Pandas_Tutorial_2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Pandas Tutorial 2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyO2tWnGX05QvEO26Q1fe/4t",
      "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/philsaurabh/Tutorials/blob/main/Pandas_Tutorial_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eTCHigPCCCFS"
      },
      "source": [
        "# Installing libraries\n",
        "Just type \n",
        "```\n",
        "!pip install pandas\n",
        "```\n",
        "and press enter to install library if not available in your workspace."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o2mzBgA8B-2B"
      },
      "source": [
        "# Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b2iRX35_B1ib"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iadbtDfNFTDM"
      },
      "source": [
        "# Dataframe\n",
        "A Pandas DataFrame is a 2 dimensional data structure, like a 2 dimensional array, or a table with rows and columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Po3bju8yEGAT"
      },
      "source": [
        "# To read data as dataframe\n",
        "df = pd.read_csv('Credit.csv')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pJBs0IgsGdJN"
      },
      "source": [
        "#  Task 1: Understanding the dataset\n",
        "\n",
        "  *   Show the values(.head, .tail)\n",
        "  *   Column names(.columns)\n",
        "  *   Shape of data (.shape)\n",
        "  *   Informtion about data (.info)\n",
        "  *   Statistical description (.describe)\n",
        "  *   Check Missing (.isNull)\n",
        "  \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "f0Vv-Y6FFO3g",
        "outputId": "96be9c81-c563-4e42-9453-3c2a274a196b"
      },
      "source": [
        "df.head(10)# By default, shows first 5 rows of data\n",
        "#df.tail()# By default, shows last 5 rows of data "
      ],
      "execution_count": 3,
      "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>Unnamed: 0</th>\n",
              "      <th>Income</th>\n",
              "      <th>Limit</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Cards</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Student</th>\n",
              "      <th>Married</th>\n",
              "      <th>Ethnicity</th>\n",
              "      <th>Balance</th>\n",
              "      <th>Defaultee</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>14.891</td>\n",
              "      <td>3606</td>\n",
              "      <td>283</td>\n",
              "      <td>2</td>\n",
              "      <td>34</td>\n",
              "      <td>11</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>333</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>106.025</td>\n",
              "      <td>6645</td>\n",
              "      <td>483</td>\n",
              "      <td>3</td>\n",
              "      <td>82</td>\n",
              "      <td>15</td>\n",
              "      <td>Female</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Asian</td>\n",
              "      <td>903</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>104.593</td>\n",
              "      <td>7075</td>\n",
              "      <td>514</td>\n",
              "      <td>4</td>\n",
              "      <td>71</td>\n",
              "      <td>11</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Asian</td>\n",
              "      <td>580</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>148.924</td>\n",
              "      <td>9504</td>\n",
              "      <td>681</td>\n",
              "      <td>3</td>\n",
              "      <td>36</td>\n",
              "      <td>11</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Asian</td>\n",
              "      <td>964</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>55.882</td>\n",
              "      <td>4897</td>\n",
              "      <td>357</td>\n",
              "      <td>2</td>\n",
              "      <td>68</td>\n",
              "      <td>16</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>331</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>80.180</td>\n",
              "      <td>8047</td>\n",
              "      <td>569</td>\n",
              "      <td>4</td>\n",
              "      <td>77</td>\n",
              "      <td>10</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>1151</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>20.996</td>\n",
              "      <td>3388</td>\n",
              "      <td>259</td>\n",
              "      <td>2</td>\n",
              "      <td>37</td>\n",
              "      <td>12</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>African American</td>\n",
              "      <td>203</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>71.408</td>\n",
              "      <td>7114</td>\n",
              "      <td>512</td>\n",
              "      <td>2</td>\n",
              "      <td>87</td>\n",
              "      <td>9</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Asian</td>\n",
              "      <td>872</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>15.125</td>\n",
              "      <td>3300</td>\n",
              "      <td>266</td>\n",
              "      <td>5</td>\n",
              "      <td>66</td>\n",
              "      <td>13</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>279</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>71.061</td>\n",
              "      <td>6819</td>\n",
              "      <td>491</td>\n",
              "      <td>3</td>\n",
              "      <td>41</td>\n",
              "      <td>19</td>\n",
              "      <td>Female</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>African American</td>\n",
              "      <td>1350</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0   Income  Limit  ...         Ethnicity  Balance  Defaultee\n",
              "0           1   14.891   3606  ...         Caucasian      333          0\n",
              "1           2  106.025   6645  ...             Asian      903          0\n",
              "2           3  104.593   7075  ...             Asian      580          0\n",
              "3           4  148.924   9504  ...             Asian      964          0\n",
              "4           5   55.882   4897  ...         Caucasian      331          0\n",
              "5           6   80.180   8047  ...         Caucasian     1151          1\n",
              "6           7   20.996   3388  ...  African American      203          0\n",
              "7           8   71.408   7114  ...             Asian      872          0\n",
              "8           9   15.125   3300  ...         Caucasian      279          0\n",
              "9          10   71.061   6819  ...  African American     1350          1\n",
              "\n",
              "[10 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dmzIQKQAIZ2n",
        "outputId": "7da3c374-b43b-4935-c6aa-3f20cf1b2499"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'Income', 'Limit', 'Rating', 'Cards', 'Age', 'Education',\n",
              "       'Gender', 'Student', 'Married', 'Ethnicity', 'Balance', 'Defaultee'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FlXpOO96Iyde",
        "outputId": "f3f0e6c4-740d-4855-c337-475de1705cf7"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(400, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P4H6ghGqI8Dy",
        "outputId": "574cf285-1eb1-4285-a45a-0b74867c8f5c"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 400 entries, 0 to 399\n",
            "Data columns (total 13 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Unnamed: 0  400 non-null    int64  \n",
            " 1   Income      400 non-null    float64\n",
            " 2   Limit       400 non-null    int64  \n",
            " 3   Rating      400 non-null    int64  \n",
            " 4   Cards       400 non-null    int64  \n",
            " 5   Age         400 non-null    int64  \n",
            " 6   Education   400 non-null    int64  \n",
            " 7   Gender      400 non-null    object \n",
            " 8   Student     400 non-null    object \n",
            " 9   Married     400 non-null    object \n",
            " 10  Ethnicity   400 non-null    object \n",
            " 11  Balance     400 non-null    int64  \n",
            " 12  Defaultee   400 non-null    int64  \n",
            "dtypes: float64(1), int64(8), object(4)\n",
            "memory usage: 40.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "kbILvo4jI_AF",
        "outputId": "36e1a416-f978-43c8-8656-836611658a63"
      },
      "source": [
        "df.describe()\n",
        "# df.describe().T # For transpose view of the same"
      ],
      "execution_count": 7,
      "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>Unnamed: 0</th>\n",
              "      <th>Income</th>\n",
              "      <th>Limit</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Cards</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Balance</th>\n",
              "      <th>Defaultee</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>200.500000</td>\n",
              "      <td>45.218885</td>\n",
              "      <td>4735.600000</td>\n",
              "      <td>354.940000</td>\n",
              "      <td>2.957500</td>\n",
              "      <td>55.667500</td>\n",
              "      <td>13.450000</td>\n",
              "      <td>520.015000</td>\n",
              "      <td>0.180000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>115.614301</td>\n",
              "      <td>35.244273</td>\n",
              "      <td>2308.198848</td>\n",
              "      <td>154.724143</td>\n",
              "      <td>1.371275</td>\n",
              "      <td>17.249807</td>\n",
              "      <td>3.125207</td>\n",
              "      <td>459.758877</td>\n",
              "      <td>0.384669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>10.354000</td>\n",
              "      <td>855.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>100.750000</td>\n",
              "      <td>21.007250</td>\n",
              "      <td>3088.000000</td>\n",
              "      <td>247.250000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>41.750000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>68.750000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>200.500000</td>\n",
              "      <td>33.115500</td>\n",
              "      <td>4622.500000</td>\n",
              "      <td>344.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>459.500000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>300.250000</td>\n",
              "      <td>57.470750</td>\n",
              "      <td>5872.750000</td>\n",
              "      <td>437.250000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>863.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>400.000000</td>\n",
              "      <td>186.634000</td>\n",
              "      <td>13913.000000</td>\n",
              "      <td>982.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>98.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>1999.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Unnamed: 0      Income  ...      Balance   Defaultee\n",
              "count  400.000000  400.000000  ...   400.000000  400.000000\n",
              "mean   200.500000   45.218885  ...   520.015000    0.180000\n",
              "std    115.614301   35.244273  ...   459.758877    0.384669\n",
              "min      1.000000   10.354000  ...     0.000000    0.000000\n",
              "25%    100.750000   21.007250  ...    68.750000    0.000000\n",
              "50%    200.500000   33.115500  ...   459.500000    0.000000\n",
              "75%    300.250000   57.470750  ...   863.000000    0.000000\n",
              "max    400.000000  186.634000  ...  1999.000000    1.000000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WyEZu9DFJEAD",
        "outputId": "83e05c94-b8bc-47f9-f13a-ac2fd45e56ea"
      },
      "source": [
        "df.isnull().values.any() # If any value is missing\n",
        "df.isnull().sum() # Total missing values"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0    0\n",
              "Income        0\n",
              "Limit         0\n",
              "Rating        0\n",
              "Cards         0\n",
              "Age           0\n",
              "Education     0\n",
              "Gender        0\n",
              "Student       0\n",
              "Married       0\n",
              "Ethnicity     0\n",
              "Balance       0\n",
              "Defaultee     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "75um4WepJvW_"
      },
      "source": [
        "# Task 2: Create age groups"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y0tIJzm3Jbep",
        "outputId": "bba16bfa-cade-4eb4-cb4c-1aff5feb8b3b"
      },
      "source": [
        "bins = [0, 2, 18, 35, 65, 105]\n",
        "names = ['<2', '2-18', '18-35', '35-65', '65+']\n",
        "\n",
        "df['AgeGroup'] = pd.cut(df['Age'], bins, labels=names)\n",
        "\n",
        "print(df.AgeGroup)\n",
        "print(df.head())"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0      18-35\n",
            "1        65+\n",
            "2        65+\n",
            "3      35-65\n",
            "4        65+\n",
            "       ...  \n",
            "395    18-35\n",
            "396    35-65\n",
            "397      65+\n",
            "398    35-65\n",
            "399    35-65\n",
            "Name: AgeGroup, Length: 400, dtype: category\n",
            "Categories (5, object): ['<2' < '2-18' < '18-35' < '35-65' < '65+']\n",
            "   Unnamed: 0   Income  Limit  Rating  ...  Ethnicity  Balance  Defaultee AgeGroup\n",
            "0           1   14.891   3606     283  ...  Caucasian      333          0    18-35\n",
            "1           2  106.025   6645     483  ...      Asian      903          0      65+\n",
            "2           3  104.593   7075     514  ...      Asian      580          0      65+\n",
            "3           4  148.924   9504     681  ...      Asian      964          0    35-65\n",
            "4           5   55.882   4897     357  ...  Caucasian      331          0      65+\n",
            "\n",
            "[5 rows x 14 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "NvVFHYsbyGVH",
        "outputId": "e6195155-d648-4dbf-e064-b27671b3170e"
      },
      "source": [
        "df.groupby(['AgeGroup']).std()"
      ],
      "execution_count": 14,
      "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>Unnamed: 0</th>\n",
              "      <th>Income</th>\n",
              "      <th>Limit</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Cards</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Student</th>\n",
              "      <th>Balance</th>\n",
              "      <th>Defaultee</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AgeGroup</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>&lt;2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2-18</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18-35</th>\n",
              "      <td>111.077983</td>\n",
              "      <td>20.031611</td>\n",
              "      <td>1540.767028</td>\n",
              "      <td>101.953330</td>\n",
              "      <td>1.300043</td>\n",
              "      <td>3.468107</td>\n",
              "      <td>3.144544</td>\n",
              "      <td>0.247676</td>\n",
              "      <td>397.867900</td>\n",
              "      <td>0.274512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35-65</th>\n",
              "      <td>116.525671</td>\n",
              "      <td>35.149990</td>\n",
              "      <td>2351.340727</td>\n",
              "      <td>157.789724</td>\n",
              "      <td>1.416025</td>\n",
              "      <td>8.307929</td>\n",
              "      <td>3.241138</td>\n",
              "      <td>0.321231</td>\n",
              "      <td>483.096268</td>\n",
              "      <td>0.406234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65+</th>\n",
              "      <td>116.213418</td>\n",
              "      <td>39.189682</td>\n",
              "      <td>2499.885177</td>\n",
              "      <td>167.477320</td>\n",
              "      <td>1.316255</td>\n",
              "      <td>6.569277</td>\n",
              "      <td>2.965925</td>\n",
              "      <td>0.291274</td>\n",
              "      <td>449.228173</td>\n",
              "      <td>0.390272</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Unnamed: 0     Income        Limit  ...   Student     Balance  Defaultee\n",
              "AgeGroup                                      ...                                 \n",
              "<2               NaN        NaN          NaN  ...       NaN         NaN        NaN\n",
              "2-18             NaN        NaN          NaN  ...       NaN         NaN        NaN\n",
              "18-35     111.077983  20.031611  1540.767028  ...  0.247676  397.867900   0.274512\n",
              "35-65     116.525671  35.149990  2351.340727  ...  0.321231  483.096268   0.406234\n",
              "65+       116.213418  39.189682  2499.885177  ...  0.291274  449.228173   0.390272\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Mcha7wwwKu2"
      },
      "source": [
        "# Task 3: Filter the female candidate data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "ApFrQwShvuOE",
        "outputId": "46c83a8b-def6-45ac-dfc4-c7c31c5541dc"
      },
      "source": [
        "data= df[df.Gender == 'Female']\n",
        "data"
      ],
      "execution_count": 10,
      "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>Unnamed: 0</th>\n",
              "      <th>Income</th>\n",
              "      <th>Limit</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Cards</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Student</th>\n",
              "      <th>Married</th>\n",
              "      <th>Ethnicity</th>\n",
              "      <th>Balance</th>\n",
              "      <th>Defaultee</th>\n",
              "      <th>AgeGroup</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>106.025</td>\n",
              "      <td>6645</td>\n",
              "      <td>483</td>\n",
              "      <td>3</td>\n",
              "      <td>82</td>\n",
              "      <td>15</td>\n",
              "      <td>Female</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Asian</td>\n",
              "      <td>903</td>\n",
              "      <td>0</td>\n",
              "      <td>65+</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>148.924</td>\n",
              "      <td>9504</td>\n",
              "      <td>681</td>\n",
              "      <td>3</td>\n",
              "      <td>36</td>\n",
              "      <td>11</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Asian</td>\n",
              "      <td>964</td>\n",
              "      <td>0</td>\n",
              "      <td>35-65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>20.996</td>\n",
              "      <td>3388</td>\n",
              "      <td>259</td>\n",
              "      <td>2</td>\n",
              "      <td>37</td>\n",
              "      <td>12</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>African American</td>\n",
              "      <td>203</td>\n",
              "      <td>0</td>\n",
              "      <td>35-65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>15.125</td>\n",
              "      <td>3300</td>\n",
              "      <td>266</td>\n",
              "      <td>5</td>\n",
              "      <td>66</td>\n",
              "      <td>13</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>279</td>\n",
              "      <td>0</td>\n",
              "      <td>65+</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>71.061</td>\n",
              "      <td>6819</td>\n",
              "      <td>491</td>\n",
              "      <td>3</td>\n",
              "      <td>41</td>\n",
              "      <td>19</td>\n",
              "      <td>Female</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>African American</td>\n",
              "      <td>1350</td>\n",
              "      <td>1</td>\n",
              "      <td>35-65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>388</th>\n",
              "      <td>389</td>\n",
              "      <td>37.878</td>\n",
              "      <td>6827</td>\n",
              "      <td>482</td>\n",
              "      <td>2</td>\n",
              "      <td>80</td>\n",
              "      <td>13</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>1129</td>\n",
              "      <td>1</td>\n",
              "      <td>65+</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>390</th>\n",
              "      <td>391</td>\n",
              "      <td>135.118</td>\n",
              "      <td>10578</td>\n",
              "      <td>747</td>\n",
              "      <td>3</td>\n",
              "      <td>81</td>\n",
              "      <td>15</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Asian</td>\n",
              "      <td>1393</td>\n",
              "      <td>1</td>\n",
              "      <td>65+</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>391</th>\n",
              "      <td>392</td>\n",
              "      <td>73.327</td>\n",
              "      <td>6555</td>\n",
              "      <td>472</td>\n",
              "      <td>2</td>\n",
              "      <td>43</td>\n",
              "      <td>15</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>721</td>\n",
              "      <td>0</td>\n",
              "      <td>35-65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>397</th>\n",
              "      <td>398</td>\n",
              "      <td>57.872</td>\n",
              "      <td>4171</td>\n",
              "      <td>321</td>\n",
              "      <td>5</td>\n",
              "      <td>67</td>\n",
              "      <td>12</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>138</td>\n",
              "      <td>0</td>\n",
              "      <td>65+</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>400</td>\n",
              "      <td>18.701</td>\n",
              "      <td>5524</td>\n",
              "      <td>415</td>\n",
              "      <td>5</td>\n",
              "      <td>64</td>\n",
              "      <td>7</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Asian</td>\n",
              "      <td>966</td>\n",
              "      <td>0</td>\n",
              "      <td>35-65</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>207 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Unnamed: 0   Income  Limit  ...  Balance  Defaultee  AgeGroup\n",
              "1             2  106.025   6645  ...      903          0       65+\n",
              "3             4  148.924   9504  ...      964          0     35-65\n",
              "6             7   20.996   3388  ...      203          0     35-65\n",
              "8             9   15.125   3300  ...      279          0       65+\n",
              "9            10   71.061   6819  ...     1350          1     35-65\n",
              "..          ...      ...    ...  ...      ...        ...       ...\n",
              "388         389   37.878   6827  ...     1129          1       65+\n",
              "390         391  135.118  10578  ...     1393          1       65+\n",
              "391         392   73.327   6555  ...      721          0     35-65\n",
              "397         398   57.872   4171  ...      138          0       65+\n",
              "399         400   18.701   5524  ...      966          0     35-65\n",
              "\n",
              "[207 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MyF_WCL2N0Kc"
      },
      "source": [
        "# Task 4: Convert Student status from Yes/No to 0/1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "_9wIJfGZNzTO",
        "outputId": "a7e18019-b676-4f5a-e8ab-8794f22ec3e7"
      },
      "source": [
        "df['Student'] = df['Student'].map({'Yes': 1, 'No': 0})\n",
        "df.head()"
      ],
      "execution_count": 11,
      "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>Unnamed: 0</th>\n",
              "      <th>Income</th>\n",
              "      <th>Limit</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Cards</th>\n",
              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Student</th>\n",
              "      <th>Married</th>\n",
              "      <th>Ethnicity</th>\n",
              "      <th>Balance</th>\n",
              "      <th>Defaultee</th>\n",
              "      <th>AgeGroup</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>14.891</td>\n",
              "      <td>3606</td>\n",
              "      <td>283</td>\n",
              "      <td>2</td>\n",
              "      <td>34</td>\n",
              "      <td>11</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>333</td>\n",
              "      <td>0</td>\n",
              "      <td>18-35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>106.025</td>\n",
              "      <td>6645</td>\n",
              "      <td>483</td>\n",
              "      <td>3</td>\n",
              "      <td>82</td>\n",
              "      <td>15</td>\n",
              "      <td>Female</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Asian</td>\n",
              "      <td>903</td>\n",
              "      <td>0</td>\n",
              "      <td>65+</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>104.593</td>\n",
              "      <td>7075</td>\n",
              "      <td>514</td>\n",
              "      <td>4</td>\n",
              "      <td>71</td>\n",
              "      <td>11</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>Asian</td>\n",
              "      <td>580</td>\n",
              "      <td>0</td>\n",
              "      <td>65+</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>148.924</td>\n",
              "      <td>9504</td>\n",
              "      <td>681</td>\n",
              "      <td>3</td>\n",
              "      <td>36</td>\n",
              "      <td>11</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>Asian</td>\n",
              "      <td>964</td>\n",
              "      <td>0</td>\n",
              "      <td>35-65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>55.882</td>\n",
              "      <td>4897</td>\n",
              "      <td>357</td>\n",
              "      <td>2</td>\n",
              "      <td>68</td>\n",
              "      <td>16</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>331</td>\n",
              "      <td>0</td>\n",
              "      <td>65+</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0   Income  Limit  Rating  ...  Ethnicity  Balance  Defaultee AgeGroup\n",
              "0           1   14.891   3606     283  ...  Caucasian      333          0    18-35\n",
              "1           2  106.025   6645     483  ...      Asian      903          0      65+\n",
              "2           3  104.593   7075     514  ...      Asian      580          0      65+\n",
              "3           4  148.924   9504     681  ...      Asian      964          0    35-65\n",
              "4           5   55.882   4897     357  ...  Caucasian      331          0      65+\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    }
  ]
}