313 lines
11 KiB
Plaintext
313 lines
11 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"1.查询5.1-5.28测量包裹尺寸的订单数据\n",
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"2.以美国的售价计算它当前应该有的售价\n",
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"3.判断"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from utils.gtools import MySQLconnect\n",
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"\n",
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"# 读取需要计算的包裹信息\n",
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"with MySQLconnect('ads') as db:\n",
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" sql = r\"\"\" \n",
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" # 限制范围是测量时间，取得SKU种类为1且数量为1的订单，且重复SKU只取最近的订单\n",
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"\n",
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"WITH\n",
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"t1 AS (\n",
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"SELECT\n",
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"order_id,\n",
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"SKU,\n",
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"order_date,\n",
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"sum(CASE WHEN opl.order_product_id LIKE '%\\_%' ESCAPE '\\\\' \n",
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" AND opl.order_product_id NOT LIKE '%\\_%\\_%' ESCAPE '\\\\' THEN product_num END) AS product_num,\n",
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"DATE_FORMAT(order_date,\"%Y-%m-%d\") AS 订单时间,\n",
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"count(opl.SKU) AS 产品种类\n",
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"FROM\n",
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"dws.fact_order_product_list opl\n",
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"WHERE\n",
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" NOT EXISTS (\n",
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" SELECT 1 \n",
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" FROM dws.log_order_reissue_detail AS r \n",
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" WHERE r.order_product_id = opl.order_product_id\n",
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" )\n",
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"AND order_date >= \"20250501\"\n",
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"AND order_date < \"20250612\"\n",
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"AND SKU <> \"\"\n",
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"GROUP BY order_id\n",
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")\n",
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",\n",
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"t2 AS (\n",
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"SELECT\t\t\t\n",
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" a.`包裹测量时间`,\n",
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"\t\t\t\t\t\tt1.order_id,\n",
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"\t\t\t\t\t\tt1.SKU,\n",
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"\t\t\t\t\t\tt1.order_date,\n",
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" a.包裹号,\n",
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" a.快递公司,\n",
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" a.运输方式,\n",
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"\t\t\t\t\t\ta.`目的国`,\n",
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" d.postcode,\n",
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" CONCAT(\n",
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" '\"', b.package, '\": {',\n",
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" '\"长\": ', length, ', ',\n",
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" '\"宽\": ', width, ', ',\n",
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" '\"高\": ', hight, ', ',\n",
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" '\"重量\": ', weight, '}'\n",
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" ) AS package_json\n",
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" FROM\n",
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"\t\t\t\tt1\n",
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" LEFT JOIN order_express a ON t1.order_id = a.单号\n",
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" JOIN package_vol_info b ON a.`包裹号` = b.package\n",
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" JOIN order_list d ON a.`单号` = d.order_id \n",
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" WHERE\n",
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" a.`包裹状态` IN ( '客户签收', '已经投递') \n",
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" AND b.hight > 0 \n",
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" AND b.length > 0 \n",
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" AND b.width > 0 \n",
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" AND b.hight > 0 \n",
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" AND b.weight > 0\n",
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"-- AND a.`目的国` = \"United States\"\n",
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"\t\t\t\t\t\tAND t1.product_num = 1\n",
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"\t\t\t\t\t\tAND t1.产品种类=1\n",
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"\t\t\t\t\t\tAND a.`包裹测量时间` >= '2025-05-01'\n",
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"\t\t\t\t\t\tAND a.`包裹测量时间` < '2025-06-12'\n",
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"),\n",
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"t3 AS (\n",
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"SELECT\n",
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"t2.*,\n",
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"sku.成本价 AS ERP采购价,\n",
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"ess.erp_package_vol AS ERP包裹数据,\n",
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"CONCAT('{', GROUP_CONCAT(package_json SEPARATOR ','), '}') AS 实际包裹数据,\n",
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"ROW_NUMBER() OVER (PARTITION BY SKU ORDER BY 包裹测量时间 DESC) as rn\n",
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"FROM\n",
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"t2\n",
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"LEFT JOIN dwd.dim_erp_sku_package_vol_info ess ON t2.SKU=ess.erp_sku\n",
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"LEFT JOIN stg_bayshop_litfad_sku sku ON t2.SKU=sku.SKU\n",
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"WHERE\n",
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"ess.`erp_package_vol`<>\"{}\" AND ess.`erp_package_vol`<>\"\"\n",
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"GROUP BY order_id\n",
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")\n",
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"SELECT\n",
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"包裹测量时间,\n",
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"order_id,\n",
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"SKU,\n",
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"DATE_FORMAT(order_date,\"%Y-%M-%D\") AS 订单时间,\n",
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"包裹号,\n",
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"`快递公司`,\n",
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"`运输方式`,\n",
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"`目的国`,\n",
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"postcode,\n",
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"ERP采购价,\n",
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"ERP包裹数据,\n",
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"实际包裹数据\n",
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"FROM\n",
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"t3\n",
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"WHERE\n",
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"rn=1\n",
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"\n",
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"\n",
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"\n",
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" \"\"\"\n",
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" df=pd.read_sql(\"SELECT * FROM `order_complet4` WHERE buy_amount is not null and `实际尺寸售价` IS NULL limit 1000 \",db.con)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def call_sell_price(price, package_dict,head_type=\"海运\"):\n",
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" import json\n",
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" from sell.sell_price import call_sell_and_order_price\n",
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" try:\n",
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" package_dict = json.loads(package_dict)\n",
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" all_sell_price, order_price, order_type = call_sell_and_order_price(price, package_dict,head_type)\n",
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" except Exception as e:\n",
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" print(f\" 报错: {e}\")\n",
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" return (\"\",\"\",\"\")\n",
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" if all_sell_price == 0:\n",
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" return (\"\",\"\",\"\")\n",
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" sell_price= all_sell_price[0]\n",
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" # logis_price = all_sell_price[1]\n",
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" return (sell_price, order_price, order_type)\n",
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"# 计算当前售价\n",
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"for index,row in df.iterrows():\n",
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" price = row['buy_amount']\n",
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" # package_dict = json.loads(row['erp_package_vol'])\n",
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" sell_price = call_sell_price(price, row['package_json'],\"海运\")\n",
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" print(sell_price)\n",
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" df.loc[index,'网站售价'] = sell_price[0]\n",
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" df.loc[index,'订单物流费'] = sell_price[1]\n",
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" df.loc[index,'尾端类型'] = sell_price[2]\n",
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" print(f\"SKU: {row['sku']} 网站售价: {sell_price[0]} 订单物流费: {sell_price[1]} 尾端类型: {sell_price[2]}\")\n",
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"df.to_clipboard(index=False)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"新的|计算欧洲各国每种货型占比"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_35372\\42756626.py:31: 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",
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" df = pd.read_sql(sql, db.con)\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"from utils.gtools import MySQLconnect\n",
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"sql=r\"\"\"SELECT\n",
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"包裹号,\n",
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"单号,\n",
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"快递公司,\n",
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"目的国,\n",
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"快递分区,\n",
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"投递时间,\n",
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"postcode,\n",
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"length,\n",
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"width,\n",
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"hight,\n",
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"weight\n",
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"FROM\n",
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"order_express oe\n",
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"LEFT JOIN package_vol_info pvi ON oe.`包裹号` = pvi.package\n",
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"left join order_list ON oe.单号 = order_list.order_id\n",
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"WHERE\n",
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"`投递时间` >='2025-05-01'\n",
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"AND `投递时间`<'2025-08-01'\n",
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"AND `包裹状态` NOT REGEXP '已作废'\n",
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"AND length >0\n",
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"AND width >0\n",
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"AND hight>0\n",
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"AND weight>0\n",
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"# AND 目的国 NOT REGEXP \"United States|Australia|United Kingdom|Japan|Canada\"\n",
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"and 目的国 REGEXP \"Australia\"\n",
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"# AND 目的国 <>''\"\"\"\n",
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"with MySQLconnect('ods') as db:\n",
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" df = pd.read_sql(sql, db.con)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(df.columns)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_35372\\4220111735.py:9: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
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" type_series = df.groupby('单号').apply(order_type)\n",
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"C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_35372\\4220111735.py:17: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
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" weight_series = df.groupby('单号').apply(order_weight)\n"
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]
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}
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],
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"source": [
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"def order_type(group):\n",
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" # 判断组中是否有任何一个包裹满足“卡派”条件\n",
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" if (group['length'] >= 200).any() or (group['weight'] >= 31500).any():\n",
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" return '卡派'\n",
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" else:\n",
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" return '快递'\n",
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"\n",
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"# 计算每个单号的类型\n",
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"type_series = df.groupby('单号').apply(order_type)\n",
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"def order_weight(group):\n",
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" # 计算单号的重量\n",
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" # 计算6000系数的体积重\n",
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" bill_weight= 0\n",
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" for i in range(len(group)):\n",
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" bill_weight += max(group.iloc[i]['length'] * group.iloc[i]['width'] * group.iloc[i]['hight'] / 4000, group.iloc[i]['weight']/1000)\n",
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" return bill_weight\n",
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"weight_series = df.groupby('单号').apply(order_weight)\n",
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"# 将结果映射回原表\n",
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"# df['类型'] = df['单号'].map(type_series)\n",
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"df['计费重'] = df['单号'].map(weight_series)\n",
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"df.to_clipboard(index= False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"from logisticsClass.logisticsTail_AU import *\n",
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"\n",
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"for i, row in df.iterrows():\n",
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" if \"POST\" in row['快递公司']:\n",
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" gel = PostLogistics_AU()\n",
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" zone = gel.is_remote(row['postcode'])\n",
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" df.loc[i,'渠道'] = \"POST\"\n",
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" elif \"TOLL\" in row['快递公司']:\n",
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" gel = TollLogistics_AU()\n",
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" zone = gel.is_remote(row['postcode'])\n",
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" df.loc[i,'渠道'] = \"TOLL\"\n",
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" elif \"ALL\" in row['快递公司']:\n",
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" gel = AllLogistics_AU()\n",
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" zone = gel.is_remote(row['postcode'])\n",
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" df.loc[i,'渠道'] = \"ALL\"\n",
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" else:\n",
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" zone = \"其他渠道\"\n",
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" df.loc[i,'分区'] = zone\n",
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"\n",
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"df.to_clipboard(index=False)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "base",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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