div.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer = pd.read_csv('./中间数据/商品分层.csv')\n",
    "predict = pd.read_csv('./示例数据/predict_sku1.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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_code</th>\n",
       "      <th>item_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20003</td>\n",
       "      <td>常规品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20028</td>\n",
       "      <td>睡眠品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20085</td>\n",
       "      <td>常规品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20219</td>\n",
       "      <td>睡眠品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20316</td>\n",
       "      <td>常规品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1748</th>\n",
       "      <td>21192</td>\n",
       "      <td>常规品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1749</th>\n",
       "      <td>20225</td>\n",
       "      <td>常规品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1750</th>\n",
       "      <td>20245</td>\n",
       "      <td>常规品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1751</th>\n",
       "      <td>21793</td>\n",
       "      <td>常规品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1752</th>\n",
       "      <td>20235</td>\n",
       "      <td>常规品</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1753 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      item_code item_type\n",
       "0         20003       常规品\n",
       "1         20028       睡眠品\n",
       "2         20085       常规品\n",
       "3         20219       睡眠品\n",
       "4         20316       常规品\n",
       "...         ...       ...\n",
       "1748      21192       常规品\n",
       "1749      20225       常规品\n",
       "1750      20245       常规品\n",
       "1751      21793       常规品\n",
       "1752      20235       常规品\n",
       "\n",
       "[1753 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>sales_region_code</th>\n",
       "      <th>item_code</th>\n",
       "      <th>first_cate_code</th>\n",
       "      <th>second_cate_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101</td>\n",
       "      <td>20002</td>\n",
       "      <td>303</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101</td>\n",
       "      <td>20003</td>\n",
       "      <td>301</td>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101</td>\n",
       "      <td>20006</td>\n",
       "      <td>307</td>\n",
       "      <td>403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101</td>\n",
       "      <td>20011</td>\n",
       "      <td>303</td>\n",
       "      <td>401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101</td>\n",
       "      <td>20014</td>\n",
       "      <td>307</td>\n",
       "      <td>403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2614</th>\n",
       "      <td>105</td>\n",
       "      <td>22066</td>\n",
       "      <td>307</td>\n",
       "      <td>403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2615</th>\n",
       "      <td>105</td>\n",
       "      <td>22072</td>\n",
       "      <td>305</td>\n",
       "      <td>412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2616</th>\n",
       "      <td>105</td>\n",
       "      <td>22075</td>\n",
       "      <td>307</td>\n",
       "      <td>403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2617</th>\n",
       "      <td>105</td>\n",
       "      <td>22083</td>\n",
       "      <td>303</td>\n",
       "      <td>401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2618</th>\n",
       "      <td>105</td>\n",
       "      <td>22084</td>\n",
       "      <td>302</td>\n",
       "      <td>408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2619 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      sales_region_code  item_code  first_cate_code  second_cate_code\n",
       "0                   101      20002              303               406\n",
       "1                   101      20003              301               405\n",
       "2                   101      20006              307               403\n",
       "3                   101      20011              303               401\n",
       "4                   101      20014              307               403\n",
       "...                 ...        ...              ...               ...\n",
       "2614                105      22066              307               403\n",
       "2615                105      22072              305               412\n",
       "2616                105      22075              307               403\n",
       "2617                105      22083              303               401\n",
       "2618                105      22084              302               408\n",
       "\n",
       "[2619 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sales_region_code', 'item_code', 'first_cate_code', 'second_cate_code',\n",
       "       'item_type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict = predict.merge(layer, on='item_code', how='left')\n",
    "predict.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['常规品', nan, '睡眠品', '新品', '流星品'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict['item_type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict['item_type'].fillna('新品', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>sales_region_code</th>\n",
       "      <th>item_code</th>\n",
       "      <th>first_cate_code</th>\n",
       "      <th>second_cate_code</th>\n",
       "      <th>item_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101</td>\n",
       "      <td>20002</td>\n",
       "      <td>303</td>\n",
       "      <td>406</td>\n",
       "      <td>常规品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101</td>\n",
       "      <td>20003</td>\n",
       "      <td>301</td>\n",
       "      <td>405</td>\n",
       "      <td>常规品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101</td>\n",
       "      <td>20006</td>\n",
       "      <td>307</td>\n",
       "      <td>403</td>\n",
       "      <td>常规品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101</td>\n",
       "      <td>20011</td>\n",
       "      <td>303</td>\n",
       "      <td>401</td>\n",
       "      <td>新品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101</td>\n",
       "      <td>20014</td>\n",
       "      <td>307</td>\n",
       "      <td>403</td>\n",
       "      <td>常规品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2614</th>\n",
       "      <td>105</td>\n",
       "      <td>22066</td>\n",
       "      <td>307</td>\n",
       "      <td>403</td>\n",
       "      <td>常规品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2615</th>\n",
       "      <td>105</td>\n",
       "      <td>22072</td>\n",
       "      <td>305</td>\n",
       "      <td>412</td>\n",
       "      <td>新品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2616</th>\n",
       "      <td>105</td>\n",
       "      <td>22075</td>\n",
       "      <td>307</td>\n",
       "      <td>403</td>\n",
       "      <td>新品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2617</th>\n",
       "      <td>105</td>\n",
       "      <td>22083</td>\n",
       "      <td>303</td>\n",
       "      <td>401</td>\n",
       "      <td>常规品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2618</th>\n",
       "      <td>105</td>\n",
       "      <td>22084</td>\n",
       "      <td>302</td>\n",
       "      <td>408</td>\n",
       "      <td>常规品</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2619 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      sales_region_code  item_code  first_cate_code  second_cate_code  \\\n",
       "0                   101      20002              303               406   \n",
       "1                   101      20003              301               405   \n",
       "2                   101      20006              307               403   \n",
       "3                   101      20011              303               401   \n",
       "4                   101      20014              307               403   \n",
       "...                 ...        ...              ...               ...   \n",
       "2614                105      22066              307               403   \n",
       "2615                105      22072              305               412   \n",
       "2616                105      22075              307               403   \n",
       "2617                105      22083              303               401   \n",
       "2618                105      22084              302               408   \n",
       "\n",
       "     item_type  \n",
       "0          常规品  \n",
       "1          常规品  \n",
       "2          常规品  \n",
       "3           新品  \n",
       "4          常规品  \n",
       "...        ...  \n",
       "2614       常规品  \n",
       "2615        新品  \n",
       "2616        新品  \n",
       "2617       常规品  \n",
       "2618       常规品  \n",
       "\n",
       "[2619 rows x 5 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(predict_sleep[\"item_code\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "401 252\n",
      "402 25\n",
      "403 309\n",
      "404 293\n",
      "405 74\n",
      "406 41\n",
      "407 649\n",
      "408 405\n",
      "409 128\n",
      "410 78\n",
      "411 20\n",
      "412 345\n",
      "2619\n"
     ]
    }
   ],
   "source": [
    "second = sorted(predict.second_cate_code.unique())\n",
    "sum = 0\n",
    "for i in second:\n",
    "    print(i, end=\" \")\n",
    "    print(predict[predict['second_cate_code']==i].shape[0])\n",
    "    sum += predict[predict['second_cate_code']==i].shape[0]\n",
    "print(sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412]\n",
      "401 57\n",
      "402 7\n",
      "403 121\n",
      "404 116\n",
      "405 9\n",
      "406 2\n",
      "407 115\n",
      "408 100\n",
      "409 28\n",
      "410 9\n",
      "411 0\n",
      "412 102\n",
      "666\n"
     ]
    }
   ],
   "source": [
    "predict_new = predict[predict['item_type']==\"新品\"]\n",
    "# predict_new\n",
    "print(second)\n",
    "sum = 0\n",
    "for i in second:\n",
    "    print(i, end=\" \")\n",
    "    print(predict_new[predict_new['second_cate_code']==i].shape[0])\n",
    "    sum += predict_new[predict_new['second_cate_code']==i].shape[0]\n",
    "print(sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_normal = predict[predict['item_type']==\"常规品\"]\n",
    "predict_normal.to_csv('./中间数据/normal_product.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1853"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(predict_normal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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",
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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>sales_region_code</th>\n",
       "      <th>item_code</th>\n",
       "      <th>first_cate_code</th>\n",
       "      <th>second_cate_code</th>\n",
       "      <th>item_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>102</td>\n",
       "      <td>20377</td>\n",
       "      <td>303</td>\n",
       "      <td>410</td>\n",
       "      <td>流星品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1064</th>\n",
       "      <td>102</td>\n",
       "      <td>21289</td>\n",
       "      <td>303</td>\n",
       "      <td>401</td>\n",
       "      <td>流星品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1828</th>\n",
       "      <td>103</td>\n",
       "      <td>21721</td>\n",
       "      <td>302</td>\n",
       "      <td>408</td>\n",
       "      <td>流星品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2060</th>\n",
       "      <td>105</td>\n",
       "      <td>20377</td>\n",
       "      <td>303</td>\n",
       "      <td>410</td>\n",
       "      <td>流星品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2284</th>\n",
       "      <td>105</td>\n",
       "      <td>21011</td>\n",
       "      <td>302</td>\n",
       "      <td>408</td>\n",
       "      <td>流星品</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      sales_region_code  item_code  first_cate_code  second_cate_code  \\\n",
       "770                 102      20377              303               410   \n",
       "1064                102      21289              303               401   \n",
       "1828                103      21721              302               408   \n",
       "2060                105      20377              303               410   \n",
       "2284                105      21011              302               408   \n",
       "\n",
       "     item_type  \n",
       "770        流星品  \n",
       "1064       流星品  \n",
       "1828       流星品  \n",
       "2060       流星品  \n",
       "2284       流星品  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_start= predict[predict['item_type']==\"流星品\"]\n",
    "predict_start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>sales_region_code</th>\n",
       "      <th>item_code</th>\n",
       "      <th>first_cate_code</th>\n",
       "      <th>second_cate_code</th>\n",
       "      <th>item_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>101</td>\n",
       "      <td>20021</td>\n",
       "      <td>305</td>\n",
       "      <td>412</td>\n",
       "      <td>睡眠品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>101</td>\n",
       "      <td>20163</td>\n",
       "      <td>302</td>\n",
       "      <td>408</td>\n",
       "      <td>睡眠品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>101</td>\n",
       "      <td>20247</td>\n",
       "      <td>306</td>\n",
       "      <td>407</td>\n",
       "      <td>睡眠品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>101</td>\n",
       "      <td>20360</td>\n",
       "      <td>303</td>\n",
       "      <td>401</td>\n",
       "      <td>睡眠品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>101</td>\n",
       "      <td>20370</td>\n",
       "      <td>305</td>\n",
       "      <td>412</td>\n",
       "      <td>睡眠品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2475</th>\n",
       "      <td>105</td>\n",
       "      <td>21612</td>\n",
       "      <td>304</td>\n",
       "      <td>409</td>\n",
       "      <td>睡眠品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2494</th>\n",
       "      <td>105</td>\n",
       "      <td>21664</td>\n",
       "      <td>307</td>\n",
       "      <td>403</td>\n",
       "      <td>睡眠品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2543</th>\n",
       "      <td>105</td>\n",
       "      <td>21828</td>\n",
       "      <td>304</td>\n",
       "      <td>409</td>\n",
       "      <td>睡眠品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2547</th>\n",
       "      <td>105</td>\n",
       "      <td>21840</td>\n",
       "      <td>302</td>\n",
       "      <td>408</td>\n",
       "      <td>睡眠品</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2609</th>\n",
       "      <td>105</td>\n",
       "      <td>22050</td>\n",
       "      <td>308</td>\n",
       "      <td>404</td>\n",
       "      <td>睡眠品</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      sales_region_code  item_code  first_cate_code  second_cate_code  \\\n",
       "8                   101      20021              305               412   \n",
       "46                  101      20163              302               408   \n",
       "73                  101      20247              306               407   \n",
       "117                 101      20360              303               401   \n",
       "120                 101      20370              305               412   \n",
       "...                 ...        ...              ...               ...   \n",
       "2475                105      21612              304               409   \n",
       "2494                105      21664              307               403   \n",
       "2543                105      21828              304               409   \n",
       "2547                105      21840              302               408   \n",
       "2609                105      22050              308               404   \n",
       "\n",
       "     item_type  \n",
       "8          睡眠品  \n",
       "46         睡眠品  \n",
       "73         睡眠品  \n",
       "117        睡眠品  \n",
       "120        睡眠品  \n",
       "...        ...  \n",
       "2475       睡眠品  \n",
       "2494       睡眠品  \n",
       "2543       睡眠品  \n",
       "2547       睡眠品  \n",
       "2609       睡眠品  \n",
       "\n",
       "[95 rows x 5 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_sleep = predict[predict['item_type']==\"睡眠品\"]\n",
    "predict_sleep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Column not found: sales_quantity'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-6282051ec434>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'item_type'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sales_region_code'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sales_quantity'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/groupby/generic.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1540\u001b[0m                 \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1541\u001b[0m             )\n\u001b[0;32m-> 1542\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1543\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1544\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_gotitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    278\u001b[0m             \u001b[0;31m# error: \"SelectionMixin\" has no attribute \"obj\"  [attr-defined]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# type: ignore[attr-defined]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Column not found: {key}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gotitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Column not found: sales_quantity'"
     ]
    }
   ],
   "source": [
    "result = predict.groupby(['item_type', 'sales_region_code'])['sales_quantity'].sum()"
   ]
  }
 ],
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