "      <td>75</td>\n",
              "      <td>57.303371</td>\n",
              "      <td>65.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Delhi Capitals</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>70.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>30</td>\n",
              "      <td>28</td>\n",
              "      <td>63</td>\n",
              "      <td>45</td>\n",
              "      <td>47.619048</td>\n",
              "      <td>62.222222</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     team  home_wins  ...  home_win_percentage  away_win_percentage\n",
              "0  Rising Pune Supergiant          5  ...            62.500000            62.500000\n",
              "1          Mumbai Indians         58  ...            57.425743            59.302326\n",
              "2     Chennai Super Kings         51  ...            57.303371            65.333333\n",
              "3          Delhi Capitals          3  ...            50.000000            70.000000\n",
              "4     Sunrisers Hyderabad         30  ...            47.619048            62.222222\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h_5S_gJafNlV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f02de09b-3c8a-4c3c-b719-d3e92e998b6f"
      },
      "source": [
        "runs.home_wins.mean()"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23.928571428571427"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qZtu3s90fOFw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "695b98c2-a638-4ff7-c493-d55ca46f8aab"
      },
      "source": [
        "runs.groupby('home_matches').\thome_wins.mean()\n",
        "#mean according to each continents"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "home_matches\n",
              "6       3\n",
              "7       2\n",
              "8       5\n",
              "14      1\n",
              "20      6\n",
              "43     18\n",
              "63     30\n",
              "67     29\n",
              "72     25\n",
              "83     34\n",
              "85     35\n",
              "89     51\n",
              "91     38\n",
              "101    58\n",
              "Name: home_wins, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2IBWbp35fOIM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "b6231d3e-4f39-4355-d8cb-b048fdab0409"
      },
      "source": [
        "runs.groupby(runs.team=='Mumbai Indians').mean()"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>home_wins</th>\n",
              "      <th>away_wins</th>\n",
              "      <th>home_matches</th>\n",
              "      <th>away_matches</th>\n",
              "      <th>home_win_percentage</th>\n",
              "      <th>away_win_percentage</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>team</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>False</th>\n",
              "      <td>21.307692</td>\n",
              "      <td>27.769231</td>\n",
              "      <td>49.846154</td>\n",
              "      <td>51.0</td>\n",
              "      <td>40.530888</td>\n",
              "      <td>55.287510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>True</th>\n",
              "      <td>58.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>101.000000</td>\n",
              "      <td>86.0</td>\n",
              "      <td>57.425743</td>\n",
              "      <td>59.302326</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       home_wins  away_wins  ...  home_win_percentage  away_win_percentage\n",
              "team                         ...                                          \n",
              "False  21.307692  27.769231  ...            40.530888            55.287510\n",
              "True   58.000000  51.000000  ...            57.425743            59.302326\n",
              "\n",
              "[2 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pNBQJmQQfOLJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "7162ceeb-c5ea-45b9-c145-d4c4df353c07"
      },
      "source": [
        "\n",
        "runs.groupby(runs.team=='Mumbai Indians').mean().plot(kind=\"bar\")"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc41533d630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rJHnuIcjfOPQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "98e8beb3-6ddf-43cf-f1dd-f6d65620f499"
      },
      "source": [
        "runs.groupby(runs.team=='Royal Challengers Bangalore').mean().plot(kind=\"bar\")"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc414ec7400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1AuPmaMPfOT7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "ef34eeb0-1aed-423a-d5e2-6fb90f22a027"
      },
      "source": [
        "runs.groupby(runs.team=='Royal Challengers Bangalore').mean()"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>home_wins</th>\n",
              "      <th>away_wins</th>\n",
              "      <th>home_matches</th>\n",
              "      <th>away_matches</th>\n",
              "      <th>home_win_percentage</th>\n",
              "      <th>away_win_percentage</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>team</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>False</th>\n",
              "      <td>23.076923</td>\n",
              "      <td>27.923077</td>\n",
              "      <td>51.076923</td>\n",
              "      <td>50.307692</td>\n",
              "      <td>41.780832</td>\n",
              "      <td>55.881616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>True</th>\n",
              "      <td>35.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>41.176471</td>\n",
              "      <td>51.578947</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       home_wins  away_wins  ...  home_win_percentage  away_win_percentage\n",
              "team                         ...                                          \n",
              "False  23.076923  27.923077  ...            41.780832            55.881616\n",
              "True   35.000000  49.000000  ...            41.176471            51.578947\n",
              "\n",
              "[2 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N84mn2i-fOYc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "f927e703-c41b-4b4c-87c6-65d117b26e2c"
      },
      "source": [
        "runs.groupby(runs.team=='Royal Challengers Bangalore').mean().plot(kind=\"bar\")"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc414f142b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wok35DhKfOWz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "84d314ba-3152-4e19-f3f3-a123ad77504c"
      },
      "source": [
        "runs.groupby(runs.team=='Royal Challengers Bangalore').mean().plot()"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc414f14c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_NkAy2V-fOSO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "outputId": "b8c64f6c-ba50-42f0-8075-663061296dcd"
      },
      "source": [
        "runs"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>team</th>\n",
              "      <th>home_wins</th>\n",
              "      <th>away_wins</th>\n",
              "      <th>home_matches</th>\n",
              "      <th>away_matches</th>\n",
              "      <th>home_win_percentage</th>\n",
              "      <th>away_win_percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Rising Pune Supergiant</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>62.500000</td>\n",
              "      <td>62.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mumbai Indians</td>\n",
              "      <td>58</td>\n",
              "      <td>51</td>\n",
              "      <td>101</td>\n",
              "      <td>86</td>\n",
              "      <td>57.425743</td>\n",
              "      <td>59.302326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Chennai Super Kings</td>\n",
              "      <td>51</td>\n",
              "      <td>49</td>\n",
              "      <td>89</td>\n",
              "      <td>75</td>\n",
              "      <td>57.303371</td>\n",
              "      <td>65.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Delhi Capitals</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>70.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Sunrisers Hyderabad</td>\n",
              "      <td>30</td>\n",
              "      <td>28</td>\n",
              "      <td>63</td>\n",
              "      <td>45</td>\n",
              "      <td>47.619048</td>\n",
              "      <td>62.222222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Rajasthan Royals</td>\n",
              "      <td>29</td>\n",
              "      <td>46</td>\n",
              "      <td>67</td>\n",
              "      <td>80</td>\n",
              "      <td>43.283582</td>\n",
              "      <td>57.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Deccan Chargers</td>\n",
              "      <td>18</td>\n",
              "      <td>11</td>\n",
              "      <td>43</td>\n",
              "      <td>32</td>\n",
              "      <td>41.860465</td>\n",
              "      <td>34.375000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Kings XI Punjab</td>\n",
              "      <td>38</td>\n",
              "      <td>44</td>\n",
              "      <td>91</td>\n",
              "      <td>85</td>\n",
              "      <td>41.758242</td>\n",
              "      <td>51.764706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Royal Challengers Bangalore</td>\n",
              "      <td>35</td>\n",
              "      <td>49</td>\n",
              "      <td>85</td>\n",
              "      <td>95</td>\n",
              "      <td>41.176471</td>\n",
              "      <td>51.578947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>34</td>\n",
              "      <td>58</td>\n",
              "      <td>83</td>\n",
              "      <td>95</td>\n",
              "      <td>40.963855</td>\n",
              "      <td>61.052632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Delhi Daredevils</td>\n",
              "      <td>25</td>\n",
              "      <td>42</td>\n",
              "      <td>72</td>\n",
              "      <td>89</td>\n",
              "      <td>34.722222</td>\n",
              "      <td>47.191011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Pune Warriors</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>20</td>\n",
              "      <td>26</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>23.076923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Kochi Tuskers Kerala</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>28.571429</td>\n",
              "      <td>57.142857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Gujarat Lions</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>14</td>\n",
              "      <td>16</td>\n",
              "      <td>7.142857</td>\n",
              "      <td>75.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           team  ...  away_win_percentage\n",
              "0        Rising Pune Supergiant  ...            62.500000\n",
              "1                Mumbai Indians  ...            59.302326\n",
              "2           Chennai Super Kings  ...            65.333333\n",
              "3                Delhi Capitals  ...            70.000000\n",
              "4           Sunrisers Hyderabad  ...            62.222222\n",
              "5              Rajasthan Royals  ...            57.500000\n",
              "6               Deccan Chargers  ...            34.375000\n",
              "7               Kings XI Punjab  ...            51.764706\n",
              "8   Royal Challengers Bangalore  ...            51.578947\n",
              "9         Kolkata Knight Riders  ...            61.052632\n",
              "10             Delhi Daredevils  ...            47.191011\n",
              "11                Pune Warriors  ...            23.076923\n",
              "12         Kochi Tuskers Kerala  ...            57.142857\n",
              "13                Gujarat Lions  ...            75.000000\n",
              "\n",
              "[14 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W3ZN3LXKfON6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "1d3d448f-6bb0-444f-fff5-28e838fd19c8"
      },
      "source": [
        "runs.home_matches"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       8\n",
              "1     101\n",
              "2      89\n",
              "3       6\n",
              "4      63\n",
              "5      67\n",
              "6      43\n",
              "7      91\n",
              "8      85\n",
              "9      83\n",
              "10     72\n",
              "11     20\n",
              "12      7\n",
              "13     14\n",
              "Name: home_matches, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5CVaWhcrfNi2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "6802902a-6dfb-48d0-a862-409a58d09ac9"
      },
      "source": [
        "runs.tail()"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>team</th>\n",
              "      <th>home_wins</th>\n",
              "      <th>away_wins</th>\n",
              "      <th>home_matches</th>\n",
              "      <th>away_matches</th>\n",
              "      <th>home_win_percentage</th>\n",
              "      <th>away_win_percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Kolkata Knight Riders</td>\n",
              "      <td>34</td>\n",
              "      <td>58</td>\n",
              "      <td>83</td>\n",
              "      <td>95</td>\n",
              "      <td>40.963855</td>\n",
              "      <td>61.052632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Delhi Daredevils</td>\n",
              "      <td>25</td>\n",
              "      <td>42</td>\n",
              "      <td>72</td>\n",
              "      <td>89</td>\n",
              "      <td>34.722222</td>\n",
              "      <td>47.191011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Pune Warriors</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>20</td>\n",
              "      <td>26</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>23.076923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Kochi Tuskers Kerala</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>28.571429</td>\n",
              "      <td>57.142857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Gujarat Lions</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>14</td>\n",
              "      <td>16</td>\n",
              "      <td>7.142857</td>\n",
              "      <td>75.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     team  home_wins  ...  home_win_percentage  away_win_percentage\n",
              "9   Kolkata Knight Riders         34  ...            40.963855            61.052632\n",
              "10       Delhi Daredevils         25  ...            34.722222            47.191011\n",
              "11          Pune Warriors          6  ...            30.000000            23.076923\n",
              "12   Kochi Tuskers Kerala          2  ...            28.571429            57.142857\n",
              "13          Gujarat Lions          1  ...             7.142857            75.000000\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
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    },
    {
      "cell_type": "code",
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        "colab_type": "code",
        "colab": {
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          "height": 34
        },
        "outputId": "c26f7246-0e6d-4af5-c28e-f9da47f57e40"
      },
      "source": [
        "pd.get_option('display.max_columns')"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A-zO0jDhhHgX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}