Assignment_1_DS_GettingStarted.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/sp1999/SHALA2020/blob/master/Assignment_1_DS.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "\n",
        "# **Assignment - 1 : Getting Started**\n",
        "\n",
        "---\n",
        "\n",
        "This assignment will get you familiarized with python libraries required for ***data science*** and ***machine learning***. You will have to complete the code snippets and run them. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "211pD45xEO0u",
        "colab_type": "text"
      },
      "source": [
        "## **Note :**   Use only the methods and attributes of the ```numpy``` library. **Do not** hard-code them. Avoid loops as much as possible."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O7v9C4mun3SQ",
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      },
      "source": [
        "---\n",
        "## Part - 1\n",
        " In this part, you will work with the library ```numpy```. \n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "846yDaXQwiMD",
        "colab_type": "text"
      },
      "source": [
        "Import ```numpy``` with an alias name ```np```. Initialize two arrays $a$, $b$ of size $3 \\times 5$ with random values and print them. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_MyeCO3_o3oq",
        "colab_type": "code",
        "outputId": "81be41a6-1c59-4bd6-9e40-2fdfca5c5e56",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "import numpy as np\n",
        "a = np.random.rand(3,5)\n",
        "b = np.random.rand(3,5)\n",
        "print(a)\n",
        "print(b)"
      ],
      "execution_count": 393,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.1920723  0.72566962 0.7849367  0.97209836 0.85097142]\n",
            " [0.54359433 0.08979087 0.48887324 0.92793635 0.7876182 ]\n",
            " [0.48509423 0.45527936 0.21798577 0.17721338 0.07362367]]\n",
            "[[0.89239319 0.64017662 0.14333232 0.41412692 0.04910892]\n",
            " [0.20937335 0.73070812 0.65112277 0.4789783  0.27478051]\n",
            " [0.65222313 0.95644951 0.43552056 0.07013251 0.05773149]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p7jtpYT4wiO6",
        "colab_type": "text"
      },
      "source": [
        "Initialize and print two arrays $c$ and $d$ having the following properties:\n",
        "*   $c$ is a row vector with all positive integer values less than 100\n",
        "* $d$ is a square matrix of size 3 having all entries as 0.0"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a2lJdRf8x9f2",
        "colab_type": "code",
        "outputId": "de000b07-6c39-45f8-f53c-e249166c40d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "c = np.arange(100)\n",
        "print(c)\n",
        "d = np.zeros(3)\n",
        "print(d)"
      ],
      "execution_count": 394,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
            " 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47\n",
            " 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71\n",
            " 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95\n",
            " 96 97 98 99]\n",
            "[0. 0. 0.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qqgtSsa88Zmh",
        "colab_type": "text"
      },
      "source": [
        "We have $e$ as following : ```e = [[1,2,3],[4,5,6],[7,8,9]]```. Change $e$ as a numpy array and print it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Krhm4bVE7RET",
        "colab_type": "code",
        "outputId": "ce6aa134-8414-45e1-a6a4-047b694ba2b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "e = [[1,2,3],[4,5,6],[7,8,9]]\n",
        "e = np.array(e)\n",
        "print(e)"
      ],
      "execution_count": 395,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1 2 3]\n",
            " [4 5 6]\n",
            " [7 8 9]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1qTikI0foam9",
        "colab_type": "text"
      },
      "source": [
        "$f$ and $g$ are two square matrices of size 5 such that all the entries of $f$ are 1 and $g$ is an identity matrix. Initialize them and print them"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DvaLckI49aBV",
        "colab_type": "code",
        "outputId": "86a509c6-439b-4547-d8eb-cfa8a3950245",
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          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "f = np.ones(5)\n",
        "print(f)\n",
        "g = np.eye(5)\n",
        "print(g)"
      ],
      "execution_count": 396,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1. 1. 1. 1. 1.]\n",
            "[[1. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0.]\n",
            " [0. 0. 1. 0. 0.]\n",
            " [0. 0. 0. 1. 0.]\n",
            " [0. 0. 0. 0. 1.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gcBZAZibAKcg",
        "colab_type": "text"
      },
      "source": [
        "Reshape $c$ into a $10 \\times 10$ matrix and name it $h$. Now flatten $h$, call it $c^\\prime$ and print **YES** when the shapes of $c$ and $c^\\prime$ are same. Otherwise print **NO**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I71vtO9bA9Pi",
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        "outputId": "5fe61d0d-b539-4d7d-fc71-0cd3545edadf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "h = c.reshape((10,10))\n",
        "c1 = h.flatten()\n",
        "if c.shape == c1.shape:\n",
        "  print('YES')\n",
        "else:\n",
        "  print('NO') "
      ],
      "execution_count": 397,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "YES\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8FrecDPiA-Bv",
        "colab_type": "text"
      },
      "source": [
        "Let $A$ denote a randomly initialized three dimensional matrix of shape (2,10,5). Transpose $A$, call it $A^\\prime$, such that its shape is (5,2,10) and print it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VbOKqSm6Bkaq",
        "colab_type": "code",
        "outputId": "be70370b-0c85-4f76-87e3-e785e010e63a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "A = np.random.rand(2,10,5)\n",
        "print(A.shape)\n",
        "print(A)\n",
        "A1 = A.transpose(2,0,1)\n",
        "print(A1.shape)\n",
        "print(A1)"
      ],
      "execution_count": 398,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2, 10, 5)\n",
            "[[[8.28710188e-02 9.59707187e-01 5.40760836e-01 8.37462433e-01\n",
            "   1.70033544e-01]\n",
            "  [2.60345073e-01 6.91977512e-01 8.95570328e-01 3.40688484e-01\n",
            "   6.46731980e-02]\n",
            "  [8.64119669e-01 2.90872446e-01 7.41082406e-01 1.58033655e-01\n",
            "   6.94963435e-01]\n",
            "  [8.41419619e-01 7.27152079e-01 3.59107525e-01 7.26689751e-01\n",
            "   1.39467124e-01]\n",
            "  [3.13819115e-01 4.19582757e-01 8.77212039e-01 1.53740209e-01\n",
            "   8.80124790e-01]\n",
            "  [7.98964319e-01 9.71624297e-01 3.67702983e-01 2.04939769e-01\n",
            "   2.40570320e-01]\n",
            "  [8.27862801e-01 9.65228149e-01 6.98809998e-01 4.82497042e-01\n",
            "   2.87049765e-01]\n",
            "  [8.33687884e-01 8.72179508e-01 9.21315918e-02 2.15949471e-01\n",
            "   8.31761090e-01]\n",
            "  [8.48303897e-01 3.14652999e-01 2.79294597e-01 4.30815022e-01\n",
            "   5.39446500e-01]\n",
            "  [9.55668150e-02 8.36912139e-01 5.34734870e-01 7.74967815e-01\n",
            "   2.30836266e-01]]\n",
            "\n",
            " [[9.65293351e-01 7.51027307e-01 3.43093864e-01 9.48527647e-01\n",
            "   7.00511779e-01]\n",
            "  [8.40561085e-01 4.54973059e-02 5.56415411e-02 7.42737274e-01\n",
            "   3.04686433e-01]\n",
            "  [5.16784366e-01 1.56262424e-01 9.77952410e-01 5.02751048e-01\n",
            "   8.29001078e-01]\n",
            "  [7.40377963e-02 4.78915452e-01 6.22794804e-02 8.84241431e-01\n",
            "   4.45810179e-01]\n",
            "  [6.85499183e-02 7.64962823e-02 5.38792658e-01 7.55664045e-02\n",
            "   1.83772318e-01]\n",
            "  [4.36357084e-01 4.97782831e-01 5.83311915e-01 6.20512681e-01\n",
            "   3.72811500e-01]\n",
            "  [6.18736583e-01 1.57244658e-01 2.75508475e-01 7.98718271e-01\n",
            "   1.53089301e-01]\n",
            "  [2.23322973e-01 2.42978180e-01 4.79507305e-01 7.45522042e-04\n",
            "   3.03113605e-02]\n",
            "  [4.61548152e-01 1.62520685e-01 6.79501804e-01 7.95204587e-01\n",
            "   5.78163359e-01]\n",
            "  [6.94764977e-01 3.90958016e-01 4.62962030e-02 4.39421533e-01\n",
            "   3.71980312e-01]]]\n",
            "(5, 2, 10)\n",
            "[[[8.28710188e-02 2.60345073e-01 8.64119669e-01 8.41419619e-01\n",
            "   3.13819115e-01 7.98964319e-01 8.27862801e-01 8.33687884e-01\n",
            "   8.48303897e-01 9.55668150e-02]\n",
            "  [9.65293351e-01 8.40561085e-01 5.16784366e-01 7.40377963e-02\n",
            "   6.85499183e-02 4.36357084e-01 6.18736583e-01 2.23322973e-01\n",
            "   4.61548152e-01 6.94764977e-01]]\n",
            "\n",
            " [[9.59707187e-01 6.91977512e-01 2.90872446e-01 7.27152079e-01\n",
            "   4.19582757e-01 9.71624297e-01 9.65228149e-01 8.72179508e-01\n",
            "   3.14652999e-01 8.36912139e-01]\n",
            "  [7.51027307e-01 4.54973059e-02 1.56262424e-01 4.78915452e-01\n",
            "   7.64962823e-02 4.97782831e-01 1.57244658e-01 2.42978180e-01\n",
            "   1.62520685e-01 3.90958016e-01]]\n",
            "\n",
            " [[5.40760836e-01 8.95570328e-01 7.41082406e-01 3.59107525e-01\n",
            "   8.77212039e-01 3.67702983e-01 6.98809998e-01 9.21315918e-02\n",
            "   2.79294597e-01 5.34734870e-01]\n",
            "  [3.43093864e-01 5.56415411e-02 9.77952410e-01 6.22794804e-02\n",
            "   5.38792658e-01 5.83311915e-01 2.75508475e-01 4.79507305e-01\n",
            "   6.79501804e-01 4.62962030e-02]]\n",
            "\n",
            " [[8.37462433e-01 3.40688484e-01 1.58033655e-01 7.26689751e-01\n",
            "   1.53740209e-01 2.04939769e-01 4.82497042e-01 2.15949471e-01\n",
            "   4.30815022e-01 7.74967815e-01]\n",
            "  [9.48527647e-01 7.42737274e-01 5.02751048e-01 8.84241431e-01\n",
            "   7.55664045e-02 6.20512681e-01 7.98718271e-01 7.45522042e-04\n",
            "   7.95204587e-01 4.39421533e-01]]\n",
            "\n",
            " [[1.70033544e-01 6.46731980e-02 6.94963435e-01 1.39467124e-01\n",
            "   8.80124790e-01 2.40570320e-01 2.87049765e-01 8.31761090e-01\n",
            "   5.39446500e-01 2.30836266e-01]\n",
            "  [7.00511779e-01 3.04686433e-01 8.29001078e-01 4.45810179e-01\n",
            "   1.83772318e-01 3.72811500e-01 1.53089301e-01 3.03113605e-02\n",
            "   5.78163359e-01 3.71980312e-01]]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xR2zd-nkCani",
        "colab_type": "text"
      },
      "source": [
        "You have a matrix $X$ of size (10,5) filled with random values. Print the following :\n",
        "* Minimum value and index where it occured, for each column\n",
        "* Maximum value and the index where it occured, for each column\n",
        "* Minimum value and index where it occured, for each row\n",
        "* Maximum value and index where it occured, for each row\n",
        "* Minimum value and index where it occured, for the entire matrix\n",
        "* Maximum value and index where it occured, for the entire matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FR2Wd4fWD_Og",
        "colab_type": "code",
        "outputId": "9216fc34-18c5-494a-e7af-0d8c60c40ab1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "import numpy as np\n",
        "np.random.seed(0)\n",
        "X = np.random.rand(10,5)\n",
        "print(X)\n",
        "print(X.shape)\n",
        "idx1 = np.argmin(X,axis=0)\n",
        "value1 = np.min(X,axis=0)\n",
        "idx2 = np.argmax(X,axis=0)\n",
        "value2 = np.max(X,axis=0)\n",
        "idx3 = np.argmin(X,axis=1)\n",
        "value3 = np.min(X,axis=1)\n",
        "idx4 = np.argmax(X,axis=1)\n",
        "value4 = np.max(X,axis=1)\n",
        "idx5 = np.argmin(X)\n",
        "value5 = np.min(X)\n",
        "idx6 = np.argmax(X)\n",
        "value6 = np.max(X)\n",
        "print(idx1,value1)  # Minimum value and index where it occured, for each column\n",
        "print(idx2,value2)  # Maximum value and the index where it occured, for each column\n",
        "print(idx3,value3)  # Minimum value and index where it occured, for each row\n",
        "print(idx4,value4)  # Maximum value and index where it occured, for each row\n",
        "print(idx5,value5)  # Minimum value and index where it occured, for the entire matrix\n",
        "print(idx6,value6)  # Maximum value and index where it occured, for the entire matrix"
      ],
      "execution_count": 399,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.5488135  0.71518937 0.60276338 0.54488318 0.4236548 ]\n",
            " [0.64589411 0.43758721 0.891773   0.96366276 0.38344152]\n",
            " [0.79172504 0.52889492 0.56804456 0.92559664 0.07103606]\n",
            " [0.0871293  0.0202184  0.83261985 0.77815675 0.87001215]\n",
            " [0.97861834 0.79915856 0.46147936 0.78052918 0.11827443]\n",
            " [0.63992102 0.14335329 0.94466892 0.52184832 0.41466194]\n",
            " [0.26455561 0.77423369 0.45615033 0.56843395 0.0187898 ]\n",
            " [0.6176355  0.61209572 0.616934   0.94374808 0.6818203 ]\n",
            " [0.3595079  0.43703195 0.6976312  0.06022547 0.66676672]\n",
            " [0.67063787 0.21038256 0.1289263  0.31542835 0.36371077]]\n",
            "(10, 5)\n",
            "[3 3 9 8 6] [0.0871293  0.0202184  0.1289263  0.06022547 0.0187898 ]\n",
            "[4 4 5 1 3] [0.97861834 0.79915856 0.94466892 0.96366276 0.87001215]\n",
            "[4 4 4 1 4 1 4 1 3 2] [0.4236548  0.38344152 0.07103606 0.0202184  0.11827443 0.14335329\n",
            " 0.0187898  0.61209572 0.06022547 0.1289263 ]\n",
            "[1 3 3 4 0 2 1 3 2 0] [0.71518937 0.96366276 0.92559664 0.87001215 0.97861834 0.94466892\n",
            " 0.77423369 0.94374808 0.6976312  0.67063787]\n",
            "34 0.018789800436355142\n",
            "20 0.978618342232764\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lDXswZl-xJ1B",
        "colab_type": "code",
        "outputId": "5670454c-95fa-4af6-e529-e1d87ae811a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 400,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lwX_gKFdsnm-",
        "colab_type": "code",
        "outputId": "0f0bf4bf-ca59-4fb6-c584-2e19cea519aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "X = np.zeros((3,3,3))\n",
        "X[0,0,1] = 1\n",
        "X[0,1,0] = 4\n",
        "print(X)\n",
        "Y = X.transpose((0,2,1)) # (0,1,2) X[i,j,k] => X[i,k,j], Y[0,0,1] = X[0,1,0]\n",
        "print(Y)"
      ],
      "execution_count": 401,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[[0. 1. 0.]\n",
            "  [4. 0. 0.]\n",
            "  [0. 0. 0.]]\n",
            "\n",
            " [[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]\n",
            "\n",
            " [[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]]\n",
            "[[[0. 4. 0.]\n",
            "  [1. 0. 0.]\n",
            "  [0. 0. 0.]]\n",
            "\n",
            " [[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]\n",
            "\n",
            " [[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YpjW2ypkEGtS",
        "colab_type": "text"
      },
      "source": [
        "Find and print the following for the matrix X: \n",
        "* Mean of each row and column\n",
        "* Standard deviation of each row and column\n",
        "* Mean of the entire matrix\n",
        "* Standard deviation of entire matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gMdm2pw0GWjf",
        "colab_type": "code",
        "outputId": "0ff3af17-5e75-48d7-b873-fc7821fbfe1a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "row_mean = np.mean(X,axis=1)\n",
        "print(row_mean) # Mean of each row\n",
        "column_mean = np.mean(X,axis=0)\n",
        "print(column_mean) # Mean of each column\n",
        "row_std = np.std(X,axis=1)\n",
        "print(row_std) # Standard deviation of each row\n",
        "column_std = np.std(X,axis=0)\n",
        "print(column_std) # Standard deviation of each column\n",
        "mean = np.mean(X)\n",
        "print(mean) # Mean of the entire matrix\n",
        "std = np.std(X)\n",
        "print(std) # Standard deviation of entire matrix"
      ],
      "execution_count": 402,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1.33333333 0.33333333 0.        ]\n",
            " [0.         0.         0.        ]\n",
            " [0.         0.         0.        ]]\n",
            "[[0.         0.33333333 0.        ]\n",
            " [1.33333333 0.         0.        ]\n",
            " [0.         0.         0.        ]]\n",
            "[[1.88561808 0.47140452 0.        ]\n",
            " [0.         0.         0.        ]\n",
            " [0.         0.         0.        ]]\n",
            "[[0.         0.47140452 0.        ]\n",
            " [1.88561808 0.         0.        ]\n",
            " [0.         0.         0.        ]]\n",
            "0.18518518518518517\n",
            "0.7715802465185059\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7dw7W8HOGXKA",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "Let $Y$ denote a square matrix of size 10 with random values. Print the following:\n",
        "* Determinant\n",
        "* Rank of the matrix\n",
        "* Diagonal Elements\n",
        "* Trace\n",
        "* Eigen values\n",
        "* Matrices obtained after Singular Value Decomposition\n",
        "* Inverse if exists, otherwise Pseudo-Inverse\n",
        "* Multiplication $Y$ with $X$\n",
        "* Element-wise multiplication of $Y$ and its inverse(or pseudo-inverse)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tswbn9hgL7yi",
        "colab_type": "code",
        "outputId": "6040684e-3ccc-407c-de5b-7e526d89aa15",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "np.random.seed(3)\n",
        "Y = np.random.rand(10,10)\n",
        "det = np.linalg.det(Y)\n",
        "print(\"determinant(Y)= {}\".format(det))\n",
        "rank = np.linalg.matrix_rank(Y)\n",
        "print(\"rank(Y)= {}\".format(rank))\n",
        "diagonal = np.diag(Y)\n",
        "print('diagonal = {}'.format(diagonal))\n",
        "trace = np.trace(Y)\n",
        "print(\"trace(Y)= {}\".format(trace))\n",
        "eigen_val,eigen_vec = np.linalg.eig(Y)\n",
        "print(\"the eigen values of Y:\\n{}\".format(eigen_val))\n",
        "print(\"the right eigen vectors of Y:\\n{}\".format(eigen_vec))\n",
        "u,s,v = np.linalg.svd(Y)\n",
        "print(\"U of Y is :\\n{}\".format(u))\n",
        "print(\"V of Y is :\\n{}\".format(v))\n",
        "print(\"singular values of Y are :\\n{}\".format(s))\n",
        "inverse = np.linalg.inv(Y)\n",
        "print(\"inverse(Y)= {}\".format(inverse))\n",
        "pseudo_inverse = np.linalg.pinv(Y)\n",
        "print(\"pseudo_inverse(Y)= {}\".format(pseudo_inverse))\n",
        "Z = np.matmul(Y,inverse)\n",
        "print(\"the multiplication of Y and inverse is:\\n{}\".format(Z))"
      ],
      "execution_count": 403,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "determinant(Y)= -0.012807808169095483\n",
            "rank(Y)= 10\n",
            "diagonal = [0.5507979  0.45683322 0.44045372 0.84575087 0.4576864  0.36217621\n",
            " 0.06152893 0.19897603 0.92672568 0.19638058]\n",
            "trace(Y)= 4.497309545724899\n",
            "the eigen values of Y:\n",
            "[ 4.797525  +0.j          0.72411408+0.52238181j  0.72411408-0.52238181j\n",
            " -0.48250062+0.55413671j -0.48250062-0.55413671j -0.6909002 +0.j\n",
            " -0.25905659+0.32357377j -0.25905659-0.32357377j  0.2127855 +0.08352477j\n",
            "  0.2127855 -0.08352477j]\n",
            "the right eigen vectors of Y:\n",
            "[[ 0.29593906+0.j          0.14213977+0.19798456j  0.14213977-0.19798456j\n",
            "  -0.37236549+0.00325385j -0.37236549-0.00325385j -0.1096481 +0.j\n",
            "  -0.0132423 +0.12273111j -0.0132423 -0.12273111j -0.56228934-0.10973258j\n",
            "  -0.56228934+0.10973258j]\n",
            " [ 0.25059285+0.j          0.01404552+0.1967944j   0.01404552-0.1967944j\n",
            "  -0.16781094-0.17307308j -0.16781094+0.17307308j -0.29711444+0.j\n",
            "  -0.04108937-0.4113554j  -0.04108937+0.4113554j   0.61892544+0.j\n",
            "   0.61892544-0.j        ]\n",
            " [ 0.31416894+0.j         -0.24186998+0.15655466j -0.24186998-0.15655466j\n",
            "  -0.16993115+0.15284371j -0.16993115-0.15284371j  0.36362726+0.j\n",
            "  -0.10206063+0.15541211j -0.10206063-0.15541211j  0.16701388+0.18450241j\n",
            "   0.16701388-0.18450241j]\n",
            " [ 0.36027404+0.j          0.52448941+0.j          0.52448941-0.j\n",
            "   0.48969059+0.j          0.48969059-0.j          0.17074825+0.j\n",
            "   0.14078898+0.01851681j  0.14078898-0.01851681j -0.13278372+0.03975259j\n",
            "  -0.13278372-0.03975259j]\n",
            " [ 0.27990836+0.j         -0.13624801+0.01805996j -0.13624801-0.01805996j\n",
            "  -0.0731246 -0.10412128j -0.0731246 +0.10412128j  0.13537705+0.j\n",
            "  -0.29263074+0.368267j   -0.29263074-0.368267j   -0.07942445-0.1381196j\n",
            "  -0.07942445+0.1381196j ]\n",
            " [ 0.30697292+0.j         -0.2288709 -0.09164898j -0.2288709 +0.09164898j\n",
            "   0.34166486-0.10037728j  0.34166486+0.10037728j  0.38961338+0.j\n",
            "   0.1575938 -0.27237341j  0.1575938 +0.27237341j -0.05056956+0.01833742j\n",
            "  -0.05056956-0.01833742j]\n",
            " [ 0.28526996+0.j         -0.07276121-0.22120787j -0.07276121+0.22120787j\n",
            "  -0.09920762+0.30369397j -0.09920762-0.30369397j -0.38942645+0.j\n",
            "  -0.35795075-0.21303717j -0.35795075+0.21303717j  0.25824685-0.05397791j\n",
            "   0.25824685+0.05397791j]\n",
            " [ 0.23358056+0.j          0.33195752+0.22875065j  0.33195752-0.22875065j\n",
            "   0.22899202+0.29805206j  0.22899202-0.29805206j -0.15526644+0.j\n",
            "   0.47450761+0.j          0.47450761-0.j          0.01343252-0.05360821j\n",
            "   0.01343252+0.05360821j]\n",
            " [ 0.39548419+0.j         -0.14569343-0.47441452j -0.14569343+0.47441452j\n",
            "  -0.29937425-0.1334132j  -0.29937425+0.1334132j   0.26931546+0.j\n",
            "   0.07126882+0.08476271j  0.07126882-0.08476271j -0.02419704-0.00279054j\n",
            "  -0.02419704+0.00279054j]\n",
            " [ 0.3952166 +0.j         -0.02644966-0.01254236j -0.02644966+0.01254236j\n",
            "   0.09200666-0.09461051j  0.09200666+0.09461051j -0.5656002 +0.j\n",
            "   0.04736886+0.16022229j  0.04736886-0.16022229j -0.31202472+0.09318458j\n",
            "  -0.31202472-0.09318458j]]\n",
            "U of Y is :\n",
            "[[-0.31776408 -0.02455508 -0.60644878 -0.17883359 -0.12672161  0.14873878\n",
            "  -0.17823942 -0.52859453 -0.02295855 -0.38570883]\n",
            " [-0.26118514 -0.10864541 -0.24911354  0.26547359  0.52168399 -0.25773901\n",
            "  -0.46995326  0.21752636 -0.39285046  0.16234491]\n",
            " [-0.31347907 -0.34483471 -0.25292297  0.1214296   0.1486964   0.33436315\n",
            "   0.54080229  0.48017168  0.05660692 -0.20966421]\n",
            " [-0.35815379  0.59490017  0.30255161  0.15850614 -0.11046295  0.3051409\n",
            "   0.07395537  0.03044889 -0.50561658 -0.18384856]\n",
            " [-0.28385845 -0.18085696 -0.1583078   0.10535533 -0.46807835 -0.08377643\n",
            "   0.24075452 -0.16170764 -0.24786952  0.69201648]\n",
            " [-0.28871348 -0.27577124  0.40271105  0.60433658  0.11931679 -0.04899205\n",
            "   0.02593316 -0.44875145  0.27874747 -0.12964796]\n",
            " [-0.27897316  0.00398904  0.20819753 -0.52536617  0.41116881 -0.41991923\n",
            "   0.42687225 -0.24145621 -0.12958507 -0.00984813]\n",
            " [-0.24788529  0.48136836 -0.15792433 -0.02303902  0.3246308   0.25755952\n",
            "  -0.01429533 -0.03088365  0.56052488  0.44079262]\n",
            " [-0.37505167 -0.36728578  0.39799373 -0.45186801 -0.10830044  0.34456765\n",
            "  -0.4447402   0.14147957  0.0716725   0.09192695]\n",
            " [-0.40049601  0.19846099 -0.02705464  0.02500837 -0.39745062 -0.57941613\n",
            "  -0.10288216  0.3673956   0.33321483 -0.22039139]]\n",
            "V of Y is :\n",
            "[[-0.28816735 -0.35560525 -0.33294235 -0.33250662 -0.36658188 -0.3625847\n",
            "  -0.26573249 -0.23278667 -0.30457183 -0.29270444]\n",
            " [ 0.53693497  0.11306854  0.39562078  0.25099719 -0.07369559 -0.37753546\n",
            "  -0.08584363  0.00333609 -0.2399022  -0.51623983]\n",
            " [-0.084609   -0.22589744 -0.00306935  0.1954239  -0.327784   -0.49318928\n",
            "   0.42323124  0.18588504  0.58200169  0.02301787]\n",
            " [ 0.01388163 -0.2631197   0.42190391 -0.53612946  0.01058633 -0.04415909\n",
            "   0.29385139  0.49501812 -0.32731018  0.15669071]\n",
            " [-0.27340804  0.28220631  0.28945859 -0.1626885   0.14790418 -0.35915036\n",
            "  -0.64971879  0.23854611  0.29589227  0.1337842 ]\n",
            " [ 0.10830593  0.27974551 -0.20571161  0.10308827  0.10096026 -0.5325142\n",
            "   0.15994871 -0.16150892 -0.3941055   0.59692177]\n",
            " [ 0.44882085  0.34972045 -0.01773924 -0.50737391 -0.52251393  0.11970007\n",
            "  -0.02807841 -0.20137782  0.25705553  0.15409283]\n",
            " [-0.35599817 -0.05022624  0.63427946  0.20935183 -0.34847081  0.11510691\n",
            "   0.03182222 -0.47748403 -0.1161671   0.21777064]\n",
            " [ 0.18417681 -0.32168889  0.15954963 -0.32397524  0.5548368  -0.1807982\n",
            "   0.08626619 -0.55063636  0.28439362 -0.01119242]\n",
            " [-0.41560913  0.59522266  0.02089304 -0.23979844  0.13515565 -0.0893732\n",
            "   0.44674689 -0.10709455 -0.02239562 -0.42098815]]\n",
            "singular values of Y are :\n",
            "[4.89636645 1.42832104 1.17173389 0.94068764 0.76892251 0.62457278\n",
            " 0.52105948 0.28969576 0.24512275 0.09350223]\n",
            "inverse(Y)= [[ 2.31470983 -1.92264991  0.7642126   0.78096977 -2.74248585  1.20175092\n",
            "   0.38693174 -1.37664514 -0.96484143  0.83123246]\n",
            " [-2.24504111  1.25605742 -0.91466223 -0.39587642  4.71823627 -1.32162185\n",
            "   0.52594984  2.39368168  0.33051878 -2.33540101]\n",
            " [-1.41290916  0.66149939  0.94968095 -0.18869248 -0.50069581 -0.55664939\n",
            "  -0.55275157  0.57384943 -0.04209635  1.11025544]\n",
            " [ 0.88051129  0.37055438  0.15632501  1.25248223 -1.81219563 -0.7249179\n",
            "  -0.19613524 -1.81766381  0.56943994  0.52226388]\n",
            " [ 0.39743656 -0.28842728 -1.09880632 -1.57965965  0.36480771  0.90235521\n",
            "  -0.47743451  2.09911148  0.53638288 -0.04562024]\n",
            " [ 0.36068175  0.22958502  0.33426258  0.10367281 -0.06700744 -0.37158555\n",
            "   0.22972702 -1.26286862 -0.45133033  0.75430018]\n",
            " [-2.01045364  0.19347423 -1.01393804 -0.74303568  3.56449935 -0.31892146\n",
            "  -0.67116163  2.01265699  0.72873355 -0.69454601]\n",
            " [ 1.18043988  0.86047396 -0.88991494  1.30425635 -0.14247318  0.69661973\n",
            "   0.5413572  -1.69774697 -0.60797727 -1.00698216]\n",
            " [-0.1679456  -0.63197036 -0.0540802  -0.73587818 -0.46423184  0.67805628\n",
            "   0.88551393  0.37662528 -0.03416891  0.42341327]\n",
            " [ 1.3938872  -0.74982353  1.96636643  1.00676681 -3.21953806  0.44078124\n",
            "  -0.40298654 -1.90114336 -0.04414834  0.55576763]]\n",
            "pseudo_inverse(Y)= [[ 2.31470983 -1.92264991  0.7642126   0.78096977 -2.74248585  1.20175092\n",
            "   0.38693174 -1.37664514 -0.96484143  0.83123246]\n",
            " [-2.24504111  1.25605742 -0.91466223 -0.39587642  4.71823627 -1.32162185\n",
            "   0.52594984  2.39368168  0.33051878 -2.33540101]\n",
            " [-1.41290916  0.66149939  0.94968095 -0.18869248 -0.50069581 -0.55664939\n",
            "  -0.55275157  0.57384943 -0.04209635  1.11025544]\n",
            " [ 0.88051129  0.37055438  0.15632501  1.25248223 -1.81219563 -0.7249179\n",
            "  -0.19613524 -1.81766381  0.56943994  0.52226388]\n",
            " [ 0.39743656 -0.28842728 -1.09880632 -1.57965965  0.36480771  0.90235521\n",
            "  -0.47743451  2.09911148  0.53638288 -0.04562024]\n",
            " [ 0.36068175  0.22958502  0.33426258  0.10367281 -0.06700744 -0.37158555\n",
            "   0.22972702 -1.26286862 -0.45133033  0.75430018]\n",
            " [-2.01045364  0.19347423 -1.01393804 -0.74303568  3.56449935 -0.31892146\n",
            "  -0.67116163  2.01265699  0.72873355 -0.69454601]\n",
            " [ 1.18043988  0.86047396 -0.88991494  1.30425635 -0.14247318  0.69661973\n",
            "   0.5413572  -1.69774697 -0.60797727 -1.00698216]\n",
            " [-0.1679456  -0.63197036 -0.0540802  -0.73587818 -0.46423184  0.67805628\n",
            "   0.88551393  0.37662528 -0.03416891  0.42341327]\n",
            " [ 1.3938872  -0.74982353  1.96636643  1.00676681 -3.21953806  0.44078124\n",
            "  -0.40298654 -1.90114336 -0.04414834  0.55576763]]\n",
            "the multiplication of Y and inverse is:\n",
            "[[ 1.00000000e+00 -3.90793144e-17  3.65935406e-16  3.11342944e-16\n",
            "  -8.91983549e-16 -4.81506510e-17 -1.42655871e-16 -6.91246638e-16\n",
            "  -6.67910040e-17  1.33795870e-16]\n",
            " [ 1.42780307e-17  1.00000000e+00 -6.98095657e-17 -1.52151045e-16\n",
            "  -4.63427918e-16 -1.05178821e-16  4.44443766e-17 -9.87040861e-17\n",
            "   1.06466168e-16 -1.58597206e-16]\n",
            " [-1.44191947e-17 -4.68953161e-17  1.00000000e+00  4.77076242e-17\n",
            "  -6.49165003e-16 -6.78141792e-17 -9.36855690e-18 -9.73600834e-16\n",
            "  -3.16087494e-17 -1.18238356e-16]\n",
            " [-1.41735142e-16  2.10254036e-16  1.43559668e-17  1.00000000e+00\n",
            "   5.52648957e-17 -8.23073740e-18 -1.72594927e-18  7.33409702e-17\n",
            "   4.69571374e-17 -2.73245897e-16]\n",
            " [-1.71385362e-17 -3.79289464e-18 -2.27088068e-16  1.03307474e-16\n",
            "   1.00000000e+00 -2.64623779e-17  1.14447652e-16  2.15787383e-17\n",
            "  -1.42660749e-17 -2.17474454e-16]\n",
            " [-8.26384805e-17  1.20440776e-16 -4.69445243e-17 -3.74729367e-17\n",
            "   9.72134814e-16  1.00000000e+00  1.81161593e-17  2.13127233e-16\n",
            "  -7.72471485e-17 -1.30656837e-16]\n",
            " [ 3.53786975e-16  2.66066463e-18 -6.15228131e-17  1.38960755e-16\n",
            "  -1.33510130e-15  2.62173396e-17  1.00000000e+00 -3.76182195e-16\n",
            "  -7.02445804e-17  2.06159764e-16]\n",
            " [-2.75842016e-16 -7.28598118e-17 -2.34103860e-16  2.80898127e-17\n",
            "  -1.39319386e-16  1.41600267e-16 -4.52961673e-17  1.00000000e+00\n",
            "  -2.64462855e-17 -7.90426564e-17]\n",
            " [-6.53027597e-17  1.25653075e-16  5.18133677e-17  1.96668895e-17\n",
            "  -3.92404830e-16 -2.59855724e-17  2.19914269e-18  2.18871633e-17\n",
            "   1.00000000e+00 -5.87793126e-18]\n",
            " [-1.92650595e-16  2.94720021e-16  1.10584317e-16  1.69940671e-16\n",
            "   3.19180173e-16 -1.66979037e-17 -4.43339340e-17  3.56743742e-17\n",
            "  -9.06248767e-18  1.00000000e+00]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qne3RnfOL6-4",
        "colab_type": "text"
      },
      "source": [
        "## End of Part - 1\n",
        " --- --- ---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LA-m4UYkMPTe",
        "colab_type": "text"
      },
      "source": [
        "## Part - 2\n",
        "---\n",
        "In this part, you will be working with the library ```pandas```. Import this library with an alias name ```pd```."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i8z7TpnZSIms",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Initial imports\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2v-HkHnyuQ3X",
        "colab_type": "text"
      },
      "source": [
        "Download dataset from: http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic3.csv\n",
        "Read the data and display the first 5 rows"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LinX5ywNTUIm",
        "colab_type": "code",
        "outputId": "066fbce2-6bc7-488f-c5a0-048ef692912a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "#Download dataset from: http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic3.csv\n",
        "\n",
        "file_url = \"http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic3.csv\"\n",
        "titanic = pd.read_csv(file_url)\n",
        "titanic.head(5)"
      ],
      "execution_count": 405,
      "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>pclass</th>\n",
              "      <th>survived</th>\n",
              "      <th>name</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>ticket</th>\n",
              "      <th>fare</th>\n",
              "      <th>cabin</th>\n",
              "      <th>embarked</th>\n",
              "      <th>boat</th>\n",
              "      <th>body</th>\n",
              "      <th>home.dest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Allen, Miss. Elisabeth Walton</td>\n",
              "      <td>female</td>\n",
              "      <td>29.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>24160</td>\n",
              "      <td>211.3375</td>\n",
              "      <td>B5</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>St Louis, MO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Allison, Master. Hudson Trevor</td>\n",
              "      <td>male</td>\n",
              "      <td>0.92</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>S</td>\n",
              "      <td>11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Allison, Miss. Helen Loraine</td>\n",
              "      <td>female</td>\n",
              "      <td>2.00</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
              "      <td>male</td>\n",
              "      <td>30.00</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>135.0</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
              "      <td>female</td>\n",
              "      <td>25.00</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>S</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   pclass  survived  ...   body                        home.dest\n",
              "0       1         1  ...    NaN                     St Louis, MO\n",
              "1       1         1  ...    NaN  Montreal, PQ / Chesterville, ON\n",
              "2       1         0  ...    NaN  Montreal, PQ / Chesterville, ON\n",
              "3       1         0  ...  135.0  Montreal, PQ / Chesterville, ON\n",
              "4       1         0  ...    NaN  Montreal, PQ / Chesterville, ON\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 405
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vTk1-mF5WYFN",
        "colab_type": "text"
      },
      "source": [
        "### Preprocessing of the Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kx9UrI9WWt17",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "**1. Convert the categorical values of columns 'sex', 'embarked', 'pclass' into dummy values and add them into titanic_data_new DataFrame**\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AwDh9njbZ0QU",
        "colab_type": "code",
        "outputId": "463c2b3f-4de3-4667-a041-c89d118a9123",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        }
      },
      "source": [
        "titanic_data_new = pd.get_dummies(titanic, columns=[\"sex\", \"embarked\", \"pclass\"])\n",
        "titanic_data_new.head(5)"
      ],
      "execution_count": 406,
      "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",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "      <th>name</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>ticket</th>\n",
              "      <th>fare</th>\n",
              "      <th>cabin</th>\n",
              "      <th>boat</th>\n",
              "      <th>body</th>\n",
              "      <th>home.dest</th>\n",
              "      <th>sex_female</th>\n",
              "      <th>sex_male</th>\n",
              "      <th>embarked_C</th>\n",
              "      <th>embarked_Q</th>\n",
              "      <th>embarked_S</th>\n",
              "      <th>pclass_1</th>\n",
              "      <th>pclass_2</th>\n",
              "      <th>pclass_3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Allen, Miss. Elisabeth Walton</td>\n",
              "      <td>29.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>24160</td>\n",
              "      <td>211.3375</td>\n",
              "      <td>B5</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>St Louis, MO</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Allison, Master. Hudson Trevor</td>\n",
              "      <td>0.92</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>Allison, Miss. Helen Loraine</td>\n",
              "      <td>2.00</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
              "      <td>30.00</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>NaN</td>\n",
              "      <td>135.0</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
              "      <td>25.00</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  ... pclass_3\n",
              "0         1  ...        0\n",
              "1         1  ...        0\n",
              "2         0  ...        0\n",
              "3         0  ...        0\n",
              "4         0  ...        0\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 406
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z9bxOfzDbY-n",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "**2. Fill the missing data in Age and Fare columns with the mean value of the respected column then add these columns to titanic_data_new**\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DNx7Pr0ehhAs",
        "colab_type": "code",
        "outputId": "91f0e6b1-ac83-4989-90d7-81f694398d89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "titanic_data_new[\"age\"].fillna(titanic_data_new[\"age\"].mean(), inplace = True)\n",
        "titanic_data_new[\"fare\"].fillna(titanic_data_new[\"fare\"].mean(), inplace = True)\n",
        "titanic_data_new.isna().sum()"
      ],
      "execution_count": 407,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "survived         0\n",
              "name             0\n",
              "age              0\n",
              "sibsp            0\n",
              "parch            0\n",
              "ticket           0\n",
              "fare             0\n",
              "cabin         1014\n",
              "boat           823\n",
              "body          1188\n",
              "home.dest      564\n",
              "sex_female       0\n",
              "sex_male         0\n",
              "embarked_C       0\n",
              "embarked_Q       0\n",
              "embarked_S       0\n",
              "pclass_1         0\n",
              "pclass_2         0\n",
              "pclass_3         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 407
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SlRzKfhxkd1M",
        "colab_type": "text"
      },
      "source": [
        "###Normalization\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "1. Normalize the columns which have numerical data\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y3hpu92TlM38",
        "colab_type": "code",
        "outputId": "82c16918-a11b-42fd-a7bb-0e236edcd357",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        }
      },
      "source": [
        "from sklearn import preprocessing\n",
        "cols_to_normalize = [\"pclass_1\", \"pclass_2\", \"pclass_3\", \"survived\", \"sibsp\", \"parch\", \"body\", \"sex_female\", \"sex_male\", \"embarked_C\", \"embarked_Q\", \"embarked_S\"]\n",
        "x = titanic_data_new[cols_to_normalize].values #returns a numpy array\n",
        "min_max_scaler = preprocessing.MinMaxScaler()\n",
        "x_scaled = min_max_scaler.fit_transform(x)\n",
        "titanic_data_new[cols_to_normalize] = pd.DataFrame(x_scaled)\n",
        "titanic_data_new.head(5)"
      ],
      "execution_count": 408,
      "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>survived</th>\n",
              "      <th>name</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>ticket</th>\n",
              "      <th>fare</th>\n",
              "      <th>cabin</th>\n",
              "      <th>boat</th>\n",
              "      <th>body</th>\n",
              "      <th>home.dest</th>\n",
              "      <th>sex_female</th>\n",
              "      <th>sex_male</th>\n",
              "      <th>embarked_C</th>\n",
              "      <th>embarked_Q</th>\n",
              "      <th>embarked_S</th>\n",
              "      <th>pclass_1</th>\n",
              "      <th>pclass_2</th>\n",
              "      <th>pclass_3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>Allen, Miss. Elisabeth Walton</td>\n",
              "      <td>29.00</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>24160</td>\n",
              "      <td>211.3375</td>\n",
              "      <td>B5</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>St Louis, MO</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>Allison, Master. Hudson Trevor</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.222222</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>Allison, Miss. Helen Loraine</td>\n",
              "      <td>2.00</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.222222</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
              "      <td>30.00</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.222222</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.409786</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
              "      <td>25.00</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.222222</td>\n",
              "      <td>113781</td>\n",
              "      <td>151.5500</td>\n",
              "      <td>C22 C26</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  ... pclass_3\n",
              "0       1.0  ...      0.0\n",
              "1       1.0  ...      0.0\n",
              "2       0.0  ...      0.0\n",
              "3       0.0  ...      0.0\n",
              "4       0.0  ...      0.0\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 408
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wB9-5x83mFAO",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "2. Drop the original age, fare columns\n",
        "3. Append the normalized columns to the titanic_data_new DataFrame.\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kb00kM0onQJy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Already done above"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uLxN3PozntKe",
        "colab_type": "text"
      },
      "source": [
        "###Plotting\n",
        "1. plot the age against the ID(continuous)\n",
        "2. plot the fare against the ID(scatter)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LdSfNEKYn_RO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 683
        },
        "outputId": "a2e999bc-8077-41c5-ed06-8d25a03e5884"
      },
      "source": [
        "plt.figure(figsize=(20,5))\n",
        "plt.plot(titanic_data_new.index.values,titanic_data_new[\"age\"])\n",
        "plt.title(\"Line plot of Age vs Row index\")\n",
        "plt.ylabel(\"Age\")\n",
        "plt.xlabel(\"Row index\")\n",
        "plt.show()\n",
        "plt.figure(figsize=(20,5))\n",
        "plt.scatter(titanic_data_new.index.values, titanic_data_new[\"fare\"], c=\"blue\")\n",
        "plt.title(\"Scatter plot of Fare vs Row index\")\n",
        "plt.ylabel(\"Fare\")\n",
        "plt.xlabel(\"Row index\")\n",
        "plt.show()"
      ],
      "execution_count": 410,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LQr7y6eevTvy",
        "colab_type": "text"
      },
      "source": [
        "## End of Part - 2\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TWhZcKvfvW2B",
        "colab_type": "text"
      },
      "source": [
        "## Part - 3\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8bY4H9DszQ2S",
        "colab_type": "text"
      },
      "source": [
        "In this part, you will work with the library ```matplotlib```. Import the ```pyplot``` from ```matplotlib``` with an alias name ```plt```."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hPN6qctRzknT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQ6KX_Aezlub",
        "colab_type": "text"
      },
      "source": [
        "Plot the sine function with domain in $[-3\\pi,3\\pi]$. Label the axes appropriately. Add title $\\sin(x)$. Use latex formatting while adding the title. Set the plot size to (10,10)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0tA3J5mA1cQ0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "outputId": "da6fc364-5dea-4cf9-8b0c-f27ad9ee58f7"
      },
      "source": [
        "x = np.arange(-3*np.pi,3*np.pi,0.1)   # start,stop,step\n",
        "y = np.sin(x)\n",
        "plt.figure(figsize=(10,10))\n",
        "plt.plot(x,y)\n",
        "plt.xlabel(\"x values from -3pi to 3pi\")\n",
        "plt.ylabel(\"sin(x)\")\n",
        "plt.title(r\"Plot of $\\sin(x)$ from -3pi to 3pi\")\n",
        "plt.show()"
      ],
      "execution_count": 413,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6KV7RqMm1d8P",
        "colab_type": "text"
      },
      "source": [
        "Plot $\\sin(x)$ and its spectrum, $\\cos(x)$ and its spectrum in a single plot, in the domain $[-2\\pi,2\\pi]$. Label the plot appropriately."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-OJuY0HO12Oo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "5d7e79c8-fd2f-470f-e8a4-b846fd020cf9"
      },
      "source": [
        "x = np.arange(-2*np.pi,2*np.pi,0.1)   # start,stop,step\n",
        "y = np.sin(x)\n",
        "z = np.cos(x)\n",
        "plt.plot(x,y,x,z)\n",
        "plt.xlabel('x values from -2pi to 2pi')  # string must be enclosed with quotes '  '\n",
        "plt.ylabel('sin(x) and cos(x)')\n",
        "plt.title('Plot of sin(x) and cos(x) from -2pi to 2pi')\n",
        "plt.legend(['sin(x)', 'cos(x)'])      # legend entries as seperate strings in a list\n",
        "plt.show()"
      ],
      "execution_count": 414,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1S6ZRm6B18ZF",
        "colab_type": "text"
      },
      "source": [
        "## End of Part - 3\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LUyszm8VqGc6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "plt.title(r'$\\alpha$')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hPJg8VUqzXOC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install spams"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}