log_sequence_seperating.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basic lib\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "from collections import OrderedDict\n",
    "\n",
    "# Plot lib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as dates\n",
    "from matplotlib.patches import Rectangle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "match_df = pd.read_feather('./data/match/ml_train_valid.fth')\n",
    "prep_log_df = pd.read_feather('./data/prep/prep_log.fth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Peak Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.signal import argrelextrema\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_group_obj = prep_log_df.groupby('user_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "is_plot = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1it [00:01,  1.74s/it]"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "41it [00:01, 22.37it/s]\n"
     ]
    }
   ],
   "source": [
    "user_seq_df_list = list()\n",
    "for idx, (user_id, user_log_df) in tqdm(enumerate(user_group_obj)):\n",
    "    \n",
    "    log_time_diff = np.diff(user_log_df.timestamp).astype('timedelta64[s]').astype(np.float64)\n",
    "    \n",
    "    c_max_index_arr = argrelextrema(\n",
    "        data=log_time_diff,\n",
    "        comparator=np.greater,\n",
    "        order=1\n",
    "        )\n",
    "    \n",
    "    if is_plot:\n",
    "        plt.plot(log_time_diff)\n",
    "        plt.scatter(c_max_index_arr[0],log_time_diff[c_max_index_arr[0]],linewidth=0.3, s=50, c='r')\n",
    "        plt.title(f\"User_id: {user_id} Log time stamp diff peak detection\")\n",
    "        plt.xlabel(\"diff index\")\n",
    "        plt.ylabel(\"timestamp diff (sec)\")\n",
    "        plt.show()\n",
    "    \n",
    "    # PD 기반 sequence 저장 \n",
    "    sequence_df_list = list()\n",
    "    for index, c_max_index in enumerate(c_max_index_arr[0]):\n",
    "        if index == len(c_max_index_arr[0]) - 1:\n",
    "            break\n",
    "        sequence_df = user_log_df[c_max_index+1:c_max_index_arr[0][index+1]+1]\n",
    "        sequence_df_list.append(sequence_df)\n",
    "\n",
    "    # 잘못 잘린 sequence 후처리\n",
    "    if len(sequence_df_list) == 0:\n",
    "        continue \n",
    "    \n",
    "    prev_sequence_df = sequence_df_list[0]\n",
    "    fixed_sequence_df_list = list()\n",
    "    for index, curr_sequence_df in enumerate(sequence_df_list[1:], 1):\n",
    "        first_event = curr_sequence_df.event.values[0]\n",
    "        if first_event == 'OpenApp':\n",
    "            fixed_sequence_df_list.append(curr_sequence_df)\n",
    "            prev_sequence_df = curr_sequence_df\n",
    "        else:\n",
    "            if len(fixed_sequence_df_list) == 0:\n",
    "                continue\n",
    "            curr_start_timestamp = curr_sequence_df.timestamp.iloc[0]\n",
    "            prev_end_timestamp = prev_sequence_df.timestamp.iloc[-1]\n",
    "            curr_prev_time_diff = (curr_start_timestamp - prev_end_timestamp)\n",
    "            merge_condition = abs(curr_prev_time_diff) <= pd.Timedelta(hours=1)\n",
    "            if merge_condition:\n",
    "                last_sequence_df = fixed_sequence_df_list.pop()\n",
    "                concated_sequence_df = pd.concat([last_sequence_df, curr_sequence_df])\n",
    "                fixed_sequence_df_list.append(concated_sequence_df)\n",
    "                \n",
    "    # 이상한 OpenApp 삭제\n",
    "    final_seq_df_list = []\n",
    "    for seq_df in fixed_sequence_df_list:\n",
    "        first_event = seq_df.event.values[0]\n",
    "        if first_event == \"OpenApp\":\n",
    "            final_seq_df_list.append(seq_df)\n",
    "    \n",
    "    if len(final_seq_df_list) != 0:\n",
    "        user_seq_df_list.append(final_seq_df_list)\n",
    "        \n",
    "    break"
   ]
  },
  {
   "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>user_id</th>\n",
       "      <th>event</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9359739</th>\n",
       "      <td>71</td>\n",
       "      <td>OpenApp</td>\n",
       "      <td>2022-05-08 20:07:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9359761</th>\n",
       "      <td>71</td>\n",
       "      <td>StartLoanApply</td>\n",
       "      <td>2022-05-08 20:08:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9359762</th>\n",
       "      <td>71</td>\n",
       "      <td>ViewLoanApplyIntro</td>\n",
       "      <td>2022-05-08 20:08:11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_id               event           timestamp\n",
       "9359739       71             OpenApp 2022-05-08 20:07:59\n",
       "9359761       71      StartLoanApply 2022-05-08 20:08:11\n",
       "9359762       71  ViewLoanApplyIntro 2022-05-08 20:08:11"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_seq_df_list[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>user_id</th>\n",
       "      <th>event</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11343004</th>\n",
       "      <td>71</td>\n",
       "      <td>OpenApp</td>\n",
       "      <td>2022-05-20 15:09:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11343078</th>\n",
       "      <td>71</td>\n",
       "      <td>StartLoanApply</td>\n",
       "      <td>2022-05-20 15:10:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11343081</th>\n",
       "      <td>71</td>\n",
       "      <td>ViewLoanApplyIntro</td>\n",
       "      <td>2022-05-20 15:10:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11343159</th>\n",
       "      <td>71</td>\n",
       "      <td>CompleteIDCertification</td>\n",
       "      <td>2022-05-20 15:10:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11343350</th>\n",
       "      <td>71</td>\n",
       "      <td>EndLoanApply</td>\n",
       "      <td>2022-05-20 15:11:58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          user_id                    event           timestamp\n",
       "11343004       71                  OpenApp 2022-05-20 15:09:59\n",
       "11343078       71           StartLoanApply 2022-05-20 15:10:25\n",
       "11343081       71       ViewLoanApplyIntro 2022-05-20 15:10:25\n",
       "11343159       71  CompleteIDCertification 2022-05-20 15:10:50\n",
       "11343350       71             EndLoanApply 2022-05-20 15:11:58"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_seq_df_list[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>event</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13189887</th>\n",
       "      <td>71</td>\n",
       "      <td>OpenApp</td>\n",
       "      <td>2022-06-03 01:24:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13189897</th>\n",
       "      <td>71</td>\n",
       "      <td>ViewLoanApplyIntro</td>\n",
       "      <td>2022-06-03 01:25:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13189899</th>\n",
       "      <td>71</td>\n",
       "      <td>StartLoanApply</td>\n",
       "      <td>2022-06-03 01:25:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13189922</th>\n",
       "      <td>71</td>\n",
       "      <td>CompleteIDCertification</td>\n",
       "      <td>2022-06-03 01:25:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13189963</th>\n",
       "      <td>71</td>\n",
       "      <td>EndLoanApply</td>\n",
       "      <td>2022-06-03 01:26:41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          user_id                    event           timestamp\n",
       "13189887       71                  OpenApp 2022-06-03 01:24:59\n",
       "13189897       71       ViewLoanApplyIntro 2022-06-03 01:25:14\n",
       "13189899       71           StartLoanApply 2022-06-03 01:25:14\n",
       "13189922       71  CompleteIDCertification 2022-06-03 01:25:39\n",
       "13189963       71             EndLoanApply 2022-06-03 01:26:41"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_seq_df_list[2]"
   ]
  },
  {
   "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>user_id</th>\n",
       "      <th>event</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9213000</th>\n",
       "      <td>71</td>\n",
       "      <td>ViewLoanApplyIntro</td>\n",
       "      <td>2022-05-06 20:57:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9213001</th>\n",
       "      <td>71</td>\n",
       "      <td>StartLoanApply</td>\n",
       "      <td>2022-05-06 20:57:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9213050</th>\n",
       "      <td>71</td>\n",
       "      <td>CompleteIDCertification</td>\n",
       "      <td>2022-05-06 20:57:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9213241</th>\n",
       "      <td>71</td>\n",
       "      <td>EndLoanApply</td>\n",
       "      <td>2022-05-06 20:59:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9220373</th>\n",
       "      <td>71</td>\n",
       "      <td>OpenApp</td>\n",
       "      <td>2022-05-06 22:21:20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_id                    event           timestamp\n",
       "9213000       71       ViewLoanApplyIntro 2022-05-06 20:57:00\n",
       "9213001       71           StartLoanApply 2022-05-06 20:57:00\n",
       "9213050       71  CompleteIDCertification 2022-05-06 20:57:43\n",
       "9213241       71             EndLoanApply 2022-05-06 20:59:48\n",
       "9220373       71                  OpenApp 2022-05-06 22:21:20"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence_df_list[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>event</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9359739</th>\n",
       "      <td>71</td>\n",
       "      <td>OpenApp</td>\n",
       "      <td>2022-05-08 20:07:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9359761</th>\n",
       "      <td>71</td>\n",
       "      <td>StartLoanApply</td>\n",
       "      <td>2022-05-08 20:08:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9359762</th>\n",
       "      <td>71</td>\n",
       "      <td>ViewLoanApplyIntro</td>\n",
       "      <td>2022-05-08 20:08:11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         user_id               event           timestamp\n",
       "9359739       71             OpenApp 2022-05-08 20:07:59\n",
       "9359761       71      StartLoanApply 2022-05-08 20:08:11\n",
       "9359762       71  ViewLoanApplyIntro 2022-05-08 20:08:11"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence_df_list[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open(\"fixed_sequence.pkl\", \"wb\") as save_file:   #Pickling\n",
    "    pickle.dump(user_seq_df_list, save_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_seq_df_list = pd.read_pickle(\"fixed_sequence.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 검증\n",
    "1. 최대길이확인\n",
    "2. OpenApp으로 시작하지 않는 sequence 존재하는지"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_seq_len = 0\n",
    "total_seq_count = 0 \n",
    "for idx, seq_df_list in enumerate(user_seq_df_list):\n",
    "    user_id = seq_df_list[0].user_id.unique()[0]\n",
    "    \n",
    "    # 1. 최대 길이 저장\n",
    "    for seq_df in seq_df_list:\n",
    "        max_seq_len = len(seq_df) if len(seq_df) > max_seq_len else max_seq_len\n",
    "    \n",
    "    # 2. OpenApp으로 시작하지 않는 sequence 존재하는지 확인\n",
    "    for seq_idx, seq_df in enumerate(seq_df_list):\n",
    "        first_event = str(seq_df.event.iloc[0])\n",
    "        if first_event != 'OpenApp':\n",
    "            print(f'🚫 User#{user_id} : Seq#{seq_idx}')\n",
    "    \n",
    "    total_seq_count += len(seq_df_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 단순 통계치 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "584636it [11:22, 856.07it/s]\n"
     ]
    }
   ],
   "source": [
    "user_event_series_list = []\n",
    "for idx, (user_id, user_log_df) in tqdm(enumerate(user_group_obj)):\n",
    "    a = user_log_df.event.value_counts()\n",
    "    b = pd.DataFrame(a).T.reset_index(drop=True)\n",
    "    b['user_id'] = user_id\n",
    "    user_event_series_list.append(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_df = pd.concat(user_event_series_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>GetCreditInfo</th>\n",
       "      <th>Login</th>\n",
       "      <th>UseLoanManage</th>\n",
       "      <th>CompleteIDCertification</th>\n",
       "      <th>EndLoanApply</th>\n",
       "      <th>OpenApp</th>\n",
       "      <th>SignUp</th>\n",
       "      <th>StartLoanApply</th>\n",
       "      <th>UseDSRCalc</th>\n",
       "      <th>UsePrepayCalc</th>\n",
       "      <th>ViewLoanApplyIntro</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>879693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>879694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>879695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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</td>\n",
       "      <td>879696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>879698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>584636 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    GetCreditInfo  Login  UseLoanManage  CompleteIDCertification  \\\n",
       "0               3      1              1                        0   \n",
       "0               1      0              0                        0   \n",
       "0               3      0              0                        0   \n",
       "0               8      0              9                        4   \n",
       "0               5     15              1                       15   \n",
       "..            ...    ...            ...                      ...   \n",
       "0               2      0              5                        6   \n",
       "0               0      0              0                        1   \n",
       "0               0      0              0                        1   \n",
       "0               3      2              2                        1   \n",
       "0               0      0              0                        0   \n",
       "\n",
       "    EndLoanApply  OpenApp  SignUp  StartLoanApply  UseDSRCalc  UsePrepayCalc  \\\n",
       "0              0        0       0               0           0              0   \n",
       "0              0        0       0               0           0              0   \n",
       "0              0        0       0               0           0              0   \n",
       "0              7        5       0               4           0              1   \n",
       "0             39       15       0              15           0              0   \n",
       "..           ...      ...     ...             ...         ...            ...   \n",
       "0             12        9       0              17           0              0   \n",
       "0              3        0       0               1           0              0   \n",
       "0              1        0       0               1           0              0   \n",
       "0              1        1       0               0           0              0   \n",
       "0              0        1       0               1           0              0   \n",
       "\n",
       "    ViewLoanApplyIntro  user_id  \n",
       "0                    0        1  \n",
       "0                    0        7  \n",
       "0                    0        9  \n",
       "0                    5       11  \n",
       "0                   15       12  \n",
       "..                 ...      ...  \n",
       "0                   10   879693  \n",
       "0                    1   879694  \n",
       "0                    1   879695  \n",
       "0                    1   879696  \n",
       "0                    1   879698  \n",
       "\n",
       "[584636 rows x 12 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_df"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.11 ('ale': conda)",
   "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": "3.7.11"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "b7aed60652aeb20dddef953ad96c993afd4530cf89ac807a056c77251a26be2f"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}