{ "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": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_31460\\3638385460.py:76: 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,db.con)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 产品品类 产品分类 \\\n", "0 126 - Outdoor 318 - Garage Storage Cabinets & Shelves \n", "1 66 - Furniture 90 - Coffee Tables \n", "2 66 - Furniture 90 - Coffee Tables \n", "3 66 - Furniture 106 - Makeup Vanities \n", "4 66 - Furniture 106 - Makeup Vanities \n", "... ... ... \n", "9176 1 - Lighting 26 - Table Lamps \n", "9177 66 - Furniture 105 - Hall Trees & Coat Racks \n", "9178 116 - Home Improvement 138 - Bathroom Sinks \n", "9179 1 - Lighting 19 - Flush Mount Light \n", "9180 None None \n", "\n", " order_id 销售额 order_date 包裹号 快递公司 运输方式 \\\n", "0 250501075201987 168.97 20250501 991484907 海MS-FEDEX-SAIR-H 海运 \n", "1 250501100002104 58.60 20250501 991487001 海MS-FEDEX-SAIR-H 海运 \n", "2 250501070802260 141.01 20250501 991491595 海MS-FEDEX-SAIR-H 海运 \n", "3 250501045815214 550.11 20250501 991492131 海MS-FEDEX-SAIR-H 海运 \n", "4 250501045815214 550.11 20250501 991492132 海MS-FEDEX-SAIR-H 海运 \n", "... ... ... ... ... ... ... \n", "9176 250610091014771 183.59 20250610 991492896 海MS-FEDEX-SAIR-H 海运 \n", "9177 250610152202124 318.40 20250610 991493003 海MS-FEDEX-SAIR-H 海运 \n", "9178 250610092801988 142.77 20250610 991493018 海MS-FEDEX-SAIR-G 海运 \n", "9179 250610064407414 112.63 20250610 991493087 海MS-FEDEX-SAIR-H 海运 \n", "9180 250610081005524 170.15 20250610 991493165 海MS-FEDEX-SAIR-H 海运 \n", "\n", " 目的国 postcode 长 宽 高 重量 \n", "0 United States 80601 100.0 50.0 22.0 11000 \n", "1 United States 19355 83.0 46.0 6.0 3000 \n", "2 United States 89052 141.0 67.0 2.0 6000 \n", "3 United States 32832 170.0 52.0 18.0 57000 \n", "4 United States 32832 130.0 49.0 15.0 21000 \n", "... ... ... ... ... ... ... \n", "9176 United States 95621 46.0 38.0 25.0 2780 \n", "9177 United States 94555 80.0 34.0 26.0 5000 \n", "9178 United States 85213 58.0 50.0 24.0 8000 \n", "9179 United States 75472-6121 31.0 31.0 13.0 1580 \n", "9180 United States 06468-1126 34.0 31.0 23.0 1800 \n", "\n", "[9181 rows x 14 columns]\n" ] } ], "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": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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产品品类产品分类order_id销售额order_date包裹号快递公司运输方式目的国postcode长宽高重量类型
0126 - Outdoor318 - Garage Storage Cabinets & Shelves250501075201987168.9720250501991484907海MS-FEDEX-SAIR-H海运United States80601100.050.022.011000快递
166 - Furniture90 - Coffee Tables25050110000210458.6020250501991487001海MS-FEDEX-SAIR-H海运United States1935583.046.06.03000快递
266 - Furniture90 - Coffee Tables250501070802260141.0120250501991491595海MS-FEDEX-SAIR-H海运United States89052141.067.02.06000快递
366 - Furniture106 - Makeup Vanities250501045815214550.1120250501991492131海MS-FEDEX-SAIR-H海运United States32832170.052.018.057000快递
466 - Furniture106 - Makeup Vanities250501045815214550.1120250501991492132海MS-FEDEX-SAIR-H海运United States32832130.049.015.021000快递
................................................
91761 - Lighting26 - Table Lamps250610091014771183.5920250610991492896海MS-FEDEX-SAIR-H海运United States9562146.038.025.02780快递
917766 - Furniture105 - Hall Trees & Coat Racks250610152202124318.4020250610991493003海MS-FEDEX-SAIR-H海运United States9455580.034.026.05000快递
9178116 - Home Improvement138 - Bathroom Sinks250610092801988142.7720250610991493018海MS-FEDEX-SAIR-G海运United States8521358.050.024.08000快递
91791 - Lighting19 - Flush Mount Light250610064407414112.6320250610991493087海MS-FEDEX-SAIR-H海运United States75472-612131.031.013.01580快递
9180NoneNone250610081005524170.1520250610991493165海MS-FEDEX-SAIR-H海运United States06468-112634.031.023.01800快递
\n", "

9181 rows × 15 columns

\n", "
" ], "text/plain": [ " 产品品类 产品分类 \\\n", "0 126 - Outdoor 318 - Garage Storage Cabinets & Shelves \n", "1 66 - Furniture 90 - Coffee Tables \n", "2 66 - Furniture 90 - Coffee Tables \n", "3 66 - Furniture 106 - Makeup Vanities \n", "4 66 - Furniture 106 - Makeup Vanities \n", "... ... ... \n", "9176 1 - Lighting 26 - Table Lamps \n", "9177 66 - Furniture 105 - Hall Trees & Coat Racks \n", "9178 116 - Home Improvement 138 - Bathroom Sinks \n", "9179 1 - Lighting 19 - Flush Mount Light \n", "9180 None None \n", "\n", " order_id 销售额 order_date 包裹号 快递公司 运输方式 \\\n", "0 250501075201987 168.97 20250501 991484907 海MS-FEDEX-SAIR-H 海运 \n", "1 250501100002104 58.60 20250501 991487001 海MS-FEDEX-SAIR-H 海运 \n", "2 250501070802260 141.01 20250501 991491595 海MS-FEDEX-SAIR-H 海运 \n", "3 250501045815214 550.11 20250501 991492131 海MS-FEDEX-SAIR-H 海运 \n", "4 250501045815214 550.11 20250501 991492132 海MS-FEDEX-SAIR-H 海运 \n", "... ... ... ... ... ... ... \n", "9176 250610091014771 183.59 20250610 991492896 海MS-FEDEX-SAIR-H 海运 \n", "9177 250610152202124 318.40 20250610 991493003 海MS-FEDEX-SAIR-H 海运 \n", "9178 250610092801988 142.77 20250610 991493018 海MS-FEDEX-SAIR-G 海运 \n", "9179 250610064407414 112.63 20250610 991493087 海MS-FEDEX-SAIR-H 海运 \n", "9180 250610081005524 170.15 20250610 991493165 海MS-FEDEX-SAIR-H 海运 \n", "\n", " 目的国 postcode 长 宽 高 重量 类型 \n", "0 United States 80601 100.0 50.0 22.0 11000 快递 \n", "1 United States 19355 83.0 46.0 6.0 3000 快递 \n", "2 United States 89052 141.0 67.0 2.0 6000 快递 \n", "3 United States 32832 170.0 52.0 18.0 57000 快递 \n", "4 United States 32832 130.0 49.0 15.0 21000 快递 \n", "... ... ... ... ... ... ... .. \n", "9176 United States 95621 46.0 38.0 25.0 2780 快递 \n", "9177 United States 94555 80.0 34.0 26.0 5000 快递 \n", "9178 United States 85213 58.0 50.0 24.0 8000 快递 \n", "9179 United States 75472-6121 31.0 31.0 13.0 1580 快递 \n", "9180 United States 06468-1126 34.0 31.0 23.0 1800 快递 \n", "\n", "[9181 rows x 15 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "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": 3, "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", "df_grouped= df.groupby('order_id')\n", "for order_num, group in df_grouped:\n", " order_num = str(int(order_num))\n", " postcode = group['postcode'].iloc[0]\n", " if pd.isna(postcode) or str(postcode).lower() == \"nan\":\n", " continue\n", " if \"海运\" in row['运输方式']:\n", " head_type = 1\n", " else:\n", " head_type = 0\n", "\n", " packages1= Package_group() # Metro-SAIR\n", " packages2= Package_group() # AM-美西\n", " packages3= Package_group() # AM-美东\n", "\n", " # 记录包裹号\n", " packages_id1=[]\n", " packages_id2=[]\n", " packages_id3=[]\n", "\n", " for index,row in group.iterrows():\n", " package = Package(row['包裹号'],row['长'],row['宽'],row['高'],row['重量'])\n", "\n", " if \"FEDEX-SAIR-G\" in row['快递公司']:\n", " company_name = \"Fedex-GROUD\"\n", " elif \"FEDEX-SAIR-H\" in row['快递公司']:\n", " company_name = \"Fedex-HOME\"\n", " # elif \"METRO\" in row['快递公司']:\n", " # company_name = \"Metro-SAIR\"\n", " # elif \"海MS-AMT-SAIR\" in row['快递公司']:\n", " # company_name = \"AM-美西\"\n", " # elif \"海NY-AMT-SAIR\" in row['快递公司']:\n", " # company_name = \"AM-美东\"\n", " # elif \"FEDEX02\" in row['快递公司']:\n", " # company_name = \"Fedex-彩虹小马\"\n", " elif \"大包\" in row['快递公司'] or row['快递公司'] == '海MS-FEDEX':\n", " company_name = \"Fedex-HOME\"\n", " # elif \"GIGA\" in row['快递公司']:\n", " # company_name = \"大健-GIGA\"\n", " # elif \"CEVA\" in row['快递公司']:\n", " # company_name = \"大健-CEVA\"\n", " elif \"USPS\" in row['快递公司']:\n", " company_name = \"Fedex-HOME\"\n", " # else:\n", " # company_name = \"大健-Metro\"\n", " if row['类型'] == \"快递\":\n", " packages= Package_group()\n", " packages.add_package(package)\n", " bill = Billing(str(index),opCountry,packages,postcode,company_name=company_name,head_type=head_type,beizhu='1')\n", " if \"USPS\" in row['快递公司']:\n", " tail_price = tail_price/2\n", " df.loc[index,'尾端费用'] = bill.tail_amount[0]\n", " df.loc[index,\"尾端货币\"] = bill.tail_amount[1]\n", " df.loc[index,'尾端渠道'] = bill.company_name\n", " elif row['类型'] == \"Metro-SAIR\":\n", " packages1.add_package(package)\n", " packages_id1.append(row['包裹号'])\n", " elif row['类型'] == \"AM-美西\":\n", " packages2.add_package(package)\n", " packages_id2.append(row['包裹号'])\n", " elif row['类型'] == \"AM-美东\":\n", " packages3.add_package(package)\n", " packages_id3.append(row['包裹号'])\n", " else:\n", " continue\n", " if len(packages1)>0:\n", " bill1 = Billing(str(index),opCountry,packages1,postcode,company_name=\"Metro-SAIR\",head_type=head_type,beizhu='1')\n", " for package_id in packages_id1:\n", " df.loc[df['包裹号']==package_id,'尾端费用'] = bill1.tail_amount[0]/len(packages1)\n", " df.loc[df['包裹号']==package_id,'尾端货币'] = bill1.tail_amount[1]\n", " df.loc[df['包裹号']==package_id,'尾端渠道'] = bill1.company_name\n", " if len(packages2)>0:\n", " bill2 = Billing(str(index),opCountry,packages2,postcode,company_name=\"AM-美西\",head_type=head_type,beizhu='1')\n", " for package_id in packages_id2:\n", " df.loc[df['包裹号']==package_id,'尾端费用'] = bill2.tail_amount[0]/len(packages2)\n", " df.loc[df['包裹号']==package_id,'尾端货币'] = bill2.tail_amount[1]\n", " df.loc[df['包裹号']==package_id,'尾端渠道'] = bill2.company_name\n", " if len(packages3)>0:\n", " bill3 = Billing(str(index),opCountry,packages3,postcode,company_name=\"AM-美东\",head_type=head_type,beizhu='1')\n", " for package_id in packages_id3:\n", " df.loc[df['包裹号']==package_id,'尾端费用'] = bill3.tail_amount[0]/len(packages3)\n", " df.loc[df['包裹号']==package_id,'尾端货币'] = bill3.tail_amount[1]\n", " df.loc[df['包裹号']==package_id,'尾端渠道'] = bill3.company_name\n", "\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "计算最优渠道费用" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "order_num:250501045815214,最优尾端费用:62.355,最优渠道:Fedex-HOME\n", "order_num:250501070802260,最优尾端费用:23.14,最优渠道:Fedex-HOME\n", "order_num:250501075201987,最优尾端费用:15.27,最优渠道:Fedex-HOME\n", "order_num:250501100002104,最优尾端费用:11.23,最优渠道:Fedex-HOME\n", "order_num:250502002202300,最优尾端费用:19.99,最优渠道:Fedex-HOME\n", "order_num:250502011626763,最优尾端费用:49.535,最优渠道:Fedex-HOME\n", "order_num:250502045802287,最优尾端费用:14.78,最优渠道:Fedex-HOME\n", "order_num:250502072013645,最优尾端费用:11.23,最优渠道:Fedex-HOME\n", "order_num:250502194002287,最优尾端费用:54.33,最优渠道:Metro-SAIR\n", "order_num:250503094802516,最优尾端费用:210.0,最优渠道:AM-美西\n", "order_num:250503112002406,最优尾端费用:14.58,最优渠道:Fedex-HOME\n", "order_num:250503193602672,最优尾端费用:16.84,最优渠道:Fedex-HOME\n", "order_num:250504071014034,最优尾端费用:29.79,最优渠道:Fedex-HOME\n", "order_num:250504083502514,最优尾端费用:59.08,最优渠道:Fedex-HOME\n", "order_num:250504084814183,最优尾端费用:10.07,最优渠道:Fedex-HOME\n", "order_num:250504121602047,最优尾端费用:33.04,最优渠道:Fedex-HOME\n", "order_num:250504125002394,最优尾端费用:10.62,最优渠道:Fedex-HOME\n", "order_num:250504225644759,最优尾端费用:19.3,最优渠道:Fedex-HOME\n", "order_num:250505004402396,最优尾端费用:43.16,最优渠道:Fedex-HOME\n", "order_num:250505012814762,最优尾端费用:56.46,最优渠道:Fedex-HOME\n", "order_num:250505020516714,最优尾端费用:25.49,最优渠道:Fedex-HOME\n", "order_num:250505024601968,最优尾端费用:27.32,最优渠道:Fedex-HOME\n", "order_num:250505030040190,最优尾端费用:109.97,最优渠道:Fedex-GROUD\n", "order_num:250505032202345,最优尾端费用:31.393333333333334,最优渠道:Fedex-HOME\n", "order_num:250505041854462,最优尾端费用:77.615,最优渠道:Metro-SAIR\n", "order_num:250505052014062,最优尾端费用:41.885,最优渠道:Fedex-HOME\n", "order_num:250505061601903,最优尾端费用:34.2,最优渠道:Fedex-HOME\n", "order_num:250505074602122,最优尾端费用:55.65,最优渠道:Fedex-HOME\n", "order_num:250505083202771,最优尾端费用:44.45,最优渠道:Fedex-HOME\n", "order_num:250505083209644,最优尾端费用:18.302,最优渠道:Fedex-HOME\n", "order_num:250505091408643,最优尾端费用:48.185,最优渠道:Fedex-HOME\n", "order_num:250505100438500,最优尾端费用:282.62,最优渠道:AM-美东\n", "order_num:250505103402059,最优尾端费用:21.236666666666668,最优渠道:Fedex-HOME\n", "order_num:250505145809240,最优尾端费用:10.07,最优渠道:Fedex-HOME\n", "order_num:250505191638203,最优尾端费用:46.51,最优渠道:Fedex-HOME\n", "order_num:250505211405109,最优尾端费用:14.56,最优渠道:Fedex-HOME\n", "order_num:250506021425688,最优尾端费用:44.45,最优渠道:Fedex-HOME\n", "order_num:250506030009243,最优尾端费用:19.52,最优渠道:Fedex-HOME\n", "order_num:250506041415802,最优尾端费用:44.45,最优渠道:Fedex-HOME\n", "order_num:250506080602413,最优尾端费用:41.88,最优渠道:Fedex-HOME\n", "order_num:250506084613841,最优尾端费用:32.24,最优渠道:Fedex-HOME\n", "order_num:250506102814221,最优尾端费用:14.78,最优渠道:Fedex-HOME\n", "order_num:250506144202566,最优尾端费用:14.09,最优渠道:Fedex-HOME\n", "order_num:250506195202194,最优尾端费用:16.84,最优渠道:Fedex-HOME\n", "order_num:250506221650068,最优尾端费用:190.0,最优渠道:AM-美西\n", "order_num:250507015209843,最优尾端费用:124.185,最优渠道:Metro-SAIR\n", "order_num:250507023202126,最优尾端费用:18.18,最优渠道:Fedex-HOME\n", "order_num:250507034402380,最优尾端费用:41.88,最优渠道:Fedex-HOME\n", "order_num:250507035602742,最优尾端费用:45.73,最优渠道:Fedex-HOME\n", "order_num:250507045414344,最优尾端费用:40.94,最优渠道:Fedex-HOME\n", "order_num:250507060202349,最优尾端费用:201.8,最优渠道:Metro-SAIR\n", "order_num:250507065428387,最优尾端费用:29.56,最优渠道:Fedex-HOME\n", "order_num:250507070202212,最优尾端费用:14.578947368421053,最优渠道:AM-美西\n", "order_num:250507111014426,最优尾端费用:129.14,最优渠道:Fedex-GROUD\n", "order_num:250507134120847,最优尾端费用:47.28,最优渠道:Fedex-HOME\n", "order_num:250507214814128,最优尾端费用:41.455,最优渠道:Fedex-HOME\n", "order_num:250507220202835,最优尾端费用:62.0,最优渠道:Fedex-HOME\n", "order_num:250507223802343,最优尾端费用:45.32,最优渠道:Fedex-HOME\n", "order_num:250508002827917,最优尾端费用:67.26666666666667,最优渠道:Metro-SAIR\n", "order_num:250508005002681,最优尾端费用:48.265,最优渠道:Fedex-HOME\n", "order_num:250508023650798,最优尾端费用:50.15,最优渠道:Fedex-HOME\n", "order_num:250508024014611,最优尾端费用:228.0,最优渠道:AM-美西\n", "order_num:250508025614017,最优尾端费用:201.8,最优渠道:Metro-SAIR\n", "order_num:250508032123500,最优尾端费用:32.245,最优渠道:Fedex-HOME\n", "order_num:250508041046615,最优尾端费用:55.0,最优渠道:AM-美西\n", "order_num:250508044700518,最优尾端费用:16.84,最优渠道:Fedex-HOME\n", "order_num:250508050633270,最优尾端费用:10.97,最优渠道:Fedex-HOME\n", "order_num:250508093401914,最优尾端费用:109.83,最优渠道:Fedex-GROUD\n", "order_num:250508100840946,最优尾端费用:10.44,最优渠道:Fedex-HOME\n", "order_num:250508102202116,最优尾端费用:28.62,最优渠道:Fedex-HOME\n", "order_num:250508125202731,最优尾端费用:31.64,最优渠道:Fedex-HOME\n", "order_num:250508130213821,最优尾端费用:17.97,最优渠道:Fedex-HOME\n", "order_num:250508130815019,最优尾端费用:25.63,最优渠道:Fedex-HOME\n", "order_num:250508201849554,最优尾端费用:100.82,最优渠道:Fedex-GROUD\n", "order_num:250508214614402,最优尾端费用:44.44,最优渠道:AM-美东\n", "order_num:250508221614665,最优尾端费用:44.445,最优渠道:Fedex-HOME\n", "order_num:250508225009196,最优尾端费用:18.52,最优渠道:Fedex-HOME\n", "order_num:250508230402613,最优尾端费用:125.67,最优渠道:Fedex-GROUD\n", "order_num:250509000304237,最优尾端费用:124.28,最优渠道:Fedex-GROUD\n", "order_num:250509001431900,最优尾端费用:74.94,最优渠道:AM-美东\n", "order_num:250509001835110,最优尾端费用:41.88,最优渠道:Fedex-HOME\n", "order_num:250509005601944,最优尾端费用:19.62,最优渠道:Fedex-HOME\n", "order_num:250509005802525,最优尾端费用:23.14,最优渠道:Fedex-HOME\n", "order_num:250509022048811,最优尾端费用:375.0,最优渠道:AM-美西\n", "order_num:250509030213999,最优尾端费用:210.0,最优渠道:AM-美西\n", "order_num:250509050035187,最优尾端费用:33.63,最优渠道:Fedex-HOME\n", "order_num:250509051813529,最优尾端费用:48.56333333333333,最优渠道:Metro-SAIR\n", "order_num:250509060425555,最优尾端费用:23.14,最优渠道:Fedex-HOME\n", "order_num:250509072613622,最优尾端费用:89.26,最优渠道:Metro-SAIR\n", "order_num:250509090213734,最优尾端费用:25.0,最优渠道:Fedex-HOME\n", "order_num:250509101042820,最优尾端费用:30.365,最优渠道:Fedex-HOME\n", "order_num:250509103602832,最优尾端费用:16.08,最优渠道:Fedex-HOME\n", "order_num:250509193036322,最优尾端费用:56.81,最优渠道:Fedex-HOME\n", "order_num:250509200626110,最优尾端费用:76.45,最优渠道:Fedex-HOME\n", "order_num:250509205825538,最优尾端费用:198.6,最优渠道:AM-美东\n", "order_num:250509210401987,最优尾端费用:53.745,最优渠道:Fedex-HOME\n", "order_num:250509212202426,最优尾端费用:41.61,最优渠道:Fedex-HOME\n", "order_num:250509215814788,最优尾端费用:33.515,最优渠道:Fedex-HOME\n", "order_num:250509222023973,最优尾端费用:31.1,最优渠道:Fedex-HOME\n", "order_num:250509225602054,最优尾端费用:10.07,最优渠道:Fedex-HOME\n", "order_num:250509230028772,最优尾端费用:104.37,最优渠道:Fedex-GROUD\n", "order_num:250509232802199,最优尾端费用:84.97,最优渠道:Fedex-GROUD\n", "order_num:250509233009110,最优尾端费用:37.192,最优渠道:AM-美东\n", "order_num:250509235102452,最优尾端费用:11.82,最优渠道:Fedex-HOME\n", "order_num:250509235602487,最优尾端费用:29.7,最优渠道:Fedex-HOME\n", "order_num:250510002609453,最优尾端费用:41.88,最优渠道:Fedex-HOME\n", "order_num:250510003209362,最优尾端费用:36.615,最优渠道:Fedex-HOME\n", "order_num:250510003700927,最优尾端费用:24.73,最优渠道:Fedex-HOME\n", "order_num:250510004048892,最优尾端费用:99.74,最优渠道:Fedex-GROUD\n", "order_num:250510004109295,最优尾端费用:54.01,最优渠道:Fedex-HOME\n", "order_num:250510005002280,最优尾端费用:15.25,最优渠道:Fedex-HOME\n", "order_num:250510020029576,最优尾端费用:21.01,最优渠道:Fedex-HOME\n", "order_num:250510025002750,最优尾端费用:26.18,最优渠道:Fedex-HOME\n", "order_num:250510025225681,最优尾端费用:44.45,最优渠道:Fedex-HOME\n", "order_num:250510030005060,最优尾端费用:11.23,最优渠道:Fedex-HOME\n", "order_num:250510030414561,最优尾端费用:109.83,最优渠道:Fedex-GROUD\n", "order_num:250510033213770,最优尾端费用:75.855,最优渠道:AM-美东\n", "order_num:250510033401907,最优尾端费用:178.52,最优渠道:Metro-SAIR\n", "order_num:250510051001993,最优尾端费用:128.37,最优渠道:Fedex-GROUD\n", "order_num:250510052602147,最优尾端费用:78.465,最优渠道:AM-美东\n", "order_num:250510060214481,最优尾端费用:91.84,最优渠道:Fedex-HOME\n", "order_num:250510061802861,最优尾端费用:117.14,最优渠道:Fedex-GROUD\n", "order_num:250510070402544,最优尾端费用:41.88,最优渠道:Fedex-HOME\n", "order_num:250510070802180,最优尾端费用:39.12,最优渠道:Fedex-HOME\n", "order_num:250510073414604,最优尾端费用:13.22,最优渠道:Fedex-HOME\n", "order_num:250510074202107,最优尾端费用:13.2,最优渠道:Fedex-HOME\n", "order_num:250510075809031,最优尾端费用:71.975,最优渠道:Fedex-GROUD\n", "order_num:250510080605085,最优尾端费用:23.14,最优渠道:Fedex-HOME\n", "order_num:250510082602759,最优尾端费用:24.381428571428568,最优渠道:Fedex-HOME\n", "order_num:250510095202387,最优尾端费用:23.835,最优渠道:Fedex-HOME\n", "order_num:250510101802116,最优尾端费用:14.03,最优渠道:Fedex-HOME\n", "order_num:250510104402713,最优尾端费用:48.365,最优渠道:Fedex-HOME\n", "order_num:250510105036999,最优尾端费用:33.22,最优渠道:Fedex-HOME\n", "order_num:250510105806149,最优尾端费用:53.4,最优渠道:AM-美东\n", "order_num:250510120602545,最优尾端费用:109.83,最优渠道:Fedex-GROUD\n", "order_num:250510193214603,最优尾端费用:10.66,最优渠道:Fedex-HOME\n", "order_num:250510195814141,最优尾端费用:21.743333333333336,最优渠道:Fedex-HOME\n", "order_num:250510201046697,最优尾端费用:121.2,最优渠道:Fedex-GROUD\n", "order_num:250510203002421,最优尾端费用:22.4,最优渠道:Fedex-HOME\n", "order_num:250510225013729,最优尾端费用:32.315,最优渠道:Fedex-HOME\n", "order_num:250510231002138,最优尾端费用:34.05,最优渠道:Fedex-HOME\n", "order_num:250511004026930,最优尾端费用:10.07,最优渠道:Fedex-HOME\n", "order_num:250511010428755,最优尾端费用:51.08,最优渠道:Fedex-HOME\n", "order_num:250511011051901,最优尾端费用:23.84,最优渠道:Fedex-HOME\n", "order_num:250511015022969,最优尾端费用:76.68,最优渠道:Fedex-GROUD\n", "order_num:250511020225544,最优尾端费用:109.83,最优渠道:Fedex-GROUD\n", "order_num:250511022602879,最优尾端费用:48.39,最优渠道:Fedex-HOME\n", "order_num:250511025825184,最优尾端费用:149.3,最优渠道:Metro-SAIR\n", "order_num:250511031414226,最优尾端费用:46.77,最优渠道:Fedex-HOME\n", "order_num:250511033002790,最优尾端费用:13.18,最优渠道:Fedex-HOME\n", 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"order_num:250608104002017,最优尾端费用:28.635,最优渠道:Fedex-HOME\n", "order_num:250608104402417,最优尾端费用:27.56,最优渠道:Fedex-HOME\n", "order_num:250608105002687,最优尾端费用:12.21,最优渠道:Fedex-HOME\n", "order_num:250608105202887,最优尾端费用:28.62,最优渠道:Fedex-HOME\n", "order_num:250608110202572,最优尾端费用:20.06,最优渠道:Fedex-HOME\n", "order_num:250608111205174,最优尾端费用:13.05,最优渠道:Fedex-HOME\n", "order_num:250608111402579,最优尾端费用:44.34,最优渠道:Fedex-HOME\n", "order_num:250608114039000,最优尾端费用:12.11,最优渠道:Fedex-HOME\n", "order_num:250608114602127,最优尾端费用:115.1,最优渠道:Fedex-GROUD\n", "order_num:250608115202862,最优尾端费用:41.885,最优渠道:Fedex-HOME\n", "order_num:250608115802181,最优尾端费用:16.915,最优渠道:Fedex-HOME\n", "order_num:250608120802620,最优尾端费用:13.47,最优渠道:Fedex-HOME\n", "order_num:250608123402560,最优尾端费用:10.07,最优渠道:Fedex-HOME\n", "order_num:250608125002633,最优尾端费用:10.07,最优渠道:Fedex-HOME\n", "order_num:250608130602100,最优尾端费用:11.0125,最优渠道:Fedex-HOME\n", "order_num:250608134402785,最优尾端费用:38.2,最优渠道:Fedex-HOME\n", "order_num:250608165601985,最优尾端费用:23.855,最优渠道:Fedex-HOME\n", "order_num:250608200214972,最优尾端费用:19.4,最优渠道:Fedex-HOME\n", "order_num:250608200414912,最优尾端费用:44.45,最优渠道:Fedex-HOME\n", "order_num:250608205015003,最优尾端费用:36.086666666666666,最优渠道:Fedex-HOME\n", "order_num:250608211427684,最优尾端费用:14.03,最优渠道:Fedex-HOME\n", "order_num:250608213802258,最优尾端费用:10.07,最优渠道:Fedex-HOME\n", "order_num:250608214202287,最优尾端费用:27.27,最优渠道:Fedex-HOME\n", "order_num:250608221414492,最优尾端费用:10.545,最优渠道:Fedex-HOME\n", "order_num:250608222414821,最优尾端费用:19.78,最优渠道:Fedex-HOME\n", "order_num:250608222602067,最优尾端费用:33.38,最优渠道:Fedex-HOME\n", "order_num:250608222802103,最优尾端费用:19.78,最优渠道:Fedex-HOME\n", "order_num:250608224014759,最优尾端费用:55.0,最优渠道:AM-美西\n", "order_num:250608224027173,最优尾端费用:16.55,最优渠道:Fedex-HOME\n", "order_num:250608225627404,最优尾端费用:16.18,最优渠道:Fedex-HOME\n", "order_num:250608230615016,最优尾端费用:26.77,最优渠道:Fedex-HOME\n", "order_num:250608231215214,最优尾端费用:20.28,最优渠道:Fedex-HOME\n", "order_num:250608232202211,最优尾端费用:30.82,最优渠道:Fedex-HOME\n", "order_num:250608232214584,最优尾端费用:20.57,最优渠道:Fedex-HOME\n", "order_num:250608234046120,最优尾端费用:10.07,最优渠道:Fedex-HOME\n", "order_num:250608234602501,最优尾端费用:12.11,最优渠道:Fedex-HOME\n", "order_num:250609000015381,最优尾端费用:38.94,最优渠道:Fedex-HOME\n", "order_num:250609001236907,最优尾端费用:44.45,最优渠道:Fedex-HOME\n", "order_num:250609002002636,最优尾端费用:26.56,最优渠道:Fedex-HOME\n", "order_num:250609003041463,最优尾端费用:41.88,最优渠道:Fedex-HOME\n", "order_num:250609011402874,最优尾端费用:13.73,最优渠道:Fedex-HOME\n", "order_num:250609013004865,最优尾端费用:53.41,最优渠道:Fedex-HOME\n", "order_num:250609015602460,最优尾端费用:12.11,最优渠道:Fedex-HOME\n", "order_num:250609020202140,最优尾端费用:27.365,最优渠道:Fedex-HOME\n", "order_num:250609020252686,最优尾端费用:70.085,最优渠道:Fedex-HOME\n", "order_num:250609020401944,最优尾端费用:11.62,最优渠道:Fedex-HOME\n", "order_num:250609020802110,最优尾端费用:12.63,最优渠道:Fedex-HOME\n", "order_num:250609023016228,最优尾端费用:25.0,最优渠道:Fedex-HOME\n", "order_num:250609023028316,最优尾端费用:12.11,最优渠道:Fedex-HOME\n", "order_num:250609023815498,最优尾端费用:17.965,最优渠道:Fedex-HOME\n", "order_num:250609024002676,最优尾端费用:27.37,最优渠道:Fedex-HOME\n", "order_num:250609030046679,最优尾端费用:36.026666666666664,最优渠道:Fedex-HOME\n", "order_num:250609031402316,最优尾端费用:20.06,最优渠道:Fedex-HOME\n", "order_num:250609032202817,最优尾端费用:17.64,最优渠道:Fedex-HOME\n", "order_num:250609034037326,最优尾端费用:12.75,最优渠道:Fedex-HOME\n", "order_num:250609034815338,最优尾端费用:52.49,最优渠道:Fedex-HOME\n", "order_num:250609035602681,最优尾端费用:41.88,最优渠道:Fedex-HOME\n", "order_num:250609041016318,最优尾端费用:28.19,最优渠道:Fedex-HOME\n", "order_num:250609041643232,最优尾端费用:97.05,最优渠道:Fedex-GROUD\n", "order_num:250609042802684,最优尾端费用:14.67,最优渠道:Fedex-HOME\n", "order_num:250609043602675,最优尾端费用:33.04,最优渠道:Fedex-HOME\n", "order_num:250609043616319,最优尾端费用:14.56,最优渠道:Fedex-HOME\n", "order_num:250609043815886,最优尾端费用:15.62,最优渠道:Fedex-HOME\n", "order_num:250609044415420,最优尾端费用:14.5,最优渠道:Fedex-HOME\n", "order_num:250609044615078,最优尾端费用:13.8,最优渠道:Fedex-HOME\n", "order_num:250609050002072,最优尾端费用:11.23,最优渠道:Fedex-HOME\n", "order_num:250609055602727,最优尾端费用:10.07,最优渠道:Fedex-HOME\n", "order_num:250609060602128,最优尾端费用:12.11,最优渠道:Fedex-HOME\n", "order_num:250609061005370,最优尾端费用:11.34,最优渠道:Fedex-HOME\n", "order_num:250609061021009,最优尾端费用:10.07,最优渠道:Fedex-HOME\n", "order_num:250609062002942,最优尾端费用:11.23,最优渠道:Fedex-HOME\n", "order_num:250609062214804,最优尾端费用:14.67,最优渠道:Fedex-HOME\n", "order_num:250609065815684,最优尾端费用:14.03,最优渠道:Fedex-HOME\n", "order_num:250609065829610,最优尾端费用:18.09,最优渠道:Fedex-HOME\n", "order_num:250609071405837,最优尾端费用:11.12,最优渠道:Fedex-HOME\n", "order_num:250609074002315,最优尾端费用:10.07,最优渠道:Fedex-HOME\n", "order_num:250609074004835,最优尾端费用:15.67,最优渠道:Fedex-HOME\n", "order_num:250609082814522,最优尾端费用:27.51,最优渠道:Fedex-HOME\n", "order_num:250609083426902,最优尾端费用:15.62,最优渠道:Fedex-HOME\n", "order_num:250609100043013,最优尾端费用:10.066666666666666,最优渠道:Fedex-HOME\n", "order_num:250609103015765,最优尾端费用:17.965,最优渠道:Fedex-HOME\n", "order_num:250609103202035,最优尾端费用:11.62,最优渠道:Fedex-HOME\n", "order_num:250609103215424,最优尾端费用:71.62,最优渠道:Fedex-HOME\n", "order_num:250609104229190,最优尾端费用:12.11,最优渠道:Fedex-HOME\n", "order_num:250609111003041,最优尾端费用:10.065999999999999,最优渠道:Fedex-HOME\n", "order_num:250609111815220,最优尾端费用:21.67,最优渠道:Fedex-HOME\n", "order_num:250609113214504,最优尾端费用:12.985,最优渠道:Fedex-HOME\n", "order_num:250609115802954,最优尾端费用:13.11,最优渠道:Fedex-HOME\n", "order_num:250609121227315,最优尾端费用:16.59,最优渠道:Fedex-HOME\n", "order_num:250609122402681,最优尾端费用:17.58,最优渠道:Fedex-HOME\n", "order_num:250610010815244,最优尾端费用:34.8,最优渠道:Fedex-HOME\n", "order_num:250610064407414,最优尾端费用:13.5,最优渠道:Fedex-HOME\n", "order_num:250610081005524,最优尾端费用:10.92,最优渠道:Fedex-HOME\n", "order_num:250610091014771,最优尾端费用:10.07,最优渠道:Fedex-HOME\n", "order_num:250610092801988,最优尾端费用:10.62,最优渠道:Fedex-HOME\n", "order_num:250610094201964,最优尾端费用:11.82,最优渠道:Fedex-HOME\n", "order_num:250610152202124,最优尾端费用:10.62,最优渠道:Fedex-HOME\n" ] } ], "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": 5, "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": [] } ], "metadata": { "kernelspec": { "display_name": "base", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 2 }