Projek Akhir IDX.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Practice Rakamin Virtual Internship.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMijSFcavhg8Wyq0DS4Zpq7",
      "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/NabilahSharfina/Rakamin-Academy/blob/main/Practice_Rakamin_Virtual_Internship.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3WzjYD7e0ddN",
        "outputId": "bbf663bc-00ad-4633-94a4-8dbdf4aabfd2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# Load data from google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np # for performing mathematical calculations behind ML algorithms\n",
        "import matplotlib.pyplot as plt # for visualization\n",
        "import pandas as pd # for handling and cleaning the dataset\n",
        "import seaborn as sns # for visualization"
      ],
      "metadata": {
        "id": "_1SO8GlR008D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MINGGU 1 - ANALYTICS APPROACH, DATA REQUIREMENTS, DATA COLLECTION"
      ],
      "metadata": {
        "id": "T6lQIGG6132x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataframe Tabel Application Record\n",
        "df_ar = pd.read_csv('/content/drive/MyDrive/RAKAMIN ACADEMY/Virtual Internship Program - ID X Partners Data Scientist/MINGGU 1/Dataset/application_record.csv')\n",
        "df_ar.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "_ZQBIjXC1bS5",
        "outputId": "bc6dfb56-31c5-4c42-f6fd-80a5e622573b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ID CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  CNT_CHILDREN  \\\n",
              "0  5008804           M            Y               Y             0   \n",
              "1  5008805           M            Y               Y             0   \n",
              "2  5008806           M            Y               Y             0   \n",
              "3  5008808           F            N               Y             0   \n",
              "4  5008809           F            N               Y             0   \n",
              "\n",
              "   AMT_INCOME_TOTAL      NAME_INCOME_TYPE            NAME_EDUCATION_TYPE  \\\n",
              "0          427500.0               Working               Higher education   \n",
              "1          427500.0               Working               Higher education   \n",
              "2          112500.0               Working  Secondary / secondary special   \n",
              "3          270000.0  Commercial associate  Secondary / secondary special   \n",
              "4          270000.0  Commercial associate  Secondary / secondary special   \n",
              "\n",
              "     NAME_FAMILY_STATUS  NAME_HOUSING_TYPE  DAYS_BIRTH  DAYS_EMPLOYED  \\\n",
              "0        Civil marriage   Rented apartment      -12005          -4542   \n",
              "1        Civil marriage   Rented apartment      -12005          -4542   \n",
              "2               Married  House / apartment      -21474          -1134   \n",
              "3  Single / not married  House / apartment      -19110          -3051   \n",
              "4  Single / not married  House / apartment      -19110          -3051   \n",
              "\n",
              "   FLAG_MOBIL  FLAG_WORK_PHONE  FLAG_PHONE  FLAG_EMAIL OCCUPATION_TYPE  \\\n",
              "0           1                1           0           0             NaN   \n",
              "1           1                1           0           0             NaN   \n",
              "2           1                0           0           0  Security staff   \n",
              "3           1                0           1           1     Sales staff   \n",
              "4           1                0           1           1     Sales staff   \n",
              "\n",
              "   CNT_FAM_MEMBERS  \n",
              "0              2.0  \n",
              "1              2.0  \n",
              "2              2.0  \n",
              "3              1.0  \n",
              "4              1.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-032b285f-896c-42ae-b712-c3d2f6c0a8f9\">\n",
              "    <div 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>ID</th>\n",
              "      <th>CODE_GENDER</th>\n",
              "      <th>FLAG_OWN_CAR</th>\n",
              "      <th>FLAG_OWN_REALTY</th>\n",
              "      <th>CNT_CHILDREN</th>\n",
              "      <th>AMT_INCOME_TOTAL</th>\n",
              "      <th>NAME_INCOME_TYPE</th>\n",
              "      <th>NAME_EDUCATION_TYPE</th>\n",
              "      <th>NAME_FAMILY_STATUS</th>\n",
              "      <th>NAME_HOUSING_TYPE</th>\n",
              "      <th>DAYS_BIRTH</th>\n",
              "      <th>DAYS_EMPLOYED</th>\n",
              "      <th>FLAG_MOBIL</th>\n",
              "      <th>FLAG_WORK_PHONE</th>\n",
              "      <th>FLAG_PHONE</th>\n",
              "      <th>FLAG_EMAIL</th>\n",
              "      <th>OCCUPATION_TYPE</th>\n",
              "      <th>CNT_FAM_MEMBERS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5008804</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Civil marriage</td>\n",
              "      <td>Rented apartment</td>\n",
              "      <td>-12005</td>\n",
              "      <td>-4542</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5008805</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Civil marriage</td>\n",
              "      <td>Rented apartment</td>\n",
              "      <td>-12005</td>\n",
              "      <td>-4542</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5008806</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>112500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-21474</td>\n",
              "      <td>-1134</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Security staff</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5008808</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-19110</td>\n",
              "      <td>-3051</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sales staff</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5008809</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-19110</td>\n",
              "      <td>-3051</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sales staff</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-032b285f-896c-42ae-b712-c3d2f6c0a8f9')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-032b285f-896c-42ae-b712-c3d2f6c0a8f9 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-032b285f-896c-42ae-b712-c3d2f6c0a8f9');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataframe Tabel Credit Record\n",
        "df_cr = pd.read_csv('/content/drive/MyDrive/RAKAMIN ACADEMY/Virtual Internship Program - ID X Partners Data Scientist/MINGGU 1/Dataset/credit_record.csv')\n",
        "df_cr.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "WZcZleK61w1v",
        "outputId": "6b8cbb80-10ed-488e-b1a7-8a313c3578aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ID  MONTHS_BALANCE STATUS\n",
              "0  5001711               0      X\n",
              "1  5001711              -1      0\n",
              "2  5001711              -2      0\n",
              "3  5001711              -3      0\n",
              "4  5001712               0      C"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a418a651-c5ed-4ec1-b2da-a9398f567ac7\">\n",
              "    <div 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>ID</th>\n",
              "      <th>MONTHS_BALANCE</th>\n",
              "      <th>STATUS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5001711</td>\n",
              "      <td>0</td>\n",
              "      <td>X</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5001711</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5001712</td>\n",
              "      <td>0</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a418a651-c5ed-4ec1-b2da-a9398f567ac7')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-a418a651-c5ed-4ec1-b2da-a9398f567ac7 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-a418a651-c5ed-4ec1-b2da-a9398f567ac7');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ar.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ms9DNaeQ7U4k",
        "outputId": "c16f817c-34ae-4677-8eb5-6f32a551e98f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 438557 entries, 0 to 438556\n",
            "Data columns (total 18 columns):\n",
            " #   Column               Non-Null Count   Dtype  \n",
            "---  ------               --------------   -----  \n",
            " 0   ID                   438557 non-null  int64  \n",
            " 1   CODE_GENDER          438557 non-null  object \n",
            " 2   FLAG_OWN_CAR         438557 non-null  object \n",
            " 3   FLAG_OWN_REALTY      438557 non-null  object \n",
            " 4   CNT_CHILDREN         438557 non-null  int64  \n",
            " 5   AMT_INCOME_TOTAL     438557 non-null  float64\n",
            " 6   NAME_INCOME_TYPE     438557 non-null  object \n",
            " 7   NAME_EDUCATION_TYPE  438557 non-null  object \n",
            " 8   NAME_FAMILY_STATUS   438557 non-null  object \n",
            " 9   NAME_HOUSING_TYPE    438557 non-null  object \n",
            " 10  DAYS_BIRTH           438557 non-null  int64  \n",
            " 11  DAYS_EMPLOYED        438557 non-null  int64  \n",
            " 12  FLAG_MOBIL           438557 non-null  int64  \n",
            " 13  FLAG_WORK_PHONE      438557 non-null  int64  \n",
            " 14  FLAG_PHONE           438557 non-null  int64  \n",
            " 15  FLAG_EMAIL           438557 non-null  int64  \n",
            " 16  OCCUPATION_TYPE      304354 non-null  object \n",
            " 17  CNT_FAM_MEMBERS      438557 non-null  float64\n",
            "dtypes: float64(2), int64(8), object(8)\n",
            "memory usage: 60.2+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cr.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cbSlZ5AW7YeH",
        "outputId": "184b97d9-4866-4f18-c3af-14fb6af791c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1048575 entries, 0 to 1048574\n",
            "Data columns (total 3 columns):\n",
            " #   Column          Non-Null Count    Dtype \n",
            "---  ------          --------------    ----- \n",
            " 0   ID              1048575 non-null  int64 \n",
            " 1   MONTHS_BALANCE  1048575 non-null  int64 \n",
            " 2   STATUS          1048575 non-null  object\n",
            "dtypes: int64(2), object(1)\n",
            "memory usage: 24.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ar.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5U9oFPglJeHI",
        "outputId": "91808f47-43d5-4255-e73e-7ee6433267c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cr.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NYjkj-EOJXF1",
        "outputId": "b5c11257-1e0f-4a0d-cfef-32d9fd920c22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MINGGU 2"
      ],
      "metadata": {
        "id": "XG5hT9NW16bS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## A. DATA UNDERSTANDING & DATA PREPARATION"
      ],
      "metadata": {
        "id": "k3Y0HxbqLc76"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Soal No. 4"
      ],
      "metadata": {
        "id": "t3GpBvg_b7qE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_ar[['FLAG_PHONE']].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "sy7uwUtYdNuo",
        "outputId": "fc528e16-6ccb-47d1-fa13-d40d63a0b937"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   FLAG_PHONE\n",
              "0           0\n",
              "1           0\n",
              "2           0\n",
              "3           1\n",
              "4           1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6e579afc-05ba-48b2-a852-b99a36dc790f\">\n",
              "    <div 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>FLAG_PHONE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6e579afc-05ba-48b2-a852-b99a36dc790f')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-6e579afc-05ba-48b2-a852-b99a36dc790f 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-6e579afc-05ba-48b2-a852-b99a36dc790f');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cek jawaban a == SALAH\n",
        "df_ar['FLAG_PHONE'] = df_ar['FLAG_PHONE'] = np.where(df_ar['FLAG_PHONE'] == 1, 'N', 'Y')\n",
        "df_ar[['FLAG_PHONE']].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Y3aNOyg7cVH8",
        "outputId": "5116dd21-6adc-44bc-cbd5-5082d91fb20b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  FLAG_PHONE\n",
              "0          Y\n",
              "1          Y\n",
              "2          Y\n",
              "3          N\n",
              "4          N"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8e22a163-985e-42a8-a294-a897fafd5201\">\n",
              "    <div 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>FLAG_PHONE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8e22a163-985e-42a8-a294-a897fafd5201')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-8e22a163-985e-42a8-a294-a897fafd5201 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-8e22a163-985e-42a8-a294-a897fafd5201');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cek jawaban b == SALAH\n",
        "df_ar['FLAG_PHONE'] = df_ar['FLAG_PHONE'] = np.where(df_ar['FLAG_PHONE'] == 0, 'Y', 'N')\n",
        "df_ar[['FLAG_PHONE']].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "w4rdKc5ecVK_",
        "outputId": "f7c03594-b412-4b59-8f7c-5636ce3ad415"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  FLAG_PHONE\n",
              "0          Y\n",
              "1          Y\n",
              "2          Y\n",
              "3          N\n",
              "4          N"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-20d7e79e-de63-4d3b-adea-6e189b483ffd\">\n",
              "    <div 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>FLAG_PHONE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-20d7e79e-de63-4d3b-adea-6e189b483ffd')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-20d7e79e-de63-4d3b-adea-6e189b483ffd 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-20d7e79e-de63-4d3b-adea-6e189b483ffd');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cek jawaban c == BETUL\n",
        "df_ar['FLAG_PHONE'] = df_ar['FLAG_PHONE'] = np.where(df_ar['FLAG_PHONE'] == 0, 'N', 'Y')\n",
        "df_ar[['FLAG_PHONE']].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "RhA3Dguab-6K",
        "outputId": "3d36b2c2-d168-4164-a1e8-0e05c2e24a4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  FLAG_PHONE\n",
              "0          N\n",
              "1          N\n",
              "2          N\n",
              "3          Y\n",
              "4          Y"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-361409dd-3b26-4706-a9be-0d69cd2466c1\">\n",
              "    <div 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>FLAG_PHONE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-361409dd-3b26-4706-a9be-0d69cd2466c1')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-361409dd-3b26-4706-a9be-0d69cd2466c1 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-361409dd-3b26-4706-a9be-0d69cd2466c1');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cek jawaban d == BETUL\n",
        "df_ar['FLAG_PHONE'] = df_ar['FLAG_PHONE'] = np.where(df_ar['FLAG_PHONE'] == 1, 'Y', 'N')\n",
        "df_ar[['FLAG_PHONE']].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "maDKt_UvcYtZ",
        "outputId": "f9bde7d2-d6b1-4264-e497-d4961ef62e19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  FLAG_PHONE\n",
              "0          N\n",
              "1          N\n",
              "2          N\n",
              "3          Y\n",
              "4          Y"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e7f0c38d-e158-4b76-a996-f09ffefc4637\">\n",
              "    <div 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>FLAG_PHONE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e7f0c38d-e158-4b76-a996-f09ffefc4637')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-e7f0c38d-e158-4b76-a996-f09ffefc4637 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-e7f0c38d-e158-4b76-a996-f09ffefc4637');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Soal No. 7"
      ],
      "metadata": {
        "id": "J2foQ-rsgcVz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_ar[['DAYS_BIRTH']].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "chmUiYwuiPad",
        "outputId": "bf808562-6621-43e6-9fd3-ceef4754605c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   DAYS_BIRTH\n",
              "0      -12005\n",
              "1      -12005\n",
              "2      -21474\n",
              "3      -19110\n",
              "4      -19110"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-97d6d19d-32e5-4ae5-a1bc-225b58d80c9d\">\n",
              "    <div 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>DAYS_BIRTH</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-12005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-12005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-21474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-19110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-19110</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-97d6d19d-32e5-4ae5-a1bc-225b58d80c9d')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-97d6d19d-32e5-4ae5-a1bc-225b58d80c9d 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-97d6d19d-32e5-4ae5-a1bc-225b58d80c9d');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ar['AGE'] = df_ar['DAYS_BIRTH'] / -365\n",
        "df_ar[['DAYS_BIRTH', 'AGE']].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "HboJ-j-fi3VO",
        "outputId": "e4bdedb7-62e2-498e-f1f7-b80ba6396087"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   DAYS_BIRTH        AGE\n",
              "0      -12005  32.890411\n",
              "1      -12005  32.890411\n",
              "2      -21474  58.832877\n",
              "3      -19110  52.356164\n",
              "4      -19110  52.356164"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c32ae62e-dc3b-403b-9a4e-126b643cb6cb\">\n",
              "    <div 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>DAYS_BIRTH</th>\n",
              "      <th>AGE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-12005</td>\n",
              "      <td>32.890411</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-12005</td>\n",
              "      <td>32.890411</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-21474</td>\n",
              "      <td>58.832877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-19110</td>\n",
              "      <td>52.356164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-19110</td>\n",
              "      <td>52.356164</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c32ae62e-dc3b-403b-9a4e-126b643cb6cb')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-c32ae62e-dc3b-403b-9a4e-126b643cb6cb 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-c32ae62e-dc3b-403b-9a4e-126b643cb6cb');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pilihan jawaban a == SALAH, karena < 0,5 ditambah 1\n",
        "df_ar['AGE'] = df_ar['AGE'].apply(np.ceil)\n",
        "df_ar[['DAYS_BIRTH', 'AGE']].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "hrSAGc-fggvJ",
        "outputId": "ec59cdd9-8b4b-43a7-88c1-8dae7cea8941"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   DAYS_BIRTH   AGE\n",
              "0      -12005  33.0\n",
              "1      -12005  33.0\n",
              "2      -21474  59.0\n",
              "3      -19110  53.0\n",
              "4      -19110  53.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f5f10203-bec6-48cd-846a-0745abe4c035\">\n",
              "    <div 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>DAYS_BIRTH</th>\n",
              "      <th>AGE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-12005</td>\n",
              "      <td>33.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-12005</td>\n",
              "      <td>33.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-21474</td>\n",
              "      <td>59.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-19110</td>\n",
              "      <td>53.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-19110</td>\n",
              "      <td>53.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f5f10203-bec6-48cd-846a-0745abe4c035')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-f5f10203-bec6-48cd-846a-0745abe4c035 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-f5f10203-bec6-48cd-846a-0745abe4c035');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pilihan jawaban b == BENAR, karena >= 0,5 ditambah 1 dan < 0,5 dibulatkan ke bawah\n",
        "df_ar['AGE'] = df_ar['AGE'].round(decimals=0)\n",
        "df_ar[['DAYS_BIRTH', 'AGE']].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "F68ziat2hvon",
        "outputId": "2663dcde-0010-410c-c530-da2ba215c186"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   DAYS_BIRTH   AGE\n",
              "0      -12005  33.0\n",
              "1      -12005  33.0\n",
              "2      -21474  59.0\n",
              "3      -19110  52.0\n",
              "4      -19110  52.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ca6c18dc-7fc0-40d4-b566-5a9c4ceae023\">\n",
              "    <div 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>DAYS_BIRTH</th>\n",
              "      <th>AGE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-12005</td>\n",
              "      <td>33.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-12005</td>\n",
              "      <td>33.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-21474</td>\n",
              "      <td>59.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-19110</td>\n",
              "      <td>52.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-19110</td>\n",
              "      <td>52.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ca6c18dc-7fc0-40d4-b566-5a9c4ceae023')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-ca6c18dc-7fc0-40d4-b566-5a9c4ceae023 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-ca6c18dc-7fc0-40d4-b566-5a9c4ceae023');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pilihan jawaban c == SALAH, karena kurang 0,1\n",
        "df_ar['AGE'] = df_ar['AGE'].round(decimals=1)\n",
        "df_ar[['DAYS_BIRTH', 'AGE']].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "uf14cpWbhvre",
        "outputId": "7b25ac4c-93b0-46fa-af9d-e1883045dd8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   DAYS_BIRTH   AGE\n",
              "0      -12005  32.9\n",
              "1      -12005  32.9\n",
              "2      -21474  58.8\n",
              "3      -19110  52.4\n",
              "4      -19110  52.4"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c5924ebe-83d5-4fb7-a715-ab5bb263adf5\">\n",
              "    <div 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>DAYS_BIRTH</th>\n",
              "      <th>AGE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-12005</td>\n",
              "      <td>32.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-12005</td>\n",
              "      <td>32.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-21474</td>\n",
              "      <td>58.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-19110</td>\n",
              "      <td>52.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-19110</td>\n",
              "      <td>52.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c5924ebe-83d5-4fb7-a715-ab5bb263adf5')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-c5924ebe-83d5-4fb7-a715-ab5bb263adf5 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-c5924ebe-83d5-4fb7-a715-ab5bb263adf5');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pilihan jawaban d == SALAH, karena dibulatkan ke bawah\n",
        "df_ar['AGE'] = df_ar['AGE'].apply(np.floor)\n",
        "df_ar[['DAYS_BIRTH', 'AGE']].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "z8vCofgwhv5X",
        "outputId": "b8b49f72-647e-43e5-9a4d-2ae0e9cd5b2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   DAYS_BIRTH   AGE\n",
              "0      -12005  32.0\n",
              "1      -12005  32.0\n",
              "2      -21474  58.0\n",
              "3      -19110  52.0\n",
              "4      -19110  52.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9223abef-0e0f-441c-b017-6bd33330abe7\">\n",
              "    <div 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>DAYS_BIRTH</th>\n",
              "      <th>AGE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-12005</td>\n",
              "      <td>32.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-12005</td>\n",
              "      <td>32.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-21474</td>\n",
              "      <td>58.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-19110</td>\n",
              "      <td>52.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-19110</td>\n",
              "      <td>52.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9223abef-0e0f-441c-b017-6bd33330abe7')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-9223abef-0e0f-441c-b017-6bd33330abe7 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-9223abef-0e0f-441c-b017-6bd33330abe7');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Soal No. 8"
      ],
      "metadata": {
        "id": "25bKxH-Nej5M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Pilihan jawaban a == BENAR\n",
        "df_ar['OCCUPATION_TYPE'] = 'Unknown'\n",
        "df_ar.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "paSg7Mkge3aF",
        "outputId": "28ca0103-6b97-4cc8-b795-47c2d00ceff9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ID CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  CNT_CHILDREN  \\\n",
              "0  5008804           M            Y               Y             0   \n",
              "1  5008805           M            Y               Y             0   \n",
              "2  5008806           M            Y               Y             0   \n",
              "3  5008808           F            N               Y             0   \n",
              "4  5008809           F            N               Y             0   \n",
              "\n",
              "   AMT_INCOME_TOTAL      NAME_INCOME_TYPE            NAME_EDUCATION_TYPE  \\\n",
              "0          427500.0               Working               Higher education   \n",
              "1          427500.0               Working               Higher education   \n",
              "2          112500.0               Working  Secondary / secondary special   \n",
              "3          270000.0  Commercial associate  Secondary / secondary special   \n",
              "4          270000.0  Commercial associate  Secondary / secondary special   \n",
              "\n",
              "     NAME_FAMILY_STATUS  NAME_HOUSING_TYPE  DAYS_BIRTH  DAYS_EMPLOYED  \\\n",
              "0        Civil marriage   Rented apartment      -12005          -4542   \n",
              "1        Civil marriage   Rented apartment      -12005          -4542   \n",
              "2               Married  House / apartment      -21474          -1134   \n",
              "3  Single / not married  House / apartment      -19110          -3051   \n",
              "4  Single / not married  House / apartment      -19110          -3051   \n",
              "\n",
              "   FLAG_MOBIL  FLAG_WORK_PHONE  FLAG_PHONE  FLAG_EMAIL OCCUPATION_TYPE  \\\n",
              "0           1                1           0           0         Unknown   \n",
              "1           1                1           0           0         Unknown   \n",
              "2           1                0           0           0         Unknown   \n",
              "3           1                0           1           1         Unknown   \n",
              "4           1                0           1           1         Unknown   \n",
              "\n",
              "   CNT_FAM_MEMBERS  \n",
              "0              2.0  \n",
              "1              2.0  \n",
              "2              2.0  \n",
              "3              1.0  \n",
              "4              1.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ff1e9679-06a2-4305-aa4b-cd89a52ab33f\">\n",
              "    <div 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>ID</th>\n",
              "      <th>CODE_GENDER</th>\n",
              "      <th>FLAG_OWN_CAR</th>\n",
              "      <th>FLAG_OWN_REALTY</th>\n",
              "      <th>CNT_CHILDREN</th>\n",
              "      <th>AMT_INCOME_TOTAL</th>\n",
              "      <th>NAME_INCOME_TYPE</th>\n",
              "      <th>NAME_EDUCATION_TYPE</th>\n",
              "      <th>NAME_FAMILY_STATUS</th>\n",
              "      <th>NAME_HOUSING_TYPE</th>\n",
              "      <th>DAYS_BIRTH</th>\n",
              "      <th>DAYS_EMPLOYED</th>\n",
              "      <th>FLAG_MOBIL</th>\n",
              "      <th>FLAG_WORK_PHONE</th>\n",
              "      <th>FLAG_PHONE</th>\n",
              "      <th>FLAG_EMAIL</th>\n",
              "      <th>OCCUPATION_TYPE</th>\n",
              "      <th>CNT_FAM_MEMBERS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5008804</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Civil marriage</td>\n",
              "      <td>Rented apartment</td>\n",
              "      <td>-12005</td>\n",
              "      <td>-4542</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5008805</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Civil marriage</td>\n",
              "      <td>Rented apartment</td>\n",
              "      <td>-12005</td>\n",
              "      <td>-4542</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5008806</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>112500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-21474</td>\n",
              "      <td>-1134</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5008808</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-19110</td>\n",
              "      <td>-3051</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5008809</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-19110</td>\n",
              "      <td>-3051</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ff1e9679-06a2-4305-aa4b-cd89a52ab33f')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-ff1e9679-06a2-4305-aa4b-cd89a52ab33f 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-ff1e9679-06a2-4305-aa4b-cd89a52ab33f');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pilihan jawaban b == BENAR\n",
        "df_ar['OCCUPATION_TYPE'] = df_ar['OCCUPATION_TYPE'].fillna('Unknown')\n",
        "df_ar.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "6TeuNpoDe3dG",
        "outputId": "e3391fff-1bb9-44e9-fed4-5d7fded2fe2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ID CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  CNT_CHILDREN  \\\n",
              "0  5008804           M            Y               Y             0   \n",
              "1  5008805           M            Y               Y             0   \n",
              "2  5008806           M            Y               Y             0   \n",
              "3  5008808           F            N               Y             0   \n",
              "4  5008809           F            N               Y             0   \n",
              "\n",
              "   AMT_INCOME_TOTAL      NAME_INCOME_TYPE            NAME_EDUCATION_TYPE  \\\n",
              "0          427500.0               Working               Higher education   \n",
              "1          427500.0               Working               Higher education   \n",
              "2          112500.0               Working  Secondary / secondary special   \n",
              "3          270000.0  Commercial associate  Secondary / secondary special   \n",
              "4          270000.0  Commercial associate  Secondary / secondary special   \n",
              "\n",
              "     NAME_FAMILY_STATUS  NAME_HOUSING_TYPE  DAYS_BIRTH  DAYS_EMPLOYED  \\\n",
              "0        Civil marriage   Rented apartment      -12005          -4542   \n",
              "1        Civil marriage   Rented apartment      -12005          -4542   \n",
              "2               Married  House / apartment      -21474          -1134   \n",
              "3  Single / not married  House / apartment      -19110          -3051   \n",
              "4  Single / not married  House / apartment      -19110          -3051   \n",
              "\n",
              "   FLAG_MOBIL  FLAG_WORK_PHONE  FLAG_PHONE  FLAG_EMAIL OCCUPATION_TYPE  \\\n",
              "0           1                1           0           0         Unknown   \n",
              "1           1                1           0           0         Unknown   \n",
              "2           1                0           0           0  Security staff   \n",
              "3           1                0           1           1     Sales staff   \n",
              "4           1                0           1           1     Sales staff   \n",
              "\n",
              "   CNT_FAM_MEMBERS  \n",
              "0              2.0  \n",
              "1              2.0  \n",
              "2              2.0  \n",
              "3              1.0  \n",
              "4              1.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-08d3f31f-ad56-4047-b89d-3f80998a0332\">\n",
              "    <div 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>ID</th>\n",
              "      <th>CODE_GENDER</th>\n",
              "      <th>FLAG_OWN_CAR</th>\n",
              "      <th>FLAG_OWN_REALTY</th>\n",
              "      <th>CNT_CHILDREN</th>\n",
              "      <th>AMT_INCOME_TOTAL</th>\n",
              "      <th>NAME_INCOME_TYPE</th>\n",
              "      <th>NAME_EDUCATION_TYPE</th>\n",
              "      <th>NAME_FAMILY_STATUS</th>\n",
              "      <th>NAME_HOUSING_TYPE</th>\n",
              "      <th>DAYS_BIRTH</th>\n",
              "      <th>DAYS_EMPLOYED</th>\n",
              "      <th>FLAG_MOBIL</th>\n",
              "      <th>FLAG_WORK_PHONE</th>\n",
              "      <th>FLAG_PHONE</th>\n",
              "      <th>FLAG_EMAIL</th>\n",
              "      <th>OCCUPATION_TYPE</th>\n",
              "      <th>CNT_FAM_MEMBERS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5008804</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Civil marriage</td>\n",
              "      <td>Rented apartment</td>\n",
              "      <td>-12005</td>\n",
              "      <td>-4542</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5008805</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Civil marriage</td>\n",
              "      <td>Rented apartment</td>\n",
              "      <td>-12005</td>\n",
              "      <td>-4542</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5008806</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>112500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-21474</td>\n",
              "      <td>-1134</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Security staff</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5008808</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-19110</td>\n",
              "      <td>-3051</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sales staff</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5008809</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-19110</td>\n",
              "      <td>-3051</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sales staff</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-08d3f31f-ad56-4047-b89d-3f80998a0332')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-08d3f31f-ad56-4047-b89d-3f80998a0332 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-08d3f31f-ad56-4047-b89d-3f80998a0332');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pilihan jawaban c == ERROR\n",
        "df_ar['OCCUPATION_TYPE'] = fillna('Unknown')\n",
        "df_ar.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "BJLHTNJQezrh",
        "outputId": "26aef78a-a9a2-4544-fd51-b9d12305b03c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-65-551e66165b68>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Pilihan jawaban c ==\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf_ar\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'OCCUPATION_TYPE'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Unknown'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mdf_ar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'fillna' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pilihan jawaban d == BENAR\n",
        "df_ar['OCCUPATION_TYPE'].fillna('Unknown', inplace=True)\n",
        "df_ar.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "zkCAO3d4fDX5",
        "outputId": "a52fa339-0123-4e0c-d5d4-b90472041144"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ID CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  CNT_CHILDREN  \\\n",
              "0  5008804           M            Y               Y             0   \n",
              "1  5008805           M            Y               Y             0   \n",
              "2  5008806           M            Y               Y             0   \n",
              "3  5008808           F            N               Y             0   \n",
              "4  5008809           F            N               Y             0   \n",
              "\n",
              "   AMT_INCOME_TOTAL      NAME_INCOME_TYPE            NAME_EDUCATION_TYPE  \\\n",
              "0          427500.0               Working               Higher education   \n",
              "1          427500.0               Working               Higher education   \n",
              "2          112500.0               Working  Secondary / secondary special   \n",
              "3          270000.0  Commercial associate  Secondary / secondary special   \n",
              "4          270000.0  Commercial associate  Secondary / secondary special   \n",
              "\n",
              "     NAME_FAMILY_STATUS  NAME_HOUSING_TYPE  DAYS_BIRTH  DAYS_EMPLOYED  \\\n",
              "0        Civil marriage   Rented apartment      -12005          -4542   \n",
              "1        Civil marriage   Rented apartment      -12005          -4542   \n",
              "2               Married  House / apartment      -21474          -1134   \n",
              "3  Single / not married  House / apartment      -19110          -3051   \n",
              "4  Single / not married  House / apartment      -19110          -3051   \n",
              "\n",
              "   FLAG_MOBIL  FLAG_WORK_PHONE  FLAG_PHONE  FLAG_EMAIL OCCUPATION_TYPE  \\\n",
              "0           1                1           0           0         Unknown   \n",
              "1           1                1           0           0         Unknown   \n",
              "2           1                0           0           0  Security staff   \n",
              "3           1                0           1           1     Sales staff   \n",
              "4           1                0           1           1     Sales staff   \n",
              "\n",
              "   CNT_FAM_MEMBERS  \n",
              "0              2.0  \n",
              "1              2.0  \n",
              "2              2.0  \n",
              "3              1.0  \n",
              "4              1.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6566b766-624e-45dd-87ad-178ec1a31b9e\">\n",
              "    <div 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>ID</th>\n",
              "      <th>CODE_GENDER</th>\n",
              "      <th>FLAG_OWN_CAR</th>\n",
              "      <th>FLAG_OWN_REALTY</th>\n",
              "      <th>CNT_CHILDREN</th>\n",
              "      <th>AMT_INCOME_TOTAL</th>\n",
              "      <th>NAME_INCOME_TYPE</th>\n",
              "      <th>NAME_EDUCATION_TYPE</th>\n",
              "      <th>NAME_FAMILY_STATUS</th>\n",
              "      <th>NAME_HOUSING_TYPE</th>\n",
              "      <th>DAYS_BIRTH</th>\n",
              "      <th>DAYS_EMPLOYED</th>\n",
              "      <th>FLAG_MOBIL</th>\n",
              "      <th>FLAG_WORK_PHONE</th>\n",
              "      <th>FLAG_PHONE</th>\n",
              "      <th>FLAG_EMAIL</th>\n",
              "      <th>OCCUPATION_TYPE</th>\n",
              "      <th>CNT_FAM_MEMBERS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5008804</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Civil marriage</td>\n",
              "      <td>Rented apartment</td>\n",
              "      <td>-12005</td>\n",
              "      <td>-4542</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5008805</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Civil marriage</td>\n",
              "      <td>Rented apartment</td>\n",
              "      <td>-12005</td>\n",
              "      <td>-4542</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5008806</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>112500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-21474</td>\n",
              "      <td>-1134</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Security staff</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5008808</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-19110</td>\n",
              "      <td>-3051</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sales staff</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5008809</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-19110</td>\n",
              "      <td>-3051</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sales staff</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6566b766-624e-45dd-87ad-178ec1a31b9e')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-6566b766-624e-45dd-87ad-178ec1a31b9e 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-6566b766-624e-45dd-87ad-178ec1a31b9e');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Soal No. 9"
      ],
      "metadata": {
        "id": "4eMLJ0z_enP4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Pilihan jawaban a == ERROR\n",
        "df_ar.ID = df_ar.ID.totype(str)\n",
        "df_ar.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "rronVmOeezJy",
        "outputId": "78aa2041-ff19-4888-fc40-a482fb6ef770"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-68-e3244160ddf3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Pilihan jawaban a ==\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf_ar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mID\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_ar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mID\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mdf_ar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5485\u001b[0m         ):\n\u001b[1;32m   5486\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5487\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5488\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5489\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'Series' object has no attribute 'totype'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pilihan jawaban b == BENAR\n",
        "df_ar.ID = df_ar.ID.astype(str)\n",
        "df_ar.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eNvuj0aFgwf5",
        "outputId": "7781e89e-7341-4def-fe6c-a2ef0d0342b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                      object\n",
              "CODE_GENDER             object\n",
              "FLAG_OWN_CAR            object\n",
              "FLAG_OWN_REALTY         object\n",
              "CNT_CHILDREN             int64\n",
              "AMT_INCOME_TOTAL       float64\n",
              "NAME_INCOME_TYPE        object\n",
              "NAME_EDUCATION_TYPE     object\n",
              "NAME_FAMILY_STATUS      object\n",
              "NAME_HOUSING_TYPE       object\n",
              "DAYS_BIRTH               int64\n",
              "DAYS_EMPLOYED            int64\n",
              "FLAG_MOBIL               int64\n",
              "FLAG_WORK_PHONE          int64\n",
              "FLAG_PHONE               int64\n",
              "FLAG_EMAIL               int64\n",
              "OCCUPATION_TYPE         object\n",
              "CNT_FAM_MEMBERS        float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pilihan jawaban c == ERROR\n",
        "df_ar.ID = df_ar.ID.cast(str)\n",
        "df_ar.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "RacafdH_gwi-",
        "outputId": "9a06e287-c415-4cca-96c9-1ebf999d8a18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-71-1aa87b2f1bdd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Pilihan jawaban c ==\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf_ar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mID\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_ar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mID\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mdf_ar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5485\u001b[0m         ):\n\u001b[1;32m   5486\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5487\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5488\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5489\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'Series' object has no attribute 'cast'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pilihan jawaban d == ERROR\n",
        "df_ar.ID = df_ar.ID.cast_as(str)\n",
        "df_ar.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "2MsQFnTQgwyg",
        "outputId": "fa4953de-87c9-4d94-87c6-2ae298d009d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-72-012068de1325>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Pilihan jawaban d ==\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf_ar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mID\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_ar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mID\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast_as\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mdf_ar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5485\u001b[0m         ):\n\u001b[1;32m   5486\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5487\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5488\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5489\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'Series' object has no attribute 'cast_as'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Soal No. 10"
      ],
      "metadata": {
        "id": "3t4dksXDQpNX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Soal No. 10 pilihan jawaban a\n",
        "df_ar.drop(['ID', 'CODE_GENDER'], axis=1, inplace=False)\n",
        "df_ar"
      ],
      "metadata": {
        "id": "NC54oFL416nv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "outputId": "e25814e8-dba2-4c0a-d9bb-5e157de23e99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             ID CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  CNT_CHILDREN  \\\n",
              "0       5008804           M            Y               Y             0   \n",
              "1       5008805           M            Y               Y             0   \n",
              "2       5008806           M            Y               Y             0   \n",
              "3       5008808           F            N               Y             0   \n",
              "4       5008809           F            N               Y             0   \n",
              "...         ...         ...          ...             ...           ...   \n",
              "438552  6840104           M            N               Y             0   \n",
              "438553  6840222           F            N               N             0   \n",
              "438554  6841878           F            N               N             0   \n",
              "438555  6842765           F            N               Y             0   \n",
              "438556  6842885           F            N               Y             0   \n",
              "\n",
              "        AMT_INCOME_TOTAL      NAME_INCOME_TYPE            NAME_EDUCATION_TYPE  \\\n",
              "0               427500.0               Working               Higher education   \n",
              "1               427500.0               Working               Higher education   \n",
              "2               112500.0               Working  Secondary / secondary special   \n",
              "3               270000.0  Commercial associate  Secondary / secondary special   \n",
              "4               270000.0  Commercial associate  Secondary / secondary special   \n",
              "...                  ...                   ...                            ...   \n",
              "438552          135000.0             Pensioner  Secondary / secondary special   \n",
              "438553          103500.0               Working  Secondary / secondary special   \n",
              "438554           54000.0  Commercial associate               Higher education   \n",
              "438555           72000.0             Pensioner  Secondary / secondary special   \n",
              "438556          121500.0               Working  Secondary / secondary special   \n",
              "\n",
              "          NAME_FAMILY_STATUS  NAME_HOUSING_TYPE  DAYS_BIRTH  DAYS_EMPLOYED  \\\n",
              "0             Civil marriage   Rented apartment      -12005          -4542   \n",
              "1             Civil marriage   Rented apartment      -12005          -4542   \n",
              "2                    Married  House / apartment      -21474          -1134   \n",
              "3       Single / not married  House / apartment      -19110          -3051   \n",
              "4       Single / not married  House / apartment      -19110          -3051   \n",
              "...                      ...                ...         ...            ...   \n",
              "438552             Separated  House / apartment      -22717         365243   \n",
              "438553  Single / not married  House / apartment      -15939          -3007   \n",
              "438554  Single / not married       With parents       -8169           -372   \n",
              "438555               Married  House / apartment      -21673         365243   \n",
              "438556               Married  House / apartment      -18858          -1201   \n",
              "\n",
              "        FLAG_MOBIL  FLAG_WORK_PHONE  FLAG_PHONE  FLAG_EMAIL OCCUPATION_TYPE  \\\n",
              "0                1                1           0           0             NaN   \n",
              "1                1                1           0           0             NaN   \n",
              "2                1                0           0           0  Security staff   \n",
              "3                1                0           1           1     Sales staff   \n",
              "4                1                0           1           1     Sales staff   \n",
              "...            ...              ...         ...         ...             ...   \n",
              "438552           1                0           0           0             NaN   \n",
              "438553           1                0           0           0        Laborers   \n",
              "438554           1                1           0           0     Sales staff   \n",
              "438555           1                0           0           0             NaN   \n",
              "438556           1                0           1           0     Sales staff   \n",
              "\n",
              "        CNT_FAM_MEMBERS  \n",
              "0                   2.0  \n",
              "1                   2.0  \n",
              "2                   2.0  \n",
              "3                   1.0  \n",
              "4                   1.0  \n",
              "...                 ...  \n",
              "438552              1.0  \n",
              "438553              1.0  \n",
              "438554              1.0  \n",
              "438555              2.0  \n",
              "438556              2.0  \n",
              "\n",
              "[438557 rows x 18 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-eac00057-d6aa-40a2-8d76-5ab8e86c17db\">\n",
              "    <div 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>ID</th>\n",
              "      <th>CODE_GENDER</th>\n",
              "      <th>FLAG_OWN_CAR</th>\n",
              "      <th>FLAG_OWN_REALTY</th>\n",
              "      <th>CNT_CHILDREN</th>\n",
              "      <th>AMT_INCOME_TOTAL</th>\n",
              "      <th>NAME_INCOME_TYPE</th>\n",
              "      <th>NAME_EDUCATION_TYPE</th>\n",
              "      <th>NAME_FAMILY_STATUS</th>\n",
              "      <th>NAME_HOUSING_TYPE</th>\n",
              "      <th>DAYS_BIRTH</th>\n",
              "      <th>DAYS_EMPLOYED</th>\n",
              "      <th>FLAG_MOBIL</th>\n",
              "      <th>FLAG_WORK_PHONE</th>\n",
              "      <th>FLAG_PHONE</th>\n",
              "      <th>FLAG_EMAIL</th>\n",
              "      <th>OCCUPATION_TYPE</th>\n",
              "      <th>CNT_FAM_MEMBERS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5008804</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Civil marriage</td>\n",
              "      <td>Rented apartment</td>\n",
              "      <td>-12005</td>\n",
              "      <td>-4542</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5008805</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Civil marriage</td>\n",
              "      <td>Rented apartment</td>\n",
              "      <td>-12005</td>\n",
              "      <td>-4542</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5008806</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>112500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-21474</td>\n",
              "      <td>-1134</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Security staff</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5008808</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-19110</td>\n",
              "      <td>-3051</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sales staff</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5008809</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-19110</td>\n",
              "      <td>-3051</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sales staff</td>\n",
              "      <td>1.0</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>438552</th>\n",
              "      <td>6840104</td>\n",
              "      <td>M</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>135000.0</td>\n",
              "      <td>Pensioner</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Separated</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-22717</td>\n",
              "      <td>365243</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>438553</th>\n",
              "      <td>6840222</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "      <td>103500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-15939</td>\n",
              "      <td>-3007</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Laborers</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>438554</th>\n",
              "      <td>6841878</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "      <td>54000.0</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>With parents</td>\n",
              "      <td>-8169</td>\n",
              "      <td>-372</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Sales staff</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>438555</th>\n",
              "      <td>6842765</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>72000.0</td>\n",
              "      <td>Pensioner</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-21673</td>\n",
              "      <td>365243</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>438556</th>\n",
              "      <td>6842885</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>121500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-18858</td>\n",
              "      <td>-1201</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Sales staff</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>438557 rows × 18 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eac00057-d6aa-40a2-8d76-5ab8e86c17db')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-eac00057-d6aa-40a2-8d76-5ab8e86c17db 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-eac00057-d6aa-40a2-8d76-5ab8e86c17db');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soal No. 10 pilihan jawaban b\n",
        "df_ar.drop(['ID', 'CODE_GENDER'], axis=0, inplace=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        },
        "id": "ZFK0kUFEhAve",
        "outputId": "2c117d6c-7d4b-4af3-ab27-747bddecf0e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-19-42be0c7be94d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Soal No. 10 pilihan jawaban b\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf_ar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ID'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'CODE_GENDER'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 )\n\u001b[0;32m--> 311\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4911\u001b[0m             \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4912\u001b[0m             \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4913\u001b[0;31m             \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4914\u001b[0m         )\n\u001b[1;32m   4915\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4148\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4149\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4150\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4152\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[1;32m   4183\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4184\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4185\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4186\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   6015\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6016\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6017\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{labels[mask]} not found in axis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6018\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6019\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"['ID' 'CODE_GENDER'] not found in axis\""
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soal No. 10 pilihan jawaban c\n",
        "df_ar.drop(['ID', 'CODE_GENDER'], axis=0, inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        },
        "id": "bVVi2jKbgUOa",
        "outputId": "83e4dd2a-f11c-4062-fef5-5454c2a418fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-7e12689e45be>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Soal No. 10 pilihan jawaban c\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf_ar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ID'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'CODE_GENDER'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 )\n\u001b[0;32m--> 311\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4911\u001b[0m             \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4912\u001b[0m             \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4913\u001b[0;31m             \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4914\u001b[0m         )\n\u001b[1;32m   4915\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4148\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4149\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4150\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4152\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[1;32m   4183\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4184\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4185\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4186\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   6015\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6016\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6017\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{labels[mask]} not found in axis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6018\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6019\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"['ID' 'CODE_GENDER'] not found in axis\""
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soal No. 10 pilihan jawaban d\n",
        "df_ar.drop(['ID', 'CODE_GENDER'], axis=1, inplace=True)\n",
        "df_ar"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "id": "xQJPuHWvhfP4",
        "outputId": "189efaef-6457-490a-d4ee-3b1c02d4b92c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        CNT_CHILDREN  AMT_INCOME_TOTAL      NAME_INCOME_TYPE  \\\n",
              "0                  0          427500.0               Working   \n",
              "1                  0          427500.0               Working   \n",
              "2                  0          112500.0               Working   \n",
              "3                  0          270000.0  Commercial associate   \n",
              "4                  0          270000.0  Commercial associate   \n",
              "...              ...               ...                   ...   \n",
              "438552             0          135000.0             Pensioner   \n",
              "438553             0          103500.0               Working   \n",
              "438554             0           54000.0  Commercial associate   \n",
              "438555             0           72000.0             Pensioner   \n",
              "438556             0          121500.0               Working   \n",
              "\n",
              "                  NAME_EDUCATION_TYPE    NAME_FAMILY_STATUS  \\\n",
              "0                    Higher education        Civil marriage   \n",
              "1                    Higher education        Civil marriage   \n",
              "2       Secondary / secondary special               Married   \n",
              "3       Secondary / secondary special  Single / not married   \n",
              "4       Secondary / secondary special  Single / not married   \n",
              "...                               ...                   ...   \n",
              "438552  Secondary / secondary special             Separated   \n",
              "438553  Secondary / secondary special  Single / not married   \n",
              "438554               Higher education  Single / not married   \n",
              "438555  Secondary / secondary special               Married   \n",
              "438556  Secondary / secondary special               Married   \n",
              "\n",
              "        NAME_HOUSING_TYPE  DAYS_BIRTH  DAYS_EMPLOYED  FLAG_MOBIL  \\\n",
              "0        Rented apartment      -12005          -4542           1   \n",
              "1        Rented apartment      -12005          -4542           1   \n",
              "2       House / apartment      -21474          -1134           1   \n",
              "3       House / apartment      -19110          -3051           1   \n",
              "4       House / apartment      -19110          -3051           1   \n",
              "...                   ...         ...            ...         ...   \n",
              "438552  House / apartment      -22717         365243           1   \n",
              "438553  House / apartment      -15939          -3007           1   \n",
              "438554       With parents       -8169           -372           1   \n",
              "438555  House / apartment      -21673         365243           1   \n",
              "438556  House / apartment      -18858          -1201           1   \n",
              "\n",
              "        FLAG_WORK_PHONE  FLAG_PHONE  FLAG_EMAIL OCCUPATION_TYPE  \\\n",
              "0                     1           0           0             NaN   \n",
              "1                     1           0           0             NaN   \n",
              "2                     0           0           0  Security staff   \n",
              "3                     0           1           1     Sales staff   \n",
              "4                     0           1           1     Sales staff   \n",
              "...                 ...         ...         ...             ...   \n",
              "438552                0           0           0             NaN   \n",
              "438553                0           0           0        Laborers   \n",
              "438554                1           0           0     Sales staff   \n",
              "438555                0           0           0             NaN   \n",
              "438556                0           1           0     Sales staff   \n",
              "\n",
              "        CNT_FAM_MEMBERS  \n",
              "0                   2.0  \n",
              "1                   2.0  \n",
              "2                   2.0  \n",
              "3                   1.0  \n",
              "4                   1.0  \n",
              "...                 ...  \n",
              "438552              1.0  \n",
              "438553              1.0  \n",
              "438554              1.0  \n",
              "438555              2.0  \n",
              "438556              2.0  \n",
              "\n",
              "[438557 rows x 14 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-86bc3151-05e5-4632-9c8d-19863cc89572\">\n",
              "    <div 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>CNT_CHILDREN</th>\n",
              "      <th>AMT_INCOME_TOTAL</th>\n",
              "      <th>NAME_INCOME_TYPE</th>\n",
              "      <th>NAME_EDUCATION_TYPE</th>\n",
              "      <th>NAME_FAMILY_STATUS</th>\n",
              "      <th>NAME_HOUSING_TYPE</th>\n",
              "      <th>DAYS_BIRTH</th>\n",
              "      <th>DAYS_EMPLOYED</th>\n",
              "      <th>FLAG_MOBIL</th>\n",
              "      <th>FLAG_WORK_PHONE</th>\n",
              "      <th>FLAG_PHONE</th>\n",
              "      <th>FLAG_EMAIL</th>\n",
              "      <th>OCCUPATION_TYPE</th>\n",
              "      <th>CNT_FAM_MEMBERS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Civil marriage</td>\n",
              "      <td>Rented apartment</td>\n",
              "      <td>-12005</td>\n",
              "      <td>-4542</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Civil marriage</td>\n",
              "      <td>Rented apartment</td>\n",
              "      <td>-12005</td>\n",
              "      <td>-4542</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>112500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-21474</td>\n",
              "      <td>-1134</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Security staff</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-19110</td>\n",
              "      <td>-3051</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sales staff</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-19110</td>\n",
              "      <td>-3051</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sales staff</td>\n",
              "      <td>1.0</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>438552</th>\n",
              "      <td>0</td>\n",
              "      <td>135000.0</td>\n",
              "      <td>Pensioner</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Separated</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-22717</td>\n",
              "      <td>365243</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>438553</th>\n",
              "      <td>0</td>\n",
              "      <td>103500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-15939</td>\n",
              "      <td>-3007</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Laborers</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>438554</th>\n",
              "      <td>0</td>\n",
              "      <td>54000.0</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>With parents</td>\n",
              "      <td>-8169</td>\n",
              "      <td>-372</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Sales staff</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>438555</th>\n",
              "      <td>0</td>\n",
              "      <td>72000.0</td>\n",
              "      <td>Pensioner</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-21673</td>\n",
              "      <td>365243</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>438556</th>\n",
              "      <td>0</td>\n",
              "      <td>121500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-18858</td>\n",
              "      <td>-1201</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Sales staff</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>438557 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-86bc3151-05e5-4632-9c8d-19863cc89572')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-86bc3151-05e5-4632-9c8d-19863cc89572 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-86bc3151-05e5-4632-9c8d-19863cc89572');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Standarisasi / Normalisasi"
      ],
      "metadata": {
        "id": "WXmvIS5Wl3eO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Soal No. 5 - standarisasi / normalisasi\n",
        "from sklearn import preprocessing\n",
        "x_array = np.array(df_ar['CNT_CHILDREN']) \n",
        "normalized_arr = preprocessing.normalize([x_array])\n",
        "print(normalized_arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vOku3EpriBVK",
        "outputId": "a17bfdbc-2120-4e4c-e768-de6e72133985"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0. 0. 0. ... 0. 0. 0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soal No. 5 - standarisasi / normalisasi\n",
        "from sklearn import preprocessing\n",
        "x_array = np.array(df_ar['AMT_INCOME_TOTAL']) \n",
        "normalized_arr = preprocessing.normalize([x_array])\n",
        "print(normalized_arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dTW3XWZVlpV3",
        "outputId": "004793da-4db5-49fb-fd94-3591a5548094"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.00296868 0.00296868 0.00078123 ... 0.00037499 0.00049999 0.00084373]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soal No. 5 - standarisasi / normalisasi\n",
        "from sklearn import preprocessing\n",
        "x_array = np.array(df_ar['CNT_FAM_MEMBERS'])\n",
        "normalized_arr = preprocessing.normalize([x_array])\n",
        "print(normalized_arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dYxubtkoiB8J",
        "outputId": "e4cef693-9a8a-4bf5-bfc3-26df5355b27e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.00127387 0.00127387 0.00127387 ... 0.00063693 0.00127387 0.00127387]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soal No. 5 - standarisasi / normalisasi\n",
        "from sklearn import preprocessing\n",
        "x_array = np.array(df_ar['DAYS_BIRTH'])\n",
        "normalized_arr = preprocessing.normalize([x_array])\n",
        "print(normalized_arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-nWe9AlkmaBA",
        "outputId": "4d2e9e1d-6124-4228-8ea2-44abecf1eba4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.00109626 -0.00109626 -0.00196093 ... -0.00074597 -0.00197911\n",
            "  -0.00172205]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soal No. 5 - standarisasi / normalisasi\n",
        "from sklearn import preprocessing\n",
        "x_array = np.array(df_ar['ID'])\n",
        "normalized_arr = preprocessing.normalize([x_array])\n",
        "print(normalized_arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a_lXHKvrmaD9",
        "outputId": "77af96b7-514a-4f78-fe1a-d80ec202d1e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.00125032 0.00125032 0.00125032 ... 0.00170789 0.00170812 0.00170815]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soal No. 5 - standarisasi / normalisasi\n",
        "from sklearn import preprocessing\n",
        "x_array = np.array(df_ar['DAYS_EMPLOYED'])\n",
        "normalized_arr = preprocessing.normalize([x_array])\n",
        "print(normalized_arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sXNHnGarl1R3",
        "outputId": "25d9b85a-b8be-4ed2-e24b-128814574429"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-4.52986183e-05 -4.52986183e-05 -1.13096947e-05 ... -3.71005857e-06\n",
            "   3.64266914e-03 -1.19779041e-05]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soal No. 5 - standarisasi / normalisasi\n",
        "from sklearn import preprocessing\n",
        "x_array = np.array(df_ar['FLAG_MOBIL']) \n",
        "normalized_arr = preprocessing.normalize([x_array])\n",
        "print(normalized_arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sZFk2GkhmofL",
        "outputId": "be37767b-807d-49c2-8775-25b389a5134d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.00151003 0.00151003 0.00151003 ... 0.00151003 0.00151003 0.00151003]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soal No. 5 - standarisasi / normalisasi\n",
        "from sklearn import preprocessing\n",
        "x_array = np.array(df_ar['FLAG_PHONE']) \n",
        "normalized_arr = preprocessing.normalize([x_array])\n",
        "print(normalized_arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ijl7RxPrmtXV",
        "outputId": "0618a7b8-6943-45cf-acdf-237e36313d30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.        0.        0.        ... 0.        0.        0.0028149]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Soal No. 5 - standarisasi / normalisasi\n",
        "from sklearn import preprocessing\n",
        "x_array = np.array(df_ar['FLAG_EMAIL']) \n",
        "normalized_arr = preprocessing.normalize([x_array])\n",
        "print(normalized_arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gVbc77O3mtaQ",
        "outputId": "aabb1764-4a89-4ef4-f648-8245fb5f8e19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0. 0. 0. ... 0. 0. 0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Label Encoding"
      ],
      "metadata": {
        "id": "Ixxf6zffo7fy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Label Encoding Using Python"
      ],
      "metadata": {
        "id": "Pz8QSdOqrf7S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "> Label Encoding can be performed in 2 ways namely:\n",
        "1. LabelEncoder class using scikit-learn library \n",
        "2. LabelEncoder class using category codes"
      ],
      "metadata": {
        "id": "E7Vste2YPwPT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### CODE_GENDER"
      ],
      "metadata": {
        "id": "1ObVW9WGszkk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###### Approach 1 – scikit-learn library approach"
      ],
      "metadata": {
        "id": "O9xTwwVWPS9Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "> And then:\n",
        "1. Create an instance of LabelEncoder() and store it in labelencoder variable/object.\n",
        "2. Apply fit and transform which does the trick to assign numerical value to categorical value and the same is stored in new column called 'CODE_GENDER_N'.\n",
        "3. Note that we have added a new column called 'CODE_GENDER_N' which contains numerical value associated to categorical value and still the column called State is present in the dataframe. This column needs to be removed before we feed the final preprocess data to machine learning model to learn."
      ],
      "metadata": {
        "id": "KYHOIYQmQAPf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "labelEncoder = LabelEncoder()\n",
        "df_ar['CODE_GENDER_N'] = labelEncoder.fit_transform(df_ar['CODE_GENDER']) \n",
        "df_ar.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "z8m6OBP2o7u2",
        "outputId": "ac15cd86-4fcf-4b8e-f425-601919ce6e93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ID CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  CNT_CHILDREN  \\\n",
              "0  5008804           M            Y               Y             0   \n",
              "1  5008805           M            Y               Y             0   \n",
              "2  5008806           M            Y               Y             0   \n",
              "3  5008808           F            N               Y             0   \n",
              "4  5008809           F            N               Y             0   \n",
              "\n",
              "   AMT_INCOME_TOTAL      NAME_INCOME_TYPE            NAME_EDUCATION_TYPE  \\\n",
              "0          427500.0               Working               Higher education   \n",
              "1          427500.0               Working               Higher education   \n",
              "2          112500.0               Working  Secondary / secondary special   \n",
              "3          270000.0  Commercial associate  Secondary / secondary special   \n",
              "4          270000.0  Commercial associate  Secondary / secondary special   \n",
              "\n",
              "     NAME_FAMILY_STATUS  NAME_HOUSING_TYPE  DAYS_BIRTH  DAYS_EMPLOYED  \\\n",
              "0        Civil marriage   Rented apartment      -12005          -4542   \n",
              "1        Civil marriage   Rented apartment      -12005          -4542   \n",
              "2               Married  House / apartment      -21474          -1134   \n",
              "3  Single / not married  House / apartment      -19110          -3051   \n",
              "4  Single / not married  House / apartment      -19110          -3051   \n",
              "\n",
              "   FLAG_MOBIL  FLAG_WORK_PHONE  FLAG_PHONE  FLAG_EMAIL OCCUPATION_TYPE  \\\n",
              "0           1                1           0           0             NaN   \n",
              "1           1                1           0           0             NaN   \n",
              "2           1                0           0           0  Security staff   \n",
              "3           1                0           1           1     Sales staff   \n",
              "4           1                0           1           1     Sales staff   \n",
              "\n",
              "   CNT_FAM_MEMBERS  CODE_GENDER_N  \n",
              "0              2.0              1  \n",
              "1              2.0              1  \n",
              "2              2.0              1  \n",
              "3              1.0              0  \n",
              "4              1.0              0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-aabd5d1a-1607-46b6-9711-f0100bf8f5e2\">\n",
              "    <div 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>ID</th>\n",
              "      <th>CODE_GENDER</th>\n",
              "      <th>FLAG_OWN_CAR</th>\n",
              "      <th>FLAG_OWN_REALTY</th>\n",
              "      <th>CNT_CHILDREN</th>\n",
              "      <th>AMT_INCOME_TOTAL</th>\n",
              "      <th>NAME_INCOME_TYPE</th>\n",
              "      <th>NAME_EDUCATION_TYPE</th>\n",
              "      <th>NAME_FAMILY_STATUS</th>\n",
              "      <th>NAME_HOUSING_TYPE</th>\n",
              "      <th>DAYS_BIRTH</th>\n",
              "      <th>DAYS_EMPLOYED</th>\n",
              "      <th>FLAG_MOBIL</th>\n",
              "      <th>FLAG_WORK_PHONE</th>\n",
              "      <th>FLAG_PHONE</th>\n",
              "      <th>FLAG_EMAIL</th>\n",
              "      <th>OCCUPATION_TYPE</th>\n",
              "      <th>CNT_FAM_MEMBERS</th>\n",
              "      <th>CODE_GENDER_N</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5008804</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Civil marriage</td>\n",
              "      <td>Rented apartment</td>\n",
              "      <td>-12005</td>\n",
              "      <td>-4542</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5008805</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Civil marriage</td>\n",
              "      <td>Rented apartment</td>\n",
              "      <td>-12005</td>\n",
              "      <td>-4542</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5008806</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>112500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-21474</td>\n",
              "      <td>-1134</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Security staff</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5008808</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-19110</td>\n",
              "      <td>-3051</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sales staff</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5008809</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-19110</td>\n",
              "      <td>-3051</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sales staff</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aabd5d1a-1607-46b6-9711-f0100bf8f5e2')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-aabd5d1a-1607-46b6-9711-f0100bf8f5e2 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-aabd5d1a-1607-46b6-9711-f0100bf8f5e2');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###### Approach 2 – Category Codes"
      ],
      "metadata": {
        "id": "sSYRBwL1NvS7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "> And then:\n",
        "1. As you had already observed that `CODE_GENDER` column datatype is an object type which is by default hence, need to convert 'CODE_GENDER' to a category type with the help of pandas.\n",
        "2. We can access the codes of the categories by running df_ar['CODE_GENDER'].cat.codes."
      ],
      "metadata": {
        "id": "NMS7nL7kN0If"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_ar['CODE_GENDER'] = df_ar['CODE_GENDER'].astype('category') \n",
        "df_ar.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dD0-abfqqGJs",
        "outputId": "45406275-5eda-4083-89b6-fad482da9d74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                        int64\n",
              "CODE_GENDER            category\n",
              "FLAG_OWN_CAR             object\n",
              "FLAG_OWN_REALTY          object\n",
              "CNT_CHILDREN              int64\n",
              "AMT_INCOME_TOTAL        float64\n",
              "NAME_INCOME_TYPE         object\n",
              "NAME_EDUCATION_TYPE      object\n",
              "NAME_FAMILY_STATUS       object\n",
              "NAME_HOUSING_TYPE        object\n",
              "DAYS_BIRTH                int64\n",
              "DAYS_EMPLOYED             int64\n",
              "FLAG_MOBIL                int64\n",
              "FLAG_WORK_PHONE           int64\n",
              "FLAG_PHONE                int64\n",
              "FLAG_EMAIL                int64\n",
              "OCCUPATION_TYPE          object\n",
              "CNT_FAM_MEMBERS         float64\n",
              "CODE_GENDER_N             int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ar['CODE_GENDER'] = df_ar['CODE_GENDER'].cat.codes\n",
        "df_ar"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "id": "Gd9DnRTCqKl8",
        "outputId": "1271ae92-5a89-4e38-ad65-5e8963631763"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             ID  CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  CNT_CHILDREN  \\\n",
              "0       5008804            1            Y               Y             0   \n",
              "1       5008805            1            Y               Y             0   \n",
              "2       5008806            1            Y               Y             0   \n",
              "3       5008808            0            N               Y             0   \n",
              "4       5008809            0            N               Y             0   \n",
              "...         ...          ...          ...             ...           ...   \n",
              "438552  6840104            1            N               Y             0   \n",
              "438553  6840222            0            N               N             0   \n",
              "438554  6841878            0            N               N             0   \n",
              "438555  6842765            0            N               Y             0   \n",
              "438556  6842885            0            N               Y             0   \n",
              "\n",
              "        AMT_INCOME_TOTAL      NAME_INCOME_TYPE            NAME_EDUCATION_TYPE  \\\n",
              "0               427500.0               Working               Higher education   \n",
              "1               427500.0               Working               Higher education   \n",
              "2               112500.0               Working  Secondary / secondary special   \n",
              "3               270000.0  Commercial associate  Secondary / secondary special   \n",
              "4               270000.0  Commercial associate  Secondary / secondary special   \n",
              "...                  ...                   ...                            ...   \n",
              "438552          135000.0             Pensioner  Secondary / secondary special   \n",
              "438553          103500.0               Working  Secondary / secondary special   \n",
              "438554           54000.0  Commercial associate               Higher education   \n",
              "438555           72000.0             Pensioner  Secondary / secondary special   \n",
              "438556          121500.0               Working  Secondary / secondary special   \n",
              "\n",
              "          NAME_FAMILY_STATUS  NAME_HOUSING_TYPE  DAYS_BIRTH  DAYS_EMPLOYED  \\\n",
              "0             Civil marriage   Rented apartment      -12005          -4542   \n",
              "1             Civil marriage   Rented apartment      -12005          -4542   \n",
              "2                    Married  House / apartment      -21474          -1134   \n",
              "3       Single / not married  House / apartment      -19110          -3051   \n",
              "4       Single / not married  House / apartment      -19110          -3051   \n",
              "...                      ...                ...         ...            ...   \n",
              "438552             Separated  House / apartment      -22717         365243   \n",
              "438553  Single / not married  House / apartment      -15939          -3007   \n",
              "438554  Single / not married       With parents       -8169           -372   \n",
              "438555               Married  House / apartment      -21673         365243   \n",
              "438556               Married  House / apartment      -18858          -1201   \n",
              "\n",
              "        FLAG_MOBIL  FLAG_WORK_PHONE  FLAG_PHONE  FLAG_EMAIL OCCUPATION_TYPE  \\\n",
              "0                1                1           0           0             NaN   \n",
              "1                1                1           0           0             NaN   \n",
              "2                1                0           0           0  Security staff   \n",
              "3                1                0           1           1     Sales staff   \n",
              "4                1                0           1           1     Sales staff   \n",
              "...            ...              ...         ...         ...             ...   \n",
              "438552           1                0           0           0             NaN   \n",
              "438553           1                0           0           0        Laborers   \n",
              "438554           1                1           0           0     Sales staff   \n",
              "438555           1                0           0           0             NaN   \n",
              "438556           1                0           1           0     Sales staff   \n",
              "\n",
              "        CNT_FAM_MEMBERS  CODE_GENDER_N  \n",
              "0                   2.0              1  \n",
              "1                   2.0              1  \n",
              "2                   2.0              1  \n",
              "3                   1.0              0  \n",
              "4                   1.0              0  \n",
              "...                 ...            ...  \n",
              "438552              1.0              1  \n",
              "438553              1.0              0  \n",
              "438554              1.0              0  \n",
              "438555              2.0              0  \n",
              "438556              2.0              0  \n",
              "\n",
              "[438557 rows x 19 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7c70e66b-3f0e-44b3-b53b-31228dcb7173\">\n",
              "    <div 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>ID</th>\n",
              "      <th>CODE_GENDER</th>\n",
              "      <th>FLAG_OWN_CAR</th>\n",
              "      <th>FLAG_OWN_REALTY</th>\n",
              "      <th>CNT_CHILDREN</th>\n",
              "      <th>AMT_INCOME_TOTAL</th>\n",
              "      <th>NAME_INCOME_TYPE</th>\n",
              "      <th>NAME_EDUCATION_TYPE</th>\n",
              "      <th>NAME_FAMILY_STATUS</th>\n",
              "      <th>NAME_HOUSING_TYPE</th>\n",
              "      <th>DAYS_BIRTH</th>\n",
              "      <th>DAYS_EMPLOYED</th>\n",
              "      <th>FLAG_MOBIL</th>\n",
              "      <th>FLAG_WORK_PHONE</th>\n",
              "      <th>FLAG_PHONE</th>\n",
              "      <th>FLAG_EMAIL</th>\n",
              "      <th>OCCUPATION_TYPE</th>\n",
              "      <th>CNT_FAM_MEMBERS</th>\n",
              "      <th>CODE_GENDER_N</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5008804</td>\n",
              "      <td>1</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Civil marriage</td>\n",
              "      <td>Rented apartment</td>\n",
              "      <td>-12005</td>\n",
              "      <td>-4542</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5008805</td>\n",
              "      <td>1</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Civil marriage</td>\n",
              "      <td>Rented apartment</td>\n",
              "      <td>-12005</td>\n",
              "      <td>-4542</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5008806</td>\n",
              "      <td>1</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>112500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-21474</td>\n",
              "      <td>-1134</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Security staff</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5008808</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-19110</td>\n",
              "      <td>-3051</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sales staff</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5008809</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-19110</td>\n",
              "      <td>-3051</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Sales staff</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>438552</th>\n",
              "      <td>6840104</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>135000.0</td>\n",
              "      <td>Pensioner</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Separated</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-22717</td>\n",
              "      <td>365243</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>438553</th>\n",
              "      <td>6840222</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "      <td>103500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-15939</td>\n",
              "      <td>-3007</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Laborers</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>438554</th>\n",
              "      <td>6841878</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>0</td>\n",
              "      <td>54000.0</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>With parents</td>\n",
              "      <td>-8169</td>\n",
              "      <td>-372</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Sales staff</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>438555</th>\n",
              "      <td>6842765</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>72000.0</td>\n",
              "      <td>Pensioner</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-21673</td>\n",
              "      <td>365243</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>438556</th>\n",
              "      <td>6842885</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>121500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>-18858</td>\n",
              "      <td>-1201</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Sales staff</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>438557 rows × 19 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7c70e66b-3f0e-44b3-b53b-31228dcb7173')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-7c70e66b-3f0e-44b3-b53b-31228dcb7173 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-7c70e66b-3f0e-44b3-b53b-31228dcb7173');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> One potential issue with label encoding is that most of the time, there is no relationship of any kind between categories, while label encoding introduces a relationship. \n",
        "\n",
        "> In the above six classes’ example for `CODE_GENDER` column, the relationship looks as follows: 0 < 1. It means that numeric values can be misjudged by algorithms as having some sort of order in them. This does not make much sense if the categories are, for example 'CODE_GENDER'. \n",
        "\n",
        "> There is no such relation in the original data with the actual State names, but, by using numerical values as we did, a number-related connection between the encoded data might be made. To overcome this problem, we can use one-hot encoding as explained below."
      ],
      "metadata": {
        "id": "Gn7AWlzQOTkx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### One-Hot Encoding Using Python"
      ],
      "metadata": {
        "id": "u8GUN5lKrcVi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### CODE_GENDER"
      ],
      "metadata": {
        "id": "jFajbXLEsoR6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###### Approach 1 – scikit-learn library approach"
      ],
      "metadata": {
        "id": "zvah_we4NTxj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "labelEncoder = preprocessing.LabelEncoder()\n",
        "df_ar['CODE_GENDER_N'] =labelEncoder.fit_transform(df_ar['CODE_GENDER'])\n",
        "df_ar[['CODE_GENDER', 'CODE_GENDER_N']].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "T8O3VYNAqj8r",
        "outputId": "a164e9f4-3ab1-4b28-8abb-2a3aa9da2bf9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  CODE_GENDER  CODE_GENDER_N\n",
              "0           M              1\n",
              "1           M              1\n",
              "2           M              1\n",
              "3           F              0\n",
              "4           F              0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e0476baf-a21c-4048-9cf9-4f0fd2b26d77\">\n",
              "    <div 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>CODE_GENDER</th>\n",
              "      <th>CODE_GENDER_N</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>F</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>F</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e0476baf-a21c-4048-9cf9-4f0fd2b26d77')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-e0476baf-a21c-4048-9cf9-4f0fd2b26d77 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-e0476baf-a21c-4048-9cf9-4f0fd2b26d77');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###### Approach 2 – Using Pandas: with the help of get_dummies function"
      ],
      "metadata": {
        "id": "uL0nJONFNXjx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ar_ohe_df = pd.get_dummies(df_ar['CODE_GENDER'], drop_first=True)\n",
        "ar_ohe_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "E0reYb9hLTgs",
        "outputId": "cbc41231-901f-4fd5-d8cb-8e3e5ddbc731"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   M\n",
              "0  1\n",
              "1  1\n",
              "2  1\n",
              "3  0\n",
              "4  0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6424fc74-247e-4f53-baaf-cbca470185de\">\n",
              "    <div 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>M</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6424fc74-247e-4f53-baaf-cbca470185de')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-6424fc74-247e-4f53-baaf-cbca470185de 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-6424fc74-247e-4f53-baaf-cbca470185de');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ar = pd.concat([df_ar, ar_ohe_df], axis=1)\n",
        "df_ar.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "6r5uRVPQL4v-",
        "outputId": "8e75acb2-dd30-47b4-a8e2-0aabc49a8d80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ID CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  CNT_CHILDREN  \\\n",
              "0  5008804           M            Y               Y             0   \n",
              "1  5008805           M            Y               Y             0   \n",
              "2  5008806           M            Y               Y             0   \n",
              "3  5008808           F            N               Y             0   \n",
              "4  5008809           F            N               Y             0   \n",
              "\n",
              "   AMT_INCOME_TOTAL      NAME_INCOME_TYPE            NAME_EDUCATION_TYPE  \\\n",
              "0          427500.0               Working               Higher education   \n",
              "1          427500.0               Working               Higher education   \n",
              "2          112500.0               Working  Secondary / secondary special   \n",
              "3          270000.0  Commercial associate  Secondary / secondary special   \n",
              "4          270000.0  Commercial associate  Secondary / secondary special   \n",
              "\n",
              "     NAME_FAMILY_STATUS  NAME_HOUSING_TYPE  ...  CODE_GENDER_M  \\\n",
              "0        Civil marriage   Rented apartment  ...              1   \n",
              "1        Civil marriage   Rented apartment  ...              1   \n",
              "2               Married  House / apartment  ...              1   \n",
              "3  Single / not married  House / apartment  ...              0   \n",
              "4  Single / not married  House / apartment  ...              0   \n",
              "\n",
              "   NAME_INCOME_TYPE_Pensioner  NAME_INCOME_TYPE_State servant  \\\n",
              "0                           0                               0   \n",
              "1                           0                               0   \n",
              "2                           0                               0   \n",
              "3                           0                               0   \n",
              "4                           0                               0   \n",
              "\n",
              "   NAME_INCOME_TYPE_Student  NAME_INCOME_TYPE_Working  Pensioner  \\\n",
              "0                         0                         1          0   \n",
              "1                         0                         1          0   \n",
              "2                         0                         1          0   \n",
              "3                         0                         0          0   \n",
              "4                         0                         0          0   \n",
              "\n",
              "  State servant  Student  Working  M  \n",
              "0             0        0        1  1  \n",
              "1             0        0        1  1  \n",
              "2             0        0        1  1  \n",
              "3             0        0        0  0  \n",
              "4             0        0        0  0  \n",
              "\n",
              "[5 rows x 29 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4f48f3eb-85ee-43fd-8780-fdc8f07b2cc2\">\n",
              "    <div 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>ID</th>\n",
              "      <th>CODE_GENDER</th>\n",
              "      <th>FLAG_OWN_CAR</th>\n",
              "      <th>FLAG_OWN_REALTY</th>\n",
              "      <th>CNT_CHILDREN</th>\n",
              "      <th>AMT_INCOME_TOTAL</th>\n",
              "      <th>NAME_INCOME_TYPE</th>\n",
              "      <th>NAME_EDUCATION_TYPE</th>\n",
              "      <th>NAME_FAMILY_STATUS</th>\n",
              "      <th>NAME_HOUSING_TYPE</th>\n",
              "      <th>...</th>\n",
              "      <th>CODE_GENDER_M</th>\n",
              "      <th>NAME_INCOME_TYPE_Pensioner</th>\n",
              "      <th>NAME_INCOME_TYPE_State servant</th>\n",
              "      <th>NAME_INCOME_TYPE_Student</th>\n",
              "      <th>NAME_INCOME_TYPE_Working</th>\n",
              "      <th>Pensioner</th>\n",
              "      <th>State servant</th>\n",
              "      <th>Student</th>\n",
              "      <th>Working</th>\n",
              "      <th>M</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5008804</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Civil marriage</td>\n",
              "      <td>Rented apartment</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5008805</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Civil marriage</td>\n",
              "      <td>Rented apartment</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5008806</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>112500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5008808</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5008809</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 29 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4f48f3eb-85ee-43fd-8780-fdc8f07b2cc2')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-4f48f3eb-85ee-43fd-8780-fdc8f07b2cc2 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-4f48f3eb-85ee-43fd-8780-fdc8f07b2cc2');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### NAME_INCOME_TYPE "
      ],
      "metadata": {
        "id": "gL5xxNvVsrG9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###### Approach 1 – scikit-learn library approach"
      ],
      "metadata": {
        "id": "UJGyMyoBNHOt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "oneHotEncoder = OneHotEncoder(sparse=False,\n",
        "                              handle_unknown='error',\n",
        "                              drop='first')\n",
        "\n",
        "oneHotEncoder_df = pd.DataFrame(oneHotEncoder.fit_transform(df_ar[['NAME_INCOME_TYPE']]))\n",
        "oneHotEncoder_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "NpZXWhsAsrRs",
        "outputId": "14e09575-a515-4282-866c-d58643cbdbb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          0    1    2    3\n",
              "0       0.0  0.0  0.0  1.0\n",
              "1       0.0  0.0  0.0  1.0\n",
              "2       0.0  0.0  0.0  1.0\n",
              "3       0.0  0.0  0.0  0.0\n",
              "4       0.0  0.0  0.0  0.0\n",
              "...     ...  ...  ...  ...\n",
              "438552  1.0  0.0  0.0  0.0\n",
              "438553  0.0  0.0  0.0  1.0\n",
              "438554  0.0  0.0  0.0  0.0\n",
              "438555  1.0  0.0  0.0  0.0\n",
              "438556  0.0  0.0  0.0  1.0\n",
              "\n",
              "[438557 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-14133e90-c513-4340-90d1-557bfc5c3626\">\n",
              "    <div 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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>438552</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>438553</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>438554</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>438555</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>438556</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>438557 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-14133e90-c513-4340-90d1-557bfc5c3626')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-14133e90-c513-4340-90d1-557bfc5c3626 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-14133e90-c513-4340-90d1-557bfc5c3626');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ar = df_ar.join(oneHotEncoder_df)\n",
        "df_ar.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        },
        "id": "rRqDP9Ott_3e",
        "outputId": "90b63692-4bf0-43de-f49a-b2fec84f664d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-80-5d955fc932d7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_ar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_ar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moneHotEncoder_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf_ar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, other, on, how, lsuffix, rsuffix, sort)\u001b[0m\n\u001b[1;32m   9098\u001b[0m         \"\"\"\n\u001b[1;32m   9099\u001b[0m         return self._join_compat(\n\u001b[0;32m-> 9100\u001b[0;31m             \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlsuffix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlsuffix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrsuffix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrsuffix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   9101\u001b[0m         )\n\u001b[1;32m   9102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_join_compat\u001b[0;34m(self, other, on, how, lsuffix, rsuffix, sort)\u001b[0m\n\u001b[1;32m   9136\u001b[0m                 \u001b[0mright_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9137\u001b[0m                 \u001b[0msuffixes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlsuffix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrsuffix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 9138\u001b[0;31m                 \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   9139\u001b[0m             )\n\u001b[1;32m   9140\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36mmerge\u001b[0;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0mvalidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     )\n\u001b[0;32m--> 121\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36mget_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    716\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    717\u001b[0m         llabels, rlabels = _items_overlap_with_suffix(\n\u001b[0;32m--> 718\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuffixes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    719\u001b[0m         )\n\u001b[1;32m    720\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/reshape/merge.py\u001b[0m in \u001b[0;36m_items_overlap_with_suffix\u001b[0;34m(left, right, suffixes)\u001b[0m\n\u001b[1;32m   2312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2313\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mlsuffix\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mrsuffix\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2314\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"columns overlap but no suffix specified: {to_rename}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2316\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrenamer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msuffix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: columns overlap but no suffix specified: Index([0], dtype='object')"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###### Approach 2 – Using Pandas: with the help of get_dummies function"
      ],
      "metadata": {
        "id": "EfAEwUCfMu-q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ar_ohe_df = pd.get_dummies(df_ar['NAME_INCOME_TYPE'], drop_first=True)\n",
        "ar_ohe_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "KO9reM01uYcM",
        "outputId": "cb53b616-369c-4925-ab5b-3ef9de94b3e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pensioner  State servant  Student  Working\n",
              "0          0              0        0        1\n",
              "1          0              0        0        1\n",
              "2          0              0        0        1\n",
              "3          0              0        0        0\n",
              "4          0              0        0        0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-93325513-6df0-4eab-bf9d-ae54c4808e22\">\n",
              "    <div 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>Pensioner</th>\n",
              "      <th>State servant</th>\n",
              "      <th>Student</th>\n",
              "      <th>Working</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-93325513-6df0-4eab-bf9d-ae54c4808e22')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-93325513-6df0-4eab-bf9d-ae54c4808e22 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-93325513-6df0-4eab-bf9d-ae54c4808e22');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ar = pd.concat([df_ar, ar_ohe_df], axis=1)\n",
        "df_ar.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "5vA9l6SeLfBP",
        "outputId": "b43df7a8-7e3e-418f-a931-4de1a3d11531"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ID CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  CNT_CHILDREN  \\\n",
              "0  5008804           M            Y               Y             0   \n",
              "1  5008805           M            Y               Y             0   \n",
              "2  5008806           M            Y               Y             0   \n",
              "3  5008808           F            N               Y             0   \n",
              "4  5008809           F            N               Y             0   \n",
              "\n",
              "   AMT_INCOME_TOTAL      NAME_INCOME_TYPE            NAME_EDUCATION_TYPE  \\\n",
              "0          427500.0               Working               Higher education   \n",
              "1          427500.0               Working               Higher education   \n",
              "2          112500.0               Working  Secondary / secondary special   \n",
              "3          270000.0  Commercial associate  Secondary / secondary special   \n",
              "4          270000.0  Commercial associate  Secondary / secondary special   \n",
              "\n",
              "     NAME_FAMILY_STATUS  NAME_HOUSING_TYPE  ...  CODE_GENDER_M  CODE_GENDER_M  \\\n",
              "0        Civil marriage   Rented apartment  ...              1              1   \n",
              "1        Civil marriage   Rented apartment  ...              1              1   \n",
              "2               Married  House / apartment  ...              1              1   \n",
              "3  Single / not married  House / apartment  ...              0              0   \n",
              "4  Single / not married  House / apartment  ...              0              0   \n",
              "\n",
              "   NAME_INCOME_TYPE_Pensioner  NAME_INCOME_TYPE_State servant  \\\n",
              "0                           0                               0   \n",
              "1                           0                               0   \n",
              "2                           0                               0   \n",
              "3                           0                               0   \n",
              "4                           0                               0   \n",
              "\n",
              "   NAME_INCOME_TYPE_Student  NAME_INCOME_TYPE_Working Pensioner  \\\n",
              "0                         0                         1         0   \n",
              "1                         0                         1         0   \n",
              "2                         0                         1         0   \n",
              "3                         0                         0         0   \n",
              "4                         0                         0         0   \n",
              "\n",
              "   State servant  Student  Working  \n",
              "0              0        0        1  \n",
              "1              0        0        1  \n",
              "2              0        0        1  \n",
              "3              0        0        0  \n",
              "4              0        0        0  \n",
              "\n",
              "[5 rows x 28 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6c52001b-9c43-4b27-867b-3229a1578ab6\">\n",
              "    <div 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>ID</th>\n",
              "      <th>CODE_GENDER</th>\n",
              "      <th>FLAG_OWN_CAR</th>\n",
              "      <th>FLAG_OWN_REALTY</th>\n",
              "      <th>CNT_CHILDREN</th>\n",
              "      <th>AMT_INCOME_TOTAL</th>\n",
              "      <th>NAME_INCOME_TYPE</th>\n",
              "      <th>NAME_EDUCATION_TYPE</th>\n",
              "      <th>NAME_FAMILY_STATUS</th>\n",
              "      <th>NAME_HOUSING_TYPE</th>\n",
              "      <th>...</th>\n",
              "      <th>CODE_GENDER_M</th>\n",
              "      <th>CODE_GENDER_M</th>\n",
              "      <th>NAME_INCOME_TYPE_Pensioner</th>\n",
              "      <th>NAME_INCOME_TYPE_State servant</th>\n",
              "      <th>NAME_INCOME_TYPE_Student</th>\n",
              "      <th>NAME_INCOME_TYPE_Working</th>\n",
              "      <th>Pensioner</th>\n",
              "      <th>State servant</th>\n",
              "      <th>Student</th>\n",
              "      <th>Working</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5008804</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Civil marriage</td>\n",
              "      <td>Rented apartment</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5008805</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>427500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Higher education</td>\n",
              "      <td>Civil marriage</td>\n",
              "      <td>Rented apartment</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5008806</td>\n",
              "      <td>M</td>\n",
              "      <td>Y</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>112500.0</td>\n",
              "      <td>Working</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5008808</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5008809</td>\n",
              "      <td>F</td>\n",
              "      <td>N</td>\n",
              "      <td>Y</td>\n",
              "      <td>0</td>\n",
              "      <td>270000.0</td>\n",
              "      <td>Commercial associate</td>\n",
              "      <td>Secondary / secondary special</td>\n",
              "      <td>Single / not married</td>\n",
              "      <td>House / apartment</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 28 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6c52001b-9c43-4b27-867b-3229a1578ab6')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-6c52001b-9c43-4b27-867b-3229a1578ab6 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-6c52001b-9c43-4b27-867b-3229a1578ab6');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Ordinal Encoding Using Python"
      ],
      "metadata": {
        "id": "YeAWJUCRUHaH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing = df_ar['NAME_HOUSING_TYPE']\n",
        "housing"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "adIIm-AcUT_Z",
        "outputId": "d06055ab-cbe3-436b-a76a-970d3f0397db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0          Rented apartment\n",
              "1          Rented apartment\n",
              "2         House / apartment\n",
              "3         House / apartment\n",
              "4         House / apartment\n",
              "                ...        \n",
              "438552    House / apartment\n",
              "438553    House / apartment\n",
              "438554         With parents\n",
              "438555    House / apartment\n",
              "438556    House / apartment\n",
              "Name: NAME_HOUSING_TYPE, Length: 438557, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "education = df_ar['NAME_EDUCATION_TYPE']\n",
        "education"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ib4RXBmCU0WS",
        "outputId": "acbc0556-3b86-43c7-f746-c458531bdceb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                      Higher education\n",
              "1                      Higher education\n",
              "2         Secondary / secondary special\n",
              "3         Secondary / secondary special\n",
              "4         Secondary / secondary special\n",
              "                      ...              \n",
              "438552    Secondary / secondary special\n",
              "438553    Secondary / secondary special\n",
              "438554                 Higher education\n",
              "438555    Secondary / secondary special\n",
              "438556    Secondary / secondary special\n",
              "Name: NAME_EDUCATION_TYPE, Length: 438557, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## B. DATA VISUALIZATION"
      ],
      "metadata": {
        "id": "bFFpvoPY2A9G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Soal No. 1"
      ],
      "metadata": {
        "id": "aTMSogqrrQGC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualisasi boxplot with pandas\n",
        "sns.boxplot('STATUS', data=df_cr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "cIoG3AZArHMD",
        "outputId": "8cc86fa6-beac-48d8-ba61-e4a3dddf8890"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-107-31a7d56323a9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Visualisasi boxplot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'STATUS'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf_cr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m             )\n\u001b[1;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mboxplot\u001b[0;34m(x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth, whis, ax, **kwargs)\u001b[0m\n\u001b[1;32m   2243\u001b[0m     plotter = _BoxPlotter(x, y, hue, data, order, hue_order,\n\u001b[1;32m   2244\u001b[0m                           \u001b[0morient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2245\u001b[0;31m                           width, dodge, fliersize, linewidth)\n\u001b[0m\u001b[1;32m   2246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2247\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth)\u001b[0m\n\u001b[1;32m    404\u001b[0m                  width, dodge, fliersize, linewidth):\n\u001b[1;32m    405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 406\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestablish_variables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue_order\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    407\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestablish_colors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mestablish_variables\u001b[0;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[1;32m    155\u001b[0m             \u001b[0;31m# Figure out the plotting orientation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    156\u001b[0m             orient = infer_orient(\n\u001b[0;32m--> 157\u001b[0;31m                 \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequire_numeric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequire_numeric\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    158\u001b[0m             )\n\u001b[1;32m    159\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/_core.py\u001b[0m in \u001b[0;36minfer_orient\u001b[0;34m(x, y, orient, require_numeric)\u001b[0m\n\u001b[1;32m   1326\u001b[0m             \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msingle_var_warning\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Vertical\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"x\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1327\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrequire_numeric\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mx_type\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"numeric\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1328\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnonnumeric_dv_error\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Horizontal\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"x\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1329\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m\"h\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1330\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: Horizontal orientation requires numeric `x` variable."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualisasi histogram with pandas\n",
        "df_cr['STATUS'].plot.hist()"
      ],
      "metadata": {
        "id": "4knu3q8L2BGi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "outputId": "bc3521d7-f361-4f8f-d98b-f83c917a961c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-97-040c54f88f31>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_cr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'STATUS'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mhist\u001b[0;34m(self, by, bins, **kwargs)\u001b[0m\n\u001b[1;32m   1311\u001b[0m             \u001b[0;34m>>\u001b[0m\u001b[0;34m>\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1312\u001b[0m         \"\"\"\n\u001b[0;32m-> 1313\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"hist\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1314\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1315\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mkde\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbw_method\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    970\u001b[0m                     \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabel_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    971\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 972\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mplot_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    973\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    974\u001b[0m     \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/plotting/_matplotlib/__init__.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(data, kind, **kwargs)\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ax\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"left_ax\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m     \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPLOT_CLASSES\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     72\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/plotting/_matplotlib/core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    284\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    285\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 286\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    287\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    288\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/plotting/_matplotlib/core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    451\u001b[0m         \u001b[0;31m# no non-numeric frames or series allowed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    452\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 453\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"no numeric data to plot\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    454\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    455\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_to_ndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: no numeric data to plot"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualisasi line chart with pandas\n",
        "df_cr(['STATUS'], axis=1).plot.line(title='Status Dataset')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        },
        "id": "X-_59v2JqlTU",
        "outputId": "6f3cf941-7d30-46fb-c6ac-32ff88394822"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-102-b223155bc811>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Visualisasi line chart\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf_cr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'STATUS'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Status Dataset'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: 'DataFrame' object is not callable"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualisasi bar chart with pandas == BENAR\n",
        "df_cr['STATUS'].value_counts().sort_index().plot.bar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "5Jdjqdk8qsK7",
        "outputId": "b2a6f04a-094c-42e1-a277-715f0b65d9f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f811b7f9610>"
            ]
          },
          "metadata": {},
          "execution_count": 103
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAD1CAYAAAClSgmzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAQCUlEQVR4nO3cfYylZXnH8e8PVihqBYTJhu6iQ8qmdtUWdQUsbWIhhQVMlzZqoI1sDLJNhBZDU1n1D1ItLfaPoqRKQmUr2FaktA1bwRIK2sY2vAxKwYVQRoSyW15WWKEWhS5e/WNu9DCee2bA2XNm4ftJTuZ5rvt+nvuafZnfOed5zqSqkCRpmD3G3YAkaekyJCRJXYaEJKnLkJAkdRkSkqSuZeNuYLEdeOCBNTk5Oe42JGm3cuutt367qiZm1190ITE5OcnU1NS425Ck3UqS+4fVfbtJktRlSEiSugwJSVKXISFJ6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLU9aL7xLUkDZrcePWin/O+809c9HMuVb6SkCR1GRKSpC5DQpLUZUhIkroMCUlSlyEhSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV0LDokkeyb5epIvtv1DktyUZDrJF5Ls1ep7t/3pNj45cI4PtfrdSY4bqK9ttekkGwfqQ9eQJI3G83klcRZw18D+x4ELqupQYAdwWqufBuxo9QvaPJKsBk4GXg+sBT7dgmdP4FPA8cBq4JQ2d641JEkjsKCQSLISOBH4TNsPcDRwZZtyKXBS217X9mnjx7T564DLq+qpqvoWMA0c3h7TVXVvVT0NXA6sm2cNSdIILPSVxCeADwI/aPsHAN+pqp1tfyuwom2vAB4AaOOPt/k/rM86plefaw1J0gjMGxJJ3gE8UlW3jqCfFyTJhiRTSaa2b98+7nYk6UVjIa8kjgJ+Pcl9zLwVdDTwSWC/JMvanJXAtra9DTgYoI3vCzw6WJ91TK/+6BxrPEdVXVxVa6pqzcTExAK+JUnSQswbElX1oapaWVWTzFx4vqGqfhv4MvDONm09cFXb3tz2aeM3VFW1+snt7qdDgFXAzcAtwKp2J9NebY3N7ZjeGpKkEfhJPidxDnB2kmlmrh9c0uqXAAe0+tnARoCq2gJcAdwJ/BNwRlU90645nAlcy8zdU1e0uXOtIUkagWXzT/mRqvoK8JW2fS8zdybNnvN94F2d488DzhtSvwa4Zkh96BqSpNHwE9eSpC5DQpLU9bzebnoxmdx49aKf877zT1z0c0rSOPlKQpLUZUhIkroMCUlSlyEhSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLUZUhIkroMCUlSlyEhSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUte8IZHkp5LcnOQ/kmxJ8oetfkiSm5JMJ/lCkr1afe+2P93GJwfO9aFWvzvJcQP1ta02nWTjQH3oGpKk0VjIK4mngKOr6heBw4C1SY4EPg5cUFWHAjuA09r804AdrX5Bm0eS1cDJwOuBtcCnk+yZZE/gU8DxwGrglDaXOdaQJI3AvCFRM77bdl/WHgUcDVzZ6pcCJ7XtdW2fNn5MkrT65VX1VFV9C5gGDm+P6aq6t6qeBi4H1rVjemtIkkZgQdck2jP+24BHgOuAbwLfqaqdbcpWYEXbXgE8ANDGHwcOGKzPOqZXP2CONWb3tyHJVJKp7du3L+RbkiQtwIJCoqqeqarDgJXMPPN/3S7t6nmqqourak1VrZmYmBh3O5L0ovG87m6qqu8AXwbeBuyXZFkbWglsa9vbgIMB2vi+wKOD9VnH9OqPzrGGJGkEFnJ300SS/dr2PsCvAXcxExbvbNPWA1e17c1tnzZ+Q1VVq5/c7n46BFgF3AzcAqxqdzLtxczF7c3tmN4akqQRWDb/FA4CLm13Ie0BXFFVX0xyJ3B5kj8Cvg5c0uZfAnwuyTTwGDM/9KmqLUmuAO4EdgJnVNUzAEnOBK4F9gQ2VdWWdq5zOmtIkkZg3pCoqtuBNw2p38vM9YnZ9e8D7+qc6zzgvCH1a4BrFrqGJGk0/MS1JKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLUZUhIkroMCUlSlyEhSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSupaNuwFJEkxuvHrRz3nf+Sf+xOeY95VEkoOTfDnJnUm2JDmr1V+d5Lok97Sv+7d6klyYZDrJ7UnePHCu9W3+PUnWD9TfkuSOdsyFSTLXGpKk0VjI2007gd+vqtXAkcAZSVYDG4Hrq2oVcH3bBzgeWNUeG4CLYOYHPnAucARwOHDuwA/9i4DTB45b2+q9NSRJIzBvSFTVg1X1tbb9P8BdwApgHXBpm3YpcFLbXgdcVjNuBPZLchBwHHBdVT1WVTuA64C1bexVVXVjVRVw2axzDVtDkjQCz+vCdZJJ4E3ATcDyqnqwDT0ELG/bK4AHBg7b2mpz1bcOqTPHGrP72pBkKsnU9u3bn8+3JEmaw4JDIskrgb8DPlBVTwyOtVcAtci9Pcdca1TVxVW1pqrWTExM7Mo2JOklZUEhkeRlzATEX1fV37fyw+2tItrXR1p9G3DwwOErW22u+soh9bnWkCSNwELubgpwCXBXVf3ZwNBm4Nk7lNYDVw3UT213OR0JPN7eMroWODbJ/u2C9bHAtW3siSRHtrVOnXWuYWtIkkZgIZ+TOAp4D3BHktta7cPA+cAVSU4D7gfe3cauAU4ApoEngfcCVNVjST4G3NLmfbSqHmvb7wc+C+wDfKk9mGMNSdIIzBsSVfVVIJ3hY4bML+CMzrk2AZuG1KeANwypPzpsDUnSaPhrOSRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLUZUhIkroMCUlSlyEhSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSuuYNiSSbkjyS5BsDtVcnuS7JPe3r/q2eJBcmmU5ye5I3Dxyzvs2/J8n6gfpbktzRjrkwSeZaQ5I0Ogt5JfFZYO2s2kbg+qpaBVzf9gGOB1a1xwbgIpj5gQ+cCxwBHA6cO/BD/yLg9IHj1s6zhiRpROYNiar6V+CxWeV1wKVt+1LgpIH6ZTXjRmC/JAcBxwHXVdVjVbUDuA5Y28ZeVVU3VlUBl80617A1JEkj8kKvSSyvqgfb9kPA8ra9AnhgYN7WVpurvnVIfa41fkySDUmmkkxt3779BXw7kqRhfuIL1+0VQC1CLy94jaq6uKrWVNWaiYmJXdmKJL2kvNCQeLi9VUT7+kirbwMOHpi3stXmqq8cUp9rDUnSiLzQkNgMPHuH0nrgqoH6qe0upyOBx9tbRtcCxybZv12wPha4to09keTIdlfTqbPONWwNSdKILJtvQpLPA28HDkyylZm7lM4HrkhyGnA/8O42/RrgBGAaeBJ4L0BVPZbkY8Atbd5Hq+rZi+HvZ+YOqn2AL7UHc6whSRqReUOiqk7pDB0zZG4BZ3TOswnYNKQ+BbxhSP3RYWtIkkbHT1xLkroMCUlSlyEhSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLUZUhIkroMCUlSlyEhSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV3Lxt2A5ja58epFP+d955+46OeU9OLkKwlJUteSD4kka5PcnWQ6ycZx9yNJLyVLOiSS7Al8CjgeWA2ckmT1eLuSpJeOpX5N4nBguqruBUhyObAOuHOsXUkCFv+amdfLlp5U1bh76EryTmBtVb2v7b8HOKKqzpw1bwOwoe3+HHD3IrdyIPDtRT7nrrA79Lk79Aj2udjsc3Htij5fW1UTs4tL/ZXEglTVxcDFu+r8Saaqas2uOv9i2R363B16BPtcbPa5uEbZ55K+JgFsAw4e2F/ZapKkEVjqIXELsCrJIUn2Ak4GNo+5J0l6yVjSbzdV1c4kZwLXAnsCm6pqyxha2WVvZS2y3aHP3aFHsM/FZp+La2R9LukL15Kk8VrqbzdJksbIkJAkdRkSkqQuQ2KWJK9Lck6SC9vjnCQ/P+6+dlftz/OYJK+cVV87rp6GSXJ4kre27dVJzk5ywrj7mk+Sy8bdw3yS/HL78zx23L08K8mhSY4aUj8qyc+Oo6dhkrxmjrFfGUUPhsSAJOcAlwMBbm6PAJ/fnX65YJL3jrsHgCS/B1wF/C7wjSTrBob/eDxd/bgk5wIXAhcl+RPgz4FXABuTfGSszQ1IsnnW4x+B33x2f9z9PSvJzQPbpzPz5/nTwLlL6P/RJ4AnhtSfaGNLxVeSfLD9HjsAkixP8lfABaNowLubBiT5T+D1VfV/s+p7AVuqatV4Ont+kvxXVXWfgYywjzuAt1XVd5NMAlcCn6uqTyb5elW9aawNNq3Pw4C9gYeAlVX1RJJ9gJuq6hfG2mCT5GvM/N6yzwBFewLDzOeHqKp/GV93PzL4d5vkFuCEqtqe5BXAjVX1xvF2ONNXVb21M3bHUugRIMn+wPnALwFnAW8Ezgb+FLioqn6wq3tY0p+TGIMfAD8D3D+rflAbWzKS3N4bApaPspc57FFV3wWoqvuSvB24MslrmelzqdhZVc8ATyb5ZlU9AVBV30uylP7e1zDzg+IjwB9U1W1JvrdUwmHAHu2H2x7MPBHdDlBV/5tk53hb+6H95hjbZ2RdzKOqdgC/k+Qs4J+B/waOrKqto+rBkHiuDwDXJ7kHeKDVXgMcCpzZPWo8lgPHATtm1QP8++jbGerhJIdV1W0A7RXFO4BNzDwjWiqeTvLyqnoSeMuzxST7soSeHLRnjRck+dv29WGW5v/hfYFbmfm3WEkOqqoH23WppfLkYCrJ6VX1F4PFJO9jpvclIcl+wMeBI4C1wAnAl5KcVVU3jKQH3256riR7MPMryle00jbglvZMc8lIcgnwl1X11SFjf1NVvzWGtmb3sZKZZ+kPDRk7qqr+bQxt/Zgke1fVU0PqBwIHVdUdY2hrXklOBI6qqg+Pu5eFSPJyYHlVfWsJ9LIc+AfgaX4UCmuAvYDfGPZvdhyS3At8GvhEVe1stcNa7f6qOmWX92BISHqpSvKrwBva7pZRPTtfqCQre28tDXsltEt6MCQkST3eAitJ6jIkJEldhoQkqcuQkCR1/T+3zMrGhAQ5kwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Soal No. 2"
      ],
      "metadata": {
        "id": "z1Q-_75VsCCZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualisasi histogram with pandas == BENAR\n",
        "df_ar['AMT_INCOME_TOTAL'].plot.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "4KQfL3jMtvjc",
        "outputId": "1134d112-e6ff-40fb-c88f-4284ae10523d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f811874d1d0>"
            ]
          },
          "metadata": {},
          "execution_count": 117
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualisasi histogram with seaborn == BENAR\n",
        "sns.distplot(df_ar['AMT_INCOME_TOTAL'], bins=10, kde=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "hYyXs5fUtEIS",
        "outputId": "f60fc9c9-4fd6-45b1-c3d2-10d0fa0a5f88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f81198957d0>"
            ]
          },
          "metadata": {},
          "execution_count": 113
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualisasi line chart with pandas\n",
        "df_ar.drop(['AMT_INCOME_TOTAL'], axis=1).plot.line(title='Income Total')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "ktNQET55v9xG",
        "outputId": "d6881e3e-4ed2-4ffe-f759-2844b1dadf4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f811870c490>"
            ]
          },
          "metadata": {},
          "execution_count": 119
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualisasi line chart with seaborn\n",
        "sns.lineplot(data=df_ar.drop(['AMT_INCOME_TOTAL'], axis=1))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "7ZTzxEtltSbw",
        "outputId": "5c314471-4015-49fe-c846-878950ad1cd9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/IPython/core/pylabtools.py:125: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualisasi box plot with seaborn == BENAR\n",
        "sns.boxplot('AMT_INCOME_TOTAL', data=df_ar)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "rT8irDD5yz1W",
        "outputId": "a436341b-cc2d-48c5-8d8d-acabe2da167c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8118714d10>"
            ]
          },
          "metadata": {},
          "execution_count": 122
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Soal No. 3"
      ],
      "metadata": {
        "id": "YzzmI9sjyNmT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualisasi box plot with seaborn == BENAR\n",
        "sns.boxplot('AGE', data=df_ar)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "JPDlWvD1yNzF",
        "outputId": "a6b6aa95-34bf-42cc-e87f-3d9e4efec27b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f81163e7590>"
            ]
          },
          "metadata": {},
          "execution_count": 123
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# calculation of the mean (e.g. for age)\n",
        "df_ar[\"AGE\"].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9JbM6252-dkB",
        "outputId": "ceea57c4-3ca1-4213-a5a1-875e30e7505c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "43.83396000975928"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# calculation of the median (e.g. for age)\n",
        "df_ar[\"AGE\"].median()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G8IEr3oU5wvR",
        "outputId": "7b1a2b0e-a763-48ad-ad74-62158ca7b2c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "43.0"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Soal No. 7"
      ],
      "metadata": {
        "id": "cEy2-pXj51Ja"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_good = df[df['STATUS'].isin(['C', '0', '1', '2'])]\n",
        "df_bad = df[df['STATUS'].isin(['C', '0', '1', '2'])]\n",
        "\n",
        "sns.distplot(df_good['AMT_INCOME_TOTAL'], ???='good')\n",
        "sns.distplot(df_bad['AMT_INCOME_TOTAL'], ???='bad')\n",
        "plt.???\n",
        "\n",
        "# Jawabannya label, label, legend"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "ylFTw-57_9V5",
        "outputId": "33ae2905-cf14-4307-83c1-c815c5db9dbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3360\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'AMT_INCOME_TOTAL'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-130-8e88d7ddc82c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdf_bad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_cr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mdf_cr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'STATUS'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'C'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'0'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'1'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_good\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'AMT_INCOME_TOTAL'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'good'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_bad\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'AMT_INCOME_TOTAL'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bad'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3456\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3457\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3458\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3459\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3460\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3361\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3365\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'AMT_INCOME_TOTAL'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Soal No. 8"
      ],
      "metadata": {
        "id": "iyraGYP9_9gR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Pilihan jawaban a == ERROR\n",
        "import seaborn as sns \n",
        "sns.countplot(data=df_ar, x='NAME_EDUCATION_TYPE')\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        },
        "id": "2BSj4fN4BGxl",
        "outputId": "0e4a1108-960e-4f2d-9ca6-3239b769b214"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-132-e784e78f1e89>\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m    sns.countplot(data=df_ar, x='NAME_EDUCATION_TYPE')\"\u001b[0m\n\u001b[0m                                                       ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pilihan jawaban b == BENAR, karena sesuai gambar\n",
        "import seaborn as sns \n",
        "sns.countplot(data=df_ar, y='NAME_EDUCATION_TYPE')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "74umykrUBjUp",
        "outputId": "90a03695-a14d-4079-c3dc-cc6941c97604"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f811656cd10>"
            ]
          },
          "metadata": {},
          "execution_count": 133
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pilihan jawaban c == SALAH\n",
        "import seaborn as sns \n",
        "sns.countplot(df_ar['NAME_EDUCATION_TYPE'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "jX3Ad6vVBjXW",
        "outputId": "60e43471-5196-4727-dc8a-fd188d6094f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8112cb5f90>"
            ]
          },
          "metadata": {},
          "execution_count": 135
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pilihan jawaban d == SALAH\n",
        "import seaborn as sns \n",
        "sns.countplot(df_ar['NAME_EDUCATION_TYPE'], orient='v')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "uXEnDhEcBjaQ",
        "outputId": "1981af0c-15a2-4599-9618-b66e5a3cf45c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f81164c5390>"
            ]
          },
          "metadata": {},
          "execution_count": 134
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Soal No. 9"
      ],
      "metadata": {
        "id": "tGB7jpBZBjzg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Pilihan jawaban a == BENAR, karena sesuai gambar\n",
        "sns.countplot(data = df_ar,\n",
        "              x = 'FLAG_PHONE',\n",
        "              hue = 'default')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "DXx3P3LIBj-g",
        "outputId": "6c859aaa-2b18-4d0e-f4ae-67b2f48b9313"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-144-2004db4fa629>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m sns.countplot(data = df_ar,\n\u001b[1;32m      3\u001b[0m               \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'FLAG_PHONE'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m               hue = 'default')\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m             )\n\u001b[1;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mcountplot\u001b[0;34m(x, y, hue, data, order, hue_order, orient, color, palette, saturation, dodge, ax, **kwargs)\u001b[0m\n\u001b[1;32m   3600\u001b[0m         \u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mci\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3601\u001b[0m         \u001b[0morient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3602\u001b[0;31m         \u001b[0merrcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcapsize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdodge\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3603\u001b[0m     )\n\u001b[1;32m   3604\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, hue, data, order, hue_order, estimator, ci, n_boot, units, seed, orient, color, palette, saturation, errcolor, errwidth, capsize, dodge)\u001b[0m\n\u001b[1;32m   1583\u001b[0m         \u001b[0;34m\"\"\"Initialize the plotter.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1584\u001b[0m         self.establish_variables(x, y, hue, data, orient,\n\u001b[0;32m-> 1585\u001b[0;31m                                  order, hue_order, units)\n\u001b[0m\u001b[1;32m   1586\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestablish_colors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1587\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimate_statistic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mci\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mestablish_variables\u001b[0;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[1;32m    151\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m                     \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Could not interpret input '{}'\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m             \u001b[0;31m# Figure out the plotting orientation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Could not interpret input 'default'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pilihan jawaban b == ValueError: Invalid RGBA argument: 'default'\n",
        "sns.countplot(data = df_ar,\n",
        "              x = 'FLAG_PHONE',\n",
        "              color = 'default')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "w_paYHWcCtjE",
        "outputId": "fd826b39-5a64-48a6-8741-b774ddb531a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-142-c13d29697499>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m sns.countplot(data = df_ar,\n\u001b[1;32m      3\u001b[0m               \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'FLAG_PHONE'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m               color = 'default')\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m             )\n\u001b[1;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mcountplot\u001b[0;34m(x, y, hue, data, order, hue_order, orient, color, palette, saturation, dodge, ax, **kwargs)\u001b[0m\n\u001b[1;32m   3600\u001b[0m         \u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mci\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3601\u001b[0m         \u001b[0morient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3602\u001b[0;31m         \u001b[0merrcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcapsize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdodge\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3603\u001b[0m     )\n\u001b[1;32m   3604\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, hue, data, order, hue_order, estimator, ci, n_boot, units, seed, orient, color, palette, saturation, errcolor, errwidth, capsize, dodge)\u001b[0m\n\u001b[1;32m   1584\u001b[0m         self.establish_variables(x, y, hue, data, orient,\n\u001b[1;32m   1585\u001b[0m                                  order, hue_order, units)\n\u001b[0;32m-> 1586\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestablish_colors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1587\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimate_statistic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mci\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mestablish_colors\u001b[0;34m(self, color, palette, saturation)\u001b[0m\n\u001b[1;32m    310\u001b[0m         \u001b[0;31m# Desaturate a bit because these are patches\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    311\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msaturation\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 312\u001b[0;31m             \u001b[0mcolors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolor_palette\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdesat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msaturation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    313\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    314\u001b[0m         \u001b[0;31m# Convert the colors to a common representations\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/palettes.py\u001b[0m in \u001b[0;36mcolor_palette\u001b[0;34m(palette, n_colors, desat, as_cmap)\u001b[0m\n\u001b[1;32m    209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdesat\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m         \u001b[0mpalette\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdesaturate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdesat\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mas_cmap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/palettes.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdesat\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m         \u001b[0mpalette\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdesaturate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdesat\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mas_cmap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/utils.py\u001b[0m in \u001b[0;36mdesaturate\u001b[0;34m(color, prop)\u001b[0m\n\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m     \u001b[0;31m# Get rgb tuple rep\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m     \u001b[0mrgb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmplcol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorConverter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_rgb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m     \u001b[0;31m# Convert to hls\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36mto_rgb\u001b[0;34m(c)\u001b[0m\n\u001b[1;32m    343\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mto_rgb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m     \u001b[0;34m\"\"\"Convert *c* to an RGB color, silently dropping the alpha channel.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    346\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36mto_rgba\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    183\u001b[0m         \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mrgba\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Suppress exception chaining of cache lookup failure.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m         \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_to_rgba_no_colorcycle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    186\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m             \u001b[0m_colors_full_map\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrgba\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36m_to_rgba_no_colorcycle\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m    259\u001b[0m                     f\"Value must be within 0-1 range\")\n\u001b[1;32m    260\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0malpha\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m1.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 261\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Invalid RGBA argument: {orig_c!r}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    262\u001b[0m     \u001b[0;31m# tuple color.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m     \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Invalid RGBA argument: 'default'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pilihan jawaban c == ValueError: default is not a valid palette name\n",
        "sns.countplot(data = df_ar,\n",
        "              x = 'FLAG_PHONE',\n",
        "              palette = 'default')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 602
        },
        "id": "KHNyINmrCtlK",
        "outputId": "6e46fdb6-c075-42f9-925a-b54dca5b5d1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/palettes.py\u001b[0m in \u001b[0;36mcolor_palette\u001b[0;34m(palette, n_colors, desat, as_cmap)\u001b[0m\n\u001b[1;32m    205\u001b[0m                 \u001b[0;31m# Perhaps a named matplotlib colormap?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m                 \u001b[0mpalette\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmpl_palette\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_colors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_cmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mas_cmap\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    207\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/palettes.py\u001b[0m in \u001b[0;36mmpl_palette\u001b[0;34m(name, n_colors, as_cmap)\u001b[0m\n\u001b[1;32m    442\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 443\u001b[0;31m         \u001b[0mcmap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_cmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    444\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/cm.py\u001b[0m in \u001b[0;36mget_cmap\u001b[0;34m(name, lut)\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 134\u001b[0;31m     \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_in_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcmap_d\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    135\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlut\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36m_check_in_list\u001b[0;34m(_values, **kwargs)\u001b[0m\n\u001b[1;32m   2144\u001b[0m                 \u001b[0;34m\"{!r} is not a valid value for {}; supported values are {}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2145\u001b[0;31m                 .format(v, k, ', '.join(map(repr, values))))\n\u001b[0m\u001b[1;32m   2146\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: 'default' is not a valid value for name; supported values are 'Accent', 'Accent_r', 'Blues', 'Blues_r', 'BrBG', 'BrBG_r', 'BuGn', 'BuGn_r', 'BuPu', 'BuPu_r', 'CMRmap', 'CMRmap_r', 'Dark2', 'Dark2_r', 'GnBu', 'GnBu_r', 'Greens', 'Greens_r', 'Greys', 'Greys_r', 'OrRd', 'OrRd_r', 'Oranges', 'Oranges_r', 'PRGn', 'PRGn_r', 'Paired', 'Paired_r', 'Pastel1', 'Pastel1_r', 'Pastel2', 'Pastel2_r', 'PiYG', 'PiYG_r', 'PuBu', 'PuBuGn', 'PuBuGn_r', 'PuBu_r', 'PuOr', 'PuOr_r', 'PuRd', 'PuRd_r', 'Purples', 'Purples_r', 'RdBu', 'RdBu_r', 'RdGy', 'RdGy_r', 'RdPu', 'RdPu_r', 'RdYlBu', 'RdYlBu_r', 'RdYlGn', 'RdYlGn_r', 'Reds', 'Reds_r', 'Set1', 'Set1_r', 'Set2', 'Set2_r', 'Set3', 'Set3_r', 'Spectral', 'Spectral_r', 'Wistia', 'Wistia_r', 'YlGn', 'YlGnBu', 'YlGnBu_r', 'YlGn_r', 'YlOrBr', 'YlOrBr_r', 'YlOrRd', 'YlOrRd_r', 'afmhot', 'afmhot_r', 'autumn', 'autumn_r', 'binary', 'binary_r', 'bone', 'bone_r', 'brg', 'brg_r', 'bwr', 'bwr_r', 'cividis', 'cividis_r', 'cool', 'cool_r', 'coolwarm', 'coolwarm_r', 'copper', 'copper_r', 'crest', 'crest_r', 'cubehelix', 'cubehelix_r', 'flag', 'flag_r', 'flare', 'flare_r', 'gist_earth', 'gist_earth_r', 'gist_gray', 'gist_gray_r', 'gist_heat', 'gist_heat_r', 'gist_ncar', 'gist_ncar_r', 'gist_rainbow', 'gist_rainbow_r', 'gist_stern', 'gist_stern_r', 'gist_yarg', 'gist_yarg_r', 'gnuplot', 'gnuplot2', 'gnuplot2_r', 'gnuplot_r', 'gray', 'gray_r', 'hot', 'hot_r', 'hsv', 'hsv_r', 'icefire', 'icefire_r', 'inferno', 'inferno_r', 'jet', 'jet_r', 'm...",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-141-97582b8cf6f3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m sns.countplot(data = df_ar,\n\u001b[1;32m      3\u001b[0m               \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'FLAG_PHONE'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m               palette = 'default')\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m             )\n\u001b[1;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mcountplot\u001b[0;34m(x, y, hue, data, order, hue_order, orient, color, palette, saturation, dodge, ax, **kwargs)\u001b[0m\n\u001b[1;32m   3600\u001b[0m         \u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mci\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3601\u001b[0m         \u001b[0morient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3602\u001b[0;31m         \u001b[0merrcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcapsize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdodge\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3603\u001b[0m     )\n\u001b[1;32m   3604\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, hue, data, order, hue_order, estimator, ci, n_boot, units, seed, orient, color, palette, saturation, errcolor, errwidth, capsize, dodge)\u001b[0m\n\u001b[1;32m   1584\u001b[0m         self.establish_variables(x, y, hue, data, orient,\n\u001b[1;32m   1585\u001b[0m                                  order, hue_order, units)\n\u001b[0;32m-> 1586\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestablish_colors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1587\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimate_statistic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mci\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mestablish_colors\u001b[0;34m(self, color, palette, saturation)\u001b[0m\n\u001b[1;32m    306\u001b[0m                 \u001b[0mpalette\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mpalette\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlevels\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 308\u001b[0;31m             \u001b[0mcolors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolor_palette\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_colors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    309\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m         \u001b[0;31m# Desaturate a bit because these are patches\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/palettes.py\u001b[0m in \u001b[0;36mcolor_palette\u001b[0;34m(palette, n_colors, desat, as_cmap)\u001b[0m\n\u001b[1;32m    206\u001b[0m                 \u001b[0mpalette\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmpl_palette\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_colors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_cmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mas_cmap\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s is not a valid palette name\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdesat\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: default is not a valid palette name"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pilihan jawaban d == TypeError: '<' not supported between instances of 'str' and 'int'\n",
        "sns.countplot(data = df_ar,\n",
        "              x = 'FLAG_PHONE',\n",
        "              saturation = 'default')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "ZLRFPqCnCtow",
        "outputId": "dfa632da-fbbc-4349-d039-a0077b7b1944"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-140-7fabf0d382f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m sns.countplot(data = df_ar,\n\u001b[1;32m      3\u001b[0m               \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'FLAG_PHONE'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m               saturation = 'default')\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m             )\n\u001b[1;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mcountplot\u001b[0;34m(x, y, hue, data, order, hue_order, orient, color, palette, saturation, dodge, ax, **kwargs)\u001b[0m\n\u001b[1;32m   3600\u001b[0m         \u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mci\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3601\u001b[0m         \u001b[0morient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3602\u001b[0;31m         \u001b[0merrcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcapsize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdodge\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3603\u001b[0m     )\n\u001b[1;32m   3604\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, hue, data, order, hue_order, estimator, ci, n_boot, units, seed, orient, color, palette, saturation, errcolor, errwidth, capsize, dodge)\u001b[0m\n\u001b[1;32m   1584\u001b[0m         self.establish_variables(x, y, hue, data, orient,\n\u001b[1;32m   1585\u001b[0m                                  order, hue_order, units)\n\u001b[0;32m-> 1586\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestablish_colors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1587\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimate_statistic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mci\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mestablish_colors\u001b[0;34m(self, color, palette, saturation)\u001b[0m\n\u001b[1;32m    309\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m         \u001b[0;31m# Desaturate a bit because these are patches\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 311\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0msaturation\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m             \u001b[0mcolors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolor_palette\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdesat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msaturation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: '<' not supported between instances of 'str' and 'int'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Soal No. 11"
      ],
      "metadata": {
        "id": "Vk8Ne0b7CSuS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Pilihan jawaban a == BENAR, karena sesuai gambar\n",
        "ggplot(mpg, aes(x = displ, y = hwy, hue = class)) + geom_point()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        },
        "id": "BBig83EYCS8t",
        "outputId": "dc0a589b-6baf-403b-dd15-019b79460cd1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-150-3d960db66a20>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    ggplot(mpg, aes(x = displ, y = hwy, hue = class)) + geom_point()\u001b[0m\n\u001b[0m                                                  ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pilihan jawaban b == SALAH, karena tidak sesuai gambar dan color='class'\n",
        "ggplot(mpg, aes(x = displ, y = hwy, color = class)) + geom_point()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        },
        "id": "W-GTABu-FOu3",
        "outputId": "bba9c79d-4f40-45cb-ea09-930e13aa6190"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-146-cddd64fd6247>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    ggplot(mpg, aes(x = displ, y = hwy, color = class)) + geom_point()\u001b[0m\n\u001b[0m                                                    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pilihan jawaban c == SALAH, karena tidak sesuai gambar \n",
        "ggplot(mpg, aes(x = displ, y = hwy, hue = class)) + geom_scatter()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        },
        "id": "QCFHP1xSFOx2",
        "outputId": "03882646-0043-4829-c815-f89757565d8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-147-5a99380153d2>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    ggplot(mpg, aes(x = displ, y = hwy, hue = class)) + geom_scatter()\u001b[0m\n\u001b[0m                                                  ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pilihan jawaban d == SALAH, karena tidak sesuai gambar dan color='class'\n",
        "ggplot(mpg, aes(x = displ, y = hwy, collor = class)) + geom_scatter()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        },
        "id": "1eTccP6DFPDJ",
        "outputId": "3e0ff42d-2778-4beb-b249-4fdee6d952f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-148-dcaa1c43aacb>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    ggplot(mpg, aes(x = displ, y = hwy, collor = class)) + geom_scatter()\u001b[0m\n\u001b[0m                                                     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MINGGU 3 - SUPERVISED & UNSUPERVISED LEARNING"
      ],
      "metadata": {
        "id": "mLRPKrnpKD_0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Soal No. 1"
      ],
      "metadata": {
        "id": "mTBTIf14f41w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_ar.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "U8eRkbmGnelX",
        "outputId": "cc489e72-9468-4617-de7d-c73d82eba17e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        ID  CNT_CHILDREN  AMT_INCOME_TOTAL  DAYS_BIRTH  \\\n",
              "ID                1.000000     -0.005178          0.011179   -0.004994   \n",
              "CNT_CHILDREN     -0.005178      1.000000          0.019177    0.349088   \n",
              "AMT_INCOME_TOTAL  0.011179      0.019177          1.000000    0.053775   \n",
              "DAYS_BIRTH       -0.004994      0.349088          0.053775    1.000000   \n",
              "DAYS_EMPLOYED    -0.002467     -0.241535         -0.141291   -0.617908   \n",
              "FLAG_MOBIL             NaN           NaN               NaN         NaN   \n",
              "FLAG_WORK_PHONE  -0.023319      0.038418         -0.033635    0.171829   \n",
              "FLAG_PHONE       -0.018992     -0.038266          0.004444   -0.037984   \n",
              "FLAG_EMAIL        0.032875      0.028457          0.112139    0.096752   \n",
              "CNT_FAM_MEMBERS  -0.001862      0.884781          0.011454    0.306179   \n",
              "\n",
              "                  DAYS_EMPLOYED  FLAG_MOBIL  FLAG_WORK_PHONE  FLAG_PHONE  \\\n",
              "ID                    -0.002467         NaN        -0.023319   -0.018992   \n",
              "CNT_CHILDREN          -0.241535         NaN         0.038418   -0.038266   \n",
              "AMT_INCOME_TOTAL      -0.141291         NaN        -0.033635    0.004444   \n",
              "DAYS_BIRTH            -0.617908         NaN         0.171829   -0.037984   \n",
              "DAYS_EMPLOYED          1.000000         NaN        -0.232208    0.004868   \n",
              "FLAG_MOBIL                  NaN         NaN              NaN         NaN   \n",
              "FLAG_WORK_PHONE       -0.232208         NaN         1.000000    0.290066   \n",
              "FLAG_PHONE             0.004868         NaN         0.290066    1.000000   \n",
              "FLAG_EMAIL            -0.074372         NaN        -0.060915   -0.001170   \n",
              "CNT_FAM_MEMBERS       -0.234373         NaN         0.049777   -0.024213   \n",
              "\n",
              "                  FLAG_EMAIL  CNT_FAM_MEMBERS  \n",
              "ID                  0.032875        -0.001862  \n",
              "CNT_CHILDREN        0.028457         0.884781  \n",
              "AMT_INCOME_TOTAL    0.112139         0.011454  \n",
              "DAYS_BIRTH          0.096752         0.306179  \n",
              "DAYS_EMPLOYED      -0.074372        -0.234373  \n",
              "FLAG_MOBIL               NaN              NaN  \n",
              "FLAG_WORK_PHONE    -0.060915         0.049777  \n",
              "FLAG_PHONE         -0.001170        -0.024213  \n",
              "FLAG_EMAIL          1.000000         0.022054  \n",
              "CNT_FAM_MEMBERS     0.022054         1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-34b08873-49f3-40c6-805c-f57bea47ae1e\">\n",
              "    <div 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>ID</th>\n",
              "      <th>CNT_CHILDREN</th>\n",
              "      <th>AMT_INCOME_TOTAL</th>\n",
              "      <th>DAYS_BIRTH</th>\n",
              "      <th>DAYS_EMPLOYED</th>\n",
              "      <th>FLAG_MOBIL</th>\n",
              "      <th>FLAG_WORK_PHONE</th>\n",
              "      <th>FLAG_PHONE</th>\n",
              "      <th>FLAG_EMAIL</th>\n",
              "      <th>CNT_FAM_MEMBERS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.005178</td>\n",
              "      <td>0.011179</td>\n",
              "      <td>-0.004994</td>\n",
              "      <td>-0.002467</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.023319</td>\n",
              "      <td>-0.018992</td>\n",
              "      <td>0.032875</td>\n",
              "      <td>-0.001862</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CNT_CHILDREN</th>\n",
              "      <td>-0.005178</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.019177</td>\n",
              "      <td>0.349088</td>\n",
              "      <td>-0.241535</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.038418</td>\n",
              "      <td>-0.038266</td>\n",
              "      <td>0.028457</td>\n",
              "      <td>0.884781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AMT_INCOME_TOTAL</th>\n",
              "      <td>0.011179</td>\n",
              "      <td>0.019177</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.053775</td>\n",
              "      <td>-0.141291</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.033635</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>0.112139</td>\n",
              "      <td>0.011454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DAYS_BIRTH</th>\n",
              "      <td>-0.004994</td>\n",
              "      <td>0.349088</td>\n",
              "      <td>0.053775</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.617908</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.171829</td>\n",
              "      <td>-0.037984</td>\n",
              "      <td>0.096752</td>\n",
              "      <td>0.306179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DAYS_EMPLOYED</th>\n",
              "      <td>-0.002467</td>\n",
              "      <td>-0.241535</td>\n",
              "      <td>-0.141291</td>\n",
              "      <td>-0.617908</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.232208</td>\n",
              "      <td>0.004868</td>\n",
              "      <td>-0.074372</td>\n",
              "      <td>-0.234373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FLAG_MOBIL</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>FLAG_WORK_PHONE</th>\n",
              "      <td>-0.023319</td>\n",
              "      <td>0.038418</td>\n",
              "      <td>-0.033635</td>\n",
              "      <td>0.171829</td>\n",
              "      <td>-0.232208</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.290066</td>\n",
              "      <td>-0.060915</td>\n",
              "      <td>0.049777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FLAG_PHONE</th>\n",
              "      <td>-0.018992</td>\n",
              "      <td>-0.038266</td>\n",
              "      <td>0.004444</td>\n",
              "      <td>-0.037984</td>\n",
              "      <td>0.004868</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.290066</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.001170</td>\n",
              "      <td>-0.024213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FLAG_EMAIL</th>\n",
              "      <td>0.032875</td>\n",
              "      <td>0.028457</td>\n",
              "      <td>0.112139</td>\n",
              "      <td>0.096752</td>\n",
              "      <td>-0.074372</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.060915</td>\n",
              "      <td>-0.001170</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.022054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CNT_FAM_MEMBERS</th>\n",
              "      <td>-0.001862</td>\n",
              "      <td>0.884781</td>\n",
              "      <td>0.011454</td>\n",
              "      <td>0.306179</td>\n",
              "      <td>-0.234373</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.049777</td>\n",
              "      <td>-0.024213</td>\n",
              "      <td>0.022054</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-34b08873-49f3-40c6-805c-f57bea47ae1e')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-34b08873-49f3-40c6-805c-f57bea47ae1e 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-34b08873-49f3-40c6-805c-f57bea47ae1e');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df_ar.corr(), vmin=0, vmax=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "cJpEP2V8pztv",
        "outputId": "05d8c5f1-8319-41ea-e937-73f546a74232"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb9165d5cd0>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df_cr.corr(), vmin=0, vmax=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "8Xby0LbFpYn7",
        "outputId": "b30a7909-23fe-400a-daf2-924e9184c82d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb9187eb0d0>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cr.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "nLDtEQZ_ns6M",
        "outputId": "9b922427-8953-4180-c466-df2e407c80f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      ID  MONTHS_BALANCE\n",
              "ID              1.000000       -0.004802\n",
              "MONTHS_BALANCE -0.004802        1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-88339ff9-7fe9-4b96-bd6d-c396be2a1892\">\n",
              "    <div 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>ID</th>\n",
              "      <th>MONTHS_BALANCE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.004802</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MONTHS_BALANCE</th>\n",
              "      <td>-0.004802</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-88339ff9-7fe9-4b96-bd6d-c396be2a1892')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-88339ff9-7fe9-4b96-bd6d-c396be2a1892 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-88339ff9-7fe9-4b96-bd6d-c396be2a1892');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ar['AMT_INCOME_TOTAL']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mICh8HeAf54c",
        "outputId": "4c3cc0ee-47a1-4131-9ffe-b92fcd2c70d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         427500.0\n",
              "1         427500.0\n",
              "2         112500.0\n",
              "3         270000.0\n",
              "4         270000.0\n",
              "            ...   \n",
              "438552    135000.0\n",
              "438553    103500.0\n",
              "438554     54000.0\n",
              "438555     72000.0\n",
              "438556    121500.0\n",
              "Name: AMT_INCOME_TOTAL, Length: 438557, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualisasi histogram with pandas\n",
        "df_ar['AMT_INCOME_TOTAL'].plot.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "0pWpZ4QHh6RY",
        "outputId": "c7ecdb82-8945-470a-b205-bc7f16d8fafd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb918817f90>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ar['FLAG_MOBIL']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "koA1uxibf56L",
        "outputId": "dc3b4a6b-1aef-406c-ae6d-397c2c95c850"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         1\n",
              "1         1\n",
              "2         1\n",
              "3         1\n",
              "4         1\n",
              "         ..\n",
              "438552    1\n",
              "438553    1\n",
              "438554    1\n",
              "438555    1\n",
              "438556    1\n",
              "Name: FLAG_MOBIL, Length: 438557, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualisasi histogram with pandas\n",
        "df_ar['FLAG_MOBIL'].plot.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "LR4ZYo92hzBo",
        "outputId": "c64a3dc8-1c68-47cb-92c4-d59a6be93c39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb9192c4bd0>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAD4CAYAAAAgs6s2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAASN0lEQVR4nO3df7DldV3H8efLXQmsEIXNnF10qbZsTU1ckcZpShlxgXSt1GA0yBi2GbHRdApwHDGNRv8ojEYoEoYfZUja6FZrRGo1/UBY0kAw5AYqiz/YAKFSocV3f5zP6ul677kH/ZxzOHefj5kz+/2+v9/v+by/u3fndb8/zvekqpAkqadHzboBSdLqY7hIkrozXCRJ3RkukqTuDBdJUndrZ93AI8Vhhx1WGzdunHUbkjRXrr/++v+sqnWL64ZLs3HjRnbt2jXrNiRpriT57FJ1T4tJkrozXCRJ3RkukqTuDBdJUneGiySpO8NFktSd4SJJ6s5wkSR1Z7hIkrrzE/rSI9zGM/9qZmN/5u0nzGxszTePXCRJ3RkukqTuDBdJUneGiySpO8NFktSd4SJJ6s5wkSR1Z7hIkrozXCRJ3RkukqTuDBdJUneGiySpO8NFktSd4SJJ6m7i4ZJkTZKPJ/nLNn9Eko8lWUjy3iQHtPp3tfmFtnzj0Huc1eq3JHnhUH1rqy0kOXOovuQYkqTpmMaRy2uBTw3NvwM4t6p+CLgXOLXVTwXubfVz23ok2QycCDwV2Aqc3wJrDfAu4DhgM3BSW3fUGJKkKZhouCTZAJwAvLvNB3g+8L62yqXAS9r0tjZPW35MW38bcEVVPVBVtwMLwFHttVBVt1XVg8AVwLYVxpAkTcGkj1zeCfwG8PU2fyjw5ara2+Z3A+vb9HrgDoC2/L62/jfqi7ZZrj5qjP8nyfYku5Ls2rNnz7e7j5KkRSYWLkl+Brirqq6f1Bjfqaq6sKq2VNWWdevWzbodSVo11k7wvZ8LvDjJ8cCBwMHA7wGHJFnbjiw2AHe29e8EDgd2J1kLPBa4e6i+z/A2S9XvHjGGJGkKJnbkUlVnVdWGqtrI4IL8R6rqFcBHgZe21U4BPtimd7R52vKPVFW1+ontbrIjgE3AtcB1wKZ2Z9gBbYwdbZvlxpAkTcEsPudyBvD6JAsMro9c1OoXAYe2+uuBMwGq6ibgSuBm4K+B06vqoXZU8hrgKgZ3o13Z1h01hiRpCiZ5WuwbqurvgL9r07cxuNNr8TpfA162zPbnAOcsUd8J7FyivuQYkqTp8BP6kqTuDBdJUneGiySpO8NFktSd4SJJ6s5wkSR1Z7hIkrozXCRJ3RkukqTuDBdJUneGiySpO8NFktSd4SJJ6s5wkSR1Z7hIkrozXCRJ3RkukqTuDBdJUneGiySpO8NFktSd4SJJ6s5wkSR1Z7hIkrozXCRJ3RkukqTuDBdJUneGiySpO8NFktSd4SJJ6s5wkSR1Z7hIkrozXCRJ3RkukqTuDBdJUneGiySpO8NFktSd4SJJ6s5wkSR1N7FwSXJgkmuT/FuSm5L8ZqsfkeRjSRaSvDfJAa3+XW1+oS3fOPReZ7X6LUleOFTf2moLSc4cqi85hiRpOiZ55PIA8Pyqegbw48DWJEcD7wDOraofAu4FTm3rnwrc2+rntvVIshk4EXgqsBU4P8maJGuAdwHHAZuBk9q6jBhDkjQFEwuXGvjvNvvo9irg+cD7Wv1S4CVtelubpy0/Jkla/YqqeqCqbgcWgKPaa6GqbquqB4ErgG1tm+XGkCRNwUSvubQjjE8AdwFXA/8BfLmq9rZVdgPr2/R64A6Atvw+4NDh+qJtlqsfOmKMxf1tT7Irya49e/Z8J7sqSRoy0XCpqoeq6seBDQyONJ4yyfEerqq6sKq2VNWWdevWzbodSVo1pnK3WFV9Gfgo8BPAIUnWtkUbgDvb9J3A4QBt+WOBu4fri7ZZrn73iDEkSVMwybvF1iU5pE0fBLwA+BSDkHlpW+0U4INtekebpy3/SFVVq5/Y7iY7AtgEXAtcB2xqd4YdwOCi/462zXJjSJKmYO3Kq3zbnghc2u7qehRwZVX9ZZKbgSuS/BbwceCitv5FwOVJFoB7GIQFVXVTkiuBm4G9wOlV9RBAktcAVwFrgIur6qb2XmcsM4YkaQomFi5VdQPwzCXqtzG4/rK4/jXgZcu81znAOUvUdwI7xx1DkjQdfkJfktSd4SJJ6s5wkSR1Z7hIkrobK1ySPG3SjUiSVo9xj1zOb084fnWSx060I0nS3BsrXKrqJ4FXMPhE/PVJ3pPkBRPtTJI0t8a+5lJVtwJvYvABxZ8Czkvy70l+blLNSZLm07jXXJ6e5FwGj295PvCiqvrRNn3uBPuTJM2hcT+h//vAu4E3VtVX9xWr6vNJ3jSRziRJc2vccDkB+OrQM70eBRxYVV+pqssn1p0kaS6Ne83lb4GDhuYf02qSJH2LccPlwKGvLKZNP2YyLUmS5t244fI/SY7cN5PkWcBXR6wvSdqPjXvN5XXAnyX5PBDg+4FfmFhXkqS5Nla4VNV1SZ4C/Egr3VJV/zu5tiRJ8+zhfFnYs4GNbZsjk1BVl02kK0nSXBsrXJJcDvwg8AngoVYuwHCRJH2LcY9ctgCbq6om2YwkaXUY926xTzK4iC9J0orGPXI5DLg5ybXAA/uKVfXiiXQlSZpr44bLWybZhCRpdRn3VuS/T/JkYFNV/W2SxwBrJtuaJGlejfvI/dOA9wF/2ErrgQ9MqilJ0nwb94L+6cBzgfvhG18c9n2TakqSNN/GDZcHqurBfTNJ1jL4nIskSd9i3HD5+yRvBA5K8gLgz4C/mFxbkqR5Nm64nAnsAW4EfgXYCfgNlJKkJY17t9jXgT9qL0mSRhr32WK3s8Q1lqr6ge4dSZLm3sN5ttg+BwIvAx7fvx1J0mow1jWXqrp76HVnVb0TOGHCvUmS5tS4p8WOHJp9FIMjmYfzXTCSpP3IuAHxO0PTe4HPAC/v3o0kaVUY926x5026EUnS6jHuabHXj1peVb/bpx1J0mrwcO4Wezawo82/CLgWuHUSTUmS5tu44bIBOLKq/gsgyVuAv6qqV06qMUnS/Br38S9PAB4cmn+w1ZaV5PAkH01yc5Kbkry21R+f5Ookt7Y/H9fqSXJekoUkNwzfoZbklLb+rUlOGao/K8mNbZvzkmTUGJKk6Rg3XC4Drk3ylnbU8jHg0hW22Qu8oao2A0cDpyfZzOA5ZR+uqk3Ah9s8wHHApvbaDlwAg6AAzgaeAxwFnD0UFhcApw1tt7XVlxtDkjQF436I8hzgVcC97fWqqvrtFbb5QlX9a5v+L+BTDL5kbBvfDKZLgZe06W3AZTVwDXBIkicCLwSurqp7qupe4Gpga1t2cFVdU1XFIACH32upMSRJUzDukQvAY4D7q+r3gN1Jjhh3wyQbgWcyOOJ5QlV9oS36It88vbYeuGNos92tNqq+e4k6I8ZY3Nf2JLuS7NqzZ8+4uyNJWsG4X3N8NnAGcFYrPRr44zG3/R7g/cDrqur+4WXtiGOiXzo2aoyqurCqtlTVlnXr1k2yDUnar4x75PKzwIuB/wGoqs8D37vSRkkezSBY/qSq/ryVv9ROadH+vKvV7wQOH9p8Q6uNqm9Yoj5qDEnSFIwbLg8OHwEk+e6VNmh3bl0EfGrRhyx3APvu+DoF+OBQ/eR219jRwH3t1NZVwLFJHtcu5B8LXNWW3Z/k6DbWyYvea6kxJElTMO7nXK5M8ocMLrKfBvwyK39x2HOBXwRuTPKJVnsj8Pb2fqcCn+WbzyjbCRwPLABfYXADAVV1T5K3Ade19d5aVfe06VcDlwAHAR9qL0aMIUmaghXDpR0VvBd4CnA/8CPAm6vq6lHbVdU/Allm8TFLrF/A6cu818XAxUvUdwE/tkT97qXGkCRNx4rhUlWVZGdVPY3BbcCSJI007jWXf03y7Il2IklaNca95vIc4JVJPsPgjrEwOKh5+qQakyTNr5HhkuRJVfU5Bp+SlyRpLCsduXyAwdOQP5vk/VX189NoSpI031a65jJ8t9cPTLIRSdLqsVK41DLTkiQta6XTYs9Icj+DI5iD2jR884L+wRPtTpI0l0aGS1WtmVYjkqTV4+E8cl+SpLEYLpKk7gwXSVJ3hoskqTvDRZLUneEiSerOcJEkdWe4SJK6M1wkSd0ZLpKk7gwXSVJ3hoskqTvDRZLUneEiSerOcJEkdWe4SJK6M1wkSd0ZLpKk7gwXSVJ3hoskqTvDRZLUneEiSerOcJEkdWe4SJK6M1wkSd0ZLpKk7gwXSVJ3hoskqTvDRZLU3cTCJcnFSe5K8smh2uOTXJ3k1vbn41o9Sc5LspDkhiRHDm1zSlv/1iSnDNWfleTGts15STJqDEnS9EzyyOUSYOui2pnAh6tqE/DhNg9wHLCpvbYDF8AgKICzgecARwFnD4XFBcBpQ9ttXWEMSdKUTCxcquofgHsWlbcBl7bpS4GXDNUvq4FrgEOSPBF4IXB1Vd1TVfcCVwNb27KDq+qaqirgskXvtdQYkqQpmfY1lydU1Rfa9BeBJ7Tp9cAdQ+vtbrVR9d1L1EeNIUmakpld0G9HHDXLMZJsT7Irya49e/ZMshVJ2q9MO1y+1E5p0f68q9XvBA4fWm9Dq42qb1iiPmqMb1FVF1bVlqrasm7dum97pyRJ/9+0w2UHsO+Or1OADw7VT253jR0N3NdObV0FHJvkce1C/rHAVW3Z/UmObneJnbzovZYaQ5I0JWsn9cZJ/hT4aeCwJLsZ3PX1duDKJKcCnwVe3lbfCRwPLABfAV4FUFX3JHkbcF1b761Vte8mgVczuCPtIOBD7cWIMSRJUzKxcKmqk5ZZdMwS6xZw+jLvczFw8RL1XcCPLVG/e6kxJEnT4yf0JUndGS6SpO4MF0lSd4aLJKk7w0WS1J3hIknqznCRJHVnuEiSujNcJEndGS6SpO4MF0lSd4aLJKk7w0WS1J3hIknqznCRJHVnuEiSujNcJEndGS6SpO4MF0lSd4aLJKk7w0WS1J3hIknqznCRJHVnuEiSujNcJEndGS6SpO4MF0lSd4aLJKk7w0WS1J3hIknqznCRJHVnuEiSujNcJEndGS6SpO4MF0lSd4aLJKk7w0WS1J3hIknqznCRJHW3asMlydYktyRZSHLmrPuRpP3JqgyXJGuAdwHHAZuBk5Jsnm1XkrT/WJXhAhwFLFTVbVX1IHAFsG3GPUnSfmPtrBuYkPXAHUPzu4HnLF4pyXZge5v97yS3TKG3ng4D/nPWTUyZ+zxFeccsRgX8d54nT16quFrDZSxVdSFw4az7+HYl2VVVW2bdxzS5z/sH93n+rdbTYncChw/Nb2g1SdIUrNZwuQ7YlOSIJAcAJwI7ZtyTJO03VuVpsaram+Q1wFXAGuDiqrppxm1Nwtye0vsOuM/7B/d5zqWqZt2DJGmVWa2nxSRJM2S4SJK6M1zmwDiPskny8iQ3J7kpyXum3WNvK+1zkicl+WiSjye5Icnxs+izlyQXJ7krySeXWZ4k57W/jxuSHDntHnsbY59f0fb1xiT/nOQZ0+6xt5X2eWi9ZyfZm+Sl0+qtN8PlEW6cR9kk2QScBTy3qp4KvG7qjXY05uN73gRcWVXPZHA34PnT7bK7S4CtI5YfB2xqr+3ABVPoadIuYfQ+3w78VFU9DXgbq+OC9yWM3ud9P//vAP5mGg1NiuHyyDfOo2xOA95VVfcCVNVdU+6xt3H2uYCD2/Rjgc9Psb/uquofgHtGrLINuKwGrgEOSfLE6XQ3GSvtc1X9876faeAaBp9Xm2tj/DsD/CrwfmCu/x8bLo98Sz3KZv2idX4Y+OEk/5TkmiQjfzOaA+Ps81uAVybZDexk8B9yNRvn72Q1OxX40KybmLQk64GfZRUcmRouq8NaBqdLfho4CfijJIfMtKPJOwm4pKo2AMcDlyfx53kVSvI8BuFyxqx7mYJ3AmdU1ddn3ch3alV+iHKVGedRNruBj1XV/wK3J/k0g7C5bjotdjfOPp9KO3ddVf+S5EAGD/6b61MJI+yXjzRK8nTg3cBxVXX3rPuZgi3AFUlg8PN8fJK9VfWB2bb18Pmb3iPfOI+y+QCDoxaSHMbgNNlt02yys3H2+XPAMQBJfhQ4ENgz1S6nawdwcrtr7Gjgvqr6wqybmqQkTwL+HPjFqvr0rPuZhqo6oqo2VtVG4H3Aq+cxWMAjl0e85R5lk+StwK6q2tGWHZvkZuAh4Nfn+be8Mff5DQxO//0ag4v7v1Rz/LiJJH/K4BeEw9p1pLOBRwNU1R8wuK50PLAAfAV41Ww67WeMfX4zcChwfvtNfu+8PzV4jH1eNXz8iySpO0+LSZK6M1wkSd0ZLpKk7gwXSVJ3hoskqTvDRZLUneEiSeru/wAVwJen2/4BNAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cr['STATUS']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2POSPruaf59m",
        "outputId": "9ac6b8d1-20a0-47eb-e705-a3963c676b55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0          X\n",
              "1          0\n",
              "2          0\n",
              "3          0\n",
              "4          C\n",
              "          ..\n",
              "1048570    C\n",
              "1048571    C\n",
              "1048572    C\n",
              "1048573    C\n",
              "1048574    C\n",
              "Name: STATUS, Length: 1048575, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualisasi histogram with pandas\n",
        "df_cr['STATUS'].plot.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "09MPzgQJiDOq",
        "outputId": "1bc23571-37db-4e74-b31d-5f23ba61801b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-18-142b67126aa6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Visualisasi histogram with pandas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf_cr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'STATUS'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mhist\u001b[0;34m(self, by, bins, **kwargs)\u001b[0m\n\u001b[1;32m   1311\u001b[0m             \u001b[0;34m>>\u001b[0m\u001b[0;34m>\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1312\u001b[0m         \"\"\"\n\u001b[0;32m-> 1313\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"hist\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1314\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1315\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mkde\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbw_method\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    970\u001b[0m                     \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabel_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    971\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 972\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mplot_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    973\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    974\u001b[0m     \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/plotting/_matplotlib/__init__.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(data, kind, **kwargs)\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ax\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"left_ax\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m     \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPLOT_CLASSES\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     72\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/plotting/_matplotlib/core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    284\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    285\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 286\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    287\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    288\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/plotting/_matplotlib/core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    451\u001b[0m         \u001b[0;31m# no non-numeric frames or series allowed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    452\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 453\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"no numeric data to plot\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    454\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    455\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_to_ndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: no numeric data to plot"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cr['MONTHS_BALANCE']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xezqyK8Mf6NK",
        "outputId": "e28e40b2-d12b-4718-f446-bf9296bd7a24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0           0\n",
              "1          -1\n",
              "2          -2\n",
              "3          -3\n",
              "4           0\n",
              "           ..\n",
              "1048570   -25\n",
              "1048571   -26\n",
              "1048572   -27\n",
              "1048573   -28\n",
              "1048574   -29\n",
              "Name: MONTHS_BALANCE, Length: 1048575, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualisasi histogram with pandas\n",
        "df_cr['MONTHS_BALANCE'].plot.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "dIXHUxdxiHDp",
        "outputId": "d9dd7b5e-1179-4709-d359-061d1b2905aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb918359d90>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Soal No. 2"
      ],
      "metadata": {
        "id": "KO_puMSjf45j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.model_selection import train_test_split\n",
        "# X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)"
      ],
      "metadata": {
        "id": "4tcgnT1MKGcE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        },
        "outputId": "830e377a-9313-49e0-e2ce-697779020f22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-27-bde729a025f1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_selection\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'X' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Soal No. 3"
      ],
      "metadata": {
        "id": "_yYhmVYTv1_5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Rumus Accuracy\n",
        "# Accuracy = (TP + TN ) / (TP+FP+FN+TN)\n",
        "# Accuracy = (TP + TN) / (TP + FP + FN + TN)\n",
        "# Accuracy = (100 + 50) / (100 + 10 + 5 + 50)\n",
        "# Accuracy = (150) / (165)\n",
        "# Accuracy = 0.9090 ~ 90,90%"
      ],
      "metadata": {
        "id": "S59cmsgiv2K4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Soal No. 12"
      ],
      "metadata": {
        "id": "K1SPaIW9v2zS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# logReg = LogisticRegression()\n",
        "# logReg.fit(X_train, Y_train)"
      ],
      "metadata": {
        "id": "amkHZE_zv2-l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MINGGU 4 -"
      ],
      "metadata": {
        "id": "Th0NbcvEM2Fg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "a6kCMqg2M2Q8"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}