01_Numpy_Exercise.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Mayank-161001/MathData/blob/main/01_Numpy_Exercise.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-g8Eh0TN_buI"
      },
      "source": [
        "# NumPy Exercises \n",
        "\n",
        "Now that we've learned about NumPy let's test your knowledge. We'll start off with a few simple tasks, and then you'll be asked some more complicated questions. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ji9zp73R_buK"
      },
      "source": [
        "#### Import NumPy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "98Mjrf8x_7ar"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "ky4Ytb0t_buK"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LWWlEPHt_buK"
      },
      "source": [
        "#### Create an array of 10 zeros "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jSUpmuKj_buK",
        "outputId": "120aeecd-89b4-4d3c-c2aa-72c1db519375"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "np.zeros(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yptn7AE8_buL"
      },
      "source": [
        "#### Create an array of 10 ones"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D54GyFPb_buL",
        "outputId": "caab8fd5-1133-42d2-b6c0-3a27197f526e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "np.ones(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aUYTsRKx_buL"
      },
      "source": [
        "#### Create an array of 10 fives"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qh3Jfrk2_buL",
        "outputId": "89db5475-a344-4b00-a49b-4574ea1adefc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "np.ones(10)*5"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lQlUjOc-_buL"
      },
      "source": [
        "#### Create an array of the integers from 10 to 50"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W8-VIyvP_buL",
        "outputId": "dcb340de-257f-4f60-e18f-5d358bda1edb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n",
              "       27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,\n",
              "       44, 45, 46, 47, 48, 49, 50])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "np.arange(10,51)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WmRSksbD_buM"
      },
      "source": [
        "#### Create an array of all the even integers from 10 to 50"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hJZUxFen_buM",
        "outputId": "ce5de850-c756-4354-b571-9bf1a38645f6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,\n",
              "       44, 46, 48, 50])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "np.arange(10,51,2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z8843x3S_buM"
      },
      "source": [
        "#### Create a 3x3 matrix with values ranging from 0 to 8"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gh3Kz7RH_buM",
        "outputId": "4f99d433-ea62-41a5-f3a7-e8ed58aa5784"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 1, 2],\n",
              "       [3, 4, 5],\n",
              "       [6, 7, 8]])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "k=np.arange(0,9)\n",
        "k.reshape(3,3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1wTzYjEq_buM"
      },
      "source": [
        "#### Create a 3x3 identity matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hAGAnqoh_buM",
        "outputId": "5ee366da-0488-48c5-d84f-8b40591eb197"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0.],\n",
              "       [0., 1., 0.],\n",
              "       [0., 0., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "np.eye(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AnkON-B6_buM"
      },
      "source": [
        "#### Use NumPy to generate a random number between 0 and 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tmolv_k2_buM",
        "outputId": "e1768d14-fb74-4ccd-c27f-8c8162bd153f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.99306009])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "np.random.rand(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OidPsds-_buN"
      },
      "source": [
        "#### Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eTHSKHhs_buN",
        "outputId": "618f1a12-56ab-443a-f835-1de88f77326d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.87972564, 0.55421567, 0.14627013, 0.40942006, 0.49205079,\n",
              "       0.18234562, 0.22014291, 0.94358572, 0.31955501, 0.44087611,\n",
              "       0.287498  , 0.88036572, 0.43268615, 0.83396603, 0.07370104,\n",
              "       0.26030152, 0.28094006, 0.72409632, 0.43222942, 0.28389971,\n",
              "       0.7446165 , 0.42774025, 0.83255225, 0.04420341, 0.21674626])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "np.random.rand(25)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RtxVW01E_buN"
      },
      "source": [
        "#### Create the following matrix:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "28yJGI7j_buN",
        "outputId": "c415ac83-4fe8-4272-fc7b-fc4e3af9ba36"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],\n",
              "       [0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],\n",
              "       [0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],\n",
              "       [0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],\n",
              "       [0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],\n",
              "       [0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],\n",
              "       [0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],\n",
              "       [0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],\n",
              "       [0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],\n",
              "       [0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1.  ]])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "k=np.arange(0.01,1.01,.01)\n",
        "k.reshape(10,10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m1nr7UtB_buN"
      },
      "source": [
        "#### Create an array of 20 linearly spaced points between 0 and 1:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jSXmda3Z_buN",
        "outputId": "ba0b3980-c078-40c0-d6ca-b07ef075c2d5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.        , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n",
              "       0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n",
              "       0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n",
              "       0.78947368, 0.84210526, 0.89473684, 0.94736842, 1.        ])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "np.linspace(0,1,20)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WRDRSWxZ_buN"
      },
      "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": "markdown",
      "metadata": {
        "id": "7cTfOpS9_buN"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8fvixuXB_buO",
        "outputId": "6294b62e-c62e-4314-c79f-ecb41b22854a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 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]]\n"
          ]
        }
      ],
      "source": [
        "\n",
        "k=np.arange(1,26)\n",
        "p=k.reshape(5,5)\n",
        "print(p)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bLyNotOn_buO",
        "outputId": "fbb67806-c16f-4041-a97c-b1500e30b27e"
      },
      "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": 15
        }
      ],
      "source": [
        "l=[]\n",
        "for i in range(2,5):\n",
        "    for j in range(1,5):\n",
        "        l.append(p[i,j])\n",
        "k=np.array(l)\n",
        "k.reshape(3,4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bJSiRhbh_buO",
        "outputId": "d72ba877-e759-4482-c27a-f98e3f6a5858"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "p[3,4]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XO9dZmW6_buO",
        "outputId": "bf7d1b54-10d3-410a-d36b-74ff6f8fb4f9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 2],\n",
              "       [ 7],\n",
              "       [12]])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "l=[]\n",
        "for i in range(0,3):\n",
        "    \n",
        "    l.append(p[i,1])\n",
        "k=np.array(l)\n",
        "k.reshape(3,1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CcDXdiRL_buO",
        "outputId": "118c0af4-81a1-438c-8546-6c2fffa5ab69"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([21, 22, 23, 24, 25])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "p[4]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pS8dMZmA_buO",
        "outputId": "11d0f956-9b19-4028-bfec-a20162420474"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[16, 17, 18, 19, 20],\n",
              "       [21, 22, 23, 24, 25]])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "l=[]\n",
        "for i in range(3,5):\n",
        "    for j in range(0,5):\n",
        "        l.append(p[i,j])\n",
        "k=np.array(l)\n",
        "k.reshape(2,5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tznOKNTI_buO"
      },
      "source": [
        "### Now do the following"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mat = np.array([[ 1, 2, 3, 4, 5], [ 6, 7, 8, 9, 10], [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25]])"
      ],
      "metadata": {
        "id": "KowRJXjtCVpu"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "raw",
      "metadata": {
        "id": "eRygS2gz_buO"
      },
      "source": [
        "mat = 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]])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IipIATIr_buO"
      },
      "source": [
        "#### Get the sum of all the values in mat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zJI70nF3_buO",
        "outputId": "0ce678ba-5ace-48e6-a414-f232555c8915"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "325\n"
          ]
        }
      ],
      "source": [
        "l=[]\n",
        "c=0\n",
        "for i in range(0,5):\n",
        "    for j in range(0,5):\n",
        "        c+=mat[i,j]\n",
        "print(c)      \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "krQi4JrH_buO"
      },
      "source": [
        "#### Get the standard deviation of the values in mat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nocLPdR5_buP",
        "outputId": "87afc074-ec66-432f-99e8-dccde959d900"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.211102550927978"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "np.std(mat)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lAeWqanA_bud"
      },
      "source": [
        "#### Get the sum of all the columns in mat"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A0oY1Tv4_bud",
        "outputId": "3ac5e9ae-902a-4f26-d242-c1191c31e1d2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[55, 60, 65, 70, 75]])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "l=[]\n",
        "c=0\n",
        "for i in range(0,5):\n",
        "    for j in range(0,5):\n",
        "        c+=mat[j,i]\n",
        "    l.append(c)\n",
        "    c=0\n",
        "k=np.array(l)\n",
        "k.reshape(1,5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "zT-jo6c4_bue"
      },
      "source": [
        "# Great Job!"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "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": "3.9.12"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}