1. Analyzing borrowers’ risk of defaulting.ipynb

{ 
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   "source": [
    "<a class=\"anchor\" id=\"chapter00\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Back to table of contents](#back1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing borrowers’ risk of defaulting\n",
    "\n",
    "By Yevgenii Atlasman  \n",
    "October 2021"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our project is to prepare a report for a bank's loan division. We'll need to find out if a customer's marital status and number of children has an impact on whether they will default on a loan. The bank already has some data on customers' credit worthiness.\n",
    "\n",
    "**The main aim of the project is to build a credit scoring of a potential customer. The credit scoring is used to evaluate the ability of a potential borrower to repay their loan.**  \n",
    "\n",
    "**Plan of our work**\n",
    " 1. Preprocess the data:\n",
    " - Identify and fill in missing values\n",
    " - Replace the real number data type with the integer type\n",
    " - Delete duplicate data\n",
    " - Categorize the data\n",
    " 2. Answer the questions:\n",
    " - Is there a connection between having kids and repaying a loan on time?\n",
    " - Is there a connection between marital status and repaying a loan on time?\n",
    " - Is there a connection between income level and repaying a loan on time?\n",
    " - How do different loan purposes affect on-time loan repayment?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"back1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of Contents\n",
    "\n",
    " [Project Descriprion](#chapter00) \n",
    "* [Open the data file and have a look at the general information](#chapter01)  \n",
    "* [Data preprocessing](#chapter02)\n",
    "    * [Processing missing values](#section_02_1)\n",
    "        * [Conclusion](#section_02_1_1)\n",
    "    * [Filling in missing values](#section_02_2)\n",
    "        * [Conclusion](#section_02_2_1)\n",
    "    * [Data type replacement](#section_02_3)\n",
    "        * [Conclusion](#section_02_3_1)\n",
    "    * [Processing duplicates](#section_02_4)\n",
    "        * [Conclusion](#section_02_4_1)\n",
    "    * [Categorizing Data](#section_02_5)\n",
    "        * [Conclusion](#section_02_5_1)\n",
    "* [Answer the questions](#chapter03)  \n",
    "    * [Is there a relation between having kids and repaying a loan on time?](#section_03_1)\n",
    "        * [Conclusion](#section_03_1_1)\n",
    "    * [Is there a relation between marital status and repaying a loan on time?](#section_03_2)\n",
    "        * [Conclusion](#section_03_2_1)\n",
    "    * [Is there a relation between income level and repaying a loan on time?](#section_03_3)\n",
    "        * [Conclusion](#section_03_3_1)\n",
    "    * [How do different loan purposes affect on-time repayment of the loan?](#section_03_4)\n",
    "        * [Conclusion](#section_03_4_1)\n",
    "* [General conclusion](#chapter04)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"chapter01\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Back to table of contents](#back1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Open the data file and have a look at the general information "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import libraries. Install `pymystem3` module."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load the Mystem library from pymystem3 and call the Counter container from the collections module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pymystem3 in /opt/conda/lib/python3.9/site-packages (0.2.0)\n",
      "Requirement already satisfied: requests in /opt/conda/lib/python3.9/site-packages (from pymystem3) (2.26.0)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.9/site-packages (from requests->pymystem3) (1.26.7)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.9/site-packages (from requests->pymystem3) (2021.10.8)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.9/site-packages (from requests->pymystem3) (3.1)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in /opt/conda/lib/python3.9/site-packages (from requests->pymystem3) (2.0.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pymystem3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pymystem3 import Mystem\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Use try-except method to read csv file, because it should work both on local computer and jupiter hub.**   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    credit_scoring = pd.read_csv('credit_scoring_eng.csv')\n",
    "except:\n",
    "    credit_scoring = pd.read_csv('/datasets/credit_scoring_eng.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>-8437.673028</td>\n",
       "      <td>42</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>0</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>40620.102</td>\n",
       "      <td>purchase of the house</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-4024.803754</td>\n",
       "      <td>36</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>17932.802</td>\n",
       "      <td>car purchase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>-5623.422610</td>\n",
       "      <td>33</td>\n",
       "      <td>Secondary Education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>23341.752</td>\n",
       "      <td>purchase of the house</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-4124.747207</td>\n",
       "      <td>32</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>42820.568</td>\n",
       "      <td>supplementary education</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>340266.072047</td>\n",
       "      <td>53</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>civil partnership</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>retiree</td>\n",
       "      <td>0</td>\n",
       "      <td>25378.572</td>\n",
       "      <td>to have a wedding</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   children  days_employed  dob_years            education  education_id  \\\n",
       "0         1   -8437.673028         42    bachelor's degree             0   \n",
       "1         1   -4024.803754         36  secondary education             1   \n",
       "2         0   -5623.422610         33  Secondary Education             1   \n",
       "3         3   -4124.747207         32  secondary education             1   \n",
       "4         0  340266.072047         53  secondary education             1   \n",
       "\n",
       "       family_status  family_status_id gender income_type  debt  total_income  \\\n",
       "0            married                 0      F    employee     0     40620.102   \n",
       "1            married                 0      F    employee     0     17932.802   \n",
       "2            married                 0      M    employee     0     23341.752   \n",
       "3            married                 0      M    employee     0     42820.568   \n",
       "4  civil partnership                 1      F     retiree     0     25378.572   \n",
       "\n",
       "                   purpose  \n",
       "0    purchase of the house  \n",
       "1             car purchase  \n",
       "2    purchase of the house  \n",
       "3  supplementary education  \n",
       "4        to have a wedding  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like there are a lot of negative values in the `days_employed` column. Probably it was mistake in inserting data. We should take a closer look at this column, and check how many values there negeative. Also in the column `purpose` we can see reasons for taking out a loan like `purchase of a car`, `buying my own car`, `to buy a car`, `car purchase`, so we'll need to unite all this values. Probably, customers were asked to write down purposes instead of making them choose from the list (with option write their own answer if they didn't find relevant purpose). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"chapter02\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Back to table of contents](#back1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preprocessing "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check what columns have missing values and what data type in each column by calling the `info()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21525 entries, 0 to 21524\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   children          21525 non-null  int64  \n",
      " 1   days_employed     19351 non-null  float64\n",
      " 2   dob_years         21525 non-null  int64  \n",
      " 3   education         21525 non-null  object \n",
      " 4   education_id      21525 non-null  int64  \n",
      " 5   family_status     21525 non-null  object \n",
      " 6   family_status_id  21525 non-null  int64  \n",
      " 7   gender            21525 non-null  object \n",
      " 8   income_type       21525 non-null  object \n",
      " 9   debt              21525 non-null  int64  \n",
      " 10  total_income      19351 non-null  float64\n",
      " 11  purpose           21525 non-null  object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "credit_scoring.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are some missing values in columns `total_income` and `days_employed`. Take a closer look at these missing values later. There is also float type in `total_income`. It will be better to work with money as integer values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also take a look at first ten rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>employee</td>\n",
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       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
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       "      <td>33</td>\n",
       "      <td>Secondary Education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>23341.752</td>\n",
       "      <td>purchase of the house</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-4124.747207</td>\n",
       "      <td>32</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>42820.568</td>\n",
       "      <td>supplementary education</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>340266.072047</td>\n",
       "      <td>53</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>civil partnership</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>retiree</td>\n",
       "      <td>0</td>\n",
       "      <td>25378.572</td>\n",
       "      <td>to have a wedding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>-926.185831</td>\n",
       "      <td>27</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>0</td>\n",
       "      <td>civil partnership</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>business</td>\n",
       "      <td>0</td>\n",
       "      <td>40922.170</td>\n",
       "      <td>purchase of the house</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>-2879.202052</td>\n",
       "      <td>43</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>0</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>business</td>\n",
       "      <td>0</td>\n",
       "      <td>38484.156</td>\n",
       "      <td>housing transactions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>-152.779569</td>\n",
       "      <td>50</td>\n",
       "      <td>SECONDARY EDUCATION</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>21731.829</td>\n",
       "      <td>education</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>-6929.865299</td>\n",
       "      <td>35</td>\n",
       "      <td>BACHELOR'S DEGREE</td>\n",
       "      <td>0</td>\n",
       "      <td>civil partnership</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>15337.093</td>\n",
       "      <td>having a wedding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>-2188.756445</td>\n",
       "      <td>41</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>23108.150</td>\n",
       "      <td>purchase of the house for my family</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   children  days_employed  dob_years            education  education_id  \\\n",
       "0         1   -8437.673028         42    bachelor's degree             0   \n",
       "1         1   -4024.803754         36  secondary education             1   \n",
       "2         0   -5623.422610         33  Secondary Education             1   \n",
       "3         3   -4124.747207         32  secondary education             1   \n",
       "4         0  340266.072047         53  secondary education             1   \n",
       "5         0    -926.185831         27    bachelor's degree             0   \n",
       "6         0   -2879.202052         43    bachelor's degree             0   \n",
       "7         0    -152.779569         50  SECONDARY EDUCATION             1   \n",
       "8         2   -6929.865299         35    BACHELOR'S DEGREE             0   \n",
       "9         0   -2188.756445         41  secondary education             1   \n",
       "\n",
       "       family_status  family_status_id gender income_type  debt  total_income  \\\n",
       "0            married                 0      F    employee     0     40620.102   \n",
       "1            married                 0      F    employee     0     17932.802   \n",
       "2            married                 0      M    employee     0     23341.752   \n",
       "3            married                 0      M    employee     0     42820.568   \n",
       "4  civil partnership                 1      F     retiree     0     25378.572   \n",
       "5  civil partnership                 1      M    business     0     40922.170   \n",
       "6            married                 0      F    business     0     38484.156   \n",
       "7            married                 0      M    employee     0     21731.829   \n",
       "8  civil partnership                 1      F    employee     0     15337.093   \n",
       "9            married                 0      M    employee     0     23108.150   \n",
       "\n",
       "                               purpose  \n",
       "0                purchase of the house  \n",
       "1                         car purchase  \n",
       "2                purchase of the house  \n",
       "3              supplementary education  \n",
       "4                    to have a wedding  \n",
       "5                purchase of the house  \n",
       "6                 housing transactions  \n",
       "7                            education  \n",
       "8                     having a wedding  \n",
       "9  purchase of the house for my family  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are some answers written in the higher case in `education` column. We'll need to make all the characters in the string lowercase later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also check how many rows in `days_employed` column have negative value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "children            15906\n",
       "days_employed       15906\n",
       "dob_years           15906\n",
       "education           15906\n",
       "education_id        15906\n",
       "family_status       15906\n",
       "family_status_id    15906\n",
       "gender              15906\n",
       "income_type         15906\n",
       "debt                15906\n",
       "total_income        15906\n",
       "purpose             15906\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring[credit_scoring['days_employed'] < 0].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`15906` negative vales from total `21525`. That's a lot. We should notify data engineers about this problem in order to find the root of it. We won't need this column in our analysis, so we can leave it as it is. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if there are some other columns with negative values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "children            47\n",
       "days_employed       44\n",
       "dob_years           47\n",
       "education           47\n",
       "education_id        47\n",
       "family_status       47\n",
       "family_status_id    47\n",
       "gender              47\n",
       "income_type         47\n",
       "debt                47\n",
       "total_income        44\n",
       "purpose             47\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring[credit_scoring['children'] < 0].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 47 rows that have negative value in the column `children`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look what variables has column `children` using the `unique()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  0,  3,  2, -1,  4, 20,  5])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring['children'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 47 customers with `-1` children. It's probably a mistake. Also we found out that there are customers with `20` children. We should change it to `1` and `2`, it might be a mistake in data entry."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Use the `abs()` method to change all values in `children` to positive values.**   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 3, 2, 4, 5])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring['children'] = abs(credit_scoring['children'])\n",
    "credit_scoring.loc[credit_scoring['children'] == 20, 'children'] = 2\n",
    "credit_scoring['children'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we saw previously, there were some values in the `education` column with differrent cases. Let's check how many different unique values in this column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([\"bachelor's degree\", 'secondary education', 'Secondary Education',\n",
       "       'SECONDARY EDUCATION', \"BACHELOR'S DEGREE\", 'some college',\n",
       "       'primary education', \"Bachelor's Degree\", 'SOME COLLEGE',\n",
       "       'Some College', 'PRIMARY EDUCATION', 'Primary Education',\n",
       "       'Graduate Degree', 'GRADUATE DEGREE', 'graduate degree'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring['education'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the same answers, only written with higher case letters. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make every variables in `education` column lowercase using the `str.lower()` method. And check the result using the `unique()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit_scoring['education'] = credit_scoring['education'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([\"bachelor's degree\", 'secondary education', 'some college',\n",
       "       'primary education', 'graduate degree'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring['education'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check if where are other columns which have `0` values creating the loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "children 14149\n",
      "days_employed 0\n",
      "dob_years 101\n",
      "education 0\n",
      "education_id 5260\n",
      "family_status 0\n",
      "family_status_id 12380\n",
      "gender 0\n",
      "income_type 0\n",
      "debt 19784\n",
      "total_income 0\n",
      "purpose 0\n"
     ]
    }
   ],
   "source": [
    "for i in credit_scoring:\n",
    "    print(i, len(credit_scoring[credit_scoring[i]==0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's fine with `children`, `education_id`, `family_status_id` and `debt` to have `0`. But for customers ages in column `dob_years` it's not. But we can skip this column, we are not going to use it in our analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"section_02_1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Back to table of contents](#back1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing missing values  <a class=\"anchor\" id=\"section_2_1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how many rows we have and how many missing values are in each column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total rows 21525\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "children               0\n",
       "days_employed       2174\n",
       "dob_years              0\n",
       "education              0\n",
       "education_id           0\n",
       "family_status          0\n",
       "family_status_id       0\n",
       "gender                 0\n",
       "income_type            0\n",
       "debt                   0\n",
       "total_income        2174\n",
       "purpose                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Total rows', len(credit_scoring))\n",
    "credit_scoring.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can assume that people who didn't indicate their income in `total_income`, don't have income and these are the same people whose `days_employed` is null (they are unemployed). These are quantitative variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We should check percentage of missing values in every column. For that count all the missing values in columns and divide amount of missing values by total number of rows and multiply by 100.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "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>missing_values</th>\n",
       "      <th>% of total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>days_employed</th>\n",
       "      <td>2174</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_income</th>\n",
       "      <td>2174</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dob_years</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education_id</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>family_status</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>family_status_id</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income_type</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>debt</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purpose</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  missing_values  % of total\n",
       "days_employed               2174        10.1\n",
       "total_income                2174        10.1\n",
       "children                       0         0.0\n",
       "dob_years                      0         0.0\n",
       "education                      0         0.0\n",
       "education_id                   0         0.0\n",
       "family_status                  0         0.0\n",
       "family_status_id               0         0.0\n",
       "gender                         0         0.0\n",
       "income_type                    0         0.0\n",
       "debt                           0         0.0\n",
       "purpose                        0         0.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report = credit_scoring.isna().sum().to_frame() \n",
    "report = report.rename(columns = {0: 'missing_values'}) \n",
    "report['% of total'] = ((report['missing_values'] / credit_scoring.shape[0])*100).round(2)\n",
    "report.sort_values(by = 'missing_values', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**It's 10% of missing values in each column. We can try and replace it by some median values. But if it was more than 10%, we couldn't do it.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if it's true. Use the `isnull()` method for finding missing values and the `unique()` method for seeing all unique values in a column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring[credit_scoring['total_income'].isnull()]['days_employed'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's correct."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at these rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>civil partnership</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>retiree</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>to have a wedding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>civil servant</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>education</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>unmarried</td>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>retiree</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>building a real estate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>civil servant</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>second-hand car purchase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>civil partnership</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>retiree</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>to have a wedding</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    children  days_employed  dob_years            education  education_id  \\\n",
       "12         0            NaN         65  secondary education             1   \n",
       "26         0            NaN         41  secondary education             1   \n",
       "29         0            NaN         63  secondary education             1   \n",
       "41         0            NaN         50  secondary education             1   \n",
       "55         0            NaN         54  secondary education             1   \n",
       "\n",
       "        family_status  family_status_id gender    income_type  debt  \\\n",
       "12  civil partnership                 1      M        retiree     0   \n",
       "26            married                 0      M  civil servant     0   \n",
       "29          unmarried                 4      F        retiree     0   \n",
       "41            married                 0      F  civil servant     0   \n",
       "55  civil partnership                 1      F        retiree     1   \n",
       "\n",
       "    total_income                   purpose  \n",
       "12           NaN         to have a wedding  \n",
       "26           NaN                 education  \n",
       "29           NaN    building a real estate  \n",
       "41           NaN  second-hand car purchase  \n",
       "55           NaN         to have a wedding  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring[credit_scoring['days_employed'].isnull()].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's strange, but people with no information in `days_employed` and `total_income` still have `income_type` mentioned."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see which different income types is in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['employee', 'retiree', 'business', 'civil servant', 'unemployed',\n",
       "       'entrepreneur', 'student', 'paternity / maternity leave'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring['income_type'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at unemployed customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3133</th>\n",
       "      <td>1</td>\n",
       "      <td>337524.466835</td>\n",
       "      <td>31</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>1</td>\n",
       "      <td>9593.119</td>\n",
       "      <td>buying property for renting out</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14798</th>\n",
       "      <td>0</td>\n",
       "      <td>395302.838654</td>\n",
       "      <td>45</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>0</td>\n",
       "      <td>civil partnership</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>0</td>\n",
       "      <td>32435.602</td>\n",
       "      <td>housing renovation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       children  days_employed  dob_years            education  education_id  \\\n",
       "3133          1  337524.466835         31  secondary education             1   \n",
       "14798         0  395302.838654         45    bachelor's degree             0   \n",
       "\n",
       "           family_status  family_status_id gender income_type  debt  \\\n",
       "3133             married                 0      M  unemployed     1   \n",
       "14798  civil partnership                 1      F  unemployed     0   \n",
       "\n",
       "       total_income                          purpose  \n",
       "3133       9593.119  buying property for renting out  \n",
       "14798     32435.602               housing renovation  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring.loc[credit_scoring['income_type'] == 'unemployed']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "They are unemloyed, but have income (maybe passive income or some allowance)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"section_02_1_1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion\n",
    "We don't know for sure how the data was collected and what `unemployed` really means. Some people that are technically unemployed can still have income. We can skip values of `days_employed` column, because we are not going to use it. We should notify data engineers about problem in this column in order to find the root of it. We won't drop this column, because we still need to look for duplicates rows and it can change the result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"section_02_2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Back to table of contents](#back1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filling in missing values\n",
    "Let's try to fill the `total_income` column by averages for some categories. Now, in order to fill in missing values in `total_income` column, lets find median total income value grouping by `income_type` column. Use the `median()` and the `groupby()` methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "income_type\n",
       "business                       27577.2720\n",
       "civil servant                  24071.6695\n",
       "employee                       22815.1035\n",
       "entrepreneur                   79866.1030\n",
       "paternity / maternity leave     8612.6610\n",
       "retiree                        18962.3180\n",
       "student                        15712.2600\n",
       "unemployed                     21014.3605\n",
       "Name: total_income, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped1 = credit_scoring.groupby(['income_type'])['total_income'].median()\n",
    "grouped1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see median income for each `income_type`. Now we need to fill in all missing values in `total_income `according to values for `income_type` above. Use the `fillna()` method and `transform()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit_scoring['clean_income1'] = credit_scoring['total_income'].fillna(credit_scoring.groupby([\n",
    "    'income_type'])['total_income'].transform('median'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
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       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "      <th>clean_income1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>civil partnership</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>retiree</td>\n",
       "      <td>0</td>\n",
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       "      <td>to have a wedding</td>\n",
       "      <td>18962.3180</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>civil servant</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>education</td>\n",
       "      <td>24071.6695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>unmarried</td>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>retiree</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>building a real estate</td>\n",
       "      <td>18962.3180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>civil servant</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>second-hand car purchase</td>\n",
       "      <td>24071.6695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>civil partnership</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>retiree</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>to have a wedding</td>\n",
       "      <td>18962.3180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    children  days_employed  dob_years            education  education_id  \\\n",
       "12         0            NaN         65  secondary education             1   \n",
       "26         0            NaN         41  secondary education             1   \n",
       "29         0            NaN         63  secondary education             1   \n",
       "41         0            NaN         50  secondary education             1   \n",
       "55         0            NaN         54  secondary education             1   \n",
       "\n",
       "        family_status  family_status_id gender    income_type  debt  \\\n",
       "12  civil partnership                 1      M        retiree     0   \n",
       "26            married                 0      M  civil servant     0   \n",
       "29          unmarried                 4      F        retiree     0   \n",
       "41            married                 0      F  civil servant     0   \n",
       "55  civil partnership                 1      F        retiree     1   \n",
       "\n",
       "    total_income                   purpose  clean_income1  \n",
       "12           NaN         to have a wedding     18962.3180  \n",
       "26           NaN                 education     24071.6695  \n",
       "29           NaN    building a real estate     18962.3180  \n",
       "41           NaN  second-hand car purchase     24071.6695  \n",
       "55           NaN         to have a wedding     18962.3180  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring[credit_scoring.total_income.isnull()].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's do the same order finding out median values grouping by a. `income_type` and `education_id`. b. `income_type`, `education_id` and `gender`, because men's and women's salary could be different for the same education and income type. We should check it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "income_type                  education_id\n",
       "business                     0               32285.6640\n",
       "                             1               25451.3100\n",
       "                             2               28778.7440\n",
       "                             3               21887.8250\n",
       "civil servant                0               27601.7775\n",
       "                             1               21864.4750\n",
       "                             2               25694.7750\n",
       "                             3               23734.2870\n",
       "                             4               17822.7570\n",
       "employee                     0               26502.5190\n",
       "                             1               21848.8175\n",
       "                             2               24209.4300\n",
       "                             3               20159.1860\n",
       "                             4               31771.3210\n",
       "entrepreneur                 0               79866.1030\n",
       "paternity / maternity leave  1                8612.6610\n",
       "retiree                      0               23078.5230\n",
       "                             1               18374.8570\n",
       "                             2               19221.9030\n",
       "                             3               16415.7850\n",
       "                             4               28334.2150\n",
       "student                      0               15712.2600\n",
       "unemployed                   0               32435.6020\n",
       "                             1                9593.1190\n",
       "Name: total_income, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped2 = credit_scoring.groupby(['income_type', 'education_id'])['total_income'].median()\n",
    "grouped2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How we can see above civil servants with graduate degree have the smallest salary in their category, but employees with graduate degree have the biggest one in employee category and also graduate degree wins in retiree category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit_scoring['clean_income2'] = credit_scoring['total_income'].fillna(credit_scoring.groupby([\n",
    "    'income_type', 'education_id'])['total_income'].transform('median'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>clean_income1</th>\n",
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       "  </thead>\n",
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       "      <th>12</th>\n",
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       "      <th>29</th>\n",
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       "      <td>secondary education</td>\n",
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       "      <td>unmarried</td>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>retiree</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>building a real estate</td>\n",
       "      <td>18962.3180</td>\n",
       "      <td>18374.857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0</td>\n",
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       "      <td>secondary education</td>\n",
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       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>civil servant</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>second-hand car purchase</td>\n",
       "      <td>24071.6695</td>\n",
       "      <td>21864.475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>civil partnership</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>retiree</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>to have a wedding</td>\n",
       "      <td>18962.3180</td>\n",
       "      <td>18374.857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    children  days_employed  dob_years            education  education_id  \\\n",
       "12         0            NaN         65  secondary education             1   \n",
       "26         0            NaN         41  secondary education             1   \n",
       "29         0            NaN         63  secondary education             1   \n",
       "41         0            NaN         50  secondary education             1   \n",
       "55         0            NaN         54  secondary education             1   \n",
       "\n",
       "        family_status  family_status_id gender    income_type  debt  \\\n",
       "12  civil partnership                 1      M        retiree     0   \n",
       "26            married                 0      M  civil servant     0   \n",
       "29          unmarried                 4      F        retiree     0   \n",
       "41            married                 0      F  civil servant     0   \n",
       "55  civil partnership                 1      F        retiree     1   \n",
       "\n",
       "    total_income                   purpose  clean_income1  clean_income2  \n",
       "12           NaN         to have a wedding     18962.3180      18374.857  \n",
       "26           NaN                 education     24071.6695      21864.475  \n",
       "29           NaN    building a real estate     18962.3180      18374.857  \n",
       "41           NaN  second-hand car purchase     24071.6695      21864.475  \n",
       "55           NaN         to have a wedding     18962.3180      18374.857  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring[credit_scoring.total_income.isnull()].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group by `income_type`, `education_id` and `gender`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "income_type                  gender  education_id\n",
       "business                     F       0               30382.9540\n",
       "                                     1               23181.0850\n",
       "                                     2               27275.4595\n",
       "                                     3               21441.9210\n",
       "                             M       0               37290.3815\n",
       "                                     1               29102.2430\n",
       "                                     2               30606.7090\n",
       "                                     3               24016.1540\n",
       "                             XNA     2               32624.8250\n",
       "civil servant                F       0               25254.6300\n",
       "                                     1               20133.0890\n",
       "                                     2               21855.8610\n",
       "                                     3               14339.0340\n",
       "                                     4               17822.7570\n",
       "                             M       0               34353.0630\n",
       "                                     1               27144.3130\n",
       "                                     2               28371.3005\n",
       "                                     3               30554.6660\n",
       "employee                     F       0               24590.6965\n",
       "                                     1               19860.3340\n",
       "                                     2               21588.0300\n",
       "                                     3               18962.6750\n",
       "                             M       0               30759.5680\n",
       "                                     1               25005.8240\n",
       "                                     2               30252.1470\n",
       "                                     3               22662.0740\n",
       "                                     4               31771.3210\n",
       "entrepreneur                 F       0               79866.1030\n",
       "                             M       0                      NaN\n",
       "paternity / maternity leave  F       1                8612.6610\n",
       "retiree                      F       0               22784.6850\n",
       "                                     1               18046.5560\n",
       "                                     2               18654.8760\n",
       "                                     3               16188.8905\n",
       "                                     4               40868.0310\n",
       "                             M       0               25193.1730\n",
       "                                     1               20175.5510\n",
       "                                     2               19946.7950\n",
       "                                     3               18099.8720\n",
       "                                     4               15800.3990\n",
       "student                      M       0               15712.2600\n",
       "unemployed                   F       0               32435.6020\n",
       "                             M       1                9593.1190\n",
       "Name: total_income, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped3 = credit_scoring.groupby(['income_type', 'gender', 'education_id'])['total_income'].median()\n",
    "grouped3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see that men with the same education and income type get much bigger salary than women. Only in category retiree-graduate degree women have bigger salary (40868) than men (15800) and entrepreneur women have (79866) and there isn't men here. But it's only outliers because of small sample of values in this group."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check this for entrepreneurs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "      <th>clean_income1</th>\n",
       "      <th>clean_income2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5936</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>0</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>buy residential real estate</td>\n",
       "      <td>79866.103</td>\n",
       "      <td>79866.103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18697</th>\n",
       "      <td>0</td>\n",
       "      <td>-520.848083</td>\n",
       "      <td>27</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>0</td>\n",
       "      <td>civil partnership</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>0</td>\n",
       "      <td>79866.103</td>\n",
       "      <td>having a wedding</td>\n",
       "      <td>79866.103</td>\n",
       "      <td>79866.103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       children  days_employed  dob_years          education  education_id  \\\n",
       "5936          0            NaN         58  bachelor's degree             0   \n",
       "18697         0    -520.848083         27  bachelor's degree             0   \n",
       "\n",
       "           family_status  family_status_id gender   income_type  debt  \\\n",
       "5936             married                 0      M  entrepreneur     0   \n",
       "18697  civil partnership                 1      F  entrepreneur     0   \n",
       "\n",
       "       total_income                      purpose  clean_income1  clean_income2  \n",
       "5936            NaN  buy residential real estate      79866.103      79866.103  \n",
       "18697     79866.103             having a wedding      79866.103      79866.103  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring[credit_scoring['income_type'] == 'entrepreneur']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes, it's only two loaners with income type as entrepreneur. So we can't compare it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit_scoring['clean_income3'] = credit_scoring['total_income'].fillna(credit_scoring.groupby([\n",
    "    'income_type', 'gender', 'education_id'])['total_income'].transform('median'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>purpose</th>\n",
       "      <th>clean_income1</th>\n",
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       "      <th>clean_income3</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>M</td>\n",
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       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>18962.3180</td>\n",
       "      <td>18374.857</td>\n",
       "      <td>20175.551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>civil servant</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>education</td>\n",
       "      <td>24071.6695</td>\n",
       "      <td>21864.475</td>\n",
       "      <td>27144.313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>unmarried</td>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>retiree</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>building a real estate</td>\n",
       "      <td>18962.3180</td>\n",
       "      <td>18374.857</td>\n",
       "      <td>18046.556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>civil servant</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>second-hand car purchase</td>\n",
       "      <td>24071.6695</td>\n",
       "      <td>21864.475</td>\n",
       "      <td>20133.089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>civil partnership</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>retiree</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>to have a wedding</td>\n",
       "      <td>18962.3180</td>\n",
       "      <td>18374.857</td>\n",
       "      <td>18046.556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    children  days_employed  dob_years            education  education_id  \\\n",
       "12         0            NaN         65  secondary education             1   \n",
       "26         0            NaN         41  secondary education             1   \n",
       "29         0            NaN         63  secondary education             1   \n",
       "41         0            NaN         50  secondary education             1   \n",
       "55         0            NaN         54  secondary education             1   \n",
       "\n",
       "        family_status  family_status_id gender    income_type  debt  \\\n",
       "12  civil partnership                 1      M        retiree     0   \n",
       "26            married                 0      M  civil servant     0   \n",
       "29          unmarried                 4      F        retiree     0   \n",
       "41            married                 0      F  civil servant     0   \n",
       "55  civil partnership                 1      F        retiree     1   \n",
       "\n",
       "    total_income                   purpose  clean_income1  clean_income2  \\\n",
       "12           NaN         to have a wedding     18962.3180      18374.857   \n",
       "26           NaN                 education     24071.6695      21864.475   \n",
       "29           NaN    building a real estate     18962.3180      18374.857   \n",
       "41           NaN  second-hand car purchase     24071.6695      21864.475   \n",
       "55           NaN         to have a wedding     18962.3180      18374.857   \n",
       "\n",
       "    clean_income3  \n",
       "12      20175.551  \n",
       "26      27144.313  \n",
       "29      18046.556  \n",
       "41      20133.089  \n",
       "55      18046.556  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring[credit_scoring.total_income.isnull()].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check now median values for each `clean_income1`, `clean_income2`, `clean_income3`, and if we got all our missing values filled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>clean_income1</th>\n",
       "      <th>clean_income2</th>\n",
       "      <th>clean_income3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>21525.000000</td>\n",
       "      <td>19351.000000</td>\n",
       "      <td>21525.000000</td>\n",
       "      <td>21525.000000</td>\n",
       "      <td>21525.000000</td>\n",
       "      <td>21525.000000</td>\n",
       "      <td>19351.000000</td>\n",
       "      <td>21525.000000</td>\n",
       "      <td>21525.000000</td>\n",
       "      <td>21524.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.479721</td>\n",
       "      <td>63046.497661</td>\n",
       "      <td>43.293380</td>\n",
       "      <td>0.817236</td>\n",
       "      <td>0.972544</td>\n",
       "      <td>0.080883</td>\n",
       "      <td>26787.568355</td>\n",
       "      <td>26436.051922</td>\n",
       "      <td>26456.597412</td>\n",
       "      <td>26456.873757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.755528</td>\n",
       "      <td>140827.311974</td>\n",
       "      <td>12.574584</td>\n",
       "      <td>0.548138</td>\n",
       "      <td>1.420324</td>\n",
       "      <td>0.272661</td>\n",
       "      <td>16475.450632</td>\n",
       "      <td>15686.986477</td>\n",
       "      <td>15703.769010</td>\n",
       "      <td>15723.367222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-18388.949901</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3306.762000</td>\n",
       "      <td>3306.762000</td>\n",
       "      <td>3306.762000</td>\n",
       "      <td>3306.762000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2747.423625</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16488.504500</td>\n",
       "      <td>17247.708000</td>\n",
       "      <td>17235.090000</td>\n",
       "      <td>17234.415000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1203.369529</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23202.870000</td>\n",
       "      <td>22815.103500</td>\n",
       "      <td>22959.405000</td>\n",
       "      <td>23181.085000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-291.095954</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32549.611000</td>\n",
       "      <td>31287.991000</td>\n",
       "      <td>31703.887000</td>\n",
       "      <td>31456.873250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>401755.400475</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>362496.645000</td>\n",
       "      <td>362496.645000</td>\n",
       "      <td>362496.645000</td>\n",
       "      <td>362496.645000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           children  days_employed     dob_years  education_id  \\\n",
       "count  21525.000000   19351.000000  21525.000000  21525.000000   \n",
       "mean       0.479721   63046.497661     43.293380      0.817236   \n",
       "std        0.755528  140827.311974     12.574584      0.548138   \n",
       "min        0.000000  -18388.949901      0.000000      0.000000   \n",
       "25%        0.000000   -2747.423625     33.000000      1.000000   \n",
       "50%        0.000000   -1203.369529     42.000000      1.000000   \n",
       "75%        1.000000    -291.095954     53.000000      1.000000   \n",
       "max        5.000000  401755.400475     75.000000      4.000000   \n",
       "\n",
       "       family_status_id          debt   total_income  clean_income1  \\\n",
       "count      21525.000000  21525.000000   19351.000000   21525.000000   \n",
       "mean           0.972544      0.080883   26787.568355   26436.051922   \n",
       "std            1.420324      0.272661   16475.450632   15686.986477   \n",
       "min            0.000000      0.000000    3306.762000    3306.762000   \n",
       "25%            0.000000      0.000000   16488.504500   17247.708000   \n",
       "50%            0.000000      0.000000   23202.870000   22815.103500   \n",
       "75%            1.000000      0.000000   32549.611000   31287.991000   \n",
       "max            4.000000      1.000000  362496.645000  362496.645000   \n",
       "\n",
       "       clean_income2  clean_income3  \n",
       "count   21525.000000   21524.000000  \n",
       "mean    26456.597412   26456.873757  \n",
       "std     15703.769010   15723.367222  \n",
       "min      3306.762000    3306.762000  \n",
       "25%     17235.090000   17234.415000  \n",
       "50%     22959.405000   23181.085000  \n",
       "75%     31703.887000   31456.873250  \n",
       "max    362496.645000  362496.645000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"section_02_2_1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We decided to fill in missing values because the percentage of missing values 10% from all rows of data. We think that it could be some mistakes in inserting the data. Because it was the same amount of missing values in `days_employed` column and `total_income`, it might be questionnaire form on the web site or on the bank clerk computer, where then you leave `days_employed` column empty, you can't get the question about `total_income` we should check it with data engineers. But for our goal it's OK for now to fill in this values.**   \n",
    "\n",
    "As we can see for clean_income3 where we looked for median value by `income_type`, `education_id` and `gender`, we've got closer value (23181) to median in `total_income` (23202). But we left one missing value apparantly in the entrepreneur-male category. It has small sample for men and women. So let's stick with filling in according only to `income_type` and `education_id`. The results obtained are quite close to original data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Common logic is that it is better to think about the strategies of filling in the missing values and if it is impossible to fill in the missing values we can simply leave missing values in the data unchanged, because sometimes when the percentage of the missing values is big and the data is rather sensitive our attempts to fill in missing values using mean or median can be risky in terms of distortion of the original data, so it is important to take it into consideration!\n",
    "       \n",
    "We can use median to fill in missing values in column `total_income` because this statistics is more resistant to outliers. \n",
    "\n",
    "Finally is important to understand the nature of the missing values, formulate hypotheses or reasons for the presence of missing values in the data, and based on these hypotheses, try to choose a way to fill the data. It is important to understand that replacing missing values with a simple median or mean by column can significantly distort the original data if the percentage of gaps in the columns exceeds 10%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"section_02_3\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Back to table of contents](#back1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data type replacement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, when we filled in missing values we can change data type in column `clean_income2` from float to integer. It's easier way to work with numbers like salary. We can use the `astype()` method with it's argument `('int')`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit_scoring['clean_income2'] = credit_scoring['clean_income2'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
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       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>-8437.673028</td>\n",
       "      <td>42</td>\n",
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       "      <td>0</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
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       "      <td>employee</td>\n",
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       "      <td>purchase of the house</td>\n",
       "      <td>40620.102</td>\n",
       "      <td>40620</td>\n",
       "      <td>40620.102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-4024.803754</td>\n",
       "      <td>36</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>17932.802</td>\n",
       "      <td>car purchase</td>\n",
       "      <td>17932.802</td>\n",
       "      <td>17932</td>\n",
       "      <td>17932.802</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>-5623.422610</td>\n",
       "      <td>33</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>23341.752</td>\n",
       "      <td>purchase of the house</td>\n",
       "      <td>23341.752</td>\n",
       "      <td>23341</td>\n",
       "      <td>23341.752</td>\n",
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       "      <th>3</th>\n",
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       "      <td>32</td>\n",
       "      <td>secondary education</td>\n",
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       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>42820.568</td>\n",
       "      <td>supplementary education</td>\n",
       "      <td>42820.568</td>\n",
       "      <td>42820</td>\n",
       "      <td>42820.568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>340266.072047</td>\n",
       "      <td>53</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>civil partnership</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>retiree</td>\n",
       "      <td>0</td>\n",
       "      <td>25378.572</td>\n",
       "      <td>to have a wedding</td>\n",
       "      <td>25378.572</td>\n",
       "      <td>25378</td>\n",
       "      <td>25378.572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   children  days_employed  dob_years            education  education_id  \\\n",
       "0         1   -8437.673028         42    bachelor's degree             0   \n",
       "1         1   -4024.803754         36  secondary education             1   \n",
       "2         0   -5623.422610         33  secondary education             1   \n",
       "3         3   -4124.747207         32  secondary education             1   \n",
       "4         0  340266.072047         53  secondary education             1   \n",
       "\n",
       "       family_status  family_status_id gender income_type  debt  total_income  \\\n",
       "0            married                 0      F    employee     0     40620.102   \n",
       "1            married                 0      F    employee     0     17932.802   \n",
       "2            married                 0      M    employee     0     23341.752   \n",
       "3            married                 0      M    employee     0     42820.568   \n",
       "4  civil partnership                 1      F     retiree     0     25378.572   \n",
       "\n",
       "                   purpose  clean_income1  clean_income2  clean_income3  \n",
       "0    purchase of the house      40620.102          40620      40620.102  \n",
       "1             car purchase      17932.802          17932      17932.802  \n",
       "2    purchase of the house      23341.752          23341      23341.752  \n",
       "3  supplementary education      42820.568          42820      42820.568  \n",
       "4        to have a wedding      25378.572          25378      25378.572  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's change data type of `days_employed` column too. For that we should fill in missing values with `0` first. Use the `fillna()` method for this column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit_scoring['days_employed'] = credit_scoring['days_employed'].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Use the `astype()` for `days_employed` column with argument `int`.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit_scoring['days_employed'] = credit_scoring['days_employed'].astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Get rid from negative values in this column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit_scoring['days_employed'] = abs(credit_scoring['days_employed'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's check how it looks like now.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>42820.568</td>\n",
       "      <td>supplementary education</td>\n",
       "      <td>42820.568</td>\n",
       "      <td>42820</td>\n",
       "      <td>42820.568</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>340266</td>\n",
       "      <td>53</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>civil partnership</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>retiree</td>\n",
       "      <td>0</td>\n",
       "      <td>25378.572</td>\n",
       "      <td>to have a wedding</td>\n",
       "      <td>25378.572</td>\n",
       "      <td>25378</td>\n",
       "      <td>25378.572</td>\n",
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      ],
      "text/plain": [
       "   children  days_employed  dob_years            education  education_id  \\\n",
       "0         1           8437         42    bachelor's degree             0   \n",
       "1         1           4024         36  secondary education             1   \n",
       "2         0           5623         33  secondary education             1   \n",
       "3         3           4124         32  secondary education             1   \n",
       "4         0         340266         53  secondary education             1   \n",
       "\n",
       "       family_status  family_status_id gender income_type  debt  total_income  \\\n",
       "0            married                 0      F    employee     0     40620.102   \n",
       "1            married                 0      F    employee     0     17932.802   \n",
       "2            married                 0      M    employee     0     23341.752   \n",
       "3            married                 0      M    employee     0     42820.568   \n",
       "4  civil partnership                 1      F     retiree     0     25378.572   \n",
       "\n",
       "                   purpose  clean_income1  clean_income2  clean_income3  \n",
       "0    purchase of the house      40620.102          40620      40620.102  \n",
       "1             car purchase      17932.802          17932      17932.802  \n",
       "2    purchase of the house      23341.752          23341      23341.752  \n",
       "3  supplementary education      42820.568          42820      42820.568  \n",
       "4        to have a wedding      25378.572          25378      25378.572  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"section_02_3_1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it's better to work with salary when it's type integer. And less mess with `days_employed`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21525 entries, 0 to 21524\n",
      "Data columns (total 15 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   children          21525 non-null  int64  \n",
      " 1   days_employed     21525 non-null  int64  \n",
      " 2   dob_years         21525 non-null  int64  \n",
      " 3   education         21525 non-null  object \n",
      " 4   education_id      21525 non-null  int64  \n",
      " 5   family_status     21525 non-null  object \n",
      " 6   family_status_id  21525 non-null  int64  \n",
      " 7   gender            21525 non-null  object \n",
      " 8   income_type       21525 non-null  object \n",
      " 9   debt              21525 non-null  int64  \n",
      " 10  total_income      19351 non-null  float64\n",
      " 11  purpose           21525 non-null  object \n",
      " 12  clean_income1     21525 non-null  float64\n",
      " 13  clean_income2     21525 non-null  int64  \n",
      " 14  clean_income3     21524 non-null  float64\n",
      "dtypes: float64(3), int64(7), object(5)\n",
      "memory usage: 2.5+ MB\n"
     ]
    }
   ],
   "source": [
    "credit_scoring.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"section_02_4\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Back to table of contents](#back1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing duplicates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check if we have duplicates in our dataset using the `duplicated()` method for finding duplicates along with the method `sum()` that returns the total number of duplicates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the result. Cut all the duplicates and put it in the table sorting by the `dob_years`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>civil partnership</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>to have a wedding</td>\n",
       "      <td>22815.1035</td>\n",
       "      <td>21848</td>\n",
       "      <td>19860.334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15892</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>unmarried</td>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>second-hand car purchase</td>\n",
       "      <td>22815.1035</td>\n",
       "      <td>21848</td>\n",
       "      <td>19860.334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19321</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>unmarried</td>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>second-hand car purchase</td>\n",
       "      <td>22815.1035</td>\n",
       "      <td>21848</td>\n",
       "      <td>19860.334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3452</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>0</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>buy residential real estate</td>\n",
       "      <td>22815.1035</td>\n",
       "      <td>26502</td>\n",
       "      <td>30759.568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       children  days_employed  dob_years            education  education_id  \\\n",
       "20297         1              0         23  secondary education             1   \n",
       "8853          1              0         23  secondary education             1   \n",
       "15892         0              0         23  secondary education             1   \n",
       "19321         0              0         23  secondary education             1   \n",
       "3452          0              0         29    bachelor's degree             0   \n",
       "\n",
       "           family_status  family_status_id gender income_type  debt  \\\n",
       "20297  civil partnership                 1      F    employee     0   \n",
       "8853   civil partnership                 1      F    employee     0   \n",
       "15892          unmarried                 4      F    employee     0   \n",
       "19321          unmarried                 4      F    employee     0   \n",
       "3452             married                 0      M    employee     0   \n",
       "\n",
       "       total_income                      purpose  clean_income1  \\\n",
       "20297           NaN            to have a wedding     22815.1035   \n",
       "8853            NaN            to have a wedding     22815.1035   \n",
       "15892           NaN     second-hand car purchase     22815.1035   \n",
       "19321           NaN     second-hand car purchase     22815.1035   \n",
       "3452            NaN  buy residential real estate     22815.1035   \n",
       "\n",
       "       clean_income2  clean_income3  \n",
       "20297          21848      19860.334  \n",
       "8853           21848      19860.334  \n",
       "15892          21848      19860.334  \n",
       "19321          21848      19860.334  \n",
       "3452           26502      30759.568  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring.loc[credit_scoring.duplicated(keep=False), :].sort_values('dob_years').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's drop duplicates using the `drop_duplicates()` method and the `reset_index()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "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",
       "    .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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "      <th>clean_income1</th>\n",
       "      <th>clean_income2</th>\n",
       "      <th>clean_income3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8437</td>\n",
       "      <td>42</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>0</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>40620.102</td>\n",
       "      <td>purchase of the house</td>\n",
       "      <td>40620.102</td>\n",
       "      <td>40620</td>\n",
       "      <td>40620.102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4024</td>\n",
       "      <td>36</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>17932.802</td>\n",
       "      <td>car purchase</td>\n",
       "      <td>17932.802</td>\n",
       "      <td>17932</td>\n",
       "      <td>17932.802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>5623</td>\n",
       "      <td>33</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>23341.752</td>\n",
       "      <td>purchase of the house</td>\n",
       "      <td>23341.752</td>\n",
       "      <td>23341</td>\n",
       "      <td>23341.752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4124</td>\n",
       "      <td>32</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>42820.568</td>\n",
       "      <td>supplementary education</td>\n",
       "      <td>42820.568</td>\n",
       "      <td>42820</td>\n",
       "      <td>42820.568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>340266</td>\n",
       "      <td>53</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>civil partnership</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>retiree</td>\n",
       "      <td>0</td>\n",
       "      <td>25378.572</td>\n",
       "      <td>to have a wedding</td>\n",
       "      <td>25378.572</td>\n",
       "      <td>25378</td>\n",
       "      <td>25378.572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   children  days_employed  dob_years            education  education_id  \\\n",
       "0         1           8437         42    bachelor's degree             0   \n",
       "1         1           4024         36  secondary education             1   \n",
       "2         0           5623         33  secondary education             1   \n",
       "3         3           4124         32  secondary education             1   \n",
       "4         0         340266         53  secondary education             1   \n",
       "\n",
       "       family_status  family_status_id gender income_type  debt  total_income  \\\n",
       "0            married                 0      F    employee     0     40620.102   \n",
       "1            married                 0      F    employee     0     17932.802   \n",
       "2            married                 0      M    employee     0     23341.752   \n",
       "3            married                 0      M    employee     0     42820.568   \n",
       "4  civil partnership                 1      F     retiree     0     25378.572   \n",
       "\n",
       "                   purpose  clean_income1  clean_income2  clean_income3  \n",
       "0    purchase of the house      40620.102          40620      40620.102  \n",
       "1             car purchase      17932.802          17932      17932.802  \n",
       "2    purchase of the house      23341.752          23341      23341.752  \n",
       "3  supplementary education      42820.568          42820      42820.568  \n",
       "4        to have a wedding      25378.572          25378      25378.572  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring = credit_scoring.drop_duplicates().reset_index(drop=True)\n",
    "credit_scoring.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"section_02_4_1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see there are 3 duplicate rows for the customer in age 71, who has secondary education, in civil partnership, female, retired, and needs loan fot having a wedding. We can't be sure that there was triple entry from the same customer and not from different 71-years old women. We should check how data was collected with different departments, ask data engineers. In this case we can drop these duplicates. There are only 71, so it won't affect on the total result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"section_02_5\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Back to table of contents](#back1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorizing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to make categorization - to organize selected data into groups according to specific criterias. We'll create a list of categories from the given data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's categorize our customers according to their's income. Call the `describe()` method to `total_income` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     21454.000000\n",
       "mean      26472.049128\n",
       "std       15726.191978\n",
       "min        3306.000000\n",
       "25%       17202.000000\n",
       "50%       22993.000000\n",
       "75%       31728.750000\n",
       "max      362496.000000\n",
       "Name: clean_income2, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring.clean_income2.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write function using the keyword `def`. Let's accept that income from 0 to 10000 equal to `poverty` category, from 10000 to 23000 - `poor`, from 23000 to 50000 - `middle-class`, from 50000 to 100000 - `wealthy`, from 100000 - `rich`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_income2_cat(x):\n",
    "    if x>=0 and x<=10000:\n",
    "        return 'poverty'\n",
    "    if x>10000 and x<=23000:\n",
    "        return 'poor'\n",
    "    if x>23000 and x<=50000:\n",
    "        return 'middle-class'\n",
    "    if x>50000 and x<=100000:\n",
    "        return 'wealthy'\n",
    "    if x>100000:\n",
    "        return 'rich'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check how our function works by creating the new column and applying the function to column `clean_income2`. Use the `apply()` method. And print dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit_scoring['income_level'] = credit_scoring['clean_income2'].apply(clean_income2_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "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",
       "\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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "      <th>clean_income1</th>\n",
       "      <th>clean_income2</th>\n",
       "      <th>clean_income3</th>\n",
       "      <th>income_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8437</td>\n",
       "      <td>42</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>0</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>40620.102</td>\n",
       "      <td>purchase of the house</td>\n",
       "      <td>40620.102</td>\n",
       "      <td>40620</td>\n",
       "      <td>40620.102</td>\n",
       "      <td>middle-class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4024</td>\n",
       "      <td>36</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>17932.802</td>\n",
       "      <td>car purchase</td>\n",
       "      <td>17932.802</td>\n",
       "      <td>17932</td>\n",
       "      <td>17932.802</td>\n",
       "      <td>poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>5623</td>\n",
       "      <td>33</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>23341.752</td>\n",
       "      <td>purchase of the house</td>\n",
       "      <td>23341.752</td>\n",
       "      <td>23341</td>\n",
       "      <td>23341.752</td>\n",
       "      <td>middle-class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4124</td>\n",
       "      <td>32</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>42820.568</td>\n",
       "      <td>supplementary education</td>\n",
       "      <td>42820.568</td>\n",
       "      <td>42820</td>\n",
       "      <td>42820.568</td>\n",
       "      <td>middle-class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>340266</td>\n",
       "      <td>53</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>civil partnership</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>retiree</td>\n",
       "      <td>0</td>\n",
       "      <td>25378.572</td>\n",
       "      <td>to have a wedding</td>\n",
       "      <td>25378.572</td>\n",
       "      <td>25378</td>\n",
       "      <td>25378.572</td>\n",
       "      <td>middle-class</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   children  days_employed  dob_years            education  education_id  \\\n",
       "0         1           8437         42    bachelor's degree             0   \n",
       "1         1           4024         36  secondary education             1   \n",
       "2         0           5623         33  secondary education             1   \n",
       "3         3           4124         32  secondary education             1   \n",
       "4         0         340266         53  secondary education             1   \n",
       "\n",
       "       family_status  family_status_id gender income_type  debt  total_income  \\\n",
       "0            married                 0      F    employee     0     40620.102   \n",
       "1            married                 0      F    employee     0     17932.802   \n",
       "2            married                 0      M    employee     0     23341.752   \n",
       "3            married                 0      M    employee     0     42820.568   \n",
       "4  civil partnership                 1      F     retiree     0     25378.572   \n",
       "\n",
       "                   purpose  clean_income1  clean_income2  clean_income3  \\\n",
       "0    purchase of the house      40620.102          40620      40620.102   \n",
       "1             car purchase      17932.802          17932      17932.802   \n",
       "2    purchase of the house      23341.752          23341      23341.752   \n",
       "3  supplementary education      42820.568          42820      42820.568   \n",
       "4        to have a wedding      25378.572          25378      25378.572   \n",
       "\n",
       "   income_level  \n",
       "0  middle-class  \n",
       "1          poor  \n",
       "2  middle-class  \n",
       "3  middle-class  \n",
       "4  middle-class  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21454 entries, 0 to 21453\n",
      "Data columns (total 16 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   children          21454 non-null  int64  \n",
      " 1   days_employed     21454 non-null  int64  \n",
      " 2   dob_years         21454 non-null  int64  \n",
      " 3   education         21454 non-null  object \n",
      " 4   education_id      21454 non-null  int64  \n",
      " 5   family_status     21454 non-null  object \n",
      " 6   family_status_id  21454 non-null  int64  \n",
      " 7   gender            21454 non-null  object \n",
      " 8   income_type       21454 non-null  object \n",
      " 9   debt              21454 non-null  int64  \n",
      " 10  total_income      19351 non-null  float64\n",
      " 11  purpose           21454 non-null  object \n",
      " 12  clean_income1     21454 non-null  float64\n",
      " 13  clean_income2     21454 non-null  int64  \n",
      " 14  clean_income3     21453 non-null  float64\n",
      " 15  income_level      21454 non-null  object \n",
      "dtypes: float64(3), int64(7), object(6)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "credit_scoring.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check how many customers in the each category grouping by `income_level` and using the `count()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "income_level\n",
       "middle-class    9403\n",
       "poor            9803\n",
       "poverty          927\n",
       "rich              99\n",
       "wealthy         1222\n",
       "Name: income_level, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring.groupby('income_level')['income_level'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's categorize our dataset according to purposes. We should check what purposes we have in the `purpose` column by calling the `unique()` method for this column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['purchase of the house', 'car purchase', 'supplementary education',\n",
       "       'to have a wedding', 'housing transactions', 'education',\n",
       "       'having a wedding', 'purchase of the house for my family',\n",
       "       'buy real estate', 'buy commercial real estate',\n",
       "       'buy residential real estate', 'construction of own property',\n",
       "       'property', 'building a property', 'buying a second-hand car',\n",
       "       'buying my own car', 'transactions with commercial real estate',\n",
       "       'building a real estate', 'housing',\n",
       "       'transactions with my real estate', 'cars', 'to become educated',\n",
       "       'second-hand car purchase', 'getting an education', 'car',\n",
       "       'wedding ceremony', 'to get a supplementary education',\n",
       "       'purchase of my own house', 'real estate transactions',\n",
       "       'getting higher education', 'to own a car', 'purchase of a car',\n",
       "       'profile education', 'university education',\n",
       "       'buying property for renting out', 'to buy a car',\n",
       "       'housing renovation', 'going to university'], dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring.purpose.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can see all the purposes, so we can create 4 categories and make list of key words for each of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "wedding_category = ['wedding','ceremony']\n",
    "house_category = ['house', 'real','estate','property','housing']\n",
    "car_category = ['car','cars']\n",
    "education_category = ['education','university', 'educated']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = Mystem()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's build up the function for lemmatization words and check if it works properly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lemmatization_func(line):\n",
    "    lemmatized = m.lemmatize(line)\n",
    "    return lemmatized"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check how it works. Take example of sentence from the column `purpose` from the first row and save into variable `example`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'purchase of the house'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example = credit_scoring.loc[0]['purpose']\n",
    "example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's lemmatize this example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['purchase', ' ', 'of', ' ', 'the', ' ', 'house', '\\n']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lemmatization_func(example)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now write and check `any()` function. It should return `True`, if it finds any word in `lemmatization_func(example)` that matches any word from list of words in variable `house_category`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "any(word in lemmatization_func(example) for word in house_category)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here write the same for list of words in variable `wedding_category`. It should return `False`, because in our variable `example` it won't find any word that matches any word from list of words in `wedding_category`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "any(word in lemmatization_func(example) for word in wedding_category)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It works properly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we should modify our `lemmatization_func(line)` function, so that it will check lemmatized words in each category list and will return name of category like `wedding`, `car`, `education`, `house` or `other` if it's doesn't match any words from categories lists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lemmatization_func(line):\n",
    "    lemmatized = m.lemmatize(line)\n",
    "    if any(word in lemmatized for word in wedding_category):\n",
    "        return 'wedding'\n",
    "    elif any(word in lemmatized for word in car_category):\n",
    "        return 'car'\n",
    "    elif any(word in lemmatized for word in education_category):\n",
    "        return 'education'\n",
    "    elif any(word in lemmatized for word in house_category):\n",
    "        return 'house'\n",
    "    else:\n",
    "        return 'other'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add new column `clean_purpose` to our dataset in order more clearly categorize loan puproses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "credit_scoring['clean_purpose'] = credit_scoring['purpose'].apply(lemmatization_func)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, check how many loans were for different purpose categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "house        10811\n",
       "car           4306\n",
       "education     4013\n",
       "wedding       2324\n",
       "Name: clean_purpose, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring['clean_purpose'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We categorized all our purposes into just 4 categories. Let's check our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "      <th>clean_income1</th>\n",
       "      <th>clean_income2</th>\n",
       "      <th>clean_income3</th>\n",
       "      <th>income_level</th>\n",
       "      <th>clean_purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8437</td>\n",
       "      <td>42</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>0</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>40620.102</td>\n",
       "      <td>purchase of the house</td>\n",
       "      <td>40620.102</td>\n",
       "      <td>40620</td>\n",
       "      <td>40620.102</td>\n",
       "      <td>middle-class</td>\n",
       "      <td>house</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4024</td>\n",
       "      <td>36</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>17932.802</td>\n",
       "      <td>car purchase</td>\n",
       "      <td>17932.802</td>\n",
       "      <td>17932</td>\n",
       "      <td>17932.802</td>\n",
       "      <td>poor</td>\n",
       "      <td>car</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>5623</td>\n",
       "      <td>33</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>23341.752</td>\n",
       "      <td>purchase of the house</td>\n",
       "      <td>23341.752</td>\n",
       "      <td>23341</td>\n",
       "      <td>23341.752</td>\n",
       "      <td>middle-class</td>\n",
       "      <td>house</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4124</td>\n",
       "      <td>32</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>42820.568</td>\n",
       "      <td>supplementary education</td>\n",
       "      <td>42820.568</td>\n",
       "      <td>42820</td>\n",
       "      <td>42820.568</td>\n",
       "      <td>middle-class</td>\n",
       "      <td>education</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>340266</td>\n",
       "      <td>53</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>civil partnership</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>retiree</td>\n",
       "      <td>0</td>\n",
       "      <td>25378.572</td>\n",
       "      <td>to have a wedding</td>\n",
       "      <td>25378.572</td>\n",
       "      <td>25378</td>\n",
       "      <td>25378.572</td>\n",
       "      <td>middle-class</td>\n",
       "      <td>wedding</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   children  days_employed  dob_years            education  education_id  \\\n",
       "0         1           8437         42    bachelor's degree             0   \n",
       "1         1           4024         36  secondary education             1   \n",
       "2         0           5623         33  secondary education             1   \n",
       "3         3           4124         32  secondary education             1   \n",
       "4         0         340266         53  secondary education             1   \n",
       "\n",
       "       family_status  family_status_id gender income_type  debt  total_income  \\\n",
       "0            married                 0      F    employee     0     40620.102   \n",
       "1            married                 0      F    employee     0     17932.802   \n",
       "2            married                 0      M    employee     0     23341.752   \n",
       "3            married                 0      M    employee     0     42820.568   \n",
       "4  civil partnership                 1      F     retiree     0     25378.572   \n",
       "\n",
       "                   purpose  clean_income1  clean_income2  clean_income3  \\\n",
       "0    purchase of the house      40620.102          40620      40620.102   \n",
       "1             car purchase      17932.802          17932      17932.802   \n",
       "2    purchase of the house      23341.752          23341      23341.752   \n",
       "3  supplementary education      42820.568          42820      42820.568   \n",
       "4        to have a wedding      25378.572          25378      25378.572   \n",
       "\n",
       "   income_level clean_purpose  \n",
       "0  middle-class         house  \n",
       "1          poor           car  \n",
       "2  middle-class         house  \n",
       "3  middle-class     education  \n",
       "4  middle-class       wedding  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_scoring.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"section_02_5_1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We categorized our customers by income and purpose to get loan. Now we can group them to different categories by their key characteristic. According to this categories we will be able to analyse relation between different categories of customers and their ability to repay a loan on time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Back to table of contents](#back1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"chapter03\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer the questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"section_03_1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Back to table of contents](#back1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Is there a relation between having kids and repaying a loan on time?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build pivot table using `pivot_table(data, index, columns, values, aggfunc, margins=True)` method, where `index`: the column or columns whose values become the indices in the pivot table, `columns`: the column or columns whose values become columns in the pivot table, `values`: the values we want to aggregate in the pivot table, `aggfunc`: the aggregate function that we're applying to the values, `margins`: the argument to include row/column totals."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look how having kids influnce on repaying a loan on time. Where `0` in debt means that loan was repayed on time, and `1` in debt means that loan was defaulted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>debt</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13028.0</td>\n",
       "      <td>1063.0</td>\n",
       "      <td>14091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4410.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>4855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1926.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>2128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>303.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>19713.0</td>\n",
       "      <td>1741.0</td>\n",
       "      <td>21454</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "debt            0       1    All\n",
       "children                        \n",
       "0         13028.0  1063.0  14091\n",
       "1          4410.0   445.0   4855\n",
       "2          1926.0   202.0   2128\n",
       "3           303.0    27.0    330\n",
       "4            37.0     4.0     41\n",
       "5             9.0     NaN      9\n",
       "All       19713.0  1741.0  21454"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "children_status = pd.pivot_table(credit_scoring, index = 'children', columns = 'debt', \n",
    "                              values = 'family_status_id', aggfunc = 'count', margins=True)\n",
    "children_status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate `default_rate` for customers with different children status and apply new column to our pivot table (`default_rate`). For this divide column with defaulted loans (has index `[1]`, because column `children` became index in our pivot table) by overall amount of loans for each amount of kids (`[All]`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "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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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>debt</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "      <th>default_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13028.0</td>\n",
       "      <td>1063.0</td>\n",
       "      <td>14091</td>\n",
       "      <td>7.543822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4410.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>4855</td>\n",
       "      <td>9.165808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1926.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>2128</td>\n",
       "      <td>9.492481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>303.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>330</td>\n",
       "      <td>8.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>41</td>\n",
       "      <td>9.756098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>19713.0</td>\n",
       "      <td>1741.0</td>\n",
       "      <td>21454</td>\n",
       "      <td>8.115037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "debt            0       1    All  default_rate\n",
       "children                                      \n",
       "0         13028.0  1063.0  14091      7.543822\n",
       "1          4410.0   445.0   4855      9.165808\n",
       "2          1926.0   202.0   2128      9.492481\n",
       "3           303.0    27.0    330      8.181818\n",
       "4            37.0     4.0     41      9.756098\n",
       "5             9.0     NaN      9           NaN\n",
       "All       19713.0  1741.0  21454      8.115037"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "children_status['default_rate'] = (children_status[1] / children_status['All']) * 100\n",
    "children_status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"section_03_1_1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "People with ***no children*** are the **best category** in term to the `default rate`, because their `default rate - 7.54%` below the `average default rate - 8.12%`. The **worst category** - people with ***4 children*** (`9.76%`). And with increasing the amount of children the default rate also increasing. It means that ***more children*** - **higher default risk**. Only people with 3 children are out of this rule and people with 5 children had no defaults. But we should keep in mind that it was only 9 people with 5 kids who took a loan. So it can be too small sample. Although maybe people with 5 children more responsible, because they know that have a lot of children to care about."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Recomendation** to the bank will be to check better people with more children. Our conclusion that people without children the most trusted group."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"section_03_2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Back to table of contents](#back1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Is there a relation between marital status and repaying a loan on time?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build pivot table to see how different **marital status** influnces on repaying a loan on time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>debt</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>family_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>civil partnership</th>\n",
       "      <td>3763</td>\n",
       "      <td>388</td>\n",
       "      <td>4151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>divorced</th>\n",
       "      <td>1110</td>\n",
       "      <td>85</td>\n",
       "      <td>1195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>11408</td>\n",
       "      <td>931</td>\n",
       "      <td>12339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unmarried</th>\n",
       "      <td>2536</td>\n",
       "      <td>274</td>\n",
       "      <td>2810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>widow / widower</th>\n",
       "      <td>896</td>\n",
       "      <td>63</td>\n",
       "      <td>959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>19713</td>\n",
       "      <td>1741</td>\n",
       "      <td>21454</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "debt                   0     1    All\n",
       "family_status                        \n",
       "civil partnership   3763   388   4151\n",
       "divorced            1110    85   1195\n",
       "married            11408   931  12339\n",
       "unmarried           2536   274   2810\n",
       "widow / widower      896    63    959\n",
       "All                19713  1741  21454"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "family_status = pd.pivot_table(credit_scoring, index = 'family_status', columns = 'debt', \n",
    "                               values = 'family_status_id', aggfunc = 'count', margins=True)\n",
    "family_status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate `default_rate` for customers with different marital status."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>debt</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "      <th>default_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>family_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>civil partnership</th>\n",
       "      <td>3763</td>\n",
       "      <td>388</td>\n",
       "      <td>4151</td>\n",
       "      <td>9.347145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>divorced</th>\n",
       "      <td>1110</td>\n",
       "      <td>85</td>\n",
       "      <td>1195</td>\n",
       "      <td>7.112971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>11408</td>\n",
       "      <td>931</td>\n",
       "      <td>12339</td>\n",
       "      <td>7.545182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unmarried</th>\n",
       "      <td>2536</td>\n",
       "      <td>274</td>\n",
       "      <td>2810</td>\n",
       "      <td>9.750890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>widow / widower</th>\n",
       "      <td>896</td>\n",
       "      <td>63</td>\n",
       "      <td>959</td>\n",
       "      <td>6.569343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>19713</td>\n",
       "      <td>1741</td>\n",
       "      <td>21454</td>\n",
       "      <td>8.115037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "debt                   0     1    All  default_rate\n",
       "family_status                                      \n",
       "civil partnership   3763   388   4151      9.347145\n",
       "divorced            1110    85   1195      7.112971\n",
       "married            11408   931  12339      7.545182\n",
       "unmarried           2536   274   2810      9.750890\n",
       "widow / widower      896    63    959      6.569343\n",
       "All                19713  1741  21454      8.115037"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "family_status['default_rate'] = (family_status[1] / family_status['All']) * 100\n",
    "family_status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's double check our result by grouping by `children` and `family_status` in pivot table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>debt</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>family_status</th>\n",
       "      <th>children</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">civil partnership</th>\n",
       "      <th>0</th>\n",
       "      <td>2501.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>2730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>882.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>322.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">divorced</th>\n",
       "      <th>0</th>\n",
       "      <td>729.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>295.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">married</th>\n",
       "      <th>0</th>\n",
       "      <td>6952.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>7468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2757.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>3004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1434.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>1582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>232.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">unmarried</th>\n",
       "      <th>0</th>\n",
       "      <td>2052.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>2262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>402.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>74.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">widow / widower</th>\n",
       "      <th>0</th>\n",
       "      <td>794.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>74.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <th></th>\n",
       "      <td>19713.0</td>\n",
       "      <td>1741.0</td>\n",
       "      <td>21454</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "debt                              0       1    All\n",
       "family_status     children                        \n",
       "civil partnership 0          2501.0   229.0   2730\n",
       "                  1           882.0   118.0   1000\n",
       "                  2           322.0    33.0    355\n",
       "                  3            48.0     8.0     56\n",
       "                  4             8.0     NaN      8\n",
       "                  5             2.0     NaN      2\n",
       "divorced          0           729.0    55.0    784\n",
       "                  1           295.0    21.0    316\n",
       "                  2            75.0     8.0     83\n",
       "                  3            10.0     1.0     11\n",
       "                  4             1.0     NaN      1\n",
       "married           0          6952.0   516.0   7468\n",
       "                  1          2757.0   247.0   3004\n",
       "                  2          1434.0   148.0   1582\n",
       "                  3           232.0    17.0    249\n",
       "                  4            26.0     3.0     29\n",
       "                  5             7.0     NaN      7\n",
       "unmarried         0          2052.0   210.0   2262\n",
       "                  1           402.0    52.0    454\n",
       "                  2            74.0    10.0     84\n",
       "                  3             7.0     1.0      8\n",
       "                  4             1.0     1.0      2\n",
       "widow / widower   0           794.0    53.0    847\n",
       "                  1            74.0     7.0     81\n",
       "                  2            21.0     3.0     24\n",
       "                  3             6.0     NaN      6\n",
       "                  4             1.0     NaN      1\n",
       "All                         19713.0  1741.0  21454"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "family_status_kids = pd.pivot_table(credit_scoring, index = ['family_status','children'], columns = 'debt', \n",
    "                               values = 'family_status_id', aggfunc = 'count', margins=True)\n",
    "family_status_kids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>debt</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "      <th>default_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>family_status</th>\n",
       "      <th>children</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">civil partnership</th>\n",
       "      <th>0</th>\n",
       "      <td>2501.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>2730</td>\n",
       "      <td>8.388278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>882.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>11.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>322.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>355</td>\n",
       "      <td>9.295775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>56</td>\n",
       "      <td>14.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">divorced</th>\n",
       "      <th>0</th>\n",
       "      <td>729.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>784</td>\n",
       "      <td>7.015306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>295.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>316</td>\n",
       "      <td>6.645570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>83</td>\n",
       "      <td>9.638554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11</td>\n",
       "      <td>9.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">married</th>\n",
       "      <th>0</th>\n",
       "      <td>6952.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>7468</td>\n",
       "      <td>6.909480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2757.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>3004</td>\n",
       "      <td>8.222370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1434.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>1582</td>\n",
       "      <td>9.355247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>232.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>249</td>\n",
       "      <td>6.827309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>29</td>\n",
       "      <td>10.344828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">unmarried</th>\n",
       "      <th>0</th>\n",
       "      <td>2052.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>2262</td>\n",
       "      <td>9.283820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>402.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>454</td>\n",
       "      <td>11.453744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>74.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>84</td>\n",
       "      <td>11.904762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8</td>\n",
       "      <td>12.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">widow / widower</th>\n",
       "      <th>0</th>\n",
       "      <td>794.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>847</td>\n",
       "      <td>6.257379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>74.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>81</td>\n",
       "      <td>8.641975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24</td>\n",
       "      <td>12.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <th></th>\n",
       "      <td>19713.0</td>\n",
       "      <td>1741.0</td>\n",
       "      <td>21454</td>\n",
       "      <td>8.115037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "debt                              0       1    All  default_rate\n",
       "family_status     children                                      \n",
       "civil partnership 0          2501.0   229.0   2730      8.388278\n",
       "                  1           882.0   118.0   1000     11.800000\n",
       "                  2           322.0    33.0    355      9.295775\n",
       "                  3            48.0     8.0     56     14.285714\n",
       "                  4             8.0     NaN      8           NaN\n",
       "                  5             2.0     NaN      2           NaN\n",
       "divorced          0           729.0    55.0    784      7.015306\n",
       "                  1           295.0    21.0    316      6.645570\n",
       "                  2            75.0     8.0     83      9.638554\n",
       "                  3            10.0     1.0     11      9.090909\n",
       "                  4             1.0     NaN      1           NaN\n",
       "married           0          6952.0   516.0   7468      6.909480\n",
       "                  1          2757.0   247.0   3004      8.222370\n",
       "                  2          1434.0   148.0   1582      9.355247\n",
       "                  3           232.0    17.0    249      6.827309\n",
       "                  4            26.0     3.0     29     10.344828\n",
       "                  5             7.0     NaN      7           NaN\n",
       "unmarried         0          2052.0   210.0   2262      9.283820\n",
       "                  1           402.0    52.0    454     11.453744\n",
       "                  2            74.0    10.0     84     11.904762\n",
       "                  3             7.0     1.0      8     12.500000\n",
       "                  4             1.0     1.0      2     50.000000\n",
       "widow / widower   0           794.0    53.0    847      6.257379\n",
       "                  1            74.0     7.0     81      8.641975\n",
       "                  2            21.0     3.0     24     12.500000\n",
       "                  3             6.0     NaN      6           NaN\n",
       "                  4             1.0     NaN      1           NaN\n",
       "All                         19713.0  1741.0  21454      8.115037"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "family_status_kids['default_rate'] = (family_status_kids[1] / family_status_kids['All']) * 100\n",
    "family_status_kids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"section_03_2_1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Married, divorced people and widow/widower** the most trusted categories, because of lower default rate (below the `average 8.12%`). People in ***civil partnership*** and ***unmarried*** have **high default rate** and have to be checked carefully. We see that unmarried people have high default rate especially with 4 kids - 50%, widows/widowers with 2 children also have high default rate -12.5%. People in civil relationships also have high default risk. Probably the **best option** for bank it's ***married people with no kids or 1 kid***. And also divorced without kids or with 1 kid and widow/widower without kids."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"section_03_3\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Back to table of contents](#back1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Is there a relation between income level and repaying a loan on time?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build pivot table for different income level to find out how it influences on repaying a loan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>debt</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income_level</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>middle-class</th>\n",
       "      <td>8666</td>\n",
       "      <td>737</td>\n",
       "      <td>9403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poor</th>\n",
       "      <td>8949</td>\n",
       "      <td>854</td>\n",
       "      <td>9803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poverty</th>\n",
       "      <td>869</td>\n",
       "      <td>58</td>\n",
       "      <td>927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rich</th>\n",
       "      <td>93</td>\n",
       "      <td>6</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wealthy</th>\n",
       "      <td>1136</td>\n",
       "      <td>86</td>\n",
       "      <td>1222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>19713</td>\n",
       "      <td>1741</td>\n",
       "      <td>21454</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "debt              0     1    All\n",
       "income_level                    \n",
       "middle-class   8666   737   9403\n",
       "poor           8949   854   9803\n",
       "poverty         869    58    927\n",
       "rich             93     6     99\n",
       "wealthy        1136    86   1222\n",
       "All           19713  1741  21454"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_level = pd.pivot_table(credit_scoring, index = 'income_level', columns = 'debt', \n",
    "                               values = 'family_status_id', aggfunc = 'count', margins=True)\n",
    "income_level"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate default rate for people with different income level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>debt</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "      <th>default_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income_level</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>middle-class</th>\n",
       "      <td>8666</td>\n",
       "      <td>737</td>\n",
       "      <td>9403</td>\n",
       "      <td>7.837924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poor</th>\n",
       "      <td>8949</td>\n",
       "      <td>854</td>\n",
       "      <td>9803</td>\n",
       "      <td>8.711619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poverty</th>\n",
       "      <td>869</td>\n",
       "      <td>58</td>\n",
       "      <td>927</td>\n",
       "      <td>6.256742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rich</th>\n",
       "      <td>93</td>\n",
       "      <td>6</td>\n",
       "      <td>99</td>\n",
       "      <td>6.060606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wealthy</th>\n",
       "      <td>1136</td>\n",
       "      <td>86</td>\n",
       "      <td>1222</td>\n",
       "      <td>7.037643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>19713</td>\n",
       "      <td>1741</td>\n",
       "      <td>21454</td>\n",
       "      <td>8.115037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "debt              0     1    All  default_rate\n",
       "income_level                                  \n",
       "middle-class   8666   737   9403      7.837924\n",
       "poor           8949   854   9803      8.711619\n",
       "poverty         869    58    927      6.256742\n",
       "rich             93     6     99      6.060606\n",
       "wealthy        1136    86   1222      7.037643\n",
       "All           19713  1741  21454      8.115037"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_level['default_rate'] = (income_level[1] / income_level['All']) * 100\n",
    "income_level"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"section_03_3_1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "People with income level group like **rich, wealthy and poverty** are repaying their loans better and have **lower default rate** than poor and middle-class. Maybe people in `poverty` category take less money for loan or repay carefully and safe money to return it to the bank. Maybe people with small total income have less kids. We don't know if it's true overall, but for our dataset we've got this result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"section_03_4\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Back to table of contents](#back1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How do different loan purposes affect on-time repayment of the loan?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build pivot table for different loan purposes to find out how it influences on repaying a loan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>debt</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clean_purpose</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>car</th>\n",
       "      <td>3903</td>\n",
       "      <td>403</td>\n",
       "      <td>4306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <td>3643</td>\n",
       "      <td>370</td>\n",
       "      <td>4013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>house</th>\n",
       "      <td>10029</td>\n",
       "      <td>782</td>\n",
       "      <td>10811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wedding</th>\n",
       "      <td>2138</td>\n",
       "      <td>186</td>\n",
       "      <td>2324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>19713</td>\n",
       "      <td>1741</td>\n",
       "      <td>21454</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "debt               0     1    All\n",
       "clean_purpose                    \n",
       "car             3903   403   4306\n",
       "education       3643   370   4013\n",
       "house          10029   782  10811\n",
       "wedding         2138   186   2324\n",
       "All            19713  1741  21454"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_purpose = pd.pivot_table(credit_scoring, index = 'clean_purpose', columns = 'debt', \n",
    "                               values = 'family_status_id', aggfunc = 'count', margins=True)\n",
    "loan_purpose"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate `default rate` for people with different loan purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "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>debt</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "      <th>default_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clean_purpose</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>car</th>\n",
       "      <td>3903</td>\n",
       "      <td>403</td>\n",
       "      <td>4306</td>\n",
       "      <td>9.359034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <td>3643</td>\n",
       "      <td>370</td>\n",
       "      <td>4013</td>\n",
       "      <td>9.220035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>house</th>\n",
       "      <td>10029</td>\n",
       "      <td>782</td>\n",
       "      <td>10811</td>\n",
       "      <td>7.233373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wedding</th>\n",
       "      <td>2138</td>\n",
       "      <td>186</td>\n",
       "      <td>2324</td>\n",
       "      <td>8.003442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>19713</td>\n",
       "      <td>1741</td>\n",
       "      <td>21454</td>\n",
       "      <td>8.115037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "debt               0     1    All  default_rate\n",
       "clean_purpose                                  \n",
       "car             3903   403   4306      9.359034\n",
       "education       3643   370   4013      9.220035\n",
       "house          10029   782  10811      7.233373\n",
       "wedding         2138   186   2324      8.003442\n",
       "All            19713  1741  21454      8.115037"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_purpose['default_rate'] = (loan_purpose[1] / loan_purpose['All']) * 100\n",
    "loan_purpose"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"section_03_4_1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Lower default risk** have people who take ***loans for houses***. And such purposes like ***buying car and education*** have **high risk** not repaying loans on time. Probably, students have less income level, have less time to work or maybe don't have degree yet to work on high-payed job. And people who buy real estate can rent it and get passive income, because a lot of people who buy property already have some money and want to invest it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"chapter04\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Back to table of contents](#back1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## General conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We examined and prepared our data to analysis. We found out in data some strange values like negative `days_employed` column and `0` values in the customer's age. Probably it was mistake in inserting data. So we would notify engineers about that and skiped this columns, because we shouldn't use these. Also we had weird numbers of children like `-1` and `20`. We thougt that it's typos and replaced it with `1` and `2`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We found missing values in `total_income`. We grouped all customers by `income_type` and `education` (we've got closer result to original data than other combination). We found median total income for each group. So we could fill missing values in with median values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After that we transofmed all education values to lowercase because it was too messed up. We replaced data type of salary to integer so it would be cleaner and easier to work with it. We found out 71 duplicate rows, so we delated it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We categorized customers by their's total income to 5 groupes: `poverty` with income `less than 10000`, `poor` with income `from 10000 to 23000`, `middle-class` with income `from 23000 to 50000`, `wealthy` with income `from 50000 to 100000` and `rich` - `more than 100000`. And got `9803 loaners in poor class`, `9403 in middle-class`, `927 in poverty class`, `1222 - wealthy` and `99 - rich`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We categorized customers by their loan purpose on `4 groups` - `car`, `house`, `wedding` and `education`. And we've got `10811` loaners who took a loan `to buy house`, `4306 - to buy car`, `4013 - on education` and `2324 - on wedding`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answering all of 4 questions we figured out that the **best categories** in terms of the loan are ***married people (default rate - 7.55%), without children (default rate - 7.55% too), whose total income more than 50000 (default rate - from 6 to 7%)*** and ***middle class with total income more than 23000 (default rate - 7.83%), who want to buy house (default rate - 7.23%)***. These are the most `truthworthy` loaners."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **worst categories** - ***people with kids (default rate - from 8.18% to 9.76%), people in civil partnership (default rate - 9.35%) and unmarried (default rate - 9.75%), whose total income less than 23000 (default rate - 8.71%), who want to buy car (default rate - 9.36%) and/or pay for education (default rate - 9.22%)***. So we `recommend` to bank to `lower credits for these categories`, `monitor them more properly` and `put to the risky folder`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Back to table of contents](#back1)"
   ]
  }
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