Helper.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4754, 90)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>custid</th>\n",
       "      <th>trade_no</th>\n",
       "      <th>bank_card_no</th>\n",
       "      <th>low_volume_percent</th>\n",
       "      <th>middle_volume_percent</th>\n",
       "      <th>take_amount_in_later_12_month_highest</th>\n",
       "      <th>trans_amount_increase_rate_lately</th>\n",
       "      <th>trans_activity_month</th>\n",
       "      <th>trans_activity_day</th>\n",
       "      <th>...</th>\n",
       "      <th>loans_max_limit</th>\n",
       "      <th>loans_avg_limit</th>\n",
       "      <th>consfin_credit_limit</th>\n",
       "      <th>consfin_credibility</th>\n",
       "      <th>consfin_org_count_current</th>\n",
       "      <th>consfin_product_count</th>\n",
       "      <th>consfin_max_limit</th>\n",
       "      <th>consfin_avg_limit</th>\n",
       "      <th>latest_query_day</th>\n",
       "      <th>loans_latest_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>2791858</td>\n",
       "      <td>20180507115231274000000023057383</td>\n",
       "      <td>卡号1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.313</td>\n",
       "      <td>...</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>1688.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>534047</td>\n",
       "      <td>20180507121002192000000023073000</td>\n",
       "      <td>卡号1</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.94</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.458</td>\n",
       "      <td>...</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>15100.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22800.0</td>\n",
       "      <td>9360.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>2849787</td>\n",
       "      <td>20180507125159718000000023114911</td>\n",
       "      <td>卡号1</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.114</td>\n",
       "      <td>...</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>1809708</td>\n",
       "      <td>20180507121358683000000388283484</td>\n",
       "      <td>卡号1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.96</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.777</td>\n",
       "      <td>...</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>1541.0</td>\n",
       "      <td>16300.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>12180.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>2499829</td>\n",
       "      <td>20180507115448545000000388205844</td>\n",
       "      <td>卡号1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.175</td>\n",
       "      <td>...</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>1630.0</td>\n",
       "      <td>8300.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8400.0</td>\n",
       "      <td>8250.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 90 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   custid                          trade_no bank_card_no  \\\n",
       "0           5  2791858  20180507115231274000000023057383          卡号1   \n",
       "1          10   534047  20180507121002192000000023073000          卡号1   \n",
       "2          12  2849787  20180507125159718000000023114911          卡号1   \n",
       "3          13  1809708  20180507121358683000000388283484          卡号1   \n",
       "4          14  2499829  20180507115448545000000388205844          卡号1   \n",
       "\n",
       "   low_volume_percent  middle_volume_percent  \\\n",
       "0                0.01                   0.99   \n",
       "1                0.02                   0.94   \n",
       "2                0.04                   0.96   \n",
       "3                0.00                   0.96   \n",
       "4                0.01                   0.99   \n",
       "\n",
       "   take_amount_in_later_12_month_highest  trans_amount_increase_rate_lately  \\\n",
       "0                                      0                               0.90   \n",
       "1                                   2000                               1.28   \n",
       "2                                      0                               1.00   \n",
       "3                                   2000                               0.13   \n",
       "4                                      0                               0.46   \n",
       "\n",
       "   trans_activity_month  trans_activity_day        ...         \\\n",
       "0                  0.55               0.313        ...          \n",
       "1                  1.00               0.458        ...          \n",
       "2                  1.00               0.114        ...          \n",
       "3                  0.57               0.777        ...          \n",
       "4                  1.00               0.175        ...          \n",
       "\n",
       "   loans_max_limit  loans_avg_limit  consfin_credit_limit  \\\n",
       "0           2900.0           1688.0                1200.0   \n",
       "1           3500.0           1758.0               15100.0   \n",
       "2           1600.0           1250.0                4200.0   \n",
       "3           3200.0           1541.0               16300.0   \n",
       "4           2300.0           1630.0                8300.0   \n",
       "\n",
       "   consfin_credibility  consfin_org_count_current  consfin_product_count  \\\n",
       "0                 75.0                        1.0                    2.0   \n",
       "1                 80.0                        5.0                    6.0   \n",
       "2                 87.0                        1.0                    1.0   \n",
       "3                 80.0                        5.0                    5.0   \n",
       "4                 79.0                        2.0                    2.0   \n",
       "\n",
       "   consfin_max_limit  consfin_avg_limit  latest_query_day  loans_latest_day  \n",
       "0             1200.0             1200.0              12.0              18.0  \n",
       "1            22800.0             9360.0               4.0               2.0  \n",
       "2             4200.0             4200.0               2.0               6.0  \n",
       "3            30000.0            12180.0               2.0               4.0  \n",
       "4             8400.0             8250.0              22.0             120.0  \n",
       "\n",
       "[5 rows x 90 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('data.csv')\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征观察"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 'Unnamed: 0', 'custid', 'trade_no', 'bank_card_no'都没用, 只有'low_volume_percent'和'middle_volume_percent'有用。\n",
    "\n",
    "> 'take_amount_in_later_12_month_highest'数值跨度可能较大, 归一化？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>custid</th>\n",
       "      <th>trade_no</th>\n",
       "      <th>bank_card_no</th>\n",
       "      <th>low_volume_percent</th>\n",
       "      <th>middle_volume_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>2791858</td>\n",
       "      <td>20180507115231274000000023057383</td>\n",
       "      <td>卡号1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>534047</td>\n",
       "      <td>20180507121002192000000023073000</td>\n",
       "      <td>卡号1</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>2849787</td>\n",
       "      <td>20180507125159718000000023114911</td>\n",
       "      <td>卡号1</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>1809708</td>\n",
       "      <td>20180507121358683000000388283484</td>\n",
       "      <td>卡号1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>2499829</td>\n",
       "      <td>20180507115448545000000388205844</td>\n",
       "      <td>卡号1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   custid                          trade_no bank_card_no  \\\n",
       "0           5  2791858  20180507115231274000000023057383          卡号1   \n",
       "1          10   534047  20180507121002192000000023073000          卡号1   \n",
       "2          12  2849787  20180507125159718000000023114911          卡号1   \n",
       "3          13  1809708  20180507121358683000000388283484          卡号1   \n",
       "4          14  2499829  20180507115448545000000388205844          卡号1   \n",
       "\n",
       "   low_volume_percent  middle_volume_percent  \n",
       "0                0.01                   0.99  \n",
       "1                0.02                   0.94  \n",
       "2                0.04                   0.96  \n",
       "3                0.00                   0.96  \n",
       "4                0.01                   0.99  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Unnamed: 0', 'custid', 'trade_no', 'bank_card_no','low_volume_percent', 'middle_volume_percent']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 0 --- 4754\n",
      "custid --- 4754\n",
      "trade_no --- 4754\n",
      "bank_card_no --- 1\n",
      "low_volume_percent --- 41\n",
      "middle_volume_percent --- 91\n"
     ]
    }
   ],
   "source": [
    "for feature in ['Unnamed: 0', 'custid', 'trade_no', 'bank_card_no','low_volume_percent','middle_volume_percent']:\n",
    "    print(feature, '---',len(data[feature].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>take_amount_in_later_12_month_highest</th>\n",
       "      <th>trans_amount_increase_rate_lately</th>\n",
       "      <th>trans_activity_month</th>\n",
       "      <th>trans_activity_day</th>\n",
       "      <th>transd_mcc</th>\n",
       "      <th>trans_days_interval_filter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.313</td>\n",
       "      <td>17.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.458</td>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.114</td>\n",
       "      <td>13.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.777</td>\n",
       "      <td>22.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.175</td>\n",
       "      <td>13.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   take_amount_in_later_12_month_highest  trans_amount_increase_rate_lately  \\\n",
       "0                                      0                               0.90   \n",
       "1                                   2000                               1.28   \n",
       "2                                      0                               1.00   \n",
       "3                                   2000                               0.13   \n",
       "4                                      0                               0.46   \n",
       "\n",
       "   trans_activity_month  trans_activity_day  transd_mcc  \\\n",
       "0                  0.55               0.313        17.0   \n",
       "1                  1.00               0.458        19.0   \n",
       "2                  1.00               0.114        13.0   \n",
       "3                  0.57               0.777        22.0   \n",
       "4                  1.00               0.175        13.0   \n",
       "\n",
       "   trans_days_interval_filter  \n",
       "0                        27.0  \n",
       "1                        30.0  \n",
       "2                        68.0  \n",
       "3                        14.0  \n",
       "4                        66.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['take_amount_in_later_12_month_highest', 'trans_amount_increase_rate_lately', 'trans_activity_month',\n",
    "      'trans_activity_day', 'transd_mcc', 'trans_days_interval_filter']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "take_amount_in_later_12_month_highest --- 166\n",
      "trans_amount_increase_rate_lately --- 783\n",
      "trans_activity_month --- 85\n",
      "trans_activity_day --- 513\n",
      "transd_mcc --- 42\n",
      "trans_days_interval_filter --- 148\n"
     ]
    }
   ],
   "source": [
    "for feature in ['take_amount_in_later_12_month_highest', 'trans_amount_increase_rate_lately', 'trans_activity_month',\n",
    "                'trans_activity_day', 'transd_mcc', 'trans_days_interval_filter']:\n",
    "    print(feature, '---',len(data[feature].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 某些特征可能包含缺失值NAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trans_days_interval</th>\n",
       "      <th>regional_mobility</th>\n",
       "      <th>student_feature</th>\n",
       "      <th>repayment_capability</th>\n",
       "      <th>is_high_user</th>\n",
       "      <th>number_of_trans_from_2011</th>\n",
       "      <th>first_transaction_time</th>\n",
       "      <th>historical_trans_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19890</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20130817.0</td>\n",
       "      <td>149050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16970</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>20160402.0</td>\n",
       "      <td>302910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9710</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20170617.0</td>\n",
       "      <td>11520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6210</td>\n",
       "      <td>0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>20130516.0</td>\n",
       "      <td>491130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11150</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20170312.0</td>\n",
       "      <td>61470</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   trans_days_interval  regional_mobility  student_feature  \\\n",
       "0                 26.0                3.0              NaN   \n",
       "1                 14.0                4.0              1.0   \n",
       "2                 22.0                1.0              NaN   \n",
       "3                  6.0                3.0              NaN   \n",
       "4                 42.0                1.0              NaN   \n",
       "\n",
       "   repayment_capability  is_high_user  number_of_trans_from_2011  \\\n",
       "0                 19890             0                       30.0   \n",
       "1                 16970             0                       23.0   \n",
       "2                  9710             0                        9.0   \n",
       "3                  6210             0                       33.0   \n",
       "4                 11150             0                       12.0   \n",
       "\n",
       "   first_transaction_time  historical_trans_amount  \n",
       "0              20130817.0                   149050  \n",
       "1              20160402.0                   302910  \n",
       "2              20170617.0                    11520  \n",
       "3              20130516.0                   491130  \n",
       "4              20170312.0                    61470  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[[ 'trans_days_interval', 'regional_mobility', 'student_feature',\n",
    "      'repayment_capability', 'is_high_user', 'number_of_trans_from_2011',\n",
    "       'first_transaction_time', 'historical_trans_amount']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trans_days_interval --- 115\n",
      "regional_mobility --- 6\n",
      "student_feature --- 3\n",
      "repayment_capability --- 2390\n",
      "is_high_user --- 2\n",
      "number_of_trans_from_2011 --- 71\n",
      "first_transaction_time --- 1694\n",
      "historical_trans_amount --- 4524\n"
     ]
    }
   ],
   "source": [
    "for feature in ['trans_days_interval', 'regional_mobility', 'student_feature',\n",
    "      'repayment_capability', 'is_high_user', 'number_of_trans_from_2011',\n",
    "       'first_transaction_time', 'historical_trans_amount']:\n",
    "    print(feature, '---',len(data[feature].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>historical_trans_day</th>\n",
       "      <th>rank_trad_1_month</th>\n",
       "      <th>trans_amount_3_month</th>\n",
       "      <th>avg_consume_less_12_valid_month</th>\n",
       "      <th>abs</th>\n",
       "      <th>top_trans_count_last_1_month</th>\n",
       "      <th>avg_price_last_12_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>151.0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>34030</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3920</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>224.0</td>\n",
       "      <td>0.35</td>\n",
       "      <td>10590</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6950</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5710</td>\n",
       "      <td>5.0</td>\n",
       "      <td>840</td>\n",
       "      <td>0.65</td>\n",
       "      <td>570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>360.0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>91690</td>\n",
       "      <td>7.0</td>\n",
       "      <td>46850</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>63.0</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9770</td>\n",
       "      <td>6.0</td>\n",
       "      <td>760</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   historical_trans_day  rank_trad_1_month  trans_amount_3_month  \\\n",
       "0                 151.0               0.40                 34030   \n",
       "1                 224.0               0.35                 10590   \n",
       "2                  31.0               1.00                  5710   \n",
       "3                 360.0               0.15                 91690   \n",
       "4                  63.0               0.65                  9770   \n",
       "\n",
       "   avg_consume_less_12_valid_month    abs  top_trans_count_last_1_month  \\\n",
       "0                              7.0   3920                          0.15   \n",
       "1                              5.0   6950                          0.05   \n",
       "2                              5.0    840                          0.65   \n",
       "3                              7.0  46850                          0.05   \n",
       "4                              6.0    760                          1.00   \n",
       "\n",
       "   avg_price_last_12_month  \n",
       "0                     1020  \n",
       "1                     1210  \n",
       "2                      570  \n",
       "3                     1290  \n",
       "4                     1110  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['historical_trans_day', 'rank_trad_1_month', 'trans_amount_3_month',\n",
    "       'avg_consume_less_12_valid_month', 'abs',\n",
    "       'top_trans_count_last_1_month', 'avg_price_last_12_month']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 'reg_preference_for_trad'需要编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_price_top_last_12_valid_month</th>\n",
       "      <th>reg_preference_for_trad</th>\n",
       "      <th>trans_top_time_last_1_month</th>\n",
       "      <th>trans_top_time_last_6_month</th>\n",
       "      <th>consume_top_time_last_1_month</th>\n",
       "      <th>consume_top_time_last_6_month</th>\n",
       "      <th>cross_consume_count_last_1_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.55</td>\n",
       "      <td>一线城市</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.50</td>\n",
       "      <td>一线城市</td>\n",
       "      <td>13.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.65</td>\n",
       "      <td>一线城市</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.45</td>\n",
       "      <td>三线城市</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.50</td>\n",
       "      <td>一线城市</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avg_price_top_last_12_valid_month reg_preference_for_trad  \\\n",
       "0                               0.55                    一线城市   \n",
       "1                               0.50                    一线城市   \n",
       "2                               0.65                    一线城市   \n",
       "3                               0.45                    三线城市   \n",
       "4                               0.50                    一线城市   \n",
       "\n",
       "   trans_top_time_last_1_month  trans_top_time_last_6_month  \\\n",
       "0                          4.0                         19.0   \n",
       "1                         13.0                         30.0   \n",
       "2                          0.0                         68.0   \n",
       "3                          6.0                          8.0   \n",
       "4                          0.0                         66.0   \n",
       "\n",
       "   consume_top_time_last_1_month  consume_top_time_last_6_month  \\\n",
       "0                            4.0                           19.0   \n",
       "1                           13.0                           30.0   \n",
       "2                            0.0                           68.0   \n",
       "3                            6.0                            8.0   \n",
       "4                            0.0                           66.0   \n",
       "\n",
       "   cross_consume_count_last_1_month  \n",
       "0                               1.0  \n",
       "1                               0.0  \n",
       "2                               0.0  \n",
       "3                               0.0  \n",
       "4                               0.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['avg_price_top_last_12_valid_month', 'reg_preference_for_trad',\n",
    "       'trans_top_time_last_1_month', 'trans_top_time_last_6_month',\n",
    "       'consume_top_time_last_1_month', 'consume_top_time_last_6_month',\n",
    "       'cross_consume_count_last_1_month']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trans_fail_top_count_enum_last_1_month</th>\n",
       "      <th>trans_fail_top_count_enum_last_6_month</th>\n",
       "      <th>trans_fail_top_count_enum_last_12_month</th>\n",
       "      <th>consume_mini_time_last_1_month</th>\n",
       "      <th>max_cumulative_consume_later_1_month</th>\n",
       "      <th>max_consume_count_later_6_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2170</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>2100</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>31700.0</td>\n",
       "      <td>8140</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   trans_fail_top_count_enum_last_1_month  \\\n",
       "0                                     1.0   \n",
       "1                                     0.0   \n",
       "2                                     3.0   \n",
       "3                                     1.0   \n",
       "4                                     3.0   \n",
       "\n",
       "   trans_fail_top_count_enum_last_6_month  \\\n",
       "0                                     2.0   \n",
       "1                                     3.0   \n",
       "2                                     6.0   \n",
       "3                                     8.0   \n",
       "4                                     3.0   \n",
       "\n",
       "   trans_fail_top_count_enum_last_12_month  consume_mini_time_last_1_month  \\\n",
       "0                                      2.0                             5.0   \n",
       "1                                      3.0                           330.0   \n",
       "2                                      6.0                             0.0   \n",
       "3                                      8.0                         31700.0   \n",
       "4                                      3.0                             0.0   \n",
       "\n",
       "   max_cumulative_consume_later_1_month  max_consume_count_later_6_month  \n",
       "0                                  2170                              6.0  \n",
       "1                                  2100                              9.0  \n",
       "2                                     0                              3.0  \n",
       "3                                  8140                              9.0  \n",
       "4                                  1000                              3.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[[ 'trans_fail_top_count_enum_last_1_month',\n",
    "       'trans_fail_top_count_enum_last_6_month',\n",
    "       'trans_fail_top_count_enum_last_12_month',\n",
    "       'consume_mini_time_last_1_month',\n",
    "       'max_cumulative_consume_later_1_month',\n",
    "       'max_consume_count_later_6_month']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 'source'没用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>railway_consume_count_last_12_month</th>\n",
       "      <th>pawns_auctions_trusts_consume_last_1_month</th>\n",
       "      <th>pawns_auctions_trusts_consume_last_6_month</th>\n",
       "      <th>jewelry_consume_count_last_6_month</th>\n",
       "      <th>status</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1970</td>\n",
       "      <td>18040</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>xs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1820</td>\n",
       "      <td>15680</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>xs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>xs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2700</td>\n",
       "      <td>27970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>xs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6410</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>xs</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   railway_consume_count_last_12_month  \\\n",
       "0                                  0.0   \n",
       "1                                  0.0   \n",
       "2                                  0.0   \n",
       "3                                  0.0   \n",
       "4                                  0.0   \n",
       "\n",
       "   pawns_auctions_trusts_consume_last_1_month  \\\n",
       "0                                        1970   \n",
       "1                                        1820   \n",
       "2                                           0   \n",
       "3                                        2700   \n",
       "4                                           0   \n",
       "\n",
       "   pawns_auctions_trusts_consume_last_6_month  \\\n",
       "0                                       18040   \n",
       "1                                       15680   \n",
       "2                                           0   \n",
       "3                                       27970   \n",
       "4                                        6410   \n",
       "\n",
       "   jewelry_consume_count_last_6_month  status source  \n",
       "0                                 0.0       1     xs  \n",
       "1                                 0.0       0     xs  \n",
       "2                                 0.0       1     xs  \n",
       "3                                 0.0       0     xs  \n",
       "4                                 0.0       1     xs  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['railway_consume_count_last_12_month',\n",
    "       'pawns_auctions_trusts_consume_last_1_month',\n",
    "       'pawns_auctions_trusts_consume_last_6_month',\n",
    "       'jewelry_consume_count_last_6_month', 'status', 'source']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "railway_consume_count_last_12_month --- 7\n",
      "pawns_auctions_trusts_consume_last_1_month --- 572\n",
      "pawns_auctions_trusts_consume_last_6_month --- 2730\n",
      "jewelry_consume_count_last_6_month --- 8\n",
      "status --- 2\n",
      "source --- 1\n"
     ]
    }
   ],
   "source": [
    "for feature in ['railway_consume_count_last_12_month',\n",
    "       'pawns_auctions_trusts_consume_last_1_month',\n",
    "       'pawns_auctions_trusts_consume_last_6_month',\n",
    "       'jewelry_consume_count_last_6_month', 'status', 'source']:\n",
    "    print(feature, '---',len(data[feature].unique()))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 'id_name'无用, 'latest_query_time是日期特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_transaction_day</th>\n",
       "      <th>trans_day_last_12_month</th>\n",
       "      <th>id_name</th>\n",
       "      <th>apply_score</th>\n",
       "      <th>apply_credibility</th>\n",
       "      <th>query_org_count</th>\n",
       "      <th>query_finance_count</th>\n",
       "      <th>query_cash_count</th>\n",
       "      <th>query_sum_count</th>\n",
       "      <th>latest_query_time</th>\n",
       "      <th>latest_one_month_apply</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1738.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>蒋红</td>\n",
       "      <td>583.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2018-04-25</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>779.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>崔向朝</td>\n",
       "      <td>653.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2018-05-03</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>338.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>王中云</td>\n",
       "      <td>654.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2018-05-05</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1831.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>何洋洋</td>\n",
       "      <td>595.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2018-05-05</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>435.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>赵洋</td>\n",
       "      <td>541.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2018-04-15</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   first_transaction_day  trans_day_last_12_month id_name  apply_score  \\\n",
       "0                 1738.0                     85.0      蒋红        583.0   \n",
       "1                  779.0                     84.0     崔向朝        653.0   \n",
       "2                  338.0                     95.0     王中云        654.0   \n",
       "3                 1831.0                     82.0     何洋洋        595.0   \n",
       "4                  435.0                     88.0      赵洋        541.0   \n",
       "\n",
       "   apply_credibility  query_org_count  query_finance_count  query_cash_count  \\\n",
       "0               79.0              8.0                  2.0               6.0   \n",
       "1               73.0              7.0                  4.0               2.0   \n",
       "2               76.0             11.0                  5.0               5.0   \n",
       "3               79.0             12.0                  7.0               4.0   \n",
       "4               75.0             11.0                  3.0               4.0   \n",
       "\n",
       "   query_sum_count latest_query_time  latest_one_month_apply  \n",
       "0             10.0        2018-04-25                     2.0  \n",
       "1              8.0        2018-05-03                     2.0  \n",
       "2             16.0        2018-05-05                     5.0  \n",
       "3             22.0        2018-05-05                     3.0  \n",
       "4             14.0        2018-04-15                     6.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['first_transaction_day', 'trans_day_last_12_month', 'id_name',\n",
    "       'apply_score', 'apply_credibility', 'query_org_count',\n",
    "       'query_finance_count', 'query_cash_count', 'query_sum_count',\n",
    "       'latest_query_time', 'latest_one_month_apply']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first_transaction_day --- 1694\n",
      "trans_day_last_12_month --- 133\n",
      "id_name --- 4310\n",
      "apply_score --- 206\n",
      "apply_credibility --- 42\n",
      "query_org_count --- 47\n",
      "query_finance_count --- 26\n",
      "query_cash_count --- 18\n",
      "query_sum_count --- 75\n",
      "latest_query_time --- 208\n",
      "latest_one_month_apply --- 37\n"
     ]
    }
   ],
   "source": [
    "for feature in ['first_transaction_day', 'trans_day_last_12_month', 'id_name',\n",
    "       'apply_score', 'apply_credibility', 'query_org_count',\n",
    "       'query_finance_count', 'query_cash_count', 'query_sum_count',\n",
    "       'latest_query_time', 'latest_one_month_apply']:\n",
    "    print(feature, '---',len(data[feature].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latest_three_month_apply</th>\n",
       "      <th>latest_six_month_apply</th>\n",
       "      <th>loans_score</th>\n",
       "      <th>loans_credibility_behavior</th>\n",
       "      <th>loans_count</th>\n",
       "      <th>loans_settle_count</th>\n",
       "      <th>loans_overdue_count</th>\n",
       "      <th>loans_org_count_behavior</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>552.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>635.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>633.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>542.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>479.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   latest_three_month_apply  latest_six_month_apply  loans_score  \\\n",
       "0                       5.0                     8.0        552.0   \n",
       "1                       6.0                     8.0        635.0   \n",
       "2                       5.0                    14.0        633.0   \n",
       "3                      16.0                    17.0        542.0   \n",
       "4                       8.0                     9.0        479.0   \n",
       "\n",
       "   loans_credibility_behavior  loans_count  loans_settle_count  \\\n",
       "0                        73.0         37.0                34.0   \n",
       "1                        76.0         37.0                36.0   \n",
       "2                        83.0          4.0                 2.0   \n",
       "3                        75.0         85.0                81.0   \n",
       "4                        73.0         37.0                32.0   \n",
       "\n",
       "   loans_overdue_count  loans_org_count_behavior  \n",
       "0                  2.0                      10.0  \n",
       "1                  0.0                      17.0  \n",
       "2                  0.0                       3.0  \n",
       "3                  4.0                      22.0  \n",
       "4                  6.0                      12.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[[ 'latest_three_month_apply', 'latest_six_month_apply', 'loans_score',\n",
    "       'loans_credibility_behavior', 'loans_count', 'loans_settle_count',\n",
    "       'loans_overdue_count', 'loans_org_count_behavior']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>consfin_org_count_behavior</th>\n",
       "      <th>loans_cash_count</th>\n",
       "      <th>latest_one_month_loan</th>\n",
       "      <th>latest_three_month_loan</th>\n",
       "      <th>latest_six_month_loan</th>\n",
       "      <th>history_suc_fee</th>\n",
       "      <th>history_fail_fee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   consfin_org_count_behavior  loans_cash_count  latest_one_month_loan  \\\n",
       "0                         1.0               9.0                    1.0   \n",
       "1                         5.0              12.0                    2.0   \n",
       "2                         1.0               2.0                    2.0   \n",
       "3                         5.0              17.0                    2.0   \n",
       "4                         2.0              10.0                    0.0   \n",
       "\n",
       "   latest_three_month_loan  latest_six_month_loan  history_suc_fee  \\\n",
       "0                      1.0                   13.0             37.0   \n",
       "1                      2.0                    8.0             49.0   \n",
       "2                      2.0                    4.0              2.0   \n",
       "3                      4.0                   34.0             91.0   \n",
       "4                      0.0                   10.0             36.0   \n",
       "\n",
       "   history_fail_fee  \n",
       "0               7.0  \n",
       "1               4.0  \n",
       "2               2.0  \n",
       "3              26.0  \n",
       "4              25.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['consfin_org_count_behavior', 'loans_cash_count',\n",
    "       'latest_one_month_loan', 'latest_three_month_loan',\n",
    "       'latest_six_month_loan', 'history_suc_fee', 'history_fail_fee']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 'loans_latest_time'是日期特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latest_one_month_suc</th>\n",
       "      <th>latest_one_month_fail</th>\n",
       "      <th>loans_long_time</th>\n",
       "      <th>loans_latest_time</th>\n",
       "      <th>loans_credit_limit</th>\n",
       "      <th>loans_credibility_limit</th>\n",
       "      <th>loans_org_count_current</th>\n",
       "      <th>loans_product_count</th>\n",
       "      <th>loans_max_limit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>2018-04-19</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>2018-05-05</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>2018-05-03</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   latest_one_month_suc  latest_one_month_fail  loans_long_time  \\\n",
       "0                   1.0                    0.0            341.0   \n",
       "1                   2.0                    1.0            353.0   \n",
       "2                   1.0                    1.0            157.0   \n",
       "3                   2.0                    0.0            355.0   \n",
       "4                   0.0                    0.0            360.0   \n",
       "\n",
       "  loans_latest_time  loans_credit_limit  loans_credibility_limit  \\\n",
       "0        2018-04-19              2200.0                     72.0   \n",
       "1        2018-05-05              2000.0                     74.0   \n",
       "2        2018-05-01              1500.0                     77.0   \n",
       "3        2018-05-03              1800.0                     74.0   \n",
       "4        2018-01-07              1800.0                     72.0   \n",
       "\n",
       "   loans_org_count_current  loans_product_count  loans_max_limit  \n",
       "0                      9.0                 10.0           2900.0  \n",
       "1                     12.0                 12.0           3500.0  \n",
       "2                      2.0                  2.0           1600.0  \n",
       "3                     17.0                 18.0           3200.0  \n",
       "4                     10.0                 10.0           2300.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['latest_one_month_suc', 'latest_one_month_fail', 'loans_long_time',\n",
    "       'loans_latest_time', 'loans_credit_limit', 'loans_credibility_limit',\n",
    "       'loans_org_count_current', 'loans_product_count', 'loans_max_limit']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loans_avg_limit</th>\n",
       "      <th>consfin_credit_limit</th>\n",
       "      <th>consfin_credibility</th>\n",
       "      <th>consfin_org_count_current</th>\n",
       "      <th>consfin_product_count</th>\n",
       "      <th>consfin_max_limit</th>\n",
       "      <th>consfin_avg_limit</th>\n",
       "      <th>latest_query_day</th>\n",
       "      <th>loans_latest_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1688.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1758.0</td>\n",
       "      <td>15100.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22800.0</td>\n",
       "      <td>9360.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1250.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1541.0</td>\n",
       "      <td>16300.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>12180.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1630.0</td>\n",
       "      <td>8300.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8400.0</td>\n",
       "      <td>8250.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   loans_avg_limit  consfin_credit_limit  consfin_credibility  \\\n",
       "0           1688.0                1200.0                 75.0   \n",
       "1           1758.0               15100.0                 80.0   \n",
       "2           1250.0                4200.0                 87.0   \n",
       "3           1541.0               16300.0                 80.0   \n",
       "4           1630.0                8300.0                 79.0   \n",
       "\n",
       "   consfin_org_count_current  consfin_product_count  consfin_max_limit  \\\n",
       "0                        1.0                    2.0             1200.0   \n",
       "1                        5.0                    6.0            22800.0   \n",
       "2                        1.0                    1.0             4200.0   \n",
       "3                        5.0                    5.0            30000.0   \n",
       "4                        2.0                    2.0             8400.0   \n",
       "\n",
       "   consfin_avg_limit  latest_query_day  loans_latest_day  \n",
       "0             1200.0              12.0              18.0  \n",
       "1             9360.0               4.0               2.0  \n",
       "2             4200.0               2.0               6.0  \n",
       "3            12180.0               2.0               4.0  \n",
       "4             8250.0              22.0             120.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[[ 'loans_avg_limit', 'consfin_credit_limit', 'consfin_credibility',\n",
    "       'consfin_org_count_current', 'consfin_product_count',\n",
    "       'consfin_max_limit', 'consfin_avg_limit', 'latest_query_day',\n",
    "       'loans_latest_day']].head()"
   ]
  }
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