Atharv_Patawar_Compute_Task_Week_2_Numpy.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/savioratharv/Compute_tasks/blob/main/Atharv_Patawar_Compute_Task_Week_2_Numpy.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X7z3U2pxCAvN"
      },
      "source": [
        "#### Import NumPy "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "jusgfjcDCAvN"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5qcAR8--CAvN"
      },
      "source": [
        "#### Create an array of 10 zeros "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xmEjfwcGCAvO",
        "outputId": "51fe4929-ee5a-46e9-ae64-e28674066140",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
          ]
        }
      ],
      "source": [
        "a=np.zeros(10)\n",
        "print(a)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-sJqSut2CAvO"
      },
      "source": [
        "#### Create an array of 10 ones"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hecGx7cECAvO",
        "outputId": "ee749312-7c71-4ea5-8336-426d10df2fd5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n"
          ]
        }
      ],
      "source": [
        "b=np.empty(10)\n",
        "b.fill(1)\n",
        "print(b)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ARYEHlU5CAvO"
      },
      "source": [
        "#### Create an array of 10 fives"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y-1rWnr7CAvP",
        "outputId": "c64ac935-6f23-48f6-d6d9-9d5799fc5e59",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[5. 5. 5. 5. 5. 5. 5. 5. 5. 5.]\n"
          ]
        }
      ],
      "source": [
        "c=np.empty(10)\n",
        "c.fill(5)\n",
        "print(c)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7IROlwxJCAvP"
      },
      "source": [
        "#### Create an array of the integers from 10 to 50 -!del!-"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UFwQhJT8CAvP",
        "outputId": "ca194127-7e09-4b82-e3c1-b02d86dfc1da",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33\n",
            " 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50]\n"
          ]
        }
      ],
      "source": [
        "d=np.array([i for i in range(10,51)])\n",
        "print(d)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wxoKdt6aCAvP"
      },
      "source": [
        "#### Create an array of all the even integers from 10 to 50  -!del!-"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7Ix-9XyZCAvP",
        "outputId": "0e1d61d6-b21b-4b4a-9b49-88661728359d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50]\n"
          ]
        }
      ],
      "source": [
        "e=np.array([i for i in range(10,51,2)])\n",
        "print(e)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0jPx7PpWCAvP"
      },
      "source": [
        "#### Create a 3x3 matrix with values ranging from 0 to 8"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vLtfhOdwCAvP",
        "outputId": "bd9a3c5a-d4de-40a6-bded-9ad1ba7bb585",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0 1 2]\n",
            " [3 4 5]\n",
            " [6 7 8]]\n"
          ]
        }
      ],
      "source": [
        "x =  np.arange(0, 9).reshape(3,3)\n",
        "print(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JcO95k-XCAvQ"
      },
      "source": [
        "#### Create a 3x3 identity matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JPVEIdl3CAvQ",
        "outputId": "2e397d98-6989-49b2-fff3-574cc4a205aa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1. 0. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 0. 1.]]\n"
          ]
        }
      ],
      "source": [
        "y=np.eye(3)\n",
        "print(y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sn7Hj9KyCAvQ"
      },
      "source": [
        "#### Use NumPy to generate a random number between 0 and 1  -!del!-"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jjB53PpTCAvQ",
        "outputId": "a1177d51-23ee-4b98-a9d4-e55bc504e01b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.1915767955647969\n"
          ]
        }
      ],
      "source": [
        "print(np.random.uniform(0,1))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xAn14oziCAvQ"
      },
      "source": [
        "#### Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MIeS99CiCAvQ",
        "outputId": "7085129f-6baf-4886-e11b-f4e258a317fa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 0.15255435  0.8034546  -0.20223255 -1.15214963  0.8378407   0.80513411\n",
            " -1.25682894  0.63742293  0.20516662  3.18744894 -0.33158159  0.5500461\n",
            " -0.38552017  0.00379528  0.61387022 -0.91456522  1.5242226   0.81553207\n",
            " -0.63356145 -0.33096537 -0.67788587 -0.6526095  -1.23370749 -0.15822104\n",
            "  0.6157122 ]\n"
          ]
        }
      ],
      "source": [
        "r=np.array([np.random.normal() for i in range(25)])\n",
        "print(r)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LbRgd6OyCAvQ"
      },
      "source": [
        "#### Create the following matrix: -!del!-"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AENeDra9CAvR",
        "outputId": "86c1d305-b30a-480f-e4e5-c8c2ba63f27f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.         0.01010101 0.02020202 0.03030303 0.04040404 0.05050505\n",
            "  0.06060606 0.07070707 0.08080808 0.09090909]\n",
            " [0.1010101  0.11111111 0.12121212 0.13131313 0.14141414 0.15151515\n",
            "  0.16161616 0.17171717 0.18181818 0.19191919]\n",
            " [0.2020202  0.21212121 0.22222222 0.23232323 0.24242424 0.25252525\n",
            "  0.26262626 0.27272727 0.28282828 0.29292929]\n",
            " [0.3030303  0.31313131 0.32323232 0.33333333 0.34343434 0.35353535\n",
            "  0.36363636 0.37373737 0.38383838 0.39393939]\n",
            " [0.4040404  0.41414141 0.42424242 0.43434343 0.44444444 0.45454545\n",
            "  0.46464646 0.47474747 0.48484848 0.49494949]\n",
            " [0.50505051 0.51515152 0.52525253 0.53535354 0.54545455 0.55555556\n",
            "  0.56565657 0.57575758 0.58585859 0.5959596 ]\n",
            " [0.60606061 0.61616162 0.62626263 0.63636364 0.64646465 0.65656566\n",
            "  0.66666667 0.67676768 0.68686869 0.6969697 ]\n",
            " [0.70707071 0.71717172 0.72727273 0.73737374 0.74747475 0.75757576\n",
            "  0.76767677 0.77777778 0.78787879 0.7979798 ]\n",
            " [0.80808081 0.81818182 0.82828283 0.83838384 0.84848485 0.85858586\n",
            "  0.86868687 0.87878788 0.88888889 0.8989899 ]\n",
            " [0.90909091 0.91919192 0.92929293 0.93939394 0.94949495 0.95959596\n",
            "  0.96969697 0.97979798 0.98989899 1.        ]]\n"
          ]
        }
      ],
      "source": [
        "x =  np.linspace(0, 1,100).reshape(10,10)\n",
        "print(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QBZWHYOFCAvR"
      },
      "source": [
        "#### Create an array of 20 linearly spaced points between 0 and 1: "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3ra8KFjgCAvR",
        "outputId": "e550ff42-2b81-402b-9886-4e76b656938a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.         0.05263158 0.10526316 0.15789474 0.21052632 0.26315789\n",
            " 0.31578947 0.36842105 0.42105263 0.47368421 0.52631579 0.57894737\n",
            " 0.63157895 0.68421053 0.73684211 0.78947368 0.84210526 0.89473684\n",
            " 0.94736842 1.        ]\n"
          ]
        }
      ],
      "source": [
        "y=np.linspace(0,1,20)\n",
        "print(y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WDFRH7mRCAvR"
      },
      "source": [
        "## Numpy Indexing and Selection\n",
        "\n",
        "Now you will be given a few matrices, and be asked to replicate the resulting matrix outputs:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B_2McpqmCAvR",
        "outputId": "5e61205b-3189-4dac-d306-6f5e62a38c3b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1,  2,  3,  4,  5],\n",
              "       [ 6,  7,  8,  9, 10],\n",
              "       [11, 12, 13, 14, 15],\n",
              "       [16, 17, 18, 19, 20],\n",
              "       [21, 22, 23, 24, 25]])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "mat = np.arange(1,26).reshape(5,5)\n",
        "mat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "oxyloFWDCAvR"
      },
      "outputs": [],
      "source": [
        "# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
        "# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
        "# BE ABLE TO SEE THE OUTPUT ANY MORE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V_GnVZsBCAvR",
        "outputId": "efe9be82-dcaf-4be5-e8ad-da61f02d4369",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[12, 13, 14, 15],\n",
              "       [17, 18, 19, 20],\n",
              "       [22, 23, 24, 25]])"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "mat[2:,1:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "OE0SKA7bCAvS"
      },
      "outputs": [],
      "source": [
        "# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
        "# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
        "# BE ABLE TO SEE THE OUTPUT ANY MORE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M9CB9jd2CAvS",
        "outputId": "3e5ebd00-472f-4863-b033-f6999ece6e7a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[20]])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "mat[3:4,4:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "5qPTLISjCAvS"
      },
      "outputs": [],
      "source": [
        "# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
        "# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
        "# BE ABLE TO SEE THE OUTPUT ANY MORE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6wT06ZgHCAvS",
        "outputId": "47ae31b3-8a8f-4028-9422-8786d87b9a38",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 2],\n",
              "       [ 7],\n",
              "       [12]])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "mat[0:3,1:2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "rQr3hBL6CAvS"
      },
      "outputs": [],
      "source": [
        "# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
        "# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
        "# BE ABLE TO SEE THE OUTPUT ANY MORE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C1VdxOrTCAvS",
        "outputId": "a0b9d83e-ca18-4aa9-e30c-d4d48a93ec4f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[21, 22, 23, 24, 25]])"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "mat[4:5,:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "ZRQgDH79CAvS"
      },
      "outputs": [],
      "source": [
        "# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
        "# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
        "# BE ABLE TO SEE THE OUTPUT ANY MORE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0fhU2bu7CAvT",
        "outputId": "75a4109d-0f9c-4409-aaac-8d037353676b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[16, 17, 18, 19, 20],\n",
              "       [21, 22, 23, 24, 25]])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "mat[3:5,:]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ih6-cRw_CAvT"
      },
      "source": [
        "### Now do the following"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mi-WfNJOCAvT"
      },
      "source": [
        "#### Get the sum of all the values in mat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T6SjcnWPCAvT",
        "outputId": "4a7fe52f-e29c-4c8e-bea7-bf614ef62c6f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "325"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "mat.sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PwZD2RewCAvT"
      },
      "source": [
        "#### Get the standard deviation of the values in mat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MxnOj36BCAvT",
        "outputId": "fd90fe87-7e6b-4fe5-fe94-3a304b8fcf1e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.211102550927978"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "mat.std()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zO38BgXWCAvU"
      },
      "source": [
        "#### Get the sum of all the columns in mat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BBJ0PLt5CAvU",
        "outputId": "91c1687b-005d-4fa3-9a03-006d1d6a37fd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([55, 60, 65, 70, 75])"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "mat.sum(axis=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Multiply two matrices using NumPy.\n",
        "\n",
        "A = [[1, 2], [2, 3]]\n",
        "\n",
        "B = [[4, 5], [6, 7]]\n",
        "\n",
        "So, A.B = [[1*4 + 2*6, 2*4 + 3*6], [1*5 + 2*7, 2*5 + 3*7]\n",
        "\n",
        "The computed answer will be: [[16, 26], [19, 31]]\n"
      ],
      "metadata": {
        "id": "AmoYw9QoljOW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a=[[1,2],[2,3]]\n",
        "b=[[4,5],[6,7]]\n",
        "np.matmul(a,b)"
      ],
      "metadata": {
        "id": "TlwnfZgYlzsQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f64d88c-3d7b-49c7-fb3d-5f685147d559"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[16, 19],\n",
              "       [26, 31]])"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Replace NaN values with average of that column\n",
        "\n",
        "Initial array :  \n",
        "\n",
        "[[ 1.3  2.5  3.6  nan]\n",
        " [ 2.6  3.3  nan  5.5]\n",
        " [ 2.1  3.2  5.4  6.5]]\n"
      ],
      "metadata": {
        "id": "0bNmuR33mE10"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a=[[ 1.3 ,2.5  ,3.6  ,np.NaN],[ 2.6  ,3.3  ,np.NaN  ,5.5],[ 2.1  ,3.2  ,5.4  ,6.5]]\n",
        "res = np.where(np.isnan(a), np.ma.array(a,\n",
        "               mask = np.isnan(a)).mean(axis = 0), a)\n",
        "print(res)"
      ],
      "metadata": {
        "id": "2lXzmm6EmRwN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "14fd2830-f81f-43cf-e43d-fbe78c74ff84"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1.3 2.5 3.6 6. ]\n",
            " [2.6 3.3 4.5 5.5]\n",
            " [2.1 3.2 5.4 6.5]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "GiRsFFT_CAvU"
      },
      "source": [
        "# You're done!"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.5.1"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}