7_"german_credit_analysis_ipynb".ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "7. \"german-credit-analysis.ipynb\"",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kkattt51/ArtInt/blob/main/7_%22german_credit_analysis_ipynb%22.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "InoAdlWmUkzl"
      },
      "source": [
        "# Анализ данных на Python с Глебом Михайловым Мастер-Класс"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0UmTccDLUtyi"
      },
      "source": [
        "Мой канал в Телеграм: https://t.me/mikhaylovgleb  \n",
        "Мой канал на Ютюб: https://www.youtube.com/c/GlebMikhaylov  \n",
        "Мой сайт: https://glebmikhaylov.com/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Pfg2Y8WGdhE"
      },
      "source": [
        "# Заливка данных"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "95cRsRYV4HQb"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7vZBZd0Y4J40"
      },
      "source": [
        "Это ссылка на файл: https://drive.google.com/file/d/1pmGSMI2LuvsiBaBG5v7N8xfPedZbF020/view?usp=sharing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PWh7Mi3U4Us3",
        "outputId": "77db9c53-1487-459b-8098-af3dfa4a18a2"
      },
      "source": [
        "!gdown --id 1pmGSMI2LuvsiBaBG5v7N8xfPedZbF020"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1pmGSMI2LuvsiBaBG5v7N8xfPedZbF020\n",
            "To: /content/german-credit.csv\n",
            "\r  0% 0.00/50.5k [00:00<?, ?B/s]\r100% 50.5k/50.5k [00:00<00:00, 39.5MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LzZFPPIU44RQ"
      },
      "source": [
        "df = pd.read_csv('/content/german-credit.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "id": "ItvTx3kH47Ds",
        "outputId": "b410c80a-6047-402c-cd4f-69659fd5af9f"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Job</th>\n",
              "      <th>Housing</th>\n",
              "      <th>Saving accounts</th>\n",
              "      <th>Checking account</th>\n",
              "      <th>Credit amount</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Purpose</th>\n",
              "      <th>default</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>own</td>\n",
              "      <td>NaN</td>\n",
              "      <td>little</td>\n",
              "      <td>1169</td>\n",
              "      <td>6</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>22</td>\n",
              "      <td>female</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>moderate</td>\n",
              "      <td>5951</td>\n",
              "      <td>48</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>49</td>\n",
              "      <td>male</td>\n",
              "      <td>1</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2096</td>\n",
              "      <td>12</td>\n",
              "      <td>education</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>45</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>7882</td>\n",
              "      <td>42</td>\n",
              "      <td>furniture/equipment</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>53</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>4870</td>\n",
              "      <td>24</td>\n",
              "      <td>car</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  Age     Sex  ...  Duration              Purpose default\n",
              "0           0   67    male  ...         6             radio/TV       0\n",
              "1           1   22  female  ...        48             radio/TV       1\n",
              "2           2   49    male  ...        12            education       0\n",
              "3           3   45    male  ...        42  furniture/equipment       0\n",
              "4           4   53    male  ...        24                  car       1\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GM2z1-6sGtMQ"
      },
      "source": [
        "# Предобработка"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gYzErQGuHCED"
      },
      "source": [
        "## Удаление столбца"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lQeUOv-x6Rcb"
      },
      "source": [
        "df.drop('Unnamed: 0',axis=1,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "id": "Z4bvqweN7C0D",
        "outputId": "959a8cdb-21c4-4b75-cc9a-2997812531b6"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Job</th>\n",
              "      <th>Housing</th>\n",
              "      <th>Saving accounts</th>\n",
              "      <th>Checking account</th>\n",
              "      <th>Credit amount</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Purpose</th>\n",
              "      <th>default</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>67</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>NaN</td>\n",
              "      <td>little</td>\n",
              "      <td>1169</td>\n",
              "      <td>6</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>22</td>\n",
              "      <td>female</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>moderate</td>\n",
              "      <td>5951</td>\n",
              "      <td>48</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>49</td>\n",
              "      <td>male</td>\n",
              "      <td>1</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2096</td>\n",
              "      <td>12</td>\n",
              "      <td>education</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>45</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>7882</td>\n",
              "      <td>42</td>\n",
              "      <td>furniture/equipment</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>53</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>4870</td>\n",
              "      <td>24</td>\n",
              "      <td>car</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age     Sex  Job  ... Duration              Purpose default\n",
              "0   67    male    2  ...        6             radio/TV       0\n",
              "1   22  female    2  ...       48             radio/TV       1\n",
              "2   49    male    1  ...       12            education       0\n",
              "3   45    male    2  ...       42  furniture/equipment       0\n",
              "4   53    male    2  ...       24                  car       1\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QJjApcegHHDl"
      },
      "source": [
        "## Переименование столбцов"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cb2kWgKG7DmI",
        "outputId": "a02ca60d-5ff8-4edf-b3b4-d5391e83fea5"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Age', 'Sex', 'Job', 'Housing', 'Saving accounts', 'Checking account',\n",
              "       'Credit amount', 'Duration', 'Purpose', 'default'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "paK4l32H7cum"
      },
      "source": [
        "df.columns = ['age', 'sex', 'job', 'housing', 'saving_accounts', 'checking_account',\n",
        "       'credit_amount', 'duration', 'purpose', 'default']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sM00YciF8Nnq"
      },
      "source": [
        "df.columns = df.columns.str.replace(' ','_').str.lower()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xUfjmOPFHKpY"
      },
      "source": [
        "## Пропуски"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E7DiK8Vj8X2C",
        "outputId": "ad1a7aa7-05f2-4de2-ead0-f97e2be75301"
      },
      "source": [
        "df.isna().mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                 0.000\n",
              "sex                 0.000\n",
              "job                 0.000\n",
              "housing             0.000\n",
              "saving_accounts     0.183\n",
              "checking_account    0.394\n",
              "credit_amount       0.000\n",
              "duration            0.000\n",
              "purpose             0.000\n",
              "default             0.000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4YpngkIiG-zh"
      },
      "source": [
        "# Сводная таблица"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g7ev-a4AWkmy"
      },
      "source": [
        "## groupby"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "5cVVWVmX059G",
        "outputId": "69da0787-5893-4fbf-dbef-4e06d969a275"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>job</th>\n",
              "      <th>housing</th>\n",
              "      <th>saving_accounts</th>\n",
              "      <th>checking_account</th>\n",
              "      <th>credit_amount</th>\n",
              "      <th>duration</th>\n",
              "      <th>purpose</th>\n",
              "      <th>default</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>NaN</td>\n",
              "      <td>little</td>\n",
              "      <td>1169</td>\n",
              "      <td>6</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>22</td>\n",
              "      <td>female</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>moderate</td>\n",
              "      <td>5951</td>\n",
              "      <td>48</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>49</td>\n",
              "      <td>male</td>\n",
              "      <td>1</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2096</td>\n",
              "      <td>12</td>\n",
              "      <td>education</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>45</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>7882</td>\n",
              "      <td>42</td>\n",
              "      <td>furniture/equipment</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>53</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>4870</td>\n",
              "      <td>24</td>\n",
              "      <td>car</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age     sex  job  ... duration              purpose default\n",
              "0   67    male    2  ...        6             radio/TV       0\n",
              "1   22  female    2  ...       48             radio/TV       1\n",
              "2   49    male    1  ...       12            education       0\n",
              "3   45    male    2  ...       42  furniture/equipment       0\n",
              "4   53    male    2  ...       24                  car       1\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zz0578zv1Jtj",
        "outputId": "67eb31ba-2810-4543-ef83-7f0c37ef601f"
      },
      "source": [
        "len(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "abBkZHLt1i3b"
      },
      "source": [
        "df['checking_account'] = df['checking_account'].fillna('no_info')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "oA8owV-D0l4C",
        "outputId": "3d8187b7-a646-4ac9-ba6c-c4e9abfff7ae"
      },
      "source": [
        "(df.groupby(['checking_account','sex'])\n",
        "[['default','credit_amount']]\n",
        " .agg({'default':['count','mean'],\n",
        "       'credit_amount':['mean','median']}))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">default</th>\n",
              "      <th colspan=\"2\" halign=\"left\">credit_amount</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>mean</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>checking_account</th>\n",
              "      <th>sex</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">little</th>\n",
              "      <th>female</th>\n",
              "      <td>88</td>\n",
              "      <td>0.522727</td>\n",
              "      <td>2564.215909</td>\n",
              "      <td>1838.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>186</td>\n",
              "      <td>0.478495</td>\n",
              "      <td>3464.295699</td>\n",
              "      <td>2589.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">moderate</th>\n",
              "      <th>female</th>\n",
              "      <td>86</td>\n",
              "      <td>0.476744</td>\n",
              "      <td>3720.034884</td>\n",
              "      <td>2651.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>183</td>\n",
              "      <td>0.349727</td>\n",
              "      <td>3878.092896</td>\n",
              "      <td>2520.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">no_info</th>\n",
              "      <th>female</th>\n",
              "      <td>116</td>\n",
              "      <td>0.155172</td>\n",
              "      <td>2708.750000</td>\n",
              "      <td>1935.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>278</td>\n",
              "      <td>0.100719</td>\n",
              "      <td>3310.169065</td>\n",
              "      <td>2393.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">rich</th>\n",
              "      <th>female</th>\n",
              "      <td>20</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>1616.050000</td>\n",
              "      <td>1400.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>43</td>\n",
              "      <td>0.232558</td>\n",
              "      <td>2438.860465</td>\n",
              "      <td>1925.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        default           credit_amount        \n",
              "                          count      mean          mean  median\n",
              "checking_account sex                                           \n",
              "little           female      88  0.522727   2564.215909  1838.5\n",
              "                 male       186  0.478495   3464.295699  2589.5\n",
              "moderate         female      86  0.476744   3720.034884  2651.0\n",
              "                 male       183  0.349727   3878.092896  2520.0\n",
              "no_info          female     116  0.155172   2708.750000  1935.0\n",
              "                 male       278  0.100719   3310.169065  2393.5\n",
              "rich             female      20  0.200000   1616.050000  1400.5\n",
              "                 male        43  0.232558   2438.860465  1925.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "67K-nqWVWol1"
      },
      "source": [
        "## pivot_table"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "coIdc-J5PfDd",
        "outputId": "eaf75796-5e61-4399-f4b7-463d03c70f36"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>job</th>\n",
              "      <th>housing</th>\n",
              "      <th>saving_accounts</th>\n",
              "      <th>checking_account</th>\n",
              "      <th>credit_amount</th>\n",
              "      <th>duration</th>\n",
              "      <th>purpose</th>\n",
              "      <th>default</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>NaN</td>\n",
              "      <td>little</td>\n",
              "      <td>1169</td>\n",
              "      <td>6</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>22</td>\n",
              "      <td>female</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>moderate</td>\n",
              "      <td>5951</td>\n",
              "      <td>48</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>49</td>\n",
              "      <td>male</td>\n",
              "      <td>1</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>no_info</td>\n",
              "      <td>2096</td>\n",
              "      <td>12</td>\n",
              "      <td>education</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>45</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>7882</td>\n",
              "      <td>42</td>\n",
              "      <td>furniture/equipment</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>53</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>4870</td>\n",
              "      <td>24</td>\n",
              "      <td>car</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age     sex  job  ... duration              purpose default\n",
              "0   67    male    2  ...        6             radio/TV       0\n",
              "1   22  female    2  ...       48             radio/TV       1\n",
              "2   49    male    1  ...       12            education       0\n",
              "3   45    male    2  ...       42  furniture/equipment       0\n",
              "4   53    male    2  ...       24                  car       1\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "J3kwG5TgPkJr",
        "outputId": "88128699-f027-4903-c0b9-0977807357be"
      },
      "source": [
        "df.pivot_table(index='checking_account',\n",
        "               columns='sex',\n",
        "               values=['default','credit_amount'],\n",
        "               aggfunc=['count','mean'],\n",
        "       margins=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"6\" halign=\"left\">count</th>\n",
              "      <th colspan=\"6\" halign=\"left\">mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"3\" halign=\"left\">credit_amount</th>\n",
              "      <th colspan=\"3\" halign=\"left\">default</th>\n",
              "      <th colspan=\"3\" halign=\"left\">credit_amount</th>\n",
              "      <th colspan=\"3\" halign=\"left\">default</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th>female</th>\n",
              "      <th>male</th>\n",
              "      <th>All</th>\n",
              "      <th>female</th>\n",
              "      <th>male</th>\n",
              "      <th>All</th>\n",
              "      <th>female</th>\n",
              "      <th>male</th>\n",
              "      <th>All</th>\n",
              "      <th>female</th>\n",
              "      <th>male</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>checking_account</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>little</th>\n",
              "      <td>88</td>\n",
              "      <td>186</td>\n",
              "      <td>274</td>\n",
              "      <td>88</td>\n",
              "      <td>186</td>\n",
              "      <td>274</td>\n",
              "      <td>2564.215909</td>\n",
              "      <td>3464.295699</td>\n",
              "      <td>3175.218978</td>\n",
              "      <td>0.522727</td>\n",
              "      <td>0.478495</td>\n",
              "      <td>0.492701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>moderate</th>\n",
              "      <td>86</td>\n",
              "      <td>183</td>\n",
              "      <td>269</td>\n",
              "      <td>86</td>\n",
              "      <td>183</td>\n",
              "      <td>269</td>\n",
              "      <td>3720.034884</td>\n",
              "      <td>3878.092896</td>\n",
              "      <td>3827.561338</td>\n",
              "      <td>0.476744</td>\n",
              "      <td>0.349727</td>\n",
              "      <td>0.390335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>no_info</th>\n",
              "      <td>116</td>\n",
              "      <td>278</td>\n",
              "      <td>394</td>\n",
              "      <td>116</td>\n",
              "      <td>278</td>\n",
              "      <td>394</td>\n",
              "      <td>2708.750000</td>\n",
              "      <td>3310.169065</td>\n",
              "      <td>3133.101523</td>\n",
              "      <td>0.155172</td>\n",
              "      <td>0.100719</td>\n",
              "      <td>0.116751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rich</th>\n",
              "      <td>20</td>\n",
              "      <td>43</td>\n",
              "      <td>63</td>\n",
              "      <td>20</td>\n",
              "      <td>43</td>\n",
              "      <td>63</td>\n",
              "      <td>1616.050000</td>\n",
              "      <td>2438.860465</td>\n",
              "      <td>2177.650794</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.232558</td>\n",
              "      <td>0.222222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <td>310</td>\n",
              "      <td>690</td>\n",
              "      <td>1000</td>\n",
              "      <td>310</td>\n",
              "      <td>690</td>\n",
              "      <td>1000</td>\n",
              "      <td>2877.774194</td>\n",
              "      <td>3448.040580</td>\n",
              "      <td>3271.258000</td>\n",
              "      <td>0.351613</td>\n",
              "      <td>0.276812</td>\n",
              "      <td>0.300000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         count             ...      mean                    \n",
              "                 credit_amount             ...   default                    \n",
              "sex                     female male   All  ...    female      male       All\n",
              "checking_account                           ...                              \n",
              "little                      88  186   274  ...  0.522727  0.478495  0.492701\n",
              "moderate                    86  183   269  ...  0.476744  0.349727  0.390335\n",
              "no_info                    116  278   394  ...  0.155172  0.100719  0.116751\n",
              "rich                        20   43    63  ...  0.200000  0.232558  0.222222\n",
              "All                        310  690  1000  ...  0.351613  0.276812  0.300000\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "z2sIahB7QJt7",
        "outputId": "1c866d68-0e0b-4945-a76d-a11af641ad80"
      },
      "source": [
        "(df.groupby(['checking_account'])\n",
        "[['default']]\n",
        " .agg(['count','mean']))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">default</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>checking_account</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>little</th>\n",
              "      <td>274</td>\n",
              "      <td>0.492701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>moderate</th>\n",
              "      <td>269</td>\n",
              "      <td>0.390335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>no_info</th>\n",
              "      <td>394</td>\n",
              "      <td>0.116751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rich</th>\n",
              "      <td>63</td>\n",
              "      <td>0.222222</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 default          \n",
              "                   count      mean\n",
              "checking_account                  \n",
              "little               274  0.492701\n",
              "moderate             269  0.390335\n",
              "no_info              394  0.116751\n",
              "rich                  63  0.222222"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "HN1vR4U2QchZ",
        "outputId": "ce9a6050-5d64-4003-fc14-5471be5e3326"
      },
      "source": [
        "(df.groupby(['checking_account'])\n",
        "[['credit_amount']]\n",
        " .agg(['count','mean','median']))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"3\" halign=\"left\">credit_amount</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>checking_account</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>little</th>\n",
              "      <td>274</td>\n",
              "      <td>3175.218978</td>\n",
              "      <td>2353.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>moderate</th>\n",
              "      <td>269</td>\n",
              "      <td>3827.561338</td>\n",
              "      <td>2622.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>no_info</th>\n",
              "      <td>394</td>\n",
              "      <td>3133.101523</td>\n",
              "      <td>2248.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rich</th>\n",
              "      <td>63</td>\n",
              "      <td>2177.650794</td>\n",
              "      <td>1881.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 credit_amount                     \n",
              "                         count         mean  median\n",
              "checking_account                                   \n",
              "little                     274  3175.218978  2353.5\n",
              "moderate                   269  3827.561338  2622.0\n",
              "no_info                    394  3133.101523  2248.0\n",
              "rich                        63  2177.650794  1881.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s_U45UNqWvT2"
      },
      "source": [
        "## Форматирование"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "bqeBKcXmUCV3",
        "outputId": "04c649f8-638e-47b7-ad72-eaf038368457"
      },
      "source": [
        "(df.groupby(['checking_account'])\n",
        "[['default']]\n",
        " .agg(['count','mean']))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">default</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>checking_account</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>little</th>\n",
              "      <td>274</td>\n",
              "      <td>0.492701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>moderate</th>\n",
              "      <td>269</td>\n",
              "      <td>0.390335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>no_info</th>\n",
              "      <td>394</td>\n",
              "      <td>0.116751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rich</th>\n",
              "      <td>63</td>\n",
              "      <td>0.222222</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 default          \n",
              "                   count      mean\n",
              "checking_account                  \n",
              "little               274  0.492701\n",
              "moderate             269  0.390335\n",
              "no_info              394  0.116751\n",
              "rich                  63  0.222222"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "DWU5wM6fUXTh",
        "outputId": "45df8fc1-5ce9-4bd3-e92d-365c2abd685e"
      },
      "source": [
        "(df.groupby('checking_account')\n",
        "['default']\n",
        " .agg(['count','mean']))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>checking_account</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>little</th>\n",
              "      <td>274</td>\n",
              "      <td>0.492701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>moderate</th>\n",
              "      <td>269</td>\n",
              "      <td>0.390335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>no_info</th>\n",
              "      <td>394</td>\n",
              "      <td>0.116751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rich</th>\n",
              "      <td>63</td>\n",
              "      <td>0.222222</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  count      mean\n",
              "checking_account                 \n",
              "little              274  0.492701\n",
              "moderate            269  0.390335\n",
              "no_info             394  0.116751\n",
              "rich                 63  0.222222"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "id": "uhltQzVPUZQu",
        "outputId": "e46a0b19-1b12-4048-ae4a-25a50628edb4"
      },
      "source": [
        "(df.groupby(['checking_account'])['default']\n",
        ".agg(['count', 'mean'])\n",
        ".sort_values('count',ascending=False)\n",
        ".style\n",
        ".format({\n",
        "    'mean': '{:,.1%}'.format,\n",
        "})\n",
        ".applymap(\n",
        "     lambda x: 'background-color : green' if x>0.3 else '', \n",
        "     subset=['mean']))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<style  type=\"text/css\" >\n",
              "#T_d1a304f0_8339_11eb_8183_0242ac1c0002row1_col1,#T_d1a304f0_8339_11eb_8183_0242ac1c0002row2_col1{\n",
              "            background-color :  green;\n",
              "        }</style><table id=\"T_d1a304f0_8339_11eb_8183_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >count</th>        <th class=\"col_heading level0 col1\" >mean</th>    </tr>    <tr>        <th class=\"index_name level0\" >checking_account</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_d1a304f0_8339_11eb_8183_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >no_info</th>\n",
              "                        <td id=\"T_d1a304f0_8339_11eb_8183_0242ac1c0002row0_col0\" class=\"data row0 col0\" >394</td>\n",
              "                        <td id=\"T_d1a304f0_8339_11eb_8183_0242ac1c0002row0_col1\" class=\"data row0 col1\" >11.7%</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_d1a304f0_8339_11eb_8183_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >little</th>\n",
              "                        <td id=\"T_d1a304f0_8339_11eb_8183_0242ac1c0002row1_col0\" class=\"data row1 col0\" >274</td>\n",
              "                        <td id=\"T_d1a304f0_8339_11eb_8183_0242ac1c0002row1_col1\" class=\"data row1 col1\" >49.3%</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_d1a304f0_8339_11eb_8183_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >moderate</th>\n",
              "                        <td id=\"T_d1a304f0_8339_11eb_8183_0242ac1c0002row2_col0\" class=\"data row2 col0\" >269</td>\n",
              "                        <td id=\"T_d1a304f0_8339_11eb_8183_0242ac1c0002row2_col1\" class=\"data row2 col1\" >39.0%</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_d1a304f0_8339_11eb_8183_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >rich</th>\n",
              "                        <td id=\"T_d1a304f0_8339_11eb_8183_0242ac1c0002row3_col0\" class=\"data row3 col0\" >63</td>\n",
              "                        <td id=\"T_d1a304f0_8339_11eb_8183_0242ac1c0002row3_col1\" class=\"data row3 col1\" >22.2%</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f3a932b0550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "PAPBVA2rUiwo",
        "outputId": "b6369ac5-a2e4-4037-e3a6-b7cbcf444ffe"
      },
      "source": [
        "df.groupby('checking_account')['default'].agg(['count','mean'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>checking_account</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>little</th>\n",
              "      <td>274</td>\n",
              "      <td>0.492701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>moderate</th>\n",
              "      <td>269</td>\n",
              "      <td>0.390335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>no_info</th>\n",
              "      <td>394</td>\n",
              "      <td>0.116751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rich</th>\n",
              "      <td>63</td>\n",
              "      <td>0.222222</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  count      mean\n",
              "checking_account                 \n",
              "little              274  0.492701\n",
              "moderate            269  0.390335\n",
              "no_info             394  0.116751\n",
              "rich                 63  0.222222"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZukdK8PLU2Yf"
      },
      "source": [
        "t = df.pivot_table(index='checking_account',\n",
        "               columns='sex',\n",
        "               values=['default','credit_amount'],\n",
        "               aggfunc=['count','mean'],\n",
        "       margins=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "JC9BXlRGU97R",
        "outputId": "2c1175bf-bb5c-412c-ef02-1670ff9e446b"
      },
      "source": [
        "t"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"6\" halign=\"left\">count</th>\n",
              "      <th colspan=\"6\" halign=\"left\">mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"3\" halign=\"left\">credit_amount</th>\n",
              "      <th colspan=\"3\" halign=\"left\">default</th>\n",
              "      <th colspan=\"3\" halign=\"left\">credit_amount</th>\n",
              "      <th colspan=\"3\" halign=\"left\">default</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th>female</th>\n",
              "      <th>male</th>\n",
              "      <th>All</th>\n",
              "      <th>female</th>\n",
              "      <th>male</th>\n",
              "      <th>All</th>\n",
              "      <th>female</th>\n",
              "      <th>male</th>\n",
              "      <th>All</th>\n",
              "      <th>female</th>\n",
              "      <th>male</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>checking_account</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>little</th>\n",
              "      <td>88</td>\n",
              "      <td>186</td>\n",
              "      <td>274</td>\n",
              "      <td>88</td>\n",
              "      <td>186</td>\n",
              "      <td>274</td>\n",
              "      <td>2564.215909</td>\n",
              "      <td>3464.295699</td>\n",
              "      <td>3175.218978</td>\n",
              "      <td>0.522727</td>\n",
              "      <td>0.478495</td>\n",
              "      <td>0.492701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>moderate</th>\n",
              "      <td>86</td>\n",
              "      <td>183</td>\n",
              "      <td>269</td>\n",
              "      <td>86</td>\n",
              "      <td>183</td>\n",
              "      <td>269</td>\n",
              "      <td>3720.034884</td>\n",
              "      <td>3878.092896</td>\n",
              "      <td>3827.561338</td>\n",
              "      <td>0.476744</td>\n",
              "      <td>0.349727</td>\n",
              "      <td>0.390335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>no_info</th>\n",
              "      <td>116</td>\n",
              "      <td>278</td>\n",
              "      <td>394</td>\n",
              "      <td>116</td>\n",
              "      <td>278</td>\n",
              "      <td>394</td>\n",
              "      <td>2708.750000</td>\n",
              "      <td>3310.169065</td>\n",
              "      <td>3133.101523</td>\n",
              "      <td>0.155172</td>\n",
              "      <td>0.100719</td>\n",
              "      <td>0.116751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rich</th>\n",
              "      <td>20</td>\n",
              "      <td>43</td>\n",
              "      <td>63</td>\n",
              "      <td>20</td>\n",
              "      <td>43</td>\n",
              "      <td>63</td>\n",
              "      <td>1616.050000</td>\n",
              "      <td>2438.860465</td>\n",
              "      <td>2177.650794</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.232558</td>\n",
              "      <td>0.222222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <td>310</td>\n",
              "      <td>690</td>\n",
              "      <td>1000</td>\n",
              "      <td>310</td>\n",
              "      <td>690</td>\n",
              "      <td>1000</td>\n",
              "      <td>2877.774194</td>\n",
              "      <td>3448.040580</td>\n",
              "      <td>3271.258000</td>\n",
              "      <td>0.351613</td>\n",
              "      <td>0.276812</td>\n",
              "      <td>0.300000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         count             ...      mean                    \n",
              "                 credit_amount             ...   default                    \n",
              "sex                     female male   All  ...    female      male       All\n",
              "checking_account                           ...                              \n",
              "little                      88  186   274  ...  0.522727  0.478495  0.492701\n",
              "moderate                    86  183   269  ...  0.476744  0.349727  0.390335\n",
              "no_info                    116  278   394  ...  0.155172  0.100719  0.116751\n",
              "rich                        20   43    63  ...  0.200000  0.232558  0.222222\n",
              "All                        310  690  1000  ...  0.351613  0.276812  0.300000\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_DAu_2NuVQo6",
        "outputId": "7e518af3-30c1-4ae3-f4bb-3de30ae0c96b"
      },
      "source": [
        "t.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultiIndex([('count', 'credit_amount', 'female'),\n",
              "            ('count', 'credit_amount',   'male'),\n",
              "            ('count', 'credit_amount',    'All'),\n",
              "            ('count',       'default', 'female'),\n",
              "            ('count',       'default',   'male'),\n",
              "            ('count',       'default',    'All'),\n",
              "            ( 'mean', 'credit_amount', 'female'),\n",
              "            ( 'mean', 'credit_amount',   'male'),\n",
              "            ( 'mean', 'credit_amount',    'All'),\n",
              "            ( 'mean',       'default', 'female'),\n",
              "            ( 'mean',       'default',   'male'),\n",
              "            ( 'mean',       'default',    'All')],\n",
              "           names=[None, None, 'sex'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gHLyHueMU_8p"
      },
      "source": [
        "t.columns = ['_'.join(col).strip() for col in t.columns.values]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        },
        "id": "9AlXpfbHVMFL",
        "outputId": "5796a133-b6a8-4c07-a704-66d42e761e4f"
      },
      "source": [
        "t"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count_credit_amount_female</th>\n",
              "      <th>count_credit_amount_male</th>\n",
              "      <th>count_credit_amount_All</th>\n",
              "      <th>count_default_female</th>\n",
              "      <th>count_default_male</th>\n",
              "      <th>count_default_All</th>\n",
              "      <th>mean_credit_amount_female</th>\n",
              "      <th>mean_credit_amount_male</th>\n",
              "      <th>mean_credit_amount_All</th>\n",
              "      <th>mean_default_female</th>\n",
              "      <th>mean_default_male</th>\n",
              "      <th>mean_default_All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>checking_account</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>little</th>\n",
              "      <td>88</td>\n",
              "      <td>186</td>\n",
              "      <td>274</td>\n",
              "      <td>88</td>\n",
              "      <td>186</td>\n",
              "      <td>274</td>\n",
              "      <td>2564.215909</td>\n",
              "      <td>3464.295699</td>\n",
              "      <td>3175.218978</td>\n",
              "      <td>0.522727</td>\n",
              "      <td>0.478495</td>\n",
              "      <td>0.492701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>moderate</th>\n",
              "      <td>86</td>\n",
              "      <td>183</td>\n",
              "      <td>269</td>\n",
              "      <td>86</td>\n",
              "      <td>183</td>\n",
              "      <td>269</td>\n",
              "      <td>3720.034884</td>\n",
              "      <td>3878.092896</td>\n",
              "      <td>3827.561338</td>\n",
              "      <td>0.476744</td>\n",
              "      <td>0.349727</td>\n",
              "      <td>0.390335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>no_info</th>\n",
              "      <td>116</td>\n",
              "      <td>278</td>\n",
              "      <td>394</td>\n",
              "      <td>116</td>\n",
              "      <td>278</td>\n",
              "      <td>394</td>\n",
              "      <td>2708.750000</td>\n",
              "      <td>3310.169065</td>\n",
              "      <td>3133.101523</td>\n",
              "      <td>0.155172</td>\n",
              "      <td>0.100719</td>\n",
              "      <td>0.116751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rich</th>\n",
              "      <td>20</td>\n",
              "      <td>43</td>\n",
              "      <td>63</td>\n",
              "      <td>20</td>\n",
              "      <td>43</td>\n",
              "      <td>63</td>\n",
              "      <td>1616.050000</td>\n",
              "      <td>2438.860465</td>\n",
              "      <td>2177.650794</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.232558</td>\n",
              "      <td>0.222222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <td>310</td>\n",
              "      <td>690</td>\n",
              "      <td>1000</td>\n",
              "      <td>310</td>\n",
              "      <td>690</td>\n",
              "      <td>1000</td>\n",
              "      <td>2877.774194</td>\n",
              "      <td>3448.040580</td>\n",
              "      <td>3271.258000</td>\n",
              "      <td>0.351613</td>\n",
              "      <td>0.276812</td>\n",
              "      <td>0.300000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  count_credit_amount_female  ...  mean_default_All\n",
              "checking_account                              ...                  \n",
              "little                                    88  ...          0.492701\n",
              "moderate                                  86  ...          0.390335\n",
              "no_info                                  116  ...          0.116751\n",
              "rich                                      20  ...          0.222222\n",
              "All                                      310  ...          0.300000\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m8F71KloVMqB",
        "outputId": "d6150296-eb0e-4979-eaac-69214eb89ee3"
      },
      "source": [
        "t.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['count_credit_amount_female', 'count_credit_amount_male',\n",
              "       'count_credit_amount_All', 'count_default_female', 'count_default_male',\n",
              "       'count_default_All', 'mean_credit_amount_female',\n",
              "       'mean_credit_amount_male', 'mean_credit_amount_All',\n",
              "       'mean_default_female', 'mean_default_male', 'mean_default_All'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MCQsyTK7ZIF_"
      },
      "source": [
        "## Пример анализа"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "6SyKcw44VVya",
        "outputId": "ce53161b-4a05-4cf7-d87f-2a86b5dc41b1"
      },
      "source": [
        "df.groupby('checking_account')['default'].agg(['count','mean'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>checking_account</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>little</th>\n",
              "      <td>274</td>\n",
              "      <td>0.492701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>moderate</th>\n",
              "      <td>269</td>\n",
              "      <td>0.390335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>no_info</th>\n",
              "      <td>394</td>\n",
              "      <td>0.116751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rich</th>\n",
              "      <td>63</td>\n",
              "      <td>0.222222</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  count      mean\n",
              "checking_account                 \n",
              "little              274  0.492701\n",
              "moderate            269  0.390335\n",
              "no_info             394  0.116751\n",
              "rich                 63  0.222222"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2xL6FYMsXVLE"
      },
      "source": [
        "Самый низкий процент должников в категории no_info"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "6M7T1RjFXlNT",
        "outputId": "5c9c6a70-fe82-458b-caaf-97d7b0f45504"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>job</th>\n",
              "      <th>housing</th>\n",
              "      <th>saving_accounts</th>\n",
              "      <th>checking_account</th>\n",
              "      <th>credit_amount</th>\n",
              "      <th>duration</th>\n",
              "      <th>purpose</th>\n",
              "      <th>default</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>NaN</td>\n",
              "      <td>little</td>\n",
              "      <td>1169</td>\n",
              "      <td>6</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>22</td>\n",
              "      <td>female</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>moderate</td>\n",
              "      <td>5951</td>\n",
              "      <td>48</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>49</td>\n",
              "      <td>male</td>\n",
              "      <td>1</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>no_info</td>\n",
              "      <td>2096</td>\n",
              "      <td>12</td>\n",
              "      <td>education</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>45</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>7882</td>\n",
              "      <td>42</td>\n",
              "      <td>furniture/equipment</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>53</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>4870</td>\n",
              "      <td>24</td>\n",
              "      <td>car</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age     sex  job  ... duration              purpose default\n",
              "0   67    male    2  ...        6             radio/TV       0\n",
              "1   22  female    2  ...       48             radio/TV       1\n",
              "2   49    male    1  ...       12            education       0\n",
              "3   45    male    2  ...       42  furniture/equipment       0\n",
              "4   53    male    2  ...       24                  car       1\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "mlwONigyYBmQ",
        "outputId": "bf981bee-18a4-48ac-8b00-1146e6cc9732"
      },
      "source": [
        "df.groupby('housing')['default'].agg(['count','mean'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>housing</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>free</th>\n",
              "      <td>108</td>\n",
              "      <td>0.407407</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>own</th>\n",
              "      <td>713</td>\n",
              "      <td>0.260870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rent</th>\n",
              "      <td>179</td>\n",
              "      <td>0.391061</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         count      mean\n",
              "housing                 \n",
              "free       108  0.407407\n",
              "own        713  0.260870\n",
              "rent       179  0.391061"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EECP6KeVYZRZ"
      },
      "source": [
        "Вывод"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "GwJ-iAlqYbek",
        "outputId": "e47ba954-a368-4c8f-bf43-5df1b2cce832"
      },
      "source": [
        "df.groupby('housing')['credit_amount'].agg(['count','mean','median'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>housing</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>free</th>\n",
              "      <td>108</td>\n",
              "      <td>4906.212963</td>\n",
              "      <td>3800.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>own</th>\n",
              "      <td>713</td>\n",
              "      <td>3060.939691</td>\n",
              "      <td>2238.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rent</th>\n",
              "      <td>179</td>\n",
              "      <td>3122.553073</td>\n",
              "      <td>2301.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         count         mean  median\n",
              "housing                            \n",
              "free       108  4906.212963  3800.5\n",
              "own        713  3060.939691  2238.0\n",
              "rent       179  3122.553073  2301.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VKCoIieQYfio"
      },
      "source": [
        "Вывод"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "rRe93Vf0bSxf",
        "outputId": "0f038909-cd91-49f9-e43c-dc3d3696e140"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>job</th>\n",
              "      <th>housing</th>\n",
              "      <th>saving_accounts</th>\n",
              "      <th>checking_account</th>\n",
              "      <th>credit_amount</th>\n",
              "      <th>duration</th>\n",
              "      <th>purpose</th>\n",
              "      <th>default</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>NaN</td>\n",
              "      <td>little</td>\n",
              "      <td>1169</td>\n",
              "      <td>6</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>22</td>\n",
              "      <td>female</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>moderate</td>\n",
              "      <td>5951</td>\n",
              "      <td>48</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>49</td>\n",
              "      <td>male</td>\n",
              "      <td>1</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>no_info</td>\n",
              "      <td>2096</td>\n",
              "      <td>12</td>\n",
              "      <td>education</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>45</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>7882</td>\n",
              "      <td>42</td>\n",
              "      <td>furniture/equipment</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>53</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>4870</td>\n",
              "      <td>24</td>\n",
              "      <td>car</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age     sex  job  ... duration              purpose default\n",
              "0   67    male    2  ...        6             radio/TV       0\n",
              "1   22  female    2  ...       48             radio/TV       1\n",
              "2   49    male    1  ...       12            education       0\n",
              "3   45    male    2  ...       42  furniture/equipment       0\n",
              "4   53    male    2  ...       24                  car       1\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cP-1ePcMdrdH"
      },
      "source": [
        "## Бины"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fe07svQ8beOS"
      },
      "source": [
        "df['credit_amount_group'] = pd.cut(df['credit_amount'],5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "0e8SltLXblQS",
        "outputId": "cac1812c-31ed-4ac0-8f9a-9bfaf501e8be"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>job</th>\n",
              "      <th>housing</th>\n",
              "      <th>saving_accounts</th>\n",
              "      <th>checking_account</th>\n",
              "      <th>credit_amount</th>\n",
              "      <th>duration</th>\n",
              "      <th>purpose</th>\n",
              "      <th>default</th>\n",
              "      <th>credit_amount_group</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>NaN</td>\n",
              "      <td>little</td>\n",
              "      <td>1169</td>\n",
              "      <td>6</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>0</td>\n",
              "      <td>(231.826, 3884.8]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>22</td>\n",
              "      <td>female</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>moderate</td>\n",
              "      <td>5951</td>\n",
              "      <td>48</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>1</td>\n",
              "      <td>(3884.8, 7519.6]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>49</td>\n",
              "      <td>male</td>\n",
              "      <td>1</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>no_info</td>\n",
              "      <td>2096</td>\n",
              "      <td>12</td>\n",
              "      <td>education</td>\n",
              "      <td>0</td>\n",
              "      <td>(231.826, 3884.8]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>45</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>7882</td>\n",
              "      <td>42</td>\n",
              "      <td>furniture/equipment</td>\n",
              "      <td>0</td>\n",
              "      <td>(7519.6, 11154.4]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>53</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>4870</td>\n",
              "      <td>24</td>\n",
              "      <td>car</td>\n",
              "      <td>1</td>\n",
              "      <td>(3884.8, 7519.6]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age     sex  job  ...              purpose default credit_amount_group\n",
              "0   67    male    2  ...             radio/TV       0   (231.826, 3884.8]\n",
              "1   22  female    2  ...             radio/TV       1    (3884.8, 7519.6]\n",
              "2   49    male    1  ...            education       0   (231.826, 3884.8]\n",
              "3   45    male    2  ...  furniture/equipment       0   (7519.6, 11154.4]\n",
              "4   53    male    2  ...                  car       1    (3884.8, 7519.6]\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "gfS40DoLbEiB",
        "outputId": "d1b4b359-b293-4080-826b-25cccc75c81c"
      },
      "source": [
        "df.groupby('credit_amount_group')['default'].agg(['count','mean'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>credit_amount_group</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>(231.826, 3884.8]</th>\n",
              "      <td>738</td>\n",
              "      <td>0.256098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(3884.8, 7519.6]</th>\n",
              "      <td>177</td>\n",
              "      <td>0.384181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(7519.6, 11154.4]</th>\n",
              "      <td>57</td>\n",
              "      <td>0.403509</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(11154.4, 14789.2]</th>\n",
              "      <td>22</td>\n",
              "      <td>0.727273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(14789.2, 18424.0]</th>\n",
              "      <td>6</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     count      mean\n",
              "credit_amount_group                 \n",
              "(231.826, 3884.8]      738  0.256098\n",
              "(3884.8, 7519.6]       177  0.384181\n",
              "(7519.6, 11154.4]       57  0.403509\n",
              "(11154.4, 14789.2]      22  0.727273\n",
              "(14789.2, 18424.0]       6  0.666667"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nbAGQbM5bP70"
      },
      "source": [
        "df['credit_amount_group2'] = pd.qcut(df['credit_amount'],5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "9oeh9U4Ib3_h",
        "outputId": "7353b257-6c09-4414-df58-c03fdcd80077"
      },
      "source": [
        "df.groupby('credit_amount_group2')['default'].agg(['count','mean'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>credit_amount_group2</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>(249.999, 1262.0]</th>\n",
              "      <td>201</td>\n",
              "      <td>0.303483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(1262.0, 1906.8]</th>\n",
              "      <td>199</td>\n",
              "      <td>0.241206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(1906.8, 2852.4]</th>\n",
              "      <td>200</td>\n",
              "      <td>0.270000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(2852.4, 4720.0]</th>\n",
              "      <td>200</td>\n",
              "      <td>0.260000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(4720.0, 18424.0]</th>\n",
              "      <td>200</td>\n",
              "      <td>0.425000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      count      mean\n",
              "credit_amount_group2                 \n",
              "(249.999, 1262.0]       201  0.303483\n",
              "(1262.0, 1906.8]        199  0.241206\n",
              "(1906.8, 2852.4]        200  0.270000\n",
              "(2852.4, 4720.0]        200  0.260000\n",
              "(4720.0, 18424.0]       200  0.425000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TX_RaEafb6Ih"
      },
      "source": [
        "df['credit_amount_group3'] = pd.cut(df['credit_amount'],[0,1000,2000,3000,5000,np.inf])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "TRHFIRRfcUm7",
        "outputId": "ebe52223-afd8-489a-ada2-26b7054e60d4"
      },
      "source": [
        "df.groupby('credit_amount_group3')['default'].agg(['count','mean'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>credit_amount_group3</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>(0.0, 1000.0]</th>\n",
              "      <td>116</td>\n",
              "      <td>0.318966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(1000.0, 2000.0]</th>\n",
              "      <td>316</td>\n",
              "      <td>0.265823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(2000.0, 3000.0]</th>\n",
              "      <td>188</td>\n",
              "      <td>0.239362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(3000.0, 5000.0]</th>\n",
              "      <td>192</td>\n",
              "      <td>0.291667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(5000.0, inf]</th>\n",
              "      <td>188</td>\n",
              "      <td>0.414894</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      count      mean\n",
              "credit_amount_group3                 \n",
              "(0.0, 1000.0]           116  0.318966\n",
              "(1000.0, 2000.0]        316  0.265823\n",
              "(2000.0, 3000.0]        188  0.239362\n",
              "(3000.0, 5000.0]        192  0.291667\n",
              "(5000.0, inf]           188  0.414894"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0hnruiUrcXLj"
      },
      "source": [
        "df['credit_amount_group4'] = pd.cut(df['credit_amount'],[0,1000,np.inf],labels=['0-1k','1k+'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "id": "Yl3gcI9bchDJ",
        "outputId": "7a15f1e5-1e09-46b2-d59f-f1e1e5f83f15"
      },
      "source": [
        "df.groupby('credit_amount_group4')['default'].agg(['count','mean'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>credit_amount_group4</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0-1k</th>\n",
              "      <td>116</td>\n",
              "      <td>0.318966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1k+</th>\n",
              "      <td>884</td>\n",
              "      <td>0.297511</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      count      mean\n",
              "credit_amount_group4                 \n",
              "0-1k                    116  0.318966\n",
              "1k+                     884  0.297511"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wbR7b06BcjKN"
      },
      "source": [
        "df['credit_amount_group5'] = pd.qcut(df['credit_amount'],5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "gJorSZ0Vc3Oc",
        "outputId": "866c205b-0302-42be-c114-fae827510260"
      },
      "source": [
        "df.groupby('credit_amount_group5')['default'].agg(['count','mean'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>credit_amount_group5</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>(249.999, 1262.0]</th>\n",
              "      <td>201</td>\n",
              "      <td>0.303483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(1262.0, 1906.8]</th>\n",
              "      <td>199</td>\n",
              "      <td>0.241206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(1906.8, 2852.4]</th>\n",
              "      <td>200</td>\n",
              "      <td>0.270000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(2852.4, 4720.0]</th>\n",
              "      <td>200</td>\n",
              "      <td>0.260000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(4720.0, 18424.0]</th>\n",
              "      <td>200</td>\n",
              "      <td>0.425000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      count      mean\n",
              "credit_amount_group5                 \n",
              "(249.999, 1262.0]       201  0.303483\n",
              "(1262.0, 1906.8]        199  0.241206\n",
              "(1906.8, 2852.4]        200  0.270000\n",
              "(2852.4, 4720.0]        200  0.260000\n",
              "(4720.0, 18424.0]       200  0.425000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hz2eIx2Ris3_"
      },
      "source": [
        "## График"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "id": "PcQ5SSwcfSz1",
        "outputId": "aa160c87-3ab9-4bf7-82f2-9c5ae4eed16a"
      },
      "source": [
        "df.groupby('credit_amount_group5')['default'].mean().plot(rot=45,ylim=0,grid=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a92cf4610>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DBHntxpHgeYy"
      },
      "source": [
        "Вывод"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RzFsM-zay3Gv"
      },
      "source": [
        "# Распределение"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f_YDkQzOzDPP"
      },
      "source": [
        "## Категориальные (value_counts)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "td0nTJwJwYn9",
        "outputId": "7c28f5fe-66aa-4b65-e711-1a52cabf3f25"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>job</th>\n",
              "      <th>housing</th>\n",
              "      <th>saving_accounts</th>\n",
              "      <th>checking_account</th>\n",
              "      <th>credit_amount</th>\n",
              "      <th>duration</th>\n",
              "      <th>purpose</th>\n",
              "      <th>default</th>\n",
              "      <th>credit_amount_group</th>\n",
              "      <th>credit_amount_group2</th>\n",
              "      <th>credit_amount_group3</th>\n",
              "      <th>credit_amount_group4</th>\n",
              "      <th>credit_amount_group5</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>NaN</td>\n",
              "      <td>little</td>\n",
              "      <td>1169</td>\n",
              "      <td>6</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>0</td>\n",
              "      <td>(231.826, 3884.8]</td>\n",
              "      <td>(249.999, 1262.0]</td>\n",
              "      <td>(1000.0, 2000.0]</td>\n",
              "      <td>1k+</td>\n",
              "      <td>(249.999, 1262.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>22</td>\n",
              "      <td>female</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>moderate</td>\n",
              "      <td>5951</td>\n",
              "      <td>48</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>1</td>\n",
              "      <td>(3884.8, 7519.6]</td>\n",
              "      <td>(4720.0, 18424.0]</td>\n",
              "      <td>(5000.0, inf]</td>\n",
              "      <td>1k+</td>\n",
              "      <td>(4720.0, 18424.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>49</td>\n",
              "      <td>male</td>\n",
              "      <td>1</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>no_info</td>\n",
              "      <td>2096</td>\n",
              "      <td>12</td>\n",
              "      <td>education</td>\n",
              "      <td>0</td>\n",
              "      <td>(231.826, 3884.8]</td>\n",
              "      <td>(1906.8, 2852.4]</td>\n",
              "      <td>(2000.0, 3000.0]</td>\n",
              "      <td>1k+</td>\n",
              "      <td>(1906.8, 2852.4]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>45</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>7882</td>\n",
              "      <td>42</td>\n",
              "      <td>furniture/equipment</td>\n",
              "      <td>0</td>\n",
              "      <td>(7519.6, 11154.4]</td>\n",
              "      <td>(4720.0, 18424.0]</td>\n",
              "      <td>(5000.0, inf]</td>\n",
              "      <td>1k+</td>\n",
              "      <td>(4720.0, 18424.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>53</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>4870</td>\n",
              "      <td>24</td>\n",
              "      <td>car</td>\n",
              "      <td>1</td>\n",
              "      <td>(3884.8, 7519.6]</td>\n",
              "      <td>(4720.0, 18424.0]</td>\n",
              "      <td>(3000.0, 5000.0]</td>\n",
              "      <td>1k+</td>\n",
              "      <td>(4720.0, 18424.0]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age     sex  ...  credit_amount_group4 credit_amount_group5\n",
              "0   67    male  ...                   1k+    (249.999, 1262.0]\n",
              "1   22  female  ...                   1k+    (4720.0, 18424.0]\n",
              "2   49    male  ...                   1k+     (1906.8, 2852.4]\n",
              "3   45    male  ...                   1k+    (4720.0, 18424.0]\n",
              "4   53    male  ...                   1k+    (4720.0, 18424.0]\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "56o7YA4Xwma_",
        "outputId": "5052abb4-a8d1-4d4c-f6b7-6ccdeda6a1ca"
      },
      "source": [
        "df['checking_account'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "no_info     394\n",
              "little      274\n",
              "moderate    269\n",
              "rich         63\n",
              "Name: checking_account, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9-V0i8ZSwzmX",
        "outputId": "a4f2a043-9e9f-4fe4-ec7c-318436524cb9"
      },
      "source": [
        "df['checking_account'].value_counts(normalize=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "no_info     0.394\n",
              "little      0.274\n",
              "moderate    0.269\n",
              "rich        0.063\n",
              "Name: checking_account, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iCPqxw6Ow7YL",
        "outputId": "9a21941c-f05d-45de-855e-c34e26967f5e"
      },
      "source": [
        "df['default'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    700\n",
              "1    300\n",
              "Name: default, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YMwpZ7IbxC_P",
        "outputId": "ec85a859-c77a-42b9-e880-5145b2b7cd90"
      },
      "source": [
        "df['default'].value_counts(normalize=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.7\n",
              "1    0.3\n",
              "Name: default, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yyT75uhExGOj",
        "outputId": "7527d4aa-8a29-42e0-f0bc-c05566141c1e"
      },
      "source": [
        "df['default'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "QE5k9rFMxORs",
        "outputId": "76005e50-2387-4198-fe2b-2609eb5ed54f"
      },
      "source": [
        "pd.get_dummies(df['checking_account'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>little</th>\n",
              "      <th>moderate</th>\n",
              "      <th>no_info</th>\n",
              "      <th>rich</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     little  moderate  no_info  rich\n",
              "0         1         0        0     0\n",
              "1         0         1        0     0\n",
              "2         0         0        1     0\n",
              "3         1         0        0     0\n",
              "4         1         0        0     0\n",
              "..      ...       ...      ...   ...\n",
              "995       0         0        1     0\n",
              "996       1         0        0     0\n",
              "997       0         0        1     0\n",
              "998       1         0        0     0\n",
              "999       0         1        0     0\n",
              "\n",
              "[1000 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xyjuNqpczKYH"
      },
      "source": [
        "## Непрерывные (hist)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n8MfmUp_xcYT",
        "outputId": "de179ab9-e240-4c52-ae34-3922d3ebc2b3"
      },
      "source": [
        "df['credit_amount'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1258    3\n",
              "1478    3\n",
              "1275    3\n",
              "1393    3\n",
              "1262    3\n",
              "       ..\n",
              "1345    1\n",
              "1343    1\n",
              "7485    1\n",
              "3345    1\n",
              "2051    1\n",
              "Name: credit_amount, Length: 921, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "X4BHTKAYxqPo",
        "outputId": "f4d0642a-e351-4d8b-87cb-05d0fc26488d"
      },
      "source": [
        "h = df['credit_amount'].hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gtw0WwkFxyTW"
      },
      "source": [
        "def get_hist(ax):\n",
        "    n,bins = [],[]\n",
        "    for rect in ax.patches:\n",
        "        ((x0, y0), (x1, y1)) = rect.get_bbox().get_points()\n",
        "        n.append(y1-y0)\n",
        "        bins.append(x0) # left edge of each bin\n",
        "    bins.append(x1) # also get right edge of last bin\n",
        "\n",
        "    return n,bins\n",
        "\n",
        "n, bins = get_hist(h)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RKvB6dXzyCJe",
        "outputId": "c813cf4a-3dcf-47bc-ec6b-7a3d7ffcfe7a"
      },
      "source": [
        "n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[445.0, 293.0, 97.0, 80.0, 38.0, 19.0, 14.0, 8.0, 5.0, 1.0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RCgil-UFyCrA",
        "outputId": "d1c138a4-9d7d-458b-c61f-0b45c8190225"
      },
      "source": [
        "bins"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[249.9999999999999,\n",
              " 2067.4,\n",
              " 3884.7999999999997,\n",
              " 5702.200000000001,\n",
              " 7519.599999999999,\n",
              " 9337.0,\n",
              " 11154.400000000003,\n",
              " 12971.800000000001,\n",
              " 14789.2,\n",
              " 16606.6,\n",
              " 18424.0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pilm6mcvVjil"
      },
      "source": [
        "## Характеристики непрерывной переменной"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wrZJea3A8ecz",
        "outputId": "b3b07c2c-8162-4075-ecf8-382308331ad7"
      },
      "source": [
        "df['credit_amount']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      1169\n",
              "1      5951\n",
              "2      2096\n",
              "3      7882\n",
              "4      4870\n",
              "       ... \n",
              "995    1736\n",
              "996    3857\n",
              "997     804\n",
              "998    1845\n",
              "999    4576\n",
              "Name: credit_amount, Length: 1000, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "1wo8qJyu8F66",
        "outputId": "a3a96aaa-37a9-4b10-9d7f-3c66c757b3e3"
      },
      "source": [
        "df['credit_amount'].hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a926ad710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E6sLOwOM8box",
        "outputId": "f24d3186-3779-4b54-e602-3ae137074db9"
      },
      "source": [
        "df['credit_amount'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3271.258"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bHDEdHyu8s7g"
      },
      "source": [
        "t = pd.DataFrame({'salary':[20,30,20,40,50,40]})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f7Y9ry049BvR",
        "outputId": "dd7ed920-e8b6-4d73-dfca-67a41a7e4284"
      },
      "source": [
        "t['salary'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "33.333333333333336"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nfn3pQc19EFR"
      },
      "source": [
        "t = pd.DataFrame({'salary':[20,30,20,40,50,40,10000000]})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P3XLY53l9L9O",
        "outputId": "d3d60b4f-4211-45ab-8e05-715587d1fe3e"
      },
      "source": [
        "t['salary'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1428600.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SsEJiGnY9OF7",
        "outputId": "3a1b3800-be93-473b-8409-1b967450f42d"
      },
      "source": [
        "t['salary'].median()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "40.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CRMgSlLJ9WhW",
        "outputId": "d9d8e94d-e0e4-47c9-d9c4-b2b7b22e7dc3"
      },
      "source": [
        "df['credit_amount'].agg(['mean','median'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mean      3271.258\n",
              "median    2319.500\n",
              "Name: credit_amount, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "uT75zzn29hEv",
        "outputId": "6c736f9e-e052-4108-a634-54273575961e"
      },
      "source": [
        "df['credit_amount'].hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a92668bd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "jZGgyaDC94PZ",
        "outputId": "898d9a77-c406-4d70-9118-d8c672707183"
      },
      "source": [
        "df.boxplot('credit_amount')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a925e6790>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "fQ4EV_2k-AI1",
        "outputId": "9eb707c7-fd3d-40a9-a878-98ae57c62038"
      },
      "source": [
        "df.boxplot('credit_amount',by='checking_account')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a9255da10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NBepj29SHMIBBZrYxLP8NGNTFODnnnMugrvaqKqVlaWMjcIyZvS3pJGCxpDHJHszMTJK1t17SNGAawKBBg6itrT2wWB9kDQ0NeRPXfOLpmjmetpnRXdL1gDMOSYXAvwInNYWZ2W5gd1h+WtIrwHHAemBYbPdhIQxgk6TBZrYxVGm92d45zWw+MB9g3Lhxli9dMb3baGZ4umaOp21mdJd07UpV1aeBl8ysuQpK0nslFYTl9xM1gr8aqqK2S/poaBe5BLgv7HY/MDksT46FO+ecy0HJdMetBp4AjpdUL6ksrJpE20bxTwLPh+659wDTzaypYf1rwG3AGuAV4JEQPhc4TdLLRJnR3C5cj3POuQzrtKrKzBLO5WBmUxKE/Zqoe26i7Z8CxiYIfxuY2Fk8nHPO5QYfOe6ccy4lnnE455xLiWcczjnnUuIZh3POuZR4xuGccy4lnnE455xLiWcczjnnUuIZh3POuZR4xuGccy4lnnG4vFNdXc3YsWOZOHEiY8eOpbq6w8fBOOfSrKvTqrsOVFdXU1FRQV1dHcXFxZSXl1NamnAGF5ek6upqysvLqaysZN++fRQUFFBWFk2f5mnr3MHhGUeG+A0uMyoqKqisrKSkpKR5iurKykpmzpzp6ercQeJVVRkSv8EVFhZSUlJCZWUlFRUV2Y5aXqurq6O+vr5FVVV9fT11dXXZjlq34NWALhle4siQuro6xo8f3yJs/PjxfoProiFDhjB79mzuuOOO5pLcxRdfzJAhQ7IdtbznpWSXLC9xZEhxcTErVqxoEbZixQqKi4uzFKPuw8w6fO8OjJeSXbK8xJEh5eXllJWVNf96q6mpoayszP8Ju2jDhg0sWLCAmTNnNnc6uOGGG5gyZUq2o5b3vJTskuUZR4Y0Fe3jN7iKigov8ndRcXExw4YNY9WqVc2N4zU1NV6SS4OmUnJJSUlzmJeSXSJeVZVBpaWlrFq1imXLlrFq1SrPNNKgqSRXU1NDY2Njc0muvLw821HLe562LlmdljgkVQGfA940s7Eh7FrgK8Dfw2ZzzOzhsO5qoAzYB1xuZktC+JnATUABcJuZzQ3hI4E7gfcATwNfMrM96bpA1714SS5zPG1d0syswxfwSeBEYFUs7Frgmwm2HQ38GegNjAReIcooCsLy+4FeYZvRYZ+7gUlh+efApZ3Fycw46aSTLF/U1NRkOwrdkqdr5njaZkY+pSvwlLVz/+20qsrMfgdsTjIfOge408x2m9laYA1wcnitMbNXLSpN3AmcI0nAp4B7wv63A+cmea6c533inXPdUVfaOGZIel5SlaSjQthQ4I3YNvUhrL3w9wBbzayxVXjea+oTP2/ePJYsWcK8efMoLy/3zCMNPEN2LrsOtFfVLcB1gIW/NwJT0xWp9kiaBkwDGDRoELW1tZk+5QGbM2cOl19+OZLYtWsX/fr1Y+bMmcyZM4fBgwdnO3p5a9myZVRWVvKtb32LkSNHsnbtWq688kpWr17NxIkTsx29bqOhoSGn/7/yVbdJ1/bqsKxl28UIYm0c7a0Drgaujq1bAnwsvJbEwq8OLwFvAYUhvMV2Hb1yvY2jR48etmfPHjN7t15zz5491qNHjyzGKv+NGTPGli9fbmbvpuvy5cttzJgxWYxV95NPdfH5JJ/Sla60cSQiKf6T+TxgVVi+H5gkqXfoLTUK+BPwJDBK0khJvYBJwP0hcjXA+WH/ycB9BxKnXOMjxzPD56pyLvuS6Y5bDUwABkqqB64BJkg6gaiq6jXgqwBm9qKku4HVQCNwmZntC8eZQVQCKQCqzOzFcIrZwJ2Svg88C1Sm7eqyyEeOZ8aQIUOYOXMmRx11FGbGjh07mDlzps9V5dxB1GnGYWaJOnG3e3M3swqgzd3RonEeDycIf5Wo11W34n3iM+Odd95h+/btHHbYYc3tR9u3b6egoCDbUXPukOFTjmRQaWkppaWlzVNjuK7bvHkzRx55JH369MHM6NOnD0cccQSbNyfbY9w511U+5UgGebfRzJgzZw5r165l+fLlrF27ljlz5mQ7Ss4dUrzEkSH+bIPMqaio4Gc/+xmvv/46xxxzDFu2bMl2lJw7pHiJI0P82QaZMWDAALZv387OnTsxM3bu3Mn27dsZMGBAtqPm3CHDSxwZ4s82yIy+ffuyb98+DjvsMAAOO+wwDj/8cPr27ZvlmDl36PASR4b4OI7M2LBhA/PmzaOoqAhJFBUVMW/ePDZs2JDtqHUL3i7nkuEljgzxcRyZ4Q9yyhxvl3NJa29Iea6/cn3KETOzRYsW2ZgxY6xHjx42ZswYW7RoUbajlPcWLVpkI0eOtOXLl9vSpUtt+fLlNnLkSE/bNPDpXDKvu0w54iWODPJxHOnnAyszx9vlXLK8jcPlHX8kb2Z4u5xLlmcczjnAnznukudVVc45wKsBXfI843DONfN2OZcMr6pyzjmXEs84XN7xQWrOZZdXVbm84oPUnMs+L3G4vOKTR2aWl+ZcMjzjcHnFB6llTnV1NVdccQU7duxofizvFVdc4ZmHa6PTjENSlaQ3Ja2Khf1Q0kuSnpd0r6QjQ/gISTslPRdeP4/tc5KkFyStkfQTSQrhAyQtlfRy+HtUJi7UdQ/FxcVccMEF9OnTh5KSEvr06cMFF1zgg9TSYNasWRQUFFBVVcVjjz1GVVUVBQUFzJo1K9tRczkmmRLHAuDMVmFLgbFm9k/AX4GrY+teMbMTwmt6LPwW4CvAqPBqOuZVwDIzGwUsC++dS2jo0KEsXryYqVOn8sADDzB16lQWL17M0KFDsx21vFdfX8/ChQtbVAMuXLiQ+vr6bEfN5ZhOMw4z+x2wuVXYY2bWGN6uBIZ1dAxJg4HDzWxlmDxrIXBuWH0OcHtYvj0W7lwbjz/+OKeeeipVVVV8/vOfp6qqilNPPZXHH38821Fz7pCRjl5VU4G7Yu9HSnoW2A5828x+DwwF4j9b6kMYwCAz2xiW/wYMSkOcXDe1e/du1q9fzyOPPNLcq2rq1Kns3r0721HLe8OGDWPy5MnccccdzY8CmDx5MsOGdfi70B2CupRxSCoHGoE7QtBG4Bgze1vSScBiSWOSPZ6ZmSTr4HzTgGkAgwYNora29oDjfjA1NDTkTVzzxRlnnMHevXvp2bNnczWVp3HXTJkyhZ/+9KdcdNFFvPnmmxx99NHs2rWLGTNmeNqmSbe5F7Q333r8BYwAVrUKmwI8AfTtYL9aYBwwGHgpFl4K/CIs/wUYHJYHA39JJk758DyOJvk0B3+uAwyws88+2+699147++yzm8Nc1/kzZDIrn+4FdPA8jgPqjivpTGAWcLaZvRMLf6+kgrD8fqJG8FctqoraLumjoTfVJcB9Ybf7gclheXIsPO95n/j0k8SYMWNYsmQJ5513HkuWLGHMmDGETnqui3zKepeMTquqJFUDE4CBkuqBa4h6UfUGloZ/2JUW9aD6JPA9SXuB/cB0M2tqWP8aUQ+tw4BHwgtgLnC3pDJgHXBBWq4sy3yEc2aYGZs2bWLw4MGsW7eOwYMHs2nTpqaSrHPuIOg04zCzRHe5yna2/TXw63bWPQWMTRD+NjCxs3jkm4qKCi666KIWU1RfdNFFPk11FxUWFrJz50769evXXMrYuXMnhYU+e45zB4v/t2XI6tWreeedd9qUOF577bVsRy2vHX744WzdupWdO3diZuzcuZOdO3dy5JFHZjtqzh0yfMqRDOnVqxczZsxoMZhqxowZ9OrVK9tRy2tbtmyhqKiIzZs3Y2Zs3ryZoqIitmzZku2oOXfI8IwjQ/bs2cO8efNaPIZz3rx57NmzJ9tRy2u9evXi7LPP5rjjjqNHjx4cd9xxnH322Z4hp4l36HDJ8KqqDBk9ejTnnntuizaOiy++mMWLF2c7anlt9+7d3HXXXVx//fWMHj2a1atXM3v2bBobGzvf2XXIO3S4pLXXTzfXX7k+jmPRokU2cuRIW758uS1dutSWL19uI0eO9H7xXdS7d2879dRTrXfv3ga0eO+6ZsyYMbZ8+XIze3e8wfLly23MmDFZjFX30l3GcXiJI0OafqHFSxzeo6rr9uzZw8qVK7nhhhuaSxyzZs1i//792Y5a3vMp612yvI0jg3wwVfr16tWLSZMmUVVVxWc/+1mqqqqYNGmSt3GkQXFxMStWrGgRtmLFCp+y3rXhJQ6XV/bs2cMf/vCHNvXw3umg68rLy7nwwgspKiri9ddf55hjjmHHjh3cdNNN2Y6ayzGecbi8Mnr0aEaNGsVZZ53F7t276d27N2eddRZ9+/bNdtS6hV27drF161b279/P+vXr6dOnT7aj5HKQV1W5vFJSUsKDDz7ID37wAx555BF+8IMf8OCDD1JSUpLtqOW9picADh06FEkMHTrUnwDoEvISh8srNTU1zJ49m6qqquZOB7Nnz/ZuzmlQX1/P+973PqqqqpqrAS+66CJ/AqBrw0scGeSDqdKvrq6Oa665pkWng2uuucZ7/qTJN77xjRazHXzjG9/IdpRcDvKMI0Oqq6u54oor2LFjB2bGjh07uOKKKzzz6CLv+ZNZN954Y4vZDm688cZsR8nlIFmeTkc9btw4e+qpp7IdjXYNHz6cxsZGFi1a1KLYX1hYyBtvvJHt6OWt9kY3+xiZrhs+fDgNDQ0ceeSRrFu3jmOPPZatW7fSr18//86mSW1tLRMmTMh2NJIi6WkzG5dwZXsjA3P9lesjxwF77LHHzOzd0aKPPfaYP6kuDfwpdZmxaNEi69+/v/Xs2dMA69mzp/Xv39/TN4185LhzWVJaWkppaWle/XrLF62fpOhPVnSJeBtHhgwbNowLLriAkSNHMnHiREaOHMkFF1zAsGHDsh015xKaNWtWczfc+F/vjuta84wjQ84991y2b9/e4oFD27dv59xzz8121JxLqL6+nj179rB+/XrMjPXr17Nnzx7vjuvaSCrjkFQl6U1Jq2JhAyQtlfRy+HtUCJekn0haI+l5SSfG9pkctn9Z0uRY+EmSXgj7/ETdoHxcU1PD1VdfzcCBA5HEwIEDufrqq6mpqcl21Jxr186dO5k7dy6PPPIIc+fOZefOndmOkstByZY4FgBntgq7ClhmZqOAZeE9wFnAqPCaBtwCUUYDXAOcApwMXNOU2YRtvhLbr/W58o6PN3D5qH///nzkIx+hsLCQj3zkI/Tv3z/bUXI5KKmMw8x+B2xuFXwOcHtYvh04Nxa+MDTMrwSOlDQYOANYamabzWwLsBQ4M6w73MxWhpb8hbFj5S0fb+DyUY8ePZg6dSpnnHEGU6dOpUcPr812bXXlWzHIzDaG5b8Bg8LyUCDe6bs+hHUUXp8gPK+Vl5dTVlbWYjBVWVkZ5eXl2Y6acwkVFha2eZJiY2MjhYXe+dK1lJZvhJmZpIyPJJQ0jaj6i0GDBlFbW5vpUx6wwYMHc/HFFzN16tTmKaq/+MUvMnjw4JyOd65I96SF3rbUuc997nPcd999bNu2DTNj27ZtvPPOO5xzzjn+nU2ThoaGbpGWXck4NkkabGYbQ3XTmyF8PTA8tt2wELYemNAqvDaED0uwfRtmNh+YD9HI8Vzvw79x40aKiooAKCoqYvTo0T7uIEmWxIwGI656iNfmfvYgxObQMGHCBM444wyWLl2KmbF161ZOO+007r333mxHrdvoLmOPulJVdT/Q1DNqMnBfLPyS0Lvqo8C2UKW1BDhd0lGhUfx0YElYt13SR0Nvqktix8pbTVNjzJs3jyVLljBv3jzKy8t9riqXs6qrq3n22Wc59thjkcSxxx7Ls88+699Z10ay3XGrgSeA4yXVSyoD5gKnSXoZ+HR4D/Aw8CqwBrgV+BqAmW0GrgOeDK/vhTDCNreFfV4BHun6pWVXRUUFlZWVLWYarayspKKiIttRcy6hWbNmNT9JsalH/J49e3wAoGsjqaoqM2tv9riJCbY14LJ2jlMFVCUIfwoYm0xc8kVdXR3jx49vETZ+/Hjvjutylj+PwyXLu0tkSHFxMd/97ndZvHhx8wOHzj33XO+O63JaSUkJM2fObP7OlpSUeFWVa8MzjgwpKSnh+uuv5/rrr2f06NGsXr2a2bNnM3369GxHzbl23X333dxwww3N31mvpnKJeMaRIf6IU5dvCgsL6WQtI3MAABZgSURBVNGjB1dddRV79+6lZ8+eFBQU+Ay5rg0fFpohdXV1HH/88S3Cjj/+eG/jcDlr37597Nmzh/379wOwf/9+9uzZw759+7IcM5drvMSRIUOGDGHWrFltngA4ZMiQbEfNuYQKCgooKChg//797Nu3jx49elBYWOgZh2vDM44M8ofiuHzS2NjI/v37+eEPf9jcxvGtb32ruQTiXBPPODJkw4YNLFiwoEUPleuvv54pU6ZkO2rOteuUU05hzpw57N69m969e3PKKafwxBNPZDtaLsd4G0eGFBcXM2zYsBbTqg8bNsy747qc9sQTTzRPdNjY2OiZhkvISxwZUl5ezoUXXkhRUVHzJIc7duzgpptuynbUnEtIEmbG4YcfzpYtW5r/ehWra81LHAdBMhP2OZdtZkZRURFHHHEEPXr04IgjjqCoqMi/v64NzzgypKKigmnTplFUVIQkioqKmDZtms9V5XLaZZdd1mJG58suSzh7kDvEeVVVhqxevZo333yz+Z9wx44dzJ8/n7feeivLMXMuscLCQm677Tbuueee5i7k559/vj/IybXh34gMKSgoYN++fS0mjDv//PMpKCjIdtScS2j69OncfPPNnHbaac3f2f3793upw7XhGUeGNDY28s4773DGGWe0mL6h9aM5ncsVH//4x7n99tvZtWtX8wDAvn378vGPfzzbUXM5xts4MmjXrl0MGDAASQwYMIBdu3ZlO0rOtauiooLLL7+c4447jh49enDcccdx+eWXe7uca8NLHBnUs2dPNm/ejJmxefNmevbsyd69e7MdLecSWr16Na+88krzD5wXX3yRV155hd27d2c5Zi7XeIkjg/bu3Uu/fv2QRL9+/TzTcDlv165dXHrppTzwwANceumlXkp2CXmJI4NOPPFEdu/ezbZt2xgyZAgjR47kmWeeyXa0nEvIzJp7Vt1yyy307NmTwsJCb5dzbRxwiUPS8ZKei722S/q6pGslrY+Ffya2z9WS1kj6i6QzYuFnhrA1kq7q6kXliueff56pU6fy0EMPMXXqVJ5//vlsR8m5DjVNdAjRtOqeabhEDrjEYWZ/AU4AkFQArAfuBb4M/NjMfhTfXtJoYBIwBhgC/FbScWH1zcBpQD3wpKT7zWz1gcbtYOlsKobGxkauvPLKlPbzUbou2wYOHMimTZua/zrXWrraOCYCr5jZug62OQe408x2m9laYA1wcnitMbNXzWwPcGfYNueZWbuvGTNmUFhYyI033sjw/7iHG2+8kcLCQmbMmNHhfs5lW1Nm4ZmGa0+62jgmAfEn2s+QdAnwFHClmW0BhgIrY9vUhzCAN1qFn5KmeGXNvHnzAJqnqJ7TuzfTp09vDnfOuXzV5YxDUi/gbODqEHQLcB1g4e+NwNSuniecaxowDWDQoEHU1tam47AZ84UvfIEvfOELTHl0BwvOjKYeyfU45xtPz/S79NJL+dSnPsXy5cu55ZZbAE/ndGloaOgWaZmOEsdZwDNmtgmg6S+ApFuBB8Pb9cDw2H7DQhgdhLdgZvOB+QDjxo2zCRMmpCH6B8GjD5E3cc0nnq5p16dPnxa9qvr06cOuXbs8ndOktra2W6RlOto4SolVU0kaHFt3HrAqLN8PTJLUW9JIYBTwJ+BJYJSkkaH0Mils65w7iAoLCykoKGDo0KH06NGDoUOHUlBQ4JMcuja69I2QVETUG+qrseAbJJ1AVFX1WtM6M3tR0t3AaqARuMzM9oXjzACWAAVAlZm92JV4OecSS6Yn4I4dOwB47bXXktrPO3UcerpU4jCzHWb2HjPbFgv7kpl92Mz+yczONrONsXUVZvYBMzvezB6JhT9sZseFdT4xjnMZ0lGPvqbegL179wagd+/enfYC9Ezj0ORTjjjnms2bN49du3Zx7OwH2bVrl/cCdAl5xuGccy4lnnE455xLiWcczjnnUuIZh3POuZR4xuGccxlWXV3N2LFjmThxImPHjqW6urrznXKYj+xxzrkMqq6upry8nMrKSvbt20dBQQFlZWUAlJaWZjl2B8ZLHM45l0EVFRVUVlZSUlJCYWEhJSUlVFZW5vWz3D3jcM65DKqrq2P8+PEtwsaPH09dXV2WYtR1nnE451wGFRcXs2LFihZhK1asoLi4OEsx6jpv43DOuQwqLy/nwgsvpKioiHXr1nHssceyY8cObrrppmxH7YB5icM55w6SziaZzBeecTjnXAZVVFRw1113sXbtWpYtW8batWu56667vHHcOedcYt447pxzLiXdsXHcMw7nnMug8vJyysrKqKmpobGxkZqaGsrKyigvL8921A6Y96pyzrkMahodPnPmTOrq6iguLqaioiJvR42DZxzOOZdxpaWllJaWUltby4QJE7IdnS7rclWVpNckvSDpOUlPhbABkpZKejn8PSqES9JPJK2R9LykE2PHmRy2f1nS5K7GyznnXGakq42jxMxOMLNx4f1VwDIzGwUsC+8BzgJGhdc04BaIMhrgGuAU4GTgmqbMxjnnXG7JVFXVOcCEsHw7UAvMDuELLXrC/UpJR0oaHLZdamabASQtBc4E8nvuYefcISPdg/ui22RuSkfGYcBjkgz4hZnNBwaZ2caw/m/AoLA8FHgjtm99CGsv3HUz//zdx9i2c2/ajjfiqofSdqwjDuvJn685PW3Hc4eWZG70I656iNfmfvYgxCaz0pFxjDez9ZKOBpZKeim+0swsZCpdJmkaURUXgwYNora2Nh2HPSjyKa6ZtG3nXhacWZSWYzU0NNCvX7+0HAtgyqM7/HOK8bTIjO6Qrl3OOMxsffj7pqR7idooNkkabGYbQ1XUm2Hz9cDw2O7DQth63q3aagqvTXCu+cB8gHHjxlne9E549KFu0ZMiLdKYFmnvoeKf07s8LTKjm6RrlxrHJRVJ6t+0DJwOrALuB5p6Rk0G7gvL9wOXhN5VHwW2hSqtJcDpko4KjeKnhzDnnHM5pqsljkHAvaFRqBBYZGaPSnoSuFtSGbAOuCBs/zDwGWAN8A7wZQAz2yzpOuDJsN33mhrKnXPO5ZYuZRxm9irwzwnC3wYmJgg34LJ2jlUFVHUlPs455zLP56pyzjmXEs84nHPOpcQzDueccynxjMM551xKPONwzjmXEs84nHPOpcSfx9GOXJ1TyedTcs5lm2cc7di2c2/aJiNL59QY6ZzUzznnDoRnHM51A+kuIYOXkl37PONwrhtIZwkZvJTsOuaN484551LiGYdzzrmUeMbhnHMuJZ5xOOecS4lnHM4551LiGYdzzrmUeMbhnHMuJT6OwznnOpCr0w9B9gZXHnDGIWk4sJDoueMGzDezmyRdC3wF+HvYdI6ZPRz2uRooA/YBl5vZkhB+JnATUADcZmZzDzRezjmXTrk6/RBkb3BlV0ocjcCVZvaMpP7A05KWhnU/NrMfxTeWNBqYBIwBhgC/lXRcWH0zcBpQDzwp6X4zW92FuDnnnMuQA844zGwjsDEs/0NSHTC0g13OAe40s93AWklrgJPDujVm9iqApDvDtp5xOOdcDkpLG4ekEcBHgD8CpwIzJF0CPEVUKtlClKmsjO1Wz7sZzRutwk9JR7xc7ulffBUfvv2q9B3w9vQdqn8xQPrme3Kuu+pyxiGpH/Br4Otmtl3SLcB1RO0e1wE3AlO7ep5wrmnANIBBgwZRW1ubjsO2K13Hb2hoSGtcM33dmfSPurksOLMoLcdqaGigX79+aTkWwJRHd+R12qYz7v6dbSlX7wWQnbTtUsYhqSdRpnGHmf0GwMw2xdbfCjwY3q4Hhsd2HxbC6CC8BTObD8wHGDdunKWzkam1/us+zMx1aTzg2+k5TP9imDDhhfQcLBsefShtjYPpbmhMZ9wOujTHPa1pm8/pCv6dTaArvaoEVAJ1ZvZfsfDBof0D4DxgVVi+H1gk6b+IGsdHAX8CBIySNJIow5gEXHSg8UqXf9TNzcmeFD5FtXMu27pS4jgV+BLwgqTnQtgcoFTSCURVVa8BXwUwsxcl3U3U6N0IXGZm+wAkzQCWEHXHrTKzF7sQL+cOOWlvO4K0tR9521H305VeVSuISgutPdzBPhVARYLwhzvazznXsXSWkMFLya5jPuWIc865lHjG4ZxzLiU+V5VzznXAxx615RmHc851IFd7WEL22o+8qso551xKPONwzjmXEs84nHPOpcTbODqQ1vrDR9NzrCMO65mW42RTLqYr5H/apr2+27+zrh0ys2zH4YCMGzfOnnrqqWxHIykjrnoorYOzXMTTNXM8bd+VywMYM/kEQElPm9m4ROu8xOGccx1IZwbaXTJkb+NwzjmXEs84nHPOpcQzDueccynxjMM551xKPONwzjmXEs84nHPOpcQzDueccynxjMM551xKcmYAoKQzgZuInjt+m5nNzXKUOiUlenJuO9ten9x2+TqS37lDXbL3g+5wL8iJjENSAXAzcBpQDzwp6X4zW53dmHUs2Q823XPwHwoOpX/Cg8l/7GROMunQXe4FuVJVdTKwxsxeNbM9wJ3AOVmOk8siM+v0VVNTk9R2fmN7V7Lp5WnrOpIrGcdQ4I3Y+/oQ5pxzLsfkRFVVsiRNA6YBDBo0iNra2uxGKEkNDQ15E9d84umaOZ62mdFd0jVXMo71wPDY+2EhrAUzmw/Mh2ha9XypK+wu9Zq5xtM1czxtM6O7pGuuVFU9CYySNFJSL2AScH+W4+Sccy6BnChxmFmjpBnAEqLuuFVm9mKWo+Wccy6BnMg4AMzsYeDhbMfDOedcx3Klqso551ye8IzDOedcSpSvA3gk/R1Yl+14JGkg8Fa2I9ENebpmjqdtZuRTuh5rZu9NtCJvM458IukpMxuX7Xh0N56umeNpmxndJV29qso551xKPONwzjmXEs84Do752Y5AN+XpmjmetpnRLdLV2zicc86lxEsczjnnUuIZRxdIagh/h0i6JyyfIOkzsW0mSPp47P21kr558GOb/yS9Jmlgmo41RdKQdBzLufZIeljSkR2sXyDp/IMZp3TwjCMNzGyDmTV9+CcAn4mtngB8vM1OLuPCkyXbMwXwjKMdkr4n6dOdbNNb0m8lPSfpwoMVt3yh6HGLnzOzrdmOS7p5xpEGkkZIWhVm9v0ecGH4Z5oNTAf+I7z/RKv9PiDpUUlPS/q9pA9lI/6ZFNLmpfDL6q+S7pD0aUn/T9LLkk6WNEDSYknPS1op6Z/Cvu+R9JikFyXdBih23C9K+lNI1180ZRKSGiTdKOnPwMckfUfSk+Hzma/I+cA44I6w/2GSTpL0ePgslkganI30yhVm9h0z+20nm30kbHuCmd11EKKV88L3/S+SFgKrgH1NpWRJl4Tv+J8l/W9st09K+oOkV/Om9JHs4yH9lfCRmQ3h7whgVVieAvw0ts21wDcTvQeWAaPC8inA8mxfUwbSaATQCHyY6IfK00AVUSZwDrAYmAdcE7b/FPBcWP4J8J2w/FnAiEbeFgMPAD3Dup8Bl4RlAy6InX9AbPl/gc+H5VpgXFjuCfwBeG94fyHRDM1ZT78upHkdcCvwIvAYcBhRaXgl8DxwL3BUB8dYAJwfll8Dvgs8A7wAfAg4GlgDbAOeAz4ATASeDdtUAb2znRZZSvv9wEdjaTcQGAP8FRgY/16GdP5V+N8YTfQI7axfR2cvL3FkiaR+RFVYv5L0HPALoLv+yl1rZi+Y2X6iG9kyi/5rXiD6RxtPdFPHzJYD75F0OPBJ4Jch/CFgSzjeROAk4MmQdhOB94d1+4Bfx85dIumPkl4gypTGJIjf8cBYYGk43reJHiaWz0YBN5vZGGAr8AVgITDbzP6JKO2vSeF4b5nZicAtRD983gT+Hfi9mZ1A9OC1BcCFZvZhopm3L03XxeSZdWa2slXYp4BfmdlbAGa2ObZusZntN7PVwKCDFcmuyJlp1Q9BPYCt4Z+uu9sdW94fe7+f6Du4N8XjCbjdzK5OsG6Xme0DkNSHqDQyzszekHQt0Ked471oZh9LMR65bK2ZPReWnyYqERxpZo+HsNuJfukm6zexY/1rgvXHh3P+NXb8y4D/TinW3cOOFLeP/3+o3a1yiJc40u8fQP8O3gNgZtuBtZL+DaKGNEn/fHCimHN+D1wMUS80ol+324HfAReF8LOAo8L2y4DzJR0d1g2QdGyC4zZlEm+FEl68/jj+ufwFeK+kj4Xj9ZSUqGSST+I3o31Auz17UjzePvwH54FYDvybpPdA9J3Ncny6xDOO9KsBRsd6mjwAnJeocZzoZlkWGnJfJKrzPxRdC5wk6XlgLjA5hH+XqOHwRaJfua8DhCL9t4HHwj5LSVDNZ1FvlluJGimXED2iuMkC4OehaqqAKFO5PnwWz9H9esJtA7bEvoNfAh7vYPtU/QUYIemDGTp+XrPoiaYVwOPhO/ZfWY5Sl/jIcee6GUkjgAfNbGx4/02gH1FHhJ8DfYFXgS+b2ZZ2jrEgHOMeSa8RVfe9JWkc8CMzmxBKh980s8+FfSYCPyIqkTwJXGpmuxMd3+U3zzicc86lxKuqnHPOpcQbuZw7hEm6GTi1VfBNZvY/2YiPyw9eVeWccy4lXlXlnHMuJZ5xOOecS4lnHM4551LiGYfrNtL1bIP2jiPpNkmju3r8XKVWz5Jxrj2ecTiXJDP79zBqvbtq/SwZ5xLyjMPlrXaeb5Dw2QaSvhWey/G8pO92coz4Oa4LJZACSbVh5HTTcz8qwn4rJQ0K4R8I71+Q9H2Fp0S2E/9+kpZJeiZsf05sXZt4SRok6d4Q9meFJ0tK+kZ43sgqSV8PYSMkrYod75thkkfCdVyv6Hkmf5X0CbV9low/mMm1y8dxuLwUJiH8NvDxMBXGAKL5fwYTTdP+IeB+4B5JpxNNM34y0eyj90v6JPB2gmPEz/FDookQv2xmJrWYuLQIWGlm5ZJuAL4CfB+4iWgcRLWk6Z1cxi7gPDPbruhhPysl3U/0XIZE8foJ8LiZnafowVX9JJ0EfJnoeS4C/ijpcd6dgr49hWZ2cqiausbMPi3pO0RTi8zoZF93iPMSh8tX7T3fINGzDU4Pr2eJHkb0IaKMpKNnJPwncISZTbfEg532AA+G5aeJnisC8DHena58USfXIOAHYaLG3wJDQ5zbi9eniJ6HgZntM7NtRJnkvWa2w8waiKY/bz2ZZiLxadJHdLCdc214icN1N4mebSDg/5rZL+IbSprZwXGeJJqxd0CrDKXJ3liGcqBTjV8MvBc4ycz2hskEEz0v5EA00vKHYevj+jTp7oB5icPlq1Seb7AEmBqeyYGkoeFZHh0d41GiKd4fktTmeSodWEn0tD2ASZ1sewTwZsg0SoCmZ4q0F69lhKfqhTaXI4ieZXKupL6SioDzQtgm4GhFz23vDXwuibgnfHaMc615xuHyUirPNzCzx4iqjZ5Q9AjZe4D+nR3DzH5F9DyP+yUdlmTUvg58I1Q/fZDoORjtuQMYF+J0CfBSJ9d2BdGjcF8gqmIabWbPED1b5E/AH4HbzOxZM9tL1Nj9J6LnlbyURNxbP0vGuYR8rirn0khSX2BnaEyfBJSa2aH6gC7XTXndpnPpdRLwU0VdsLYCU7McH+fSzksczmWYpA8DrceI7DazU7IRH+e6yjMO55xzKfHGceeccynxjMM551xKPONwzjmXEs84nHPOpcQzDueccyn5/3r6B5ZjGOhcAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bWHDK-Ax-bSs",
        "outputId": "98a88f56-9efa-4eb4-a572-132977f1a336"
      },
      "source": [
        "df['credit_amount'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     1000.000000\n",
              "mean      3271.258000\n",
              "std       2822.736876\n",
              "min        250.000000\n",
              "25%       1365.500000\n",
              "50%       2319.500000\n",
              "75%       3972.250000\n",
              "max      18424.000000\n",
              "Name: credit_amount, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_-p7XGgUeN5j"
      },
      "source": [
        "# Скаттер"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GoEfmW17eTfz"
      },
      "source": [
        "## Скаттер плот"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "VMiSQDvv-5MV",
        "outputId": "e5518d5b-257f-49a1-c155-3b16f9d321e1"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>job</th>\n",
              "      <th>housing</th>\n",
              "      <th>saving_accounts</th>\n",
              "      <th>checking_account</th>\n",
              "      <th>credit_amount</th>\n",
              "      <th>duration</th>\n",
              "      <th>purpose</th>\n",
              "      <th>default</th>\n",
              "      <th>credit_amount_group</th>\n",
              "      <th>credit_amount_group2</th>\n",
              "      <th>credit_amount_group3</th>\n",
              "      <th>credit_amount_group4</th>\n",
              "      <th>credit_amount_group5</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>NaN</td>\n",
              "      <td>little</td>\n",
              "      <td>1169</td>\n",
              "      <td>6</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>0</td>\n",
              "      <td>(231.826, 3884.8]</td>\n",
              "      <td>(249.999, 1262.0]</td>\n",
              "      <td>(1000.0, 2000.0]</td>\n",
              "      <td>1k+</td>\n",
              "      <td>(249.999, 1262.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>22</td>\n",
              "      <td>female</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>moderate</td>\n",
              "      <td>5951</td>\n",
              "      <td>48</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>1</td>\n",
              "      <td>(3884.8, 7519.6]</td>\n",
              "      <td>(4720.0, 18424.0]</td>\n",
              "      <td>(5000.0, inf]</td>\n",
              "      <td>1k+</td>\n",
              "      <td>(4720.0, 18424.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>49</td>\n",
              "      <td>male</td>\n",
              "      <td>1</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>no_info</td>\n",
              "      <td>2096</td>\n",
              "      <td>12</td>\n",
              "      <td>education</td>\n",
              "      <td>0</td>\n",
              "      <td>(231.826, 3884.8]</td>\n",
              "      <td>(1906.8, 2852.4]</td>\n",
              "      <td>(2000.0, 3000.0]</td>\n",
              "      <td>1k+</td>\n",
              "      <td>(1906.8, 2852.4]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>45</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>7882</td>\n",
              "      <td>42</td>\n",
              "      <td>furniture/equipment</td>\n",
              "      <td>0</td>\n",
              "      <td>(7519.6, 11154.4]</td>\n",
              "      <td>(4720.0, 18424.0]</td>\n",
              "      <td>(5000.0, inf]</td>\n",
              "      <td>1k+</td>\n",
              "      <td>(4720.0, 18424.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>53</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>4870</td>\n",
              "      <td>24</td>\n",
              "      <td>car</td>\n",
              "      <td>1</td>\n",
              "      <td>(3884.8, 7519.6]</td>\n",
              "      <td>(4720.0, 18424.0]</td>\n",
              "      <td>(3000.0, 5000.0]</td>\n",
              "      <td>1k+</td>\n",
              "      <td>(4720.0, 18424.0]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age     sex  ...  credit_amount_group4 credit_amount_group5\n",
              "0   67    male  ...                   1k+    (249.999, 1262.0]\n",
              "1   22  female  ...                   1k+    (4720.0, 18424.0]\n",
              "2   49    male  ...                   1k+     (1906.8, 2852.4]\n",
              "3   45    male  ...                   1k+    (4720.0, 18424.0]\n",
              "4   53    male  ...                   1k+    (4720.0, 18424.0]\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "8YUPeyncXRbV",
        "outputId": "1d79954b-e910-4dbb-bf0a-22584d4101bb"
      },
      "source": [
        "df.plot(x='age',y='credit_amount',kind='scatter',figsize=(10,5),alpha=0.3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a9242eed0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UVm0M_khYsus"
      },
      "source": [
        "df['age_group'] = pd.qcut(df['age'],5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "_YBydnzNZOoU",
        "outputId": "afb1640f-94e6-4fb4-bad9-f43ec127816e"
      },
      "source": [
        "df.groupby('age_group')['credit_amount'].agg(['count','mean','median'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age_group</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>(18.999, 26.0]</th>\n",
              "      <td>240</td>\n",
              "      <td>2987.645833</td>\n",
              "      <td>2112.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(26.0, 30.0]</th>\n",
              "      <td>171</td>\n",
              "      <td>3436.578947</td>\n",
              "      <td>2463.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(30.0, 36.0]</th>\n",
              "      <td>216</td>\n",
              "      <td>3240.439815</td>\n",
              "      <td>2427.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(36.0, 45.0]</th>\n",
              "      <td>187</td>\n",
              "      <td>3618.401070</td>\n",
              "      <td>2522.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(45.0, 75.0]</th>\n",
              "      <td>186</td>\n",
              "      <td>3172.000000</td>\n",
              "      <td>2064.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                count         mean  median\n",
              "age_group                                 \n",
              "(18.999, 26.0]    240  2987.645833  2112.5\n",
              "(26.0, 30.0]      171  3436.578947  2463.0\n",
              "(30.0, 36.0]      216  3240.439815  2427.0\n",
              "(36.0, 45.0]      187  3618.401070  2522.0\n",
              "(45.0, 75.0]      186  3172.000000  2064.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "t8LhK76QZVfO",
        "outputId": "5584be7a-a245-45fd-c785-5a32e4d8500b"
      },
      "source": [
        "df.groupby('age_group')['credit_amount'].median().plot(ylim=0,grid=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a923c3750>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jo_1tYOBdILS"
      },
      "source": [
        "## Корреляция"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "id": "1i7PakYtZhuM",
        "outputId": "f0dd34ea-a7a1-4f31-f4da-c2f0a7dead0f"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>job</th>\n",
              "      <th>housing</th>\n",
              "      <th>saving_accounts</th>\n",
              "      <th>checking_account</th>\n",
              "      <th>credit_amount</th>\n",
              "      <th>duration</th>\n",
              "      <th>purpose</th>\n",
              "      <th>default</th>\n",
              "      <th>credit_amount_group</th>\n",
              "      <th>credit_amount_group2</th>\n",
              "      <th>credit_amount_group3</th>\n",
              "      <th>credit_amount_group4</th>\n",
              "      <th>credit_amount_group5</th>\n",
              "      <th>age_group</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>NaN</td>\n",
              "      <td>little</td>\n",
              "      <td>1169</td>\n",
              "      <td>6</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>0</td>\n",
              "      <td>(231.826, 3884.8]</td>\n",
              "      <td>(249.999, 1262.0]</td>\n",
              "      <td>(1000.0, 2000.0]</td>\n",
              "      <td>1k+</td>\n",
              "      <td>(249.999, 1262.0]</td>\n",
              "      <td>(45.0, 75.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>22</td>\n",
              "      <td>female</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>moderate</td>\n",
              "      <td>5951</td>\n",
              "      <td>48</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>1</td>\n",
              "      <td>(3884.8, 7519.6]</td>\n",
              "      <td>(4720.0, 18424.0]</td>\n",
              "      <td>(5000.0, inf]</td>\n",
              "      <td>1k+</td>\n",
              "      <td>(4720.0, 18424.0]</td>\n",
              "      <td>(18.999, 26.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>49</td>\n",
              "      <td>male</td>\n",
              "      <td>1</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>no_info</td>\n",
              "      <td>2096</td>\n",
              "      <td>12</td>\n",
              "      <td>education</td>\n",
              "      <td>0</td>\n",
              "      <td>(231.826, 3884.8]</td>\n",
              "      <td>(1906.8, 2852.4]</td>\n",
              "      <td>(2000.0, 3000.0]</td>\n",
              "      <td>1k+</td>\n",
              "      <td>(1906.8, 2852.4]</td>\n",
              "      <td>(45.0, 75.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>45</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>7882</td>\n",
              "      <td>42</td>\n",
              "      <td>furniture/equipment</td>\n",
              "      <td>0</td>\n",
              "      <td>(7519.6, 11154.4]</td>\n",
              "      <td>(4720.0, 18424.0]</td>\n",
              "      <td>(5000.0, inf]</td>\n",
              "      <td>1k+</td>\n",
              "      <td>(4720.0, 18424.0]</td>\n",
              "      <td>(36.0, 45.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>53</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>4870</td>\n",
              "      <td>24</td>\n",
              "      <td>car</td>\n",
              "      <td>1</td>\n",
              "      <td>(3884.8, 7519.6]</td>\n",
              "      <td>(4720.0, 18424.0]</td>\n",
              "      <td>(3000.0, 5000.0]</td>\n",
              "      <td>1k+</td>\n",
              "      <td>(4720.0, 18424.0]</td>\n",
              "      <td>(45.0, 75.0]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age     sex  job  ... credit_amount_group4 credit_amount_group5       age_group\n",
              "0   67    male    2  ...                  1k+    (249.999, 1262.0]    (45.0, 75.0]\n",
              "1   22  female    2  ...                  1k+    (4720.0, 18424.0]  (18.999, 26.0]\n",
              "2   49    male    1  ...                  1k+     (1906.8, 2852.4]    (45.0, 75.0]\n",
              "3   45    male    2  ...                  1k+    (4720.0, 18424.0]    (36.0, 45.0]\n",
              "4   53    male    2  ...                  1k+    (4720.0, 18424.0]    (45.0, 75.0]\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "id": "zZdmvl21aCLJ",
        "outputId": "b3667b70-5150-4800-b767-39c91bb9ae05"
      },
      "source": [
        "df[['age','credit_amount']].corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>credit_amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.032716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>credit_amount</th>\n",
              "      <td>0.032716</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    age  credit_amount\n",
              "age            1.000000       0.032716\n",
              "credit_amount  0.032716       1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tVXkuiGJaZmx"
      },
      "source": [
        "t = pd.DataFrame({'x':range(10)})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R5he2vs6apBX"
      },
      "source": [
        "t['y'] = t['x'] + 7"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "Yob-bxs5apa-",
        "outputId": "4b00a96e-0e28-4fe4-e5a5-117467e4fb8f"
      },
      "source": [
        "t.plot(x='x',y='y',kind='scatter')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a9233ba90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "id": "p5cjCvAmax2u",
        "outputId": "f5cea98f-d42d-4dc6-c254-6ff3db15c6c5"
      },
      "source": [
        "t.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>x</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>x</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>y</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     x    y\n",
              "x  1.0  1.0\n",
              "y  1.0  1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BZaPPud_a0ns"
      },
      "source": [
        "t['y'] = -t['x'] - 7"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "KK2OBZw8a_Nh",
        "outputId": "87b4a161-62b7-436e-d77f-75e7341b0314"
      },
      "source": [
        "t.plot(x='x',y='y',kind='scatter')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a922ff6d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "id": "2SMN6lk4bAv4",
        "outputId": "0c753039-2ee1-447a-9128-77305ee8d542"
      },
      "source": [
        "t.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>x</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>y</th>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     x    y\n",
              "x  1.0 -1.0\n",
              "y -1.0  1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f6eYmU6QbCkS"
      },
      "source": [
        "t = pd.DataFrame({'x':np.linspace(-10,10,100)})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U9ywNc9UbJrD"
      },
      "source": [
        "t['y'] = t['x'] ** 2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "FAiXXNaFbMgK",
        "outputId": "fe35048e-ad45-44de-ba84-25a824aa9467"
      },
      "source": [
        "t.plot(x='x',y='y',kind='scatter')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a92414590>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "id": "jy-psaFhbRgO",
        "outputId": "35f4f10d-1942-41c6-b8d6-d4b5eff82870"
      },
      "source": [
        "t.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>x</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>3.205184e-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>y</th>\n",
              "      <td>3.205184e-17</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              x             y\n",
              "x  1.000000e+00  3.205184e-17\n",
              "y  3.205184e-17  1.000000e+00"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Flz6QcmKbW6X"
      },
      "source": [
        "# Пример анализа"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mWtbTdQots5J"
      },
      "source": [
        "## Предобработка"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tlmC4WRNt7uR"
      },
      "source": [
        "Age (numeric)  \n",
        "Sex (text: male, female)  \n",
        "Job (numeric: 0 - unskilled and non-resident, 1 - unskilled and resident, 2 - skilled, 3 - highly skilled)  \n",
        "Housing (text: own, rent, or free)  \n",
        "Saving accounts (text - little, moderate, quite rich, rich)  \n",
        "Checking account (numeric, in DM - Deutsch Mark)  \n",
        "Credit amount (numeric, in DM)  \n",
        "Duration (numeric, in month)  \n",
        "Purpose (text: car, furniture/equipment, radio/TV, domestic appliances, repairs, education, business, vacation/others)  \n",
        "default (1 - bad, 0 - good)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9FStnqPpuNIZ"
      },
      "source": [
        "Задание: проанализировать от чего зависит default"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6FoB8pfjrtMN"
      },
      "source": [
        "df = pd.read_csv('/content/german-credit.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "id": "MsiKW51VsuNu",
        "outputId": "6dbd2677-e098-42e1-b195-b2eebe153e20"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Job</th>\n",
              "      <th>Housing</th>\n",
              "      <th>Saving accounts</th>\n",
              "      <th>Checking account</th>\n",
              "      <th>Credit amount</th>\n",
              "      <th>Duration</th>\n",
              "      <th>Purpose</th>\n",
              "      <th>default</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>67</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>NaN</td>\n",
              "      <td>little</td>\n",
              "      <td>1169</td>\n",
              "      <td>6</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>22</td>\n",
              "      <td>female</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>moderate</td>\n",
              "      <td>5951</td>\n",
              "      <td>48</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>49</td>\n",
              "      <td>male</td>\n",
              "      <td>1</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2096</td>\n",
              "      <td>12</td>\n",
              "      <td>education</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>45</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>7882</td>\n",
              "      <td>42</td>\n",
              "      <td>furniture/equipment</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>53</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>4870</td>\n",
              "      <td>24</td>\n",
              "      <td>car</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  Age     Sex  ...  Duration              Purpose default\n",
              "0           0   67    male  ...         6             radio/TV       0\n",
              "1           1   22  female  ...        48             radio/TV       1\n",
              "2           2   49    male  ...        12            education       0\n",
              "3           3   45    male  ...        42  furniture/equipment       0\n",
              "4           4   53    male  ...        24                  car       1\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MDXET6hzs95B"
      },
      "source": [
        "df.drop('Unnamed: 0',axis=1,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wEfYygzktC1R"
      },
      "source": [
        "df.columns = df.columns.str.lower().str.replace(' ','_')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "spLfziSQtDhb",
        "outputId": "b9e7260b-3c5a-4a29-d197-54e077e0c587"
      },
      "source": [
        "df.isna().mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                 0.000\n",
              "sex                 0.000\n",
              "job                 0.000\n",
              "housing             0.000\n",
              "saving_accounts     0.183\n",
              "checking_account    0.394\n",
              "credit_amount       0.000\n",
              "duration            0.000\n",
              "purpose             0.000\n",
              "default             0.000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sxHOKIgVtnc3"
      },
      "source": [
        "Сделаю техническую замену пропусков"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AgU9-tGhtO3u"
      },
      "source": [
        "df['saving_accounts'].fillna('no_info',inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wCIz5xNBtgWi"
      },
      "source": [
        "df['checking_account'].fillna('no_info',inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GZv7zwUZtjz1",
        "outputId": "c6bb7368-9172-401b-9375-0c1ccfc64d1f"
      },
      "source": [
        "df.isna().mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                 0.0\n",
              "sex                 0.0\n",
              "job                 0.0\n",
              "housing             0.0\n",
              "saving_accounts     0.0\n",
              "checking_account    0.0\n",
              "credit_amount       0.0\n",
              "duration            0.0\n",
              "purpose             0.0\n",
              "default             0.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O-X9CX50tkye"
      },
      "source": [
        "## Анализ"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "eca7MYJ7tyej",
        "outputId": "4bbd20af-e0b1-497d-dcd4-87b95fa7a7ae"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>job</th>\n",
              "      <th>housing</th>\n",
              "      <th>saving_accounts</th>\n",
              "      <th>checking_account</th>\n",
              "      <th>credit_amount</th>\n",
              "      <th>duration</th>\n",
              "      <th>purpose</th>\n",
              "      <th>default</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>no_info</td>\n",
              "      <td>little</td>\n",
              "      <td>1169</td>\n",
              "      <td>6</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>22</td>\n",
              "      <td>female</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>moderate</td>\n",
              "      <td>5951</td>\n",
              "      <td>48</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>49</td>\n",
              "      <td>male</td>\n",
              "      <td>1</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>no_info</td>\n",
              "      <td>2096</td>\n",
              "      <td>12</td>\n",
              "      <td>education</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>45</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>7882</td>\n",
              "      <td>42</td>\n",
              "      <td>furniture/equipment</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>53</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>4870</td>\n",
              "      <td>24</td>\n",
              "      <td>car</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age     sex  job  ... duration              purpose default\n",
              "0   67    male    2  ...        6             radio/TV       0\n",
              "1   22  female    2  ...       48             radio/TV       1\n",
              "2   49    male    1  ...       12            education       0\n",
              "3   45    male    2  ...       42  furniture/equipment       0\n",
              "4   53    male    2  ...       24                  car       1\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9p8JiD7KucKH"
      },
      "source": [
        "df['age_group'] = pd.qcut(df['age'],5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "f3A5OKhNtzff",
        "outputId": "f70224d8-b112-47e2-da33-eed9928d7a37"
      },
      "source": [
        "df.groupby('age_group')['default'].agg(['count','mean'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age_group</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>(18.999, 26.0]</th>\n",
              "      <td>240</td>\n",
              "      <td>0.391667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(26.0, 30.0]</th>\n",
              "      <td>171</td>\n",
              "      <td>0.315789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(30.0, 36.0]</th>\n",
              "      <td>216</td>\n",
              "      <td>0.259259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(36.0, 45.0]</th>\n",
              "      <td>187</td>\n",
              "      <td>0.262032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(45.0, 75.0]</th>\n",
              "      <td>186</td>\n",
              "      <td>0.252688</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                count      mean\n",
              "age_group                      \n",
              "(18.999, 26.0]    240  0.391667\n",
              "(26.0, 30.0]      171  0.315789\n",
              "(30.0, 36.0]      216  0.259259\n",
              "(36.0, 45.0]      187  0.262032\n",
              "(45.0, 75.0]      186  0.252688"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "x8cQLKVquZzc",
        "outputId": "08fb99ce-748c-4ef7-a558-5f44abcf0a3e"
      },
      "source": [
        "df.groupby('age_group')['default'].mean().plot(ylim=0,grid=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a8d1effd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "41AVkseEuoYB"
      },
      "source": [
        "С увеличением возраста процент дефолтников снижается!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "id": "rX6fvnmQuz0M",
        "outputId": "9bfeac21-6168-462c-bd92-16ad2ba9422e"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>job</th>\n",
              "      <th>housing</th>\n",
              "      <th>saving_accounts</th>\n",
              "      <th>checking_account</th>\n",
              "      <th>credit_amount</th>\n",
              "      <th>duration</th>\n",
              "      <th>purpose</th>\n",
              "      <th>default</th>\n",
              "      <th>age_group</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>no_info</td>\n",
              "      <td>little</td>\n",
              "      <td>1169</td>\n",
              "      <td>6</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>0</td>\n",
              "      <td>(45.0, 75.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>22</td>\n",
              "      <td>female</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>moderate</td>\n",
              "      <td>5951</td>\n",
              "      <td>48</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>1</td>\n",
              "      <td>(18.999, 26.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>49</td>\n",
              "      <td>male</td>\n",
              "      <td>1</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>no_info</td>\n",
              "      <td>2096</td>\n",
              "      <td>12</td>\n",
              "      <td>education</td>\n",
              "      <td>0</td>\n",
              "      <td>(45.0, 75.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>45</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>7882</td>\n",
              "      <td>42</td>\n",
              "      <td>furniture/equipment</td>\n",
              "      <td>0</td>\n",
              "      <td>(36.0, 45.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>53</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>4870</td>\n",
              "      <td>24</td>\n",
              "      <td>car</td>\n",
              "      <td>1</td>\n",
              "      <td>(45.0, 75.0]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age     sex  job  ...              purpose default       age_group\n",
              "0   67    male    2  ...             radio/TV       0    (45.0, 75.0]\n",
              "1   22  female    2  ...             radio/TV       1  (18.999, 26.0]\n",
              "2   49    male    1  ...            education       0    (45.0, 75.0]\n",
              "3   45    male    2  ...  furniture/equipment       0    (36.0, 45.0]\n",
              "4   53    male    2  ...                  car       1    (45.0, 75.0]\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "id": "YWojAFOHu9Jt",
        "outputId": "d014581d-4d6c-467d-8bb7-edb42297eaaa"
      },
      "source": [
        "df.groupby('sex')['default'].agg(['count','mean'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>310</td>\n",
              "      <td>0.351613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>690</td>\n",
              "      <td>0.276812</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        count      mean\n",
              "sex                    \n",
              "female    310  0.351613\n",
              "male      690  0.276812"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x9UA1A8LvhuS"
      },
      "source": [
        "Выборка перекошена -- мужчин в 2 раза больше чем женщин. И среди женщин дефолта сильно больше. Возможно влияние другой переменной? Возраст?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "id": "6jElLJ8ivAs9",
        "outputId": "fa2fc697-7140-4364-f520-96c18fd0ea23"
      },
      "source": [
        "df.groupby('sex')['age'].agg(['count','mean','median'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>310</td>\n",
              "      <td>32.803226</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>690</td>\n",
              "      <td>36.778261</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        count       mean  median\n",
              "sex                             \n",
              "female    310  32.803226      29\n",
              "male      690  36.778261      35"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-8CuSTZpvOrd"
      },
      "source": [
        "Действительно! Женщины в этой выборке моложе! Посмотрим в разрезе пола и возраста"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FHdMXr8ev-og"
      },
      "source": [
        "df['age_group2'] = pd.qcut(df['age'],3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "_-7fT48DwJFF",
        "outputId": "ddbbc6aa-584e-409d-bd10-0286c8d51ad6"
      },
      "source": [
        "df.groupby(['age_group2','sex'])['default'].agg(['count','mean'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age_group2</th>\n",
              "      <th>sex</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">(18.999, 28.0]</th>\n",
              "      <th>female</th>\n",
              "      <td>154</td>\n",
              "      <td>0.409091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>180</td>\n",
              "      <td>0.327778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">(28.0, 38.0]</th>\n",
              "      <th>female</th>\n",
              "      <td>83</td>\n",
              "      <td>0.385542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>263</td>\n",
              "      <td>0.235741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">(38.0, 75.0]</th>\n",
              "      <th>female</th>\n",
              "      <td>73</td>\n",
              "      <td>0.191781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>247</td>\n",
              "      <td>0.283401</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       count      mean\n",
              "age_group2     sex                    \n",
              "(18.999, 28.0] female    154  0.409091\n",
              "               male      180  0.327778\n",
              "(28.0, 38.0]   female     83  0.385542\n",
              "               male      263  0.235741\n",
              "(38.0, 75.0]   female     73  0.191781\n",
              "               male      247  0.283401"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_1nF8elkwK7L"
      },
      "source": [
        "В молодом и среднем возрасте женщины хуже возвращают долг, а в более старшем наоборот: женщины возвращают долг лучше чем мужчины"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "id": "QLjIodgzwhSY",
        "outputId": "5629bc27-601f-4a5d-811a-de1ab7ec1d80"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>job</th>\n",
              "      <th>housing</th>\n",
              "      <th>saving_accounts</th>\n",
              "      <th>checking_account</th>\n",
              "      <th>credit_amount</th>\n",
              "      <th>duration</th>\n",
              "      <th>purpose</th>\n",
              "      <th>default</th>\n",
              "      <th>age_group</th>\n",
              "      <th>age_group2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>no_info</td>\n",
              "      <td>little</td>\n",
              "      <td>1169</td>\n",
              "      <td>6</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>0</td>\n",
              "      <td>(45.0, 75.0]</td>\n",
              "      <td>(38.0, 75.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>22</td>\n",
              "      <td>female</td>\n",
              "      <td>2</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>moderate</td>\n",
              "      <td>5951</td>\n",
              "      <td>48</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>1</td>\n",
              "      <td>(18.999, 26.0]</td>\n",
              "      <td>(18.999, 28.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>49</td>\n",
              "      <td>male</td>\n",
              "      <td>1</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>no_info</td>\n",
              "      <td>2096</td>\n",
              "      <td>12</td>\n",
              "      <td>education</td>\n",
              "      <td>0</td>\n",
              "      <td>(45.0, 75.0]</td>\n",
              "      <td>(38.0, 75.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>45</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>7882</td>\n",
              "      <td>42</td>\n",
              "      <td>furniture/equipment</td>\n",
              "      <td>0</td>\n",
              "      <td>(36.0, 45.0]</td>\n",
              "      <td>(38.0, 75.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>53</td>\n",
              "      <td>male</td>\n",
              "      <td>2</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>4870</td>\n",
              "      <td>24</td>\n",
              "      <td>car</td>\n",
              "      <td>1</td>\n",
              "      <td>(45.0, 75.0]</td>\n",
              "      <td>(38.0, 75.0]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age     sex  job  ... default       age_group      age_group2\n",
              "0   67    male    2  ...       0    (45.0, 75.0]    (38.0, 75.0]\n",
              "1   22  female    2  ...       1  (18.999, 26.0]  (18.999, 28.0]\n",
              "2   49    male    1  ...       0    (45.0, 75.0]    (38.0, 75.0]\n",
              "3   45    male    2  ...       0    (36.0, 45.0]    (38.0, 75.0]\n",
              "4   53    male    2  ...       1    (45.0, 75.0]    (38.0, 75.0]\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qVjiKMNgwzu3"
      },
      "source": [
        "0 - unskilled and non-resident, 1 - unskilled and resident, 2 - skilled, 3 - highly skilled"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKfohzQsw1yn"
      },
      "source": [
        "job_dict = {0:\"unskilled and non-resident\",\n",
        "            1:\"unskilled and resident\",\n",
        "            2:\"skilled\",\n",
        "            3: \"highly skilled\"}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IoYvZ0wQxAnj",
        "outputId": "b1ceb4b6-4004-449f-c799-9e3e473aa99c"
      },
      "source": [
        "job_dict"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'unskilled and non-resident',\n",
              " 1: 'unskilled and resident',\n",
              " 2: 'skilled',\n",
              " 3: 'highly skilled'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3YdYiGj9xCG4"
      },
      "source": [
        "df['job'] = df['job'].map(job_dict)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "r0JfrVHMwpcW",
        "outputId": "05602f3c-91b3-423c-d2ac-2fe8b1b02e60"
      },
      "source": [
        "df.groupby('job')['default'].agg(['count','mean'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>job</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>highly skilled</th>\n",
              "      <td>148</td>\n",
              "      <td>0.344595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>skilled</th>\n",
              "      <td>630</td>\n",
              "      <td>0.295238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unskilled and non-resident</th>\n",
              "      <td>22</td>\n",
              "      <td>0.318182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unskilled and resident</th>\n",
              "      <td>200</td>\n",
              "      <td>0.280000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            count      mean\n",
              "job                                        \n",
              "highly skilled                148  0.344595\n",
              "skilled                       630  0.295238\n",
              "unskilled and non-resident     22  0.318182\n",
              "unskilled and resident        200  0.280000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eJGnlLv2xWjU"
      },
      "source": [
        "Странно, что в skilled самый высокий дефолт. Возможно влияние другой перменной"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "-0n9_6TBxV4V",
        "outputId": "309a7476-f9f3-47db-9591-bcc620e0208a"
      },
      "source": [
        "df.groupby(['job','age_group2'])['default'].agg(['count','mean'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>job</th>\n",
              "      <th>age_group2</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">highly skilled</th>\n",
              "      <th>(18.999, 28.0]</th>\n",
              "      <td>21</td>\n",
              "      <td>0.523810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(28.0, 38.0]</th>\n",
              "      <td>68</td>\n",
              "      <td>0.220588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(38.0, 75.0]</th>\n",
              "      <td>59</td>\n",
              "      <td>0.423729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">skilled</th>\n",
              "      <th>(18.999, 28.0]</th>\n",
              "      <td>240</td>\n",
              "      <td>0.350000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(28.0, 38.0]</th>\n",
              "      <td>220</td>\n",
              "      <td>0.268182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(38.0, 75.0]</th>\n",
              "      <td>170</td>\n",
              "      <td>0.252941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">unskilled and non-resident</th>\n",
              "      <th>(18.999, 28.0]</th>\n",
              "      <td>9</td>\n",
              "      <td>0.555556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(28.0, 38.0]</th>\n",
              "      <td>4</td>\n",
              "      <td>0.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(38.0, 75.0]</th>\n",
              "      <td>9</td>\n",
              "      <td>0.111111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">unskilled and resident</th>\n",
              "      <th>(18.999, 28.0]</th>\n",
              "      <td>64</td>\n",
              "      <td>0.343750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(28.0, 38.0]</th>\n",
              "      <td>54</td>\n",
              "      <td>0.351852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(38.0, 75.0]</th>\n",
              "      <td>82</td>\n",
              "      <td>0.182927</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                           count      mean\n",
              "job                        age_group2                     \n",
              "highly skilled             (18.999, 28.0]     21  0.523810\n",
              "                           (28.0, 38.0]       68  0.220588\n",
              "                           (38.0, 75.0]       59  0.423729\n",
              "skilled                    (18.999, 28.0]    240  0.350000\n",
              "                           (28.0, 38.0]      220  0.268182\n",
              "                           (38.0, 75.0]      170  0.252941\n",
              "unskilled and non-resident (18.999, 28.0]      9  0.555556\n",
              "                           (28.0, 38.0]        4  0.250000\n",
              "                           (38.0, 75.0]        9  0.111111\n",
              "unskilled and resident     (18.999, 28.0]     64  0.343750\n",
              "                           (28.0, 38.0]       54  0.351852\n",
              "                           (38.0, 75.0]       82  0.182927"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tCYYsngdywCD"
      },
      "source": [
        "В skilled много молодых и это влияет на уровень дефолта"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "Oi8kJkacxVu_",
        "outputId": "f26ead73-843c-4932-c015-69f0098daa21"
      },
      "source": [
        "df.groupby('job')['credit_amount'].agg(['count','mean','median'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "        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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>job</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>highly skilled</th>\n",
              "      <td>148</td>\n",
              "      <td>5435.493243</td>\n",
              "      <td>4459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>skilled</th>\n",
              "      <td>630</td>\n",
              "      <td>3070.965079</td>\n",
              "      <td>2324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unskilled and non-resident</th>\n",
              "      <td>22</td>\n",
              "      <td>2745.136364</td>\n",
              "      <td>1416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unskilled and resident</th>\n",
              "      <td>200</td>\n",
              "      <td>2358.520000</td>\n",
              "      <td>1622</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            count         mean  median\n",
              "job                                                   \n",
              "highly skilled                148  5435.493243    4459\n",
              "skilled                       630  3070.965079    2324\n",
              "unskilled and non-resident     22  2745.136364    1416\n",
              "unskilled and resident        200  2358.520000    1622"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yGs1-GuSy2Xm"
      },
      "source": [
        "Но похоже, что главное влияние оказывает размер кредита! В hs и s размер кредита выше чем в unskilled. Посмотрим на влияние размера кредита на дефолт вообще"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TfMuQhSUyB8z"
      },
      "source": [
        "df['credit_amount_group'] = pd.qcut(df['credit_amount'],5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "EYZspWkaySRA",
        "outputId": "9fbb52fb-417a-4f94-e461-3296ecf280aa"
      },
      "source": [
        "df.groupby('credit_amount_group')['default'].agg(['count','mean'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "        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>count</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>credit_amount_group</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>(249.999, 1262.0]</th>\n",
              "      <td>201</td>\n",
              "      <td>0.303483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(1262.0, 1906.8]</th>\n",
              "      <td>199</td>\n",
              "      <td>0.241206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(1906.8, 2852.4]</th>\n",
              "      <td>200</td>\n",
              "      <td>0.270000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(2852.4, 4720.0]</th>\n",
              "      <td>200</td>\n",
              "      <td>0.260000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(4720.0, 18424.0]</th>\n",
              "      <td>200</td>\n",
              "      <td>0.425000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     count      mean\n",
              "credit_amount_group                 \n",
              "(249.999, 1262.0]      201  0.303483\n",
              "(1262.0, 1906.8]       199  0.241206\n",
              "(1906.8, 2852.4]       200  0.270000\n",
              "(2852.4, 4720.0]       200  0.260000\n",
              "(4720.0, 18424.0]      200  0.425000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CjR4t5fAyegj"
      },
      "source": [
        "Да, чем больше кредит, тем выше дефолт рейт."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "ovdHzxQHzP0q",
        "outputId": "a99461bc-f44b-4535-efe0-2afc1270cf6b"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>job</th>\n",
              "      <th>housing</th>\n",
              "      <th>saving_accounts</th>\n",
              "      <th>checking_account</th>\n",
              "      <th>credit_amount</th>\n",
              "      <th>duration</th>\n",
              "      <th>purpose</th>\n",
              "      <th>default</th>\n",
              "      <th>age_group</th>\n",
              "      <th>age_group2</th>\n",
              "      <th>credit_amount_group</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67</td>\n",
              "      <td>male</td>\n",
              "      <td>skilled</td>\n",
              "      <td>own</td>\n",
              "      <td>no_info</td>\n",
              "      <td>little</td>\n",
              "      <td>1169</td>\n",
              "      <td>6</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>0</td>\n",
              "      <td>(45.0, 75.0]</td>\n",
              "      <td>(38.0, 75.0]</td>\n",
              "      <td>(249.999, 1262.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>22</td>\n",
              "      <td>female</td>\n",
              "      <td>skilled</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>moderate</td>\n",
              "      <td>5951</td>\n",
              "      <td>48</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>1</td>\n",
              "      <td>(18.999, 26.0]</td>\n",
              "      <td>(18.999, 28.0]</td>\n",
              "      <td>(4720.0, 18424.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>49</td>\n",
              "      <td>male</td>\n",
              "      <td>unskilled and resident</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>no_info</td>\n",
              "      <td>2096</td>\n",
              "      <td>12</td>\n",
              "      <td>education</td>\n",
              "      <td>0</td>\n",
              "      <td>(45.0, 75.0]</td>\n",
              "      <td>(38.0, 75.0]</td>\n",
              "      <td>(1906.8, 2852.4]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>45</td>\n",
              "      <td>male</td>\n",
              "      <td>skilled</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>7882</td>\n",
              "      <td>42</td>\n",
              "      <td>furniture/equipment</td>\n",
              "      <td>0</td>\n",
              "      <td>(36.0, 45.0]</td>\n",
              "      <td>(38.0, 75.0]</td>\n",
              "      <td>(4720.0, 18424.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>53</td>\n",
              "      <td>male</td>\n",
              "      <td>skilled</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>4870</td>\n",
              "      <td>24</td>\n",
              "      <td>car</td>\n",
              "      <td>1</td>\n",
              "      <td>(45.0, 75.0]</td>\n",
              "      <td>(38.0, 75.0]</td>\n",
              "      <td>(4720.0, 18424.0]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age     sex  ...      age_group2 credit_amount_group\n",
              "0   67    male  ...    (38.0, 75.0]   (249.999, 1262.0]\n",
              "1   22  female  ...  (18.999, 28.0]   (4720.0, 18424.0]\n",
              "2   49    male  ...    (38.0, 75.0]    (1906.8, 2852.4]\n",
              "3   45    male  ...    (38.0, 75.0]   (4720.0, 18424.0]\n",
              "4   53    male  ...    (38.0, 75.0]   (4720.0, 18424.0]\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "hagnw5zlzXTv",
        "outputId": "71b25a41-7d57-46fc-eeb1-5cd3899dca14"
      },
      "source": [
        "df.groupby('housing')['default'].agg(['count','mean'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>housing</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>free</th>\n",
              "      <td>108</td>\n",
              "      <td>0.407407</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>own</th>\n",
              "      <td>713</td>\n",
              "      <td>0.260870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rent</th>\n",
              "      <td>179</td>\n",
              "      <td>0.391061</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         count      mean\n",
              "housing                 \n",
              "free       108  0.407407\n",
              "own        713  0.260870\n",
              "rent       179  0.391061"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XrByooAkzbjj"
      },
      "source": [
        "Если дом в собственности, то дефолт гораздо ниже."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "5N1GmrXczmwE",
        "outputId": "4a6fdb5e-41c5-45ad-bcae-2f0956b3de1d"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>job</th>\n",
              "      <th>housing</th>\n",
              "      <th>saving_accounts</th>\n",
              "      <th>checking_account</th>\n",
              "      <th>credit_amount</th>\n",
              "      <th>duration</th>\n",
              "      <th>purpose</th>\n",
              "      <th>default</th>\n",
              "      <th>age_group</th>\n",
              "      <th>age_group2</th>\n",
              "      <th>credit_amount_group</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>67</td>\n",
              "      <td>male</td>\n",
              "      <td>skilled</td>\n",
              "      <td>own</td>\n",
              "      <td>no_info</td>\n",
              "      <td>little</td>\n",
              "      <td>1169</td>\n",
              "      <td>6</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>0</td>\n",
              "      <td>(45.0, 75.0]</td>\n",
              "      <td>(38.0, 75.0]</td>\n",
              "      <td>(249.999, 1262.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>22</td>\n",
              "      <td>female</td>\n",
              "      <td>skilled</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>moderate</td>\n",
              "      <td>5951</td>\n",
              "      <td>48</td>\n",
              "      <td>radio/TV</td>\n",
              "      <td>1</td>\n",
              "      <td>(18.999, 26.0]</td>\n",
              "      <td>(18.999, 28.0]</td>\n",
              "      <td>(4720.0, 18424.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>49</td>\n",
              "      <td>male</td>\n",
              "      <td>unskilled and resident</td>\n",
              "      <td>own</td>\n",
              "      <td>little</td>\n",
              "      <td>no_info</td>\n",
              "      <td>2096</td>\n",
              "      <td>12</td>\n",
              "      <td>education</td>\n",
              "      <td>0</td>\n",
              "      <td>(45.0, 75.0]</td>\n",
              "      <td>(38.0, 75.0]</td>\n",
              "      <td>(1906.8, 2852.4]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>45</td>\n",
              "      <td>male</td>\n",
              "      <td>skilled</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>7882</td>\n",
              "      <td>42</td>\n",
              "      <td>furniture/equipment</td>\n",
              "      <td>0</td>\n",
              "      <td>(36.0, 45.0]</td>\n",
              "      <td>(38.0, 75.0]</td>\n",
              "      <td>(4720.0, 18424.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>53</td>\n",
              "      <td>male</td>\n",
              "      <td>skilled</td>\n",
              "      <td>free</td>\n",
              "      <td>little</td>\n",
              "      <td>little</td>\n",
              "      <td>4870</td>\n",
              "      <td>24</td>\n",
              "      <td>car</td>\n",
              "      <td>1</td>\n",
              "      <td>(45.0, 75.0]</td>\n",
              "      <td>(38.0, 75.0]</td>\n",
              "      <td>(4720.0, 18424.0]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age     sex  ...      age_group2 credit_amount_group\n",
              "0   67    male  ...    (38.0, 75.0]   (249.999, 1262.0]\n",
              "1   22  female  ...  (18.999, 28.0]   (4720.0, 18424.0]\n",
              "2   49    male  ...    (38.0, 75.0]    (1906.8, 2852.4]\n",
              "3   45    male  ...    (38.0, 75.0]   (4720.0, 18424.0]\n",
              "4   53    male  ...    (38.0, 75.0]   (4720.0, 18424.0]\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        }
      ]
    }
  ]
}