1094 lines
42 KiB
Plaintext
1094 lines
42 KiB
Plaintext
{
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"cells": [
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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('ods') as db:\n",
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" sql = r\"\"\" \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 >= \"20250201\"\n",
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"AND order_date < \"20250601\"\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` >= '2025-03-01'\n",
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"\t\t\t\t\t\tAND a.`包裹测量时间` < '2025-06-01'\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.包裹数据 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 ads.new_erp_sku_size ess ON t2.SKU=ess.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.`包裹数据`<>''\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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" df=pd.read_sql(sql,db.con)\n",
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" print(df)\n",
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"df"
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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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"list_order_id = df[\"order_id\"].drop_duplicates().tolist()\n",
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"param_order_id = \",\".join(f\"'{order_id}'\" for order_id in list_order_id)"
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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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"先计算美国的实际利润率"
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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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"from utils.gtools import MySQLconnect\n",
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"\n",
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"ods = MySQLconnect(\"ods\")\n",
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"engine = ods.engine()\n",
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"cursor = ods.connect().cursor()\n",
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"\n",
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"batch_size = 50000 # 每次查询 500 个 order_id，避免 SQL 语句过长\n",
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"order_id_list = df[\"order_id\"].drop_duplicates().tolist() # 取出所有 order_id\n",
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"# df['postcode'] = \"38016\"\n",
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"# 存储分批查询的结果\n",
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"result_dfs1 = []\n",
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"result_dfs2 = []\n",
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"for i in range(0, len(order_id_list), batch_size):\n",
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" batch_order_ids = order_id_list[i:i + batch_size] # 取当前批次的 order_id\n",
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" param = \",\".join(f\"'{order_id}'\" for order_id in batch_order_ids)\n",
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"\n",
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" purchase_order_sql = f\"\"\"\n",
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" WITH t1 AS (\n",
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" SELECT LEFT(ol.out_detials_outlink_id, 15) AS order_id,\n",
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" SUM(out_detials_qty * price) AS instock_cost,\n",
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" NULL AS buy_cost\n",
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" FROM ods.outstock_list ol\n",
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" JOIN ods.instock_list il ON ol.store_in_id = il.id \n",
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" WHERE LEFT(ol.out_detials_outlink_id, 15) IN ({param})\n",
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" GROUP BY LEFT(ol.out_detials_outlink_id, 15)\n",
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" \n",
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" UNION ALL\n",
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" \n",
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" SELECT LEFT(order_product_id, 15) AS order_id, \n",
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" NULL AS instock_cost,\n",
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" SUM(buy_num * actual_price) AS buy_cost\n",
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" FROM warehouse_purchasing\n",
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" WHERE LEFT(order_product_id, 15) IN ({param}) \n",
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" AND buy_audit = \"采购完成\"\n",
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" GROUP BY LEFT(order_product_id, 15)\n",
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" )\n",
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" SELECT order_id,\n",
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" SUM(CASE \n",
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" WHEN instock_cost IS NULL THEN buy_cost\n",
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" ELSE instock_cost \n",
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" END) AS 采购成本\n",
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" FROM t1 \n",
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" GROUP BY order_id\n",
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" \"\"\"\n",
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" \n",
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"\n",
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" batch_df1 = pd.read_sql(purchase_order_sql, con=engine) # 运行 SQL 查询\n",
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" result_dfs1.append(batch_df1) # 存入结果列表\n",
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" print(f\"已完成 {i + batch_size} 个 order_id 的查询\")\n",
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"\n",
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"# 合并所有查询结果\n",
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"purchase_order_df1 = pd.concat(result_dfs1, ignore_index=True)\n",
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"purchase_order_df1[\"order_id\"] = purchase_order_df1[\"order_id\"].astype(str)\n",
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"\n",
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"\n",
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"# 转换数据类型，确保匹配\n",
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"df[\"order_id\"] = df[\"order_id\"].astype(str)\n",
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"\n",
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"# 进行合并\n",
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"df = pd.merge(df, purchase_order_df1, on='order_id', how='left')\n",
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"# 复制到剪贴板\n",
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"df.to_clipboard(index=False)\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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"import pandas as pd\n",
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"df = pd.read_clipboard()\n",
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"df"
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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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"from utils.countryOperator import OperateCountry\n",
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"from utils.logisticsBill import BillFactory\n",
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"from utils.Package import Package, Package_group\n",
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"import pandas as pd\n",
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"import json\n",
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"import re\n",
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"# 美国 \n",
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"from utils.logisticsBill import Billing\n",
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"import requests\n",
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"\n",
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"for index, row in df.iterrows():\n",
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" opCountry = OperateCountry('US')\n",
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" postcode = row['postcode']\n",
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" if pd.isna(postcode) or str(postcode).lower() == \"nan\":\n",
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" continue\n",
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" try:\n",
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" package_dict = json.loads(row['实际包裹数据'])\n",
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" except Exception as e:\n",
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" print(f\"行 {index} 解析失败: {e}\")\n",
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" print(row['实际包裹数据'])\n",
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" continue\n",
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" packages = Package_group()\n",
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" def extract_number(value):\n",
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" # 提取字符串中的第一个数字\n",
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" match = re.search(r\"[-+]?\\d*\\.\\d+|\\d+\", str(value))\n",
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" return float(match.group()) if match else 0.0\n",
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" for key, package in package_dict.items():\n",
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" package['长'] = extract_number(package['长'])\n",
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" package['宽'] = extract_number(package['宽'])\n",
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" package['高'] = extract_number(package['高'])\n",
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" package['重量'] = extract_number(package['重量'])\n",
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" \n",
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" if package['长'] == 0 or package['宽'] == 0 or package['高'] == 0 or package['重量'] == 0:\n",
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" continue\n",
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" packages.add_package(Package(key,package['长'], package['宽'], package['高'], package['重量']))\n",
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" if packages is None:\n",
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" continue\n",
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" if \"海运\" in row['运输方式']:\n",
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" head_type = 1\n",
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" else:\n",
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" head_type = 0\n",
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"\n",
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" # if \"FEDEX-SAIR-G\" in row['快递公司']:\n",
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" # company_name = \"Fedex-GROUD\"\n",
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" # elif \"FEDEX-SAIR-H\" in row['快递公司']:\n",
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" # company_name = \"Fedex-HOME\"\n",
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" # elif \"FEDEX02\" in row['快递公司']:\n",
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" # company_name = \"Fedex-彩虹小马\"\n",
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" # elif \"大包\" in row['快递公司'] or row['快递公司'] == '海MS-FEDEX':\n",
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" # company_name = \"Fedex-金宏亚\"\n",
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" # elif \"GIGA\" in row['快递公司']:\n",
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" # company_name = \"大健-GIGA\"\n",
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" # elif \"CEVA\" in row['快递公司']:\n",
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" # company_name = \"大健-CEVA\"\n",
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" # elif \"USPS\" in row['快递公司']:\n",
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" # company_name = \"Fedex-GROUD\"\n",
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" # else:\n",
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" # company_name = \"大健-Metro\"\n",
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" \n",
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" bill = Billing(str(index),opCountry,packages,postcode,company_name=\"Fedex-GROUD\",head_type=head_type,beizhu='1')\n",
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" head_price = bill.head_amount[0]\n",
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" tail_price = bill.tail_amount[0]\n",
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" if \"USPS\" in row['快递公司']:\n",
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" tail_price = tail_price/2\n",
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" # df.loc[index,'头程CNY'] = head_price\n",
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" df.loc[index,'头程CNY'] = head_price\n",
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" # df.loc[index,'最优渠道'] = bill.company_name\n",
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" print(f\"行 {index} 处理完成\")\n",
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" \n",
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"df.to_clipboard(index=False)\n",
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" "
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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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"us_df = df[(df['目的国']=='United States')]"
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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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"from utils.countryOperator import OperateCountry\n",
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"from utils.logisticsBill import BillFactory\n",
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"from utils.Package import Package, Package_group\n",
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"import pandas as pd\n",
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"# 美国 \n",
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"df1=pd.read_excel(r\"D:\\test\\logistics\\拦截数据\\1-3月利润分段.xlsx\",sheet_name=\"11-4月全球订单\")\n",
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"df = df1[(df1['尾端渠道']==\"大健-Metro\")|(df1['尾端渠道']==\"大健-CEVA\")]\n",
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"# df['postcode'] = df['postcode'].astype(str)"
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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": [
|
||
"from utils.logisticsBill import Billing\n",
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"opCountry = OperateCountry('US')\n",
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"billFactory = BillFactory()\n",
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"for index,row in df.iterrows():\n",
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" print(row['postcode'])\n",
|
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" package = Package(row['包裹号'],row['长'],row['宽'],row['高'],row['重量'])\n",
|
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" packages= Package_group([package])\n",
|
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" postcode = row['postcode']\n",
|
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" head_type = 1 if row['运输方式'] == '海运' else 0\n",
|
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" try:\n",
|
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" bill = Billing(str(row['包裹号']),opCountry,packages,postcode=str(postcode),company_name=\"大健-Metro\",head_type=head_type,beizhu=0)\n",
|
||
" df.loc[index,\"美西\"] = bill.tail_amount[0]\n",
|
||
" except:\n",
|
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" bill = None\n",
|
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" df.loc[index,\"美西\"] = \"不可算\"\n",
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" \n",
|
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" print(index)\n",
|
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" print(bill)\n",
|
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"# bill_df = billFactory.bills_to_df()\n",
|
||
"# bill_df['ID'] = bill_df['ID'].astype(int)\n",
|
||
"# bill_df.to_clipboard()\n",
|
||
"\n",
|
||
"# merged_df = pd.merge(df, bill_df, left_on=\"包裹号\",right_on=\"ID\", how='inner')\n",
|
||
"df.to_clipboard(index=False)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import re\n",
|
||
"from data.us_zone import zone_west\n",
|
||
"def get_west_zone(postcode):\n",
|
||
" \"\"\"获取美西邮编分区\"\"\"\n",
|
||
" if not re.match(r'\\d{5}-\\d{4}|\\d{5}', postcode):\n",
|
||
" return \"邮编格式不合法\"\n",
|
||
" postcode = postcode[:5]\n",
|
||
" postcode = int(postcode)\n",
|
||
" for zone, postcodes in zone_west.items():\n",
|
||
" start =int(postcodes[0].split(\"-\")[0])\n",
|
||
" end = int(postcodes[-1].split(\"-\")[-1])\n",
|
||
" if start > postcode or postcode > end:\n",
|
||
" continue\n",
|
||
" for postcode_range in postcodes:\n",
|
||
" if \"-\" in postcode_range:\n",
|
||
" start, end = map(int, postcode_range.split(\"-\"))\n",
|
||
" if start <= postcode <= end:\n",
|
||
" return zone\n",
|
||
" else:\n",
|
||
" if int(zone) == postcode:\n",
|
||
" return zone\n",
|
||
" return \"未查询到邮编分区\"\n",
|
||
"df['分区']=df['postcode'].apply(get_west_zone)\n",
|
||
"df.to_clipboard(index=False)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 取sku所属SPU下所有sku及其现在售价\n",
|
||
"from sell.sell_price import call_sell_and_order_price\n",
|
||
"import json\n",
|
||
"sku_list = (\n",
|
||
" order_id_df_cal['SKU']\n",
|
||
" .apply(pd.to_numeric, errors='coerce') # 转数字，非法的变 NaN\n",
|
||
" .dropna()\n",
|
||
" .astype(int)\n",
|
||
" .astype(str)\n",
|
||
" .tolist()\n",
|
||
")\n",
|
||
"placeholders = ','.join(['%s'] * len(sku_list)) \n",
|
||
"# result = []\n",
|
||
"with MySQLconnect('ods') as db:\n",
|
||
" enginal = db.engine()\n",
|
||
" sql = f\"\"\"SELECT\n",
|
||
" SPU,\n",
|
||
" sku.SKU,\n",
|
||
" sku.`成本价`,\n",
|
||
" spi.`包裹数据`\n",
|
||
" FROM\n",
|
||
" stg_bayshop_litfad_sku sku\n",
|
||
" LEFT JOIN stg_bayshop_litfad_spu spu ON sku.`产品PID` = spu.`产品PID` \n",
|
||
" LEFT JOIN ads.new_erp_sku_size spi ON sku.SKU =spi.SKU\n",
|
||
" WHERE\n",
|
||
" spu.SPU IN (\n",
|
||
" SELECT\n",
|
||
" SPU \n",
|
||
" FROM\n",
|
||
" stg_bayshop_litfad_sku sku\n",
|
||
" LEFT JOIN stg_bayshop_litfad_spu spu ON sku.`产品PID` = spu.`产品PID` \n",
|
||
" WHERE\n",
|
||
" sku.SKU IN ({placeholders}) \n",
|
||
" )\n",
|
||
" \"\"\"\n",
|
||
" result = pd.read_sql(sql, enginal,params= tuple(sku_list))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import json\n",
|
||
"from sell.sell_price import call_sell_and_order_price\n",
|
||
"# 计算当前售价\n",
|
||
"for index,row in df.iterrows():\n",
|
||
" price = row['采购成本']\n",
|
||
" try:\n",
|
||
" package_dict = json.loads(row['实际包裹数据'])\n",
|
||
" sell_price, order_price, order_type = call_sell_and_order_price(price, package_dict)\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"SKU: {row['SKU']} 报错: {e}\")\n",
|
||
" continue\n",
|
||
" if sell_price == 0:\n",
|
||
" continue\n",
|
||
" df.loc[index, '实际应有售价'] = sell_price[0]\n",
|
||
" df.loc[index, '实际物流分摊费'] = sell_price[1]\n",
|
||
" df.loc[index, '实际头程cny'] = sell_price[2]\n",
|
||
" df.loc[index, '实际头程USD'] = sell_price[3]\n",
|
||
" df.loc[index, '订单物流费'] = order_price\n",
|
||
" df.loc[index, '尾端类型'] = order_type\n",
|
||
" print(f\" SKU {row['SKU']} \")\n",
|
||
"df.to_clipboard(index=False)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"写计算物流费的海运空运函数\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import math\n",
|
||
"express_price = pd.read_excel(r'D:\\test\\logistics\\data\\售价尾端价格.xlsx', sheet_name='Sheet1')\n",
|
||
"key_column = express_price.iloc[:, 8] # 第 I 列\n",
|
||
"value_column = express_price.iloc[:, 9] # 第 J 列\n",
|
||
"small_column = express_price.iloc[:, 10] # 第 K 列\n",
|
||
"big_column = express_price.iloc[:, 11] # 第 L 列\n",
|
||
"air_small_dict = dict(zip(key_column, small_column))\n",
|
||
"air_big_dict = dict(zip(key_column, big_column))\n",
|
||
"# 转换成字典\n",
|
||
"ocean_price_dict = dict(zip(key_column, value_column))\n",
|
||
"def ocean_order_price(packages):\n",
|
||
" express_fee = 0 # 快递基础费\n",
|
||
" long_fee = 0 # 超长费\n",
|
||
" weight_fee = 0 # 超重费\n",
|
||
" big_fee = 0 # 大包裹费\n",
|
||
" express_type = ''\n",
|
||
" for package in packages:\n",
|
||
" for key, value in ocean_price_dict.items():\n",
|
||
" if package.weight <=key:\n",
|
||
" express_fee+=value\n",
|
||
" break\n",
|
||
" if package.fst_size>=116 or package.sed_size>=71 or package.girth>=251:\n",
|
||
" long_fee += 16.3\n",
|
||
" express_type +=\"超长\"\n",
|
||
" if package.weight>=21000 and package.fst_size<238 and package.girth<315:\n",
|
||
" weight_fee+=25.5\n",
|
||
" express_type +=\"超重\"\n",
|
||
" if package.fst_size>=238 or package.girth>=315:\n",
|
||
" big_fee+=61.6\n",
|
||
" express_type +=\"大包裹\"\n",
|
||
" express_fee = express_fee + long_fee + weight_fee + big_fee\n",
|
||
"\n",
|
||
" # 卡派（步长为3）\n",
|
||
" ltl_base = 0\n",
|
||
" ltl_fee = 0\n",
|
||
" count1 = 0\n",
|
||
" count2 = 0\n",
|
||
" count3 = 0\n",
|
||
" count4 = 0\n",
|
||
" order_type2 = '卡派'\n",
|
||
" order_other_type1 = ''\n",
|
||
" order_other_type2 = ''\n",
|
||
" order_other_type3 = ''\n",
|
||
" order_other_type4 = ''\n",
|
||
" order_ltl_oversize = 0\n",
|
||
" order_ltl_overweight1 = 0\n",
|
||
" order_ltl_overweight2 = 0\n",
|
||
" order_ltl_overpackage = 0\n",
|
||
" sku_total_cubic_feet = 0\n",
|
||
" for package in packages:\n",
|
||
" cubic_feet= package.length * package.width * package.height / 1000000 * 35.3\n",
|
||
" sku_total_cubic_feet += cubic_feet\n",
|
||
" # 卡派额外费用\n",
|
||
" if package.fst_size>= 250:\n",
|
||
" count1 += 1\n",
|
||
" order_ltl_oversize = 118\n",
|
||
" order_other_type1 = '超长'\n",
|
||
" if package.weight >= 111000:\n",
|
||
" count2 += 1\n",
|
||
" order_ltl_overweight1 = 78\n",
|
||
" order_other_type2 = '超重'\n",
|
||
" if package.weight >= 130000:\n",
|
||
" count3 += 1\n",
|
||
" order_ltl_overweight2 = 30\n",
|
||
" order_other_type3 = '超重'\n",
|
||
" if package.fst_size >= 310:\n",
|
||
" count4 += 1\n",
|
||
" order_ltl_overpackage = 30\n",
|
||
" order_other_type4 = '大包裹'\n",
|
||
" order_type2 += order_other_type3 + order_other_type1 + order_other_type2 + order_other_type4\n",
|
||
"\n",
|
||
" # 卡派基础费用 体积/1000000 *35.3\n",
|
||
" if sku_total_cubic_feet < 25:\n",
|
||
" ltl_base = round(163 / 0.45 / 2, 2) # 181.11\n",
|
||
"\n",
|
||
" elif sku_total_cubic_feet < 35:\n",
|
||
" ltl_base = round(180 / 0.45 / 2, 2) # 200\n",
|
||
" else:\n",
|
||
"\t # 大于一个立方的（35立方英尺） 按照每立方英尺*5美金\n",
|
||
" # 最低为190美金\n",
|
||
" ltl_base = round(max(190, 5 * sku_total_cubic_feet) / 0.359 / 2)\n",
|
||
"\n",
|
||
" \n",
|
||
" ltl_fee = math.ceil(count1 / 3) * order_ltl_oversize + math.ceil(count2 / 3) * order_ltl_overweight1 + math.ceil(\n",
|
||
" count3 / 3) * order_ltl_overweight2 + math.ceil(count4 / 3) * order_ltl_overpackage + ltl_base\n",
|
||
"\n",
|
||
" if ltl_fee < express_fee:\n",
|
||
" ocean_fee = ltl_fee\n",
|
||
" order_type = order_type2\n",
|
||
" else:\n",
|
||
" ocean_fee = express_fee\n",
|
||
" order_type = express_type\n",
|
||
" return ocean_fee, order_type\n",
|
||
"\n",
|
||
"def air_order_price(packages):\n",
|
||
" express_fee = 0 \n",
|
||
" express_type = ''\n",
|
||
" for package in packages:\n",
|
||
" price=0\n",
|
||
" bill_weight = max(package.weight, package.get_volume_weight(8500))\n",
|
||
" if package.weight<=420 and package.fst_size<=50 and package.sed_size<=40 and package.trd_size<=30:\n",
|
||
" for key, value in air_small_dict.items():\n",
|
||
" if package.weight <=key:\n",
|
||
" price =value\n",
|
||
" break\n",
|
||
" elif package.weight<=2718 and package.fst_size<=50 and package.sed_size<=40 and package.trd_size<=30:\n",
|
||
" for key, value in air_small_dict.items():\n",
|
||
" if bill_weight <=key:\n",
|
||
" price =value\n",
|
||
" break\n",
|
||
" else:\n",
|
||
" for key, value in air_big_dict.items():\n",
|
||
" if bill_weight <=key:\n",
|
||
" price =value\n",
|
||
" break\n",
|
||
" if package.weight<=420:\n",
|
||
" express_fee+=((((min(max(package.density,37),337)*0.093+27.7)/6+0.65)*package.get_volume_weight(6000))*0.3+price)/0.45\n",
|
||
" express_type+='USPS'\n",
|
||
" elif package.weight<=2718:\n",
|
||
" express_fee+=(((min(max(package.density,37),337)*0.093+27.7)/6+0.65)*package.get_volume_weight(8500)*0.3+price)/0.45\n",
|
||
" express_type+='UandF'\n",
|
||
" else:\n",
|
||
" express_fee+=(((min(max(package.density,37),337)*0.093+27.7-1.08)/6+0.65-1.06)*package.get_volume_weight(8500))/0.45+price\n",
|
||
" express_type+='FEDEX'\n",
|
||
" return express_fee, express_type\n",
|
||
" "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 记录包裹的最大重量和\n",
|
||
"\n",
|
||
"from utils.Package import Package, Package_group\n",
|
||
"import re\n",
|
||
"for index, row in df.iterrows():\n",
|
||
" try:\n",
|
||
" package_dict = json.loads(row['ERP包裹数据'])\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"行 {index} 解析失败: {e}\")\n",
|
||
" print(row['实际包裹数据'])\n",
|
||
" continue\n",
|
||
" packages = Package_group()\n",
|
||
" def extract_number(value):\n",
|
||
" # 提取字符串中的第一个数字\n",
|
||
" match = re.search(r\"[-+]?\\d*\\.\\d+|\\d+\", str(value))\n",
|
||
" return float(match.group()) if match else 0.0\n",
|
||
" for key, package in package_dict.items():\n",
|
||
" package['长'] = extract_number(package['长'])\n",
|
||
" package['宽'] = extract_number(package['宽'])\n",
|
||
" package['高'] = extract_number(package['高'])\n",
|
||
" package['重量'] = extract_number(package['重量'])\n",
|
||
" \n",
|
||
" if package['长'] == 0 or package['宽'] == 0 or package['高'] == 0 or package['重量'] == 0:\n",
|
||
" continue\n",
|
||
" packages.add_package(Package(key,package['长'], package['宽'], package['高'], package['重量']))\n",
|
||
" if packages is None:\n",
|
||
" continue\n",
|
||
" if row['运输方式']=='海运':\n",
|
||
" order_fee, order_type = ocean_order_price(packages)\n",
|
||
" else:\n",
|
||
" order_fee, order_type = air_order_price(packages)\n",
|
||
" \n",
|
||
" # 订单信息\n",
|
||
" df.loc[index, 'ERP物流费'] = order_fee\n",
|
||
" df.loc[index, '尾端类型'] = order_type\n",
|
||
" print(order_fee, order_type)\n",
|
||
"df.to_clipboard(index=False)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 取sku所属SPU下所有sku及其现在售价\n",
|
||
"import json\n",
|
||
"# 单个品类一个一个处理\n",
|
||
"category = \"66 - Furniture\"\n",
|
||
"df_one = order_id_df_cal[order_id_df_cal['产品品类']==category]\n",
|
||
"sku_list = (\n",
|
||
" df_one['SKU']\n",
|
||
" .apply(pd.to_numeric, errors='coerce') # 转数字，非法的变 NaN\n",
|
||
" .dropna()\n",
|
||
" .astype(int)\n",
|
||
" .astype(str)\n",
|
||
" .tolist()\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"placeholders = ','.join(['%s'] * len(sku_list)) \n",
|
||
"\n",
|
||
"# 从哪个表查询\n",
|
||
"with MySQLconnect('ods') as db:\n",
|
||
" enginal = db.engine()\n",
|
||
" sql = f\"\"\"SELECT\n",
|
||
" SKU,\n",
|
||
" 价格 AS ERP采购价,\n",
|
||
" 规格,\n",
|
||
" cpmaso规格,\n",
|
||
" `标准/预设属性集`,\n",
|
||
" 自定义属性集\n",
|
||
"\n",
|
||
" FROM\n",
|
||
" erp_furniture_sku sku\n",
|
||
" LEFT JOIN stg_bayshop_litfad_spu spu ON sku.`产品PID` = spu.`产品PID`\n",
|
||
" WHERE\n",
|
||
" spu.SPU IN (\n",
|
||
" SELECT\n",
|
||
" SPU \n",
|
||
" FROM\n",
|
||
" stg_bayshop_litfad_sku sku\n",
|
||
" LEFT JOIN stg_bayshop_litfad_spu spu ON sku.`产品PID` = spu.`产品PID` \n",
|
||
" WHERE\n",
|
||
" sku.SKU IN ({placeholders}) \n",
|
||
" )\n",
|
||
" \"\"\"\n",
|
||
" result = pd.read_sql(sql, enginal,params= tuple(sku_list))\n",
|
||
"df_one = pd.merge(df_one, result, on=['SKU'], how='left')\n",
|
||
"df_one.to_excel(f'{category}.xlsx')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"1.找到SPU最新到仓的SKU及其体积\n",
|
||
"2.根据算法F(x)计算这个SPU的其他SKU理论长宽高重量\n",
|
||
"3.找到该SPU其他有过实际体积的SKU，记录这些实际体积\n",
|
||
"4.分析这些SKU的实际体积和理论体积的差距"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"df = pd.read_excel(r'D:\\test\\logistics\\拦截数据\\一票一件发货订单.xlsx',sheet_name=\"单包裹数据\")\n",
|
||
"# 写一个cal_size算法，输入原属性和新属性，返回涨幅系数\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import re\n",
|
||
"import json\n",
|
||
"for index, row in df.iterrows():\n",
|
||
" package_dict = json.loads(row['包裹数据'])\n",
|
||
" for package in package_dict.values():\n",
|
||
" item = {}\n",
|
||
" for key, value in package.items():\n",
|
||
" try:\n",
|
||
" # 使用正则表达式提取数字部分\n",
|
||
" number_str = re.findall(r\"[-+]?\\d*\\.\\d+|\\d+\", str(value))\n",
|
||
" if number_str:\n",
|
||
" item[key] = float(number_str[0]) # 取第一个匹配到的数字并转换为 float\n",
|
||
" else:\n",
|
||
" item[key] = value # 如果没有数字部分，保留原值\n",
|
||
" except ValueError:\n",
|
||
" item[key] = value # 如果遇到无法转换的值，保留原值\n",
|
||
" size = []\n",
|
||
" size.append(item['长'])\n",
|
||
" size.append(item['宽'])\n",
|
||
" size.append(item['高'])\n",
|
||
" weight = item['重量']\n",
|
||
" size.sort()\n",
|
||
" length = size[2]\n",
|
||
" width = size[1]\n",
|
||
" height = size[0]\n",
|
||
" df.loc[index, 'ERP长'] = length\n",
|
||
" df.loc[index, 'ERP宽'] = width\n",
|
||
" df.loc[index, 'ERP高'] = height\n",
|
||
" df.loc[index, 'ERP重量'] = weight\n",
|
||
" print(f\"{row['SKU']}尺寸为:{width},h:{height},d:{length},w:{weight}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def cal_size(old,new):\n",
|
||
" try:\n",
|
||
" old = float(old)\n",
|
||
" new = float(new)\n",
|
||
" if old == 0:\n",
|
||
" return None # 或 return 0，防止除以0\n",
|
||
" return (new - old) / old\n",
|
||
" except (ValueError, TypeError):\n",
|
||
" return None # 遇到不能转为 float 的就返回 None\n",
|
||
"# 按SPU分组，first_df取每个SPU组里订单月份最大的那行数据，如果订单月份相同，取第一条\n",
|
||
"first_df = df.groupby('SPU').apply(lambda x: x.loc[x['订单月份'].idxmax()]).reset_index(drop=True)\n",
|
||
"# 按SPU分组，计算每个SPU的涨幅系数\n",
|
||
"for index, row in first_df.iterrows():\n",
|
||
" first_df.loc[index, '长系数'] = cal_size(row['ERP长'],row['长'])\n",
|
||
" first_df.loc[index, '宽系数'] = cal_size(row['ERP宽'],row['宽'])\n",
|
||
" first_df.loc[index, '高系数'] = cal_size(row['ERP高'],row['高'])\n",
|
||
" first_df.loc[index, '重量系数'] = cal_size(row['ERP重量'],row['重量'])\n",
|
||
" print(\n",
|
||
" f\"{row['SPU']} 的系数为 \"\n",
|
||
" f\"{first_df.loc[index, '长系数']}, \"\n",
|
||
" f\"{first_df.loc[index, '宽系数']}, \"\n",
|
||
" f\"{first_df.loc[index, '高系数']}, \"\n",
|
||
" f\"{first_df.loc[index, '重量系数']}\"\n",
|
||
" )\n",
|
||
" \n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 将每个SPU的系数，并入df表中\n",
|
||
"df = pd.merge(df, first_df[['SPU','长系数','宽系数','高系数','重量系数']], on='SPU',how='left')\n",
|
||
"print(\"合并完成\")\n",
|
||
"# 根据系数计算每个SKU的理论尺寸\n",
|
||
"df['理论长'] = ((1 + df['长系数']) * df['ERP长']).round(2)\n",
|
||
"df['理论宽'] = ((1 + df['宽系数']) * df['ERP宽']).round(2)\n",
|
||
"df['理论高'] = ((1 + df['高系数']) * df['ERP高']).round(2)\n",
|
||
"df['理论重量'] = ((1 + df['重量系数']) * df['ERP重量']).round(2)\n",
|
||
"\n",
|
||
"df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 构造一个set，存储first_df中的 (SKU, 订单月份)\n",
|
||
"first_packages = set(first_df['包裹号'])\n",
|
||
"\n",
|
||
"# 新增列，包裹号在first_packages中标1，否则0\n",
|
||
"df['is_first'] = df['包裹号'].apply(lambda x: 1 if x in first_packages else 0)\n",
|
||
"df.to_clipboard(index=False)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 计算预测后的尺寸下，一票一件订单的售价和订单价格\n",
|
||
"from sell.sell_price import call_sell_and_order_price\n",
|
||
"for index,row in df.iterrows():\n",
|
||
" price = row['成本价']\n",
|
||
" package_dict1={}\n",
|
||
" package_dict2={}\n",
|
||
" try:\n",
|
||
" package_dict1['包裹1'] = {}\n",
|
||
" package_dict2['包裹1'] = {}\n",
|
||
" package_dict1['包裹1']['长'] = row['长']\n",
|
||
" package_dict1['包裹1']['宽'] = row['宽']\n",
|
||
" package_dict1['包裹1']['高'] = row['高']\n",
|
||
" package_dict1['包裹1']['重量'] = row['重量']\n",
|
||
" package_dict2['包裹1']['长'] = row['理论长']\n",
|
||
" package_dict2['包裹1']['宽'] = row['理论宽'] \n",
|
||
" package_dict2['包裹1']['高'] = row['理论高']\n",
|
||
" package_dict2['包裹1']['重量'] = row['理论重量']\n",
|
||
" sell_price1, order_price1, order_type1 = call_sell_and_order_price(price, package_dict1)\n",
|
||
" sell_price2, order_price2, order_type2 = call_sell_and_order_price(price, package_dict2)\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"SKU: {row['SKU']} 报错: {e}\")\n",
|
||
" continue\n",
|
||
" df.loc[index, '实际体积售价'] = sell_price1\n",
|
||
" df.loc[index, '实际体积订单价'] = order_price1\n",
|
||
" df.loc[index, '实际体积订单类型'] = order_type1\n",
|
||
" df.loc[index, '理论体积售价'] = sell_price2\n",
|
||
" df.loc[index, '理论体积订单价'] = order_price2\n",
|
||
" df.loc[index, '理论体积订单类型'] = order_type2\n",
|
||
" print(f\"SPU: {row['SPU']}, SKU {row['SKU']} 实际体积售价: {sell_price1}, 理论体积售价: {sell_price2},\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"计算SPU下所有SKU的网站售价，实际尺寸售价，预测尺寸售价"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 取表格数据is_first为1的数据，取spu,长宽高重量系数\n",
|
||
"import pandas as pd\n",
|
||
"df = pd.read_excel(r'D:\\test\\logistics\\拦截数据\\一票一件发货订单.xlsx',sheet_name=\"单包裹系数计算\")\n",
|
||
"# 写一个cal_size算法，输入原属性和新属性，返回涨幅系数\n",
|
||
"df1=df[['is_first','SPU','SKU','长','宽','高','重量']]\n",
|
||
"base_df = df[df['is_first']==1]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"\n",
|
||
"# 取这些SPU下的所有SKU及其现在售价\n",
|
||
"from sell.sell_price import call_sell_and_order_price\n",
|
||
"import json\n",
|
||
"from utils.gtools import MySQLconnect\n",
|
||
"import pandas as pd\n",
|
||
"spu_list = (\n",
|
||
" base_df['SPU']\n",
|
||
" .apply(pd.to_numeric, errors='coerce') # 转数字，非法的变 NaN\n",
|
||
" .dropna()\n",
|
||
" .astype(int)\n",
|
||
" .astype(str)\n",
|
||
" .tolist()\n",
|
||
")\n",
|
||
"placeholders = ','.join(['%s'] * len(spu_list)) \n",
|
||
"# result = []\n",
|
||
"with MySQLconnect('ods') as db:\n",
|
||
" enginal = db.engine()\n",
|
||
" sql = f\"\"\"SELECT\n",
|
||
" 产品品类,\n",
|
||
" 产品分类,\n",
|
||
" SPU,\n",
|
||
" sku.SKU,\n",
|
||
" sku.`成本价`,\n",
|
||
" spi.`包裹数据`,\n",
|
||
" 物流分摊,\n",
|
||
" 产品售价\n",
|
||
" FROM\n",
|
||
" stg_bayshop_litfad_sku sku\n",
|
||
" LEFT JOIN stg_bayshop_litfad_spu spu ON sku.`产品PID` = spu.`产品PID` \n",
|
||
" LEFT JOIN ads.new_erp_sku_size spi ON sku.SKU =spi.SKU\n",
|
||
" WHERE\n",
|
||
" spu.SPU IN ({placeholders}) \n",
|
||
" \"\"\"\n",
|
||
" result = pd.read_sql(sql, enginal,params= tuple(spu_list))\n",
|
||
"# 合并df\n",
|
||
"all_df = pd.merge(result,df, on=['SKU'], how='left')\n",
|
||
"all_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 先把ERP包裹数据拆出来\n",
|
||
"import re\n",
|
||
"import json\n",
|
||
"for index, row in all_df.iterrows():\n",
|
||
" if not isinstance(row['包裹数据'], str) or not row['包裹数据']:\n",
|
||
" print(f\"第{index}行包裹数据为空或非字符串，跳过\")\n",
|
||
" continue\n",
|
||
" try:\n",
|
||
" package_dict = json.loads(row['包裹数据'])\n",
|
||
" except json.JSONDecodeError as e:\n",
|
||
" print(f\"解析失败：第{index}行，错误信息：{e}\")\n",
|
||
" continue\n",
|
||
" try:\n",
|
||
" for package in package_dict.values():\n",
|
||
" item = {}\n",
|
||
" for key, value in package.items():\n",
|
||
" try:\n",
|
||
" # 使用正则表达式提取数字部分\n",
|
||
" number_str = re.findall(r\"[-+]?\\d*\\.\\d+|\\d+\", str(value))\n",
|
||
" if number_str:\n",
|
||
" item[key] = float(number_str[0]) # 取第一个匹配到的数字并转换为 float\n",
|
||
" else:\n",
|
||
" item[key] = value # 如果没有数字部分，保留原值\n",
|
||
" except ValueError:\n",
|
||
" item[key] = value # 如果遇到无法转换的值，保留原值\n",
|
||
" except AttributeError:\n",
|
||
" print(f\"解析失败：第{index}行，错误信息：包裹数据为空\")\n",
|
||
" continue\n",
|
||
" size = []\n",
|
||
" size.append(item['长'])\n",
|
||
" size.append(item['宽'])\n",
|
||
" size.append(item['高'])\n",
|
||
" weight = item['重量']\n",
|
||
" size.sort()\n",
|
||
" length = size[2]\n",
|
||
" width = size[1]\n",
|
||
" height = size[0]\n",
|
||
" all_df.loc[index, 'ERP长'] = length\n",
|
||
" all_df.loc[index, 'ERP宽'] = width\n",
|
||
" all_df.loc[index, 'ERP高'] = height\n",
|
||
" all_df.loc[index, 'ERP重量'] = weight\n",
|
||
" print(f\"{row['SKU']}尺寸为:{width},h:{height},d:{length},w:{weight}\")\n",
|
||
" "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 计算每个SPU的长宽高重量系数\n",
|
||
"def cal_size(old,new):\n",
|
||
" try:\n",
|
||
" old = float(old)\n",
|
||
" new = float(new)\n",
|
||
" if old == 0:\n",
|
||
" return None # 或 return 0，防止除以0\n",
|
||
" return (new - old) / old\n",
|
||
" except (ValueError, TypeError):\n",
|
||
" return None # 遇到不能转为 float 的就返回 None\n",
|
||
" \n",
|
||
"test_df = all_df[all_df['is_first']==1]\n",
|
||
"# 取基准数据SPU的系数\n",
|
||
"for index, row in test_df.iterrows():\n",
|
||
" test_df.loc[index, '长系数'] = cal_size(row['ERP长'],row['长'])\n",
|
||
" test_df.loc[index, '宽系数'] = cal_size(row['ERP宽'],row['宽'])\n",
|
||
" test_df.loc[index, '高系数'] = cal_size(row['ERP高'],row['高'])\n",
|
||
" test_df.loc[index, '重量系数'] = cal_size(row['ERP重量'],row['重量'])\n",
|
||
" print(\n",
|
||
" f\"{row['SPU_x']} 的系数为 \"\n",
|
||
" f\"{test_df.loc[index, '长系数']}, \"\n",
|
||
" f\"{test_df.loc[index, '宽系数']}, \"\n",
|
||
" f\"{test_df.loc[index, '高系数']}, \"\n",
|
||
" f\"{test_df.loc[index, '重量系数']}\"\n",
|
||
" )\n",
|
||
"# 将SPU的基准系数合并至all_df\n",
|
||
"all_df = pd.merge(all_df, test_df[['SPU_x', '长系数', '宽系数', '高系数', '重量系数']], on='SPU_x', how='left')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 根据系数 得到所有SKU的预测尺寸\n",
|
||
"# 根据系数计算每个SKU的理论尺寸\n",
|
||
"all_df['理论长'] = ((1 + all_df['长系数']) * all_df['ERP长']).round(2)\n",
|
||
"all_df['理论宽'] = ((1 + all_df['宽系数']) * all_df['ERP宽']).round(2)\n",
|
||
"all_df['理论高'] = ((1 + all_df['高系数']) * all_df['ERP高']).round(2)\n",
|
||
"all_df['理论重量'] = ((1 + all_df['重量系数']) * all_df['ERP重量']).round(2)\n",
|
||
"all_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 计算三种尺寸下的售价\n",
|
||
"# 计算预测后的尺寸下，一票一件订单的售价和订单价格\n",
|
||
"from sell.sell_price import call_sell_and_order_price\n",
|
||
"for index,row in all_df.iterrows():\n",
|
||
" price = row['成本价']\n",
|
||
" package_dict1={}\n",
|
||
" package_dict2={}\n",
|
||
" package_dict3={}\n",
|
||
" try:\n",
|
||
" package_dict1['包裹1'] = {}\n",
|
||
" package_dict2['包裹1'] = {}\n",
|
||
" package_dict3['包裹1'] = {}\n",
|
||
" package_dict1['包裹1']['长'] = row['长']\n",
|
||
" package_dict1['包裹1']['宽'] = row['宽']\n",
|
||
" package_dict1['包裹1']['高'] = row['高']\n",
|
||
" package_dict1['包裹1']['重量'] = row['重量']\n",
|
||
" package_dict2['包裹1']['长'] = row['理论长']\n",
|
||
" package_dict2['包裹1']['宽'] = row['理论宽'] \n",
|
||
" package_dict2['包裹1']['高'] = row['理论高']\n",
|
||
" package_dict2['包裹1']['重量'] = row['理论重量']\n",
|
||
" package_dict3['包裹1']['长'] = row['ERP长']\n",
|
||
" package_dict3['包裹1']['宽'] = row['ERP宽'] \n",
|
||
" package_dict3['包裹1']['高'] = row['ERP高']\n",
|
||
" package_dict3['包裹1']['重量'] = row['ERP重量']\n",
|
||
" sell_price1, order_price1, order_type1 = call_sell_and_order_price(price, package_dict1)\n",
|
||
" sell_price2, order_price2, order_type2 = call_sell_and_order_price(price, package_dict2)\n",
|
||
" sell_price3, order_price3, order_type3 = call_sell_and_order_price(price, package_dict3)\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"SKU: {row['SKU']} 报错: {e}\")\n",
|
||
" continue\n",
|
||
" all_df.loc[index, 'ERP售价'] = sell_price3\n",
|
||
" all_df.loc[index, '实际体积售价'] = sell_price1\n",
|
||
" all_df.loc[index, '理论体积售价'] = sell_price2\n",
|
||
"\n",
|
||
" all_df.loc[index, 'ERP订单价'] = order_price3\n",
|
||
" all_df.loc[index, '实际体积订单价'] = order_price1\n",
|
||
" all_df.loc[index, '理论体积订单价'] = order_price2\n",
|
||
" # all_df.loc[index, '理论体积订单类型'] = order_type2\n",
|
||
" print(f\"SPU: {row['SPU_x']}, SKU {row['SKU']} ,ERP售价: {sell_price3}, 实际体积售价: {sell_price1}, 理论体积售价: {sell_price2},\")\n",
|
||
"all_df.to_excel('单包裹SKU售价分析.xlsx', index=False)"
|
||
]
|
||
}
|
||
],
|
||
"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
|
||
}
|