{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.gtools import MySQLconnect\n",
    "import pandas as pd\n",
    "ads = MySQLconnect('ads')\n",
    "engine = ads.engine()\n",
    "conn = ads.connect()\n",
    "# sql = \"\"\"   SELECT\n",
    "#                 sku.SKU,\n",
    "#                 包裹数据,\n",
    "#                 成本价\n",
    "#             FROM\n",
    "#                 ods.`stg_bayshop_litfad_sku` sku\n",
    "#                 LEFT JOIN ads.new_erp_sku_size size ON sku.SKU=size.SKU\n",
    "# \t\t\t\t\t\t\t WHERE sku.状态 = \"启用\"\n",
    "# \t\t\t\t\t\t\t AND EXISTS (SELECT 1 FROM ods.stg_bayshop_litfad_spu s1 where s1.`产品PID` = sku.`产品PID`  AND s1.状态 = \"正常销售\" and s1.`产品分类` regexp \"light\")  AND sku.添加时间 >=\"2024-01-01\"\n",
    "# \"\"\"\n",
    "# df = pd.read_sql(sql, conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_9392\\3242304332.py:43: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql(sql, conn)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "日期:2024-01-01, 售价计算完成\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_9392\\3242304332.py:43: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql(sql, conn)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "日期:2024-02-01, 售价计算完成\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_9392\\3242304332.py:43: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql(sql, conn)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "日期:2024-03-01, 售价计算完成\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_9392\\3242304332.py:43: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql(sql, conn)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "日期:2024-04-01, 售价计算完成\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_9392\\3242304332.py:43: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql(sql, conn)\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[3], line 44\u001b[0m\n\u001b[0;32m     26\u001b[0m     sql \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\u001b[38;5;124m   SELECT\u001b[39m\n\u001b[0;32m     27\u001b[0m \u001b[38;5;124m\tsku.SKU,\u001b[39m\n\u001b[0;32m     28\u001b[0m \u001b[38;5;124m\t包裹数据,\u001b[39m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     41\u001b[0m \u001b[38;5;124m\tAND DATE_FORMAT( sku.添加时间, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m%Y-%m-01\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m )= \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdate\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     42\u001b[0m \u001b[38;5;124m    \u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[0;32m     43\u001b[0m     df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_sql(sql, conn)\n\u001b[1;32m---> 44\u001b[0m     df1 \u001b[38;5;241m=\u001b[39m cal_sell_price(df)\n\u001b[0;32m     45\u001b[0m     df1\u001b[38;5;241m.\u001b[39mto_excel(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m售价_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdate\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.xlsx\u001b[39m\u001b[38;5;124m'\u001b[39m, index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m     46\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m日期:\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdate\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, 售价计算完成\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "Cell \u001b[1;32mIn[3], line 16\u001b[0m, in \u001b[0;36mcal_sell_price\u001b[1;34m(df)\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     15\u001b[0m     package_dict \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mloads(row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m包裹数据\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m---> 16\u001b[0m     sell_price,order_price,order_type \u001b[38;5;241m=\u001b[39m call_sell_and_order_price(row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m成本价\u001b[39m\u001b[38;5;124m'\u001b[39m], package_dict)\n\u001b[0;32m     17\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     18\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrow[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSKU\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m 报错: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32md:\\test\\logistics\\sell\\sell_price.py:120\u001b[0m, in \u001b[0;36mcall_sell_and_order_price\u001b[1;34m(price, package_dict)\u001b[0m\n\u001b[0;32m    118\u001b[0m \u001b[38;5;66;03m# 修改版本\u001b[39;00m\n\u001b[0;32m    119\u001b[0m sell_price \u001b[38;5;241m=\u001b[39m litfad\u001b[38;5;241m.\u001b[39mcal_sell_price_2025()\n\u001b[1;32m--> 120\u001b[0m order_price, order_type \u001b[38;5;241m=\u001b[39m us_ocean_order_price(packages)\n\u001b[0;32m    121\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mround\u001b[39m(sell_price,\u001b[38;5;241m2\u001b[39m),order_price,order_type\n",
      "File \u001b[1;32md:\\test\\logistics\\sell\\sell_price.py:23\u001b[0m, in \u001b[0;36mus_ocean_order_price\u001b[1;34m(packages)\u001b[0m\n\u001b[0;32m     21\u001b[0m     base_fee \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m999999\u001b[39m\n\u001b[0;32m     22\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m---> 23\u001b[0m base_fee \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m df1[df1[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mg\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m lbs_weight][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m费用\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39miloc[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m     24\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m package\u001b[38;5;241m.\u001b[39mfst_size \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m116\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m package\u001b[38;5;241m.\u001b[39msed_size \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m71\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m package\u001b[38;5;241m.\u001b[39mgirth \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m251\u001b[39m:\n\u001b[0;32m     25\u001b[0m     other_fee \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m16.3\u001b[39m\n",
      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:4093\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   4091\u001b[0m \u001b[38;5;66;03m# Do we have a (boolean) 1d indexer?\u001b[39;00m\n\u001b[0;32m   4092\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m com\u001b[38;5;241m.\u001b[39mis_bool_indexer(key):\n\u001b[1;32m-> 4093\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_bool_array(key)\n\u001b[0;32m   4095\u001b[0m \u001b[38;5;66;03m# We are left with two options: a single key, and a collection of keys,\u001b[39;00m\n\u001b[0;32m   4096\u001b[0m \u001b[38;5;66;03m# We interpret tuples as collections only for non-MultiIndex\u001b[39;00m\n\u001b[0;32m   4097\u001b[0m is_single_key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_list_like(key)\n",
      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:4155\u001b[0m, in \u001b[0;36mDataFrame._getitem_bool_array\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   4152\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m   4154\u001b[0m indexer \u001b[38;5;241m=\u001b[39m key\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m-> 4155\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_take_with_is_copy(indexer, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n",
      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:4153\u001b[0m, in \u001b[0;36mNDFrame._take_with_is_copy\u001b[1;34m(self, indices, axis)\u001b[0m\n\u001b[0;32m   4142\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m   4143\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_take_with_is_copy\u001b[39m(\u001b[38;5;28mself\u001b[39m, indices, axis: Axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Self:\n\u001b[0;32m   4144\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   4145\u001b[0m \u001b[38;5;124;03m    Internal version of the `take` method that sets the `_is_copy`\u001b[39;00m\n\u001b[0;32m   4146\u001b[0m \u001b[38;5;124;03m    attribute to keep track of the parent dataframe (using in indexing\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   4151\u001b[0m \u001b[38;5;124;03m    See the docstring of `take` for full explanation of the parameters.\u001b[39;00m\n\u001b[0;32m   4152\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 4153\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(indices\u001b[38;5;241m=\u001b[39mindices, axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[0;32m   4154\u001b[0m     \u001b[38;5;66;03m# Maybe set copy if we didn't actually change the index.\u001b[39;00m\n\u001b[0;32m   4155\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m result\u001b[38;5;241m.\u001b[39m_get_axis(axis)\u001b[38;5;241m.\u001b[39mequals(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_axis(axis)):\n",
      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:4133\u001b[0m, in \u001b[0;36mNDFrame.take\u001b[1;34m(self, indices, axis, **kwargs)\u001b[0m\n\u001b[0;32m   4128\u001b[0m     \u001b[38;5;66;03m# We can get here with a slice via DataFrame.__getitem__\u001b[39;00m\n\u001b[0;32m   4129\u001b[0m     indices \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marange(\n\u001b[0;32m   4130\u001b[0m         indices\u001b[38;5;241m.\u001b[39mstart, indices\u001b[38;5;241m.\u001b[39mstop, indices\u001b[38;5;241m.\u001b[39mstep, dtype\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mintp\n\u001b[0;32m   4131\u001b[0m     )\n\u001b[1;32m-> 4133\u001b[0m new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mgr\u001b[38;5;241m.\u001b[39mtake(\n\u001b[0;32m   4134\u001b[0m     indices,\n\u001b[0;32m   4135\u001b[0m     axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_block_manager_axis(axis),\n\u001b[0;32m   4136\u001b[0m     verify\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m   4137\u001b[0m )\n\u001b[0;32m   4138\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor_from_mgr(new_data, axes\u001b[38;5;241m=\u001b[39mnew_data\u001b[38;5;241m.\u001b[39maxes)\u001b[38;5;241m.\u001b[39m__finalize__(\n\u001b[0;32m   4139\u001b[0m     \u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtake\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   4140\u001b[0m )\n",
      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:894\u001b[0m, in \u001b[0;36mBaseBlockManager.take\u001b[1;34m(self, indexer, axis, verify)\u001b[0m\n\u001b[0;32m    891\u001b[0m indexer \u001b[38;5;241m=\u001b[39m maybe_convert_indices(indexer, n, verify\u001b[38;5;241m=\u001b[39mverify)\n\u001b[0;32m    893\u001b[0m new_labels \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes[axis]\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[1;32m--> 894\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreindex_indexer(\n\u001b[0;32m    895\u001b[0m     new_axis\u001b[38;5;241m=\u001b[39mnew_labels,\n\u001b[0;32m    896\u001b[0m     indexer\u001b[38;5;241m=\u001b[39mindexer,\n\u001b[0;32m    897\u001b[0m     axis\u001b[38;5;241m=\u001b[39maxis,\n\u001b[0;32m    898\u001b[0m     allow_dups\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m    899\u001b[0m     copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m    900\u001b[0m )\n",
      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:687\u001b[0m, in \u001b[0;36mBaseBlockManager.reindex_indexer\u001b[1;34m(self, new_axis, indexer, axis, fill_value, allow_dups, copy, only_slice, use_na_proxy)\u001b[0m\n\u001b[0;32m    680\u001b[0m     new_blocks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_slice_take_blocks_ax0(\n\u001b[0;32m    681\u001b[0m         indexer,\n\u001b[0;32m    682\u001b[0m         fill_value\u001b[38;5;241m=\u001b[39mfill_value,\n\u001b[0;32m    683\u001b[0m         only_slice\u001b[38;5;241m=\u001b[39monly_slice,\n\u001b[0;32m    684\u001b[0m         use_na_proxy\u001b[38;5;241m=\u001b[39muse_na_proxy,\n\u001b[0;32m    685\u001b[0m     )\n\u001b[0;32m    686\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 687\u001b[0m     new_blocks \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m    688\u001b[0m         blk\u001b[38;5;241m.\u001b[39mtake_nd(\n\u001b[0;32m    689\u001b[0m             indexer,\n\u001b[0;32m    690\u001b[0m             axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m    691\u001b[0m             fill_value\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m    692\u001b[0m                 fill_value \u001b[38;5;28;01mif\u001b[39;00m fill_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m blk\u001b[38;5;241m.\u001b[39mfill_value\n\u001b[0;32m    693\u001b[0m             ),\n\u001b[0;32m    694\u001b[0m         )\n\u001b[0;32m    695\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m blk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblocks\n\u001b[0;32m    696\u001b[0m     ]\n\u001b[0;32m    698\u001b[0m new_axes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n\u001b[0;32m    699\u001b[0m new_axes[axis] \u001b[38;5;241m=\u001b[39m new_axis\n",
      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:688\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    680\u001b[0m     new_blocks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_slice_take_blocks_ax0(\n\u001b[0;32m    681\u001b[0m         indexer,\n\u001b[0;32m    682\u001b[0m         fill_value\u001b[38;5;241m=\u001b[39mfill_value,\n\u001b[0;32m    683\u001b[0m         only_slice\u001b[38;5;241m=\u001b[39monly_slice,\n\u001b[0;32m    684\u001b[0m         use_na_proxy\u001b[38;5;241m=\u001b[39muse_na_proxy,\n\u001b[0;32m    685\u001b[0m     )\n\u001b[0;32m    686\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    687\u001b[0m     new_blocks \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m--> 688\u001b[0m         blk\u001b[38;5;241m.\u001b[39mtake_nd(\n\u001b[0;32m    689\u001b[0m             indexer,\n\u001b[0;32m    690\u001b[0m             axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m    691\u001b[0m             fill_value\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m    692\u001b[0m                 fill_value \u001b[38;5;28;01mif\u001b[39;00m fill_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m blk\u001b[38;5;241m.\u001b[39mfill_value\n\u001b[0;32m    693\u001b[0m             ),\n\u001b[0;32m    694\u001b[0m         )\n\u001b[0;32m    695\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m blk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblocks\n\u001b[0;32m    696\u001b[0m     ]\n\u001b[0;32m    698\u001b[0m new_axes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n\u001b[0;32m    699\u001b[0m new_axes[axis] \u001b[38;5;241m=\u001b[39m new_axis\n",
      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py:1307\u001b[0m, in \u001b[0;36mBlock.take_nd\u001b[1;34m(self, indexer, axis, new_mgr_locs, fill_value)\u001b[0m\n\u001b[0;32m   1304\u001b[0m     allow_fill \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m   1306\u001b[0m \u001b[38;5;66;03m# Note: algos.take_nd has upcast logic similar to coerce_to_target_dtype\u001b[39;00m\n\u001b[1;32m-> 1307\u001b[0m new_values \u001b[38;5;241m=\u001b[39m algos\u001b[38;5;241m.\u001b[39mtake_nd(\n\u001b[0;32m   1308\u001b[0m     values, indexer, axis\u001b[38;5;241m=\u001b[39maxis, allow_fill\u001b[38;5;241m=\u001b[39mallow_fill, fill_value\u001b[38;5;241m=\u001b[39mfill_value\n\u001b[0;32m   1309\u001b[0m )\n\u001b[0;32m   1311\u001b[0m \u001b[38;5;66;03m# Called from three places in managers, all of which satisfy\u001b[39;00m\n\u001b[0;32m   1312\u001b[0m \u001b[38;5;66;03m#  these assertions\u001b[39;00m\n\u001b[0;32m   1313\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m, ExtensionBlock):\n\u001b[0;32m   1314\u001b[0m     \u001b[38;5;66;03m# NB: in this case, the 'axis' kwarg will be ignored in the\u001b[39;00m\n\u001b[0;32m   1315\u001b[0m     \u001b[38;5;66;03m#  algos.take_nd call above.\u001b[39;00m\n",
      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\array_algos\\take.py:117\u001b[0m, in \u001b[0;36mtake_nd\u001b[1;34m(arr, indexer, axis, fill_value, allow_fill)\u001b[0m\n\u001b[0;32m    114\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mtake(indexer, fill_value\u001b[38;5;241m=\u001b[39mfill_value, allow_fill\u001b[38;5;241m=\u001b[39mallow_fill)\n\u001b[0;32m    116\u001b[0m arr \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(arr)\n\u001b[1;32m--> 117\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _take_nd_ndarray(arr, indexer, axis, fill_value, allow_fill)\n",
      "File \u001b[1;32mc:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\array_algos\\take.py:162\u001b[0m, in \u001b[0;36m_take_nd_ndarray\u001b[1;34m(arr, indexer, axis, fill_value, allow_fill)\u001b[0m\n\u001b[0;32m    157\u001b[0m     out \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mempty(out_shape, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[0;32m    159\u001b[0m func \u001b[38;5;241m=\u001b[39m _get_take_nd_function(\n\u001b[0;32m    160\u001b[0m     arr\u001b[38;5;241m.\u001b[39mndim, arr\u001b[38;5;241m.\u001b[39mdtype, out\u001b[38;5;241m.\u001b[39mdtype, axis\u001b[38;5;241m=\u001b[39maxis, mask_info\u001b[38;5;241m=\u001b[39mmask_info\n\u001b[0;32m    161\u001b[0m )\n\u001b[1;32m--> 162\u001b[0m func(arr, indexer, out, fill_value)\n\u001b[0;32m    164\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m flip_order:\n\u001b[0;32m    165\u001b[0m     out \u001b[38;5;241m=\u001b[39m out\u001b[38;5;241m.\u001b[39mT\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# 计算售价\n",
    "\n",
    "\n",
    "from sell.sell_price import call_sell_and_order_price\n",
    "import json\n",
    "import pandas as pd\n",
    "DATE_LIST =     [\"2024-01-01\",\"2024-02-01\",\"2024-03-01\",\"2024-04-01\",\"2024-05-01\",\"2024-06-01\",\"2024-07-01\",\"2024-08-01\",\"2024-09-01\",\"2024-10-01\",\"2024-11-01\",\"2024-12-01\",\"2025-01-01\",\"2025-02-01\",\"2025-03-01\"]\n",
    "\n",
    "def cal_sell_price(df):\n",
    "    \"\"\"\n",
    "    计算所有SKU的售价,物流分摊费\n",
    "    \"\"\"\n",
    "    for index, row in df.iterrows():\n",
    "        try:\n",
    "            package_dict = json.loads(row['包裹数据'])\n",
    "            sell_price,order_price,order_type = call_sell_and_order_price(row['成本价'], package_dict)\n",
    "        except Exception as e:\n",
    "            print(f\" {row['SKU']} 报错: {e}\")\n",
    "            continue\n",
    "        df.loc[index, '售价'] = sell_price\n",
    "        df.loc[index, '物流分摊费'] = order_price\n",
    "        df.loc[index, '物流类型'] = order_type\n",
    "        # print(f\"sku:{row['SKU']},售价:{sell_price},物流分摊费:{order_price},物流类型:{order_type}\")\n",
    "    return df\n",
    "for date in DATE_LIST:\n",
    "    sql = f\"\"\"   SELECT\n",
    "\tsku.SKU,\n",
    "\t包裹数据,\n",
    "\t成本价,\n",
    "\t产品售价,\n",
    "\t`产品品类`,\n",
    "\t`产品分类` \n",
    "FROM\n",
    "\tods.`stg_bayshop_litfad_sku` sku\n",
    "\tLEFT JOIN ads.new_erp_sku_size size ON sku.SKU = size.SKU \n",
    "\tLEFT JOIN ods.stg_bayshop_litfad_spu spu ON sku.`产品PID` =spu.`产品PID`\n",
    "WHERE\n",
    "\tsku.状态 = \"启用\" \n",
    "\tAND spu.`产品品类` REGEXP \"66\"\n",
    "\tAND spu.`状态` =\"正常销售\"\n",
    "\tAND DATE_FORMAT( sku.添加时间, \"%Y-%m-01\" )= \"{date}\"\n",
    "    \"\"\"\n",
    "    df = pd.read_sql(sql, conn)\n",
    "    df1 = cal_sell_price(df)\n",
    "    df1.to_excel(f'售价_{date}.xlsx', index=False)\n",
    "    print(f\"日期:{date}, 售价计算完成\")\n",
    "\n"
   ]
  }
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