adatelokeszites.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Programs\\miniconda\\envs\\dap2020_jupyter\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3062: DtypeWarning: Columns (44,45) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "train_data = pd.read_csv('./data/public_train.csv')\n",
    "test_data = pd.read_csv('./data/public_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['train_or_test'] = 'train'\n",
    "test_data['train_or_test'] = 'test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([train_data, test_data], sort=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['AGE', 'AME_FLAG', 'APP_SUB_TYPE', 'BILL_AMOUNT_MONTH_1',\n",
       "       'BILL_AMOUNT_MONTH_2', 'BILL_AMOUNT_MONTH_3', 'BILL_AMOUNT_MONTH_4',\n",
       "       'BILL_AMOUNT_MONTH_5', 'BILL_AMOUNT_MONTH_6', 'BIRTH_CITY',\n",
       "       'BIRTH_STATE', 'CARS_FLAG', 'CONF_CITIZEN_CARD_NUM', 'CONF_TAX_STATUS',\n",
       "       'C_NAME_GIVEN', 'C_PRODUCT', 'DAY_OF_PAYMENT', 'EDU_LEVEL',\n",
       "       'EMAIL_FLAG', 'HOME_PHONE_FLAG', 'ID', 'L_BALANCE', 'MATE_PROF_CODE',\n",
       "       'MCARD_FLAG', 'MONTHS_IN_RES', 'M_IN_THE_JOB', 'M_STATUS', 'NAT',\n",
       "       'NUMBERO_OF_BANK_ACCOUNTS', 'NUMBER_OF_DEPENDANTS',\n",
       "       'NUMBER_OF_SPECIAL_BANK_ACCOUNTS', 'OCARDS_FLAG', 'OCC_TYPE',\n",
       "       'O_INCOMES', 'PAY_AMOUNT_MONTH_1', 'PAY_AMOUNT_MONTH_2',\n",
       "       'PAY_AMOUNT_MONTH_3', 'PAY_AMOUNT_MONTH_4', 'PAY_AMOUNT_MONTH_5',\n",
       "       'PAY_AMOUNT_MONTH_6', 'PAY_MONTH_0', 'PAY_MONTH_2', 'PAY_MONTH_3',\n",
       "       'PAY_MONTH_4', 'PAY_MONTH_5', 'PAY_MONTH_6', 'PROF_BOROUGH',\n",
       "       'PROF_CITY', 'PROF_CODE', 'PROF_PHONE_AREA_CODE', 'PROF_PHONE_GIVEN',\n",
       "       'PROF_STATE', 'PROF_ZIP', 'P_ADDRESS_TYPE', 'P_ASSETS_VALUE',\n",
       "       'P_MONTHLY_INCOME', 'RES_BOROUGH', 'RES_CITY', 'RES_PHONE_AREA_CODE',\n",
       "       'RES_STATE', 'RES_TYPE', 'RES_ZIP', 'SEX', 'S_FLAG', 'TARGET',\n",
       "       'VISA_FLAG', 'train_or_test'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    W\n",
       "1    C\n",
       "2    W\n",
       "3    W\n",
       "4    W\n",
       "Name: APP_SUB_TYPE, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['APP_SUB_TYPE'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>AGE</th>\n",
       "      <th>AME_FLAG</th>\n",
       "      <th>APP_SUB_TYPE</th>\n",
       "      <th>BILL_AMOUNT_MONTH_1</th>\n",
       "      <th>BILL_AMOUNT_MONTH_2</th>\n",
       "      <th>BILL_AMOUNT_MONTH_3</th>\n",
       "      <th>BILL_AMOUNT_MONTH_4</th>\n",
       "      <th>BILL_AMOUNT_MONTH_5</th>\n",
       "      <th>BILL_AMOUNT_MONTH_6</th>\n",
       "      <th>BIRTH_CITY</th>\n",
       "      <th>BIRTH_STATE</th>\n",
       "      <th>CARS_FLAG</th>\n",
       "      <th>CONF_CITIZEN_CARD_NUM</th>\n",
       "      <th>CONF_TAX_STATUS</th>\n",
       "      <th>C_NAME_GIVEN</th>\n",
       "      <th>C_PRODUCT</th>\n",
       "      <th>DAY_OF_PAYMENT</th>\n",
       "      <th>EDU_LEVEL</th>\n",
       "      <th>EMAIL_FLAG</th>\n",
       "      <th>HOME_PHONE_FLAG</th>\n",
       "      <th>ID</th>\n",
       "      <th>L_BALANCE</th>\n",
       "      <th>MATE_PROF_CODE</th>\n",
       "      <th>MCARD_FLAG</th>\n",
       "      <th>MONTHS_IN_RES</th>\n",
       "      <th>M_IN_THE_JOB</th>\n",
       "      <th>M_STATUS</th>\n",
       "      <th>NAT</th>\n",
       "      <th>NUMBERO_OF_BANK_ACCOUNTS</th>\n",
       "      <th>NUMBER_OF_DEPENDANTS</th>\n",
       "      <th>NUMBER_OF_SPECIAL_BANK_ACCOUNTS</th>\n",
       "      <th>OCARDS_FLAG</th>\n",
       "      <th>OCC_TYPE</th>\n",
       "      <th>O_INCOMES</th>\n",
       "      <th>PAY_AMOUNT_MONTH_1</th>\n",
       "      <th>PAY_AMOUNT_MONTH_2</th>\n",
       "      <th>PAY_AMOUNT_MONTH_3</th>\n",
       "      <th>PAY_AMOUNT_MONTH_4</th>\n",
       "      <th>PAY_AMOUNT_MONTH_5</th>\n",
       "      <th>PAY_AMOUNT_MONTH_6</th>\n",
       "      <th>PAY_MONTH_0</th>\n",
       "      <th>PAY_MONTH_2</th>\n",
       "      <th>PAY_MONTH_3</th>\n",
       "      <th>PAY_MONTH_4</th>\n",
       "      <th>PAY_MONTH_5</th>\n",
       "      <th>PAY_MONTH_6</th>\n",
       "      <th>PROF_BOROUGH</th>\n",
       "      <th>PROF_CITY</th>\n",
       "      <th>PROF_CODE</th>\n",
       "      <th>PROF_PHONE_AREA_CODE</th>\n",
       "      <th>PROF_PHONE_GIVEN</th>\n",
       "      <th>PROF_STATE</th>\n",
       "      <th>PROF_ZIP</th>\n",
       "      <th>P_ADDRESS_TYPE</th>\n",
       "      <th>P_ASSETS_VALUE</th>\n",
       "      <th>P_MONTHLY_INCOME</th>\n",
       "      <th>RES_BOROUGH</th>\n",
       "      <th>RES_CITY</th>\n",
       "      <th>RES_PHONE_AREA_CODE</th>\n",
       "      <th>RES_STATE</th>\n",
       "      <th>RES_TYPE</th>\n",
       "      <th>RES_ZIP</th>\n",
       "      <th>SEX</th>\n",
       "      <th>S_FLAG</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>VISA_FLAG</th>\n",
       "      <th>train_or_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50000.00000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>34255</td>\n",
       "      <td>47936</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>50000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>17662.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>21116.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>46223.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.00000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>42687.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.0000</td>\n",
       "      <td>5.000000e+04</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.00000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>12371</td>\n",
       "      <td>15576</td>\n",
       "      <td>42244.000000</td>\n",
       "      <td>50000</td>\n",
       "      <td>50000</td>\n",
       "      <td>15693</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>50000</td>\n",
       "      <td>5.000000e+04</td>\n",
       "      <td>49472.000000</td>\n",
       "      <td>25783</td>\n",
       "      <td>40347</td>\n",
       "      <td>50000</td>\n",
       "      <td>50000</td>\n",
       "      <td>48651.000000</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>49935</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</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>1471</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>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>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",
       "      <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>1796</td>\n",
       "      <td>1221</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>1433.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1796</td>\n",
       "      <td>1522</td>\n",
       "      <td>102</td>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1433.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</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>City26</td>\n",
       "      <td>State4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</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",
       "      <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",
       "      <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>Borough0</td>\n",
       "      <td>City25</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "      <td>State5</td>\n",
       "      <td>960.0</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Borough0</td>\n",
       "      <td>City16</td>\n",
       "      <td></td>\n",
       "      <td>State5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>960.0</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28206</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>2064</td>\n",
       "      <td>5717</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27959</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41809</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",
       "      <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",
       "      <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>4558</td>\n",
       "      <td>652</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36532</td>\n",
       "      <td>36510</td>\n",
       "      <td>2400</td>\n",
       "      <td>911.0</td>\n",
       "      <td>49673</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4946</td>\n",
       "      <td>1672</td>\n",
       "      <td>8212</td>\n",
       "      <td>8773</td>\n",
       "      <td>NaN</td>\n",
       "      <td>911.0</td>\n",
       "      <td>30805</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>43.24852</td>\n",
       "      <td>0.001740</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60641.670080</td>\n",
       "      <td>57355.324260</td>\n",
       "      <td>53292.235900</td>\n",
       "      <td>48868.057480</td>\n",
       "      <td>45637.257120</td>\n",
       "      <td>44628.478760</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.336140</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.275700</td>\n",
       "      <td>12.869920</td>\n",
       "      <td>0.296003</td>\n",
       "      <td>0.802280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25000.500000</td>\n",
       "      <td>164094.600000</td>\n",
       "      <td>3.797926</td>\n",
       "      <td>0.097460</td>\n",
       "      <td>9.727149</td>\n",
       "      <td>0.009320</td>\n",
       "      <td>2.14840</td>\n",
       "      <td>0.961600</td>\n",
       "      <td>0.357840</td>\n",
       "      <td>0.650520</td>\n",
       "      <td>0.357840</td>\n",
       "      <td>0.002040</td>\n",
       "      <td>2.484316</td>\n",
       "      <td>2963.729060</td>\n",
       "      <td>5619.1078</td>\n",
       "      <td>5.436837e+03</td>\n",
       "      <td>5146.842240</td>\n",
       "      <td>5568.31044</td>\n",
       "      <td>5326.496520</td>\n",
       "      <td>4930.801480</td>\n",
       "      <td>0.031700</td>\n",
       "      <td>-0.042900</td>\n",
       "      <td>-0.112940</td>\n",
       "      <td>-0.170260</td>\n",
       "      <td>-0.215300</td>\n",
       "      <td>-0.248160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.061784</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.451491e+05</td>\n",
       "      <td>180633.123690</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.252225</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001320</td>\n",
       "      <td>0.424320</td>\n",
       "      <td>0.111440</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.98905</td>\n",
       "      <td>0.041677</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72452.016459</td>\n",
       "      <td>69798.265537</td>\n",
       "      <td>66190.142456</td>\n",
       "      <td>63214.005733</td>\n",
       "      <td>61091.066392</td>\n",
       "      <td>60077.818698</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.472392</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.988286</td>\n",
       "      <td>6.608385</td>\n",
       "      <td>0.955688</td>\n",
       "      <td>0.398284</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14433.901067</td>\n",
       "      <td>123839.020397</td>\n",
       "      <td>5.212168</td>\n",
       "      <td>0.296586</td>\n",
       "      <td>10.668841</td>\n",
       "      <td>0.383453</td>\n",
       "      <td>1.32285</td>\n",
       "      <td>0.202105</td>\n",
       "      <td>0.479953</td>\n",
       "      <td>1.193655</td>\n",
       "      <td>0.479953</td>\n",
       "      <td>0.045121</td>\n",
       "      <td>1.532261</td>\n",
       "      <td>15782.379761</td>\n",
       "      <td>16557.9558</td>\n",
       "      <td>1.724584e+04</td>\n",
       "      <td>18372.905193</td>\n",
       "      <td>22290.83708</td>\n",
       "      <td>18960.915332</td>\n",
       "      <td>16648.887358</td>\n",
       "      <td>1.073573</td>\n",
       "      <td>1.143348</td>\n",
       "      <td>1.117841</td>\n",
       "      <td>1.074325</td>\n",
       "      <td>1.045413</td>\n",
       "      <td>1.069317</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.220104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.281880e+06</td>\n",
       "      <td>134589.611245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.867833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.036308</td>\n",
       "      <td>0.494249</td>\n",
       "      <td>0.314679</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-165580.000000</td>\n",
       "      <td>-33350.000000</td>\n",
       "      <td>-61506.000000</td>\n",
       "      <td>-65167.000000</td>\n",
       "      <td>-37594.000000</td>\n",
       "      <td>-51443.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</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>0.0000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>31.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6136.000000</td>\n",
       "      <td>4989.000000</td>\n",
       "      <td>4144.000000</td>\n",
       "      <td>3517.500000</td>\n",
       "      <td>2475.000000</td>\n",
       "      <td>2160.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12500.750000</td>\n",
       "      <td>80000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</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",
       "      <td>0.000000</td>\n",
       "      <td>1200.0000</td>\n",
       "      <td>1.023000e+03</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>500.00000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>416.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>94000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>41.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43990.000000</td>\n",
       "      <td>40679.000000</td>\n",
       "      <td>34859.000000</td>\n",
       "      <td>28698.000000</td>\n",
       "      <td>26152.000000</td>\n",
       "      <td>25833.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25000.500000</td>\n",
       "      <td>120000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2784.0000</td>\n",
       "      <td>2.438000e+03</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>1959.00000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>1894.000000</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>133200.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>53.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86422.000000</td>\n",
       "      <td>82789.000000</td>\n",
       "      <td>78366.000000</td>\n",
       "      <td>70899.000000</td>\n",
       "      <td>66895.000000</td>\n",
       "      <td>65239.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37500.250000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5000.0000</td>\n",
       "      <td>5.000000e+03</td>\n",
       "      <td>4407.000000</td>\n",
       "      <td>4059.00000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>3900.000000</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>215576.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>106.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>964511.000000</td>\n",
       "      <td>983931.000000</td>\n",
       "      <td>693131.000000</td>\n",
       "      <td>891586.000000</td>\n",
       "      <td>927171.000000</td>\n",
       "      <td>961664.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>228.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>7.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>873552.0000</td>\n",
       "      <td>1.215471e+06</td>\n",
       "      <td>896040.000000</td>\n",
       "      <td>621000.00000</td>\n",
       "      <td>417990.000000</td>\n",
       "      <td>528666.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.683611e+07</td>\n",
       "      <td>999976.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                AGE      AME_FLAG APP_SUB_TYPE  BILL_AMOUNT_MONTH_1  \\\n",
       "count   50000.00000  50000.000000        50000         50000.000000   \n",
       "unique          NaN           NaN            3                  NaN   \n",
       "top             NaN           NaN            W                  NaN   \n",
       "freq            NaN           NaN        28206                  NaN   \n",
       "mean       43.24852      0.001740          NaN         60641.670080   \n",
       "std        14.98905      0.041677          NaN         72452.016459   \n",
       "min         6.00000      0.000000          NaN       -165580.000000   \n",
       "25%        31.00000      0.000000          NaN          6136.000000   \n",
       "50%        41.00000      0.000000          NaN         43990.000000   \n",
       "75%        53.00000      0.000000          NaN         86422.000000   \n",
       "max       106.00000      1.000000          NaN        964511.000000   \n",
       "\n",
       "        BILL_AMOUNT_MONTH_2  BILL_AMOUNT_MONTH_3  BILL_AMOUNT_MONTH_4  \\\n",
       "count          50000.000000         50000.000000         50000.000000   \n",
       "unique                  NaN                  NaN                  NaN   \n",
       "top                     NaN                  NaN                  NaN   \n",
       "freq                    NaN                  NaN                  NaN   \n",
       "mean           57355.324260         53292.235900         48868.057480   \n",
       "std            69798.265537         66190.142456         63214.005733   \n",
       "min           -33350.000000        -61506.000000        -65167.000000   \n",
       "25%             4989.000000          4144.000000          3517.500000   \n",
       "50%            40679.000000         34859.000000         28698.000000   \n",
       "75%            82789.000000         78366.000000         70899.000000   \n",
       "max           983931.000000        693131.000000        891586.000000   \n",
       "\n",
       "        BILL_AMOUNT_MONTH_5  BILL_AMOUNT_MONTH_6 BIRTH_CITY BIRTH_STATE  \\\n",
       "count          50000.000000         50000.000000      34255       47936   \n",
       "unique                  NaN                  NaN       1471          28   \n",
       "top                     NaN                  NaN     City26      State4   \n",
       "freq                    NaN                  NaN       2064        5717   \n",
       "mean           45637.257120         44628.478760        NaN         NaN   \n",
       "std            61091.066392         60077.818698        NaN         NaN   \n",
       "min           -37594.000000        -51443.000000        NaN         NaN   \n",
       "25%             2475.000000          2160.750000        NaN         NaN   \n",
       "50%            26152.000000         25833.000000        NaN         NaN   \n",
       "75%            66895.000000         65239.000000        NaN         NaN   \n",
       "max           927171.000000        961664.000000        NaN         NaN   \n",
       "\n",
       "           CARS_FLAG  CONF_CITIZEN_CARD_NUM  CONF_TAX_STATUS C_NAME_GIVEN  \\\n",
       "count   50000.000000                50000.0          50000.0        50000   \n",
       "unique           NaN                    NaN              NaN            2   \n",
       "top              NaN                    NaN              NaN           no   \n",
       "freq             NaN                    NaN              NaN        27959   \n",
       "mean        0.336140                    0.0              0.0          NaN   \n",
       "std         0.472392                    0.0              0.0          NaN   \n",
       "min         0.000000                    0.0              0.0          NaN   \n",
       "25%         0.000000                    0.0              0.0          NaN   \n",
       "50%         0.000000                    0.0              0.0          NaN   \n",
       "75%         1.000000                    0.0              0.0          NaN   \n",
       "max         1.000000                    0.0              0.0          NaN   \n",
       "\n",
       "           C_PRODUCT  DAY_OF_PAYMENT     EDU_LEVEL    EMAIL_FLAG  \\\n",
       "count   50000.000000    50000.000000  17662.000000  50000.000000   \n",
       "unique           NaN             NaN           NaN           NaN   \n",
       "top              NaN             NaN           NaN           NaN   \n",
       "freq             NaN             NaN           NaN           NaN   \n",
       "mean        1.275700       12.869920      0.296003      0.802280   \n",
       "std         0.988286        6.608385      0.955688      0.398284   \n",
       "min         1.000000        1.000000      0.000000      0.000000   \n",
       "25%         1.000000       10.000000      0.000000      1.000000   \n",
       "50%         1.000000       10.000000      0.000000      1.000000   \n",
       "75%         1.000000       15.000000      0.000000      1.000000   \n",
       "max         7.000000       25.000000      5.000000      1.000000   \n",
       "\n",
       "       HOME_PHONE_FLAG            ID       L_BALANCE  MATE_PROF_CODE  \\\n",
       "count            50000  50000.000000    50000.000000    21116.000000   \n",
       "unique               2           NaN             NaN             NaN   \n",
       "top                yes           NaN             NaN             NaN   \n",
       "freq             41809           NaN             NaN             NaN   \n",
       "mean               NaN  25000.500000   164094.600000        3.797926   \n",
       "std                NaN  14433.901067   123839.020397        5.212168   \n",
       "min                NaN      1.000000    10000.000000        0.000000   \n",
       "25%                NaN  12500.750000    80000.000000        0.000000   \n",
       "50%                NaN  25000.500000   120000.000000        0.000000   \n",
       "75%                NaN  37500.250000   200000.000000       11.000000   \n",
       "max                NaN  50000.000000  1000000.000000       17.000000   \n",
       "\n",
       "          MCARD_FLAG  MONTHS_IN_RES  M_IN_THE_JOB     M_STATUS           NAT  \\\n",
       "count   50000.000000   46223.000000  50000.000000  50000.00000  50000.000000   \n",
       "unique           NaN            NaN           NaN          NaN           NaN   \n",
       "top              NaN            NaN           NaN          NaN           NaN   \n",
       "freq             NaN            NaN           NaN          NaN           NaN   \n",
       "mean        0.097460       9.727149      0.009320      2.14840      0.961600   \n",
       "std         0.296586      10.668841      0.383453      1.32285      0.202105   \n",
       "min         0.000000       0.000000      0.000000      0.00000      0.000000   \n",
       "25%         0.000000       1.000000      0.000000      1.00000      1.000000   \n",
       "50%         0.000000       6.000000      0.000000      2.00000      1.000000   \n",
       "75%         0.000000      15.000000      0.000000      2.00000      1.000000   \n",
       "max         1.000000     228.000000     35.000000      7.00000      2.000000   \n",
       "\n",
       "        NUMBERO_OF_BANK_ACCOUNTS  NUMBER_OF_DEPENDANTS  \\\n",
       "count               50000.000000          50000.000000   \n",
       "unique                       NaN                   NaN   \n",
       "top                          NaN                   NaN   \n",
       "freq                         NaN                   NaN   \n",
       "mean                    0.357840              0.650520   \n",
       "std                     0.479953              1.193655   \n",
       "min                     0.000000              0.000000   \n",
       "25%                     0.000000              0.000000   \n",
       "50%                     0.000000              0.000000   \n",
       "75%                     1.000000              1.000000   \n",
       "max                     2.000000             53.000000   \n",
       "\n",
       "        NUMBER_OF_SPECIAL_BANK_ACCOUNTS   OCARDS_FLAG      OCC_TYPE  \\\n",
       "count                      50000.000000  50000.000000  42687.000000   \n",
       "unique                              NaN           NaN           NaN   \n",
       "top                                 NaN           NaN           NaN   \n",
       "freq                                NaN           NaN           NaN   \n",
       "mean                           0.357840      0.002040      2.484316   \n",
       "std                            0.479953      0.045121      1.532261   \n",
       "min                            0.000000      0.000000      0.000000   \n",
       "25%                            0.000000      0.000000      1.000000   \n",
       "50%                            0.000000      0.000000      2.000000   \n",
       "75%                            1.000000      0.000000      4.000000   \n",
       "max                            2.000000      1.000000      5.000000   \n",
       "\n",
       "            O_INCOMES  PAY_AMOUNT_MONTH_1  PAY_AMOUNT_MONTH_2  \\\n",
       "count    50000.000000          50000.0000        5.000000e+04   \n",
       "unique            NaN                 NaN                 NaN   \n",
       "top               NaN                 NaN                 NaN   \n",
       "freq              NaN                 NaN                 NaN   \n",
       "mean      2963.729060           5619.1078        5.436837e+03   \n",
       "std      15782.379761          16557.9558        1.724584e+04   \n",
       "min          0.000000              0.0000        0.000000e+00   \n",
       "25%          0.000000           1200.0000        1.023000e+03   \n",
       "50%          0.000000           2784.0000        2.438000e+03   \n",
       "75%          0.000000           5000.0000        5.000000e+03   \n",
       "max     200000.000000         873552.0000        1.215471e+06   \n",
       "\n",
       "        PAY_AMOUNT_MONTH_3  PAY_AMOUNT_MONTH_4  PAY_AMOUNT_MONTH_5  \\\n",
       "count         50000.000000         50000.00000        50000.000000   \n",
       "unique                 NaN                 NaN                 NaN   \n",
       "top                    NaN                 NaN                 NaN   \n",
       "freq                   NaN                 NaN                 NaN   \n",
       "mean           5146.842240          5568.31044         5326.496520   \n",
       "std           18372.905193         22290.83708        18960.915332   \n",
       "min               0.000000             0.00000            0.000000   \n",
       "25%             700.000000           500.00000          500.000000   \n",
       "50%            2000.000000          1959.00000         2000.000000   \n",
       "75%            4407.000000          4059.00000         4000.000000   \n",
       "max          896040.000000        621000.00000       417990.000000   \n",
       "\n",
       "        PAY_AMOUNT_MONTH_6   PAY_MONTH_0   PAY_MONTH_2   PAY_MONTH_3  \\\n",
       "count         50000.000000  50000.000000  50000.000000  50000.000000   \n",
       "unique                 NaN           NaN           NaN           NaN   \n",
       "top                    NaN           NaN           NaN           NaN   \n",
       "freq                   NaN           NaN           NaN           NaN   \n",
       "mean           4930.801480      0.031700     -0.042900     -0.112940   \n",
       "std           16648.887358      1.073573      1.143348      1.117841   \n",
       "min               0.000000     -2.000000     -2.000000     -2.000000   \n",
       "25%             416.000000      0.000000     -1.000000     -1.000000   \n",
       "50%            1894.000000      0.000000      0.000000      0.000000   \n",
       "75%            3900.000000      0.000000      0.000000      0.000000   \n",
       "max          528666.000000      8.000000      7.000000      7.000000   \n",
       "\n",
       "         PAY_MONTH_4   PAY_MONTH_5   PAY_MONTH_6 PROF_BOROUGH PROF_CITY  \\\n",
       "count   50000.000000  50000.000000  50000.000000        12371     15576   \n",
       "unique           NaN           NaN           NaN         1796      1221   \n",
       "top              NaN           NaN           NaN     Borough0    City25   \n",
       "freq             NaN           NaN           NaN         4558       652   \n",
       "mean       -0.170260     -0.215300     -0.248160          NaN       NaN   \n",
       "std         1.074325      1.045413      1.069317          NaN       NaN   \n",
       "min        -2.000000     -2.000000     -2.000000          NaN       NaN   \n",
       "25%        -1.000000     -1.000000     -1.000000          NaN       NaN   \n",
       "50%         0.000000      0.000000      0.000000          NaN       NaN   \n",
       "75%         0.000000      0.000000      0.000000          NaN       NaN   \n",
       "max         7.000000      7.000000      7.000000          NaN       NaN   \n",
       "\n",
       "           PROF_CODE PROF_PHONE_AREA_CODE PROF_PHONE_GIVEN PROF_STATE  \\\n",
       "count   42244.000000                50000            50000      15693   \n",
       "unique           NaN                   87                2         27   \n",
       "top              NaN                                    No     State5   \n",
       "freq             NaN                36532            36510       2400   \n",
       "mean        8.061784                  NaN              NaN        NaN   \n",
       "std         3.220104                  NaN              NaN        NaN   \n",
       "min         0.000000                  NaN              NaN        NaN   \n",
       "25%         9.000000                  NaN              NaN        NaN   \n",
       "50%         9.000000                  NaN              NaN        NaN   \n",
       "75%         9.000000                  NaN              NaN        NaN   \n",
       "max        18.000000                  NaN              NaN        NaN   \n",
       "\n",
       "        PROF_ZIP P_ADDRESS_TYPE  P_ASSETS_VALUE  P_MONTHLY_INCOME RES_BOROUGH  \\\n",
       "count    50000.0          50000    5.000000e+04      49472.000000       25783   \n",
       "unique    1433.0              2             NaN               NaN        1796   \n",
       "top        960.0              A             NaN               NaN    Borough0   \n",
       "freq       911.0          49673             NaN               NaN        4946   \n",
       "mean         NaN            NaN    4.451491e+05     180633.123690         NaN   \n",
       "std          NaN            NaN    2.281880e+06     134589.611245         NaN   \n",
       "min          NaN            NaN    0.000000e+00      10000.000000         NaN   \n",
       "25%          NaN            NaN    0.000000e+00      94000.000000         NaN   \n",
       "50%          NaN            NaN    0.000000e+00     133200.000000         NaN   \n",
       "75%          NaN            NaN    0.000000e+00     215576.000000         NaN   \n",
       "max          NaN            NaN    1.683611e+07     999976.400000         NaN   \n",
       "\n",
       "       RES_CITY RES_PHONE_AREA_CODE RES_STATE      RES_TYPE  RES_ZIP     SEX  \\\n",
       "count     40347               50000     50000  48651.000000  50000.0   49935   \n",
       "unique     1522                 102        27           NaN   1433.0       2   \n",
       "top      City16                        State5           NaN    960.0  female   \n",
       "freq       1672                8212      8773           NaN    911.0   30805   \n",
       "mean        NaN                 NaN       NaN      1.252225      NaN     NaN   \n",
       "std         NaN                 NaN       NaN      0.867833      NaN     NaN   \n",
       "min         NaN                 NaN       NaN      0.000000      NaN     NaN   \n",
       "25%         NaN                 NaN       NaN      1.000000      NaN     NaN   \n",
       "50%         NaN                 NaN       NaN      1.000000      NaN     NaN   \n",
       "75%         NaN                 NaN       NaN      1.000000      NaN     NaN   \n",
       "max         NaN                 NaN       NaN      5.000000      NaN     NaN   \n",
       "\n",
       "              S_FLAG        TARGET     VISA_FLAG train_or_test  \n",
       "count   50000.000000  25000.000000  50000.000000         50000  \n",
       "unique           NaN           NaN           NaN             2  \n",
       "top              NaN           NaN           NaN         train  \n",
       "freq             NaN           NaN           NaN         25000  \n",
       "mean        0.001320      0.424320      0.111440           NaN  \n",
       "std         0.036308      0.494249      0.314679           NaN  \n",
       "min         0.000000      0.000000      0.000000           NaN  \n",
       "25%         0.000000      0.000000      0.000000           NaN  \n",
       "50%         0.000000      0.000000      0.000000           NaN  \n",
       "75%         0.000000      1.000000      0.000000           NaN  \n",
       "max         1.000000      1.000000      1.000000           NaN  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>ID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PAY_MONTH_0</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <td>3797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1</th>\n",
       "      <td>8127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ID\n",
       "PAY_MONTH_0       \n",
       "-2            3797\n",
       "-1            8127\n",
       " 0           27365\n",
       " 1            5105\n",
       " 2            5041\n",
       " 3             393\n",
       " 4              78\n",
       " 5              20\n",
       " 6              16\n",
       " 7              32\n",
       " 8              26"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['ID', 'PAY_MONTH_0']].groupby(['PAY_MONTH_0']).agg({'ID':'count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          4181\n",
       "1          5100\n",
       "2         97989\n",
       "3         44970\n",
       "4           390\n",
       "          ...  \n",
       "24995    128671\n",
       "24996     70381\n",
       "24997      5988\n",
       "24998      6954\n",
       "24999    186248\n",
       "Name: BILL_AMOUNT_MONTH_1, Length: 50000, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['BILL_AMOUNT_MONTH_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>BILL_AMOUNT_MONTH_1</th>\n",
       "      <th>PAY_AMOUNT_MONTH_1</th>\n",
       "      <th>P_MONTHLY_INCOME</th>\n",
       "      <th>TARGET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4181</td>\n",
       "      <td>26034</td>\n",
       "      <td>245200.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5100</td>\n",
       "      <td>9861</td>\n",
       "      <td>203200.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44970</td>\n",
       "      <td>3855</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>73267</td>\n",
       "      <td>2971</td>\n",
       "      <td>91200.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6926</td>\n",
       "      <td>1747</td>\n",
       "      <td>225600.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24988</th>\n",
       "      <td>14237</td>\n",
       "      <td>15031</td>\n",
       "      <td>347680.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24990</th>\n",
       "      <td>34580</td>\n",
       "      <td>1300</td>\n",
       "      <td>105200.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24992</th>\n",
       "      <td>336</td>\n",
       "      <td>0</td>\n",
       "      <td>96800.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24993</th>\n",
       "      <td>175539</td>\n",
       "      <td>3826</td>\n",
       "      <td>236240.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24999</th>\n",
       "      <td>226007</td>\n",
       "      <td>10000</td>\n",
       "      <td>428600.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10608 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       BILL_AMOUNT_MONTH_1  PAY_AMOUNT_MONTH_1  P_MONTHLY_INCOME  TARGET\n",
       "0                     4181               26034          245200.0     1.0\n",
       "1                     5100                9861          203200.0     1.0\n",
       "3                    44970                3855           49200.0     1.0\n",
       "11                   73267                2971           91200.0     1.0\n",
       "16                    6926                1747          225600.0     1.0\n",
       "...                    ...                 ...               ...     ...\n",
       "24988                14237               15031          347680.0     1.0\n",
       "24990                34580                1300          105200.0     1.0\n",
       "24992                  336                   0           96800.0     1.0\n",
       "24993               175539                3826          236240.0     1.0\n",
       "24999               226007               10000          428600.0     1.0\n",
       "\n",
       "[10608 rows x 4 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = df[['BILL_AMOUNT_MONTH_1', 'PAY_AMOUNT_MONTH_1', 'P_MONTHLY_INCOME', 'TARGET']]\n",
    "a = a[a['TARGET'] == 1]\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['MEAN_PAY_AMOUNT_MONTH'] = (df['PAY_AMOUNT_MONTH_1'] + df['PAY_AMOUNT_MONTH_2'] \n",
    "                               + df['PAY_AMOUNT_MONTH_3'] + df['PAY_AMOUNT_MONTH_4']\n",
    "                               + df['PAY_AMOUNT_MONTH_5'] + df['PAY_AMOUNT_MONTH_6']) / 6\n",
    "df['MEAN_BILL_AMOUNT_MONTH'] = (df['BILL_AMOUNT_MONTH_1'] + df['BILL_AMOUNT_MONTH_2'] \n",
    "                                + df['BILL_AMOUNT_MONTH_3'] + df['BILL_AMOUNT_MONTH_4']\n",
    "                                + df['BILL_AMOUNT_MONTH_5'] + df['BILL_AMOUNT_MONTH_6']) / 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>AME_FLAG</th>\n",
       "      <th>APP_SUB_TYPE</th>\n",
       "      <th>BILL_AMOUNT_MONTH_1</th>\n",
       "      <th>BILL_AMOUNT_MONTH_2</th>\n",
       "      <th>BILL_AMOUNT_MONTH_3</th>\n",
       "      <th>BILL_AMOUNT_MONTH_4</th>\n",
       "      <th>BILL_AMOUNT_MONTH_5</th>\n",
       "      <th>BILL_AMOUNT_MONTH_6</th>\n",
       "      <th>BIRTH_CITY</th>\n",
       "      <th>BIRTH_STATE</th>\n",
       "      <th>CARS_FLAG</th>\n",
       "      <th>CONF_CITIZEN_CARD_NUM</th>\n",
       "      <th>CONF_TAX_STATUS</th>\n",
       "      <th>C_NAME_GIVEN</th>\n",
       "      <th>C_PRODUCT</th>\n",
       "      <th>DAY_OF_PAYMENT</th>\n",
       "      <th>EDU_LEVEL</th>\n",
       "      <th>EMAIL_FLAG</th>\n",
       "      <th>HOME_PHONE_FLAG</th>\n",
       "      <th>ID</th>\n",
       "      <th>L_BALANCE</th>\n",
       "      <th>MATE_PROF_CODE</th>\n",
       "      <th>MCARD_FLAG</th>\n",
       "      <th>MONTHS_IN_RES</th>\n",
       "      <th>M_IN_THE_JOB</th>\n",
       "      <th>M_STATUS</th>\n",
       "      <th>NAT</th>\n",
       "      <th>NUMBERO_OF_BANK_ACCOUNTS</th>\n",
       "      <th>NUMBER_OF_DEPENDANTS</th>\n",
       "      <th>NUMBER_OF_SPECIAL_BANK_ACCOUNTS</th>\n",
       "      <th>OCARDS_FLAG</th>\n",
       "      <th>OCC_TYPE</th>\n",
       "      <th>O_INCOMES</th>\n",
       "      <th>PAY_AMOUNT_MONTH_1</th>\n",
       "      <th>PAY_AMOUNT_MONTH_2</th>\n",
       "      <th>PAY_AMOUNT_MONTH_3</th>\n",
       "      <th>PAY_AMOUNT_MONTH_4</th>\n",
       "      <th>PAY_AMOUNT_MONTH_5</th>\n",
       "      <th>PAY_AMOUNT_MONTH_6</th>\n",
       "      <th>PAY_MONTH_0</th>\n",
       "      <th>PAY_MONTH_2</th>\n",
       "      <th>PAY_MONTH_3</th>\n",
       "      <th>PAY_MONTH_4</th>\n",
       "      <th>PAY_MONTH_5</th>\n",
       "      <th>PAY_MONTH_6</th>\n",
       "      <th>PROF_BOROUGH</th>\n",
       "      <th>PROF_CITY</th>\n",
       "      <th>PROF_CODE</th>\n",
       "      <th>PROF_PHONE_AREA_CODE</th>\n",
       "      <th>PROF_PHONE_GIVEN</th>\n",
       "      <th>PROF_STATE</th>\n",
       "      <th>PROF_ZIP</th>\n",
       "      <th>P_ADDRESS_TYPE</th>\n",
       "      <th>P_ASSETS_VALUE</th>\n",
       "      <th>P_MONTHLY_INCOME</th>\n",
       "      <th>RES_BOROUGH</th>\n",
       "      <th>RES_CITY</th>\n",
       "      <th>RES_PHONE_AREA_CODE</th>\n",
       "      <th>RES_STATE</th>\n",
       "      <th>RES_TYPE</th>\n",
       "      <th>RES_ZIP</th>\n",
       "      <th>SEX</th>\n",
       "      <th>S_FLAG</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>VISA_FLAG</th>\n",
       "      <th>train_or_test</th>\n",
       "      <th>SUM_PAY_AMOUNT_MONTH</th>\n",
       "      <th>MEAN_PAY_AMOUNT_MONTH</th>\n",
       "      <th>MEAN_BILL_AMOUNT_MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>W</td>\n",
       "      <td>4181</td>\n",
       "      <td>25988</td>\n",
       "      <td>3177</td>\n",
       "      <td>49174</td>\n",
       "      <td>11823</td>\n",
       "      <td>669</td>\n",
       "      <td>City0</td>\n",
       "      <td>State0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>150000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26034</td>\n",
       "      <td>3182</td>\n",
       "      <td>49174</td>\n",
       "      <td>11823</td>\n",
       "      <td>669</td>\n",
       "      <td>1534</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>595</td>\n",
       "      <td>A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>245200.0</td>\n",
       "      <td>Borough0</td>\n",
       "      <td>City2115</td>\n",
       "      <td>105</td>\n",
       "      <td>State0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>595</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>train</td>\n",
       "      <td>15402.666667</td>\n",
       "      <td>15402.666667</td>\n",
       "      <td>15835.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>5100</td>\n",
       "      <td>9861</td>\n",
       "      <td>11021</td>\n",
       "      <td>16883</td>\n",
       "      <td>150</td>\n",
       "      <td>4661</td>\n",
       "      <td>City1</td>\n",
       "      <td>State1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>180000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9861</td>\n",
       "      <td>11021</td>\n",
       "      <td>16883</td>\n",
       "      <td>150</td>\n",
       "      <td>4661</td>\n",
       "      <td>873</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230</td>\n",
       "      <td>A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>203200.0</td>\n",
       "      <td>Borough1</td>\n",
       "      <td>City1</td>\n",
       "      <td>20</td>\n",
       "      <td>State1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>230</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "      <td>7241.500000</td>\n",
       "      <td>7241.500000</td>\n",
       "      <td>7946.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>W</td>\n",
       "      <td>97989</td>\n",
       "      <td>101458</td>\n",
       "      <td>103840</td>\n",
       "      <td>105646</td>\n",
       "      <td>86127</td>\n",
       "      <td>67269</td>\n",
       "      <td>City3</td>\n",
       "      <td>State2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>3</td>\n",
       "      <td>120000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5100</td>\n",
       "      <td>5500</td>\n",
       "      <td>5170</td>\n",
       "      <td>3543</td>\n",
       "      <td>3000</td>\n",
       "      <td>3238</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>545</td>\n",
       "      <td>A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>City3</td>\n",
       "      <td></td>\n",
       "      <td>State2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>545</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "      <td>4258.500000</td>\n",
       "      <td>4258.500000</td>\n",
       "      <td>93721.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>W</td>\n",
       "      <td>44970</td>\n",
       "      <td>48101</td>\n",
       "      <td>46993</td>\n",
       "      <td>47940</td>\n",
       "      <td>50903</td>\n",
       "      <td>49794</td>\n",
       "      <td>City9</td>\n",
       "      <td>State7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>4</td>\n",
       "      <td>50000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3855</td>\n",
       "      <td>0</td>\n",
       "      <td>1694</td>\n",
       "      <td>3713</td>\n",
       "      <td>2000</td>\n",
       "      <td>1900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>607</td>\n",
       "      <td>A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>Borough10</td>\n",
       "      <td>City16</td>\n",
       "      <td>107</td>\n",
       "      <td>State7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>607</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "      <td>2193.666667</td>\n",
       "      <td>2193.666667</td>\n",
       "      <td>48116.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>W</td>\n",
       "      <td>390</td>\n",
       "      <td>1780</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>City10</td>\n",
       "      <td>State3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>5</td>\n",
       "      <td>100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4780</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>Borough0</td>\n",
       "      <td>City10</td>\n",
       "      <td>9.0</td>\n",
       "      <td>32</td>\n",
       "      <td>Yes</td>\n",
       "      <td>State3</td>\n",
       "      <td>384</td>\n",
       "      <td>A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>106040.0</td>\n",
       "      <td>Borough11</td>\n",
       "      <td>City10</td>\n",
       "      <td>32</td>\n",
       "      <td>State3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>384</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "      <td>796.666667</td>\n",
       "      <td>796.666667</td>\n",
       "      <td>361.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24995</th>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>W</td>\n",
       "      <td>128671</td>\n",
       "      <td>129901</td>\n",
       "      <td>127658</td>\n",
       "      <td>127362</td>\n",
       "      <td>128726</td>\n",
       "      <td>129081</td>\n",
       "      <td>NaN</td>\n",
       "      <td>State9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>49996</td>\n",
       "      <td>130000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7000</td>\n",
       "      <td>4710</td>\n",
       "      <td>4500</td>\n",
       "      <td>9400</td>\n",
       "      <td>3600</td>\n",
       "      <td>5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>725</td>\n",
       "      <td>A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137960.0</td>\n",
       "      <td>Borough728</td>\n",
       "      <td>City8</td>\n",
       "      <td>66</td>\n",
       "      <td>State21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>725</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>test</td>\n",
       "      <td>5785.000000</td>\n",
       "      <td>5785.000000</td>\n",
       "      <td>128566.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24996</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>W</td>\n",
       "      <td>70381</td>\n",
       "      <td>68660</td>\n",
       "      <td>70081</td>\n",
       "      <td>70899</td>\n",
       "      <td>75331</td>\n",
       "      <td>74005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>State7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>49997</td>\n",
       "      <td>80000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3126</td>\n",
       "      <td>2578</td>\n",
       "      <td>5616</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>635</td>\n",
       "      <td>A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>City239</td>\n",
       "      <td>112</td>\n",
       "      <td>State7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>635</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>test</td>\n",
       "      <td>2386.666667</td>\n",
       "      <td>2386.666667</td>\n",
       "      <td>71559.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24997</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>Z</td>\n",
       "      <td>5988</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>City32</td>\n",
       "      <td>State0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>49998</td>\n",
       "      <td>80000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>36700.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>591</td>\n",
       "      <td>A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105</td>\n",
       "      <td>State0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>591</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>test</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>998.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24998</th>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>W</td>\n",
       "      <td>6954</td>\n",
       "      <td>4110</td>\n",
       "      <td>1030</td>\n",
       "      <td>357</td>\n",
       "      <td>357</td>\n",
       "      <td>370</td>\n",
       "      <td>City1442</td>\n",
       "      <td>State5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>49999</td>\n",
       "      <td>130000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>1034</td>\n",
       "      <td>727</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1330</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>882</td>\n",
       "      <td>A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>142440.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>City459</td>\n",
       "      <td></td>\n",
       "      <td>State5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>882</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>test</td>\n",
       "      <td>519.333333</td>\n",
       "      <td>519.333333</td>\n",
       "      <td>2196.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24999</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>W</td>\n",
       "      <td>186248</td>\n",
       "      <td>178001</td>\n",
       "      <td>145248</td>\n",
       "      <td>58142</td>\n",
       "      <td>22312</td>\n",
       "      <td>7340</td>\n",
       "      <td>City3754</td>\n",
       "      <td>State5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>50000</td>\n",
       "      <td>180000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18000</td>\n",
       "      <td>5044</td>\n",
       "      <td>6506</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Borough315</td>\n",
       "      <td>City3754</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9</td>\n",
       "      <td>Yes</td>\n",
       "      <td>State5</td>\n",
       "      <td>124</td>\n",
       "      <td>A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>194626.4</td>\n",
       "      <td>Borough2259</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>State5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>124</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>test</td>\n",
       "      <td>5425.000000</td>\n",
       "      <td>5425.000000</td>\n",
       "      <td>99548.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       AGE  AME_FLAG APP_SUB_TYPE  BILL_AMOUNT_MONTH_1  BILL_AMOUNT_MONTH_2  \\\n",
       "0       32         0            W                 4181                25988   \n",
       "1       34         0            C                 5100                 9861   \n",
       "2       61         0            W                97989               101458   \n",
       "3       36         0            W                44970                48101   \n",
       "4       29         0            W                  390                 1780   \n",
       "...    ...       ...          ...                  ...                  ...   \n",
       "24995   59         0            W               128671               129901   \n",
       "24996   38         0            W                70381                68660   \n",
       "24997   37         0            Z                 5988                    0   \n",
       "24998   68         0            W                 6954                 4110   \n",
       "24999   27         0            W               186248               178001   \n",
       "\n",
       "       BILL_AMOUNT_MONTH_3  BILL_AMOUNT_MONTH_4  BILL_AMOUNT_MONTH_5  \\\n",
       "0                     3177                49174                11823   \n",
       "1                    11021                16883                  150   \n",
       "2                   103840               105646                86127   \n",
       "3                    46993                47940                50903   \n",
       "4                        0                    0                    0   \n",
       "...                    ...                  ...                  ...   \n",
       "24995               127658               127362               128726   \n",
       "24996                70081                70899                75331   \n",
       "24997                    0                    0                    0   \n",
       "24998                 1030                  357                  357   \n",
       "24999               145248                58142                22312   \n",
       "\n",
       "       BILL_AMOUNT_MONTH_6 BIRTH_CITY BIRTH_STATE  CARS_FLAG  \\\n",
       "0                      669      City0      State0          0   \n",
       "1                     4661      City1      State1          0   \n",
       "2                    67269      City3      State2          0   \n",
       "3                    49794      City9      State7          0   \n",
       "4                        0     City10      State3          0   \n",
       "...                    ...        ...         ...        ...   \n",
       "24995               129081        NaN      State9          0   \n",
       "24996                74005        NaN      State7          0   \n",
       "24997                    0     City32      State0          1   \n",
       "24998                  370   City1442      State5          0   \n",
       "24999                 7340   City3754      State5          0   \n",
       "\n",
       "       CONF_CITIZEN_CARD_NUM  CONF_TAX_STATUS C_NAME_GIVEN  C_PRODUCT  \\\n",
       "0                          0                0           no          1   \n",
       "1                          0                0          yes          1   \n",
       "2                          0                0           no          1   \n",
       "3                          0                0          yes          1   \n",
       "4                          0                0          yes          2   \n",
       "...                      ...              ...          ...        ...   \n",
       "24995                      0                0           no          1   \n",
       "24996                      0                0           no          2   \n",
       "24997                      0                0           no          1   \n",
       "24998                      0                0           no          1   \n",
       "24999                      0                0          yes          1   \n",
       "\n",
       "       DAY_OF_PAYMENT  EDU_LEVEL  EMAIL_FLAG HOME_PHONE_FLAG     ID  \\\n",
       "0                   5        NaN           1             yes      1   \n",
       "1                  15        NaN           1             yes      2   \n",
       "2                  20        NaN           1              no      3   \n",
       "3                  25        NaN           1             yes      4   \n",
       "4                  15        NaN           1             yes      5   \n",
       "...               ...        ...         ...             ...    ...   \n",
       "24995              10        NaN           1             yes  49996   \n",
       "24996              15        NaN           1             yes  49997   \n",
       "24997              15        0.0           0             yes  49998   \n",
       "24998              15        NaN           1              no  49999   \n",
       "24999              10        NaN           1              no  50000   \n",
       "\n",
       "       L_BALANCE  MATE_PROF_CODE  MCARD_FLAG  MONTHS_IN_RES  M_IN_THE_JOB  \\\n",
       "0         150000             NaN           1           15.0             0   \n",
       "1         180000            11.0           0            1.0             0   \n",
       "2         120000             NaN           0            NaN             0   \n",
       "3          50000             NaN           1           10.0             0   \n",
       "4         100000             NaN           0            6.0             0   \n",
       "...          ...             ...         ...            ...           ...   \n",
       "24995     130000             NaN           1           15.0             0   \n",
       "24996      80000             NaN           0            1.0             0   \n",
       "24997      80000             0.0           0            3.0             0   \n",
       "24998     130000             NaN           0            5.0             0   \n",
       "24999     180000             NaN           0            9.0             0   \n",
       "\n",
       "       M_STATUS  NAT  NUMBERO_OF_BANK_ACCOUNTS  NUMBER_OF_DEPENDANTS  \\\n",
       "0             6    1                         0                     1   \n",
       "1             2    1                         0                     0   \n",
       "2             2    1                         0                     0   \n",
       "3             2    1                         0                     0   \n",
       "4             1    1                         0                     1   \n",
       "...         ...  ...                       ...                   ...   \n",
       "24995         2    1                         0                     2   \n",
       "24996         1    1                         0                     0   \n",
       "24997         1    1                         1                     1   \n",
       "24998         2    1                         0                     0   \n",
       "24999         2    1                         0                     2   \n",
       "\n",
       "       NUMBER_OF_SPECIAL_BANK_ACCOUNTS  OCARDS_FLAG  OCC_TYPE  O_INCOMES  \\\n",
       "0                                    0            0       4.0        0.0   \n",
       "1                                    0            0       4.0        0.0   \n",
       "2                                    0            0       NaN        0.0   \n",
       "3                                    0            0       4.0        0.0   \n",
       "4                                    0            0       2.0        0.0   \n",
       "...                                ...          ...       ...        ...   \n",
       "24995                                0            0       NaN        0.0   \n",
       "24996                                0            0       1.0        0.0   \n",
       "24997                                1            0       2.0    36700.0   \n",
       "24998                                0            0       NaN        0.0   \n",
       "24999                                0            0       2.0        0.0   \n",
       "\n",
       "       PAY_AMOUNT_MONTH_1  PAY_AMOUNT_MONTH_2  PAY_AMOUNT_MONTH_3  \\\n",
       "0                   26034                3182               49174   \n",
       "1                    9861               11021               16883   \n",
       "2                    5100                5500                5170   \n",
       "3                    3855                   0                1694   \n",
       "4                    4780                   0                   0   \n",
       "...                   ...                 ...                 ...   \n",
       "24995                7000                4710                4500   \n",
       "24996                   0                3126                2578   \n",
       "24997                   0                   0                   0   \n",
       "24998                  12                1034                 727   \n",
       "24999               18000                5044                6506   \n",
       "\n",
       "       PAY_AMOUNT_MONTH_4  PAY_AMOUNT_MONTH_5  PAY_AMOUNT_MONTH_6  \\\n",
       "0                   11823                 669                1534   \n",
       "1                     150                4661                 873   \n",
       "2                    3543                3000                3238   \n",
       "3                    3713                2000                1900   \n",
       "4                       0                   0                   0   \n",
       "...                   ...                 ...                 ...   \n",
       "24995                9400                3600                5500   \n",
       "24996                5616                   0                3000   \n",
       "24997                   0                   0                   0   \n",
       "24998                   0                  13                1330   \n",
       "24999                 500                 500                2000   \n",
       "\n",
       "       PAY_MONTH_0  PAY_MONTH_2  PAY_MONTH_3  PAY_MONTH_4  PAY_MONTH_5  \\\n",
       "0               -2           -2           -2           -2           -2   \n",
       "1               -2           -2           -2           -2           -2   \n",
       "2                0            0            0            0            0   \n",
       "3                0            0            2            0            0   \n",
       "4               -2           -2           -2           -2           -2   \n",
       "...            ...          ...          ...          ...          ...   \n",
       "24995            0            0            0            0            0   \n",
       "24996            2            2            0            0            0   \n",
       "24997            0            0           -2           -2           -2   \n",
       "24998           -1            2           -1           -1            0   \n",
       "24999            0            0            0            0            0   \n",
       "\n",
       "       PAY_MONTH_6 PROF_BOROUGH PROF_CITY  PROF_CODE PROF_PHONE_AREA_CODE  \\\n",
       "0               -2          NaN       NaN        9.0                        \n",
       "1               -2          NaN       NaN       11.0                        \n",
       "2                0          NaN       NaN        NaN                        \n",
       "3                2          NaN       NaN        9.0                        \n",
       "4               -2     Borough0    City10        9.0                   32   \n",
       "...            ...          ...       ...        ...                  ...   \n",
       "24995            2          NaN       NaN        NaN                        \n",
       "24996            2          NaN       NaN        9.0                        \n",
       "24997           -2          NaN       NaN        9.0                        \n",
       "24998            0          NaN       NaN        NaN                        \n",
       "24999            0   Borough315  City3754        9.0                    9   \n",
       "\n",
       "      PROF_PHONE_GIVEN PROF_STATE PROF_ZIP P_ADDRESS_TYPE  P_ASSETS_VALUE  \\\n",
       "0                   No        NaN      595              A             0.0   \n",
       "1                   No        NaN      230              A             0.0   \n",
       "2                   No        NaN      545              A             0.0   \n",
       "3                   No        NaN      607              A             0.0   \n",
       "4                  Yes     State3      384              A             0.0   \n",
       "...                ...        ...      ...            ...             ...   \n",
       "24995               No        NaN      725              A             0.0   \n",
       "24996               No        NaN      635              A             0.0   \n",
       "24997               No        NaN      591              A             0.0   \n",
       "24998               No        NaN      882              A             0.0   \n",
       "24999              Yes     State5      124              A             0.0   \n",
       "\n",
       "       P_MONTHLY_INCOME  RES_BOROUGH  RES_CITY RES_PHONE_AREA_CODE RES_STATE  \\\n",
       "0              245200.0     Borough0  City2115                 105    State0   \n",
       "1              203200.0     Borough1     City1                  20    State1   \n",
       "2              133200.0          NaN     City3                        State2   \n",
       "3               49200.0    Borough10    City16                 107    State7   \n",
       "4              106040.0    Borough11    City10                  32    State3   \n",
       "...                 ...          ...       ...                 ...       ...   \n",
       "24995          137960.0   Borough728     City8                  66   State21   \n",
       "24996           91200.0          NaN   City239                 112    State7   \n",
       "24997           91200.0          NaN       NaN                 105    State0   \n",
       "24998          142440.0          NaN   City459                        State5   \n",
       "24999          194626.4  Borough2259       NaN                        State5   \n",
       "\n",
       "       RES_TYPE RES_ZIP     SEX  S_FLAG  TARGET  VISA_FLAG train_or_test  \\\n",
       "0           1.0     595  female       0     1.0          1         train   \n",
       "1           1.0     230  female       0     1.0          0         train   \n",
       "2           NaN     545  female       0     0.0          0         train   \n",
       "3           1.0     607  female       0     1.0          0         train   \n",
       "4           1.0     384  female       0     0.0          0         train   \n",
       "...         ...     ...     ...     ...     ...        ...           ...   \n",
       "24995       1.0     725  female       0     NaN          0          test   \n",
       "24996       1.0     635  female       0     NaN          0          test   \n",
       "24997       2.0     591  female       0     NaN          0          test   \n",
       "24998       1.0     882    male       0     NaN          0          test   \n",
       "24999       1.0     124  female       0     NaN          0          test   \n",
       "\n",
       "       SUM_PAY_AMOUNT_MONTH  MEAN_PAY_AMOUNT_MONTH  MEAN_BILL_AMOUNT_MONTH  \n",
       "0              15402.666667           15402.666667            15835.333333  \n",
       "1               7241.500000            7241.500000             7946.000000  \n",
       "2               4258.500000            4258.500000            93721.500000  \n",
       "3               2193.666667            2193.666667            48116.833333  \n",
       "4                796.666667             796.666667              361.666667  \n",
       "...                     ...                    ...                     ...  \n",
       "24995           5785.000000            5785.000000           128566.500000  \n",
       "24996           2386.666667            2386.666667            71559.500000  \n",
       "24997              0.000000               0.000000              998.000000  \n",
       "24998            519.333333             519.333333             2196.333333  \n",
       "24999           5425.000000            5425.000000            99548.500000  \n",
       "\n",
       "[50000 rows x 70 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['INCOME_RATE'] = (df['MEAN_BILL_AMOUNT_MONTH'] + df['MEAN_PAY_AMOUNT_MONTH']) / (df['P_MONTHLY_INCOME'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>TARGET</th>\n",
       "      <th>INCOME_RATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.353512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.386888</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TARGET  INCOME_RATE\n",
       "0     0.0     0.353512\n",
       "1     1.0     0.386888"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['INCOME_RATE', 'TARGET']].groupby(['TARGET'], as_index=False).agg({'INCOME_RATE':'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>TARGET</th>\n",
       "      <th>MEAN_BILL_AMOUNT_MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>50668.352406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>52843.399038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TARGET  MEAN_BILL_AMOUNT_MONTH\n",
       "0     0.0            50668.352406\n",
       "1     1.0            52843.399038"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['MEAN_BILL_AMOUNT_MONTH', 'TARGET']].groupby(['TARGET'], as_index=False).agg({'MEAN_BILL_AMOUNT_MONTH':'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>TARGET</th>\n",
       "      <th>MEAN_PAY_AMOUNT_MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5879.096975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4702.732073</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TARGET  MEAN_PAY_AMOUNT_MONTH\n",
       "0     0.0            5879.096975\n",
       "1     1.0            4702.732073"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['MEAN_PAY_AMOUNT_MONTH', 'TARGET']].groupby(['TARGET'], as_index=False).agg({'MEAN_PAY_AMOUNT_MONTH':'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       -2.000000\n",
       "1       -2.000000\n",
       "2        0.000000\n",
       "3        0.666667\n",
       "4       -2.000000\n",
       "           ...   \n",
       "24995    0.333333\n",
       "24996    1.000000\n",
       "24997   -1.333333\n",
       "24998   -0.166667\n",
       "24999    0.000000\n",
       "Name: MEAN_PAY_MONTH, Length: 50000, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MEAN_PAY_MONTH'] = (df['PAY_MONTH_0'] + df['PAY_MONTH_2'] + df['PAY_MONTH_3'] \n",
    "                        + df['PAY_MONTH_4'] + df['PAY_MONTH_5'] + df['PAY_MONTH_6']) / 6\n",
    "df['MEAN_PAY_MONTH']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>TARGET</th>\n",
       "      <th>MEAN_PAY_MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.282877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.081825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TARGET  MEAN_PAY_MONTH\n",
       "0     0.0       -0.282877\n",
       "1     1.0        0.081825"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['TARGET'] == 1].groupby(['ID'], as_index=False).agg({'MEAN_PAY_MONTH':'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>AGE</th>\n",
       "      <th>AME_FLAG</th>\n",
       "      <th>APP_SUB_TYPE</th>\n",
       "      <th>BILL_AMOUNT_MONTH_1</th>\n",
       "      <th>BILL_AMOUNT_MONTH_2</th>\n",
       "      <th>BILL_AMOUNT_MONTH_3</th>\n",
       "      <th>BILL_AMOUNT_MONTH_4</th>\n",
       "      <th>BILL_AMOUNT_MONTH_5</th>\n",
       "      <th>BILL_AMOUNT_MONTH_6</th>\n",
       "      <th>BIRTH_CITY</th>\n",
       "      <th>BIRTH_STATE</th>\n",
       "      <th>CARS_FLAG</th>\n",
       "      <th>CONF_CITIZEN_CARD_NUM</th>\n",
       "      <th>CONF_TAX_STATUS</th>\n",
       "      <th>C_NAME_GIVEN</th>\n",
       "      <th>C_PRODUCT</th>\n",
       "      <th>DAY_OF_PAYMENT</th>\n",
       "      <th>EDU_LEVEL</th>\n",
       "      <th>EMAIL_FLAG</th>\n",
       "      <th>HOME_PHONE_FLAG</th>\n",
       "      <th>ID</th>\n",
       "      <th>L_BALANCE</th>\n",
       "      <th>MATE_PROF_CODE</th>\n",
       "      <th>MCARD_FLAG</th>\n",
       "      <th>MONTHS_IN_RES</th>\n",
       "      <th>M_IN_THE_JOB</th>\n",
       "      <th>M_STATUS</th>\n",
       "      <th>NAT</th>\n",
       "      <th>NUMBERO_OF_BANK_ACCOUNTS</th>\n",
       "      <th>NUMBER_OF_DEPENDANTS</th>\n",
       "      <th>NUMBER_OF_SPECIAL_BANK_ACCOUNTS</th>\n",
       "      <th>OCARDS_FLAG</th>\n",
       "      <th>OCC_TYPE</th>\n",
       "      <th>O_INCOMES</th>\n",
       "      <th>PAY_AMOUNT_MONTH_1</th>\n",
       "      <th>...</th>\n",
       "      <th>PAY_AMOUNT_MONTH_4</th>\n",
       "      <th>PAY_AMOUNT_MONTH_5</th>\n",
       "      <th>PAY_AMOUNT_MONTH_6</th>\n",
       "      <th>PAY_MONTH_0</th>\n",
       "      <th>PAY_MONTH_2</th>\n",
       "      <th>PAY_MONTH_3</th>\n",
       "      <th>PAY_MONTH_4</th>\n",
       "      <th>PAY_MONTH_5</th>\n",
       "      <th>PAY_MONTH_6</th>\n",
       "      <th>PROF_BOROUGH</th>\n",
       "      <th>PROF_CITY</th>\n",
       "      <th>PROF_CODE</th>\n",
       "      <th>PROF_PHONE_AREA_CODE</th>\n",
       "      <th>PROF_PHONE_GIVEN</th>\n",
       "      <th>PROF_STATE</th>\n",
       "      <th>PROF_ZIP</th>\n",
       "      <th>P_ADDRESS_TYPE</th>\n",
       "      <th>P_ASSETS_VALUE</th>\n",
       "      <th>P_MONTHLY_INCOME</th>\n",
       "      <th>RES_BOROUGH</th>\n",
       "      <th>RES_CITY</th>\n",
       "      <th>RES_PHONE_AREA_CODE</th>\n",
       "      <th>RES_STATE</th>\n",
       "      <th>RES_TYPE</th>\n",
       "      <th>RES_ZIP</th>\n",
       "      <th>SEX</th>\n",
       "      <th>S_FLAG</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>VISA_FLAG</th>\n",
       "      <th>train_or_test</th>\n",
       "      <th>SUM_PAY_AMOUNT_MONTH</th>\n",
       "      <th>MEAN_PAY_AMOUNT_MONTH</th>\n",
       "      <th>MEAN_BILL_AMOUNT_MONTH</th>\n",
       "      <th>INCOME_RATE</th>\n",
       "      <th>MEAN_PAY_MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>W</td>\n",
       "      <td>4181</td>\n",
       "      <td>25988</td>\n",
       "      <td>3177</td>\n",
       "      <td>49174</td>\n",
       "      <td>11823</td>\n",
       "      <td>669</td>\n",
       "      <td>City0</td>\n",
       "      <td>State0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>150000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26034</td>\n",
       "      <td>...</td>\n",
       "      <td>11823</td>\n",
       "      <td>669</td>\n",
       "      <td>1534</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>595</td>\n",
       "      <td>A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>245200.0</td>\n",
       "      <td>Borough0</td>\n",
       "      <td>City2115</td>\n",
       "      <td>105</td>\n",
       "      <td>State0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>595</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>train</td>\n",
       "      <td>15402.666667</td>\n",
       "      <td>15402.666667</td>\n",
       "      <td>15835.333333</td>\n",
       "      <td>0.127398</td>\n",
       "      <td>-2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>5100</td>\n",
       "      <td>9861</td>\n",
       "      <td>11021</td>\n",
       "      <td>16883</td>\n",
       "      <td>150</td>\n",
       "      <td>4661</td>\n",
       "      <td>City1</td>\n",
       "      <td>State1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>180000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9861</td>\n",
       "      <td>...</td>\n",
       "      <td>150</td>\n",
       "      <td>4661</td>\n",
       "      <td>873</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230</td>\n",
       "      <td>A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>203200.0</td>\n",
       "      <td>Borough1</td>\n",
       "      <td>City1</td>\n",
       "      <td>20</td>\n",
       "      <td>State1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>230</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "      <td>7241.500000</td>\n",
       "      <td>7241.500000</td>\n",
       "      <td>7946.000000</td>\n",
       "      <td>0.074742</td>\n",
       "      <td>-2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>W</td>\n",
       "      <td>97989</td>\n",
       "      <td>101458</td>\n",
       "      <td>103840</td>\n",
       "      <td>105646</td>\n",
       "      <td>86127</td>\n",
       "      <td>67269</td>\n",
       "      <td>City3</td>\n",
       "      <td>State2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>3</td>\n",
       "      <td>120000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5100</td>\n",
       "      <td>...</td>\n",
       "      <td>3543</td>\n",
       "      <td>3000</td>\n",
       "      <td>3238</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>545</td>\n",
       "      <td>A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>City3</td>\n",
       "      <td></td>\n",
       "      <td>State2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>545</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "      <td>4258.500000</td>\n",
       "      <td>4258.500000</td>\n",
       "      <td>93721.500000</td>\n",
       "      <td>0.735586</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>W</td>\n",
       "      <td>44970</td>\n",
       "      <td>48101</td>\n",
       "      <td>46993</td>\n",
       "      <td>47940</td>\n",
       "      <td>50903</td>\n",
       "      <td>49794</td>\n",
       "      <td>City9</td>\n",
       "      <td>State7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>4</td>\n",
       "      <td>50000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3855</td>\n",
       "      <td>...</td>\n",
       "      <td>3713</td>\n",
       "      <td>2000</td>\n",
       "      <td>1900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>607</td>\n",
       "      <td>A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49200.0</td>\n",
       "      <td>Borough10</td>\n",
       "      <td>City16</td>\n",
       "      <td>107</td>\n",
       "      <td>State7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>607</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "      <td>2193.666667</td>\n",
       "      <td>2193.666667</td>\n",
       "      <td>48116.833333</td>\n",
       "      <td>1.022571</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>W</td>\n",
       "      <td>390</td>\n",
       "      <td>1780</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>City10</td>\n",
       "      <td>State3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>5</td>\n",
       "      <td>100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4780</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>Borough0</td>\n",
       "      <td>City10</td>\n",
       "      <td>9.0</td>\n",
       "      <td>32</td>\n",
       "      <td>Yes</td>\n",
       "      <td>State3</td>\n",
       "      <td>384</td>\n",
       "      <td>A</td>\n",
       "      <td>0.0</td>\n",
       "      <td>106040.0</td>\n",
       "      <td>Borough11</td>\n",
       "      <td>City10</td>\n",
       "      <td>32</td>\n",
       "      <td>State3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>384</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "      <td>796.666667</td>\n",
       "      <td>796.666667</td>\n",
       "      <td>361.666667</td>\n",
       "      <td>0.010924</td>\n",
       "      <td>-2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   AGE  AME_FLAG APP_SUB_TYPE  BILL_AMOUNT_MONTH_1  BILL_AMOUNT_MONTH_2  \\\n",
       "0   32         0            W                 4181                25988   \n",
       "1   34         0            C                 5100                 9861   \n",
       "2   61         0            W                97989               101458   \n",
       "3   36         0            W                44970                48101   \n",
       "4   29         0            W                  390                 1780   \n",
       "\n",
       "   BILL_AMOUNT_MONTH_3  BILL_AMOUNT_MONTH_4  BILL_AMOUNT_MONTH_5  \\\n",
       "0                 3177                49174                11823   \n",
       "1                11021                16883                  150   \n",
       "2               103840               105646                86127   \n",
       "3                46993                47940                50903   \n",
       "4                    0                    0                    0   \n",
       "\n",
       "   BILL_AMOUNT_MONTH_6 BIRTH_CITY BIRTH_STATE  CARS_FLAG  \\\n",
       "0                  669      City0      State0          0   \n",
       "1                 4661      City1      State1          0   \n",
       "2                67269      City3      State2          0   \n",
       "3                49794      City9      State7          0   \n",
       "4                    0     City10      State3          0   \n",
       "\n",
       "   CONF_CITIZEN_CARD_NUM  CONF_TAX_STATUS C_NAME_GIVEN  C_PRODUCT  \\\n",
       "0                      0                0           no          1   \n",
       "1                      0                0          yes          1   \n",
       "2                      0                0           no          1   \n",
       "3                      0                0          yes          1   \n",
       "4                      0                0          yes          2   \n",
       "\n",
       "   DAY_OF_PAYMENT  EDU_LEVEL  EMAIL_FLAG HOME_PHONE_FLAG  ID  L_BALANCE  \\\n",
       "0               5        NaN           1             yes   1     150000   \n",
       "1              15        NaN           1             yes   2     180000   \n",
       "2              20        NaN           1              no   3     120000   \n",
       "3              25        NaN           1             yes   4      50000   \n",
       "4              15        NaN           1             yes   5     100000   \n",
       "\n",
       "   MATE_PROF_CODE  MCARD_FLAG  MONTHS_IN_RES  M_IN_THE_JOB  M_STATUS  NAT  \\\n",
       "0             NaN           1           15.0             0         6    1   \n",
       "1            11.0           0            1.0             0         2    1   \n",
       "2             NaN           0            NaN             0         2    1   \n",
       "3             NaN           1           10.0             0         2    1   \n",
       "4             NaN           0            6.0             0         1    1   \n",
       "\n",
       "   NUMBERO_OF_BANK_ACCOUNTS  NUMBER_OF_DEPENDANTS  \\\n",
       "0                         0                     1   \n",
       "1                         0                     0   \n",
       "2                         0                     0   \n",
       "3                         0                     0   \n",
       "4                         0                     1   \n",
       "\n",
       "   NUMBER_OF_SPECIAL_BANK_ACCOUNTS  OCARDS_FLAG  OCC_TYPE  O_INCOMES  \\\n",
       "0                                0            0       4.0        0.0   \n",
       "1                                0            0       4.0        0.0   \n",
       "2                                0            0       NaN        0.0   \n",
       "3                                0            0       4.0        0.0   \n",
       "4                                0            0       2.0        0.0   \n",
       "\n",
       "   PAY_AMOUNT_MONTH_1  ...  PAY_AMOUNT_MONTH_4  PAY_AMOUNT_MONTH_5  \\\n",
       "0               26034  ...               11823                 669   \n",
       "1                9861  ...                 150                4661   \n",
       "2                5100  ...                3543                3000   \n",
       "3                3855  ...                3713                2000   \n",
       "4                4780  ...                   0                   0   \n",
       "\n",
       "   PAY_AMOUNT_MONTH_6  PAY_MONTH_0  PAY_MONTH_2  PAY_MONTH_3  PAY_MONTH_4  \\\n",
       "0                1534           -2           -2           -2           -2   \n",
       "1                 873           -2           -2           -2           -2   \n",
       "2                3238            0            0            0            0   \n",
       "3                1900            0            0            2            0   \n",
       "4                   0           -2           -2           -2           -2   \n",
       "\n",
       "   PAY_MONTH_5  PAY_MONTH_6  PROF_BOROUGH  PROF_CITY PROF_CODE  \\\n",
       "0           -2           -2           NaN        NaN       9.0   \n",
       "1           -2           -2           NaN        NaN      11.0   \n",
       "2            0            0           NaN        NaN       NaN   \n",
       "3            0            2           NaN        NaN       9.0   \n",
       "4           -2           -2      Borough0     City10       9.0   \n",
       "\n",
       "  PROF_PHONE_AREA_CODE  PROF_PHONE_GIVEN PROF_STATE PROF_ZIP P_ADDRESS_TYPE  \\\n",
       "0                                     No        NaN      595              A   \n",
       "1                                     No        NaN      230              A   \n",
       "2                                     No        NaN      545              A   \n",
       "3                                     No        NaN      607              A   \n",
       "4                   32               Yes     State3      384              A   \n",
       "\n",
       "  P_ASSETS_VALUE P_MONTHLY_INCOME  RES_BOROUGH  RES_CITY RES_PHONE_AREA_CODE  \\\n",
       "0            0.0         245200.0     Borough0  City2115                 105   \n",
       "1            0.0         203200.0     Borough1     City1                  20   \n",
       "2            0.0         133200.0          NaN     City3                       \n",
       "3            0.0          49200.0    Borough10    City16                 107   \n",
       "4            0.0         106040.0    Borough11    City10                  32   \n",
       "\n",
       "  RES_STATE RES_TYPE RES_ZIP     SEX S_FLAG TARGET  VISA_FLAG  train_or_test  \\\n",
       "0    State0      1.0     595  female      0    1.0          1          train   \n",
       "1    State1      1.0     230  female      0    1.0          0          train   \n",
       "2    State2      NaN     545  female      0    0.0          0          train   \n",
       "3    State7      1.0     607  female      0    1.0          0          train   \n",
       "4    State3      1.0     384  female      0    0.0          0          train   \n",
       "\n",
       "   SUM_PAY_AMOUNT_MONTH MEAN_PAY_AMOUNT_MONTH  MEAN_BILL_AMOUNT_MONTH  \\\n",
       "0          15402.666667          15402.666667            15835.333333   \n",
       "1           7241.500000           7241.500000             7946.000000   \n",
       "2           4258.500000           4258.500000            93721.500000   \n",
       "3           2193.666667           2193.666667            48116.833333   \n",
       "4            796.666667            796.666667              361.666667   \n",
       "\n",
       "   INCOME_RATE  MEAN_PAY_MONTH  \n",
       "0     0.127398       -2.000000  \n",
       "1     0.074742       -2.000000  \n",
       "2     0.735586        0.000000  \n",
       "3     1.022571        0.666667  \n",
       "4     0.010924       -2.000000  \n",
       "\n",
       "[5 rows x 72 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "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",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDU_LEVEL</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>15995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ID\n",
       "EDU_LEVEL       \n",
       "0.0        15995\n",
       "1.0           56\n",
       "2.0          342\n",
       "3.0          621\n",
       "4.0          615\n",
       "5.0           33"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['EDU_LEVEL']).agg({'ID':'count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AGE': dtype('int64'),\n",
       " 'AME_FLAG': dtype('int64'),\n",
       " 'APP_SUB_TYPE': dtype('O'),\n",
       " 'BILL_AMOUNT_MONTH_1': dtype('int64'),\n",
       " 'BILL_AMOUNT_MONTH_2': dtype('int64'),\n",
       " 'BILL_AMOUNT_MONTH_3': dtype('int64'),\n",
       " 'BILL_AMOUNT_MONTH_4': dtype('int64'),\n",
       " 'BILL_AMOUNT_MONTH_5': dtype('int64'),\n",
       " 'BILL_AMOUNT_MONTH_6': dtype('int64'),\n",
       " 'BIRTH_CITY': dtype('O'),\n",
       " 'BIRTH_STATE': dtype('O'),\n",
       " 'CARS_FLAG': dtype('int64'),\n",
       " 'CONF_CITIZEN_CARD_NUM': dtype('int64'),\n",
       " 'CONF_TAX_STATUS': dtype('int64'),\n",
       " 'C_NAME_GIVEN': dtype('O'),\n",
       " 'C_PRODUCT': dtype('int64'),\n",
       " 'DAY_OF_PAYMENT': dtype('int64'),\n",
       " 'EDU_LEVEL': dtype('float64'),\n",
       " 'EMAIL_FLAG': dtype('int64'),\n",
       " 'HOME_PHONE_FLAG': dtype('O'),\n",
       " 'ID': dtype('int64'),\n",
       " 'L_BALANCE': dtype('int64'),\n",
       " 'MATE_PROF_CODE': dtype('float64'),\n",
       " 'MCARD_FLAG': dtype('int64'),\n",
       " 'MONTHS_IN_RES': dtype('float64'),\n",
       " 'M_IN_THE_JOB': dtype('int64'),\n",
       " 'M_STATUS': dtype('int64'),\n",
       " 'NAT': dtype('int64'),\n",
       " 'NUMBERO_OF_BANK_ACCOUNTS': dtype('int64'),\n",
       " 'NUMBER_OF_DEPENDANTS': dtype('int64'),\n",
       " 'NUMBER_OF_SPECIAL_BANK_ACCOUNTS': dtype('int64'),\n",
       " 'OCARDS_FLAG': dtype('int64'),\n",
       " 'OCC_TYPE': dtype('float64'),\n",
       " 'O_INCOMES': dtype('float64'),\n",
       " 'PAY_AMOUNT_MONTH_1': dtype('int64'),\n",
       " 'PAY_AMOUNT_MONTH_2': dtype('int64'),\n",
       " 'PAY_AMOUNT_MONTH_3': dtype('int64'),\n",
       " 'PAY_AMOUNT_MONTH_4': dtype('int64'),\n",
       " 'PAY_AMOUNT_MONTH_5': dtype('int64'),\n",
       " 'PAY_AMOUNT_MONTH_6': dtype('int64'),\n",
       " 'PAY_MONTH_0': dtype('int64'),\n",
       " 'PAY_MONTH_2': dtype('int64'),\n",
       " 'PAY_MONTH_3': dtype('int64'),\n",
       " 'PAY_MONTH_4': dtype('int64'),\n",
       " 'PAY_MONTH_5': dtype('int64'),\n",
       " 'PAY_MONTH_6': dtype('int64'),\n",
       " 'PROF_BOROUGH': dtype('O'),\n",
       " 'PROF_CITY': dtype('O'),\n",
       " 'PROF_CODE': dtype('float64'),\n",
       " 'PROF_PHONE_AREA_CODE': dtype('O'),\n",
       " 'PROF_PHONE_GIVEN': dtype('O'),\n",
       " 'PROF_STATE': dtype('O'),\n",
       " 'PROF_ZIP': dtype('O'),\n",
       " 'P_ADDRESS_TYPE': dtype('O'),\n",
       " 'P_ASSETS_VALUE': dtype('float64'),\n",
       " 'P_MONTHLY_INCOME': dtype('float64'),\n",
       " 'RES_BOROUGH': dtype('O'),\n",
       " 'RES_CITY': dtype('O'),\n",
       " 'RES_PHONE_AREA_CODE': dtype('O'),\n",
       " 'RES_STATE': dtype('O'),\n",
       " 'RES_TYPE': dtype('float64'),\n",
       " 'RES_ZIP': dtype('O'),\n",
       " 'SEX': dtype('O'),\n",
       " 'S_FLAG': dtype('int64'),\n",
       " 'TARGET': dtype('float64'),\n",
       " 'VISA_FLAG': dtype('int64'),\n",
       " 'train_or_test': dtype('O'),\n",
       " 'SUM_PAY_AMOUNT_MONTH': dtype('float64'),\n",
       " 'MEAN_PAY_AMOUNT_MONTH': dtype('float64'),\n",
       " 'MEAN_BILL_AMOUNT_MONTH': dtype('float64'),\n",
       " 'INCOME_RATE': dtype('float64'),\n",
       " 'MEAN_PAY_MONTH': dtype('float64')}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = dict()\n",
    "for att in df.columns:\n",
    "    d[att] = df[att].dtype\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>EDU_LEVEL</th>\n",
       "      <th>MATE_PROF_CODE</th>\n",
       "      <th>MONTHS_IN_RES</th>\n",
       "      <th>OCC_TYPE</th>\n",
       "      <th>O_INCOMES</th>\n",
       "      <th>PROF_CODE</th>\n",
       "      <th>P_ASSETS_VALUE</th>\n",
       "      <th>P_MONTHLY_INCOME</th>\n",
       "      <th>RES_TYPE</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>SUM_PAY_AMOUNT_MONTH</th>\n",
       "      <th>MEAN_PAY_AMOUNT_MONTH</th>\n",
       "      <th>MEAN_BILL_AMOUNT_MONTH</th>\n",
       "      <th>INCOME_RATE</th>\n",
       "      <th>MEAN_PAY_MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17662.000000</td>\n",
       "      <td>21116.000000</td>\n",
       "      <td>46223.000000</td>\n",
       "      <td>42687.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>42244.000000</td>\n",
       "      <td>5.000000e+04</td>\n",
       "      <td>49472.000000</td>\n",
       "      <td>48651.000000</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>49472.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.296003</td>\n",
       "      <td>3.797926</td>\n",
       "      <td>9.727149</td>\n",
       "      <td>2.484316</td>\n",
       "      <td>2963.729060</td>\n",
       "      <td>8.061784</td>\n",
       "      <td>4.451491e+05</td>\n",
       "      <td>180633.123690</td>\n",
       "      <td>1.252225</td>\n",
       "      <td>0.424320</td>\n",
       "      <td>5338.065830</td>\n",
       "      <td>5338.065830</td>\n",
       "      <td>51737.170600</td>\n",
       "      <td>0.368367</td>\n",
       "      <td>-0.126310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.955688</td>\n",
       "      <td>5.212168</td>\n",
       "      <td>10.668841</td>\n",
       "      <td>1.532261</td>\n",
       "      <td>15782.379761</td>\n",
       "      <td>3.220104</td>\n",
       "      <td>2.281880e+06</td>\n",
       "      <td>134589.611245</td>\n",
       "      <td>0.867833</td>\n",
       "      <td>0.494249</td>\n",
       "      <td>9997.410420</td>\n",
       "      <td>9997.410420</td>\n",
       "      <td>61598.721738</td>\n",
       "      <td>0.315703</td>\n",
       "      <td>0.915897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\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>0.000000e+00</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-10470.833333</td>\n",
       "      <td>-0.018756</td>\n",
       "      <td>-2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>94000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1370.541667</td>\n",
       "      <td>1370.541667</td>\n",
       "      <td>7177.166667</td>\n",
       "      <td>0.063600</td>\n",
       "      <td>-0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>133200.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2609.833333</td>\n",
       "      <td>2609.833333</td>\n",
       "      <td>35977.833333</td>\n",
       "      <td>0.312633</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>215576.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5400.000000</td>\n",
       "      <td>5400.000000</td>\n",
       "      <td>73460.291667</td>\n",
       "      <td>0.626938</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>228.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.683611e+07</td>\n",
       "      <td>999976.400000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>627344.333333</td>\n",
       "      <td>627344.333333</td>\n",
       "      <td>877313.833333</td>\n",
       "      <td>4.007344</td>\n",
       "      <td>5.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          EDU_LEVEL  MATE_PROF_CODE  MONTHS_IN_RES      OCC_TYPE  \\\n",
       "count  17662.000000    21116.000000   46223.000000  42687.000000   \n",
       "mean       0.296003        3.797926       9.727149      2.484316   \n",
       "std        0.955688        5.212168      10.668841      1.532261   \n",
       "min        0.000000        0.000000       0.000000      0.000000   \n",
       "25%        0.000000        0.000000       1.000000      1.000000   \n",
       "50%        0.000000        0.000000       6.000000      2.000000   \n",
       "75%        0.000000       11.000000      15.000000      4.000000   \n",
       "max        5.000000       17.000000     228.000000      5.000000   \n",
       "\n",
       "           O_INCOMES     PROF_CODE  P_ASSETS_VALUE  P_MONTHLY_INCOME  \\\n",
       "count   50000.000000  42244.000000    5.000000e+04      49472.000000   \n",
       "mean     2963.729060      8.061784    4.451491e+05     180633.123690   \n",
       "std     15782.379761      3.220104    2.281880e+06     134589.611245   \n",
       "min         0.000000      0.000000    0.000000e+00      10000.000000   \n",
       "25%         0.000000      9.000000    0.000000e+00      94000.000000   \n",
       "50%         0.000000      9.000000    0.000000e+00     133200.000000   \n",
       "75%         0.000000      9.000000    0.000000e+00     215576.000000   \n",
       "max    200000.000000     18.000000    1.683611e+07     999976.400000   \n",
       "\n",
       "           RES_TYPE        TARGET  SUM_PAY_AMOUNT_MONTH  \\\n",
       "count  48651.000000  25000.000000          50000.000000   \n",
       "mean       1.252225      0.424320           5338.065830   \n",
       "std        0.867833      0.494249           9997.410420   \n",
       "min        0.000000      0.000000              0.000000   \n",
       "25%        1.000000      0.000000           1370.541667   \n",
       "50%        1.000000      0.000000           2609.833333   \n",
       "75%        1.000000      1.000000           5400.000000   \n",
       "max        5.000000      1.000000         627344.333333   \n",
       "\n",
       "       MEAN_PAY_AMOUNT_MONTH  MEAN_BILL_AMOUNT_MONTH   INCOME_RATE  \\\n",
       "count           50000.000000            50000.000000  49472.000000   \n",
       "mean             5338.065830            51737.170600      0.368367   \n",
       "std              9997.410420            61598.721738      0.315703   \n",
       "min                 0.000000           -10470.833333     -0.018756   \n",
       "25%              1370.541667             7177.166667      0.063600   \n",
       "50%              2609.833333            35977.833333      0.312633   \n",
       "75%              5400.000000            73460.291667      0.626938   \n",
       "max            627344.333333           877313.833333      4.007344   \n",
       "\n",
       "       MEAN_PAY_MONTH  \n",
       "count    50000.000000  \n",
       "mean        -0.126310  \n",
       "std          0.915897  \n",
       "min         -2.000000  \n",
       "25%         -0.666667  \n",
       "50%          0.000000  \n",
       "75%          0.000000  \n",
       "max          5.500000  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=['float64'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}