Intellipact_Numpy_Practice_.ipynb

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      "name": "Intellipact Numpy  Practice .ipynb",
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "hHhnGjfHTnIj"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "# one dimensional array "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ODU4MUEeTnIq",
        "outputId": "09f0bf3a-04bf-481c-ee39-f8d1015578d2"
      },
      "source": [
        "l1=[1,2,3,4]\n",
        "l1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3, 4]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TYeR_qBLTnIs",
        "outputId": "741e6560-699e-491f-d1f0-83ace4f54389"
      },
      "source": [
        "n1=np.array(l1)\n",
        "n1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 4])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rTPUgXFiTnIs",
        "outputId": "0ce283e8-41bc-412b-ef29-3a1ebf85911b"
      },
      "source": [
        "#Multi dimensional array\n",
        "\n",
        "\n",
        "l2=[6,7,8,9,10,1]\n",
        "l2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[6, 7, 8, 9, 10, 1]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B_r3QuUjTnIt"
      },
      "source": [
        "l3=np.array([[l1],[l2]])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kXjDu2qFTnIt",
        "outputId": "92bef4b4-6099-479f-c8c0-8239f2eb1a7b"
      },
      "source": [
        "l3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[list([1, 2, 3, 4])],\n",
              "       [list([6, 7, 8, 9, 10, 11])]], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rWjErLwvTnIu",
        "outputId": "764aab7b-2ac1-41d5-f670-b26092dc2c96"
      },
      "source": [
        "type(l3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JSOeiePCTnIu",
        "outputId": "fae2fd61-6051-41a2-a283-a8bef038efbb"
      },
      "source": [
        "#numpy as using the zero value \n",
        "\n",
        "import numpy as np\n",
        "\n",
        "np.ones((2,3))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 1., 1.],\n",
              "       [1., 1., 1.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fTz-EqVYTnIu"
      },
      "source": [
        "#arrange method\n",
        "\n",
        "n=np.arange(1,10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QvQn8BNhTnIv",
        "outputId": "c2423714-ec1f-49bb-8ac0-431d34e2af8f"
      },
      "source": [
        "n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PdG1y2YLTnIv",
        "outputId": "8c631ffd-dd85-4429-adf0-94dd6c2659be"
      },
      "source": [
        "# arange the array with respect x and y and intervals is z\n",
        "\n",
        "#odd value and even value\n",
        "\n",
        "np.arange(1,10,2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 3, 5, 7, 9])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZxhJWDgTnIv",
        "outputId": "2a5884d8-ee84-4f3d-acef-279ba7b6a92b"
      },
      "source": [
        "#even value \n",
        "\n",
        "np.arange(10,20,2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([10, 12, 14, 16, 18])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2_MbaZBwTnIw"
      },
      "source": [
        "# arange the z number  between x and y"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gf4kldKcTnIw",
        "outputId": "0089037a-e22e-4141-c095-11341f88c240"
      },
      "source": [
        "np.linspace(10,20,10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([10.        , 11.11111111, 12.22222222, 13.33333333, 14.44444444,\n",
              "       15.55555556, 16.66666667, 17.77777778, 18.88888889, 20.        ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yFwGJ0YTTnIw",
        "outputId": "4e3318eb-7096-4089-8536-736c76eb6432"
      },
      "source": [
        "np.linspace(0,10,10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.        ,  1.11111111,  2.22222222,  3.33333333,  4.44444444,\n",
              "        5.55555556,  6.66666667,  7.77777778,  8.88888889, 10.        ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "vz8xkFpITnIw",
        "outputId": "164c45c7-1ee0-4161-b247-aa788b6c342b"
      },
      "source": [
        "#filling the same no in x and y\n",
        "#using the full method \n",
        "\n",
        "np.full((5,5),10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10, 10, 10, 10, 10],\n",
              "       [10, 10, 10, 10, 10],\n",
              "       [10, 10, 10, 10, 10],\n",
              "       [10, 10, 10, 10, 10],\n",
              "       [10, 10, 10, 10, 10]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kGRRO8PKTnIx"
      },
      "source": [
        "#random method \n",
        "\n",
        "#filling the random value in x and y"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZSXEO-3jTnIx",
        "outputId": "d67a5de3-c38a-4711-9cdb-b01bf88e1ace"
      },
      "source": [
        "np.random.randint(100,200,10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([184, 161, 184, 151, 117, 139, 155, 179, 100, 131])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cg9jh14iTnIx"
      },
      "source": [
        "##INSPECTING A NUMPY ARRAY\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "l3=np.array([[l1],[l2]])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wa0pcfU5TnIx",
        "outputId": "cf86655a-cd6b-4068-acc2-4f3e27e52467"
      },
      "source": [
        "l3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[list([1, 2, 3, 4])],\n",
              "       [list([6, 7, 8, 9, 10, 1])]], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jUmb_dl5TnIy",
        "outputId": "447caf9d-51fd-432e-92c1-e0c0559e6118"
      },
      "source": [
        "np.shape(l3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sXS8-E_TTnIy"
      },
      "source": [
        "l1=[1,2,3,4]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LczeKD0STnIy"
      },
      "source": [
        "l2=[5,5,5,5,5,5]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-PXUEzp5TnIy",
        "outputId": "62d355c8-b8df-47db-b68e-cf4aa4a30758"
      },
      "source": [
        "n=np.array([[1,2,3,4],[5,5,5,5]])\n",
        "n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2, 3, 4],\n",
              "       [5, 5, 5, 5]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E0uo5EjHTnIy",
        "outputId": "2fe09229-7a9b-4fdd-a9b6-52e824dd1d7b"
      },
      "source": [
        "n.shape\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ieosk0ysTnIz",
        "outputId": "0caeb82b-32e3-48a3-b4c0-4525195bcb60"
      },
      "source": [
        "n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2, 3, 4],\n",
              "       [5, 5, 5, 5]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wM1pBaE0TnIz"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XLJJyjaPTnIz",
        "outputId": "1aaa409f-3f32-42fb-ec0f-91ce90445189"
      },
      "source": [
        "n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2, 3, 4],\n",
              "       [5, 5, 5, 5]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zRgEiB__TnIz"
      },
      "source": [
        "\n",
        "a=np.arange(24)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0bBeOtxsTnI0",
        "outputId": "f912dd9d-fcdd-42f0-d526-f236a7a456c4"
      },
      "source": [
        "a.ndim"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IzlXLdkiTnI0",
        "outputId": "68535523-55db-49cc-ea1d-510b31ab0173"
      },
      "source": [
        "b=a.reshape(12,2)  #trick is that calculate the factor 24:1,2,3,4,6,8,12,24\n",
        "b"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0,  1],\n",
              "       [ 2,  3],\n",
              "       [ 4,  5],\n",
              "       [ 6,  7],\n",
              "       [ 8,  9],\n",
              "       [10, 11],\n",
              "       [12, 13],\n",
              "       [14, 15],\n",
              "       [16, 17],\n",
              "       [18, 19],\n",
              "       [20, 21],\n",
              "       [22, 23]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LQ5jFDduTnI0",
        "outputId": "5ec248e2-047a-4809-d926-bcec44a1321e"
      },
      "source": [
        "#sind the no of array element in array\n",
        "\n",
        "a.size"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tczhnS5VTnI0"
      },
      "source": [
        "#find the data type of the array\n",
        "\n",
        "a=np.arange(24,dtype=float)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pPhYr6EyTnI0",
        "outputId": "dbf3de38-32c1-41ce-9271-b5a7fc09992a"
      },
      "source": [
        "print(a.dtype)\n",
        "a"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12.,\n",
              "       13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LQ1cNXtkTnI1"
      },
      "source": [
        " #PERFORMING MATHEMATICAL FUNCTIONS USING NUMPY"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N888YbSJTnI1"
      },
      "source": [
        "#Addtion\n",
        "#Subtraction\n",
        "#multiplication\n",
        "#Divide\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8W8SrA5ETnI1"
      },
      "source": [
        "# some other mathmatical function"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YqjXt1h1TnI1"
      },
      "source": [
        "log\n",
        "exponencial\n",
        "sin\n",
        "cos \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tG2JxgYuTnI1"
      },
      "source": [
        "#Addtion\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DCiyGiLnTnI2"
      },
      "source": [
        "l1=[2,4,6]\n",
        "l2=[1,3,5,]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5QIbnnAvTnI2"
      },
      "source": [
        "import numpy as np\n",
        "#when we need the two array value  in seprate "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_QTn7xlQTnI2",
        "outputId": "5ce416a2-1ffe-4fa3-e43b-d9a936d91a08"
      },
      "source": [
        "np.sum([[2,4,6],[1,3,5,]],axis=0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 3,  7, 11])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MWaz-TmCTnI2",
        "outputId": "ab7c0c0e-17e6-4cdc-9e66-e9ca2449bb88"
      },
      "source": [
        "\n",
        "#sum of the function with puting axis =1\n",
        "\n",
        "np.sum([[2,4,6],[1,3,5,]],axis=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([12,  9])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "6ZgjCZ2ZTnI2",
        "outputId": "a5b4f6c1-922c-4441-bc1c-f60ab73c6bdd"
      },
      "source": [
        "#Subtraction\n",
        "\n",
        "np.subtract(30,20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lk5TAeOOTnI3",
        "outputId": "2c5273d5-416c-4d26-b4c6-5140576605f5"
      },
      "source": [
        "#multiplication\n",
        "\n",
        "\n",
        "np.multiply(20,30)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "600"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HLaaRghPTnI3"
      },
      "source": [
        "#divide "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gpK6mutbTnI3",
        "outputId": "3c301eae-3aef-43af-bd6b-d1ead3de9639"
      },
      "source": [
        "\n",
        "np.divide(30,20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-N-Qu0hITnI3",
        "outputId": "3f0c5603-7930-4c48-b7a5-2ec2de672aaa"
      },
      "source": [
        "\n",
        "\n",
        "l1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2, 4, 6]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "96b3ao68TnI3",
        "outputId": "c16d9b7e-9787-4ef9-a5b1-d930426bcab5"
      },
      "source": [
        "l2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 3, 5]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gzGbpQj1TnI4",
        "outputId": "6dfce623-0bd2-46cb-a694-674f155a55e0"
      },
      "source": [
        "np.multiply([2,4,6],[1,3,5,])\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2, 12, 30])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "omYKDKeWTnI4",
        "outputId": "1b256a9d-6c85-43af-da2f-ca0e4c7d1a00"
      },
      "source": [
        "np.divide([2,4,6],[1,3,5,])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2.        , 1.33333333, 1.2       ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lw4sGcM7TnI4",
        "outputId": "a4b738a1-fb09-4a2f-cd1c-18e3447ac875"
      },
      "source": [
        "print(np.exp(l1))\n",
        "print(np.cos(l1))\n",
        "print(np.sin(l1))\n",
        "print(np.log(l1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[  7.3890561   54.59815003 403.42879349]\n",
            "[-0.41614684 -0.65364362  0.96017029]\n",
            "[ 0.90929743 -0.7568025  -0.2794155 ]\n",
            "[0.69314718 1.38629436 1.79175947]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8zM9D2GQTnI4",
        "outputId": "6f922d1b-9843-440b-e3e3-82d42a8cb0be"
      },
      "source": [
        "print(np.exp(l2))\n",
        "print(np.cos(l2))\n",
        "print(np.sin(l2))\n",
        "print(np.log(l2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[  2.71828183  20.08553692 148.4131591 ]\n",
            "[ 0.54030231 -0.9899925   0.28366219]\n",
            "[ 0.84147098  0.14112001 -0.95892427]\n",
            "[0.         1.09861229 1.60943791]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1IdEon5BTnI5",
        "outputId": "62e0dd74-98d5-4097-a5d9-9dab814357f2"
      },
      "source": [
        "l1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2, 4, 6]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aalO0oNhTnI5",
        "outputId": "290ff79f-c1cb-4764-f089-09a35e797050"
      },
      "source": [
        "l2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 3, 5]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1FHHdk0FTnI5",
        "outputId": "fa9e0cd6-db7d-4260-b7de-883a5f30d384"
      },
      "source": [
        "#comparison of the elemet \n",
        "\n",
        "np.equal(l1,l2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([False, False, False])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1zyZuOGFTnI5"
      },
      "source": [
        "#comparison of the array"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NmOzk_OqTnI5",
        "outputId": "8f7ba763-d929-43dc-9b66-e6acc35b913a"
      },
      "source": [
        "np.array_equal(l1,l2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3LqzBDJhTnI6",
        "outputId": "531eeec5-ecb2-4428-d2c3-847ca1dcc4a7"
      },
      "source": [
        "l1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2, 4, 6]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JmqjuiX6TnI6",
        "outputId": "a173681d-fb67-4a8b-eb46-cf7288a7ac2f"
      },
      "source": [
        "l2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 3, 5]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FgJd9zI4TnI6",
        "outputId": "e240704f-85d5-486a-ccd2-9a3c4f1f2cfc"
      },
      "source": [
        "l3=[10,20,30]\n",
        "l3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[10, 20, 30]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "huJEph0HTnI6"
      },
      "source": [
        "# aggregate function\n",
        "\n",
        "#array wise sum\n",
        "#min of an array\n",
        "#means of the array\n",
        "#median of the array\n",
        "#correlation of the array\n",
        "#standard deviation of the array\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jQGf36JbTnI6"
      },
      "source": [
        "#array wise sum\n",
        "\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ExSRt4IvTnI7",
        "outputId": "77b1ec6c-b33e-4e9a-c429-09aa4e737f20"
      },
      "source": [
        "#L1\n",
        "print(np.sum(l1))\n",
        "print(np.min(l1))\n",
        "print(np.mean(l1))\n",
        "print(np.median(l1))\n",
        "print(np.corrcoef(l1))\n",
        "print(np.std(l1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "12\n",
            "2\n",
            "4.0\n",
            "4.0\n",
            "1.0\n",
            "1.632993161855452\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2hzyA5FeTnI7",
        "outputId": "076627ce-907e-431a-fcbd-dbbf940fb7ed"
      },
      "source": [
        "l2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 3, 5]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CPZPyLBnTnI7"
      },
      "source": [
        "#L2\n",
        "l2=[1, 3, 5]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gr0V3ZExTnI7",
        "outputId": "725fdd03-4798-4b70-d936-d51affcb9632"
      },
      "source": [
        "l2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 3, 5]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3dqfBrADTnI7",
        "outputId": "765e39b3-88d4-4265-feb9-1f6b3d959140"
      },
      "source": [
        "\n",
        "print(np.sum(l2))\n",
        "print(np.min(l2))\n",
        "print(np.mean(l2))\n",
        "print(np.median(l2))\n",
        "print(np.corrcoef(l2))\n",
        "print(np.std(l2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "9\n",
            "1\n",
            "3.0\n",
            "3.0\n",
            "1.0\n",
            "1.632993161855452\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E3wSaQcvTnI8"
      },
      "source": [
        "# Array comparison"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pjvtb4bFTnI8",
        "outputId": "da158625-e7e1-4c73-8345-7b2f8b9b0711"
      },
      "source": [
        "#element wise comparison\n",
        "\n",
        "a=[1,2,4]\n",
        "b=[1,2,3]\n",
        "c=[1,2,4]\n",
        "\n",
        "np.equal(a,b)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True,  True, False])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JYC6EGNITnI8",
        "outputId": "9ab8d320-5af8-40c2-db3c-52ff33eeeb41"
      },
      "source": [
        "np.equal(a,c)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True,  True,  True])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2WxhHdEFTnI8"
      },
      "source": [
        "#array wise comparison\n",
        "\n",
        "a=[1,2,4]\n",
        "b=[1,2,3]\n",
        "c=[1,2,4]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xe7D-90TTnI9",
        "outputId": "26e829f3-167a-476f-ba7c-e41bb9e2e185"
      },
      "source": [
        "a"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 4]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TOErsNxyTnI9",
        "outputId": "318e0ed6-5587-4da0-a212-011668f7d00f"
      },
      "source": [
        "b"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lCyZ_BaBTnI9",
        "outputId": "29521e9c-4cff-4018-d3b6-3efc54eee62f"
      },
      "source": [
        "c"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 4]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aGZrkVcwTnI9",
        "outputId": "40f4c527-4450-41b1-f11d-22e9bb7819c5"
      },
      "source": [
        "np.array_equal(a,b)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9gBJNbpbTnI-",
        "outputId": "7c82d55c-ef7b-4dd8-b0ae-6894d2171ebe"
      },
      "source": [
        "np.array_equal(a,c)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EuqgsjPUTnI-",
        "outputId": "774a31c8-f1be-4d79-d7c5-ba175ad24631"
      },
      "source": [
        "#aggregate THE FUNTION WITH VALUE \n",
        "\n",
        "\n",
        "print(np.sum(a))\n",
        "print(np.min(a))\n",
        "print(np.mean(a))\n",
        "print(np.median(a))\n",
        "print(np.corrcoef(a))\n",
        "print(np.std(a))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7\n",
            "1\n",
            "2.3333333333333335\n",
            "2.0\n",
            "1.0\n",
            "1.247219128924647\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9zu5Ntm_TnI-"
      },
      "source": [
        "#CONCEPT OF THE BRODCATINGS IN GIVEN FUNCTIONS\n",
        "\n",
        "a=np.array([[0,0,0],[2,4,6],[1,3,5,],[4,5,6]])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q4ucR5shTnI-",
        "outputId": "3fba0669-b962-41a7-8bf9-a8a21b8aba67"
      },
      "source": [
        "a"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0],\n",
              "       [2, 4, 6],\n",
              "       [1, 3, 5],\n",
              "       [4, 5, 6]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ON6iJpPFTnI-"
      },
      "source": [
        "b=np.array([[3,4,5]])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f9K-U_vzTnI_",
        "outputId": "abbaf127-8793-428f-c05c-468945d39501"
      },
      "source": [
        "b"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[3, 4, 5]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TSE7tm7STnI_"
      },
      "source": [
        "c=a+b"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I0oXGBcgTnI_",
        "outputId": "483ffae1-d70d-465a-8c54-b11ada5a7b7e"
      },
      "source": [
        "c"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 3,  4,  5],\n",
              "       [ 5,  8, 11],\n",
              "       [ 4,  7, 10],\n",
              "       [ 7,  9, 11]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eVDXftFnTnI_",
        "outputId": "9b0e6105-b598-40f2-dde4-15748ea2eb9b"
      },
      "source": [
        "#second way to write \n",
        "\n",
        "a=np.array([[0,0,0],[2,4,6],[1,3,5,],[4,5,6]])\n",
        "b=np.array([[3,4,5]])\n",
        "print(\"First Array:\\n\",a,'\\n')\n",
        "print(\"Second Array:\\n\",b,'\\n')\n",
        "print(\"First Array+Second Array:\\n\",a+b,'\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "First Array:\n",
            " [[0 0 0]\n",
            " [2 4 6]\n",
            " [1 3 5]\n",
            " [4 5 6]] \n",
            "\n",
            "Second Array:\n",
            " [[3 4 5]] \n",
            "\n",
            "First Array+Second Array:\n",
            " [[ 3  4  5]\n",
            " [ 5  8 11]\n",
            " [ 4  7 10]\n",
            " [ 7  9 11]] \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1GRFt2ffTnJA",
        "outputId": "eb06e351-1516-4137-c7cb-f723318879d7"
      },
      "source": [
        "#ARRAY multiplication\n",
        "\n",
        "l1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2, 4, 6]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a6G3NehPTnJA",
        "outputId": "16296383-c3f7-4347-c63c-6b24f0697306"
      },
      "source": [
        "l2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 3, 5]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wobC_KvSTnJA",
        "outputId": "c5d5585c-60b2-422f-e04e-ada56ebee1a7"
      },
      "source": [
        "#concatenate two array\n",
        "\n",
        "np.concatenate((l1,l2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 4, 6, 1, 3, 5])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R4jHGsMPTnJA",
        "outputId": "439ff2fb-8a44-43f1-c94d-8a67e9116f19"
      },
      "source": [
        "#stack array row wise:   vertically \n",
        "\n",
        "np.vstack((l1,l2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2, 4, 6],\n",
              "       [1, 3, 5]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "febsY4gVTnJA",
        "outputId": "2b3383b3-5da4-4455-d1f0-c61ed327ddbd"
      },
      "source": [
        "##stack array row wise:   horizontally \n",
        "\n",
        "np.hstack((l1,l2))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 4, 6, 1, 3, 5])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h7QENZIJTnJB",
        "outputId": "949de335-7f20-41b2-ec37-c092585fddc1"
      },
      "source": [
        "#stack array column wise:\n",
        "\n",
        "np.column_stack((l1,l2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2, 1],\n",
              "       [4, 3],\n",
              "       [6, 5]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iR5zLn49TnJB"
      },
      "source": [
        "#split array \n",
        "\n",
        "x=np.arange(16.0).reshape(4,4)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gqON6Sv5TnJB",
        "outputId": "d0af523e-f8e8-4d29-a77b-c28d9a00aa0c"
      },
      "source": [
        "x"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.,  1.,  2.,  3.],\n",
              "       [ 4.,  5.,  6.,  7.],\n",
              "       [ 8.,  9., 10., 11.],\n",
              "       [12., 13., 14., 15.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xy9fzhbUTnJB",
        "outputId": "533f1e7c-284b-43b1-e2e3-7927a90a49ca"
      },
      "source": [
        "#split array \n",
        "\n",
        "np.hsplit(x,2)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[ 0.,  1.],\n",
              "        [ 4.,  5.],\n",
              "        [ 8.,  9.],\n",
              "        [12., 13.]]),\n",
              " array([[ 2.,  3.],\n",
              "        [ 6.,  7.],\n",
              "        [10., 11.],\n",
              "        [14., 15.]])]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2M9ZEmJUTnJC",
        "outputId": "9e279a74-f180-48d7-d8e4-47a293275ac2"
      },
      "source": [
        "np.hsplit(x,[3,6])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[ 0.,  1.,  2.],\n",
              "        [ 4.,  5.,  6.],\n",
              "        [ 8.,  9., 10.],\n",
              "        [12., 13., 14.]]),\n",
              " array([[ 3.],\n",
              "        [ 7.],\n",
              "        [11.],\n",
              "        [15.]]),\n",
              " array([], shape=(4, 0), dtype=float64)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lmA4VlBpTnJC"
      },
      "source": [
        "# INDEXING AND SLICING USING NUMPY"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wdChZhxsTnJC",
        "outputId": "ed2262c6-c3f9-4a2c-c2dd-a8525a6db4ca"
      },
      "source": [
        "a=['m','o','n','d','a','y']\n",
        "a"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['m', 'o', 'n', 'd', 'a', 'y']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7BNeIE9FTnJC",
        "outputId": "ba8a6a76-244c-4308-8849-aade8ad967c1"
      },
      "source": [
        "#last value  extracted\n",
        "\n",
        "a[-1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'y'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yZEKekvfTnJC"
      },
      "source": [
        "\n",
        "#assign the value \n",
        "a[-1]=5\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X7RdzkgjTnJD",
        "outputId": "fac50db8-9cb4-408e-c7fc-d777ead56685"
      },
      "source": [
        "a"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['m', 'o', 'n', 'd', 'a', 5]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2KU7lqSFTnJD",
        "outputId": "689ac62d-4a10-40c6-8a91-bf5004c2c72e"
      },
      "source": [
        "#frnt of the  index value \n",
        "#just before value a\n",
        "\n",
        "a[:1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['m']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tDQvlHJDTnJD",
        "outputId": "19a8d664-63b7-4921-dc85-18f2cfb8af83"
      },
      "source": [
        "#remove the index \n",
        "a[1:]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['o', 'n', 'd', 'a', 5]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w_GwptCxTnJD",
        "outputId": "252d8d7e-e68c-4713-fba9-e45879d0ac5c"
      },
      "source": [
        "#range of the value \n",
        "a[1:4]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['o', 'n', 'd']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ly9fAOpeTnJD"
      },
      "source": [
        "a[1]='satyam'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "guBa89u4TnJE",
        "outputId": "3f9c9ea2-e734-4ec1-d5a0-20870ee9cb09"
      },
      "source": [
        "a"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['m', 'satyam', 'n', 'd', 'a', 5]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sIghzW8cTnJE"
      },
      "source": [
        "a=np.array([[0,0,0],[2,4,6],[1,3,5,],[4,5,6]])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dtjcKjtvTnJE",
        "outputId": "87a77c5e-6fee-48ec-abe7-d230595a096f"
      },
      "source": [
        "a"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0],\n",
              "       [2, 4, 6],\n",
              "       [1, 3, 5],\n",
              "       [4, 5, 6]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oVZifSUOTnJE",
        "outputId": "894727dd-d51f-4b25-e52e-982ec2b4bbe7"
      },
      "source": [
        "#extract the first array \n",
        "\n",
        "a[0]\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LyyJLjMqTnJE",
        "outputId": "fdcb236e-b10a-4844-f810-ab46c59e72b2"
      },
      "source": [
        "a[:1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EeqPOzo5TnJF",
        "outputId": "9fc35ed3-153d-474d-be08-f9b4b51ee602"
      },
      "source": [
        "a[:1,1:]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aAZYJ6LITnJF",
        "outputId": "cc1a0cfd-7134-4b67-facb-610fd8db4b20"
      },
      "source": [
        "a[:2]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0],\n",
              "       [2, 4, 6]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hwMQEFRyTnJF",
        "outputId": "a1ab8ddd-601b-4fa9-87a4-4b3ce5fbb6ef"
      },
      "source": [
        "a[:2,1:]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0],\n",
              "       [4, 6]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QS43yvBKTnJG"
      },
      "source": [
        "# Advantage of the numpy  list "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7DaPH0DDTnJG",
        "outputId": "30ab85f0-2911-4d6e-e418-c24d62139f4b"
      },
      "source": [
        "#numpy vs list: Memory size \n",
        "\n",
        "import numpy as np\n",
        "import sys\n",
        "#define a  list \n",
        "\n",
        "l=range(1000)\n",
        "print(\"size of the list:\",sys.getsizeof(1)*len(l))\n",
        "\n",
        "\n",
        "#define the array\n",
        "a=np.arange(1000)\n",
        "print(\"size of an array:\",a.size*a.itemsize)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "size of the list: 28000\n",
            "size of an array: 4000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9rmRPf8iTnJG",
        "outputId": "4550287d-e270-45e4-a817-bdbd22ebfddf"
      },
      "source": [
        "\n",
        "#scipy #it is scientific calculation  of the library \n",
        "\n",
        "scipy.cluster   #it is used for vector size of the calculation\n",
        "scipy.stats      #it is used for statistics\n",
        "scipy.optimize   #it is used for optimization (max and min the function)\n",
        "scipy.integrate  #it is used for integration of the fuction \n",
        "scipy.linalg      #it is used for the linear alzebra of the function \n",
        "scipy.fftpack    #it is used for the fftpack of the function \n",
        "scipy.signal     #it is used for the signal processing of the function"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'scipy' is not defined",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[1;32m<ipython-input-86-4c1ebac79306>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#scipy #it is scientific calculation  of the library\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mscipy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcluster\u001b[0m   \u001b[1;31m#it is used for vector size of the calculation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mscipy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstats\u001b[0m      \u001b[1;31m#it is used for statistics\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mscipy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptimize\u001b[0m   \u001b[1;31m#it is used for optimization (max and min the function)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;31mNameError\u001b[0m: name 'scipy' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hmdi9nPCTnJG",
        "outputId": "35fb30c1-2ec8-4088-8a5e-5c56c97c5579"
      },
      "source": [
        "scipy.cluster "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'scipy' is not defined",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[1;32m<ipython-input-87-5795a3d0446e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mscipy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcluster\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[1;31mNameError\u001b[0m: name 'scipy' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T3G4A3JHTnJH"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M1w9BIYWTnJH",
        "outputId": "2fe718f7-25ec-43e3-ad35-9f41ebaf37c4"
      },
      "source": [
        "car=pd.read_csv('panda\\mtcars2.csv')\n",
        "car"
      ],
      "execution_count": null,
      "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>S.No</th>\n",
              "      <th>Unnamed: 1</th>\n",
              "      <th>mpg</th>\n",
              "      <th>cyl</th>\n",
              "      <th>disp</th>\n",
              "      <th>hp</th>\n",
              "      <th>drat</th>\n",
              "      <th>wt</th>\n",
              "      <th>qsec</th>\n",
              "      <th>vs</th>\n",
              "      <th>am</th>\n",
              "      <th>gear</th>\n",
              "      <th>carb</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Mazda RX4</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6</td>\n",
              "      <td>160.0</td>\n",
              "      <td>110</td>\n",
              "      <td>3.90</td>\n",
              "      <td>2.620</td>\n",
              "      <td>16.46</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Mazda RX4 Wag</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6</td>\n",
              "      <td>160.0</td>\n",
              "      <td>110</td>\n",
              "      <td>3.90</td>\n",
              "      <td>2.875</td>\n",
              "      <td>17.02</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Datsun 710</td>\n",
              "      <td>22.8</td>\n",
              "      <td>4</td>\n",
              "      <td>108.0</td>\n",
              "      <td>93</td>\n",
              "      <td>3.85</td>\n",
              "      <td>2.320</td>\n",
              "      <td>18.61</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Hornet 4 Drive</td>\n",
              "      <td>21.4</td>\n",
              "      <td>6</td>\n",
              "      <td>258.0</td>\n",
              "      <td>110</td>\n",
              "      <td>3.08</td>\n",
              "      <td>3.215</td>\n",
              "      <td>19.44</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Hornet Sportabout</td>\n",
              "      <td>18.7</td>\n",
              "      <td>8</td>\n",
              "      <td>360.0</td>\n",
              "      <td>175</td>\n",
              "      <td>3.15</td>\n",
              "      <td>3.440</td>\n",
              "      <td>17.02</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>Valiant</td>\n",
              "      <td>18.1</td>\n",
              "      <td>6</td>\n",
              "      <td>225.0</td>\n",
              "      <td>105</td>\n",
              "      <td>2.76</td>\n",
              "      <td>3.460</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>Duster 360</td>\n",
              "      <td>14.3</td>\n",
              "      <td>8</td>\n",
              "      <td>360.0</td>\n",
              "      <td>245</td>\n",
              "      <td>3.21</td>\n",
              "      <td>3.570</td>\n",
              "      <td>15.84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>Merc 240D</td>\n",
              "      <td>24.4</td>\n",
              "      <td>4</td>\n",
              "      <td>146.7</td>\n",
              "      <td>62</td>\n",
              "      <td>3.69</td>\n",
              "      <td>3.190</td>\n",
              "      <td>20.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>Merc 230</td>\n",
              "      <td>22.8</td>\n",
              "      <td>4</td>\n",
              "      <td>140.8</td>\n",
              "      <td>95</td>\n",
              "      <td>3.92</td>\n",
              "      <td>3.150</td>\n",
              "      <td>22.90</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>Merc 280</td>\n",
              "      <td>19.2</td>\n",
              "      <td>6</td>\n",
              "      <td>167.6</td>\n",
              "      <td>123</td>\n",
              "      <td>3.92</td>\n",
              "      <td>3.440</td>\n",
              "      <td>18.30</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>Merc 280C</td>\n",
              "      <td>17.8</td>\n",
              "      <td>6</td>\n",
              "      <td>167.6</td>\n",
              "      <td>123</td>\n",
              "      <td>3.92</td>\n",
              "      <td>3.440</td>\n",
              "      <td>18.90</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>Merc 450SE</td>\n",
              "      <td>16.4</td>\n",
              "      <td>8</td>\n",
              "      <td>275.8</td>\n",
              "      <td>180</td>\n",
              "      <td>3.07</td>\n",
              "      <td>4.070</td>\n",
              "      <td>17.40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>Merc 450SL</td>\n",
              "      <td>17.3</td>\n",
              "      <td>8</td>\n",
              "      <td>275.8</td>\n",
              "      <td>180</td>\n",
              "      <td>3.07</td>\n",
              "      <td>3.730</td>\n",
              "      <td>17.60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>Merc 450SLC</td>\n",
              "      <td>15.2</td>\n",
              "      <td>8</td>\n",
              "      <td>275.8</td>\n",
              "      <td>180</td>\n",
              "      <td>3.07</td>\n",
              "      <td>3.780</td>\n",
              "      <td>18.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>Cadillac Fleetwood</td>\n",
              "      <td>10.4</td>\n",
              "      <td>8</td>\n",
              "      <td>472.0</td>\n",
              "      <td>205</td>\n",
              "      <td>2.93</td>\n",
              "      <td>5.250</td>\n",
              "      <td>17.98</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>Lincoln Continental</td>\n",
              "      <td>10.4</td>\n",
              "      <td>8</td>\n",
              "      <td>460.0</td>\n",
              "      <td>215</td>\n",
              "      <td>3.00</td>\n",
              "      <td>5.424</td>\n",
              "      <td>17.82</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>17</td>\n",
              "      <td>Chrysler Imperial</td>\n",
              "      <td>14.7</td>\n",
              "      <td>8</td>\n",
              "      <td>440.0</td>\n",
              "      <td>230</td>\n",
              "      <td>3.23</td>\n",
              "      <td>5.345</td>\n",
              "      <td>17.42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>18</td>\n",
              "      <td>Fiat 128</td>\n",
              "      <td>32.4</td>\n",
              "      <td>4</td>\n",
              "      <td>78.7</td>\n",
              "      <td>66</td>\n",
              "      <td>4.08</td>\n",
              "      <td>2.200</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>19</td>\n",
              "      <td>Honda Civic</td>\n",
              "      <td>30.4</td>\n",
              "      <td>4</td>\n",
              "      <td>75.7</td>\n",
              "      <td>52</td>\n",
              "      <td>4.93</td>\n",
              "      <td>1.615</td>\n",
              "      <td>18.52</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20</td>\n",
              "      <td>Toyota Corolla</td>\n",
              "      <td>33.9</td>\n",
              "      <td>4</td>\n",
              "      <td>71.1</td>\n",
              "      <td>65</td>\n",
              "      <td>4.22</td>\n",
              "      <td>1.835</td>\n",
              "      <td>19.90</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>21</td>\n",
              "      <td>Toyota Corona</td>\n",
              "      <td>21.5</td>\n",
              "      <td>4</td>\n",
              "      <td>120.1</td>\n",
              "      <td>97</td>\n",
              "      <td>3.70</td>\n",
              "      <td>2.465</td>\n",
              "      <td>20.01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>22</td>\n",
              "      <td>Dodge Challenger</td>\n",
              "      <td>15.5</td>\n",
              "      <td>8</td>\n",
              "      <td>318.0</td>\n",
              "      <td>150</td>\n",
              "      <td>2.76</td>\n",
              "      <td>3.520</td>\n",
              "      <td>16.87</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>23</td>\n",
              "      <td>AMC Javelin</td>\n",
              "      <td>15.2</td>\n",
              "      <td>8</td>\n",
              "      <td>304.0</td>\n",
              "      <td>150</td>\n",
              "      <td>3.15</td>\n",
              "      <td>3.435</td>\n",
              "      <td>17.30</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>24</td>\n",
              "      <td>Camaro Z28</td>\n",
              "      <td>13.3</td>\n",
              "      <td>8</td>\n",
              "      <td>350.0</td>\n",
              "      <td>245</td>\n",
              "      <td>3.73</td>\n",
              "      <td>3.840</td>\n",
              "      <td>15.41</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>25</td>\n",
              "      <td>Pontiac Firebird</td>\n",
              "      <td>19.2</td>\n",
              "      <td>8</td>\n",
              "      <td>400.0</td>\n",
              "      <td>175</td>\n",
              "      <td>3.08</td>\n",
              "      <td>3.845</td>\n",
              "      <td>17.05</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>26</td>\n",
              "      <td>Fiat X1-9</td>\n",
              "      <td>27.3</td>\n",
              "      <td>4</td>\n",
              "      <td>79.0</td>\n",
              "      <td>66</td>\n",
              "      <td>4.08</td>\n",
              "      <td>1.935</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>27</td>\n",
              "      <td>Porsche 914-2</td>\n",
              "      <td>26.0</td>\n",
              "      <td>4</td>\n",
              "      <td>120.3</td>\n",
              "      <td>91</td>\n",
              "      <td>4.43</td>\n",
              "      <td>2.140</td>\n",
              "      <td>16.70</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>28</td>\n",
              "      <td>Lotus Europa</td>\n",
              "      <td>30.4</td>\n",
              "      <td>4</td>\n",
              "      <td>95.1</td>\n",
              "      <td>113</td>\n",
              "      <td>3.77</td>\n",
              "      <td>1.513</td>\n",
              "      <td>16.90</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>29</td>\n",
              "      <td>Ford Pantera L</td>\n",
              "      <td>15.8</td>\n",
              "      <td>8</td>\n",
              "      <td>351.0</td>\n",
              "      <td>264</td>\n",
              "      <td>4.22</td>\n",
              "      <td>3.170</td>\n",
              "      <td>14.50</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>30</td>\n",
              "      <td>Ferrari Dino</td>\n",
              "      <td>19.7</td>\n",
              "      <td>6</td>\n",
              "      <td>145.0</td>\n",
              "      <td>175</td>\n",
              "      <td>3.62</td>\n",
              "      <td>2.770</td>\n",
              "      <td>15.50</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>31</td>\n",
              "      <td>Maserati Bora</td>\n",
              "      <td>15.0</td>\n",
              "      <td>8</td>\n",
              "      <td>301.0</td>\n",
              "      <td>335</td>\n",
              "      <td>3.54</td>\n",
              "      <td>3.570</td>\n",
              "      <td>14.60</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>32</td>\n",
              "      <td>Volvo 142E</td>\n",
              "      <td>21.4</td>\n",
              "      <td>4</td>\n",
              "      <td>121.0</td>\n",
              "      <td>109</td>\n",
              "      <td>4.11</td>\n",
              "      <td>2.780</td>\n",
              "      <td>18.60</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    S.No           Unnamed: 1   mpg  cyl   disp   hp  drat     wt   qsec  vs  \\\n",
              "0      1            Mazda RX4  21.0    6  160.0  110  3.90  2.620  16.46   0   \n",
              "1      2        Mazda RX4 Wag  21.0    6  160.0  110  3.90  2.875  17.02   0   \n",
              "2      3           Datsun 710  22.8    4  108.0   93  3.85  2.320  18.61   1   \n",
              "3      4       Hornet 4 Drive  21.4    6  258.0  110  3.08  3.215  19.44   1   \n",
              "4      5    Hornet Sportabout  18.7    8  360.0  175  3.15  3.440  17.02   0   \n",
              "5      6              Valiant  18.1    6  225.0  105  2.76  3.460    NaN   1   \n",
              "6      7           Duster 360  14.3    8  360.0  245  3.21  3.570  15.84   0   \n",
              "7      8            Merc 240D  24.4    4  146.7   62  3.69  3.190  20.00   1   \n",
              "8      9             Merc 230  22.8    4  140.8   95  3.92  3.150  22.90   1   \n",
              "9     10             Merc 280  19.2    6  167.6  123  3.92  3.440  18.30   1   \n",
              "10    11            Merc 280C  17.8    6  167.6  123  3.92  3.440  18.90   1   \n",
              "11    12           Merc 450SE  16.4    8  275.8  180  3.07  4.070  17.40   0   \n",
              "12    13           Merc 450SL  17.3    8  275.8  180  3.07  3.730  17.60   0   \n",
              "13    14          Merc 450SLC  15.2    8  275.8  180  3.07  3.780  18.00   0   \n",
              "14    15   Cadillac Fleetwood  10.4    8  472.0  205  2.93  5.250  17.98   0   \n",
              "15    16  Lincoln Continental  10.4    8  460.0  215  3.00  5.424  17.82   0   \n",
              "16    17    Chrysler Imperial  14.7    8  440.0  230  3.23  5.345  17.42   0   \n",
              "17    18             Fiat 128  32.4    4   78.7   66  4.08  2.200    NaN   1   \n",
              "18    19          Honda Civic  30.4    4   75.7   52  4.93  1.615  18.52   1   \n",
              "19    20       Toyota Corolla  33.9    4   71.1   65  4.22  1.835  19.90   1   \n",
              "20    21        Toyota Corona  21.5    4  120.1   97  3.70  2.465  20.01   1   \n",
              "21    22     Dodge Challenger  15.5    8  318.0  150  2.76  3.520  16.87   0   \n",
              "22    23          AMC Javelin  15.2    8  304.0  150  3.15  3.435  17.30   0   \n",
              "23    24           Camaro Z28  13.3    8  350.0  245  3.73  3.840  15.41   0   \n",
              "24    25     Pontiac Firebird  19.2    8  400.0  175  3.08  3.845  17.05   0   \n",
              "25    26            Fiat X1-9  27.3    4   79.0   66  4.08  1.935    NaN   1   \n",
              "26    27        Porsche 914-2  26.0    4  120.3   91  4.43  2.140  16.70   0   \n",
              "27    28         Lotus Europa  30.4    4   95.1  113  3.77  1.513  16.90   1   \n",
              "28    29       Ford Pantera L  15.8    8  351.0  264  4.22  3.170  14.50   0   \n",
              "29    30         Ferrari Dino  19.7    6  145.0  175  3.62  2.770  15.50   0   \n",
              "30    31        Maserati Bora  15.0    8  301.0  335  3.54  3.570  14.60   0   \n",
              "31    32           Volvo 142E  21.4    4  121.0  109  4.11  2.780  18.60   1   \n",
              "\n",
              "    am  gear  carb  \n",
              "0    1     4     4  \n",
              "1    1     4     4  \n",
              "2    1     4     1  \n",
              "3    0     3     1  \n",
              "4    0     3     2  \n",
              "5    0     3     1  \n",
              "6    0     3     4  \n",
              "7    0     4     2  \n",
              "8    0     4     2  \n",
              "9    0     4     4  \n",
              "10   0     4     4  \n",
              "11   0     3     3  \n",
              "12   0     3     3  \n",
              "13   0     3     3  \n",
              "14   0     3     4  \n",
              "15   0     3     4  \n",
              "16   0     3     4  \n",
              "17   1     4     1  \n",
              "18   1     4     2  \n",
              "19   1     4     1  \n",
              "20   0     3     1  \n",
              "21   0     3     2  \n",
              "22   0     3     2  \n",
              "23   0     3     4  \n",
              "24   0     3     2  \n",
              "25   1     4     1  \n",
              "26   1     5     2  \n",
              "27   1     5     2  \n",
              "28   1     5     4  \n",
              "29   1     5     6  \n",
              "30   1     5     8  \n",
              "31   1     4     2  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1kJ2_fddTnJH",
        "outputId": "c506afcb-897b-4731-d1b9-409906c00038"
      },
      "source": [
        "car.drop(columns=[\"S.No\",\"Unnamed: 1\"])"
      ],
      "execution_count": null,
      "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>mpg</th>\n",
              "      <th>cyl</th>\n",
              "      <th>disp</th>\n",
              "      <th>hp</th>\n",
              "      <th>drat</th>\n",
              "      <th>wt</th>\n",
              "      <th>qsec</th>\n",
              "      <th>vs</th>\n",
              "      <th>am</th>\n",
              "      <th>gear</th>\n",
              "      <th>carb</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>21.0</td>\n",
              "      <td>6</td>\n",
              "      <td>160.0</td>\n",
              "      <td>110</td>\n",
              "      <td>3.90</td>\n",
              "      <td>2.620</td>\n",
              "      <td>16.46</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>21.0</td>\n",
              "      <td>6</td>\n",
              "      <td>160.0</td>\n",
              "      <td>110</td>\n",
              "      <td>3.90</td>\n",
              "      <td>2.875</td>\n",
              "      <td>17.02</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>22.8</td>\n",
              "      <td>4</td>\n",
              "      <td>108.0</td>\n",
              "      <td>93</td>\n",
              "      <td>3.85</td>\n",
              "      <td>2.320</td>\n",
              "      <td>18.61</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>21.4</td>\n",
              "      <td>6</td>\n",
              "      <td>258.0</td>\n",
              "      <td>110</td>\n",
              "      <td>3.08</td>\n",
              "      <td>3.215</td>\n",
              "      <td>19.44</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>18.7</td>\n",
              "      <td>8</td>\n",
              "      <td>360.0</td>\n",
              "      <td>175</td>\n",
              "      <td>3.15</td>\n",
              "      <td>3.440</td>\n",
              "      <td>17.02</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>18.1</td>\n",
              "      <td>6</td>\n",
              "      <td>225.0</td>\n",
              "      <td>105</td>\n",
              "      <td>2.76</td>\n",
              "      <td>3.460</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>14.3</td>\n",
              "      <td>8</td>\n",
              "      <td>360.0</td>\n",
              "      <td>245</td>\n",
              "      <td>3.21</td>\n",
              "      <td>3.570</td>\n",
              "      <td>15.84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>24.4</td>\n",
              "      <td>4</td>\n",
              "      <td>146.7</td>\n",
              "      <td>62</td>\n",
              "      <td>3.69</td>\n",
              "      <td>3.190</td>\n",
              "      <td>20.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>22.8</td>\n",
              "      <td>4</td>\n",
              "      <td>140.8</td>\n",
              "      <td>95</td>\n",
              "      <td>3.92</td>\n",
              "      <td>3.150</td>\n",
              "      <td>22.90</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>19.2</td>\n",
              "      <td>6</td>\n",
              "      <td>167.6</td>\n",
              "      <td>123</td>\n",
              "      <td>3.92</td>\n",
              "      <td>3.440</td>\n",
              "      <td>18.30</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>17.8</td>\n",
              "      <td>6</td>\n",
              "      <td>167.6</td>\n",
              "      <td>123</td>\n",
              "      <td>3.92</td>\n",
              "      <td>3.440</td>\n",
              "      <td>18.90</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>16.4</td>\n",
              "      <td>8</td>\n",
              "      <td>275.8</td>\n",
              "      <td>180</td>\n",
              "      <td>3.07</td>\n",
              "      <td>4.070</td>\n",
              "      <td>17.40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>17.3</td>\n",
              "      <td>8</td>\n",
              "      <td>275.8</td>\n",
              "      <td>180</td>\n",
              "      <td>3.07</td>\n",
              "      <td>3.730</td>\n",
              "      <td>17.60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>15.2</td>\n",
              "      <td>8</td>\n",
              "      <td>275.8</td>\n",
              "      <td>180</td>\n",
              "      <td>3.07</td>\n",
              "      <td>3.780</td>\n",
              "      <td>18.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>10.4</td>\n",
              "      <td>8</td>\n",
              "      <td>472.0</td>\n",
              "      <td>205</td>\n",
              "      <td>2.93</td>\n",
              "      <td>5.250</td>\n",
              "      <td>17.98</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>10.4</td>\n",
              "      <td>8</td>\n",
              "      <td>460.0</td>\n",
              "      <td>215</td>\n",
              "      <td>3.00</td>\n",
              "      <td>5.424</td>\n",
              "      <td>17.82</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>14.7</td>\n",
              "      <td>8</td>\n",
              "      <td>440.0</td>\n",
              "      <td>230</td>\n",
              "      <td>3.23</td>\n",
              "      <td>5.345</td>\n",
              "      <td>17.42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>32.4</td>\n",
              "      <td>4</td>\n",
              "      <td>78.7</td>\n",
              "      <td>66</td>\n",
              "      <td>4.08</td>\n",
              "      <td>2.200</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>30.4</td>\n",
              "      <td>4</td>\n",
              "      <td>75.7</td>\n",
              "      <td>52</td>\n",
              "      <td>4.93</td>\n",
              "      <td>1.615</td>\n",
              "      <td>18.52</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>33.9</td>\n",
              "      <td>4</td>\n",
              "      <td>71.1</td>\n",
              "      <td>65</td>\n",
              "      <td>4.22</td>\n",
              "      <td>1.835</td>\n",
              "      <td>19.90</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>21.5</td>\n",
              "      <td>4</td>\n",
              "      <td>120.1</td>\n",
              "      <td>97</td>\n",
              "      <td>3.70</td>\n",
              "      <td>2.465</td>\n",
              "      <td>20.01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>15.5</td>\n",
              "      <td>8</td>\n",
              "      <td>318.0</td>\n",
              "      <td>150</td>\n",
              "      <td>2.76</td>\n",
              "      <td>3.520</td>\n",
              "      <td>16.87</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>15.2</td>\n",
              "      <td>8</td>\n",
              "      <td>304.0</td>\n",
              "      <td>150</td>\n",
              "      <td>3.15</td>\n",
              "      <td>3.435</td>\n",
              "      <td>17.30</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>13.3</td>\n",
              "      <td>8</td>\n",
              "      <td>350.0</td>\n",
              "      <td>245</td>\n",
              "      <td>3.73</td>\n",
              "      <td>3.840</td>\n",
              "      <td>15.41</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>19.2</td>\n",
              "      <td>8</td>\n",
              "      <td>400.0</td>\n",
              "      <td>175</td>\n",
              "      <td>3.08</td>\n",
              "      <td>3.845</td>\n",
              "      <td>17.05</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>27.3</td>\n",
              "      <td>4</td>\n",
              "      <td>79.0</td>\n",
              "      <td>66</td>\n",
              "      <td>4.08</td>\n",
              "      <td>1.935</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>26.0</td>\n",
              "      <td>4</td>\n",
              "      <td>120.3</td>\n",
              "      <td>91</td>\n",
              "      <td>4.43</td>\n",
              "      <td>2.140</td>\n",
              "      <td>16.70</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>30.4</td>\n",
              "      <td>4</td>\n",
              "      <td>95.1</td>\n",
              "      <td>113</td>\n",
              "      <td>3.77</td>\n",
              "      <td>1.513</td>\n",
              "      <td>16.90</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>15.8</td>\n",
              "      <td>8</td>\n",
              "      <td>351.0</td>\n",
              "      <td>264</td>\n",
              "      <td>4.22</td>\n",
              "      <td>3.170</td>\n",
              "      <td>14.50</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>19.7</td>\n",
              "      <td>6</td>\n",
              "      <td>145.0</td>\n",
              "      <td>175</td>\n",
              "      <td>3.62</td>\n",
              "      <td>2.770</td>\n",
              "      <td>15.50</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>15.0</td>\n",
              "      <td>8</td>\n",
              "      <td>301.0</td>\n",
              "      <td>335</td>\n",
              "      <td>3.54</td>\n",
              "      <td>3.570</td>\n",
              "      <td>14.60</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>21.4</td>\n",
              "      <td>4</td>\n",
              "      <td>121.0</td>\n",
              "      <td>109</td>\n",
              "      <td>4.11</td>\n",
              "      <td>2.780</td>\n",
              "      <td>18.60</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     mpg  cyl   disp   hp  drat     wt   qsec  vs  am  gear  carb\n",
              "0   21.0    6  160.0  110  3.90  2.620  16.46   0   1     4     4\n",
              "1   21.0    6  160.0  110  3.90  2.875  17.02   0   1     4     4\n",
              "2   22.8    4  108.0   93  3.85  2.320  18.61   1   1     4     1\n",
              "3   21.4    6  258.0  110  3.08  3.215  19.44   1   0     3     1\n",
              "4   18.7    8  360.0  175  3.15  3.440  17.02   0   0     3     2\n",
              "5   18.1    6  225.0  105  2.76  3.460    NaN   1   0     3     1\n",
              "6   14.3    8  360.0  245  3.21  3.570  15.84   0   0     3     4\n",
              "7   24.4    4  146.7   62  3.69  3.190  20.00   1   0     4     2\n",
              "8   22.8    4  140.8   95  3.92  3.150  22.90   1   0     4     2\n",
              "9   19.2    6  167.6  123  3.92  3.440  18.30   1   0     4     4\n",
              "10  17.8    6  167.6  123  3.92  3.440  18.90   1   0     4     4\n",
              "11  16.4    8  275.8  180  3.07  4.070  17.40   0   0     3     3\n",
              "12  17.3    8  275.8  180  3.07  3.730  17.60   0   0     3     3\n",
              "13  15.2    8  275.8  180  3.07  3.780  18.00   0   0     3     3\n",
              "14  10.4    8  472.0  205  2.93  5.250  17.98   0   0     3     4\n",
              "15  10.4    8  460.0  215  3.00  5.424  17.82   0   0     3     4\n",
              "16  14.7    8  440.0  230  3.23  5.345  17.42   0   0     3     4\n",
              "17  32.4    4   78.7   66  4.08  2.200    NaN   1   1     4     1\n",
              "18  30.4    4   75.7   52  4.93  1.615  18.52   1   1     4     2\n",
              "19  33.9    4   71.1   65  4.22  1.835  19.90   1   1     4     1\n",
              "20  21.5    4  120.1   97  3.70  2.465  20.01   1   0     3     1\n",
              "21  15.5    8  318.0  150  2.76  3.520  16.87   0   0     3     2\n",
              "22  15.2    8  304.0  150  3.15  3.435  17.30   0   0     3     2\n",
              "23  13.3    8  350.0  245  3.73  3.840  15.41   0   0     3     4\n",
              "24  19.2    8  400.0  175  3.08  3.845  17.05   0   0     3     2\n",
              "25  27.3    4   79.0   66  4.08  1.935    NaN   1   1     4     1\n",
              "26  26.0    4  120.3   91  4.43  2.140  16.70   0   1     5     2\n",
              "27  30.4    4   95.1  113  3.77  1.513  16.90   1   1     5     2\n",
              "28  15.8    8  351.0  264  4.22  3.170  14.50   0   1     5     4\n",
              "29  19.7    6  145.0  175  3.62  2.770  15.50   0   1     5     6\n",
              "30  15.0    8  301.0  335  3.54  3.570  14.60   0   1     5     8\n",
              "31  21.4    4  121.0  109  4.11  2.780  18.60   1   1     4     2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DI515CCDTnJH",
        "outputId": "608bdc96-16c4-4cfe-b00c-9ed250c52ced"
      },
      "source": [
        "from scipy.cluster.vq import kmeans,vq,\n",
        "centroid,_=kmeans(data,3)\n",
        "idx,_=vq(data,centroid)\n",
        "idx"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "trailing comma not allowed without surrounding parentheses (<ipython-input-23-ebc4f544e454>, line 1)",
          "traceback": [
            "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-23-ebc4f544e454>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    from scipy.cluster.vq import kmeans,vq,\u001b[0m\n\u001b[1;37m                                 ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m trailing comma not allowed without surrounding parentheses\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1nFmHo_jTnJI"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}