Lab01_AIML.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shahrukh-1052/AIML_LAB_2024/blob/main/Lab01_AIML.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b50d918d",
      "metadata": {
        "id": "b50d918d"
      },
      "source": [
        "## Lab01- Implement basic data structures of Numpy, Pandas, Matplotlib and Access Data from sources\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0128a62e",
      "metadata": {
        "id": "0128a62e"
      },
      "source": [
        "### Part 01: Lab Task is implement the following data structures using Numpy and Pandas.**\n",
        "1. Lists\n",
        "2. Arrays\n",
        "3. Identify their type using type()\n",
        "4. Perform mathematical operations on these datasets created - multiplication, division, poweroff\n",
        "5. Combine text with Numpy fuction to generate a textual output - ”Addition of Two: array1 +array 2\n",
        "6. Implement np.sin(), log(), log2(), np.exp())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "53ac920a",
      "metadata": {
        "id": "53ac920a"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "lst1=[1,2,3]\n",
        "array1 = np.array(lst1)\n",
        "array1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e40e19b2",
      "metadata": {
        "id": "e40e19b2"
      },
      "outputs": [],
      "source": [
        "type(lst1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b44611f1",
      "metadata": {
        "id": "b44611f1"
      },
      "outputs": [],
      "source": [
        "type(array1)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3b08fedd",
      "metadata": {
        "id": "3b08fedd"
      },
      "source": [
        "***Perform Mathematical Operations on these datasets - Multiplication, Division, Poweroff***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cbbb5ca5",
      "metadata": {
        "id": "cbbb5ca5"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "208e5e57",
      "metadata": {
        "id": "208e5e57"
      },
      "outputs": [],
      "source": [
        "# sine function\n",
        "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))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6ad654be",
      "metadata": {
        "id": "6ad654be"
      },
      "outputs": [],
      "source": [
        "list2=[3,2,1]\n",
        "array2=np.array(list2)\n",
        "array2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7ffe0ebb",
      "metadata": {
        "id": "7ffe0ebb"
      },
      "outputs": [],
      "source": [
        "print(\"Adding two numpy arrays {array1} and {array2} together:\", array1+array2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e2d45eed",
      "metadata": {
        "id": "e2d45eed"
      },
      "outputs": [],
      "source": [
        "print(f\"Adding two numpy arrays {array1} and {array2} together: {array1+array2}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7edeb66c",
      "metadata": {
        "id": "7edeb66c"
      },
      "source": [
        "### Part-02: Lab Task is to visualize the datasets or data using Matplotlib builtin function Graphs"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cdd82673",
      "metadata": {
        "id": "cdd82673"
      },
      "source": [
        "### Generate Scatterplot\n",
        "**Let's suppose we collected age (in years), height (in cm), and weight (in kilograms) information from few patients visitng a clinic.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "70b89ee2",
      "metadata": {
        "id": "70b89ee2"
      },
      "outputs": [],
      "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]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "183f441b",
      "metadata": {
        "id": "183f441b"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d4b57ac8",
      "metadata": {
        "id": "d4b57ac8",
        "outputId": "993e551f-9787-4537-f85a-fbcc7515ea21"
      },
      "outputs": [
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "de2fc765",
      "metadata": {
        "id": "de2fc765"
      },
      "source": [
        "### Generate Bar Plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "371361d4",
      "metadata": {
        "id": "371361d4",
        "outputId": "c704dfb9-ad7a-4cc2-aeb7-eb6a82827aaa"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3495be05",
      "metadata": {
        "id": "3495be05"
      },
      "source": [
        "### Generate Histogram"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cae1cb88",
      "metadata": {
        "id": "cae1cb88",
        "outputId": "c158fe85-0288-41c5-e2e1-2c6ac4330b99"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d72cfb41",
      "metadata": {
        "id": "d72cfb41"
      },
      "source": [
        "### Part-03: Access Data from various sources and formats"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9985c098",
      "metadata": {
        "id": "9985c098"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c4b282d0",
      "metadata": {
        "scrolled": true,
        "id": "c4b282d0"
      },
      "outputs": [],
      "source": [
        "df1 = pd.read_table(\"Lab01-Text.txt\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a6d4a7b4",
      "metadata": {
        "id": "a6d4a7b4"
      },
      "outputs": [],
      "source": [
        "df1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e8d9521b",
      "metadata": {
        "id": "e8d9521b"
      },
      "outputs": [],
      "source": [
        "data2 = pd.read_csv(\"Lab01-Text.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "212f008e",
      "metadata": {
        "id": "212f008e"
      },
      "outputs": [],
      "source": [
        "data2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1df68dba",
      "metadata": {
        "id": "1df68dba"
      },
      "outputs": [],
      "source": [
        "data3 = pd.read_excel(\"Height_weight.xlsx\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8660c8e4",
      "metadata": {
        "id": "8660c8e4"
      },
      "outputs": [],
      "source": [
        "data3"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.15"
    },
    "colab": {
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  "nbformat_minor": 5
}