AIML_Lab_01.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM9WtqlaQ09rIlRweFtrJ/e",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/KMadhu10/AIML_1374/blob/main/AIML_Lab_01.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Lab-1\n"
      ],
      "metadata": {
        "id": "e7B-imGUEo_F"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "PwSZkn7VE6EO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Part 01: Lab Task is implement the following data structures using Numpy and Pandas.**\n",
        "Lists\n",
        "Arrays\n",
        "Identify their type using type()\n",
        "Perform mathematical operations on these datasets created - multiplication, division, poweroff\n",
        "Combine text with Numpy fuction to generate a textual output - ”Addition of Two: array1 +array 2\n",
        "Implement np.sin(), log(), log2(), np.exp())\n"
      ],
      "metadata": {
        "id": "0Q_ON3UpFLHG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "lst1=[1,2,3]\n",
        "array1 = np.array(lst1)\n",
        "array1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RHaR3tJ6FWBV",
        "outputId": "8b4a0456-1f6e-4fbc-d279-296f9f0d6de5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(lst1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K79XJy9KFzDt",
        "outputId": "7331781d-ed75-4da5-961a-ad326220746f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "list"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(array1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1-NufJpGF6K-",
        "outputId": "f79be9b9-7349-45be-b7be-f7fbb16ce1a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "QzMlMzFBFrkd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Perform Mathematical Operations on these datasets - Multiplication, Division, Poweroff"
      ],
      "metadata": {
        "id": "JaAVcYUkGAQ2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "list2=[3,2,1]\n",
        "array2=np.array(list2)\n",
        "array2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "csRo9L1pGD9G",
        "outputId": "54a4df90-5f07-4e1c-c608-4a287748b0d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 2, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"array2 multiplied by array1: \",array1*array2)\n",
        "print(\"array2 divided by array1: \",array2/array1)\n",
        "print(\"array2 raised to the power of array1: \",array2**array1)\n",
        "print(f\" {array2} raised to the power of {array1}:,{array2**array1}\")"
      ],
      "metadata": {
        "id": "bGLHCYaaGPHz",
        "outputId": "0a7edd06-2d45-4358-fbac-79ba996f1cd1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "array2 multiplied by array1:  [3 4 3]\n",
            "array2 divided by array1:  [3.         1.         0.33333333]\n",
            "array2 raised to the power of array1:  [3 4 1]\n",
            " [3 2 1] raised to the power of [1 2 3]:,[3 4 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"array2 multiplied by array1: \",array1*array2)\n",
        "print(\"array2 divided by array1: \",array2/array1)\n",
        "print(\"array2 raised to the power of array1: \",array2**array1)\n",
        "print(f\" {array2} raised to the power of {array1}:,{array2**array1}\")"
      ],
      "metadata": {
        "id": "EsmSD_MKGZW1",
        "outputId": "c34e4a2c-6fc0-414e-c29f-3ecae6388cfe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "array2 multiplied by array1:  [3 4 3]\n",
            "array2 divided by array1:  [3.         1.         0.33333333]\n",
            "array2 raised to the power of array1:  [3 4 1]\n",
            " [3 2 1] raised to the power of [1 2 3]:,[3 4 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Sine: \",np.sin(array1))\n",
        "# logarithm\n",
        "print(\"Natural logarithm: \",np.log(array1))\n",
        "print(\"Base-10 logarithm: \",np.log10(array1))\n",
        "print(\"Base-2 logarithm: \",np.log2(array1))\n",
        "# Exponential\n",
        "print(\"Exponential: \",np.exp(array1))"
      ],
      "metadata": {
        "id": "Kclgg-l3GlVd",
        "outputId": "679972e9-9b5f-4495-b352-8b0bb1f0c53a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sine:  [0.84147098 0.90929743 0.14112001]\n",
            "Natural logarithm:  [0.         0.69314718 1.09861229]\n",
            "Base-10 logarithm:  [0.         0.30103    0.47712125]\n",
            "Base-2 logarithm:  [0.        1.        1.5849625]\n",
            "Exponential:  [ 2.71828183  7.3890561  20.08553692]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Adding two numpy arrays {array1} and {array2} together:\", array1+array2)"
      ],
      "metadata": {
        "id": "Q0gssRmFGp3u",
        "outputId": "1bff6ed6-41f5-4b54-8a8d-1167126a1073",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Adding two numpy arrays {array1} and {array2} together: [4 4 4]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Adding two numpy arrays {array1} and {array2} together: {array1+array2}\")"
      ],
      "metadata": {
        "id": "vPoMly8mG5pt",
        "outputId": "92c7a5d7-93fa-40b1-d147-9b62b8a16f6e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Adding two numpy arrays [1 2 3] and [3 2 1] together: [4 4 4]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Part-02: Lab Task is to visualize the datasets or data using Matplotlib"
      ],
      "metadata": {
        "id": "ArQyYj97u5rX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "builtin function Graphs"
      ],
      "metadata": {
        "id": "_TJM5_Oxu-uQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Generate Scatterplot"
      ],
      "metadata": {
        "id": "zHd-NP3IvHxH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's suppose we collected age (in years), height (in cm), and weight (in kilograms) information from few patients visitng a clinic."
      ],
      "metadata": {
        "id": "VadG9N6fvKMc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "people = ['Ann','Brandon','Chen','David','Emily','Farook',\n",
        "          'Gagan','Hamish','Imran','Julio','Katherine','Lily']\n",
        "age = [21,12,32,45,37,18,28,52,5,40,48,15]\n",
        "weight = [55,35,77,68,70,60,72,69,18,65,82,48]\n",
        "height = [160,135,170,165,173,168,175,159,105,171,155,158]"
      ],
      "metadata": {
        "id": "d_YK551EvNUm"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "0thB__CRwFx5"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(weight, height)\n",
        "plt.title(\"Relationship between Weight and Height of Patients\")\n",
        "plt.ylabel(\"Height in Meters\")\n",
        "plt.xlabel(\"Weight of Patients in Kgs\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "Ce2omyoTwIpV",
        "outputId": "af407439-bd23-4f0b-ccf3-82aef6ab0dc9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Generate Bar Plot"
      ],
      "metadata": {
        "id": "z5mWsSdkwO2N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,4))\n",
        "plt.title(\"People's weight in kgs\",fontsize=16,\n",
        "          fontstyle='italic')\n",
        "\n",
        "          # Main plot function 'bar'\n",
        "plt.bar(x=people,height=weight, width=0.6,\n",
        "                  color='orange',edgecolor='k',alpha=0.6)\n",
        "\n",
        "plt.xlabel(\"People\",fontsize=15)\n",
        "                  #plt.xticks(fontsize=14,rotation=30)\n",
        "                  #plt.yticks(fontsize=14)\n",
        "plt.ylabel(\"Weight (in kgs)\",fontsize=15)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "L6ysPO0EwTlF",
        "outputId": "20bcaac4-fc0e-492d-af41-570dcd5fdd44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Generate Histogram"
      ],
      "metadata": {
        "id": "WNnTmu3ewhWv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "plt.figure(figsize=(7,5))\n",
        "\n",
        "# Main plot function 'hist'\n",
        "plt.hist(weight,color='red',edgecolor='k', alpha=0.75,bins=5)\n",
        "\n",
        "plt.title(\"Histogram of patient weight\",fontsize=18)\n",
        "plt.xlabel(\"Weight in kgs\",fontsize=15)\n",
        "#plt.xticks(fontsize=15)\n",
        "#plt.yticks(fontsize=15)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "lSD3PKqbwkHN",
        "outputId": "b07e17ef-b1ea-4336-b40d-39c2126b22f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        }
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Part-03: Access Data from various sources and formats"
      ],
      "metadata": {
        "id": "W3VgtCJwwrbT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n"
      ],
      "metadata": {
        "id": "bO57enyRwtg-"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = pd.read_table(\"Lab01-Text.txt\")\n"
      ],
      "metadata": {
        "id": "WfysCAgkxDJV"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1"
      ],
      "metadata": {
        "id": "WgbVFCEL57b-",
        "outputId": "647ac7e1-28a7-45c5-d464-d4370cbac12b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Name  Age Branch\n",
              "0   Vishnu   20    CSE\n",
              "1   Harshu   19    CSE\n",
              "2    Rahul   21    EEE\n",
              "3    Pavan   19    ECE\n",
              "4     Sonu   18    CSE\n",
              "5   Hasini   18     DS\n",
              "6  Hepsiba   19  Civil"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c4f8888a-9441-4614-88ea-f6aaaf93eff0\" class=\"colab-df-container\">\n",
              "    <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>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Branch</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Vishnu</td>\n",
              "      <td>20</td>\n",
              "      <td>CSE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Harshu</td>\n",
              "      <td>19</td>\n",
              "      <td>CSE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Rahul</td>\n",
              "      <td>21</td>\n",
              "      <td>EEE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Pavan</td>\n",
              "      <td>19</td>\n",
              "      <td>ECE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Sonu</td>\n",
              "      <td>18</td>\n",
              "      <td>CSE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Hasini</td>\n",
              "      <td>18</td>\n",
              "      <td>DS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Hepsiba</td>\n",
              "      <td>19</td>\n",
              "      <td>Civil</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c4f8888a-9441-4614-88ea-f6aaaf93eff0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c4f8888a-9441-4614-88ea-f6aaaf93eff0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c4f8888a-9441-4614-88ea-f6aaaf93eff0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d6e52d63-1534-4b5c-a055-c757e213b274\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d6e52d63-1534-4b5c-a055-c757e213b274')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d6e52d63-1534-4b5c-a055-c757e213b274 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_20562d79-a919-494e-971d-e59adc7d1796\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df1')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_20562d79-a919-494e-971d-e59adc7d1796 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df1');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df1",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Vishnu\",\n          \"Harshu\",\n          \"Hasini\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 18,\n        \"max\": 21,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          19,\n          18,\n          20\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Branch\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"EEE\",\n          \"Civil\",\n          \"ECE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data2 = pd.read_csv(\"Lab01-Text.csv\")"
      ],
      "metadata": {
        "id": "BFCdwuJT6KhF"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data3 = pd.read_excel(\"Height_weight.xlsx\")"
      ],
      "metadata": {
        "id": "Pcixx7fRBrpU"
      },
      "execution_count": 27,
      "outputs": []
    }
  ]
}