Electronic-Commerce/阿里跨境电商智能算法大赛-数据探索与可视化.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "哈罗，各位 ~ 本文对[安泰杯 —— 跨境电商智能算法大赛](https://tianchi.aliyun.com/competition/entrance/231718/information)数据进行了初探与可视化处理，便于各位更为直观理解赛题数据与建模目标。\n",
    "\n",
    "    \n",
    "[赛题目标](https://tianchi.aliyun.com/competition/entrance/231718/introduction)：通过用户历史订单数据，预测用户下一次购买的商品。\n",
    "\n",
    "[赛题数据](https://tianchi.aliyun.com/competition/entrance/231718/information)：数据保存为四个文件中，训练数据(Antai_AE_round1_train_20190626.csv)、测试数据(Antai_AE_round1_test_20190626.csv)、商品信息(Antai_AE_round1_item_attr_20190626.csv)、提交示例(Antai_AE_round1_submit_20190715.csv)\n",
    "\n",
    "1. 训练数据：用户每次购买的商品id，订单日期以及用户国家标识\n",
    "\n",
    "2. 测试数据：较于训练数据，测试数据剔除了用户需要预测最后一次购买记录\n",
    "\n",
    "3. 商品信息：商品id、品类id、店铺id和商品价格\n",
    "\n",
    "4. 提交示例：预测用户购买商品Top30的item_id依概率从高到低排序，buyer_admin_id,predict 1,predict 2,…,predict 30\n",
    "\n",
    "\n",
    "代码运行环境：\n",
    "    \n",
    "    JupyterLab\n",
    "    Python 3.5\n",
    "    Pandas 0.24.2\n",
    "    matplotlib 3.0.2\n",
    "    seaborn 0.9.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 载入数据分析库包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "import gc\n",
    "%matplotlib inline\n",
    "# 禁用科学计数法\n",
    "pd.set_option('display.float_format',lambda x : '%.2f' % x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "item = pd.read_csv('../data/Antai_AE_round1_item_attr_20190626.csv')\n",
    "train = pd.read_csv('../data/Antai_AE_round1_train_20190626.csv')\n",
    "test = pd.read_csv('../data/Antai_AE_round1_test_20190626.csv')\n",
    "submit = pd.read_csv('../data/Antai_AE_round1_submit_20190715.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据预处理\n",
    "* 合并train和test文件\n",
    "* 提取日期年月日等信息\n",
    "* 关联商品价格、品类、店铺\n",
    "* 转化每列数据类型为可存储的最小值，减少内存消耗\n",
    "* 保存为hdf5格式文件，加速读取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([train.assign(is_train=1), test.assign(is_train=0)])\n",
    "\n",
    "df['create_order_time'] = pd.to_datetime(df['create_order_time'])\n",
    "df['date'] = df['create_order_time'].dt.date\n",
    "df['day'] = df['create_order_time'].dt.day\n",
    "df['hour'] = df['create_order_time'].dt.hour\n",
    "\n",
    "df = pd.merge(df, item, how='left', on='item_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before memory usage of properties dataframe is : 1292.8728713989258  MB\n",
      "After memory usage of properties dataframe is : 696.1623153686523  MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "memory = df.memory_usage().sum() / 1024**2 \n",
    "print('Before memory usage of properties dataframe is :', memory, \" MB\")\n",
    "\n",
    "dtype_dict = {'buyer_admin_id' : 'int32', \n",
    "              'item_id' : 'int32', \n",
    "              'store_id' : pd.Int32Dtype(),\n",
    "              'irank' : 'int16',\n",
    "              'item_price' : pd.Int16Dtype(),\n",
    "              'cate_id' : pd.Int16Dtype(),\n",
    "              'is_train' : 'int8',\n",
    "              'day' : 'int8',\n",
    "              'hour' : 'int8',\n",
    "             }\n",
    "\n",
    "df = df.astype(dtype_dict)\n",
    "memory = df.memory_usage().sum() / 1024**2 \n",
    "print('After memory usage of properties dataframe is :', memory, \" MB\")\n",
    "del train,test; gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in ['store_id', 'item_price', 'cate_id']:\n",
    "    df[col] = df[col].fillna(0).astype(np.int32).replace(0, np.nan)\n",
    "df.to_hdf('../data/train_test.h5', '1.0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.55 s, sys: 2.91 s, total: 6.46 s\n",
      "Wall time: 6.46 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df = pd.read_hdf('../data/train_test.h5', '1.0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 13.3 s, sys: 1.29 s, total: 14.6 s\n",
      "Wall time: 14.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train = pd.read_csv('../data/Antai_AE_round1_train_20190626.csv')\n",
    "test = pd.read_csv('../data/Antai_AE_round1_test_20190626.csv')\n",
    "item = pd.read_csv('../data/Antai_AE_round1_item_attr_20190626.csv')\n",
    "del train, test; gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "经过前处理后:\n",
    "* 文件内存占用从1200M减少至600M\n",
    "* 采用hdf5格式存储，读取时间从15秒减少到仅需5秒"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview: 数据内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>buyer_country_id</th>\n",
       "      <th>buyer_admin_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>create_order_time</th>\n",
       "      <th>irank</th>\n",
       "      <th>is_train</th>\n",
       "      <th>date</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>cate_id</th>\n",
       "      <th>store_id</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>xx</td>\n",
       "      <td>8362078</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-08-10 23:49:44</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-08-10</td>\n",
       "      <td>10</td>\n",
       "      <td>23</td>\n",
       "      <td>2324.00</td>\n",
       "      <td>10013.00</td>\n",
       "      <td>4501.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>xx</td>\n",
       "      <td>9694304</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-08-03 23:55:07</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-08-03</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>3882.00</td>\n",
       "      <td>4485.00</td>\n",
       "      <td>2751.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>yy</td>\n",
       "      <td>101887</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-08-27 08:31:26</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-08-27</td>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>155.00</td>\n",
       "      <td>8341.00</td>\n",
       "      <td>656.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>xx</td>\n",
       "      <td>8131786</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-08-31 06:00:19</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>155.00</td>\n",
       "      <td>8341.00</td>\n",
       "      <td>656.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>xx</td>\n",
       "      <td>9778613</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-08-21 06:01:56</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-08-21</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>1191.00</td>\n",
       "      <td>1949.00</td>\n",
       "      <td>1689.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  buyer_country_id  buyer_admin_id  item_id   create_order_time  irank  \\\n",
       "0               xx         8362078        1 2018-08-10 23:49:44     12   \n",
       "1               xx         9694304        2 2018-08-03 23:55:07      9   \n",
       "2               yy          101887        3 2018-08-27 08:31:26      3   \n",
       "3               xx         8131786        3 2018-08-31 06:00:19      9   \n",
       "4               xx         9778613        5 2018-08-21 06:01:56     14   \n",
       "\n",
       "   is_train        date  day  hour  cate_id  store_id  item_price  \n",
       "0         1  2018-08-10   10    23  2324.00  10013.00     4501.00  \n",
       "1         1  2018-08-03    3    23  3882.00   4485.00     2751.00  \n",
       "2         1  2018-08-27   27     8   155.00   8341.00      656.00  \n",
       "3         1  2018-08-31   31     6   155.00   8341.00      656.00  \n",
       "4         1  2018-08-21   21     6  1191.00   1949.00     1689.00  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "buyer_country_id 0\n",
      "buyer_admin_id 0\n",
      "item_id 0\n",
      "create_order_time 0\n",
      "irank 0\n",
      "is_train 0\n",
      "date 0\n",
      "day 0\n",
      "hour 0\n",
      "cate_id 26119\n",
      "store_id 26119\n",
      "item_price 26119\n",
      "item_id 0\n",
      "cate_id 0\n",
      "store_id 0\n",
      "item_price 0\n"
     ]
    }
   ],
   "source": [
    "# Null 空值统计\n",
    "for pdf in [df, item]:\n",
    "    for col in pdf.columns:\n",
    "        print(col, pdf[col].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\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>buyer_admin_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>irank</th>\n",
       "      <th>is_train</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>cate_id</th>\n",
       "      <th>store_id</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13035341.00</td>\n",
       "      <td>13035341.00</td>\n",
       "      <td>13035341.00</td>\n",
       "      <td>13035341.00</td>\n",
       "      <td>13035341.00</td>\n",
       "      <td>13035341.00</td>\n",
       "      <td>13009222.00</td>\n",
       "      <td>13009222.00</td>\n",
       "      <td>13009222.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6527293.86</td>\n",
       "      <td>6522519.78</td>\n",
       "      <td>143.62</td>\n",
       "      <td>0.99</td>\n",
       "      <td>18.62</td>\n",
       "      <td>9.06</td>\n",
       "      <td>1498.53</td>\n",
       "      <td>40575.67</td>\n",
       "      <td>1099.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3764280.24</td>\n",
       "      <td>3765432.09</td>\n",
       "      <td>1573.84</td>\n",
       "      <td>0.11</td>\n",
       "      <td>9.21</td>\n",
       "      <td>6.56</td>\n",
       "      <td>903.26</td>\n",
       "      <td>24284.46</td>\n",
       "      <td>2880.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-32768.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3269515.00</td>\n",
       "      <td>3261386.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>616.00</td>\n",
       "      <td>20648.00</td>\n",
       "      <td>123.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6528429.00</td>\n",
       "      <td>6522878.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>19.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1505.00</td>\n",
       "      <td>39368.00</td>\n",
       "      <td>246.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9787265.00</td>\n",
       "      <td>9784900.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>27.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>2010.00</td>\n",
       "      <td>59273.00</td>\n",
       "      <td>700.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13046721.00</td>\n",
       "      <td>13046734.00</td>\n",
       "      <td>32767.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>4243.00</td>\n",
       "      <td>95105.00</td>\n",
       "      <td>20230.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       buyer_admin_id     item_id       irank    is_train         day  \\\n",
       "count     13035341.00 13035341.00 13035341.00 13035341.00 13035341.00   \n",
       "mean       6527293.86  6522519.78      143.62        0.99       18.62   \n",
       "std        3764280.24  3765432.09     1573.84        0.11        9.21   \n",
       "min              1.00        1.00   -32768.00        0.00        1.00   \n",
       "25%        3269515.00  3261386.00        4.00        1.00       10.00   \n",
       "50%        6528429.00  6522878.00        8.00        1.00       19.00   \n",
       "75%        9787265.00  9784900.00       16.00        1.00       27.00   \n",
       "max       13046721.00 13046734.00    32767.00        1.00       31.00   \n",
       "\n",
       "             hour     cate_id    store_id  item_price  \n",
       "count 13035341.00 13009222.00 13009222.00 13009222.00  \n",
       "mean         9.06     1498.53    40575.67     1099.75  \n",
       "std          6.56      903.26    24284.46     2880.00  \n",
       "min          0.00        1.00        1.00        1.00  \n",
       "25%          4.00      616.00    20648.00      123.00  \n",
       "50%          8.00     1505.00    39368.00      246.00  \n",
       "75%         13.00     2010.00    59273.00      700.00  \n",
       "max         23.00     4243.00    95105.00    20230.00  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>item_id</th>\n",
       "      <th>cate_id</th>\n",
       "      <th>store_id</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2832669.00</td>\n",
       "      <td>2832669.00</td>\n",
       "      <td>2832669.00</td>\n",
       "      <td>2832669.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6429138.00</td>\n",
       "      <td>1481.10</td>\n",
       "      <td>40256.46</td>\n",
       "      <td>1124.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3725431.44</td>\n",
       "      <td>923.09</td>\n",
       "      <td>24370.92</td>\n",
       "      <td>2110.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3224114.00</td>\n",
       "      <td>600.00</td>\n",
       "      <td>19850.00</td>\n",
       "      <td>180.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6391845.00</td>\n",
       "      <td>1499.00</td>\n",
       "      <td>38954.00</td>\n",
       "      <td>400.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9636216.00</td>\n",
       "      <td>2050.00</td>\n",
       "      <td>58406.00</td>\n",
       "      <td>1200.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13046734.00</td>\n",
       "      <td>4243.00</td>\n",
       "      <td>95105.00</td>\n",
       "      <td>20230.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          item_id    cate_id   store_id  item_price\n",
       "count  2832669.00 2832669.00 2832669.00  2832669.00\n",
       "mean   6429138.00    1481.10   40256.46     1124.00\n",
       "std    3725431.44     923.09   24370.92     2110.62\n",
       "min          1.00       1.00       1.00        1.00\n",
       "25%    3224114.00     600.00   19850.00      180.00\n",
       "50%    6391845.00    1499.00   38954.00      400.00\n",
       "75%    9636216.00    2050.00   58406.00     1200.00\n",
       "max   13046734.00    4243.00   95105.00    20230.00"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据内容：\n",
    "* 用户、商品、店铺、品类乃至商品价格都是从1开始用整数编号\n",
    "* 订单日期格式为：YYYY-mm-dd HH:mm:ss\n",
    "* 源数据中都木有空值，但是由于某些商品，不在商品表，因此缺少了一些价格、品类信息。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据探查"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下一步，我们依次对每个文件的特征进行基础统计和可视化处理，这是对数据进一步理解的基础。\n",
    "\n",
    "[]~(￣▽￣)~* Let's do it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练集与测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = df['is_train']==1\n",
    "test = df['is_train']==0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集样本量是 12868509\n",
      "测试集样本量是 166832\n",
      "样本比例为： 77.13453653975256\n"
     ]
    }
   ],
   "source": [
    "train_count = len(df[train])\n",
    "print('训练集样本量是',train_count)\n",
    "test_count = len(df[test])\n",
    "print('测试集样本量是',test_count)\n",
    "print('样本比例为：', train_count/test_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### buyer_country_id 国家编号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def groupby_cnt_ratio(df, col):\n",
    "    if isinstance(col, str):\n",
    "        col = [col]\n",
    "    key = ['is_train', 'buyer_country_id'] + col\n",
    "    \n",
    "    # groupby function\n",
    "    cnt_stat = df.groupby(key).size().to_frame('count')\n",
    "    ratio_stat = (cnt_stat / cnt_stat.groupby(['is_train', 'buyer_country_id']).sum()).rename(columns={'count':'count_ratio'})\n",
    "    return pd.merge(cnt_stat, ratio_stat, on=key, how='outer').sort_values(by=['count'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>count_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_train</th>\n",
       "      <th>buyer_country_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>xx</th>\n",
       "      <td>10635642</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yy</th>\n",
       "      <td>2232867</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th>yy</th>\n",
       "      <td>166832</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              count  count_ratio\n",
       "is_train buyer_country_id                       \n",
       "1        xx                10635642         1.00\n",
       "         yy                 2232867         1.00\n",
       "0        yy                  166832         1.00"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby_cnt_ratio(df, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.countplot(x='is_train', data = df, palette=['red', 'blue'], hue='buyer_country_id', order=[1, 0])\n",
    "plt.xticks(np.arange(2), ('训练集', '测试集'))\n",
    "plt.xlabel('数据文件')\n",
    "plt.title('国家编号');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "buyer_country_id 国家编号\n",
    "\n",
    "> 本次比赛给出若干日内来自成熟国家的部分用户的行为数据，以及来自待成熟国家的A部分用户的行为数据，以及待成熟国家的B部分用户的行为数据去除每个用户的最后一条购买数据，让参赛人预测B部分用户的最后一条行为数据。\n",
    "\n",
    "* 训练集中有2个国家数据，xx国家样本数10635642，占比83%，yy国家样本数2232867条，仅占17%\n",
    "* 预测集中有yy国家的166832数据, 训练集中yy国样本数量是测试集中的13倍，如赛题目的所言，期望通过大量成熟国家来预测少量带成熟国家的用户购买行为"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### buyer_admin_id 用户编号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集中用户数量 809213\n",
      "测试集中用户数量 11398\n"
     ]
    }
   ],
   "source": [
    "print('训练集中用户数量',len(df[train]['buyer_admin_id'].unique()))\n",
    "print('测试集中用户数量',len(df[test]['buyer_admin_id'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "同时在训练集测试集出现的有6位用户，id如下： [12647969, 13000419, 3106927, 12858772, 12929117, 12368445]\n"
     ]
    }
   ],
   "source": [
    "union = list(set(df[train]['buyer_admin_id'].unique()).intersection(set(df[test]['buyer_admin_id'].unique())))\n",
    "print('同时在训练集测试集出现的有6位用户，id如下：',union)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        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>buyer_country_id</th>\n",
       "      <th>buyer_admin_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>create_order_time</th>\n",
       "      <th>irank</th>\n",
       "      <th>is_train</th>\n",
       "      <th>date</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>cate_id</th>\n",
       "      <th>store_id</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7546704</th>\n",
       "      <td>xx</td>\n",
       "      <td>3106927</td>\n",
       "      <td>7645546</td>\n",
       "      <td>2018-08-30 02:49:22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-08-30</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>1164.00</td>\n",
       "      <td>73781.00</td>\n",
       "      <td>770.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4582539</th>\n",
       "      <td>xx</td>\n",
       "      <td>3106927</td>\n",
       "      <td>4639151</td>\n",
       "      <td>2018-08-30 02:49:22</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-08-30</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>2214.00</td>\n",
       "      <td>53190.00</td>\n",
       "      <td>1669.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11953258</th>\n",
       "      <td>xx</td>\n",
       "      <td>3106927</td>\n",
       "      <td>12122118</td>\n",
       "      <td>2018-08-30 02:49:22</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-08-30</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>236.00</td>\n",
       "      <td>73781.00</td>\n",
       "      <td>884.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255625</th>\n",
       "      <td>xx</td>\n",
       "      <td>3106927</td>\n",
       "      <td>258860</td>\n",
       "      <td>2018-08-30 02:49:22</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-08-30</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>189.00</td>\n",
       "      <td>24221.00</td>\n",
       "      <td>900.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7402817</th>\n",
       "      <td>xx</td>\n",
       "      <td>3106927</td>\n",
       "      <td>7499372</td>\n",
       "      <td>2018-08-30 02:49:22</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-08-30</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>2214.00</td>\n",
       "      <td>32535.00</td>\n",
       "      <td>2714.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9483312</th>\n",
       "      <td>xx</td>\n",
       "      <td>3106927</td>\n",
       "      <td>9613063</td>\n",
       "      <td>2018-08-30 02:49:22</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-08-30</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>3069.00</td>\n",
       "      <td>73781.00</td>\n",
       "      <td>110.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2740080</th>\n",
       "      <td>xx</td>\n",
       "      <td>3106927</td>\n",
       "      <td>2773189</td>\n",
       "      <td>2018-08-27 08:18:23</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-08-27</td>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>1865.00</td>\n",
       "      <td>49499.00</td>\n",
       "      <td>20067.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12152249</th>\n",
       "      <td>xx</td>\n",
       "      <td>3106927</td>\n",
       "      <td>12324030</td>\n",
       "      <td>2018-08-27 07:15:05</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-08-27</td>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>880.00</td>\n",
       "      <td>92968.00</td>\n",
       "      <td>1764.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2201292</th>\n",
       "      <td>xx</td>\n",
       "      <td>3106927</td>\n",
       "      <td>2227720</td>\n",
       "      <td>2018-08-19 02:36:36</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-08-19</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>1164.00</td>\n",
       "      <td>6404.00</td>\n",
       "      <td>1900.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6717641</th>\n",
       "      <td>xx</td>\n",
       "      <td>3106927</td>\n",
       "      <td>6804187</td>\n",
       "      <td>2018-08-19 02:33:39</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-08-19</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>1164.00</td>\n",
       "      <td>52421.00</td>\n",
       "      <td>230.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         buyer_country_id  buyer_admin_id   item_id   create_order_time  \\\n",
       "7546704                xx         3106927   7645546 2018-08-30 02:49:22   \n",
       "4582539                xx         3106927   4639151 2018-08-30 02:49:22   \n",
       "11953258               xx         3106927  12122118 2018-08-30 02:49:22   \n",
       "255625                 xx         3106927    258860 2018-08-30 02:49:22   \n",
       "7402817                xx         3106927   7499372 2018-08-30 02:49:22   \n",
       "9483312                xx         3106927   9613063 2018-08-30 02:49:22   \n",
       "2740080                xx         3106927   2773189 2018-08-27 08:18:23   \n",
       "12152249               xx         3106927  12324030 2018-08-27 07:15:05   \n",
       "2201292                xx         3106927   2227720 2018-08-19 02:36:36   \n",
       "6717641                xx         3106927   6804187 2018-08-19 02:33:39   \n",
       "\n",
       "          irank  is_train        date  day  hour  cate_id  store_id  \\\n",
       "7546704       1         1  2018-08-30   30     2  1164.00  73781.00   \n",
       "4582539       2         1  2018-08-30   30     2  2214.00  53190.00   \n",
       "11953258      3         1  2018-08-30   30     2   236.00  73781.00   \n",
       "255625        4         1  2018-08-30   30     2   189.00  24221.00   \n",
       "7402817       5         1  2018-08-30   30     2  2214.00  32535.00   \n",
       "9483312       6         1  2018-08-30   30     2  3069.00  73781.00   \n",
       "2740080      10         1  2018-08-27   27     8  1865.00  49499.00   \n",
       "12152249     11         1  2018-08-27   27     7   880.00  92968.00   \n",
       "2201292      12         1  2018-08-19   19     2  1164.00   6404.00   \n",
       "6717641      13         1  2018-08-19   19     2  1164.00  52421.00   \n",
       "\n",
       "          item_price  \n",
       "7546704       770.00  \n",
       "4582539      1669.00  \n",
       "11953258      884.00  \n",
       "255625        900.00  \n",
       "7402817      2714.00  \n",
       "9483312       110.00  \n",
       "2740080     20067.00  \n",
       "12152249     1764.00  \n",
       "2201292      1900.00  \n",
       "6717641       230.00  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[train][df['buyer_admin_id'].isin(union)].sort_values(by=['buyer_admin_id','irank']).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "         buyer_country_id  buyer_admin_id  item_id   create_order_time  irank  \\\n",
       "13016145               yy         3106927   202354 2018-08-30 02:48:40      7   \n",
       "13008981               yy         3106927  6994414 2018-08-29 05:48:06      8   \n",
       "13008982               yy         3106927  6994414 2018-08-29 05:48:06      9   \n",
       "\n",
       "          is_train        date  day  hour  cate_id  store_id  item_price  \n",
       "13016145         0  2018-08-30   30     2   642.00  24221.00      989.00  \n",
       "13008981         0  2018-08-29   29     5     7.00  37411.00     1521.00  \n",
       "13008982         0  2018-08-29   29     5     7.00  37411.00     1521.00  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[test][df['buyer_admin_id'].isin(union)].sort_values(by=['buyer_admin_id','irank']).head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>73781.00</td>\n",
       "      <td>770.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        buyer_country_id  buyer_admin_id  item_id   create_order_time  irank  \\\n",
       "3488248               xx        12858772  3530835 2018-08-27 20:49:33      1   \n",
       "6404074               xx        12368445  6486053 2018-08-31 04:24:46      1   \n",
       "7546704               xx         3106927  7645546 2018-08-30 02:49:22      1   \n",
       "\n",
       "         is_train        date  day  hour  cate_id  store_id  item_price  \n",
       "3488248         1  2018-08-27   27    20   579.00  38151.00      399.00  \n",
       "6404074         1  2018-08-31   31     4  1423.00  55861.00      400.00  \n",
       "7546704         1  2018-08-30   30     2  1164.00  73781.00      770.00  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(train) & (df['irank']==1) & (df['buyer_admin_id'].isin(['12858772','3106927','12368445']))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "emmm... 为啥同一个用户在训练集和测试集国家不一样了呢？但是其他信息能对上。。。，而且rank=1的结果直接给出来了。。。\n",
    "\n",
    "id为12858772、3106927、12368445直接把结果给出来\n",
    "\n",
    "可能是数据清洗出问题了，后面再看看怎么处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 用户记录数分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1144848</th>\n",
       "      <td>286</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          count  count_ratio\n",
       "is_train buyer_country_id buyer_admin_id                    \n",
       "1        xx               10828801        42751         0.00\n",
       "                          10951390        23569         0.00\n",
       "                          11223615        19933         0.00\n",
       "         yy               2381782          3480         0.00\n",
       "                          2333316          1944         0.00\n",
       "                          2365356          1686         0.00\n",
       "0        yy               2041038          1386         0.01\n",
       "                          2070430           399         0.00\n",
       "                          1144848           286         0.00"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "admin_cnt = groupby_cnt_ratio(df, 'buyer_admin_id')\n",
    "admin_cnt.groupby(['is_train','buyer_country_id']).head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                             max  min  median\n",
       "is_train buyer_country_id                    \n",
       "0        yy                 1386    7      11\n",
       "1        xx                42751    8      11\n",
       "         yy                 3480    8      12"
      ]
     },
     "execution_count": 23,
     "metadata": {},
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   ],
   "source": [
    "# 用户购买记录数——最多、最少、中位数\n",
    "admin_cnt.groupby(['is_train','buyer_country_id'])['count'].agg(['max','min','median'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2 ,figsize=(16,6))\n",
    "ax[0].set(xlabel='用户记录数')\n",
    "sns.kdeplot(admin_cnt.loc[(1, 'xx')]['count'].values, ax=ax[0]).set_title('训练集--xx国用户记录数')\n",
    "\n",
    "ax[1].legend(labels=['训练集', '测试集'], loc=\"upper right\")\n",
    "ax[1].set(xlabel='用户记录数')\n",
    "sns.kdeplot(admin_cnt[admin_cnt['count']<50].loc[(1, 'yy')]['count'].values, ax=ax[1]).set_title('yy国用户记录数')\n",
    "sns.kdeplot(admin_cnt[admin_cnt['count']<50].loc[(0, 'yy')]['count'].values, ax=ax[1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用户记录数进行了一波简单的探查：\n",
    "* 训练集中记录了*809213*个用户的数据，其中id为10828801的用户拔得头筹，有42751条购买记录，用户至少都有8条记录\n",
    "* 训练集中记录了*11398*个用户的数据，其中id为2041038的用户勇冠三军，有1386条购买记录，用户至少有7条记录\n",
    "\n",
    "Notes: 验证集中用户最少仅有7条，是因为最后一条记录被抹去"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上面数据和图表看到，用户记录数大都都分布在0~50，少量用户记录甚至超过了10000条，下一步对用户记录数分布继续探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>人数</th>\n",
       "      <th>人数占比</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_train</th>\n",
       "      <th>buyer_country_id</th>\n",
       "      <th>记录数</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">xx</th>\n",
       "      <th>8</th>\n",
       "      <td>118155</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>91757</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>72936</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>57678</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>46534</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   人数  人数占比\n",
       "is_train buyer_country_id 记录数              \n",
       "1        xx               8    118155  0.18\n",
       "                          9     91757  0.14\n",
       "                          10    72936  0.11\n",
       "                          11    57678  0.09\n",
       "                          12    46534  0.07"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "admin_cnt.columns = ['记录数', '占比']\n",
    "admin_user_cnt = groupby_cnt_ratio(admin_cnt, '记录数')\n",
    "admin_user_cnt.columns = ['人数', '人数占比']\n",
    "admin_user_cnt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>记录数</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "      <th>50</th>\n",
       "      <th>51</th>\n",
       "      <th>52</th>\n",
       "      <th>53</th>\n",
       "      <th>54</th>\n",
       "      <th>55</th>\n",
       "      <th>56</th>\n",
       "      <th>60</th>\n",
       "      <th>...</th>\n",
       "      <th>346</th>\n",
       "      <th>348</th>\n",
       "      <th>352</th>\n",
       "      <th>355</th>\n",
       "      <th>360</th>\n",
       "      <th>361</th>\n",
       "      <th>457</th>\n",
       "      <th>460</th>\n",
       "      <th>461</th>\n",
       "      <th>618</th>\n",
       "      <th>573</th>\n",
       "      <th>582</th>\n",
       "      <th>584</th>\n",
       "      <th>585</th>\n",
       "      <th>594</th>\n",
       "      <th>597</th>\n",
       "      <th>604</th>\n",
       "      <th>607</th>\n",
       "      <th>619</th>\n",
       "      <th>568</th>\n",
       "      <th>630</th>\n",
       "      <th>638</th>\n",
       "      <th>640</th>\n",
       "      <th>649</th>\n",
       "      <th>655</th>\n",
       "      <th>656</th>\n",
       "      <th>659</th>\n",
       "      <th>660</th>\n",
       "      <th>570</th>\n",
       "      <th>559</th>\n",
       "      <th>468</th>\n",
       "      <th>504</th>\n",
       "      <th>475</th>\n",
       "      <th>476</th>\n",
       "      <th>477</th>\n",
       "      <th>479</th>\n",
       "      <th>481</th>\n",
       "      <th>488</th>\n",
       "      <th>494</th>\n",
       "      <th>497</th>\n",
       "      <th>521</th>\n",
       "      <th>556</th>\n",
       "      <th>526</th>\n",
       "      <th>528</th>\n",
       "      <th>529</th>\n",
       "      <th>537</th>\n",
       "      <th>545</th>\n",
       "      <th>549</th>\n",
       "      <th>550</th>\n",
       "      <th>554</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>人数</th>\n",
       "      <td>118155.00</td>\n",
       "      <td>91757.00</td>\n",
       "      <td>72936.00</td>\n",
       "      <td>57678.00</td>\n",
       "      <td>46534.00</td>\n",
       "      <td>38114.00</td>\n",
       "      <td>31432.00</td>\n",
       "      <td>26735.00</td>\n",
       "      <td>22352.00</td>\n",
       "      <td>18742.00</td>\n",
       "      <td>16113.00</td>\n",
       "      <td>13677.00</td>\n",
       "      <td>12092.00</td>\n",
       "      <td>10097.00</td>\n",
       "      <td>8974.00</td>\n",
       "      <td>7749.00</td>\n",
       "      <td>6816.00</td>\n",
       "      <td>5910.00</td>\n",
       "      <td>5387.00</td>\n",
       "      <td>4751.00</td>\n",
       "      <td>4166.00</td>\n",
       "      <td>3769.00</td>\n",
       "      <td>3525.00</td>\n",
       "      <td>3005.00</td>\n",
       "      <td>2828.00</td>\n",
       "      <td>2476.00</td>\n",
       "      <td>2289.00</td>\n",
       "      <td>2069.00</td>\n",
       "      <td>1817.00</td>\n",
       "      <td>1683.00</td>\n",
       "      <td>1626.00</td>\n",
       "      <td>1464.00</td>\n",
       "      <td>1364.00</td>\n",
       "      <td>1218.00</td>\n",
       "      <td>1150.00</td>\n",
       "      <td>1001.00</td>\n",
       "      <td>988.00</td>\n",
       "      <td>963.00</td>\n",
       "      <td>802.00</td>\n",
       "      <td>768.00</td>\n",
       "      <td>710.00</td>\n",
       "      <td>681.00</td>\n",
       "      <td>597.00</td>\n",
       "      <td>595.00</td>\n",
       "      <td>548.00</td>\n",
       "      <td>533.00</td>\n",
       "      <td>503.00</td>\n",
       "      <td>473.00</td>\n",
       "      <td>448.00</td>\n",
       "      <td>428.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>人数占比</th>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 506 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "记录数        8        9        10       11       12       13       14       15   \\\n",
       "人数   118155.00 91757.00 72936.00 57678.00 46534.00 38114.00 31432.00 26735.00   \n",
       "人数占比      0.18     0.14     0.11     0.09     0.07     0.06     0.05     0.04   \n",
       "\n",
       "记录数       16       17       18       19       20       21      22      23   \\\n",
       "人数   22352.00 18742.00 16113.00 13677.00 12092.00 10097.00 8974.00 7749.00   \n",
       "人数占比     0.03     0.03     0.02     0.02     0.02     0.02    0.01    0.01   \n",
       "\n",
       "记录数      24      25      26      27      28      29      30      31      32   \\\n",
       "人数   6816.00 5910.00 5387.00 4751.00 4166.00 3769.00 3525.00 3005.00 2828.00   \n",
       "人数占比    0.01    0.01    0.01    0.01    0.01    0.01    0.01    0.00    0.00   \n",
       "\n",
       "记录数      33      34      35      36      37      38      39      40      41   \\\n",
       "人数   2476.00 2289.00 2069.00 1817.00 1683.00 1626.00 1464.00 1364.00 1218.00   \n",
       "人数占比    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00    0.00   \n",
       "\n",
       "记录数      42      43     44     45     46     47     48     49     50     51   \\\n",
       "人数   1150.00 1001.00 988.00 963.00 802.00 768.00 710.00 681.00 597.00 595.00   \n",
       "人数占比    0.00    0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.00   \n",
       "\n",
       "记录数     52     53     54     55     56     60   ...  346  348  352  355  360  \\\n",
       "人数   548.00 533.00 503.00 473.00 448.00 428.00  ... 1.00 1.00 1.00 1.00 1.00   \n",
       "人数占比   0.00   0.00   0.00   0.00   0.00   0.00  ... 0.00 0.00 0.00 0.00 0.00   \n",
       "\n",
       "记录数   361  457  460  461  618  573  582  584  585  594  597  604  607  619  \\\n",
       "人数   1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00   \n",
       "人数占比 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00   \n",
       "\n",
       "记录数   568  630  638  640  649  655  656  659  660  570  559  468  504  475  \\\n",
       "人数   1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00   \n",
       "人数占比 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00   \n",
       "\n",
       "记录数   476  477  479  481  488  494  497  521  556  526  528  529  537  545  \\\n",
       "人数   1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00   \n",
       "人数占比 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00   \n",
       "\n",
       "记录数   549  550  554  \n",
       "人数   1.00 1.00 1.00  \n",
       "人数占比 0.00 0.00 0.00  \n",
       "\n",
       "[2 rows x 506 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# xx国——用户记录数与用户数\n",
    "admin_user_cnt.loc[(1,'xx')][['人数','人数占比']].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">人数占比</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>is_train</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>buyer_country_id</th>\n",
       "      <th>记录数</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">yy</th>\n",
       "      <th>8</th>\n",
       "      <td>0.13</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.09</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.06</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     人数占比     \n",
       "is_train                0    1\n",
       "buyer_country_id 记录数          \n",
       "yy               8   0.13 0.16\n",
       "                 9   0.10 0.13\n",
       "                 10  0.09 0.10\n",
       "                 11  0.08 0.08\n",
       "                 12  0.06 0.07\n",
       "                 13  0.05 0.06\n",
       "                 14  0.04 0.05\n",
       "                 15  0.04 0.04\n",
       "                 16  0.03 0.03\n",
       "                 17  0.03 0.03"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# yy国——记录数与用户数占比\n",
    "admin_user_cnt.loc[([1,0],'yy',slice(None))][['人数','人数占比']].unstack(0).drop('人数',1).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 1, figsize=(16,10))\n",
    "admin_plot = admin_user_cnt.reset_index()\n",
    "sns.barplot(x='记录数', y='人数占比', data=admin_plot[(admin_plot['记录数']<50) & (admin_plot['buyer_country_id']=='xx')], \n",
    "            estimator=np.mean, ax=ax[0]).set_title('训练集——xx国记录数与人数占比');\n",
    "\n",
    "sns.barplot(x='记录数', y='人数占比', hue='is_train', data=admin_plot[(admin_plot['记录数']<50) & (admin_plot['buyer_country_id']=='yy')], \n",
    "            estimator=np.mean, ax=ax[1]).set_title('yy国记录数与人数占比');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用户记录数进一步探查结论：\n",
    "    * 不管是训练集还是验证集，99%的用户购买记录都在50条内，这是比较符合正常逻辑\n",
    "    * TODO:对于发生大量购买行为的用户，后面再单独探查，是否有其他规律或疑似刷单现象"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### item_id 商品编号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "商品表中商品数： 2832669\n",
      "训练集中商品数： 2812048\n",
      "验证集中商品数： 104735\n",
      "仅训练集有的商品数： 2735801\n",
      "仅验证集有的商品数： 28488\n",
      "训练集验证集共同商品数： 76247\n",
      "训练集中不在商品表的商品数： 7733\n",
      "验证集中不在商品表的商品数： 313\n"
     ]
    }
   ],
   "source": [
    "print('商品表中商品数：',len(item['item_id'].unique()))\n",
    "print('训练集中商品数：',len(df[train]['item_id'].unique()))\n",
    "print('验证集中商品数：',len(df[test]['item_id'].unique()))\n",
    "print('仅训练集有的商品数：',len(list(set(df[train]['item_id'].unique()).difference(set(df[test]['item_id'].unique())))))\n",
    "print('仅验证集有的商品数：',len(list(set(df[test]['item_id'].unique()).difference(set(df[train]['item_id'].unique())))))\n",
    "print('训练集验证集共同商品数：',len(list(set(df[train]['item_id'].unique()).intersection(set(df[test]['item_id'].unique())))))\n",
    "print('训练集中不在商品表的商品数：',len(list(set(df[train]['item_id'].unique()).difference(set(item['item_id'].unique())))))\n",
    "print('验证集中不在商品表的商品数：',len(list(set(df[test]['item_id'].unique()).difference(set(item['item_id'].unique())))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 商品销量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "item_cnt = groupby_cnt_ratio(df, 'item_id')\n",
    "item_cnt.columns=['销量', '总销量占比']\n",
    "item_cnt.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_item_plot = item_cnt.groupby(['is_train','buyer_country_id']).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 1, figsize=(16,12))\n",
    "sns.barplot(x='item_id', y='销量', data=top_item_plot[top_item_plot['buyer_country_id']=='xx'], \n",
    "            order=top_item_plot['item_id'][top_item_plot['buyer_country_id']=='xx'], ax=ax[0], estimator=np.mean).set_title('xx国-TOP热销商品')\n",
    "sns.barplot(x='item_id', y='销量', hue='is_train', data=top_item_plot[top_item_plot['buyer_country_id']=='yy'], \n",
    "            order=top_item_plot['item_id'][top_item_plot['buyer_country_id']=='yy'], ax=ax[1], estimator=np.mean).set_title('yy国-TOP热销商品');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "初步数据发现：\n",
    "* 训练集中出售最多商品是12691565，卖了112659次。\n",
    "* 训练集中出售最多商品是5595070，卖了112659次。\n",
    "* 大部分商品只有1次出售记录，符合电商长尾属性\n",
    "* 比较奇怪的yy国中，训练集和测试集中热销商品并不太一样"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 整体商品销量分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "item_order_cnt = groupby_cnt_ratio(item_cnt, '销量')\n",
    "item_order_cnt.columns = ['商品数', '占比']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>商品数</th>\n",
       "      <th>占比</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_train</th>\n",
       "      <th>buyer_country_id</th>\n",
       "      <th>销量</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">xx</th>\n",
       "      <th>1</th>\n",
       "      <td>1331056</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>361829</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>164953</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>95629</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>61551</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">yy</th>\n",
       "      <th>1</th>\n",
       "      <td>81212</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13083</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4350</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2123</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1200</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">yy</th>\n",
       "      <th>1</th>\n",
       "      <td>625041</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>139607</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55274</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29848</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17782</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  商品数   占比\n",
       "is_train buyer_country_id 销量              \n",
       "1        xx               1   1331056 0.58\n",
       "                          2    361829 0.16\n",
       "                          3    164953 0.07\n",
       "                          4     95629 0.04\n",
       "                          5     61551 0.03\n",
       "0        yy               1     81212 0.78\n",
       "                          2     13083 0.12\n",
       "                          3      4350 0.04\n",
       "                          4      2123 0.02\n",
       "                          5      1200 0.01\n",
       "1        yy               1    625041 0.67\n",
       "                          2    139607 0.15\n",
       "                          3     55274 0.06\n",
       "                          4     29848 0.03\n",
       "                          5     17782 0.02"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_order_cnt.groupby(['is_train','buyer_country_id']).head(5).sort_values(by=['buyer_country_id','is_train'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "item_order_plot = item_order_cnt.reset_index()\n",
    "item_order_plot = item_order_plot[item_order_plot['销量']<=8]\n",
    "\n",
    "xx_item_order_plot = item_order_plot[item_order_plot['buyer_country_id']=='xx']\n",
    "yy_item_order_plot = item_order_plot[item_order_plot['buyer_country_id']=='yy']\n",
    "yy_item_order_plot_1 = yy_item_order_plot[yy_item_order_plot['is_train']==1]\n",
    "yy_item_order_plot_0 = yy_item_order_plot[yy_item_order_plot['is_train']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 商品销量饼图\n",
    "def text_style_func(pct, allvals):\n",
    "    absolute = int(pct/100.*np.sum(allvals))\n",
    "    return \"{:.1f}%({:d})\".format(pct, absolute)\n",
    "\n",
    "def pie_param(ax, df, color_palette):\n",
    "    return ax.pie(df['占比'].values, autopct=lambda pct: text_style_func(pct, df['商品数']), labels = df['销量'], \n",
    "                  explode = [0.1]+ np.zeros(len(df)-1).tolist(), pctdistance = 0.7, colors=sns.color_palette(color_palette, 8))\n",
    "\n",
    "fig, ax = plt.subplots(1, 3, figsize=(16,12))\n",
    "ax[0].set(xlabel='xx国-商品销量')\n",
    "ax[0].set(ylabel='xx国-商品数量比例')\n",
    "pie_param(ax[0], xx_item_order_plot, \"coolwarm\")\n",
    "ax[1].set(xlabel='yy国-训练集商品销量')\n",
    "pie_param(ax[1], yy_item_order_plot_1, \"Set3\")\n",
    "ax[2].set(xlabel='yy国测试集集商品销量')\n",
    "pie_param(ax[2], yy_item_order_plot_0, \"Set3\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9215598591503256\n",
      "0.9643666018897873\n",
      "0.9884661288012603\n"
     ]
    }
   ],
   "source": [
    "print(xx_item_order_plot.head(10)['占比'].sum())\n",
    "print(yy_item_order_plot_1.head(10)['占比'].sum())\n",
    "print(yy_item_order_plot_0.head(10)['占比'].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "总体来看，由于训练集数据远多于测试集数据：\n",
    "* 训练集商品销量大于测试集商品销量\n",
    "* 长尾趋势严重，热门商品少，大量商品仅有数次销售记录，1单商品占了绝大部分(均超过50%)\n",
    "* 训练集中92%的商品销量不超过10件，而在测试集中97%的商品销量不超过10件\n",
    "* 此外训练集中yy国的商品销量略大于测试集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cate_id 品类编号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "商品品类数 4243\n",
      "训练集商品品类数 4239\n",
      "测试集商品品类数 2727\n"
     ]
    }
   ],
   "source": [
    "print('商品品类数', len(item['cate_id'].unique()))\n",
    "print('训练集商品品类数', len(df[train]['cate_id'].unique()))\n",
    "print('测试集商品品类数', len(df[test]['cate_id'].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 各个品类下商品数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>cate_id</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>579</td>\n",
       "      <td>170074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2395</th>\n",
       "      <td>2396</td>\n",
       "      <td>44467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497</th>\n",
       "      <td>1498</td>\n",
       "      <td>42676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>600</td>\n",
       "      <td>39729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1591</th>\n",
       "      <td>1592</td>\n",
       "      <td>34820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      cate_id   count\n",
       "578       579  170074\n",
       "2395     2396   44467\n",
       "1497     1498   42676\n",
       "599       600   39729\n",
       "1591     1592   34820"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cate_cnt = item.groupby(['cate_id']).size().to_frame('count').reset_index()\n",
    "cate_cnt.sort_values(by=['count'], ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "sns.kdeplot(data=cate_cnt[cate_cnt['count']<1000]['count']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们发现：\n",
    "    * 579品类一花独秀有17W个商品，可能是平台主营方向\n",
    "    * 大部分品类都在100个以上"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### store_id 店铺编号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "商品店铺数 95105\n",
      "训练集店铺数 94970\n",
      "测试集店铺数 94970\n"
     ]
    }
   ],
   "source": [
    "print('商品店铺数', len(item['store_id'].unique()))\n",
    "print('训练集店铺数', len(df[train]['store_id'].unique()))\n",
    "print('测试集店铺数', len(df[train]['store_id'].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 店铺下品类数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>store_id</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45594</th>\n",
       "      <td>45595</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44983</th>\n",
       "      <td>44984</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1870</th>\n",
       "      <td>1871</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22034</th>\n",
       "      <td>22035</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       store_id  count\n",
       "0             1    326\n",
       "45594     45595    280\n",
       "44983     44984    274\n",
       "1870       1871    258\n",
       "22034     22035    241"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_cate_cnt = item.groupby(['store_id'])['cate_id'].nunique().to_frame('count').reset_index()\n",
    "store_cate_cnt.sort_values(by=['count'], ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_cnt_cate_cnt = store_cate_cnt.groupby(['count']).size().reset_index()\n",
    "store_cnt_cate_cnt.columns = ['店铺品类数', '店铺数量']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2R2wuv4XyQUGSJEma8DqtMb+1+vcQ8CvgHsre5fdQ1oU/VN1/W4/7KEmSJG302ibm1dU0rwAepOxbvrS6vZRyYuajlO0UV7R7DEmSJEn1dFrKMnr//ZS9zKcBf1WVHwlcT7n65hQ8+VKSJElaK922S1xNSbp/BVyVme8AFlf/nwF8KzOP6XEfJUmSpI1e28Q8Ip5BScwProreV+0d/rKI2Ixyxc33RcTTe99NSZIkaePWaSnL6cD2lN1ZVgG7UnZNeSZl7fmkKv4TwOG97aYkSZK0cet08uergL+nXMDnAuAjmbkfcEdm7peZ+2bm3pStFCVJkiSthY5rzDPzBuAlwPeBz1fFb2qqs7InPZMkSZI2IR13ZYmIQeDizHxNRNwC7AFMjoj/QdkucQAYysybet9VSZIkaePVMTHPzJGI2K6p7gWU/cwnAbtTLja0X896KEmSJG0Cuu1jDjC6VGX1aEG11pyIuGX0tiRJkqTx67aPOcCMakvEwWr2vDGZX90mRpIkSdIYtJ0xj4jZwJeAnYCrKGvKrwb+bH10TJIkSdqUdFrK8mvK/uSnV9siAhARP+55ryRJkqRNTNvEPDN/B/wuItZYrhIRn6Wc/Lljdft9mflo77opSZIkbdzqnPzZ7EPADMpJoTcAQ5Qrg0qSJEkapzqJ+W4RcRPlRM+VwHLgd8B8SmL+vcw0MZckSZLWQp3E/EXAMsqs+CCwbfXvOcCngPuAOb3qoCRJkrQp6JqYZ+ZtTUV3VP9/OyI+AWyzznslSZIkbWLGs8b8MZm5GvjjOuqLJEmStMmqc4EhSZIkST22VjPm6m7rLacxeWiwVt2VK0Z4YNEjPe6RJEmS+pGJeY9NHhrkD2d9ulbdpxz/XsDEXJIkaVPkUhZJkiSpD5iYS5IkSX3AxFySJEnqAybmkiRJUh/o+cmfETEIfBs4PTOvjYinAV+v7v5KZp5Z1TsSOAFYBByXmRkRU4BLgO2B24BjM3NVROwGnAuMAJ/KzG/1+nlIkiRJvdTTGfMqsb4MeHpD8bnAqcCewKERsWNE7Ax8ENgLOAY4o6p7IjA/M/cE7gUOq8ovAo4CZgMnR8RmvXwekiRJUq+tj6UsxwK3AkTEZGC3zJxXXTX0GkpyvQ9wRWYuzcxfA7OqunOAC6rHmQv8dUTsBCzLzJ9n5nLgZmCP9fA8JEmSpJ7p6VKWzHwUuCciRoumA79rqLIQ2A5YCtzdUP4wMBMYbigfrTvcVHe0vGdmzhxeb3HjbUuSJEkT2/q+wNDDwNSGn4eBScBinphcN5ZvXv3fXNZct2cWLFgCjD1pXrBgybhiJEmSNHENDExim21mjD2uB31pKzNXAg9UJ4AC7A7MB24E9gWIiGFgFrCgKt+vqe7dwPYRMb2pXJIkSZqwNsR2iZ8GLo2If6GsDZ+bmb8CFkXEecDlwHnVMphzKCd3/hPwYeCcam362cDcqv5kyjpzSZIkacJaL0tZMvNNDbcvi4hfUWa6T8nMh6q7XgccBIxk5tVV3bsiYjawP7BvZt5RlX8hIm4CdgHelpmr1sfzkCRJknplfa8xByAzbwdubypbRdl5pbnu/ZTtEZvLbwFu6VUfJUmSpPXJK39KkiRJfcDEXJIkSeoDJuaSJElSHzAxlyRJkvqAibkkSZLUB0zMJUmSpD5gYi5JkiT1ARNzSZIkqQ+YmEuSJEl9YINc+VPdbb3lVCYPDdWqu3LFCh5YtLzHPZIkSVIvmZj3qclDQ9x35km16s76u9MAE3NJkqSJzKUskiRJUh8wMZckSZL6gIm5JEmS1AdMzCVJkqQ+YGIuSZIk9QETc0mSJKkPmJhLkiRJfcDEXJIkSeoDJuaSJElSHzAxlyRJkvqAibkkSZLUB0zMJUmSpD5gYi5JkiT1gSkbugNat7becojJQ1Nr1V25YjkPLFrR4x5JkiSpDhPzjczkoanc/fnDatXd8R1fBUzMJUmS+oFLWSRJkqQ+YGIuSZIk9YENspQlIm4D7q9+/A/gM8ClwGbAtZl5clXvAOCTwMPAiZl5Q1X+eeCFwG+BIzPz4fX7DCRJkqR1a73PmEfEDsAdmTm7+vcR4HTg0sx8CfC0iNgzIoaBs4EDgYOBM6r41wNPycwXAFcA71rfz0GSJEla1zbEjPlLgd0j4rqq/XcC+wMnVvfPBf4aGAL+MzN/DxARf4yIHYE5wAUNdb8KfKKXHZ45c3i9xa3PttYmTpIkSevWhkjMbwP2z8zbI+KllNnyVZm5uLp/IbAdMAzc3RDXqny0rKcWLFgCjD2JXbBgybhixtvW2sRJkiRp3RgYmMQ228wYe1wP+tJNZubt1e2fAM8GRiJiUlU2DEwCFgObN8S1Kh8tkyRJkia0DZGYnxkR+1e3/xa4FfgxsGdVtjswvyp7YUQMRMQA8JfAncCNwH5NdSVJkqQJbUMsZTkF+GpEfBa4B3grsC3w5YiYB7weeHFmLo6I7wLfrOJuysz7IuJrwPURsSXwauDd6/0ZSJIkSevYek/MM/Mu4MVNxXdFxN8AewNnZOa9Vd0TI2JfYCvg8qpsUUTsBRwEXJSZP11/vZckSZJ6Y4PsY95KlbDf1aL8/7YoW0zZjUXrwJO2HGLK0NRadR9dsZwHF63ocY8kSZI2PX2TmGvDmTI0ldu/+MpadXd922WAibkkSdK6tiFO/pQkSZLUxMRckiRJ6gMm5pIkSVIfMDGXJEmS+oCJuSRJktQH3JVF4+Y2i5IkSeuOibnGbcrQVG49+xW16j7/rd/BbRYlSZLacymLJEmS1AdMzCVJkqQ+YGIuSZIk9QHXmGu986RRSZKkNZmYa72bMjSV6//t4Fp19z5mLp40KkmSNgUuZZEkSZL6gIm5JEmS1AdcyqIJYasthxisuS59ZMVyFrouXZIkTTAm5poQBoem8t1zD6pVd/+3zMN16ZIkaaJxKYskSZLUB5wx10bNJTCSJGmiMDHXRm1waCrfOe/AWnVfcfSVuARGkiRtKCbmUgvOtEuSpPXNxFxqYXBoKt/48str1X3NUVfhTLskSVpbJubSOrLVVoMMDk6rVXdk5BEWLhzpcY8kSdJEYmIurSODg9O44Pw5teq+4U1XAybmkiTpcSbm0ga25VaDDNWcaV8x8giLqpn28cZJkqT+ZGIubWBDg9P4twvqzbQf84bHZ9qHBqfx+Yvqxb3j8BI33mR+i62GmDpY72TY5SPLWbzQNfeSJI2Vibm0CRkanMYnv1ovmX//YY9/CJg6OJUPfaPeybAff83jJ8OOJ6Ef3mqIaTVjHhlZzpLqQ8DwVlOZNjhUM24FSxYur1VXkqT1ZcIm5hHxfuB1wP3AGzPzDxu4S5KaTB2cyt9dWi+hP/PQktBPG5zKgZcfXCvmykPmsqT6EDBtcIgDv/32enGv+gJLKIn5eBL64a2mMW1wsGbMCEsWPlK1tf7i1n8fN2PaYL23lEdGHmXJwmXjipGkjdmETMwjYk/gEGAPYB/gVODYGqGTR28MDG9eu72BgUmP3x6eMea4geEtxtXW5OEnjTNu5pjjBoe3HVdbQ+OMmzpj7HHTxhEDsNk446bPeMqY4zYfRwzAjM3HFzc8jrgtxtnWVtPHF7f1OOK23Wx8v7Ntp2895rhpg0O86ZpP14o5/4D3snRgBdMGB3nT1WfVi5lzPEsHlldtDXLUVefVivvyy49uiruwZtwRLB1YXmKu/Fq9mANf94S2jp53aa248w46tCFuCkfP+07NuFewdGAS0wan8OZ5V9aK+dJBB7K0+p3N2GIaU2sm9MtHHuWhxY+MO278bW3G1MHJXSJG41by0OJl445bn20BDG8xnaHBgVpxK0ZWsWTxw2yxxXQGa8aMjKxi8eKHAdZr3JZbTGdKzZhHR1axqGpry+HpTBmqGbdiFYuWjD9uq+HpTK4Zs3LFKhZWbW01vBmTh+r9rleuWMnCJcvGHTf+tqYxeaje39rKFY+ycMkj445bX21V6r0YlUmrV68eS/2+EBGnAH/MzC9ExCTgZ5n5FzVC9wKu72nnJEmSpGJv4Ad1K0/IGXNgGPhPgMxcHRF1p79vobxA9wEre9Q3SZIkbdomA7MouWdtEzUxXww0JuN114osZwyfWiRJkqRx+u+xBtRbqNR/bgT2A4iIZwB/3LDdkSRJktbORE3M/x14dkT8C/A14HMbuD+SJEnSWpmQJ38CRMRU4BXAfZn5ww3dH0mSJGltTNjEXJIkSdqYTNSlLJIkSdJGxcRckiRJ6gMTdbvECS0iZgAvAe7PzB9v6P6of0XEc4GnAT/MzMUbuj/qL46P/jee4/143yNaxXUbI+uyrX6LW999nIgi4s+AZwM3ZeaCDd0fucb8MRExCHwbOD0zr61Rfxi4GBgCtgbenpk31Yy7BpgL7ANcmZn/PIZ+XgJcnZnn16x/G3B/9eN/ZOZHxtDWG4GXZuaba9T9X8CxDUUvAHbJzPu6xG0HnAdMq4qOzMzfdonZGvgi8FRgFfDOzPxFtz6OR6txERH7Ae/PzDl1YiLidcAJwArgF8DxmbnGH16LuGOBgykXJ3g98KI2b6wtx25E/AXwzcyMGm29ETgJ+ENV5ajMnF/z9diMckXd/5WZv6r5mlxD+dsB2Ba4PjOP6xLzDuB1VdzXM/PTNdt6LfB2ykTE9zLzw63ixqrdMaDT+GgVA+xEl/HRJu6v6DI+Oh2n2o2PNm09iy7jo8Pr0XF8tGnvVDqMjzYxL6DL+GgT93RqjI9xJoYtj/c1xsgTYoB7qTdGmuOW0mGMdHo/6nL8aNXWA9QbI61ejzpjpLm9A+k+RppjHqXeGGmO+x09OIY0tfvYe3v1YeqCzNy9bhxwF/BPwMPAQspr+UiXmPuBE6vbRwFzMvPOOn2sfp4F/FdmblOjf/OB86t+AnwgM2+s01Z1hfdrgI9n5n+0i2lqbz9gx6p4C2BBu/fspriHgHdRftc3ZOYJNWLuBv6hivkF5dj3aKd+duJSFiAipgCXUQ7QdR0JXFj9oj8GfKBuc8BpmfmPwHuAtgOlRT9fC7xyDPV3AO7IzNnVv7Ek5X8GvBd4d536mXnxaDvA+4Fvd0vKK+8CzqvizqEc2Lv5B+DazNwXOI2SpHcUEYMRcUVEzK5+flpE3Fj9+7s2MWuMi4h4NXAyMLVOTERMo7wh7pOZewN7UBKqrm1RPnj8bWaeCtxOmdWoE0dEDFFezzX62SZmb+DwhrHSKilv93fyGeArHZLyNeIy84CG8fJr4POdYqo3y2Mzcy/gRcBREfGUbm1FxFOBU4ADKEnVfqNjoCFmOCK+ExFXR8QtEfHCOuODFseAbuOjRcxHqTE+WrVFjfHRJq7j+GgT03V8tGuLLuOjVVy38dEi5hPUGB8t4j5Kl/EBj42/7wLPBz4ZEe+uOUbWON7XGCOt3iPqjJFWcd3GSMv3oy7jo11cnTHS7v2v2xhZI67GGGmOOZh6Y6RVH0+hyxh5LDjikoh4U0RsERHfi4gfRsQ/tqtfxTz23h4RLwY+Sfng2FFTTvAO4KDM3A9YDby8RswzKePjNMoHkRfVbGvUv/LEiz12itkbeE/D+OiUlDe3dSLlA0C3pPyxuMx8Q8P4uBFoOwHa1N4ngP0y80XAcyJijy4xUyl/K6+t/j6HgSM69bMbE/PHHQvcWrdyZp6ZmV+rftyWMqNRJ+7WzLwsIp5FSTAvqBNXJRgnAmfV7SPwUmD3iLguIm6IiOfXbGsAuJDyye8NEdH203Ab/0j9Dyr3A8+PiOnAC4Ff1oj5C8ofGpRP3x2v/NomoTyXMiu3J3BoROzYKpY1x8WPgW7fIDwWk5mPZOarMvORqh9b8viMUse2MvNjwJTqAPAU4D9r9hHgI5SZwa59rOwNfD4iboqITtcFeEJcRBwIvBaYVs0C1m1vNH4/4PeZ+V9dYh4BNqs+MD4DmESZFerW1p8Dv6x+D6spMxvN46VVQtl1fLQ5BnQcHy1iflNnfLRqq8746HCcajs+2sR0HR+t4uqMj07H0nbjo0XMr6gxPlrEDdF9fEDrZK3OGGl1vO82RtaIqTlGWsV1HCMd3o86Hj/axNUZI2vE1Rwjbd83O4yRVjF1xkhz3A3UGyPNCd7pwKWZ+RLgaRGxZ5uY5vf2e4COera9AAANAElEQVRDWtXtFJeZh2bmA9XdLXOSFjFnAIur38FewPdq9pGIOAZI4Pc1Y14KnFx9kL04ypbXXeMi4q+ADwEPRETbicl2OVJEPBPYKTOvqhn3MPCXUb7Nn0X5fXeKmQksyceXAXXNSboxMQcy89HMvGc8sRHxZMqB+rQxhu5L+bRa96ql51Bmr5eMoY3bgP0z86WUmejTa8YdSVkicgLwU+D7UZYHdBURewN3Z+ZdXSsXXweeB7yT8kcwr0bMxZQ/8D2BTwFfqRHzWLIWEZOB3TJzXnWgvQaY3RzQalxkh6/52sU0OBH4Trb4JqFD3FOBV9NmnLSKi4gXArvR5puE5pgoXxN+mHJgfhHl4l371OzjF4G3AV8C3hwRR43huUEZl5/sFpOZI8A3KLNCbwEuyczlNdq6FdgxIl4VZbnViym/78aY5mTtPmqMj1GNx4Bu46NVTENx2/HRIa7j+GgV1218tIj5JDXGR5s+dh0fHZ4btBkfLWL+iRrjo0Xcm+gyPqBlsvYVxjBGaDje1x0jtH6P6DpGWsTVGSOPxdQdH01xf2IMY6Spj7XHCK1fk45jpCHmPsYwRhrifkKNMdIiwdufMsEFZTb6r9u084T39sz8bWau7NCvlnEN/Xg9ZZzdXDNmC+BQyuRHu+UXT4iLiJ0pfzsnj6F/5wAvzswXV20dVjPuDMqEyReAl0TER2vGjXo/ncdHc9y/AccDbwW+T+u/m8aYe4AHI+LoiDgYOBz4Zof2ujIxXwtVsnoJcFJmrvGpqpPMPAt4DeUNpVs7bwZuy8wfjLGLmZm3V7d/QuuvulvZAzg3M+/NzOuB5ZQZhjreAZw9hj5+Anhvlq/SjgP+d7eAzDyPMlP1Msrs+Zld6jcna9MpawZHLQS2G0Ofxywi5lC+WqyzVOcxmfmbzHw9Za1o12VPUdZq/jNwTLZYx96mjdXA5Zm5urr9M2qMlSq5GcjMr2XmH4GLKOs+a4mIZwMjmfmbGnWfATwrM9+dmScC20REuze6x2RZY7kPZW3uB4FPZPt1l42JYa3xMZ5jQKuYOuOjVVyd8dEYByygxvhoausuao6PprYepub4aPOadBwfTW1Npeb4aNFWrfFRGU3WHmIMx5CxHO/bxdQ9hjTH1RkjTTG1jx8NcZ9gDMeQhrizGMMxpMVr0vUY0hBzCWM4hjTEfYR6Y6Q5wVuVj6/nbzk+xvve3i4uInajLEE9um5MZv4pM48BfkCZlOsWN0B5rse3+2DTpq15mbmiut0yH2kT9zzgjMx8kLJGfY3x0eH12Bp4XmZeV7OfM4AjMvPNWc4juJOy9r5bWwcCv6VMZl6YmbVWULRjYj5O1azrxcBlmXn5GOLeHBGjM0LbUE6Y6ebVwN4RcS3lU+pJEdH1qy7gzIjYv7r9t9RfqnMbsGvV36cAO1BO3OgoIrYEntvmk3o704DRE1z2o8zUd5Xla8sXUU6y6DTr0crDPHHt5DDla82eqGb2Pw4cWrevETEpIn5azcJAWW9YZ6y8hDIDcnE1Xp4aEZd2aWsn4KqImBzlBLc5wI+6NVS9ka6MiJlV0d6U5U91vRH4as26U0tXY3qU9b57UH+sLKXM6C2mLD9YQ1OSdw81xsd4jgGtYuqMj+a4uuOjRXtdx0eLtnaixvhojqs7Pjq8jm3HR4uYWuOjVVt1xseohmTtFOqNkTEf71vF1BwjreI6jpEWMbtT4/jR5nnVGSPNcb+h3hhp9zp2GiPNMT+j3hhZo61uY6RNsjYS5dtIaP8eM9739lZx7wS+TFnr/GDNmFVRTvJ97LnWiDuTskTwcw1j5Noo5yW0i/kAcFtEbFYda19J63xkjT5Sco/RicF27zHtXsfXAd9qUb9d3AnAsyLiyVGW2ryENcdIqz6+nLKs56mUpbxrxe0Sx+9oyskksyLiMOCuzDy8RtyFlHV1P6R8nfO2bgGZefDo7Yg4BbizZiJwCvDViPgsJdl4a40YKH/c/xYRN1AO5u/LzGU14uZQzqwfi1OB8yLii5S1WW+pE1T90S3JzCvH2B6ZuTIiHoiIp2XZAWZ34DtjfZwx+AZlhu2yiAD4SGb+e5c+ro6Ik4C5EfEIZSeAH3ZrKDO/Bzx39OeIuDMzD+0Sc2dEzKOcHLYMOGsMH67eAVxeHWwfoKwVresVlK8pu8rM/4qIKyhjZIjyml5bJzbKORP/DBzXahawTbJcZ3yM5xiwRgxlCUS38dEqrs74aNXHbuOjVVt1xkeruDrjo93r2Gl8tGqrzvhoFXckHcYHPJZ4PSMzT6IkMH+krInuNkbGfLxvE/PvdB8jreJ2pvMYaY7ZNTMfO8+nw/GjVVuvpPsYaRX3Z3QfI+1ex05jpDnmeMra/m5jZI22uh1DKMnaVlWytlMVty3l/IMfUsbHGpNb431vbxUHHAM8Cfjf1Rg5KzMv6hLzX5T3+hWUCbk1PuS06eP5DWV3ZjnBslv/VlFmypdTvl35Rs3n9Yuqj4OUk1rXGB/tXsfqfe3U5vpd2nsS5UPcFpS/u692i6nauhQ4ITMfbtdeXW6XqE1GRJwPnJ9lC71XUk4ouYHyNdTumfnQhuyfNowoJzF9jsdn+O6inPvg+BAA1ezZBZRvD0cTw8AxoiZNCeWXKR9qX09ZXz2uc9m0aTEx1yYrInalzGTMa/PVnzZhjg914xhRJxHxdMryi++v7bpjbTpMzCVJkqQ+4MmfkiRJUh8wMZck9UzDzhSSpC7clUWSJriI2IGya8j/bXP/ZZTdlbKh7FDKJeY3o+zg0O5y6HXafw7wgsz8ckS8BjggM4+JiFcAB1F2xBit+3bKlnPzgL+nbDV2DuViYRe323NYkjYFJuaSNPHtAHwpInapttqcBAxm5oooe2DvAPym2nJsEuUKfyPVv4uB4YgYyMxVANXexj+k7DPdaEfKXtrXNpU/DHw8In5dPebyiJhOSbZPaKo7hbKbyaPA5sBhmXl6RLysqi9JmywTc0maYKoZ6v8H/LSheGfgxxHxEDAZuLeavf4MZYu/71MuDb4HZY/lJ1EufDKb8l5wHGVfYyhXibwhM1/e1O75lCsgNpZNpWwx+feUS7KP7oP9gqrNqyJiamYuj4hpwL2U/YifVLV9XUS8FJgxegXH0frjfHkkacIyMZekiWcZ8MvM3Gu0ICISeHVmzm8oO5oy6/0GHr/y4DMplwd/IWUv7guARdXVdEd1WhfefN9llCR7hHJRjr8A7qNcNW8p5VLfU6Nc/nwV8FJK0v58ygVpHqAsdRmIiFsps/JLImK3zFyCJG1CTMwlaeJZRZl1bjRCucrjSPXzSkryvQUl+f0lZanIHMoM+uaUS0i/APgbSiI9agrlUts/adH2E2ayR2fVo1xu8Hzg7Krt31P2cH5XZt5e1RmkfFDYvurDQuBByhUSv5CZp0bEV4HPmJRL2hSZmEvSxDNISX6bzWlxdcEzIuJVwMGZ+VBE7EtZsrIVZfb7FuCAxoDqJNHd6nQkIl5IudT9M4BjgecAL8vMj0fEbMpl5P81Mz9NWev+R+B9wMeAjwCvqp7P/6geckfgv+u0LUkbGxNzSZp4tqWcYHkw8PGqbBfg6oYZ8zdSTrA8n5I0fz8i/r26768oM+Y7AC9ufOCIuJMymw2wNXASZenL24BFVfnOmTm1uj0d+GZmfreK3wqYAZCZ10bEC4BZDY/3buCjlO16v0OZ6d8TuLaKHc7MB8bzokjSRGdiLkkTz7bA7ygnb16bme9qvDMirgWGMvOnwB7Vz6Mz5l8AvgI8mbLO+7PAfg3hKzLzWdXjnAQ8RDnh8x8y89yqvHFrxc8DSyPiI9XPOwHbRsQPqp8nATMi4j2Z+b2I2Iuy7vxFlGU1B2Xmsoi4lLJe/ftr99JI0sRlYi5JE89zgPmUGfF2mu+bFhFPrW6fQzn+D1OWsTTutLKqxWM1lz32c2Y+d/R2RDyZss3ihcC9mfmhFo+1HfAj4CrgWcCHq/JLgdOAU9s/JUnauE1avbr5/CFJUr+KiAHKNonvpywZ+Tzw2+ZqwOzM/FEV8wNK8v0pypru83l8xvzTwNzMnF3V/RWPJ95bA++lnCT6bh5fyrJDZs5o6NNkyoWETqMsfbmCsk3js4GPZuYNDXUnAX9NWc5yLvCXwHWUvc0voiyZeX1m3jS+V0iSJi5nzCVpYtmGsuPJ94DXApe0W8pS3Z5CSdxPzszfVHubQ0myfw+8A/hZQ/hQZu5UxZ5AeZ8Y4olLWUZ3WRkAvgzsA1wPvCozf109zrsj4gDgcxHxJMo68iWUpP0nlAsV/T4iDgf+jnKhoV9FxI+AUyPiVZn58Fq/WpI0gThjLkkTVJV0D2Tmiq6VOzxGZnZaEtMt/tnAnZ2S6IjYKTPvHG8bkrSpMDGXJEmS+sDAhu6AJEmSJBNzSZIkqS+YmEuSJEl9wMRckiRJ6gMm5pIkSVIf+P/0UMVTT6/OGgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "sns.barplot(x='店铺品类数', y='店铺数量', data=store_cnt_cate_cnt[store_cnt_cate_cnt['店铺品类数']<50], estimator=np.mean);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 店铺下商品数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>store_id</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68671</th>\n",
       "      <td>68672</td>\n",
       "      <td>2435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33381</th>\n",
       "      <td>33382</td>\n",
       "      <td>2220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17544</th>\n",
       "      <td>17545</td>\n",
       "      <td>2053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82146</th>\n",
       "      <td>82147</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       store_id  count\n",
       "0             1   6911\n",
       "68671     68672   2435\n",
       "33381     33382   2220\n",
       "17544     17545   2053\n",
       "82146     82147   2016"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_item_cnt = item.groupby(['store_id'])['item_id'].nunique().to_frame('count').reset_index()\n",
    "store_item_cnt.sort_values(by=['count'], ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "store_cnt_item_cnt = store_item_cnt.groupby(['count']).size().reset_index()\n",
    "store_cnt_item_cnt.columns = ['店铺商品数', '店铺数量']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "      <th>...</th>\n",
       "      <th>579</th>\n",
       "      <th>580</th>\n",
       "      <th>581</th>\n",
       "      <th>582</th>\n",
       "      <th>583</th>\n",
       "      <th>584</th>\n",
       "      <th>585</th>\n",
       "      <th>586</th>\n",
       "      <th>587</th>\n",
       "      <th>588</th>\n",
       "      <th>589</th>\n",
       "      <th>590</th>\n",
       "      <th>591</th>\n",
       "      <th>592</th>\n",
       "      <th>593</th>\n",
       "      <th>594</th>\n",
       "      <th>595</th>\n",
       "      <th>596</th>\n",
       "      <th>597</th>\n",
       "      <th>598</th>\n",
       "      <th>599</th>\n",
       "      <th>600</th>\n",
       "      <th>601</th>\n",
       "      <th>602</th>\n",
       "      <th>603</th>\n",
       "      <th>604</th>\n",
       "      <th>605</th>\n",
       "      <th>606</th>\n",
       "      <th>607</th>\n",
       "      <th>608</th>\n",
       "      <th>609</th>\n",
       "      <th>610</th>\n",
       "      <th>611</th>\n",
       "      <th>612</th>\n",
       "      <th>613</th>\n",
       "      <th>614</th>\n",
       "      <th>615</th>\n",
       "      <th>616</th>\n",
       "      <th>617</th>\n",
       "      <th>618</th>\n",
       "      <th>619</th>\n",
       "      <th>620</th>\n",
       "      <th>621</th>\n",
       "      <th>622</th>\n",
       "      <th>623</th>\n",
       "      <th>624</th>\n",
       "      <th>625</th>\n",
       "      <th>626</th>\n",
       "      <th>627</th>\n",
       "      <th>628</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>店铺商品数</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "      <td>33</td>\n",
       "      <td>34</td>\n",
       "      <td>35</td>\n",
       "      <td>36</td>\n",
       "      <td>37</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>40</td>\n",
       "      <td>41</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>44</td>\n",
       "      <td>45</td>\n",
       "      <td>46</td>\n",
       "      <td>47</td>\n",
       "      <td>48</td>\n",
       "      <td>49</td>\n",
       "      <td>50</td>\n",
       "      <td>...</td>\n",
       "      <td>747</td>\n",
       "      <td>765</td>\n",
       "      <td>767</td>\n",
       "      <td>771</td>\n",
       "      <td>774</td>\n",
       "      <td>782</td>\n",
       "      <td>797</td>\n",
       "      <td>809</td>\n",
       "      <td>816</td>\n",
       "      <td>834</td>\n",
       "      <td>835</td>\n",
       "      <td>841</td>\n",
       "      <td>842</td>\n",
       "      <td>845</td>\n",
       "      <td>861</td>\n",
       "      <td>871</td>\n",
       "      <td>881</td>\n",
       "      <td>897</td>\n",
       "      <td>910</td>\n",
       "      <td>915</td>\n",
       "      <td>926</td>\n",
       "      <td>955</td>\n",
       "      <td>974</td>\n",
       "      <td>975</td>\n",
       "      <td>991</td>\n",
       "      <td>999</td>\n",
       "      <td>1019</td>\n",
       "      <td>1026</td>\n",
       "      <td>1051</td>\n",
       "      <td>1081</td>\n",
       "      <td>1083</td>\n",
       "      <td>1094</td>\n",
       "      <td>1138</td>\n",
       "      <td>1145</td>\n",
       "      <td>1161</td>\n",
       "      <td>1207</td>\n",
       "      <td>1276</td>\n",
       "      <td>1307</td>\n",
       "      <td>1374</td>\n",
       "      <td>1554</td>\n",
       "      <td>1556</td>\n",
       "      <td>1558</td>\n",
       "      <td>1642</td>\n",
       "      <td>1820</td>\n",
       "      <td>1997</td>\n",
       "      <td>2016</td>\n",
       "      <td>2053</td>\n",
       "      <td>2220</td>\n",
       "      <td>2435</td>\n",
       "      <td>6911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>店铺数量</th>\n",
       "      <td>9129</td>\n",
       "      <td>6897</td>\n",
       "      <td>5593</td>\n",
       "      <td>4879</td>\n",
       "      <td>4206</td>\n",
       "      <td>3698</td>\n",
       "      <td>3374</td>\n",
       "      <td>3050</td>\n",
       "      <td>2773</td>\n",
       "      <td>2486</td>\n",
       "      <td>2251</td>\n",
       "      <td>2030</td>\n",
       "      <td>1966</td>\n",
       "      <td>1799</td>\n",
       "      <td>1673</td>\n",
       "      <td>1478</td>\n",
       "      <td>1437</td>\n",
       "      <td>1293</td>\n",
       "      <td>1246</td>\n",
       "      <td>1219</td>\n",
       "      <td>1171</td>\n",
       "      <td>1059</td>\n",
       "      <td>1044</td>\n",
       "      <td>958</td>\n",
       "      <td>910</td>\n",
       "      <td>885</td>\n",
       "      <td>772</td>\n",
       "      <td>792</td>\n",
       "      <td>709</td>\n",
       "      <td>675</td>\n",
       "      <td>656</td>\n",
       "      <td>626</td>\n",
       "      <td>596</td>\n",
       "      <td>582</td>\n",
       "      <td>538</td>\n",
       "      <td>532</td>\n",
       "      <td>479</td>\n",
       "      <td>456</td>\n",
       "      <td>496</td>\n",
       "      <td>437</td>\n",
       "      <td>441</td>\n",
       "      <td>415</td>\n",
       "      <td>401</td>\n",
       "      <td>383</td>\n",
       "      <td>374</td>\n",
       "      <td>371</td>\n",
       "      <td>330</td>\n",
       "      <td>325</td>\n",
       "      <td>335</td>\n",
       "      <td>306</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 629 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0     1     2     3     4     5     6     7     8     9     10    11   \\\n",
       "店铺商品数     1     2     3     4     5     6     7     8     9    10    11    12   \n",
       "店铺数量   9129  6897  5593  4879  4206  3698  3374  3050  2773  2486  2251  2030   \n",
       "\n",
       "        12    13    14    15    16    17    18    19    20    21    22   23   \\\n",
       "店铺商品数    13    14    15    16    17    18    19    20    21    22    23   24   \n",
       "店铺数量   1966  1799  1673  1478  1437  1293  1246  1219  1171  1059  1044  958   \n",
       "\n",
       "       24   25   26   27   28   29   30   31   32   33   34   35   36   37   \\\n",
       "店铺商品数   25   26   27   28   29   30   31   32   33   34   35   36   37   38   \n",
       "店铺数量   910  885  772  792  709  675  656  626  596  582  538  532  479  456   \n",
       "\n",
       "       38   39   40   41   42   43   44   45   46   47   48   49   ...  579  \\\n",
       "店铺商品数   39   40   41   42   43   44   45   46   47   48   49   50  ...  747   \n",
       "店铺数量   496  437  441  415  401  383  374  371  330  325  335  306  ...    1   \n",
       "\n",
       "       580  581  582  583  584  585  586  587  588  589  590  591  592  593  \\\n",
       "店铺商品数  765  767  771  774  782  797  809  816  834  835  841  842  845  861   \n",
       "店铺数量     1    1    1    1    1    2    1    1    2    1    1    1    1    1   \n",
       "\n",
       "       594  595  596  597  598  599  600  601  602  603  604   605   606  \\\n",
       "店铺商品数  871  881  897  910  915  926  955  974  975  991  999  1019  1026   \n",
       "店铺数量     1    1    1    1    1    1    1    1    1    1    1     1     1   \n",
       "\n",
       "        607   608   609   610   611   612   613   614   615   616   617   618  \\\n",
       "店铺商品数  1051  1081  1083  1094  1138  1145  1161  1207  1276  1307  1374  1554   \n",
       "店铺数量      1     1     1     1     1     1     1     1     1     1     1     1   \n",
       "\n",
       "        619   620   621   622   623   624   625   626   627   628  \n",
       "店铺商品数  1556  1558  1642  1820  1997  2016  2053  2220  2435  6911  \n",
       "店铺数量      1     1     1     1     1     1     1     1     1     1  \n",
       "\n",
       "[2 rows x 629 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store_cnt_item_cnt.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,4))\n",
    "sns.barplot(x='店铺商品数', y='店铺数量', data=store_cnt_item_cnt[store_cnt_item_cnt['店铺商品数']<80], estimator=np.mean);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### item_price 商品价格"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20230 1 1124.0030755446542 400.0\n"
     ]
    }
   ],
   "source": [
    "print(item['item_price'].max(), item['item_price'].min(), item['item_price'].mean(), item['item_price'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,4))\n",
    "plt.subplot(121)\n",
    "sns.kdeplot(item['item_price'])\n",
    "plt.subplot(122)\n",
    "sns.kdeplot(item['item_price'][item['item_price']<1000]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>item_price</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>200</td>\n",
       "      <td>75872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>500</td>\n",
       "      <td>61283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>100</td>\n",
       "      <td>33826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>400</td>\n",
       "      <td>27670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>300</td>\n",
       "      <td>26044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>199</td>\n",
       "      <td>18032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1000</td>\n",
       "      <td>16739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>700</td>\n",
       "      <td>12028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>101</td>\n",
       "      <td>11972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>121</td>\n",
       "      <td>11833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     item_price  count\n",
       "199         200  75872\n",
       "499         500  61283\n",
       "99          100  33826\n",
       "399         400  27670\n",
       "299         300  26044\n",
       "198         199  18032\n",
       "999        1000  16739\n",
       "699         700  12028\n",
       "100         101  11972\n",
       "120         121  11833"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_cnt = item.groupby(['item_price']).size().to_frame('count').reset_index()\n",
    "price_cnt.sort_values(by=['count'], ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "关于商品价格：商品价格是通过函数转化成了从1开始的整数，最大值为20230，最小值为1。\n",
    "    * 经常对商品价格统计，大部门商品都是整百数，Top5价格200\\500\\100\\400\\300\n",
    "    * TODO：整百商品探查"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 有售商品价格"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20230.0 1.0 1103.9060915681803 245.0\n",
      "19907.0 1.0 778.7395852140734 270.0\n"
     ]
    }
   ],
   "source": [
    "print(df[train]['item_price'].max(), df[train]['item_price'].min(), df[train]['item_price'].mean(), df[train]['item_price'].median())\n",
    "print(df[test]['item_price'].max(), df[test]['item_price'].min(), df[test]['item_price'].mean(), df[test]['item_price'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "sns.kdeplot(df[train][df[train]['item_price']<1000][['item_id','item_price']].drop_duplicates()['item_price'])\n",
    "sns.kdeplot(df[test][df[test]['item_price']<1000][['item_id','item_price']].drop_duplicates()['item_price']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "商品价格与销量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>商品数量</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_price</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.00</th>\n",
       "      <td>1305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.00</th>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.00</th>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.00</th>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            商品数量\n",
       "item_price      \n",
       "1.00         266\n",
       "2.00        1305\n",
       "3.00         204\n",
       "4.00         152\n",
       "5.00         133"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[train].groupby(['item_price'])['item_id'].nunique().to_frame('商品数量').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>count_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_train</th>\n",
       "      <th>buyer_country_id</th>\n",
       "      <th>item_price</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">1</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">xx</th>\n",
       "      <th>200.00</th>\n",
       "      <td>404366</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500.00</th>\n",
       "      <td>201061</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100.00</th>\n",
       "      <td>186473</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17844.00</th>\n",
       "      <td>114631</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121.00</th>\n",
       "      <td>92956</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">yy</th>\n",
       "      <th>200.00</th>\n",
       "      <td>82625</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500.00</th>\n",
       "      <td>48651</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100.00</th>\n",
       "      <td>38838</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300.00</th>\n",
       "      <td>27452</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400.00</th>\n",
       "      <td>24144</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">yy</th>\n",
       "      <th>200.00</th>\n",
       "      <td>6846</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500.00</th>\n",
       "      <td>3582</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100.00</th>\n",
       "      <td>3019</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300.00</th>\n",
       "      <td>2084</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400.00</th>\n",
       "      <td>1765</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       count  count_ratio\n",
       "is_train buyer_country_id item_price                     \n",
       "1        xx               200.00      404366         0.04\n",
       "                          500.00      201061         0.02\n",
       "                          100.00      186473         0.02\n",
       "                          17844.00    114631         0.01\n",
       "                          121.00       92956         0.01\n",
       "         yy               200.00       82625         0.04\n",
       "                          500.00       48651         0.02\n",
       "                          100.00       38838         0.02\n",
       "                          300.00       27452         0.01\n",
       "                          400.00       24144         0.01\n",
       "0        yy               200.00        6846         0.04\n",
       "                          500.00        3582         0.02\n",
       "                          100.00        3019         0.02\n",
       "                          300.00        2084         0.01\n",
       "                          400.00        1765         0.01"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_cnt = groupby_cnt_ratio(df, 'item_price')\n",
    "price_cnt.groupby(['is_train', 'buyer_country_id']).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "似乎价格与销量并无直接关系\n",
    "    * 但是价格为100、200、300、400、500整百数位居销量榜\n",
    "    * xx国，17844如此高价格的商品销量这么高？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### create_order_time 订单日期"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-07-13 05:54:54 2018-08-31 23:59:57\n",
      "2018-07-17 07:43:40 2018-08-31 23:58:56\n"
     ]
    }
   ],
   "source": [
    "print(df[train]['create_order_time'].min(), df[train]['create_order_time'].max())\n",
    "print(df[test]['create_order_time'].min(), df[test]['create_order_time'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_seven = df[train][df[train]['create_order_time']<pd.to_datetime('2018-08-01')]\n",
    "train_df_eight = df[train][df[train]['create_order_time']>pd.to_datetime('2018-08-01')]\n",
    "train_df_seven = df[train][df[train]['create_order_time']<pd.to_datetime('2018-08-01')]\n",
    "train_df_eight = df[train][df[train]['create_order_time']>pd.to_datetime('2018-08-01')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7月数据量 17856 \n",
      "8月数据量 12838990\n"
     ]
    }
   ],
   "source": [
    "print('7月数据量',len(df[train][df[train]['create_order_time']<pd.to_datetime('2018-08-01')]),\n",
    "      '\\n8月数据量',len(df[train][df[train]['create_order_time']>pd.to_datetime('2018-08-02')]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_cnt = groupby_cnt_ratio(df, 'date')\n",
    "date_cnt.columns = ['当天销量', \"占比\"]\n",
    "date_cnt = date_cnt.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 1, figsize=(16,10))\n",
    "sns.lineplot(x='date', y='当天销量', hue='buyer_country_id', data=date_cnt[(date_cnt['is_train']==1)], \n",
    "            estimator=np.mean, ax=ax[0]).set_title('训练集——每日销量');\n",
    "\n",
    "sns.lineplot(x='date', y='当天销量', hue='is_train', data=date_cnt[(date_cnt['buyer_country_id']=='yy')], \n",
    "            estimator=np.mean, ax=ax[1]).set_title('yy国每日销量');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "很明显：\n",
    "* 训练集中7月份数据远小于8月份数据\n",
    "* 训练集中xx国和yy国每日销量趋势十分相似，且在27日有个波峰"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "seven = date_cnt[date_cnt['date']<pd.to_datetime('2018-08-02')]\n",
    "eight = date_cnt[date_cnt['date']>=pd.to_datetime('2018-08-02')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1152 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 3, figsize=(20,16))\n",
    "def barplot(ax, df, title):\n",
    "    df['date'] = df['date'].astype(str)\n",
    "    sns.barplot(y='date', x='当天销量' ,data=df, order=sorted(df['date'].unique()), ax=ax, estimator=np.mean)\\\n",
    "    .set_title(title)\n",
    "    \n",
    "barplot(ax[0][0], seven[(seven['is_train']==1) & (seven['buyer_country_id']=='xx')], 'xx国7月份销量')\n",
    "barplot(ax[1][0], eight[(eight['is_train']==1) & (eight['buyer_country_id']=='xx')], 'xx国8月份销量')\n",
    "barplot(ax[0][1], seven[(seven['is_train']==1) & (seven['buyer_country_id']=='yy')], '训练集-yy国7月份销量')\n",
    "barplot(ax[1][1], eight[(eight['is_train']==1) & (eight['buyer_country_id']=='yy')], '训练集-yy国8月份销量')\n",
    "barplot(ax[0][2], seven[(seven['is_train']==0) & (seven['buyer_country_id']=='yy')], '测试集-yy国7月份销量')\n",
    "barplot(ax[1][2], eight[(eight['is_train']==0) & (eight['buyer_country_id']=='yy')], '测试集-yy国8月份销量')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据放大后看：\n",
    "* 训练集和测试集在8月份有相似的波动规律，27号出现波峰，当天剧增数据有待下一步探查"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 每日uv与商品数(去重)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique = df.groupby(['is_train', 'buyer_country_id', 'date']).agg({'buyer_admin_id':'nunique','item_id':['nunique','size']})\n",
    "unique.columns = ['uv','商品数(去重)', '销量']\n",
    "unique = unique.reset_index()\n",
    "unique = pd.melt(unique, id_vars=['is_train', 'buyer_country_id', 'date'], value_vars=['uv', '商品数(去重)', '销量'])\n",
    "unique['date'] = unique['date'].astype(str)\n",
    "unique = unique[unique['date']>='2018-08-02']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(3, 1, figsize=(16,8), sharex=True)\n",
    "sns.lineplot(x='date', y='value', hue='variable', data=unique[(unique['is_train']==1) & (unique['buyer_country_id']=='xx')], \n",
    "             estimator=np.mean, ax=ax[0]).set_title('xx国每日销售数据');\n",
    "\n",
    "sns.lineplot(x='date', y='value', hue='variable', data=unique[(unique['is_train']==0) & (unique['buyer_country_id']=='yy')], \n",
    "            estimator=np.mean, ax=ax[1]).set_title('训练集-yy国每日销量');\n",
    "\n",
    "sns.lineplot(x='date', y='value', hue='variable', data=unique[(unique['is_train']==1) & (unique['buyer_country_id']=='yy')], \n",
    "            estimator=np.mean, ax=ax[2]).set_title('测试集-yy国每日销量')\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对每日的uv、商品数和销量作图发现：\n",
    "* 三者基本上呈正相关，xx国的商品单品销量更高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BASELINE\n",
    "选取用户近30次购买记录作为预测值，越近购买的商品放在越靠前的列，不够30次购买记录的用热销商品5595070填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv('../data/Antai_AE_round1_test_20190626.csv')\n",
    "tmp = test[test['irank']<=31].sort_values(by=['buyer_country_id', 'buyer_admin_id', 'irank'])[['buyer_admin_id','item_id','irank']]\n",
    "sub = tmp.set_index(['buyer_admin_id', 'irank']).unstack(-1)\n",
    "sub.fillna(5595070).astype(int).reset_index().to_csv('../submit/sub.csv', index=False, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 最终提交文件格式\n",
    "sub = pd.read_csv('../submit/sub.csv', header = None)\n",
    "sub.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 感谢阅读\n",
    "\n",
    "本文代码已放于[github](https://github.com/RainFung/awesome-visualization/blob/master/Electronic-Commerce/%E8%B7%A8%E5%A2%83%E7%94%B5%E5%95%86%E6%99%BA%E8%83%BD%E7%AE%97%E6%B3%95%E5%A4%A7%E8%B5%9B-%E6%95%B0%E6%8D%AE%E6%8E%A2%E7%B4%A2%E4%B8%8E%E5%8F%AF%E8%A7%86%E5%8C%96.ipynb)与[阿里天池实验室]()\n",
    "\n",
    "\n",
    "欢迎关注作者专栏[数与码](https://zhuanlan.zhihu.com/shuma)，专栏将持续传播数据、算法以及编程相关知识。\n",
    "\n",
    "> [ML理论&实践](https://zhuanlan.zhihu.com/DataAI)\n",
    "\n",
    "> 微信公众号：kaggle竞赛宝典，一个用心分享算法的地方！\n",
    "\n",
    "> QQ群：机器学习kaggle阿里天池 892165076\n",
    "\n",
    "\n",
    "作者才疏学浅，文章中可能出现纰漏，忘读者多多指正交流，感谢支持"
   ]
  }
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