hackathon2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"applications.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>LOAN_DEFAULT</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>AMT_GOODS_PRICE</th>\n",
       "      <th>REGION_POPULATION_RELATIVE</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30751.0000</td>\n",
       "      <td>23397.000000</td>\n",
       "      <td>23397.000000</td>\n",
       "      <td>2.339700e+04</td>\n",
       "      <td>2.339700e+04</td>\n",
       "      <td>23397.000000</td>\n",
       "      <td>2.337500e+04</td>\n",
       "      <td>23397.000000</td>\n",
       "      <td>23397.000000</td>\n",
       "      <td>23397.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>23397.000000</td>\n",
       "      <td>23397.000000</td>\n",
       "      <td>23397.000000</td>\n",
       "      <td>23397.000000</td>\n",
       "      <td>20181.000000</td>\n",
       "      <td>20181.000000</td>\n",
       "      <td>20181.000000</td>\n",
       "      <td>20181.000000</td>\n",
       "      <td>20181.000000</td>\n",
       "      <td>20181.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15376.0000</td>\n",
       "      <td>0.083173</td>\n",
       "      <td>0.420524</td>\n",
       "      <td>1.675337e+05</td>\n",
       "      <td>5.969064e+05</td>\n",
       "      <td>27082.672137</td>\n",
       "      <td>5.360546e+05</td>\n",
       "      <td>0.020647</td>\n",
       "      <td>-15995.186178</td>\n",
       "      <td>64023.880327</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008249</td>\n",
       "      <td>0.000598</td>\n",
       "      <td>0.000556</td>\n",
       "      <td>0.000427</td>\n",
       "      <td>0.006888</td>\n",
       "      <td>0.007928</td>\n",
       "      <td>0.033299</td>\n",
       "      <td>0.268768</td>\n",
       "      <td>0.256727</td>\n",
       "      <td>1.891878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8877.1934</td>\n",
       "      <td>0.276150</td>\n",
       "      <td>0.727529</td>\n",
       "      <td>9.856913e+04</td>\n",
       "      <td>3.989251e+05</td>\n",
       "      <td>14569.858280</td>\n",
       "      <td>3.662844e+05</td>\n",
       "      <td>0.013882</td>\n",
       "      <td>4357.713390</td>\n",
       "      <td>141443.142415</td>\n",
       "      <td>...</td>\n",
       "      <td>0.090450</td>\n",
       "      <td>0.024455</td>\n",
       "      <td>0.023566</td>\n",
       "      <td>0.020670</td>\n",
       "      <td>0.087935</td>\n",
       "      <td>0.122885</td>\n",
       "      <td>0.201779</td>\n",
       "      <td>0.933736</td>\n",
       "      <td>0.603627</td>\n",
       "      <td>1.864967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.565000e+04</td>\n",
       "      <td>4.500000e+04</td>\n",
       "      <td>2052.000000</td>\n",
       "      <td>4.500000e+04</td>\n",
       "      <td>0.000533</td>\n",
       "      <td>-25166.000000</td>\n",
       "      <td>-16365.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7688.5000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.125000e+05</td>\n",
       "      <td>2.700000e+05</td>\n",
       "      <td>16447.500000</td>\n",
       "      <td>2.385000e+05</td>\n",
       "      <td>0.010006</td>\n",
       "      <td>-19642.000000</td>\n",
       "      <td>-2750.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15376.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.440000e+05</td>\n",
       "      <td>5.123385e+05</td>\n",
       "      <td>24939.000000</td>\n",
       "      <td>4.500000e+05</td>\n",
       "      <td>0.018850</td>\n",
       "      <td>-15721.000000</td>\n",
       "      <td>-1221.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>23063.5000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.025000e+05</td>\n",
       "      <td>8.086500e+05</td>\n",
       "      <td>34596.000000</td>\n",
       "      <td>6.795000e+05</td>\n",
       "      <td>0.028663</td>\n",
       "      <td>-12323.000000</td>\n",
       "      <td>-288.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30751.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.825000e+06</td>\n",
       "      <td>4.050000e+06</td>\n",
       "      <td>225000.000000</td>\n",
       "      <td>4.050000e+06</td>\n",
       "      <td>0.072508</td>\n",
       "      <td>-7689.000000</td>\n",
       "      <td>365243.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 106 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  LOAN_DEFAULT  CNT_CHILDREN  AMT_INCOME_TOTAL    AMT_CREDIT  \\\n",
       "count  30751.0000  23397.000000  23397.000000      2.339700e+04  2.339700e+04   \n",
       "mean   15376.0000      0.083173      0.420524      1.675337e+05  5.969064e+05   \n",
       "std     8877.1934      0.276150      0.727529      9.856913e+04  3.989251e+05   \n",
       "min        1.0000      0.000000      0.000000      2.565000e+04  4.500000e+04   \n",
       "25%     7688.5000      0.000000      0.000000      1.125000e+05  2.700000e+05   \n",
       "50%    15376.0000      0.000000      0.000000      1.440000e+05  5.123385e+05   \n",
       "75%    23063.5000      0.000000      1.000000      2.025000e+05  8.086500e+05   \n",
       "max    30751.0000      1.000000      9.000000      3.825000e+06  4.050000e+06   \n",
       "\n",
       "         AMT_ANNUITY  AMT_GOODS_PRICE  REGION_POPULATION_RELATIVE  \\\n",
       "count   23397.000000     2.337500e+04                23397.000000   \n",
       "mean    27082.672137     5.360546e+05                    0.020647   \n",
       "std     14569.858280     3.662844e+05                    0.013882   \n",
       "min      2052.000000     4.500000e+04                    0.000533   \n",
       "25%     16447.500000     2.385000e+05                    0.010006   \n",
       "50%     24939.000000     4.500000e+05                    0.018850   \n",
       "75%     34596.000000     6.795000e+05                    0.028663   \n",
       "max    225000.000000     4.050000e+06                    0.072508   \n",
       "\n",
       "         DAYS_BIRTH  DAYS_EMPLOYED             ...              \\\n",
       "count  23397.000000   23397.000000             ...               \n",
       "mean  -15995.186178   64023.880327             ...               \n",
       "std     4357.713390  141443.142415             ...               \n",
       "min   -25166.000000  -16365.000000             ...               \n",
       "25%   -19642.000000   -2750.000000             ...               \n",
       "50%   -15721.000000   -1221.000000             ...               \n",
       "75%   -12323.000000    -288.000000             ...               \n",
       "max    -7689.000000  365243.000000             ...               \n",
       "\n",
       "       FLAG_DOCUMENT_18  FLAG_DOCUMENT_19  FLAG_DOCUMENT_20  FLAG_DOCUMENT_21  \\\n",
       "count      23397.000000      23397.000000      23397.000000      23397.000000   \n",
       "mean           0.008249          0.000598          0.000556          0.000427   \n",
       "std            0.090450          0.024455          0.023566          0.020670   \n",
       "min            0.000000          0.000000          0.000000          0.000000   \n",
       "25%            0.000000          0.000000          0.000000          0.000000   \n",
       "50%            0.000000          0.000000          0.000000          0.000000   \n",
       "75%            0.000000          0.000000          0.000000          0.000000   \n",
       "max            1.000000          1.000000          1.000000          1.000000   \n",
       "\n",
       "       AMT_REQ_CREDIT_BUREAU_HOUR  AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "count                20181.000000               20181.000000   \n",
       "mean                     0.006888                   0.007928   \n",
       "std                      0.087935                   0.122885   \n",
       "min                      0.000000                   0.000000   \n",
       "25%                      0.000000                   0.000000   \n",
       "50%                      0.000000                   0.000000   \n",
       "75%                      0.000000                   0.000000   \n",
       "max                      3.000000                   6.000000   \n",
       "\n",
       "       AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "count                20181.000000               20181.000000   \n",
       "mean                     0.033299                   0.268768   \n",
       "std                      0.201779                   0.933736   \n",
       "min                      0.000000                   0.000000   \n",
       "25%                      0.000000                   0.000000   \n",
       "50%                      0.000000                   0.000000   \n",
       "75%                      0.000000                   0.000000   \n",
       "max                      6.000000                  24.000000   \n",
       "\n",
       "       AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "count               20181.000000                20181.000000  \n",
       "mean                    0.256727                    1.891878  \n",
       "std                     0.603627                    1.864967  \n",
       "min                     0.000000                    0.000000  \n",
       "25%                     0.000000                    0.000000  \n",
       "50%                     0.000000                    1.000000  \n",
       "75%                     0.000000                    3.000000  \n",
       "max                     8.000000                   25.000000  \n",
       "\n",
       "[8 rows x 106 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'LOAN_ID', 'LOAN_DEFAULT', 'CONTRACT_TYPE', 'GENDER',\n",
       "       'OWN_CAR', 'OWN_REALTY', 'CNT_CHILDREN', 'AMT_INCOME_TOTAL',\n",
       "       'AMT_CREDIT',\n",
       "       ...\n",
       "       'FLAG_DOCUMENT_18', 'FLAG_DOCUMENT_19', 'FLAG_DOCUMENT_20',\n",
       "       'FLAG_DOCUMENT_21', 'AMT_REQ_CREDIT_BUREAU_HOUR',\n",
       "       'AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_WEEK',\n",
       "       'AMT_REQ_CREDIT_BUREAU_MON', 'AMT_REQ_CREDIT_BUREAU_QRT',\n",
       "       'AMT_REQ_CREDIT_BUREAU_YEAR'],\n",
       "      dtype='object', length=123)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>LOAN_ID</th>\n",
       "      <th>LOAN_DEFAULT</th>\n",
       "      <th>CONTRACT_TYPE</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>OWN_CAR</th>\n",
       "      <th>OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>100002,1,Cash loans,M,N,Y,0,202500.0,406597.5,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>100003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>100004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>100006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312682.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>100007</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>100008,0,Cash loans,M,N,Y,0,99000.0,490495.5,2...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>100009</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>171000.0</td>\n",
       "      <td>1560726.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>100010</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>360000.0</td>\n",
       "      <td>1530000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>100011</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112500.0</td>\n",
       "      <td>1019610.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>100012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>405000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 123 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                            LOAN_ID  \\\n",
       "0           1  100002,1,Cash loans,M,N,Y,0,202500.0,406597.5,...   \n",
       "1           2                                             100003   \n",
       "2           3                                             100004   \n",
       "3           4                                             100006   \n",
       "4           5                                             100007   \n",
       "5           6  100008,0,Cash loans,M,N,Y,0,99000.0,490495.5,2...   \n",
       "6           7                                             100009   \n",
       "7           8                                             100010   \n",
       "8           9                                             100011   \n",
       "9          10                                             100012   \n",
       "\n",
       "   LOAN_DEFAULT    CONTRACT_TYPE GENDER OWN_CAR OWN_REALTY  CNT_CHILDREN  \\\n",
       "0           NaN              NaN    NaN     NaN        NaN           NaN   \n",
       "1           0.0       Cash loans      F       N          N           0.0   \n",
       "2           0.0  Revolving loans      M       Y          Y           0.0   \n",
       "3           0.0       Cash loans      F       N          Y           0.0   \n",
       "4           0.0       Cash loans      M       N          Y           0.0   \n",
       "5           NaN              NaN    NaN     NaN        NaN           NaN   \n",
       "6           0.0       Cash loans      F       Y          Y           1.0   \n",
       "7           0.0       Cash loans      M       Y          Y           0.0   \n",
       "8           0.0       Cash loans      F       N          Y           0.0   \n",
       "9           0.0  Revolving loans      M       N          Y           0.0   \n",
       "\n",
       "   AMT_INCOME_TOTAL  AMT_CREDIT             ...              FLAG_DOCUMENT_18  \\\n",
       "0               NaN         NaN             ...                           NaN   \n",
       "1          270000.0   1293502.5             ...                           0.0   \n",
       "2           67500.0    135000.0             ...                           0.0   \n",
       "3          135000.0    312682.5             ...                           0.0   \n",
       "4          121500.0    513000.0             ...                           0.0   \n",
       "5               NaN         NaN             ...                           NaN   \n",
       "6          171000.0   1560726.0             ...                           0.0   \n",
       "7          360000.0   1530000.0             ...                           0.0   \n",
       "8          112500.0   1019610.0             ...                           0.0   \n",
       "9          135000.0    405000.0             ...                           0.0   \n",
       "\n",
       "   FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21  \\\n",
       "0               NaN              NaN              NaN   \n",
       "1               0.0              0.0              0.0   \n",
       "2               0.0              0.0              0.0   \n",
       "3               0.0              0.0              0.0   \n",
       "4               0.0              0.0              0.0   \n",
       "5               NaN              NaN              NaN   \n",
       "6               0.0              0.0              0.0   \n",
       "7               0.0              0.0              0.0   \n",
       "8               0.0              0.0              0.0   \n",
       "9               0.0              0.0              0.0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "0                        NaN                       NaN   \n",
       "1                        0.0                       0.0   \n",
       "2                        0.0                       0.0   \n",
       "3                        NaN                       NaN   \n",
       "4                        0.0                       0.0   \n",
       "5                        NaN                       NaN   \n",
       "6                        0.0                       0.0   \n",
       "7                        0.0                       0.0   \n",
       "8                        0.0                       0.0   \n",
       "9                        NaN                       NaN   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "0                        NaN                        NaN   \n",
       "1                        0.0                        0.0   \n",
       "2                        0.0                        0.0   \n",
       "3                        NaN                        NaN   \n",
       "4                        0.0                        0.0   \n",
       "5                        NaN                        NaN   \n",
       "6                        0.0                        1.0   \n",
       "7                        0.0                        0.0   \n",
       "8                        0.0                        0.0   \n",
       "9                        NaN                        NaN   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                        NaN                         NaN  \n",
       "1                        0.0                         0.0  \n",
       "2                        0.0                         0.0  \n",
       "3                        NaN                         NaN  \n",
       "4                        0.0                         0.0  \n",
       "5                        NaN                         NaN  \n",
       "6                        1.0                         2.0  \n",
       "7                        0.0                         0.0  \n",
       "8                        0.0                         1.0  \n",
       "9                        NaN                         NaN  \n",
       "\n",
       "[10 rows x 123 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['to_clean'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['LOAN_DEFAULT'].isnull(), \"to_clean\"] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean = df[~df['LOAN_DEFAULT'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ = df[df['LOAN_DEFAULT'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30751, 124)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df_['list_string'] = df['LOAN_ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df_['list_string'] = df_['list_string'].apply(lambda x: x.replace('\\\"', \"\").replace(\", \", \"-\").split(\",\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['100002',\n",
       " '1',\n",
       " 'Cash loans',\n",
       " 'M',\n",
       " 'N',\n",
       " 'Y',\n",
       " '0',\n",
       " '202500.0',\n",
       " '406597.5',\n",
       " '24700.5',\n",
       " '351000.0',\n",
       " 'Unaccompanied',\n",
       " 'Working',\n",
       " 'Secondary / secondary special',\n",
       " 'Single / not married',\n",
       " 'House / apartment',\n",
       " '0.018801',\n",
       " '-9461',\n",
       " '-637',\n",
       " '-3648.0',\n",
       " '-2120',\n",
       " '',\n",
       " '1',\n",
       " '1',\n",
       " '0',\n",
       " '1',\n",
       " '1',\n",
       " '0',\n",
       " 'Laborers',\n",
       " '1.0',\n",
       " '2',\n",
       " '2',\n",
       " 'WEDNESDAY',\n",
       " '10',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " 'Business Entity Type 3',\n",
       " '0.08303696739132256',\n",
       " '0.2629485927471776',\n",
       " '0.13937578009978951',\n",
       " '0.0247',\n",
       " '0.0369',\n",
       " '0.9722',\n",
       " '0.6192',\n",
       " '0.0143',\n",
       " '0.0',\n",
       " '0.069',\n",
       " '0.0833',\n",
       " '0.125',\n",
       " '0.0369',\n",
       " '0.0202',\n",
       " '0.019',\n",
       " '0.0',\n",
       " '0.0',\n",
       " '0.0252',\n",
       " '0.0383',\n",
       " '0.9722',\n",
       " '0.6341',\n",
       " '0.0144',\n",
       " '0.0',\n",
       " '0.069',\n",
       " '0.0833',\n",
       " '0.125',\n",
       " '0.0377',\n",
       " '0.022',\n",
       " '0.0198',\n",
       " '0.0',\n",
       " '0.0',\n",
       " '0.025',\n",
       " '0.0369',\n",
       " '0.9722',\n",
       " '0.6243',\n",
       " '0.0144',\n",
       " '0.0',\n",
       " '0.069',\n",
       " '0.0833',\n",
       " '0.125',\n",
       " '0.0375',\n",
       " '0.0205',\n",
       " '0.0193',\n",
       " '0.0',\n",
       " '0.0',\n",
       " 'reg oper account',\n",
       " 'block of flats',\n",
       " '0.0149',\n",
       " 'Stone-brick',\n",
       " 'No',\n",
       " '2.0',\n",
       " '2.0',\n",
       " '2.0',\n",
       " '2.0',\n",
       " '-1134.0',\n",
       " '0',\n",
       " '1',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0',\n",
       " '0.0',\n",
       " '0.0',\n",
       " '0.0',\n",
       " '0.0',\n",
       " '0.0',\n",
       " '1.0']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_['list_string'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_['list_string'][0].index(\"Stone-brick\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_['list_string'][0].index(\"Stone-brick\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "124"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.columns[1:]).index(\"WALLSMATERIAL_MODE\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_clean = list(df_['list_string'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'LOAN_ID', 'LOAN_DEFAULT', 'CONTRACT_TYPE', 'GENDER',\n",
       "       'OWN_CAR', 'OWN_REALTY', 'CNT_CHILDREN', 'AMT_INCOME_TOTAL',\n",
       "       'AMT_CREDIT',\n",
       "       ...\n",
       "       'FLAG_DOCUMENT_19', 'FLAG_DOCUMENT_20', 'FLAG_DOCUMENT_21',\n",
       "       'AMT_REQ_CREDIT_BUREAU_HOUR', 'AMT_REQ_CREDIT_BUREAU_DAY',\n",
       "       'AMT_REQ_CREDIT_BUREAU_WEEK', 'AMT_REQ_CREDIT_BUREAU_MON',\n",
       "       'AMT_REQ_CREDIT_BUREAU_QRT', 'AMT_REQ_CREDIT_BUREAU_YEAR', 'to_clean'],\n",
       "      dtype='object', length=124)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cleaned = pd.DataFrame(data_clean, columns=list(df.columns[1:-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LOAN_ID</th>\n",
       "      <th>LOAN_DEFAULT</th>\n",
       "      <th>CONTRACT_TYPE</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>OWN_CAR</th>\n",
       "      <th>OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100002</td>\n",
       "      <td>1</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>24700.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100008</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>99000.0</td>\n",
       "      <td>490495.5</td>\n",
       "      <td>27517.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100022</td>\n",
       "      <td>0</td>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>112500.0</td>\n",
       "      <td>157500.0</td>\n",
       "      <td>7875.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100024</td>\n",
       "      <td>0</td>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>427500.0</td>\n",
       "      <td>21375.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>4</th>\n",
       "      <td>100030</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>11074.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  LOAN_ID LOAN_DEFAULT    CONTRACT_TYPE GENDER OWN_CAR OWN_REALTY  \\\n",
       "0  100002            1       Cash loans      M       N          Y   \n",
       "1  100008            0       Cash loans      M       N          Y   \n",
       "2  100022            0  Revolving loans      F       N          Y   \n",
       "3  100024            0  Revolving loans      M       Y          Y   \n",
       "4  100030            0       Cash loans      F       N          Y   \n",
       "\n",
       "  CNT_CHILDREN AMT_INCOME_TOTAL AMT_CREDIT AMT_ANNUITY  \\\n",
       "0            0         202500.0   406597.5     24700.5   \n",
       "1            0          99000.0   490495.5     27517.5   \n",
       "2            0         112500.0   157500.0      7875.0   \n",
       "3            0         135000.0   427500.0     21375.0   \n",
       "4            0          90000.0   225000.0     11074.5   \n",
       "\n",
       "             ...             FLAG_DOCUMENT_18 FLAG_DOCUMENT_19  \\\n",
       "0            ...                            0                0   \n",
       "1            ...                            0                0   \n",
       "2            ...                            0                0   \n",
       "3            ...                            0                0   \n",
       "4            ...                            0                0   \n",
       "\n",
       "  FLAG_DOCUMENT_20 FLAG_DOCUMENT_21 AMT_REQ_CREDIT_BUREAU_HOUR  \\\n",
       "0                0                0                        0.0   \n",
       "1                0                0                        0.0   \n",
       "2                0                0                        0.0   \n",
       "3                0                0                              \n",
       "4                0                0                        0.0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_DAY AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
       "0                       0.0                        0.0   \n",
       "1                       0.0                        0.0   \n",
       "2                       0.0                        0.0   \n",
       "3                                                        \n",
       "4                       0.0                        0.0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_MON AMT_REQ_CREDIT_BUREAU_QRT  \\\n",
       "0                       0.0                       0.0   \n",
       "1                       0.0                       1.0   \n",
       "2                       0.0                       0.0   \n",
       "3                                                       \n",
       "4                       0.0                       0.0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                        1.0  \n",
       "1                        1.0  \n",
       "2                        0.0  \n",
       "3                             \n",
       "4                        0.0  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cleaned.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>LOAN_ID</th>\n",
       "      <th>LOAN_DEFAULT</th>\n",
       "      <th>CONTRACT_TYPE</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>OWN_CAR</th>\n",
       "      <th>OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "      <th>to_clean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>100003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>100004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>100006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312682.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>100007</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>100009</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>171000.0</td>\n",
       "      <td>1560726.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 124 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 LOAN_ID  LOAN_DEFAULT    CONTRACT_TYPE GENDER OWN_CAR  \\\n",
       "1           2  100003           0.0       Cash loans      F       N   \n",
       "2           3  100004           0.0  Revolving loans      M       Y   \n",
       "3           4  100006           0.0       Cash loans      F       N   \n",
       "4           5  100007           0.0       Cash loans      M       N   \n",
       "6           7  100009           0.0       Cash loans      F       Y   \n",
       "\n",
       "  OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT    ...     \\\n",
       "1          N           0.0          270000.0   1293502.5    ...      \n",
       "2          Y           0.0           67500.0    135000.0    ...      \n",
       "3          Y           0.0          135000.0    312682.5    ...      \n",
       "4          Y           0.0          121500.0    513000.0    ...      \n",
       "6          Y           1.0          171000.0   1560726.0    ...      \n",
       "\n",
       "   FLAG_DOCUMENT_19  FLAG_DOCUMENT_20 FLAG_DOCUMENT_21  \\\n",
       "1               0.0               0.0              0.0   \n",
       "2               0.0               0.0              0.0   \n",
       "3               0.0               0.0              0.0   \n",
       "4               0.0               0.0              0.0   \n",
       "6               0.0               0.0              0.0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "1                        0.0                       0.0   \n",
       "2                        0.0                       0.0   \n",
       "3                        NaN                       NaN   \n",
       "4                        0.0                       0.0   \n",
       "6                        0.0                       0.0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_WEEK AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "1                        0.0                       0.0   \n",
       "2                        0.0                       0.0   \n",
       "3                        NaN                       NaN   \n",
       "4                        0.0                       0.0   \n",
       "6                        0.0                       1.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  to_clean  \n",
       "1                        0.0                         0.0         0  \n",
       "2                        0.0                         0.0         0  \n",
       "3                        NaN                         NaN         0  \n",
       "4                        0.0                         0.0         0  \n",
       "6                        1.0                         2.0         0  \n",
       "\n",
       "[5 rows x 124 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=True'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass sort=False\n",
      "\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df_out = pd.concat([df_clean, df_cleaned], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_out.to_csv(\"applications_clean.csv\", sep=\",\", encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#####\n",
    "# 2, 'LOAN_DEFAULT'\n",
    "# 9, 'CNT_CHILDREN'\n",
    "# 17, 'NAME_FAMILY_STATUS'\n",
    "# 32, 'CNT_FAM_MEMBERS'\n",
    "# 35, 'WEEKDAY_APPR_PROCESS_START'\n",
    "# 36, 'HOUR_APPR_PROCESS_START'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1143f2438>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_out.groupby('GENDER')['LOAN_DEFAULT'].count().sort_values(ascending=False).plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1aa38c18>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_out.groupby('NAME_FAMILY_STATUS')['LOAN_DEFAULT'].count().sort_values(ascending=False).plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1aa722e8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['GENDER', 'LOAN_DEFAULT']).size().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['HAS_JOB'] = \"employed\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[(df['OCCUPATION_TYPE'].isnull()) & (df['ORGANIZATION_TYPE'] == 'XNA'), \"HAS_JOB\"] = \"unemployed\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GENDER</th>\n",
       "      <th>NAME_FAMILY_STATUS</th>\n",
       "      <th>LOAN_DEFAULT</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">F</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Civil marriage</th>\n",
       "      <th>0.0</th>\n",
       "      <td>1372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Married</th>\n",
       "      <th>0.0</th>\n",
       "      <td>8773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Separated</th>\n",
       "      <th>0.0</th>\n",
       "      <td>1031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Single / not married</th>\n",
       "      <th>0.0</th>\n",
       "      <td>1939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Widow</th>\n",
       "      <th>0.0</th>\n",
       "      <td>1085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">M</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Civil marriage</th>\n",
       "      <th>0.0</th>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Married</th>\n",
       "      <th>0.0</th>\n",
       "      <td>5209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Separated</th>\n",
       "      <th>0.0</th>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Single / not married</th>\n",
       "      <th>0.0</th>\n",
       "      <td>1063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Widow</th>\n",
       "      <th>0.0</th>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             0\n",
       "GENDER NAME_FAMILY_STATUS   LOAN_DEFAULT      \n",
       "F      Civil marriage       0.0           1372\n",
       "                            1.0            125\n",
       "       Married              0.0           8773\n",
       "                            1.0            639\n",
       "       Separated            0.0           1031\n",
       "                            1.0             87\n",
       "       Single / not married 0.0           1939\n",
       "                            1.0            174\n",
       "       Widow                0.0           1085\n",
       "                            1.0             69\n",
       "M      Civil marriage       0.0            603\n",
       "                            1.0             93\n",
       "       Married              0.0           5209\n",
       "                            1.0            526\n",
       "       Separated            0.0            302\n",
       "                            1.0             41\n",
       "       Single / not married 0.0           1063\n",
       "                            1.0            186\n",
       "       Widow                0.0             74\n",
       "                            1.0              6"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df.groupby(['GENDER', 'NAME_FAMILY_STATUS', 'LOAN_DEFAULT']).size())\n",
    "# 6.9 female defaulted\n",
    "# 10.1 males defaulted\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAME_INCOME_TYPE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Businessman</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Commercial associate</th>\n",
       "      <td>5283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pensioner</th>\n",
       "      <td>4224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State servant</th>\n",
       "      <td>1673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unemployed</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Working</th>\n",
       "      <td>12213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          0\n",
       "NAME_INCOME_TYPE           \n",
       "Businessman               1\n",
       "Commercial associate   5283\n",
       "Pensioner              4224\n",
       "State servant          1673\n",
       "Unemployed                3\n",
       "Working               12213"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df.groupby('NAME_INCOME_TYPE').size())\n",
    "\n",
    "# 6.9 female defaulted\n",
    "# 10.1 males defaulted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HAS_JOB</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>employed</th>\n",
       "      <td>26525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unemployed</th>\n",
       "      <td>4226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0\n",
       "HAS_JOB          \n",
       "employed    26525\n",
       "unemployed   4226"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df.groupby('HAS_JOB').size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a17e8b400>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['GENDER', 'LOAN_DEFAULT']).size().plot.bar(stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1be055f8>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x= df[['GENDER', 'LOAN_DEFAULT']]\n",
    "y= x.set_index('GENDER')\n",
    "z=y.groupby(['GENDER', 'LOAN_DEFAULT']).size()\n",
    "z.plot.bar(stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'COUNT')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "df.groupby('GENDER')['LOAN_DEFAULT'].value_counts().unstack().plot.bar(stacked=True)\n",
    "plt.xlabel('GENDER', fontsize=12)\n",
    "plt.ylabel('COUNT', fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'COUNT')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "df.groupby('HAS_JOB')['LOAN_DEFAULT'].value_counts().unstack().plot.bar(stacked=True)\n",
    "plt.xlabel('EMPLOYMENT_STATUS', fontsize=12)\n",
    "plt.ylabel('COUNT', fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'COUNT')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "df.groupby('NAME_FAMILY_STATUS')['LOAN_DEFAULT'].value_counts().unstack().sort_values(by=0, ascending=False).plot.bar(stacked=True)\n",
    "plt.xlabel('FAMILY STATUS', fontsize=12)\n",
    "plt.ylabel('COUNT', fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'COUNT')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "df.groupby('WEEKDAY_APPR_PROCESS_START')['LOAN_DEFAULT'].value_counts().unstack().sort_values(by=0, ascending=False).plot.bar(stacked=True)\n",
    "plt.xlabel('DAY OF THE WEEK', fontsize=12)\n",
    "plt.ylabel('COUNT', fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_married = df[df['NAME_FAMILY_STATUS'] == 'Married']\n",
    "df_single = df[df['NAME_FAMILY_STATUS'] == 'Single / not married']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>LOAN_DEFAULT</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0.0</th>\n",
       "      <th>0.0</th>\n",
       "      <td>9073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.0</th>\n",
       "      <th>0.0</th>\n",
       "      <td>3092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2.0</th>\n",
       "      <th>0.0</th>\n",
       "      <td>1545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3.0</th>\n",
       "      <th>0.0</th>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">4.0</th>\n",
       "      <th>0.0</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">5.0</th>\n",
       "      <th>0.0</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <th>0.0</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <th>0.0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <th>0.0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              0\n",
       "CNT_CHILDREN LOAN_DEFAULT      \n",
       "0.0          0.0           9073\n",
       "             1.0            673\n",
       "1.0          0.0           3092\n",
       "             1.0            300\n",
       "2.0          0.0           1545\n",
       "             1.0            158\n",
       "3.0          0.0            242\n",
       "             1.0             25\n",
       "4.0          0.0             23\n",
       "             1.0              7\n",
       "5.0          0.0              2\n",
       "             1.0              2\n",
       "6.0          0.0              3\n",
       "7.0          0.0              1\n",
       "8.0          0.0              1"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df_married.groupby(['CNT_CHILDREN', 'LOAN_DEFAULT']).size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'COUNT')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "df_married['CNT_CHILDREN'] = df_married['CNT_CHILDREN'].astype(float)\n",
    "df_married[df_married['CNT_CHILDREN'] <= 4].groupby('CNT_CHILDREN')['LOAN_DEFAULT'].value_counts().unstack().sort_values(by=0, ascending=False).plot.bar(stacked=True)\n",
    "plt.xlabel('NO. OF CHILDREN', fontsize=12)\n",
    "plt.ylabel('COUNT', fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['LOAN_DEFAULT', 'NAME_FAMILY_STATUS', 'WEEKDAY_APPR_PROCESS_START', 'GENDER', 'HAS_JOB', 'CNT_FAM_MEMBERS', 'CNT_CHILDREN']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.pairplot(df, hue=\"LOAN_DEFAULT\", vars=['GENDER', 'LOAN_DEFAULT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'COUNT')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "df.groupby('WEEKDAY_APPR_PROCESS_START')['LOAN_DEFAULT'].value_counts().unstack().sort_values(by=0, ascending=False).plot.bar(stacked=True)\n",
    "plt.xlabel('DAY OF THE WEEK', fontsize=12)\n",
    "plt.ylabel('COUNT', fontsize=12)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}