Data Preprocessing .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hello Johannah!\n",
    "\n",
    "My name is Dmitry.  I'm glad to review your work today.\n",
    "I will mark your mistakes and give you some hints how it is possible to fix them. We are getting ready for real job, where your team leader/senior colleague will do exactly the same. Don't worry and study with pleasure! \n",
    "\n",
    "Below you will find my comments - **please do not move, modify or delete them**.\n",
    "\n",
    "You can find my comments in green, yellow or red boxes like this:\n",
    "\n",
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Success. Everything is done succesfully.\n",
    "</div>\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Remarks. Some recommendations.\n",
    "</div>\n",
    "\n",
    "<div class=\"alert alert-block alert-danger\">\n",
    "\n",
    "<b>Reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Needs fixing. The block requires some corrections. Work can't be accepted with the red comments.\n",
    "</div>\n",
    "\n",
    "You can answer me by using this:\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Student answer.</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Text here.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing borrowers’ risk of defaulting\n",
    "\n",
    "My project is to prepare a report for a bank’s loan division. I’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",
    "My report will be considered when building the **credit score** of a potential customer. The **credit score** is used to evaluate the ability of a potential borrower to repay their loan.\n",
    "\n",
    "My hypothesis is that there is a coorelation between a borrowers chance of defaulting on a loan when looking at their income, education and reason for requesting the loan. I will test my hypothesis in the following analysis \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Great start with an introduction!\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Open the data file and have a look at the general information. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "df= pd.read_csv('/datasets/credit_scoring_eng.csv')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 1. Data exploration\n",
    "\n",
    "**Description of the data**\n",
    "- `children` - the number of children in the family\n",
    "- `days_employed` - work experience in days\n",
    "- `dob_years` - client's age in years\n",
    "- `education` - client's education\n",
    "- `education_id` - education identifier\n",
    "- `family_status` - marital status\n",
    "- `family_status_id` - marital status identifier\n",
    "- `gender` - gender of the client\n",
    "- `income_type` - type of employment\n",
    "- `debt` - was there any debt on loan repayment\n",
    "- `total_income` - monthly income\n",
    "- `purpose` - the purpose of obtaining a loan\n",
    "\n",
    "Now let's explore the data. I want to see how many columns and rows it has, and look at a few rows to check for potential issues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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_id</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.538908</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.381587</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.000000</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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20.000000</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",
       "    </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       0.538908   63046.497661     43.293380      0.817236   \n",
       "std        1.381587  140827.311974     12.574584      0.548138   \n",
       "min       -1.000000  -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       20.000000  401755.400475     75.000000      4.000000   \n",
       "\n",
       "       family_status_id          debt   total_income  \n",
       "count      21525.000000  21525.000000   19351.000000  \n",
       "mean           0.972544      0.080883   26787.568355  \n",
       "std            1.420324      0.272661   16475.450632  \n",
       "min            0.000000      0.000000    3306.762000  \n",
       "25%            0.000000      0.000000   16488.504500  \n",
       "50%            0.000000      0.000000   23202.870000  \n",
       "75%            1.000000      0.000000   32549.611000  \n",
       "max            4.000000      1.000000  362496.645000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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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",
       "    <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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "I am seeing that the education column has mixed upper and lower case useage, to be more uniform I will change that column to all lower case. I will also convert the float data type in the 'days_employed' ans 'total_income' columns to an integer type. The 'days_employed' column shows many negative values. I am not sure what that means and will need to investigate further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "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": [
    "df.info()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The columns 'days_employed' and 'total_income are missing some data. The rest of the columns appear to be complete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2174\n",
      "2174\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(df['days_employed'].isna().sum())\n",
    "print(df['total_income'].isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The missing data in the two columns appears to be symmetrical. In order to check this I will check all the rows with missing data to confirm the numbers are actually related. The code below indicates the missing values in the columns are also equal to the missing columns in the rows. The rows that are missing data for 'days_employed' are also missing data for 'total_income'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.isna().sum(axis='rows')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "children            2174\n",
      "days_employed          0\n",
      "dob_years           2174\n",
      "education           2174\n",
      "education_id        2174\n",
      "family_status       2174\n",
      "family_status_id    2174\n",
      "gender              2174\n",
      "income_type         2174\n",
      "debt                2174\n",
      "total_income           0\n",
      "purpose             2174\n",
      "dtype: int64\n",
      "    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  \n"
     ]
    }
   ],
   "source": [
    "# Let's apply multiple conditions for filtering data and look at the number of rows in the filtered table.\n",
    "filtered_df = df.loc[(df['days_employed'].isna())&(df['total_income'].isna())]\n",
    "\n",
    "print(filtered_df.count())\n",
    "print(filtered_df.head(5))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Intermediate conclusion**\n",
    "\n",
    "The number of rows in the filtered table matches the number of missing values. We can conclude that the missing data is missing from the same form in each entry. To determine if the missing values account for a significant piece of the data I will find the percentage of the missing values compared to the total dataset.\n",
    "\n",
    "\n",
    "\n",
    "[Explain your next steps and how they correlate with the conclusions you made so far.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Correct.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "children             0.000000\n",
       "days_employed       10.099884\n",
       "dob_years            0.000000\n",
       "education            0.000000\n",
       "education_id         0.000000\n",
       "family_status        0.000000\n",
       "family_status_id     0.000000\n",
       "gender               0.000000\n",
       "income_type          0.000000\n",
       "debt                 0.000000\n",
       "total_income        10.099884\n",
       "purpose              0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()*100/len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The missing data accounts for around 10% of the total dataset. This is significant, I will be investigating why the values are missing for these entries. I suspect employment status such as retirement and unemployment could be a reason why days employed and income would be empty.In my next steps I will look into these reasonings "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>children</th>\n",
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       "      <th>21311</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>buying property for renting out</td>\n",
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       "    <tr>\n",
       "      <th>21321</th>\n",
       "      <td>0</td>\n",
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       "      <td>Secondary Education</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>retiree</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>real estate transactions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21414</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
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       "      <th>21415</th>\n",
       "      <td>0</td>\n",
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       "      <td>purchase of a car</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>413 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       children  days_employed  dob_years            education  education_id  \\\n",
       "12            0            NaN         65  secondary education             1   \n",
       "29            0            NaN         63  secondary education             1   \n",
       "55            0            NaN         54  secondary education             1   \n",
       "67            0            NaN         52    bachelor's degree             0   \n",
       "145           0            NaN         62  secondary education             1   \n",
       "...         ...            ...        ...                  ...           ...   \n",
       "21311         0            NaN         49  secondary education             1   \n",
       "21321         0            NaN         56  Secondary Education             1   \n",
       "21414         0            NaN         65  secondary education             1   \n",
       "21415         0            NaN         54  secondary education             1   \n",
       "21423         0            NaN         63  secondary education             1   \n",
       "\n",
       "           family_status  family_status_id gender income_type  debt  \\\n",
       "12     civil partnership                 1      M     retiree     0   \n",
       "29             unmarried                 4      F     retiree     0   \n",
       "55     civil partnership                 1      F     retiree     1   \n",
       "67               married                 0      F     retiree     0   \n",
       "145              married                 0      M     retiree     0   \n",
       "...                  ...               ...    ...         ...   ...   \n",
       "21311            married                 0      F     retiree     0   \n",
       "21321            married                 0      F     retiree     0   \n",
       "21414            married                 0      F     retiree     0   \n",
       "21415            married                 0      F     retiree     0   \n",
       "21423            married                 0      M     retiree     0   \n",
       "\n",
       "       total_income                              purpose  \n",
       "12              NaN                    to have a wedding  \n",
       "29              NaN               building a real estate  \n",
       "55              NaN                    to have a wedding  \n",
       "67              NaN  purchase of the house for my family  \n",
       "145             NaN                  building a property  \n",
       "...             ...                                  ...  \n",
       "21311           NaN      buying property for renting out  \n",
       "21321           NaN             real estate transactions  \n",
       "21414           NaN             purchase of my own house  \n",
       "21415           NaN                 housing transactions  \n",
       "21423           NaN                    purchase of a car  \n",
       "\n",
       "[413 rows x 12 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "filtered_df[filtered_df['income_type']=='retiree']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>21489</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>21497</th>\n",
       "      <td>0</td>\n",
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       "      <td>BACHELOR'S DEGREE</td>\n",
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       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>21510</th>\n",
       "      <td>2</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
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       "<p>2174 rows × 12 columns</p>\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",
       "21489         2            NaN         47  Secondary Education             1   \n",
       "21495         1            NaN         50  secondary education             1   \n",
       "21497         0            NaN         48    BACHELOR'S DEGREE             0   \n",
       "21502         1            NaN         42  secondary education             1   \n",
       "21510         2            NaN         28  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",
       "21489            married                 0      M       business     0   \n",
       "21495  civil partnership                 1      F       employee     0   \n",
       "21497            married                 0      F       business     0   \n",
       "21502            married                 0      F       employee     0   \n",
       "21510            married                 0      F       employee     0   \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  \n",
       "...             ...                       ...  \n",
       "21489           NaN         purchase of a car  \n",
       "21495           NaN          wedding ceremony  \n",
       "21497           NaN       building a property  \n",
       "21502           NaN    building a real estate  \n",
       "21510           NaN              car purchase  \n",
       "\n",
       "[2174 rows x 12 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_df[filtered_df['days_employed'].isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on these filters I see that there is more detail to these missing values. Some seem to be random but others could be due to retirement or unemployment.This is definitly a blind spot in this data set ( that there is no option for reporting unemployment, or there is and people dont want to report it and seem unworthy of credit).  It may be better to fill with missing values with categorized medians/means. This way the blank incomes are standardized and more accurate than leaving them blank, especially for further analysis.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "**Possible reasons for missing values in data**\n",
    "\n",
    "I think the values may be missing due to random error, unemployment, and retirement. I have already seen some pattern showing retirement could be a reason for the missing data in the filtered tables bit now I will check the whole data set to see the distribution from a different angle.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "You are right!\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "       children  days_employed  dob_years            education  education_id  \\\n",
       "4             0  340266.072047         53  secondary education             1   \n",
       "12            0            NaN         65  secondary education             1   \n",
       "18            0  400281.136913         53  secondary education             1   \n",
       "24            1  338551.952911         57  secondary education             1   \n",
       "25            0  363548.489348         67  secondary education             1   \n",
       "...         ...            ...        ...                  ...           ...   \n",
       "21505         0  338904.866406         53  secondary education             1   \n",
       "21508         0  386497.714078         62  secondary education             1   \n",
       "21509         0  362161.054124         59    bachelor's degree             0   \n",
       "21518         0  373995.710838         59  SECONDARY EDUCATION             1   \n",
       "21521         0  343937.404131         67  secondary education             1   \n",
       "\n",
       "           family_status  family_status_id gender income_type  debt  \\\n",
       "4      civil partnership                 1      F     retiree     0   \n",
       "12     civil partnership                 1      M     retiree     0   \n",
       "18       widow / widower                 2      F     retiree     0   \n",
       "24             unmarried                 4      F     retiree     0   \n",
       "25               married                 0      M     retiree     0   \n",
       "...                  ...               ...    ...         ...   ...   \n",
       "21505  civil partnership                 1      M     retiree     0   \n",
       "21508            married                 0      M     retiree     0   \n",
       "21509            married                 0      M     retiree     0   \n",
       "21518            married                 0      F     retiree     0   \n",
       "21521            married                 0      F     retiree     0   \n",
       "\n",
       "       total_income                                   purpose  \n",
       "4         25378.572                         to have a wedding  \n",
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       "18         9091.804                  buying a second-hand car  \n",
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       "...             ...                                       ...  \n",
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       "\n",
       "[3856 rows x 12 columns]"
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   "source": [
    "\n",
    "df.loc[df['income_type']=='retiree']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "       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",
       "21489         2            NaN         47  Secondary Education             1   \n",
       "21495         1            NaN         50  secondary education             1   \n",
       "21497         0            NaN         48    BACHELOR'S DEGREE             0   \n",
       "21502         1            NaN         42  secondary education             1   \n",
       "21510         2            NaN         28  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",
       "21489            married                 0      M       business     0   \n",
       "21495  civil partnership                 1      F       employee     0   \n",
       "21497            married                 0      F       business     0   \n",
       "21502            married                 0      F       employee     0   \n",
       "21510            married                 0      F       employee     0   \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  \n",
       "...             ...                       ...  \n",
       "21489           NaN         purchase of a car  \n",
       "21495           NaN          wedding ceremony  \n",
       "21497           NaN       building a property  \n",
       "21502           NaN    building a real estate  \n",
       "21510           NaN              car purchase  \n",
       "\n",
       "[2174 rows x 12 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['days_employed'].isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Intermediate conclusion**\n",
    "\n",
    "Interesting! The distribution is not similar to that in the filtered table. This makes me think it could be due to random error.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusions**\n",
    "\n",
    "After looking into filtered tables and the wider dataset I did not find a patern in the distribution of the missing data. This leads me to believe it is random.\n",
    "Because the data is missing in total_income and days_employed I will need to consider the categories I can use to help fill the data and whether to use means or medians. Next I will transform the data by fixing the duplicates, redundant entries, incorrect artifacts, and missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Good job.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data transformation\n",
    "\n",
    "Ill go through each column to see where there are issues. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "secondary education    13750\n",
       "bachelor's degree       4718\n",
       "SECONDARY EDUCATION      772\n",
       "Secondary Education      711\n",
       "some college             668\n",
       "BACHELOR'S DEGREE        274\n",
       "Bachelor's Degree        268\n",
       "primary education        250\n",
       "Some College              47\n",
       "SOME COLLEGE              29\n",
       "PRIMARY EDUCATION         17\n",
       "Primary Education         15\n",
       "graduate degree            4\n",
       "GRADUATE DEGREE            1\n",
       "Graduate Degree            1\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df['education'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['education']= df['education'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "secondary education    15233\n",
       "bachelor's degree       5260\n",
       "some college             744\n",
       "primary education        282\n",
       "graduate degree            6\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df['education'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By lowering the case I was able to consolidate the values in the education column. Nice!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Great!\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0     14149\n",
       " 1      4818\n",
       " 2      2055\n",
       " 3       330\n",
       " 20       76\n",
       "-1        47\n",
       " 4        41\n",
       " 5         9\n",
       "Name: children, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['children'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " There are negative values in this column which must be accidental. I am also skeptical that there are 76 people with 20 children. I believe this is an accident as well. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.0603448275862069]\n"
     ]
    }
   ],
   "source": [
    "children_percentage= [(76+47)*100/df['children'].sum()]\n",
    "print(children_percentage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df[df.children !=-1]\n",
    "df=df[df.children !=20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because these values are making up only one percent of the data I think it is best to drop the rows where people reported having -1 or 20 children "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "We can also say -1 = 1 and 20 = 2\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    14149\n",
       "1     4818\n",
       "2     2055\n",
       "3      330\n",
       "4       41\n",
       "5        9\n",
       "Name: children, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['children'].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will check the 'days_employed' column because I noticed in the head() that there weres some negative values reported"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-327.685916     1\n",
       "-1580.622577    1\n",
       "-4122.460569    1\n",
       "-2828.237691    1\n",
       "-2636.090517    1\n",
       "               ..\n",
       "-201.643573     1\n",
       "-7120.517564    1\n",
       "-2146.884040    1\n",
       "-881.454684     1\n",
       "-3382.113891    1\n",
       "Name: days_employed, Length: 19240, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df['days_employed'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "children            73.866928\n",
      "days_employed       73.866928\n",
      "dob_years           73.866928\n",
      "education           73.866928\n",
      "education_id        73.866928\n",
      "family_status       73.866928\n",
      "family_status_id    73.866928\n",
      "gender              73.866928\n",
      "income_type         73.866928\n",
      "debt                73.866928\n",
      "total_income        73.866928\n",
      "purpose             73.866928\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "negative_days= df[df['days_employed']<0].count()\n",
    "print(negative_days*100/len(df['days_employed']))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "73% of the entries are negative in 'days_employed' this must be a technical issue. Perhaps they are using days passed? I will convert the negative days to positive. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['days_employed']= df['days_employed'].abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142.276217     1\n",
       "402.974768     1\n",
       "3601.450735    1\n",
       "1849.622944    1\n",
       "5849.845620    1\n",
       "              ..\n",
       "3951.652030    1\n",
       "847.043824     1\n",
       "1745.884477    1\n",
       "1801.512744    1\n",
       "1636.419775    1\n",
       "Name: days_employed, Length: 19240, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df['days_employed'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [children, days_employed, dob_years, education, education_id, family_status, family_status_id, gender, income_type, debt, total_income, purpose]\n",
       "Index: []"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.loc[df['days_employed']<0]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Well done!\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35    614\n",
       "40    603\n",
       "41    603\n",
       "34    597\n",
       "38    595\n",
       "42    592\n",
       "33    577\n",
       "39    572\n",
       "31    556\n",
       "36    553\n",
       "29    543\n",
       "44    543\n",
       "48    536\n",
       "30    536\n",
       "37    531\n",
       "43    510\n",
       "50    509\n",
       "32    506\n",
       "49    505\n",
       "28    501\n",
       "45    494\n",
       "27    490\n",
       "52    483\n",
       "56    482\n",
       "47    480\n",
       "54    476\n",
       "46    469\n",
       "58    461\n",
       "53    457\n",
       "57    457\n",
       "51    446\n",
       "59    441\n",
       "55    441\n",
       "26    406\n",
       "60    376\n",
       "25    356\n",
       "61    353\n",
       "62    351\n",
       "63    268\n",
       "24    263\n",
       "64    263\n",
       "23    252\n",
       "65    194\n",
       "66    183\n",
       "22    183\n",
       "67    167\n",
       "21    110\n",
       "0     100\n",
       "68     99\n",
       "69     83\n",
       "70     65\n",
       "71     58\n",
       "20     51\n",
       "72     33\n",
       "19     14\n",
       "73      8\n",
       "74      6\n",
       "75      1\n",
       "Name: dob_years, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "df['dob_years'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 101 people reporting their age as 0.They account for .4% of the data. I will drop those rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "children            0.467246\n",
       "days_employed       0.420521\n",
       "dob_years           0.467246\n",
       "education           0.467246\n",
       "education_id        0.467246\n",
       "family_status       0.467246\n",
       "family_status_id    0.467246\n",
       "gender              0.467246\n",
       "income_type         0.467246\n",
       "debt                0.467246\n",
       "total_income        0.420521\n",
       "purpose             0.467246\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['dob_years']==0].count()*100/len(df['dob_years'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 0 age makes up .4% of the data, I am comfortable dropping those rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df[df.dob_years !=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35    614\n",
       "41    603\n",
       "40    603\n",
       "34    597\n",
       "38    595\n",
       "42    592\n",
       "33    577\n",
       "39    572\n",
       "31    556\n",
       "36    553\n",
       "44    543\n",
       "29    543\n",
       "48    536\n",
       "30    536\n",
       "37    531\n",
       "43    510\n",
       "50    509\n",
       "32    506\n",
       "49    505\n",
       "28    501\n",
       "45    494\n",
       "27    490\n",
       "52    483\n",
       "56    482\n",
       "47    480\n",
       "54    476\n",
       "46    469\n",
       "58    461\n",
       "57    457\n",
       "53    457\n",
       "51    446\n",
       "55    441\n",
       "59    441\n",
       "26    406\n",
       "60    376\n",
       "25    356\n",
       "61    353\n",
       "62    351\n",
       "63    268\n",
       "64    263\n",
       "24    263\n",
       "23    252\n",
       "65    194\n",
       "22    183\n",
       "66    183\n",
       "67    167\n",
       "21    110\n",
       "68     99\n",
       "69     83\n",
       "70     65\n",
       "71     58\n",
       "20     51\n",
       "72     33\n",
       "19     14\n",
       "73      8\n",
       "74      6\n",
       "75      1\n",
       "Name: dob_years, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dob_years'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "married              12254\n",
       "civil partnership     4139\n",
       "unmarried             2783\n",
       "divorced              1179\n",
       "widow / widower        947\n",
       "Name: family_status, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df['family_status'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No problems here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F      14083\n",
       "M       7218\n",
       "XNA        1\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df[df.gender !='XNA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F    14083\n",
       "M     7218\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I dropped XNA as it appears to be a technical error.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "<s>\"XNA\" seems to be technical error. Better to drop it.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Well done!\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "employee                       10996\n",
       "business                        5033\n",
       "retiree                         3819\n",
       "civil servant                   1447\n",
       "unemployed                         2\n",
       "entrepreneur                       2\n",
       "paternity / maternity leave        1\n",
       "student                            1\n",
       "Name: income_type, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df['income_type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All good"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I will check for duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>2849</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>F</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>purchase of the house for my family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3290</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58</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>NaN</td>\n",
       "      <td>to have a wedding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4182</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34</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>NaN</td>\n",
       "      <td>wedding ceremony</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4851</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</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>NaN</td>\n",
       "      <td>wedding ceremony</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5557</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58</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>NaN</td>\n",
       "      <td>to have a wedding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20702</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>retiree</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>supplementary education</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21032</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>retiree</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>to become educated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21132</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47</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>NaN</td>\n",
       "      <td>housing renovation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21281</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</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>NaN</td>\n",
       "      <td>buy commercial real estate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21415</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>retiree</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>housing transactions</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       children  days_employed  dob_years            education  education_id  \\\n",
       "2849          0            NaN         41  secondary education             1   \n",
       "3290          0            NaN         58  secondary education             1   \n",
       "4182          1            NaN         34    bachelor's degree             0   \n",
       "4851          0            NaN         60  secondary education             1   \n",
       "5557          0            NaN         58  secondary education             1   \n",
       "...         ...            ...        ...                  ...           ...   \n",
       "20702         0            NaN         64  secondary education             1   \n",
       "21032         0            NaN         60  secondary education             1   \n",
       "21132         0            NaN         47  secondary education             1   \n",
       "21281         1            NaN         30    bachelor's degree             0   \n",
       "21415         0            NaN         54  secondary education             1   \n",
       "\n",
       "           family_status  family_status_id gender income_type  debt  \\\n",
       "2849             married                 0      F    employee     0   \n",
       "3290   civil partnership                 1      F     retiree     0   \n",
       "4182   civil partnership                 1      F    employee     0   \n",
       "4851   civil partnership                 1      F     retiree     0   \n",
       "5557   civil partnership                 1      F     retiree     0   \n",
       "...                  ...               ...    ...         ...   ...   \n",
       "20702            married                 0      F     retiree     0   \n",
       "21032            married                 0      F     retiree     0   \n",
       "21132            married                 0      F    employee     0   \n",
       "21281            married                 0      F    employee     0   \n",
       "21415            married                 0      F     retiree     0   \n",
       "\n",
       "       total_income                              purpose  \n",
       "2849            NaN  purchase of the house for my family  \n",
       "3290            NaN                    to have a wedding  \n",
       "4182            NaN                     wedding ceremony  \n",
       "4851            NaN                     wedding ceremony  \n",
       "5557            NaN                    to have a wedding  \n",
       "...             ...                                  ...  \n",
       "20702           NaN              supplementary education  \n",
       "21032           NaN                   to become educated  \n",
       "21132           NaN                   housing renovation  \n",
       "21281           NaN           buy commercial real estate  \n",
       "21415           NaN                 housing transactions  \n",
       "\n",
       "[71 rows x 12 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_duplicated = df[df.duplicated()]\n",
    "df_duplicated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.33331768461574574"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()*100/len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "making up only .33% I will drop these duplicates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df = df.drop_duplicates().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21230"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.986295005807201"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)/21525 #this is the length of the original dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "The dataframe has been cleaned up and I was able to conserve 98.6% of the data. Duplicated rows were dropped. All columns were checked for strange values, if any were found they were either dropped or fixed. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Great job!\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the IDs provided I know that I can make a couple dictionaries based on family status:family_status_id and \n",
    "education:education_id\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 'married', 1: 'civil partnership', 2: 'widow / widower', 3: 'divorced', 4: 'unmarried'}\n"
     ]
    }
   ],
   "source": [
    "family_dict= df.set_index('family_status_id').to_dict()['family_status']\n",
    "print(family_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: \"bachelor's degree\", 1: 'secondary education', 2: 'some college', 3: 'primary education', 4: 'graduate degree'}\n"
     ]
    }
   ],
   "source": [
    "education_dict=df.set_index('education_id').to_dict()['education']\n",
    "print(education_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Restoring missing values in `total_income`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "As I found earlier there are missing values in total_income and in days_employed. I will fix them by filling them with an appropriate mean or median based on my investigation below\n",
    "\n",
    "To help me find a good fit for the population I will create a category for the ages of the clients. This will be a new column in the dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def age_group(dob_years):\n",
    "    \n",
    "    if dob_years <= 30 :\n",
    "        return 'young adult'\n",
    "    if dob_years <= 50 :\n",
    "        return 'adult'\n",
    "    return 'older adult'\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "young adult\n",
      "adult\n",
      "older adult\n"
     ]
    }
   ],
   "source": [
    "# Test if the function works\n",
    "print(age_group(28))\n",
    "print(age_group(48))\n",
    "print(age_group(58))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</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>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "      <th>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8437.673028</td>\n",
       "      <td>42</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>40620.102</td>\n",
       "      <td>purchase of the house</td>\n",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\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>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5623.422610</td>\n",
       "      <td>33</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
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       "      <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>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\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",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\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>older adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\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",
       "      <td>young adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\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",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>152.779569</td>\n",
       "      <td>50</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>21731.829</td>\n",
       "      <td>education</td>\n",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>6929.865299</td>\n",
       "      <td>35</td>\n",
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       "      <td>15337.093</td>\n",
       "      <td>having a wedding</td>\n",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\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",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>4171.483647</td>\n",
       "      <td>36</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>business</td>\n",
       "      <td>0</td>\n",
       "      <td>18230.959</td>\n",
       "      <td>buy real estate</td>\n",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>792.701887</td>\n",
       "      <td>40</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>12331.077</td>\n",
       "      <td>buy commercial real estate</td>\n",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\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",
       "      <td>older adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1846.641941</td>\n",
       "      <td>54</td>\n",
       "      <td>some college</td>\n",
       "      <td>2</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>20873.317</td>\n",
       "      <td>car purchase</td>\n",
       "      <td>older adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1844.956182</td>\n",
       "      <td>56</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>business</td>\n",
       "      <td>1</td>\n",
       "      <td>26420.466</td>\n",
       "      <td>buy residential real estate</td>\n",
       "      <td>older adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>972.364419</td>\n",
       "      <td>26</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>18691.345</td>\n",
       "      <td>construction of own property</td>\n",
       "      <td>young adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1719.934226</td>\n",
       "      <td>35</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>46272.433</td>\n",
       "      <td>property</td>\n",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>2369.999720</td>\n",
       "      <td>33</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>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>14465.694</td>\n",
       "      <td>building a property</td>\n",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>400281.136913</td>\n",
       "      <td>53</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>widow / widower</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>retiree</td>\n",
       "      <td>0</td>\n",
       "      <td>9091.804</td>\n",
       "      <td>buying a second-hand car</td>\n",
       "      <td>older adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>10038.818549</td>\n",
       "      <td>48</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>divorced</td>\n",
       "      <td>3</td>\n",
       "      <td>F</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>38852.977</td>\n",
       "      <td>buying my own car</td>\n",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  children  days_employed  dob_years            education  \\\n",
       "0       0         1    8437.673028         42    bachelor's degree   \n",
       "1       1         1    4024.803754         36  secondary education   \n",
       "2       2         0    5623.422610         33  secondary education   \n",
       "3       3         3    4124.747207         32  secondary education   \n",
       "4       4         0  340266.072047         53  secondary education   \n",
       "5       5         0     926.185831         27    bachelor's degree   \n",
       "6       6         0    2879.202052         43    bachelor's degree   \n",
       "7       7         0     152.779569         50  secondary education   \n",
       "8       8         2    6929.865299         35    bachelor's degree   \n",
       "9       9         0    2188.756445         41  secondary education   \n",
       "10     10         2    4171.483647         36    bachelor's degree   \n",
       "11     11         0     792.701887         40  secondary education   \n",
       "12     12         0            NaN         65  secondary education   \n",
       "13     13         0    1846.641941         54         some college   \n",
       "14     14         0    1844.956182         56    bachelor's degree   \n",
       "15     15         1     972.364419         26  secondary education   \n",
       "16     16         0    1719.934226         35  secondary education   \n",
       "17     17         0    2369.999720         33    bachelor's degree   \n",
       "18     18         0  400281.136913         53  secondary education   \n",
       "19     19         0   10038.818549         48  secondary education   \n",
       "\n",
       "    education_id      family_status  family_status_id gender income_type  \\\n",
       "0              0            married                 0      F    employee   \n",
       "1              1            married                 0      F    employee   \n",
       "2              1            married                 0      M    employee   \n",
       "3              1            married                 0      M    employee   \n",
       "4              1  civil partnership                 1      F     retiree   \n",
       "5              0  civil partnership                 1      M    business   \n",
       "6              0            married                 0      F    business   \n",
       "7              1            married                 0      M    employee   \n",
       "8              0  civil partnership                 1      F    employee   \n",
       "9              1            married                 0      M    employee   \n",
       "10             0            married                 0      M    business   \n",
       "11             1            married                 0      F    employee   \n",
       "12             1  civil partnership                 1      M     retiree   \n",
       "13             2            married                 0      F    employee   \n",
       "14             0  civil partnership                 1      F    business   \n",
       "15             1            married                 0      F    employee   \n",
       "16             1            married                 0      F    employee   \n",
       "17             0  civil partnership                 1      M    employee   \n",
       "18             1    widow / widower                 2      F     retiree   \n",
       "19             1           divorced                 3      F    employee   \n",
       "\n",
       "    debt  total_income                              purpose    age_group  \n",
       "0      0     40620.102                purchase of the house        adult  \n",
       "1      0     17932.802                         car purchase        adult  \n",
       "2      0     23341.752                purchase of the house        adult  \n",
       "3      0     42820.568              supplementary education        adult  \n",
       "4      0     25378.572                    to have a wedding  older adult  \n",
       "5      0     40922.170                purchase of the house  young adult  \n",
       "6      0     38484.156                 housing transactions        adult  \n",
       "7      0     21731.829                            education        adult  \n",
       "8      0     15337.093                     having a wedding        adult  \n",
       "9      0     23108.150  purchase of the house for my family        adult  \n",
       "10     0     18230.959                      buy real estate        adult  \n",
       "11     0     12331.077           buy commercial real estate        adult  \n",
       "12     0           NaN                    to have a wedding  older adult  \n",
       "13     0     20873.317                         car purchase  older adult  \n",
       "14     1     26420.466          buy residential real estate  older adult  \n",
       "15     0     18691.345         construction of own property  young adult  \n",
       "16     0     46272.433                             property        adult  \n",
       "17     0     14465.694                  building a property        adult  \n",
       "18     0      9091.804             buying a second-hand car  older adult  \n",
       "19     0     38852.977                    buying my own car        adult  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df['age_group'] = df['dob_years'].apply(age_group)\n",
    "df.head(20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "adult          10918\n",
       "older adult     6614\n",
       "young adult     3698\n",
       "Name: age_group, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age_group'].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Thinking about income: It typically depends on ones employment, which could depend on ones education, as well as ones age. I am going to look at the distribution of these factors and how they effect ones income in these data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Nice approach!\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 19149 entries, 0 to 21229\n",
      "Data columns (total 14 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   index             19149 non-null  int64  \n",
      " 1   children          19149 non-null  int64  \n",
      " 2   days_employed     19149 non-null  float64\n",
      " 3   dob_years         19149 non-null  int64  \n",
      " 4   education         19149 non-null  object \n",
      " 5   education_id      19149 non-null  int64  \n",
      " 6   family_status     19149 non-null  object \n",
      " 7   family_status_id  19149 non-null  int64  \n",
      " 8   gender            19149 non-null  object \n",
      " 9   income_type       19149 non-null  object \n",
      " 10  debt              19149 non-null  int64  \n",
      " 11  total_income      19149 non-null  float64\n",
      " 12  purpose           19149 non-null  object \n",
      " 13  age_group         19149 non-null  object \n",
      "dtypes: float64(2), int64(6), object(6)\n",
      "memory usage: 2.2+ MB\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</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>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\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",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\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>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\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>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\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",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\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>older adult</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  children  days_employed  dob_years            education  \\\n",
       "0      0         1    8437.673028         42    bachelor's degree   \n",
       "1      1         1    4024.803754         36  secondary education   \n",
       "2      2         0    5623.422610         33  secondary education   \n",
       "3      3         3    4124.747207         32  secondary education   \n",
       "4      4         0  340266.072047         53  secondary education   \n",
       "\n",
       "   education_id      family_status  family_status_id gender income_type  debt  \\\n",
       "0             0            married                 0      F    employee     0   \n",
       "1             1            married                 0      F    employee     0   \n",
       "2             1            married                 0      M    employee     0   \n",
       "3             1            married                 0      M    employee     0   \n",
       "4             1  civil partnership                 1      F     retiree     0   \n",
       "\n",
       "   total_income                  purpose    age_group  \n",
       "0     40620.102    purchase of the house        adult  \n",
       "1     17932.802             car purchase        adult  \n",
       "2     23341.752    purchase of the house        adult  \n",
       "3     42820.568  supplementary education        adult  \n",
       "4     25378.572        to have a wedding  older adult  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_drop= df.dropna()\n",
    "df_drop.info()\n",
    "df_drop.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "<b>Reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "<s>Please check comment in the end of block before filling NANs.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "<b>Reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "<s>Let's take a look at how to improve your approach. <br>\n",
    "    Income depends on several factors. Consider to pivot data with several levels to find suitable filler for NAN in every group. <br>\n",
    "    Please check https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.pivot.html\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "<b>Reviewer's comment v2</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "<s>Let's go deeper in working with missing values in this task. <br>\n",
    "    Our recommendation here is to group people by several characteristics for more accurate result in median/mean. <br>\n",
    "    Propably you will need custom function to fill NANs with result from pivot table =) <br>\n",
    "You've already done a lot, I am sure you'll take care of it! <br>\n",
    "    Please check example below:\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reviewer's example\n",
    "#df.pivot_table(index=['age_group','income_type' ], columns='education', values='total_income', aggfunc='median')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>education</th>\n",
       "      <th>bachelor's degree</th>\n",
       "      <th>graduate degree</th>\n",
       "      <th>primary education</th>\n",
       "      <th>secondary education</th>\n",
       "      <th>some college</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_group</th>\n",
       "      <th>income_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">adult</th>\n",
       "      <th>business</th>\n",
       "      <td>33702.3130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27305.9010</td>\n",
       "      <td>26144.0300</td>\n",
       "      <td>31437.1180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>civil servant</th>\n",
       "      <td>28178.5190</td>\n",
       "      <td>17822.7570</td>\n",
       "      <td>30545.9490</td>\n",
       "      <td>21740.9130</td>\n",
       "      <td>29281.5325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employee</th>\n",
       "      <td>27096.0190</td>\n",
       "      <td>25161.5835</td>\n",
       "      <td>19887.1210</td>\n",
       "      <td>22166.6270</td>\n",
       "      <td>27035.7210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paternity / maternity leave</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8612.6610</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retiree</th>\n",
       "      <td>32491.0720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15412.8750</td>\n",
       "      <td>21219.0680</td>\n",
       "      <td>7015.4225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unemployed</th>\n",
       "      <td>32435.6020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9593.1190</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">older adult</th>\n",
       "      <th>business</th>\n",
       "      <td>34987.9720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23066.1310</td>\n",
       "      <td>25063.4520</td>\n",
       "      <td>33073.7630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>civil servant</th>\n",
       "      <td>29314.0030</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12709.2755</td>\n",
       "      <td>22940.3780</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employee</th>\n",
       "      <td>27979.3760</td>\n",
       "      <td>42945.7940</td>\n",
       "      <td>20896.0125</td>\n",
       "      <td>21854.6310</td>\n",
       "      <td>27143.4650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retiree</th>\n",
       "      <td>22784.6850</td>\n",
       "      <td>28334.2150</td>\n",
       "      <td>16415.7850</td>\n",
       "      <td>18259.1760</td>\n",
       "      <td>19633.0750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">young adult</th>\n",
       "      <th>business</th>\n",
       "      <td>28448.9855</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21167.1430</td>\n",
       "      <td>23912.5945</td>\n",
       "      <td>25570.8550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>civil servant</th>\n",
       "      <td>24483.1205</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30563.3830</td>\n",
       "      <td>21032.8640</td>\n",
       "      <td>21640.4795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employee</th>\n",
       "      <td>24434.1270</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23533.5420</td>\n",
       "      <td>20273.9605</td>\n",
       "      <td>22024.3690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entrepreneur</th>\n",
       "      <td>79866.1030</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retiree</th>\n",
       "      <td>34394.1280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12807.0710</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>student</th>\n",
       "      <td>15712.2600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "education                                bachelor's degree  graduate degree  \\\n",
       "age_group   income_type                                                       \n",
       "adult       business                            33702.3130              NaN   \n",
       "            civil servant                       28178.5190       17822.7570   \n",
       "            employee                            27096.0190       25161.5835   \n",
       "            paternity / maternity leave                NaN              NaN   \n",
       "            retiree                             32491.0720              NaN   \n",
       "            unemployed                          32435.6020              NaN   \n",
       "older adult business                            34987.9720              NaN   \n",
       "            civil servant                       29314.0030              NaN   \n",
       "            employee                            27979.3760       42945.7940   \n",
       "            retiree                             22784.6850       28334.2150   \n",
       "young adult business                            28448.9855              NaN   \n",
       "            civil servant                       24483.1205              NaN   \n",
       "            employee                            24434.1270              NaN   \n",
       "            entrepreneur                        79866.1030              NaN   \n",
       "            retiree                             34394.1280              NaN   \n",
       "            student                             15712.2600              NaN   \n",
       "\n",
       "education                                primary education  \\\n",
       "age_group   income_type                                      \n",
       "adult       business                            27305.9010   \n",
       "            civil servant                       30545.9490   \n",
       "            employee                            19887.1210   \n",
       "            paternity / maternity leave                NaN   \n",
       "            retiree                             15412.8750   \n",
       "            unemployed                                 NaN   \n",
       "older adult business                            23066.1310   \n",
       "            civil servant                       12709.2755   \n",
       "            employee                            20896.0125   \n",
       "            retiree                             16415.7850   \n",
       "young adult business                            21167.1430   \n",
       "            civil servant                       30563.3830   \n",
       "            employee                            23533.5420   \n",
       "            entrepreneur                               NaN   \n",
       "            retiree                                    NaN   \n",
       "            student                                    NaN   \n",
       "\n",
       "education                                secondary education  some college  \n",
       "age_group   income_type                                                     \n",
       "adult       business                              26144.0300    31437.1180  \n",
       "            civil servant                         21740.9130    29281.5325  \n",
       "            employee                              22166.6270    27035.7210  \n",
       "            paternity / maternity leave            8612.6610           NaN  \n",
       "            retiree                               21219.0680     7015.4225  \n",
       "            unemployed                             9593.1190           NaN  \n",
       "older adult business                              25063.4520    33073.7630  \n",
       "            civil servant                         22940.3780           NaN  \n",
       "            employee                              21854.6310    27143.4650  \n",
       "            retiree                               18259.1760    19633.0750  \n",
       "young adult business                              23912.5945    25570.8550  \n",
       "            civil servant                         21032.8640    21640.4795  \n",
       "            employee                              20273.9605    22024.3690  \n",
       "            entrepreneur                                 NaN           NaN  \n",
       "            retiree                               12807.0710           NaN  \n",
       "            student                                      NaN           NaN  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = df_drop.pivot_table(index=['age_group','income_type' ], columns='education', values='total_income', aggfunc='median')\n",
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If I group the data by age_group, education level, and income type and create medians for individuals based on these groupings I believe this will be the most concise and accurate way to fill the missing incomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "median = df.groupby(['age_group', 'education', 'income_type'])['total_income'].transform('median')\n",
    "\n",
    "df['total_income'] = df['total_income'].fillna(median)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "This is a good approach too. Well done!\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21230 entries, 0 to 21229\n",
      "Data columns (total 14 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   index             21230 non-null  int64  \n",
      " 1   children          21230 non-null  int64  \n",
      " 2   days_employed     19149 non-null  float64\n",
      " 3   dob_years         21230 non-null  int64  \n",
      " 4   education         21230 non-null  object \n",
      " 5   education_id      21230 non-null  int64  \n",
      " 6   family_status     21230 non-null  object \n",
      " 7   family_status_id  21230 non-null  int64  \n",
      " 8   gender            21230 non-null  object \n",
      " 9   income_type       21230 non-null  object \n",
      " 10  debt              21230 non-null  int64  \n",
      " 11  total_income      21229 non-null  float64\n",
      " 12  purpose           21230 non-null  object \n",
      " 13  age_group         21230 non-null  object \n",
      "dtypes: float64(2), int64(6), object(6)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I see there is one NaN remaining. Lets look closer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</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>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5880</th>\n",
       "      <td>5936</td>\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>older adult</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      index  children  days_employed  dob_years          education  \\\n",
       "5880   5936         0            NaN         58  bachelor's degree   \n",
       "\n",
       "      education_id family_status  family_status_id gender   income_type  debt  \\\n",
       "5880             0       married                 0      M  entrepreneur     0   \n",
       "\n",
       "      total_income                      purpose    age_group  \n",
       "5880           NaN  buy residential real estate  older adult  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['total_income'].isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This person is quite unique and a median for their circumstance did not make it into my table. I will fill their income based on their income_type and education."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[5880,['total_income']]= 79866.1030\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21230 entries, 0 to 21229\n",
      "Data columns (total 14 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   index             21230 non-null  int64  \n",
      " 1   children          21230 non-null  int64  \n",
      " 2   days_employed     19149 non-null  float64\n",
      " 3   dob_years         21230 non-null  int64  \n",
      " 4   education         21230 non-null  object \n",
      " 5   education_id      21230 non-null  int64  \n",
      " 6   family_status     21230 non-null  object \n",
      " 7   family_status_id  21230 non-null  int64  \n",
      " 8   gender            21230 non-null  object \n",
      " 9   income_type       21230 non-null  object \n",
      " 10  debt              21230 non-null  int64  \n",
      " 11  total_income      21230 non-null  float64\n",
      " 12  purpose           21230 non-null  object \n",
      " 13  age_group         21230 non-null  object \n",
      "dtypes: float64(2), int64(6), object(6)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22166    480\n",
       "18259    294\n",
       "26144    187\n",
       "20273    162\n",
       "21854    146\n",
       "        ... \n",
       "12160      1\n",
       "14211      1\n",
       "6023       1\n",
       "18317      1\n",
       "52973      1\n",
       "Name: total_income, Length: 15276, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['total_income'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>index</th>\n",
       "      <th>children</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>age_group</th>\n",
       "      <th>purpose_category</th>\n",
       "      <th>income_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\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</td>\n",
       "      <td>purchase of the house</td>\n",
       "      <td>adult</td>\n",
       "      <td>housing</td>\n",
       "      <td>middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\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</td>\n",
       "      <td>car purchase</td>\n",
       "      <td>adult</td>\n",
       "      <td>car transaction</td>\n",
       "      <td>low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\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</td>\n",
       "      <td>purchase of the house</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4124</td>\n",
       "      <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</td>\n",
       "      <td>supplementary education</td>\n",
       "      <td>adult</td>\n",
       "      <td>education</td>\n",
       "      <td>middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\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</td>\n",
       "      <td>to have a wedding</td>\n",
       "      <td>older adult</td>\n",
       "      <td>wedding</td>\n",
       "      <td>middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>926</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</td>\n",
       "      <td>purchase of the house</td>\n",
       "      <td>young adult</td>\n",
       "      <td>housing</td>\n",
       "      <td>middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2879</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</td>\n",
       "      <td>housing transactions</td>\n",
       "      <td>adult</td>\n",
       "      <td>housing</td>\n",
       "      <td>middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>152</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</td>\n",
       "      <td>education</td>\n",
       "      <td>adult</td>\n",
       "      <td>education</td>\n",
       "      <td>middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>6929</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</td>\n",
       "      <td>having a wedding</td>\n",
       "      <td>adult</td>\n",
       "      <td>wedding</td>\n",
       "      <td>low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2188</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</td>\n",
       "      <td>purchase of the house for my family</td>\n",
       "      <td>adult</td>\n",
       "      <td>housing</td>\n",
       "      <td>middle income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  children  days_employed  dob_years            education  \\\n",
       "0      0         1           8437         42    bachelor's degree   \n",
       "1      1         1           4024         36  secondary education   \n",
       "2      2         0           5623         33  secondary education   \n",
       "3      3         3           4124         32  secondary education   \n",
       "4      4         0         340266         53  secondary education   \n",
       "5      5         0            926         27    bachelor's degree   \n",
       "6      6         0           2879         43    bachelor's degree   \n",
       "7      7         0            152         50  secondary education   \n",
       "8      8         2           6929         35    bachelor's degree   \n",
       "9      9         0           2188         41  secondary education   \n",
       "\n",
       "   education_id      family_status  family_status_id gender income_type  debt  \\\n",
       "0             0            married                 0      F    employee     0   \n",
       "1             1            married                 0      F    employee     0   \n",
       "2             1            married                 0      M    employee     0   \n",
       "3             1            married                 0      M    employee     0   \n",
       "4             1  civil partnership                 1      F     retiree     0   \n",
       "5             0  civil partnership                 1      M    business     0   \n",
       "6             0            married                 0      F    business     0   \n",
       "7             1            married                 0      M    employee     0   \n",
       "8             0  civil partnership                 1      F    employee     0   \n",
       "9             1            married                 0      M    employee     0   \n",
       "\n",
       "   total_income                              purpose    age_group  \\\n",
       "0         40620                purchase of the house        adult   \n",
       "1         17932                         car purchase        adult   \n",
       "2         23341                purchase of the house        adult   \n",
       "3         42820              supplementary education        adult   \n",
       "4         25378                    to have a wedding  older adult   \n",
       "5         40922                purchase of the house  young adult   \n",
       "6         38484                 housing transactions        adult   \n",
       "7         21731                            education        adult   \n",
       "8         15337                     having a wedding        adult   \n",
       "9         23108  purchase of the house for my family        adult   \n",
       "\n",
       "  purpose_category   income_level  \n",
       "0          housing  middle income  \n",
       "1  car transaction     low income  \n",
       "2          housing  middle income  \n",
       "3        education  middle income  \n",
       "4          wedding  middle income  \n",
       "5          housing  middle income  \n",
       "6          housing  middle income  \n",
       "7        education  middle income  \n",
       "8          wedding     low income  \n",
       "9          housing  middle income  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['total_income']=df.total_income.astype(int)\n",
    "df['total_income'].head()\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the missing total_income values have been replaced with the median for each entries age group, income type, and education level. I have also changed the data type to integer. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Restoring values in `days_employed`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " I am going to do a similar analysis to what I did in for the other missing values. This will help me determine the best fit for the population. Thinking about what may impact ones days employed, age is a big factor as well as education level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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",
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       "\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>days_employed</th>\n",
       "      <th>days_employed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>adult</th>\n",
       "      <td>10395.136690</td>\n",
       "      <td>1867.970299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>older adult</th>\n",
       "      <td>197541.560600</td>\n",
       "      <td>333946.445320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>young adult</th>\n",
       "      <td>1925.799317</td>\n",
       "      <td>1046.095163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      mean         median\n",
       "             days_employed  days_employed\n",
       "age_group                                \n",
       "adult         10395.136690    1867.970299\n",
       "older adult  197541.560600  333946.445320\n",
       "young adult    1925.799317    1046.095163"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = pd.pivot_table(data = df_drop,\n",
    "                      index = ['age_group'],\n",
    "                       values = 'days_employed',\n",
    "                       aggfunc = ['mean', 'median']\n",
    "                      )\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>days_employed</th>\n",
       "      <th>days_employed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>82474.919277</td>\n",
       "      <td>2540.740460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>37176.432951</td>\n",
       "      <td>1667.140839</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                mean        median\n",
       "       days_employed days_employed\n",
       "gender                            \n",
       "F       82474.919277   2540.740460\n",
       "M       37176.432951   1667.140839"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = pd.pivot_table(data = df_drop,\n",
    "                      index = ['gender'],\n",
    "                       values = 'days_employed',\n",
    "                       aggfunc = ['mean', 'median']\n",
    "                      )\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>days_employed</th>\n",
       "      <th>days_employed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bachelor's degree</th>\n",
       "      <td>42451.577484</td>\n",
       "      <td>1896.569279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>graduate degree</th>\n",
       "      <td>121323.630206</td>\n",
       "      <td>5660.057032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary education</th>\n",
       "      <td>130340.426349</td>\n",
       "      <td>3043.933615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary education</th>\n",
       "      <td>76508.592640</td>\n",
       "      <td>2393.978448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>some college</th>\n",
       "      <td>20823.242105</td>\n",
       "      <td>1209.230373</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              mean        median\n",
       "                     days_employed days_employed\n",
       "education                                       \n",
       "bachelor's degree     42451.577484   1896.569279\n",
       "graduate degree      121323.630206   5660.057032\n",
       "primary education    130340.426349   3043.933615\n",
       "secondary education   76508.592640   2393.978448\n",
       "some college          20823.242105   1209.230373"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = pd.pivot_table(data = df_drop,\n",
    "                      index = ['education'],\n",
    "                       values = 'days_employed',\n",
    "                       aggfunc = ['mean', 'median']\n",
    "                      )\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>days_employed</th>\n",
       "      <th>days_employed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>business</th>\n",
       "      <td>2119.512866</td>\n",
       "      <td>1555.993659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>civil servant</th>\n",
       "      <td>3392.119263</td>\n",
       "      <td>2672.903939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employee</th>\n",
       "      <td>2325.740892</td>\n",
       "      <td>1573.791064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entrepreneur</th>\n",
       "      <td>520.848083</td>\n",
       "      <td>520.848083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paternity / maternity leave</th>\n",
       "      <td>3296.759962</td>\n",
       "      <td>3296.759962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retiree</th>\n",
       "      <td>365037.486121</td>\n",
       "      <td>365269.100414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>student</th>\n",
       "      <td>578.751554</td>\n",
       "      <td>578.751554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unemployed</th>\n",
       "      <td>366413.652744</td>\n",
       "      <td>366413.652744</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      mean         median\n",
       "                             days_employed  days_employed\n",
       "income_type                                              \n",
       "business                       2119.512866    1555.993659\n",
       "civil servant                  3392.119263    2672.903939\n",
       "employee                       2325.740892    1573.791064\n",
       "entrepreneur                    520.848083     520.848083\n",
       "paternity / maternity leave    3296.759962    3296.759962\n",
       "retiree                      365037.486121  365269.100414\n",
       "student                         578.751554     578.751554\n",
       "unemployed                   366413.652744  366413.652744"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = pd.pivot_table(data = df_drop,\n",
    "                      index = ['income_type'],\n",
    "                       values = 'days_employed',\n",
    "                       aggfunc = ['mean', 'median']\n",
    "                      )\n",
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am going to use the medians for the income type to fill the days employed. What a person reports as their job is a good indicator of how long a person would be employed. Also the distribution of the age group was pretty extreme, I would not feel comfortable filling unknow values with those numbers. Filling with the median helps remove any issues with outliers skewing our distribution. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Correct.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['days_employed'] = (df.groupby('income_type')['days_employed']\n",
    "                      .transform(lambda x: x.fillna(x.median())))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['days_employed']=df['days_employed'].astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that I have resolved the NaN values I am able to convert the data type from float to integer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1573      1067\n",
       "1555       497\n",
       "365269     380\n",
       "2672       146\n",
       "327         16\n",
       "          ... \n",
       "360640       1\n",
       "391558       1\n",
       "395796       1\n",
       "4475         1\n",
       "2049         1\n",
       "Name: days_employed, Length: 9041, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df['days_employed'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21230 entries, 0 to 21229\n",
      "Data columns (total 14 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   index             21230 non-null  int64  \n",
      " 1   children          21230 non-null  int64  \n",
      " 2   days_employed     21230 non-null  int64  \n",
      " 3   dob_years         21230 non-null  int64  \n",
      " 4   education         21230 non-null  object \n",
      " 5   education_id      21230 non-null  int64  \n",
      " 6   family_status     21230 non-null  object \n",
      " 7   family_status_id  21230 non-null  int64  \n",
      " 8   gender            21230 non-null  object \n",
      " 9   income_type       21230 non-null  object \n",
      " 10  debt              21230 non-null  int64  \n",
      " 11  total_income      21230 non-null  float64\n",
      " 12  purpose           21230 non-null  object \n",
      " 13  age_group         21230 non-null  object \n",
      "dtypes: float64(1), int64(7), object(6)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the missing values have been filled. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorization of data\n",
    "\n",
    " To help me complete my task I will categorize the data based off of the categories of interest to the bank. These are: debt, children, family status, and purpose for the loan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>family_status</th>\n",
       "      <th>children</th>\n",
       "      <th>debt</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>married</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>purchase of the house</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>married</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>car purchase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>purchase of the house</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>married</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>supplementary education</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>civil partnership</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>to have a wedding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>civil partnership</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>purchase of the house</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>housing transactions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>education</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>civil partnership</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>having a wedding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>purchase of the house for my family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>married</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>buy real estate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>buy commercial real estate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>civil partnership</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>to have a wedding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>car purchase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>civil partnership</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>buy residential real estate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        family_status  children  debt                              purpose\n",
       "0             married         1     0                purchase of the house\n",
       "1             married         1     0                         car purchase\n",
       "2             married         0     0                purchase of the house\n",
       "3             married         3     0              supplementary education\n",
       "4   civil partnership         0     0                    to have a wedding\n",
       "5   civil partnership         0     0                purchase of the house\n",
       "6             married         0     0                 housing transactions\n",
       "7             married         0     0                            education\n",
       "8   civil partnership         2     0                     having a wedding\n",
       "9             married         0     0  purchase of the house for my family\n",
       "10            married         2     0                      buy real estate\n",
       "11            married         0     0           buy commercial real estate\n",
       "12  civil partnership         0     0                    to have a wedding\n",
       "13            married         0     0                         car purchase\n",
       "14  civil partnership         0     1          buy residential real estate"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat= df[['family_status','children','debt','purpose']]\n",
    "\n",
    "df_cat.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I can see there is a need for a few category in the purpose column. I will find the unique values to see which categories I will make."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat.purpose.unique()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " I can see I need a category for real estate, housing, education, car purchase, and  wedding. lets check the rest of the columns before moving on. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['married', 'civil partnership', 'widow / widower', 'divorced',\n",
       "       'unmarried'], dtype=object)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat.family_status.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat.debt.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 3, 2, 4, 5])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat.children.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am seeing that I can make categories for the purposes for the loan requests. I will make a new column for these catagories. I will make a category for education, wedding, car, real estate, house"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "wedding ceremony                            785\n",
       "having a wedding                            759\n",
       "to have a wedding                           755\n",
       "real estate transactions                    669\n",
       "buy commercial real estate                  655\n",
       "buying property for renting out             647\n",
       "transactions with commercial real estate    643\n",
       "housing transactions                        641\n",
       "purchase of the house for my family         636\n",
       "housing                                     635\n",
       "purchase of the house                       634\n",
       "property                                    627\n",
       "construction of own property                626\n",
       "transactions with my real estate            623\n",
       "building a property                         619\n",
       "purchase of my own house                    618\n",
       "building a real estate                      617\n",
       "buy real estate                             612\n",
       "housing renovation                          602\n",
       "buy residential real estate                 599\n",
       "buying my own car                           501\n",
       "going to university                         493\n",
       "car                                         487\n",
       "second-hand car purchase                    479\n",
       "cars                                        474\n",
       "buying a second-hand car                    471\n",
       "to own a car                                470\n",
       "to buy a car                                465\n",
       "car purchase                                457\n",
       "purchase of a car                           454\n",
       "supplementary education                     453\n",
       "university education                        443\n",
       "to get a supplementary education            442\n",
       "getting an education                        439\n",
       "education                                   439\n",
       "profile education                           431\n",
       "getting higher education                    423\n",
       "to become educated                          407\n",
       "Name: purpose, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['purpose'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "def purpose_category(purpose):\n",
    "    if 'wedding'in purpose:\n",
    "        return 'wedding'\n",
    "    if 'real estate'in purpose:\n",
    "        return 'real estate'\n",
    "    if 'car' in purpose:\n",
    "        return 'car transaction'\n",
    "    if 'education'in purpose:\n",
    "        return 'education'\n",
    "    if 'property' in purpose:\n",
    "        return 'real estate'\n",
    "    if 'house' or 'housing' in purpose:\n",
    "        return 'housing'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['purpose_category']= df['purpose'].apply(purpose_category)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21230"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['purpose_category'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "real estate        6937\n",
       "housing            4666\n",
       "car transaction    4258\n",
       "education          3070\n",
       "wedding            2299\n",
       "Name: purpose_category, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['purpose_category'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Well done!\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</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>age_group</th>\n",
       "      <th>purpose_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>42</td>\n",
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       "      <td>40620.102</td>\n",
       "      <td>purchase of the house</td>\n",
       "      <td>adult</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\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",
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       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>17932.802</td>\n",
       "      <td>car purchase</td>\n",
       "      <td>adult</td>\n",
       "      <td>car transaction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5623</td>\n",
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       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
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       "      <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",
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       "      <td>3</td>\n",
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       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>42820.568</td>\n",
       "      <td>supplementary education</td>\n",
       "      <td>adult</td>\n",
       "      <td>education</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\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>older adult</td>\n",
       "      <td>wedding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>926</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",
       "      <td>young adult</td>\n",
       "      <td>housing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2879</td>\n",
       "      <td>43</td>\n",
       "      <td>bachelor's degree</td>\n",
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       "      <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",
       "      <td>adult</td>\n",
       "      <td>housing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>50</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
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       "      <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",
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       "      <td>education</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>6929</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",
       "      <td>adult</td>\n",
       "      <td>wedding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2188</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",
       "      <td>adult</td>\n",
       "      <td>housing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>4171</td>\n",
       "      <td>36</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>business</td>\n",
       "      <td>0</td>\n",
       "      <td>18230.959</td>\n",
       "      <td>buy real estate</td>\n",
       "      <td>adult</td>\n",
       "      <td>real estate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>792</td>\n",
       "      <td>40</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>12331.077</td>\n",
       "      <td>buy commercial real estate</td>\n",
       "      <td>adult</td>\n",
       "      <td>real estate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>365269</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>18259.176</td>\n",
       "      <td>to have a wedding</td>\n",
       "      <td>older adult</td>\n",
       "      <td>wedding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1846</td>\n",
       "      <td>54</td>\n",
       "      <td>some college</td>\n",
       "      <td>2</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>20873.317</td>\n",
       "      <td>car purchase</td>\n",
       "      <td>older adult</td>\n",
       "      <td>car transaction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1844</td>\n",
       "      <td>56</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>business</td>\n",
       "      <td>1</td>\n",
       "      <td>26420.466</td>\n",
       "      <td>buy residential real estate</td>\n",
       "      <td>older adult</td>\n",
       "      <td>real estate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>972</td>\n",
       "      <td>26</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>18691.345</td>\n",
       "      <td>construction of own property</td>\n",
       "      <td>young adult</td>\n",
       "      <td>real estate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1719</td>\n",
       "      <td>35</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>46272.433</td>\n",
       "      <td>property</td>\n",
       "      <td>adult</td>\n",
       "      <td>real estate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>2369</td>\n",
       "      <td>33</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>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>14465.694</td>\n",
       "      <td>building a property</td>\n",
       "      <td>adult</td>\n",
       "      <td>real estate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>400281</td>\n",
       "      <td>53</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>widow / widower</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>retiree</td>\n",
       "      <td>0</td>\n",
       "      <td>9091.804</td>\n",
       "      <td>buying a second-hand car</td>\n",
       "      <td>older adult</td>\n",
       "      <td>car transaction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>10038</td>\n",
       "      <td>48</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>divorced</td>\n",
       "      <td>3</td>\n",
       "      <td>F</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>38852.977</td>\n",
       "      <td>buying my own car</td>\n",
       "      <td>adult</td>\n",
       "      <td>car transaction</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  children  days_employed  dob_years            education  \\\n",
       "0       0         1           8437         42    bachelor's degree   \n",
       "1       1         1           4024         36  secondary education   \n",
       "2       2         0           5623         33  secondary education   \n",
       "3       3         3           4124         32  secondary education   \n",
       "4       4         0         340266         53  secondary education   \n",
       "5       5         0            926         27    bachelor's degree   \n",
       "6       6         0           2879         43    bachelor's degree   \n",
       "7       7         0            152         50  secondary education   \n",
       "8       8         2           6929         35    bachelor's degree   \n",
       "9       9         0           2188         41  secondary education   \n",
       "10     10         2           4171         36    bachelor's degree   \n",
       "11     11         0            792         40  secondary education   \n",
       "12     12         0         365269         65  secondary education   \n",
       "13     13         0           1846         54         some college   \n",
       "14     14         0           1844         56    bachelor's degree   \n",
       "15     15         1            972         26  secondary education   \n",
       "16     16         0           1719         35  secondary education   \n",
       "17     17         0           2369         33    bachelor's degree   \n",
       "18     18         0         400281         53  secondary education   \n",
       "19     19         0          10038         48  secondary education   \n",
       "\n",
       "    education_id      family_status  family_status_id gender income_type  \\\n",
       "0              0            married                 0      F    employee   \n",
       "1              1            married                 0      F    employee   \n",
       "2              1            married                 0      M    employee   \n",
       "3              1            married                 0      M    employee   \n",
       "4              1  civil partnership                 1      F     retiree   \n",
       "5              0  civil partnership                 1      M    business   \n",
       "6              0            married                 0      F    business   \n",
       "7              1            married                 0      M    employee   \n",
       "8              0  civil partnership                 1      F    employee   \n",
       "9              1            married                 0      M    employee   \n",
       "10             0            married                 0      M    business   \n",
       "11             1            married                 0      F    employee   \n",
       "12             1  civil partnership                 1      M     retiree   \n",
       "13             2            married                 0      F    employee   \n",
       "14             0  civil partnership                 1      F    business   \n",
       "15             1            married                 0      F    employee   \n",
       "16             1            married                 0      F    employee   \n",
       "17             0  civil partnership                 1      M    employee   \n",
       "18             1    widow / widower                 2      F     retiree   \n",
       "19             1           divorced                 3      F    employee   \n",
       "\n",
       "    debt  total_income                              purpose    age_group  \\\n",
       "0      0     40620.102                purchase of the house        adult   \n",
       "1      0     17932.802                         car purchase        adult   \n",
       "2      0     23341.752                purchase of the house        adult   \n",
       "3      0     42820.568              supplementary education        adult   \n",
       "4      0     25378.572                    to have a wedding  older adult   \n",
       "5      0     40922.170                purchase of the house  young adult   \n",
       "6      0     38484.156                 housing transactions        adult   \n",
       "7      0     21731.829                            education        adult   \n",
       "8      0     15337.093                     having a wedding        adult   \n",
       "9      0     23108.150  purchase of the house for my family        adult   \n",
       "10     0     18230.959                      buy real estate        adult   \n",
       "11     0     12331.077           buy commercial real estate        adult   \n",
       "12     0     18259.176                    to have a wedding  older adult   \n",
       "13     0     20873.317                         car purchase  older adult   \n",
       "14     1     26420.466          buy residential real estate  older adult   \n",
       "15     0     18691.345         construction of own property  young adult   \n",
       "16     0     46272.433                             property        adult   \n",
       "17     0     14465.694                  building a property        adult   \n",
       "18     0      9091.804             buying a second-hand car  older adult   \n",
       "19     0     38852.977                    buying my own car        adult   \n",
       "\n",
       "   purpose_category  \n",
       "0           housing  \n",
       "1   car transaction  \n",
       "2           housing  \n",
       "3         education  \n",
       "4           wedding  \n",
       "5           housing  \n",
       "6           housing  \n",
       "7         education  \n",
       "8           wedding  \n",
       "9           housing  \n",
       "10      real estate  \n",
       "11      real estate  \n",
       "12          wedding  \n",
       "13  car transaction  \n",
       "14      real estate  \n",
       "15      real estate  \n",
       "16      real estate  \n",
       "17      real estate  \n",
       "18  car transaction  \n",
       "19  car transaction  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14398      3306.762\n",
       "12846      3392.845\n",
       "15963      3418.824\n",
       "1581       3471.216\n",
       "14095      3503.298\n",
       "            ...    \n",
       "16953    273809.483\n",
       "20526    274402.943\n",
       "9068     276204.162\n",
       "19340    352136.354\n",
       "12257    362496.645\n",
       "Name: total_income, Length: 21230, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['total_income'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26489.032407842675"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['total_income'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22166.627\n",
       "dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['total_income'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22925.296"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['total_income'].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "based off of these ranges I will do less than 20000 total income as 'low income'\n",
    "20000 to 100000 as 'middle income' and above 100000 as 'high income'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "def income_level(total_income):\n",
    "    if total_income < 20000:\n",
    "        return 'low income'\n",
    "    if 20000 < total_income < 100000:\n",
    "        return 'middle income'\n",
    "    return 'high income'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>index</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>age_group</th>\n",
       "      <th>purpose_category</th>\n",
       "      <th>income_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\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>adult</td>\n",
       "      <td>housing</td>\n",
       "      <td>middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\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>adult</td>\n",
       "      <td>car transaction</td>\n",
       "      <td>low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\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>adult</td>\n",
       "      <td>housing</td>\n",
       "      <td>middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\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>adult</td>\n",
       "      <td>education</td>\n",
       "      <td>middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\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>older adult</td>\n",
       "      <td>wedding</td>\n",
       "      <td>middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>926</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",
       "      <td>young adult</td>\n",
       "      <td>housing</td>\n",
       "      <td>middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2879</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",
       "      <td>adult</td>\n",
       "      <td>housing</td>\n",
       "      <td>middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>152</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",
       "      <td>adult</td>\n",
       "      <td>education</td>\n",
       "      <td>middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>6929</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",
       "      <td>adult</td>\n",
       "      <td>wedding</td>\n",
       "      <td>low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2188</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",
       "      <td>adult</td>\n",
       "      <td>housing</td>\n",
       "      <td>middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>4171</td>\n",
       "      <td>36</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>business</td>\n",
       "      <td>0</td>\n",
       "      <td>18230.959</td>\n",
       "      <td>buy real estate</td>\n",
       "      <td>adult</td>\n",
       "      <td>real estate</td>\n",
       "      <td>low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>792</td>\n",
       "      <td>40</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>12331.077</td>\n",
       "      <td>buy commercial real estate</td>\n",
       "      <td>adult</td>\n",
       "      <td>real estate</td>\n",
       "      <td>low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>365269</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>18259.176</td>\n",
       "      <td>to have a wedding</td>\n",
       "      <td>older adult</td>\n",
       "      <td>wedding</td>\n",
       "      <td>low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1846</td>\n",
       "      <td>54</td>\n",
       "      <td>some college</td>\n",
       "      <td>2</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>20873.317</td>\n",
       "      <td>car purchase</td>\n",
       "      <td>older adult</td>\n",
       "      <td>car transaction</td>\n",
       "      <td>middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1844</td>\n",
       "      <td>56</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>business</td>\n",
       "      <td>1</td>\n",
       "      <td>26420.466</td>\n",
       "      <td>buy residential real estate</td>\n",
       "      <td>older adult</td>\n",
       "      <td>real estate</td>\n",
       "      <td>middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>972</td>\n",
       "      <td>26</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>18691.345</td>\n",
       "      <td>construction of own property</td>\n",
       "      <td>young adult</td>\n",
       "      <td>real estate</td>\n",
       "      <td>low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1719</td>\n",
       "      <td>35</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>46272.433</td>\n",
       "      <td>property</td>\n",
       "      <td>adult</td>\n",
       "      <td>real estate</td>\n",
       "      <td>middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>2369</td>\n",
       "      <td>33</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>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>14465.694</td>\n",
       "      <td>building a property</td>\n",
       "      <td>adult</td>\n",
       "      <td>real estate</td>\n",
       "      <td>low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>400281</td>\n",
       "      <td>53</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>widow / widower</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>retiree</td>\n",
       "      <td>0</td>\n",
       "      <td>9091.804</td>\n",
       "      <td>buying a second-hand car</td>\n",
       "      <td>older adult</td>\n",
       "      <td>car transaction</td>\n",
       "      <td>low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>10038</td>\n",
       "      <td>48</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>divorced</td>\n",
       "      <td>3</td>\n",
       "      <td>F</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>38852.977</td>\n",
       "      <td>buying my own car</td>\n",
       "      <td>adult</td>\n",
       "      <td>car transaction</td>\n",
       "      <td>middle income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  children  days_employed  dob_years            education  \\\n",
       "0       0         1           8437         42    bachelor's degree   \n",
       "1       1         1           4024         36  secondary education   \n",
       "2       2         0           5623         33  secondary education   \n",
       "3       3         3           4124         32  secondary education   \n",
       "4       4         0         340266         53  secondary education   \n",
       "5       5         0            926         27    bachelor's degree   \n",
       "6       6         0           2879         43    bachelor's degree   \n",
       "7       7         0            152         50  secondary education   \n",
       "8       8         2           6929         35    bachelor's degree   \n",
       "9       9         0           2188         41  secondary education   \n",
       "10     10         2           4171         36    bachelor's degree   \n",
       "11     11         0            792         40  secondary education   \n",
       "12     12         0         365269         65  secondary education   \n",
       "13     13         0           1846         54         some college   \n",
       "14     14         0           1844         56    bachelor's degree   \n",
       "15     15         1            972         26  secondary education   \n",
       "16     16         0           1719         35  secondary education   \n",
       "17     17         0           2369         33    bachelor's degree   \n",
       "18     18         0         400281         53  secondary education   \n",
       "19     19         0          10038         48  secondary education   \n",
       "\n",
       "    education_id      family_status  family_status_id gender income_type  \\\n",
       "0              0            married                 0      F    employee   \n",
       "1              1            married                 0      F    employee   \n",
       "2              1            married                 0      M    employee   \n",
       "3              1            married                 0      M    employee   \n",
       "4              1  civil partnership                 1      F     retiree   \n",
       "5              0  civil partnership                 1      M    business   \n",
       "6              0            married                 0      F    business   \n",
       "7              1            married                 0      M    employee   \n",
       "8              0  civil partnership                 1      F    employee   \n",
       "9              1            married                 0      M    employee   \n",
       "10             0            married                 0      M    business   \n",
       "11             1            married                 0      F    employee   \n",
       "12             1  civil partnership                 1      M     retiree   \n",
       "13             2            married                 0      F    employee   \n",
       "14             0  civil partnership                 1      F    business   \n",
       "15             1            married                 0      F    employee   \n",
       "16             1            married                 0      F    employee   \n",
       "17             0  civil partnership                 1      M    employee   \n",
       "18             1    widow / widower                 2      F     retiree   \n",
       "19             1           divorced                 3      F    employee   \n",
       "\n",
       "    debt  total_income                              purpose    age_group  \\\n",
       "0      0     40620.102                purchase of the house        adult   \n",
       "1      0     17932.802                         car purchase        adult   \n",
       "2      0     23341.752                purchase of the house        adult   \n",
       "3      0     42820.568              supplementary education        adult   \n",
       "4      0     25378.572                    to have a wedding  older adult   \n",
       "5      0     40922.170                purchase of the house  young adult   \n",
       "6      0     38484.156                 housing transactions        adult   \n",
       "7      0     21731.829                            education        adult   \n",
       "8      0     15337.093                     having a wedding        adult   \n",
       "9      0     23108.150  purchase of the house for my family        adult   \n",
       "10     0     18230.959                      buy real estate        adult   \n",
       "11     0     12331.077           buy commercial real estate        adult   \n",
       "12     0     18259.176                    to have a wedding  older adult   \n",
       "13     0     20873.317                         car purchase  older adult   \n",
       "14     1     26420.466          buy residential real estate  older adult   \n",
       "15     0     18691.345         construction of own property  young adult   \n",
       "16     0     46272.433                             property        adult   \n",
       "17     0     14465.694                  building a property        adult   \n",
       "18     0      9091.804             buying a second-hand car  older adult   \n",
       "19     0     38852.977                    buying my own car        adult   \n",
       "\n",
       "   purpose_category   income_level  \n",
       "0           housing  middle income  \n",
       "1   car transaction     low income  \n",
       "2           housing  middle income  \n",
       "3         education  middle income  \n",
       "4           wedding  middle income  \n",
       "5           housing  middle income  \n",
       "6           housing  middle income  \n",
       "7         education  middle income  \n",
       "8           wedding     low income  \n",
       "9           housing  middle income  \n",
       "10      real estate     low income  \n",
       "11      real estate     low income  \n",
       "12          wedding     low income  \n",
       "13  car transaction  middle income  \n",
       "14      real estate  middle income  \n",
       "15      real estate     low income  \n",
       "16      real estate  middle income  \n",
       "17      real estate     low income  \n",
       "18  car transaction     low income  \n",
       "19  car transaction  middle income  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['income_level']= df['total_income'].apply(income_level)\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Good job.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking the Hypotheses\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Is there a correlation between having children and paying back on time?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "children\n",
       "5    0.000000\n",
       "0    7.545824\n",
       "3    8.231707\n",
       "1    9.202838\n",
       "2    9.514468\n",
       "4    9.756098\n",
       "Name: debt, dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "100*(df_cat.groupby('children')['debt'].mean()).sort_values()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion**\n",
    "\n",
    "On average there is very little trend for number of children and the chance the borrower defaulting. In fact according to our data families with 5 children have never defaulted on their loans. We have a very small sample for such instances but I do not see evidence of a higher likelyhood of default based on children. There is a higher likelyhood of default if a person has children vs if they do not. About 2 percent difference."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "You are right.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Is there a correlation between family status and paying back on time?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "family_status\n",
       "widow / widower      6.553911\n",
       "divorced             7.124682\n",
       "married              7.557521\n",
       "civil partnership    9.314202\n",
       "unmarried            9.784173\n",
       "Name: debt, dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "100*(df_cat.groupby('family_status')['debt'].mean()).sort_values()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion**\n",
    "\n",
    " Those who are widowed, divorced or married are about 2% less likely to default vs. those in a civil partnership or those who are unmarried. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "<b>Reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "<s>Why? 9.78 / 7.56 -> 30% higher!\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Correct.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Is there a correlation between income level and paying back on time?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "income_level\n",
       "high income      6.060606\n",
       "middle income    8.022774\n",
       "low income       8.321283\n",
       "Name: debt, dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100*(df.groupby('income_level')['debt'].mean()).sort_values()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion**\n",
    "\n",
    "Lower and middle incomes are 2 percent more likely to default on thier loans. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Correct.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How does credit purpose affect the default rate?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "purpose_category\n",
       "housing            7.201029\n",
       "real estate        7.539282\n",
       "wedding            7.872988\n",
       "car transaction    9.323626\n",
       "education          9.348534\n",
       "Name: debt, dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100*(df.groupby('purpose_category')['debt'].mean()).sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion**\n",
    "Car transactions and education have 2% more risk of default vs. wedding, housing, and real estate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Great.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# General Conclusion \n",
    "\n",
    "My analysis is complete. I have resolved many issues with the data, including replacing missing values (populated with optimized values based on each entry), removed implicit duplicates, fixed the data types and unified mix cases. I also built multiple categories to visualize the data. Through my analysis the bank will be able to develop a credit score for their borrowers. \n",
    "\n",
    "I was able to conclude that there is, on average, a 2% trend towards default when a borrower is: young to middle age, low to middle income, and borrowing for education or a car. These should be useful when considering the credit score.\n",
    "\n",
    "I am grateful for the opportunity to handle these data. Thank you very much for your time reviewing my work. I am looking forward to your feedback.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Good overall analysis!\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Overall reviewer's comment</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Johannah, thank you for sending your project. You've done a really good job on it! <br>\n",
    "    \n",
    "There are a lot of correct conclusions and nice code. <br>\n",
    "    \n",
    "However, there are some issues. I wrote comments. Please elaborate them.\n",
    "    \n",
    "You have very good analytic skills, keep up the good work! I will be waiting for your corrected project.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Overall reviewer's comment v2</b> <a class=\"tocSkip\"></a>\n",
    "\n",
    "Johannah, I really appreciate the corrections you sent in! Thanks for taking the time to do so. <br>\n",
    "    Now your project is a true \"A\". Congratulations! <br>\n",
    "    Your project has passed code review. Keep up the good work!\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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