{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "取订单包裹数据，计算它打单的费用，和理论最小费用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from utils.gtools import MySQLconnect\n",
    "\n",
    "# 读取需要计算的包裹信息\n",
    "with MySQLconnect('ods') as db:\n",
    "    sql = r\"\"\"  \n",
    "WITH\n",
    "t1 AS (\n",
    "SELECT\n",
    "order_id,\n",
    "spu.`产品品类`,\n",
    "spu.`产品分类`,\n",
    "SUM(as_value_amount) AS 销售额,\n",
    "sum(CASE WHEN opl.order_product_id LIKE '%\\_%' ESCAPE '\\\\' \n",
    "         AND opl.order_product_id NOT LIKE '%\\_%\\_%' ESCAPE '\\\\' THEN product_num END) AS product_num,\n",
    "order_date,\n",
    "count(opl.SKU) AS 产品数\n",
    "FROM\n",
    "dws.fact_order_product_list opl\n",
    "LEFT JOIN stg_bayshop_litfad_sku sku ON opl.SKU=sku.SKU\n",
    "LEFT JOIN stg_bayshop_litfad_spu spu ON sku.产品PID = spu.`产品PID`\n",
    "WHERE\n",
    "  NOT EXISTS (\n",
    "    SELECT 1 \n",
    "    FROM dws.log_order_reissue_detail AS r \n",
    "    WHERE r.order_product_id = opl.order_product_id\n",
    "  )\n",
    "\tAND order_date >= \"20250501\"\n",
    "AND order_date < \"20250612\"\n",
    "GROUP BY order_id\n",
    ")\n",
    "-- ,\n",
    "-- t2 AS (\n",
    "SELECT\t\t\tt1.`产品品类`,\n",
    "\t\t\t\t\t\tt1.`产品分类`,\n",
    "            t1.order_id,\n",
    "\t\t\t\t\t\tt1.销售额,\n",
    "\t\t\t\t\t\tt1.order_date,\n",
    "            a.包裹号,\n",
    "            a.快递公司,\n",
    "            a.运输方式,\n",
    "\t\t\t\t\t\ta.`目的国`,\n",
    "            d.postcode,\n",
    "            b.length AS `长`,\n",
    "            b.width AS `宽`,\n",
    "            b.hight AS `高`,\n",
    "            b.weight AS `重量`\n",
    "        FROM\n",
    "\t\t\t\tt1\n",
    "            LEFT JOIN order_express a ON t1.order_id = a.单号\n",
    "            JOIN package_vol_info b ON a.`包裹号` = b.package\n",
    "            JOIN order_list d ON a.`单号` = d.order_id \n",
    "        WHERE\n",
    "            a.`包裹状态` not IN ( '已作废') \n",
    "            AND b.hight > 0 \n",
    "            AND b.length > 0 \n",
    "            AND b.width > 0 \n",
    "            AND b.hight > 0 \n",
    "            AND b.weight > 0\n",
    "            AND a.`目的国` = \"United States\"\n",
    "AND `快递公司` NOT REGEXP \"易可达|USPS|XMILES|WWEX|美西卡派|美东卡派|大包\"\n",
    "AND `运输方式` REGEXP \"海运\"\n",
    "AND a.`投递时间` >= '2025-05-17'\n",
    "AND a.`投递时间` < '2025-06-12'\n",
    "ORDER BY order_date\n",
    "-- )\n",
    "-- SELECT\n",
    "-- `快递公司`,\n",
    "-- count(*)\n",
    "-- FROM\n",
    "-- t2\n",
    "-- GROUP BY 快递公司\n",
    "-- \n",
    "\n",
    "    \"\"\"\n",
    "    df=pd.read_sql(sql,db.con)\n",
    "    print(df)\n",
    "    df.to_clipboard(index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "计算实际物流渠道打单的预估费用，以订单为单位计算，然后价格给每个订单的每个包裹"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for index,row in df.iterrows():\n",
    "    if \"METRO-SAIR\" in row['快递公司']:\n",
    "        df.loc[index,'类型'] = \"Metro-SAIR\"\n",
    "    elif \"海MS-AMT-SAIR\" in row['快递公司']:\n",
    "        df.loc[index,'类型'] = \"AM-美西\"\n",
    "    elif \"海NY-AMT-SAIR\" in row['快递公司']:\n",
    "        df.loc[index,'类型'] = \"AM-美东\"\n",
    "    elif any(x in row['快递公司'] for x in [\"FEDEX-SAIR-G\", \"FEDEX-SAIR-H\", \"FEDEX\"]):\n",
    "        df.loc[index,'类型'] = \"快递\"\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "# 取数据源\n",
    "df = pd.read_excel(r'C:\\Users\\Admin\\Desktop\\订单利润率分析1.1.xlsx', sheet_name='包裹详情')\n",
    "df = df[df['尾端费用'] == None]\n",
    "# 快递公司映射\n",
    "company_mapping =pd.read_excel(r'C:\\Users\\Admin\\Desktop\\订单利润率分析1.1.xlsx', sheet_name='快递公司',usecols=[0, 1])\n",
    "company_mapping = dict(zip(company_mapping['快递公司'], company_mapping['company']))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.countryOperator import OperateCountry\n",
    "from utils.logisticsBill import BillFactory\n",
    "from utils.Package import Package, Package_group\n",
    "import pandas as pd\n",
    "import json\n",
    "import re\n",
    "# 美国 \n",
    "from utils.logisticsBill import Billing\n",
    "# opCountry = OperateCountry('US')\n",
    "df_grouped= df.groupby('快递跟踪号')\n",
    "for order_num, group in df_grouped:\n",
    "    postcode = group['postcode'].iloc[0]\n",
    "    if pd.isna(postcode) or str(postcode).lower() == \"nan\":\n",
    "        continue\n",
    "\n",
    "    packages= Package_group() # Metro-SAIR\n",
    "    company_name = company_mapping[group['快递公司'].iloc[0]]\n",
    "    if company_name == \"无\" or company_name == \"可补\":\n",
    "        continue\n",
    "    opCountry = OperateCountry(group['目的国'].iloc[0])\n",
    "    total_weight=0 # 按体积重分费用\n",
    "    for index,row in group.iterrows():\n",
    "        if row['length'] == 0 or row['width'] == 0 or row['hight'] == 0 or row['weight'] == 0:\n",
    "            continue\n",
    "        total_weight = row['体积重']\n",
    "        package = Package(row['包裹号'],row['length'],row['width'],row['hight'],row['weight'])\n",
    "        packages.add_package(package)\n",
    "    try:\n",
    "        bill1 = Billing(str(index),opCountry,packages,postcode,company_name=company_name,head_type=1,beizhu='1')\n",
    "        for index,row in group.iterrows():\n",
    "            propertion = row['体积重']/total_weight\n",
    "            df.loc[df['包裹号']==row['包裹号'],'尾端费用'] = bill1.tail_amount[0]*propertion\n",
    "        df.loc[df['快递跟踪号']==order_num,'账单详情'] = bill1.bill_dict()\n",
    "    except:\n",
    "        continue\n",
    "    print(index,bill1.bill_dict())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel(r'C:\\Users\\Admin\\Desktop\\订单利润率分析1.xlsx',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "计算最优渠道费用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.countryOperator import OperateCountry\n",
    "from utils.logisticsBill import BillFactory\n",
    "from utils.Package import Package, Package_group\n",
    "import pandas as pd\n",
    "import json\n",
    "import re\n",
    "# 美国 \n",
    "from utils.logisticsBill import Billing\n",
    "import requests\n",
    "opCountry = OperateCountry('US')\n",
    "for order_num, group in df_grouped:\n",
    "    postcode = group['postcode'].iloc[0]\n",
    "    if pd.isna(postcode) or str(postcode).lower() == \"nan\":\n",
    "        continue\n",
    "    \n",
    "    \n",
    "    if \"海运\" in row['运输方式']:\n",
    "        head_type = 1\n",
    "    else:\n",
    "        head_type = 0\n",
    "    # 包裹打包\n",
    "    packages = Package_group()\n",
    "    for index,row in group.iterrows():\n",
    "        package = Package(row['包裹号'],row['长'],row['宽'],row['高'],row['重量'])\n",
    "        packages.add_package(package)\n",
    "    if packages is None:\n",
    "        continue\n",
    "\n",
    "    bill = Billing(str(order_num),opCountry,packages,postcode,company_name=None,head_type=head_type,beizhu='1')\n",
    "    head_price = bill.head_amount[0]\n",
    "    tail_price = bill.tail_amount[0]\n",
    "    if \"USPS\" in row['快递公司']:\n",
    "        tail_price = tail_price/2\n",
    "    df.loc[df['order_id']==order_num,'最优尾端费用'] = tail_price/len(packages)\n",
    "    # df.loc[index,\"尾端货币\"] = bill.tail_amount[1]\n",
    "    df.loc[df['order_id']==order_num,'最优渠道'] = bill.company_name\n",
    "    print(f\"order_num:{order_num},最优尾端费用:{tail_price/len(packages)},最优渠道:{bill.company_name}\")\n",
    "df.to_clipboard(index=False)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_clipboard(index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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