545 lines
19 KiB
Plaintext
545 lines
19 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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"取数据"
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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('ods') 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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"# 测量时间D +2 天进行汇总数据\n",
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"# 订单汇总产品数和取出\n",
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"# 测量时间D +2 天进行汇总数据\n",
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"# 订单汇总产品数和取出\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 REGEXP \"[0-9]{15}_[0-9]*$\"\n",
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" 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(DISTINCT opl.SKU) AS 产品种类\n",
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"FROM\n",
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"dws.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 left(r.order_product_id,15) = opl.order_id\n",
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" \n",
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" )\n",
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"AND order_date >= \"2025-05-01\"\n",
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"AND order_date < \"2025-09-18\"\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 \n",
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" a.`包裹测量时间`,\n",
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" t1.order_id,\n",
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" t1.SKU,\n",
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" t1.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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" a.`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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" t1\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` != '--'\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 t1.product_num = 1\n",
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" AND t1.产品种类=1\n",
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" AND a.`包裹测量时间` >= '2025-06-01'\n",
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" AND a.`包裹测量时间` < '2025-09-16'\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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"SPU,\n",
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"sku.成本价 AS ERP采购价,\n",
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"CONCAT('{', GROUP_CONCAT(package_json SEPARATOR ','), '}') AS 实际包裹数据,\n",
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"count(package_json) AS 包裹数,\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 stg_bayshop_litfad_sku sku ON t2.SKU=sku.SKU\n",
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"left JOIN stg_bayshop_litfad_spu spu ON sku.产品PID=spu.产品PID\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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"SPU,\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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"实际包裹数据,\n",
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"包裹数,\n",
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"rn AS 从新到旧\n",
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"FROM\n",
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"t3\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.to_clipboard(index=False)\n",
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"\n",
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"# df=df[df['实际包裹数量']==1]\n",
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"df"
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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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"拆开实际包裹数据，并标记为1"
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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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"# 先把ERP包裹数据拆出来\n",
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"import re\n",
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"import json\n",
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"base_df=df.copy()\n",
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"for index, row in base_df.iterrows():\n",
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" if not isinstance(row['实际包裹数据'], str) or not row['实际包裹数据']:\n",
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" print(f\"第{index}行包裹数据为空或非字符串，跳过\")\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 json.JSONDecodeError as e:\n",
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" print(f\"解析失败：第{index}行，错误信息：{e}\")\n",
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" continue\n",
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" count = len(package_dict)\n",
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" print(f\"第{index}行，包裹数量：{count}\")\n",
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" if count !=1:\n",
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" print(f\"第{index}行，包裹数量不为1，跳过\")\n",
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" continue\n",
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" try:\n",
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" for package in package_dict.values():\n",
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" item = {}\n",
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"\n",
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" for key, value in package.items():\n",
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" try:\n",
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" # 使用正则表达式提取数字部分\n",
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" number_str = re.findall(r\"[-+]?\\d*\\.\\d+|\\d+\", str(value))\n",
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" if number_str:\n",
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" item[key] = float(number_str[0]) # 取第一个匹配到的数字并转换为 float\n",
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" else:\n",
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" item[key] = value # 如果没有数字部分，保留原值\n",
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" except ValueError:\n",
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" item[key] = value # 如果遇到无法转换的值，保留原值\n",
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" except AttributeError:\n",
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" print(f\"解析失败：第{index}行，错误信息：包裹数据为空\")\n",
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" continue\n",
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" size = []\n",
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" size.append(item['长'])\n",
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" size.append(item['宽'])\n",
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" size.append(item['高'])\n",
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" weight = item['重量']\n",
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" size.sort()\n",
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" length = size[2]\n",
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" width = size[1]\n",
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" height = size[0]\n",
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" base_df.loc[index,'is_first'] = 1\n",
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" base_df.loc[index, '长'] = length\n",
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" base_df.loc[index, '宽'] = width\n",
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" base_df.loc[index, '高'] = height\n",
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" base_df.loc[index, '重量'] = weight\n",
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"\n",
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" print(f\"{row['SKU']}尺寸为:{width},h:{height},d:{length},w:{weight}\")\n",
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"base_df"
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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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"取SPU下所有SKU"
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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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"\n",
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"# 取这些SPU下的所有SKU及其现在售价\n",
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"# from sell.sell_price import call_sell_and_order_price\n",
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"import json\n",
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"from utils.gtools import MySQLconnect\n",
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"import pandas as pd\n",
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"base_df=df.copy()\n",
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"spu_list = (\n",
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" base_df['SPU']\n",
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" .apply(pd.to_numeric, errors='coerce')\n",
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" .dropna()\n",
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" .astype(int)\n",
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" .astype(str)\n",
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" .drop_duplicates() # 加这一行\n",
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" .tolist()\n",
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")\n",
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"\n",
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"def chunk_list(lst, size):\n",
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" for i in range(0, len(lst), size):\n",
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" yield lst[i:i+size]\n",
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"\n",
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"result_list = []\n",
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"with MySQLconnect('ods') as db:\n",
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" enginal = db.engine()\n",
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" for chunk in chunk_list(spu_list, 100):\n",
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" quoted_spus = ','.join([f\"'{spu}'\" for spu in chunk]) # 加引号防止 SQL 错误\n",
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" sql = f\"\"\"\n",
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" SELECT\n",
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" 产品品类,\n",
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" 产品分类,\n",
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" SPU,\n",
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" SKU,\n",
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" sku.成本价,\n",
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" 物流分摊,\n",
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" 产品售价\n",
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" from stg_bayshop_litfad_spu spu \n",
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" LEFT JOIN stg_bayshop_litfad_sku sku ON sku.产品PID = spu.产品PID\n",
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" WHERE spu.SPU IN ({quoted_spus})\n",
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" \"\"\"\n",
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" df_chunk = pd.read_sql(sql, enginal)\n",
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" result_list.append(df_chunk)\n",
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" print(f\"已处理 {len(result_list) * 100} 个SPU\")\n",
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"\n",
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"result = pd.concat(result_list, ignore_index=True)\n",
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"\n",
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"# 合并df\n",
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"all_df = pd.merge(result,base_df, on=['SPU','SKU'], how='left')\n",
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"all_df"
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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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"# 所有的SKU 分类和汇总 ->层级一样的SKU"
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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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"# 按 SPU 分组；\n",
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"# 在组内按 成本价升序 排序；\n",
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"# 成本价相同的 SKU 属于同一个层次；\n",
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"# 层次号就是「第几种不同的成本价」。# \n",
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"all_df['层次'] = all_df.groupby('SPU')['成本价'].rank(method='dense').astype(int)\n",
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"all_df"
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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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"# 哪几个层级有实际数据，估算其他没有数据的层级的数据\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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},
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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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"# 先把ERP包裹数据拆出来\n",
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"import re\n",
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"import json\n",
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"for index, row in all_df.iterrows():\n",
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" if not isinstance(row['ERP包裹数据'], str) or not row['ERP包裹数据']:\n",
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" print(f\"第{index}行包裹数据为空或非字符串，跳过\")\n",
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" continue\n",
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" try:\n",
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" package_dict = json.loads(row['ERP包裹数据'])\n",
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" except json.JSONDecodeError as e:\n",
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" print(f\"解析失败：第{index}行，错误信息：{e}\")\n",
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" continue\n",
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" count = len(package_dict)\n",
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" print(f\"第{index}行，包裹数量：{count}\")\n",
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" if count !=1:\n",
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" print(f\"第{index}行，包裹数量不为1，跳过\")\n",
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" continue\n",
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" \n",
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" try:\n",
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" for package in package_dict.values():\n",
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" item = {}\n",
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"\n",
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" for key, value in package.items():\n",
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" try:\n",
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" # 使用正则表达式提取数字部分\n",
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" number_str = re.findall(r\"[-+]?\\d*\\.\\d+|\\d+\", str(value))\n",
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" if number_str:\n",
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" item[key] = float(number_str[0]) # 取第一个匹配到的数字并转换为 float\n",
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" else:\n",
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" item[key] = value # 如果没有数字部分，保留原值\n",
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" except ValueError:\n",
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" item[key] = value # 如果遇到无法转换的值，保留原值\n",
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" except AttributeError:\n",
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" print(f\"解析失败：第{index}行，错误信息：包裹数据为空\")\n",
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" continue\n",
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" size = []\n",
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" size.append(item['长'])\n",
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" size.append(item['宽'])\n",
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" size.append(item['高'])\n",
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" weight = item['重量']\n",
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" size.sort()\n",
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" length = size[2]\n",
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" width = size[1]\n",
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" height = size[0]\n",
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" all_df.loc[index, 'ERP包裹数'] = count\n",
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" all_df.loc[index, 'ERP长'] = length\n",
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" all_df.loc[index, 'ERP宽'] = width\n",
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" all_df.loc[index, 'ERP高'] = height\n",
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" all_df.loc[index, 'ERP重量'] = weight\n",
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" print(f\"{row['SKU']}尺寸为:{width},h:{height},d:{length},w:{weight}\")\n",
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" "
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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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"计算每个SPU的长宽高重量系数"
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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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"# 计算每个SPU的长宽高重量系数\n",
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"def cal_size(old,new):\n",
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" try:\n",
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" old = float(old)\n",
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" new = float(new)\n",
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" if old == 0:\n",
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" return None # 或 return 0，防止除以0\n",
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" return (new - old) / old\n",
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" except (ValueError, TypeError):\n",
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" return None # 遇到不能转为 float 的就返回 None\n",
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" \n",
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"test_df = all_df[all_df['is_first']==1]\n",
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"# 取基准数据SPU的系数\n",
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"for index, row in test_df.iterrows():\n",
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" test_df.loc[index, '长系数'] = cal_size(row['ERP长'],row['长'])\n",
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" test_df.loc[index, '宽系数'] = cal_size(row['ERP宽'],row['宽'])\n",
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" test_df.loc[index, '高系数'] = cal_size(row['ERP高'],row['高'])\n",
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" test_df.loc[index, '重量系数'] = cal_size(row['ERP重量'],row['重量'])\n",
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" print(\n",
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" f\"{row['SPU']} 的系数为 \"\n",
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" f\"{test_df.loc[index, '长系数']}, \"\n",
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" f\"{test_df.loc[index, '宽系数']}, \"\n",
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" f\"{test_df.loc[index, '高系数']}, \"\n",
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" f\"{test_df.loc[index, '重量系数']}\"\n",
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" )\n",
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"# 将SPU的基准系数合并至all_df\n",
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"all_df = pd.merge(all_df, test_df[['SPU', '长系数', '宽系数', '高系数', '重量系数']], on='SPU', how='left')\n",
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"all_df.to_excel('单包裹SKU售价分析.xlsx', 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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"计算每个sku的理论尺寸\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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"all_df = pd.read_excel('单包裹SKU售价分析.xlsx')\n",
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"\n",
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"# 根据系数 得到所有SKU的预测尺寸\n",
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"# 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.to_excel('单包裹SKU售价分析.xlsx', index=False)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"计算三种尺寸下的售价，计算预测后的尺寸下，一票一件订单的售价和订单价格"
|
||
]
|
||
},
|
||
{
|
||
"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,head_type=\"海运\")\n",
|
||
" sell_price2, order_price2, order_type2 = call_sell_and_order_price(price, package_dict2,head_type=\"海运\")\n",
|
||
" sell_price3, order_price3, order_type3 = call_sell_and_order_price(price, package_dict3,head_type=\"海运\")\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"SKU: {row['SKU']} 报错: {e}\")\n",
|
||
" continue\n",
|
||
" if isinstance(sell_price3, (int, float)):\n",
|
||
" all_df.loc[index, 'ERP售价'] = sell_price3\n",
|
||
" else:\n",
|
||
" all_df.loc[index, 'ERP售价'] = sell_price3[0]\n",
|
||
" if isinstance(sell_price1, (int, float)):\n",
|
||
" all_df.loc[index, '实际体积售价'] = sell_price1\n",
|
||
" else:\n",
|
||
" all_df.loc[index, '实际体积售价'] = sell_price1[0]\n",
|
||
"\n",
|
||
" if isinstance(sell_price2, (int, float)):\n",
|
||
" all_df.loc[index, '理论体积售价'] = sell_price2\n",
|
||
" else:\n",
|
||
" all_df.loc[index, '理论体积售价'] = sell_price2[0]\n",
|
||
"\n",
|
||
"\n",
|
||
" all_df.loc[index, 'ERP订单物流'] = order_price3\n",
|
||
" all_df.loc[index, '实际体积订单物流'] = order_price1\n",
|
||
" all_df.loc[index, '理论体积订单物流'] = order_price2\n",
|
||
" # all_df.loc[index, '理论体积订单类型'] = order_type2\n",
|
||
" print(f\"SPU: {row['SPU']}, SKU {row['SKU']} ,网站售价: {row['产品售价']}, ERP售价: {sell_price3}, 实际体积售价: {sell_price1}, 理论体积售价: {sell_price2},\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"写成可以上传批量修改尺寸的格式"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"all_df = all_df[(all_df['是否有过修改记录']==\"否\")&(all_df['使用尺寸售价']!=\"ERP售价\")]\n",
|
||
"all_df['SPU最大涨幅']=all_df.groupby('SPU')['售价涨跌幅'].transform(max)\n",
|
||
"all_df['SPU最小涨幅']=all_df.groupby('SPU')['售价涨跌幅'].transform(min)\n",
|
||
"\n",
|
||
"filtered_df = all_df[(all_df['SPU最大涨幅'] <= 0.5) & (all_df['SPU最小涨幅'] >= -0.5)]\n",
|
||
"for index,row in filtered_df.iterrows():\n",
|
||
" if row['使用尺寸售价']==\"实际体积售价\":\n",
|
||
" length = str(row['长'])\n",
|
||
" width = str(row['宽'])\n",
|
||
" height = str(row['高'])\n",
|
||
" weight = str(row['重量'])\n",
|
||
" else:\n",
|
||
" length = str(row['理论长'])\n",
|
||
" width = str(row['理论宽'])\n",
|
||
" height = str(row['理论高'])\n",
|
||
" weight = str(row['理论重量'])\n",
|
||
" filtered_df.loc[index, '尺寸重量'] = f\"{weight}|{length}*{width}*{height}*1,\"\n",
|
||
"\n",
|
||
" print(index)\n",
|
||
"spu_list = filtered_df['SPU'].unique()\n",
|
||
"filtered_df = filtered_df[['SKU','成本价','尺寸重量']]\n",
|
||
"filtered_df "
|
||
]
|
||
}
|
||
],
|
||
"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
|
||
}
|