查询销量变化幅度大的[小店铺].ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymysql\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#连接数据库\n",
    "from sqlalchemy import create_engine  \n",
    "      \n",
    "db_info_report = {'user':'lr',  \n",
    "    'password':'hooya5000',  \n",
    "    'host':'192.168.0.239',  \n",
    "    'database':'daily_data'  # 这里我们事先指定了数据库，后续操作只需要表即可\n",
    "}  \n",
    "\n",
    "#engine = create_engine('mysql+pymysql://lr:hooya5000@192.168.0.239/daily_data',encoding='utf-8')\n",
    "      \n",
    "engine_report = create_engine('mysql+pymysql://%(user)s:%(password)s@%(host)s/%(database)s?charset=utf8' % db_info_report,encoding='utf-8')    #这里直接使用pymysql连接,echo=True，会显示在加载数据库所执行的SQL语句。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#下面获取的time period需要覆盖我们要研究的日期,数据格式为yyyy-mm-dd\n",
    "#导入数据库数据\n",
    "df = pd.read_sql_query(\"select * from mini_shop_advertisement where date between '2020-10-09'and '2020-10-14'\", con = engine_report,parse_dates = ['date'],index_col = ['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据准备工作,研究对象数据获取\n",
    "df1 = df[(df.index >= '2020-10-13') & (df.index <= '2020-10-14')]#获取我们要分析的所有asin(以会员日期间有数据的为研究对象)\n",
    "df2 = df[(df.index >= '2020-10-09') & (df.index <= '2020-10-12')]#用作对比的数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>shopname</th>\n",
       "      <th>portfolio</th>\n",
       "      <th>campaign</th>\n",
       "      <th>adgroup</th>\n",
       "      <th>sku</th>\n",
       "      <th>asin</th>\n",
       "      <th>impression</th>\n",
       "      <th>click</th>\n",
       "      <th>spend</th>\n",
       "      <th>sales</th>\n",
       "      <th>units</th>\n",
       "      <th>order</th>\n",
       "      <th>create_date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-10-09</th>\n",
       "      <td>2870</td>\n",
       "      <td>casart</td>\n",
       "      <td>Not grouped</td>\n",
       "      <td>Advertised product</td>\n",
       "      <td>Advertised product</td>\n",
       "      <td>Casart-0900-HT</td>\n",
       "      <td>B0812PM5RB</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-10-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-09</th>\n",
       "      <td>2871</td>\n",
       "      <td>casart</td>\n",
       "      <td>Not grouped</td>\n",
       "      <td>Advertised product</td>\n",
       "      <td>Advertised product</td>\n",
       "      <td>Casart-1021-HT</td>\n",
       "      <td>B08136RZMZ</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-10-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-09</th>\n",
       "      <td>2872</td>\n",
       "      <td>casart</td>\n",
       "      <td>Not grouped</td>\n",
       "      <td>Advertised product</td>\n",
       "      <td>Advertised product</td>\n",
       "      <td>Casart-10945-CL</td>\n",
       "      <td>B07ZVTLBHB</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-10-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-09</th>\n",
       "      <td>2873</td>\n",
       "      <td>casart</td>\n",
       "      <td>Not grouped</td>\n",
       "      <td>Advertised product</td>\n",
       "      <td>Advertised product</td>\n",
       "      <td>Casart-11589-CL</td>\n",
       "      <td>B081C2CMF2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-10-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-09</th>\n",
       "      <td>2874</td>\n",
       "      <td>casart</td>\n",
       "      <td>Not grouped</td>\n",
       "      <td>Advertised product</td>\n",
       "      <td>Advertised product</td>\n",
       "      <td>Casart-21559-EP</td>\n",
       "      <td>B0816NDPYV</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2020-10-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id shopname    portfolio            campaign  \\\n",
       "date                                                         \n",
       "2020-10-09  2870   casart  Not grouped  Advertised product   \n",
       "2020-10-09  2871   casart  Not grouped  Advertised product   \n",
       "2020-10-09  2872   casart  Not grouped  Advertised product   \n",
       "2020-10-09  2873   casart  Not grouped  Advertised product   \n",
       "2020-10-09  2874   casart  Not grouped  Advertised product   \n",
       "\n",
       "                       adgroup              sku        asin  impression  \\\n",
       "date                                                                      \n",
       "2020-10-09  Advertised product   Casart-0900-HT  B0812PM5RB           7   \n",
       "2020-10-09  Advertised product   Casart-1021-HT  B08136RZMZ           5   \n",
       "2020-10-09  Advertised product  Casart-10945-CL  B07ZVTLBHB           1   \n",
       "2020-10-09  Advertised product  Casart-11589-CL  B081C2CMF2           1   \n",
       "2020-10-09  Advertised product  Casart-21559-EP  B0816NDPYV          13   \n",
       "\n",
       "            click  spend  sales  units  order create_date  \n",
       "date                                                       \n",
       "2020-10-09      0      0      0      0      0  2020-10-16  \n",
       "2020-10-09      0      0      0      0      0  2020-10-16  \n",
       "2020-10-09      0      0      0      0      0  2020-10-16  \n",
       "2020-10-09      0      0      0      0      0  2020-10-16  \n",
       "2020-10-09      0      0      0      0      0  2020-10-16  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[:5]#取前5行看看dataframe长啥样子\n",
    "df2[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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'B08D3D16R1', 'B08D3GYHS6', 'B08D3K5DGN', 'B08D3DPY5G', 'B08D97V656', 'B08D987M1P', 'B08D96J681', 'B08D97D83Y', 'B08DCZMTZQ', 'B08DCGZ4GN', 'B08DCDSMQ2', 'B08DKJR6JR', 'B08DKKHSC4', 'B08DK3F6GV', 'B08DLQYVK6', 'B08DLSHJXH', 'B08DT8TDKK', 'B08F21QX8W', 'B08DXXJYCQ', 'B08F56MZ8F', 'B08F56DQ7Y', 'B08FC8RMGM', 'B08FC92J59', 'B08FFFQ3S7', 'B08FMKMVCK', 'B08FYCG2Z9', 'B08FYG6TSN', 'B08FYJ568G', 'B08FMMJL3D', 'B08FMM89CH', 'B08GQXWS95', 'B08FB4DKKY', 'B08HS1DB7H', 'B08HRTPP6B', 'B08HRN4LTP', 'B08HQSZ7VC', 'B08HGSM3XT', 'B08HGZXPJM', 'B08HGC5KCC', 'B08HRK5NF2', 'B08HRK36MY', 'B08HWWPH76', 'B08HWVXFC8', 'B08HVFB5PC', 'B08HZ24Q79', 'B08HZ1ZLRV', 'B08HRWPLZG', 'B08JCM6TLJ', 'B08J9NB4PX', 'B08JC1RGPT', 'B08JCVN5DB', 'B08JCTVTQ9', 'B08J7LR5F9', 'B08J7JDB1X', 'B08JM2472N', 'B08JM375P7', 'B08JM33YJ2', 'B08JLY2LCM', 'B08JM3C3PP', 'B08JLX4ZDB', 'B08JQ5BB3Y', 'B08JPGY8VY', 'B08JPJL919', 'B08KDL6Y5V', 'B08KDNS2YD', 'B08KCYGXGZ', 'B08KCR6Z63', 'B08KDNV7C3', 'B08KDQBS5T', 'B08K8MMMG6', 'B08K8MZPCK', 'B07ZSWYLZX', 'B08FYJHB37']\n"
     ]
    }
   ],
   "source": [
    "df1_repli=np.array(df1['asin']).tolist()#将asin列转化为list,这个list中asin有重复\n",
    "asin = []\n",
    "[asin.append(i) for i in df1_repli if not i in asin]\n",
    "print(asin)#获取到了最终的在会员日有广告的产品asin,不重复"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-7-351d543277c2>:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1['{}'.format(i)] = df1['{}'.format(i)].astype(str)\n",
      "<ipython-input-7-351d543277c2>:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df2['{}'.format(i)] = df2['{}'.format(i)].astype(str)\n",
      "<ipython-input-7-351d543277c2>:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1['{}'.format(i)] = df1['{}'.format(i)].astype(int)\n",
      "<ipython-input-7-351d543277c2>:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df2['{}'.format(i)] = df2['{}'.format(i)].astype(int)\n",
      "<ipython-input-7-351d543277c2>:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1['{}'.format(i)] = pd.to_datetime(df1['{}'.format(i)])\n",
      "<ipython-input-7-351d543277c2>:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df2['{}'.format(i)] = pd.to_datetime(df2['{}'.format(i)])\n"
     ]
    }
   ],
   "source": [
    "##转换数据格式,转换为数值型后面使用聚合函数就不会报错了\n",
    "# 归类不同字段类型\n",
    "ad_fieldtype_str = ['id','portfolio','campaign','adgroup','shopname','sku','asin']\n",
    "ad_fieldtype_int = ['impression','click','spend','sales','units','order']\n",
    "ad_fieldtype_time = ['create_date']\n",
    "\n",
    "# 修改列数据属性\n",
    "for i in ad_fieldtype_str:\n",
    "    df1['{}'.format(i)] = df1['{}'.format(i)].astype(str)\n",
    "    df2['{}'.format(i)] = df2['{}'.format(i)].astype(str)\n",
    "\n",
    "for i in ad_fieldtype_int:\n",
    "    df1['{}'.format(i)] = df1['{}'.format(i)].astype(int)\n",
    "    df2['{}'.format(i)] = df2['{}'.format(i)].astype(int)\n",
    "    \n",
    "for i in ad_fieldtype_time:\n",
    "    df1['{}'.format(i)] = pd.to_datetime(df1['{}'.format(i)])\n",
    "    df2['{}'.format(i)] = pd.to_datetime(df2['{}'.format(i)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-8-15acdfb9d6b0>:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  df_compare = df2.groupby(['asin','shopname'])['impression','click','spend','sales','units','order'].sum()\n"
     ]
    },
    {
     "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>impression</th>\n",
       "      <th>click</th>\n",
       "      <th>spend</th>\n",
       "      <th>sales</th>\n",
       "      <th>units</th>\n",
       "      <th>order</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asin</th>\n",
       "      <th>shopname</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B00THZ1PPI</th>\n",
       "      <th>adtest</th>\n",
       "      <td>2210</td>\n",
       "      <td>15</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B00WWR2CWS</th>\n",
       "      <th>adtest</th>\n",
       "      <td>6649</td>\n",
       "      <td>98</td>\n",
       "      <td>525</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B00XC4Y1OW</th>\n",
       "      <th>adtest</th>\n",
       "      <td>3272</td>\n",
       "      <td>14</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B017OWD1N4</th>\n",
       "      <th>adtest</th>\n",
       "      <td>982</td>\n",
       "      <td>7</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B0197WZR2C</th>\n",
       "      <th>adtest</th>\n",
       "      <td>5272</td>\n",
       "      <td>30</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     impression  click  spend  sales  units  order\n",
       "asin       shopname                                               \n",
       "B00THZ1PPI adtest          2210     15     90      0      0      0\n",
       "B00WWR2CWS adtest          6649     98    525      0      0      0\n",
       "B00XC4Y1OW adtest          3272     14     89      0      0      0\n",
       "B017OWD1N4 adtest           982      7     34      0      0      0\n",
       "B0197WZR2C adtest          5272     30    192      0      0      0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_compare = df2.groupby(['asin','shopname'])['impression','click','spend','sales','units','order'].sum()\n",
    "df_compare[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-9-93f0a421d086>:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  df_temp = df1.groupby(['asin','shopname'])['impression','click','spend','sales','units','order'].sum()\n"
     ]
    },
    {
     "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>impression</th>\n",
       "      <th>click</th>\n",
       "      <th>spend</th>\n",
       "      <th>sales</th>\n",
       "      <th>units</th>\n",
       "      <th>order</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asin</th>\n",
       "      <th>shopname</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B00THZ1PPI</th>\n",
       "      <th>adtest</th>\n",
       "      <td>1232</td>\n",
       "      <td>13</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B00WWR2CWS</th>\n",
       "      <th>adtest</th>\n",
       "      <td>403</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B00XC4Y1OW</th>\n",
       "      <th>adtest</th>\n",
       "      <td>1210</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>11999</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B017OWD1N4</th>\n",
       "      <th>adtest</th>\n",
       "      <td>1271</td>\n",
       "      <td>8</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B0197WZR2C</th>\n",
       "      <th>adtest</th>\n",
       "      <td>2142</td>\n",
       "      <td>9</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     impression  click  spend  sales  units  order\n",
       "asin       shopname                                               \n",
       "B00THZ1PPI adtest          1232     13     97      0      0      0\n",
       "B00WWR2CWS adtest           403      4     21      0      0      0\n",
       "B00XC4Y1OW adtest          1210      4     21  11999      1      1\n",
       "B017OWD1N4 adtest          1271      8     61      0      0      0\n",
       "B0197WZR2C adtest          2142      9     67      0      0      0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_prime = df1.groupby(['asin','shopname'])['impression','click','spend','sales','units','order'].sum()\n",
    "df_prime[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['adtest'], dtype='object', name='shopname')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_prime.index.get_level_values(\"shopname\")[df_prime.index.get_level_values(\"asin\") == 'B00XC4Y1OW']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['B00XC4Y1OW', 'B01AL363GU', 'B01FZ4Z3TI', 'B01KVJZSFQ', 'B01M15JZNB',\n",
       "       'B075YPTZT6', 'B076WTGW62', 'B078967ZF1', 'B078GL8N74', 'B07B8N254R',\n",
       "       ...\n",
       "       'B08JGBJGNQ', 'B08JGNZ32F', 'B08JM3C3PP', 'B08JV62XGM', 'B08JVD54GL',\n",
       "       'B08K8WTBX5', 'B08KG3SKJW', 'B08KTFVXC2', 'B08KTHL78K', 'B08L3BN93Q'],\n",
       "      dtype='object', name='asin', length=280)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_prime.index.get_level_values(\"asin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['B07KLQBHMX' 'B07Q5FGNY2' 'B07WDH754K' 'B07ZVC3V21' 'B081Q9P5Z4'\n",
      " 'B08G4WWL6D']\n",
      "           asin shop_name  performance impression click  spend   sales\n",
      "0    B01AL363GU   safstar     0.823529      30476   154   8609   34795\n",
      "1    B01KVJZSFQ   safstar     3.500000      86891   400  13840  134887\n",
      "2    B075YPTZT6     gymax     5.000000       6193    41    433   29495\n",
      "3    B078967ZF1    casart     2.000000       7989   110   1311    4999\n",
      "4    B07DRBQRZC     gymax     2.000000      28087    68   3939   29999\n",
      "..          ...       ...          ...        ...   ...    ...     ...\n",
      "145  B08HRSR399     gymax     2.800000     216117   987  30580  293986\n",
      "146  B08HVPJ4CR   goflame     2.000000      10113    54   1348    9999\n",
      "147  B08HWMNVN2   goflame     1.000000      62676   189   4906    9999\n",
      "148  B08JM3C3PP  honeyjoy     1.000000      18070    53   1417    8599\n",
      "149  B08JVD54GL     homgx     0.181818      44914   356   8690   11999\n",
      "\n",
      "[150 rows x 7 columns]\n",
      "           asin shop_name  performance impression click  spend   sales\n",
      "1    B01KVJZSFQ   safstar     3.500000      86891   400  13840  134887\n",
      "2    B075YPTZT6     gymax     5.000000       6193    41    433   29495\n",
      "3    B078967ZF1    casart     2.000000       7989   110   1311    4999\n",
      "4    B07DRBQRZC     gymax     2.000000      28087    68   3939   29999\n",
      "5    B07J3BRYMF   goflame     2.000000       5419    11    299    3699\n",
      "..          ...       ...          ...        ...   ...    ...     ...\n",
      "142  B08HGQ2CRM     gymax     4.333333      72736   319  23288  285987\n",
      "143  B08HMKDLW5   goflame     2.000000      21519    72   1777   11198\n",
      "144  B08HRPV2VZ   goflame     4.000000       2189    34    881   27998\n",
      "145  B08HRSR399     gymax     2.800000     216117   987  30580  293986\n",
      "146  B08HVPJ4CR   goflame     2.000000      10113    54   1348    9999\n",
      "\n",
      "[101 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "manual_needed = np.setdiff1d(df_prime.index.get_level_values(\"asin\"),df_compare.index.get_level_values(\"asin\"))#asin在df_prime.index中有在df_compare.index中却没有的\n",
    "#新建一个dataframe用来存放判断后的结果\n",
    "columns = ['asin','shop_name','performance','impression','click','spend','sales']\n",
    "df_performance = pd.DataFrame(columns = columns)\n",
    "for i in df_prime.index.get_level_values(\"asin\"):\n",
    "    shop = df_prime.index.get_level_values(\"shopname\")[df_prime.index.get_level_values(\"asin\") == i][0]\n",
    "    temp = {}\n",
    "    temp['asin'] = i\n",
    "    temp['shop_name'] = shop\n",
    "    temp['impression'] = df_prime.loc[(i,shop),'impression']\n",
    "    temp['click'] = df_prime.loc[(i,shop),'click']\n",
    "    temp['spend'] = df_prime.loc[(i,shop),'spend']\n",
    "    temp['sales'] = df_prime.loc[(i,shop),'sales']\n",
    "    #performance列的构造\n",
    "    if i in manual_needed:\n",
    "        continue\n",
    "    else:\n",
    "        if (df_compare.loc[(i,shop),'units'] >= 1):\n",
    "            temp['performance'] = (df_prime.loc[(i,shop),'units']/2) /(df_compare.loc[(i,shop),'units']/4)\n",
    "        else:\n",
    "            continue\n",
    "               \n",
    "    df_performance = df_performance.append(temp,ignore_index = True)\n",
    "    \n",
    "promising_asin = df_performance[df_performance['performance'] >= 2]\n",
    "                \n",
    "print(manual_needed)\n",
    "print(df_performance)\n",
    "print(promising_asin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>asin</th>\n",
       "      <th>shop_name</th>\n",
       "      <th>performance</th>\n",
       "      <th>impression</th>\n",
       "      <th>click</th>\n",
       "      <th>spend</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B01KVJZSFQ</td>\n",
       "      <td>safstar</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>86891</td>\n",
       "      <td>400</td>\n",
       "      <td>13840</td>\n",
       "      <td>134887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B075YPTZT6</td>\n",
       "      <td>gymax</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6193</td>\n",
       "      <td>41</td>\n",
       "      <td>433</td>\n",
       "      <td>29495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B078967ZF1</td>\n",
       "      <td>casart</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7989</td>\n",
       "      <td>110</td>\n",
       "      <td>1311</td>\n",
       "      <td>4999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B07DRBQRZC</td>\n",
       "      <td>gymax</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>28087</td>\n",
       "      <td>68</td>\n",
       "      <td>3939</td>\n",
       "      <td>29999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B07J3BRYMF</td>\n",
       "      <td>goflame</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5419</td>\n",
       "      <td>11</td>\n",
       "      <td>299</td>\n",
       "      <td>3699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>B08HGQ2CRM</td>\n",
       "      <td>gymax</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>72736</td>\n",
       "      <td>319</td>\n",
       "      <td>23288</td>\n",
       "      <td>285987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>B08HMKDLW5</td>\n",
       "      <td>goflame</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>21519</td>\n",
       "      <td>72</td>\n",
       "      <td>1777</td>\n",
       "      <td>11198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>B08HRPV2VZ</td>\n",
       "      <td>goflame</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2189</td>\n",
       "      <td>34</td>\n",
       "      <td>881</td>\n",
       "      <td>27998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>B08HRSR399</td>\n",
       "      <td>gymax</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>216117</td>\n",
       "      <td>987</td>\n",
       "      <td>30580</td>\n",
       "      <td>293986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>B08HVPJ4CR</td>\n",
       "      <td>goflame</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>10113</td>\n",
       "      <td>54</td>\n",
       "      <td>1348</td>\n",
       "      <td>9999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           asin shop_name  performance impression click  spend   sales\n",
       "1    B01KVJZSFQ   safstar     3.500000      86891   400  13840  134887\n",
       "2    B075YPTZT6     gymax     5.000000       6193    41    433   29495\n",
       "3    B078967ZF1    casart     2.000000       7989   110   1311    4999\n",
       "4    B07DRBQRZC     gymax     2.000000      28087    68   3939   29999\n",
       "5    B07J3BRYMF   goflame     2.000000       5419    11    299    3699\n",
       "..          ...       ...          ...        ...   ...    ...     ...\n",
       "142  B08HGQ2CRM     gymax     4.333333      72736   319  23288  285987\n",
       "143  B08HMKDLW5   goflame     2.000000      21519    72   1777   11198\n",
       "144  B08HRPV2VZ   goflame     4.000000       2189    34    881   27998\n",
       "145  B08HRSR399     gymax     2.800000     216117   987  30580  293986\n",
       "146  B08HVPJ4CR   goflame     2.000000      10113    54   1348    9999\n",
       "\n",
       "[101 rows x 7 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "promising_asin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           asin shop_name  performance  impression  click  spend   sales\n",
      "0    B0172W2D6M  tangkula     5.454545       99922    965  38125  326576\n",
      "1    B01M641PUR  tangkula     3.600000      136578    515  22990  241967\n",
      "2    B01MUICHSX  tangkula     3.818182       89941    343  11611   90985\n",
      "3    B01N0XMGQT  tangkula     3.882353      104837    427  32649  337778\n",
      "4    B01N25WLAG  tangkula     4.285714       24924    162   6554  108988\n",
      "..          ...       ...          ...         ...    ...    ...     ...\n",
      "306  B08HGQ2CRM     gymax     4.333333       72736    319  23288  285987\n",
      "307  B08HMKDLW5   goflame     2.000000       21519     72   1777   11198\n",
      "308  B08HRPV2VZ   goflame     4.000000        2189     34    881   27998\n",
      "309  B08HRSR399     gymax     2.800000      216117    987  30580  293986\n",
      "310  B08HVPJ4CR   goflame     2.000000       10113     54   1348    9999\n",
      "\n",
      "[311 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "with open('promising_asin.csv', 'a', encoding='utf-8',newline=\"\") as f:\n",
    "        #pd.DataFrame(columns = columns).to_csv(f, index=False) # 去除index,第二次运行的时候这一段需要注释掉\n",
    "        # 追加, 不写索引和表头\n",
    "        promising_asin.to_csv(f, mode='a+', index=False,  header=False)\n",
    "\n",
    "promising_asin_result = pd.read_csv('promising_asin.csv')\n",
    "print(promising_asin_result)\n",
    "# 关闭文件\n",
    "f.close()#只有在文件关闭状态下才不会报错"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index.get_level_values"
   ]
  }
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