311 lines
13 KiB
Plaintext
311 lines
13 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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"一丶stock_date表示备货日期"
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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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"# 取备货时间date符合方案的SKU\n",
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"\"\"\"\n",
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"1.订单上线时间三个月以上\n",
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"2.前一个月订单数>=4或前两个月订单数>=5\n",
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"数据提取方案：\n",
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"1.设置变量stock_date为备货时间\n",
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"2.计算出stock_date的两个月前的order_date2和一个月前的order_date1\n",
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"3.查询order_date1至stock_date的订单数和order_date2至stock_date的订单数\n",
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"4.筛选出order_date1至stock_date的订单数>=4或order_date2至stock_date的订单数>=5的SKU\n",
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"5.计算上线时间和date的差值,筛选>3个月天的产品\n",
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"\"\"\"\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"from datetime import datetime\n",
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"from dateutil.relativedelta import relativedelta\n",
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"\n",
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"stock_date = '2025-11-18'\n",
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"stock_datetime = datetime.strptime(stock_date, '%Y-%m-%d')\n",
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"\n",
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"# 计算一个月前和两个月前\n",
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"order_date1 = (stock_datetime - relativedelta(months=1)).strftime('%Y-%m-%d') # 一个月前订单时间\n",
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"order_date2 = (stock_datetime - relativedelta(months=2)).strftime('%Y-%m-%d') # 两个月前订单时间\n",
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"online_date = (stock_datetime - relativedelta(months=3)).strftime('%Y-%m-%d') # 上线时间\n",
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"\n",
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"after_date1 = (stock_datetime + relativedelta(months=1)).strftime('%Y-%m-%d') # 一个月后订单时间\n",
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"after_date2 = (stock_datetime + relativedelta(months=2)).strftime('%Y-%m-%d') # 两个月后订单时间\n",
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"after_date3 = (stock_datetime + relativedelta(months=3)).strftime('%Y-%m-%d') # 三个月后订单时间\n",
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"after_date4 = (stock_datetime + relativedelta(months=4)).strftime('%Y-%m-%d') # 四个月后订单时间\n",
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"\n",
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"print(after_date1, after_date2, after_date3, after_date4, order_date1,order_date2,online_date)"
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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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"当前板块直接跑是计算美国区的备货情况，如果需要看全球的，请搜索delivery_country并把这行注释掉（在sql里）"
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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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"# 读取订单数据\n",
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"from utils.gtools import MySQLconnect\n",
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"with MySQLconnect('ods') as db:\n",
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" enginal = db.engine()\n",
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" sql = f\"\"\"\n",
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" # 取采购价\n",
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" with \n",
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" pur_cost AS (\n",
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" SELECT\n",
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" SUBSTRING_INDEX(opl.order_product_id, '_', 2) AS order_product_id,\n",
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" SUM(actual_price) AS 采购成本,\n",
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" SKU,\n",
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" ROW_NUMBER() OVER (PARTITION BY SKU ORDER BY order_date DESC) as rn\n",
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" FROM\n",
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" dws.order_product_list opl\n",
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" LEFT JOIN ods.warehouse_purchasing wp ON opl.order_product_id = wp.order_product_id\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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" AND order_date >= '{order_date1}'\n",
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" AND order_date < '{stock_datetime}'\n",
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" AND opl.fun_audit NOT REGEXP \"等待\"\n",
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" AND opl.site_name REGEXP \"Litfad\"\n",
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" AND opl.product_audit REGEXP \"采购完成\"\n",
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" AND opl.SKU IS NOT NULL and opl.SKU <>0\n",
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" GROUP BY \n",
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" SUBSTRING_INDEX(opl.order_product_id, '_', 2) \n",
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"\n",
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" ),\n",
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" t1 AS (\n",
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" SELECT\n",
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" spu.`产品品类`,\n",
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" spu.`产品分类`,\n",
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" pc.采购成本,\n",
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" opl.SKU,\n",
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" COUNT(DISTINCT CASE WHEN DATE_FORMAT(opl.order_date, '%%Y-%%m-%%d') >= '{order_date2}' and DATE_FORMAT(opl.order_date, '%%Y-%%m-%%d') < '{order_date1}' THEN opl.order_id END) AS 前第二月订单数,\n",
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" COUNT(DISTINCT CASE WHEN DATE_FORMAT(opl.order_date, '%%Y-%%m-%%d') >= '{order_date1}' and DATE_FORMAT(opl.order_date, '%%Y-%%m-%%d') < '{stock_datetime}' THEN opl.order_id END) AS 前一月订单数,\n",
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" SUM(CASE WHEN (DATE_FORMAT(opl.order_date, '%%Y-%%m-%%d') >= '{stock_datetime}' AND DATE_FORMAT(opl.order_date, '%%Y-%%m') < '{after_date1}') \n",
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" THEN opl.product_num ELSE 0 END) AS 后第一月产品数,\n",
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" SUM(CASE WHEN (DATE_FORMAT(opl.order_date, '%%Y-%%m') >= '{after_date1}' AND DATE_FORMAT(opl.order_date, '%%Y-%%m-%%d') <'{after_date2}') \n",
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" THEN opl.product_num ELSE 0 END) AS 后第两月产品数,\n",
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" SUM(CASE WHEN (DATE_FORMAT(opl.order_date, '%%Y-%%m') >= '{after_date2}' AND DATE_FORMAT(opl.order_date, '%%Y-%%m') < '{after_date3}') \n",
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" THEN opl.product_num ELSE 0 END) AS 后第三月产品数,\n",
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" SUM(CASE WHEN DATE_FORMAT(opl.order_date, '%%Y-%%m') >= '{after_date3}' \n",
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" THEN opl.product_num ELSE 0 END) AS 后第四月产品数,\n",
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" DATE_FORMAT(sku.添加时间,'%%Y-%%m-%%d') AS 上线时间,\n",
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" DATE_FORMAT(sku.更新时间,'%%Y-%%m-%%d') AS 更新时间\n",
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" \n",
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" FROM\n",
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" dws.order_product_list opl\n",
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" LEFT JOIN pur_cost pc ON opl.SKU = pc.SKU \n",
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" LEFT JOIN ods.order_list ol ON opl.order_id =ol.order_id\n",
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" LEFT JOIN ods.stg_bayshop_litfad_sku sku ON opl.SKU = sku.SKU\n",
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" LEFT JOIN ods.stg_bayshop_litfad_spu spu ON sku.`产品PID` = spu.`产品PID` \n",
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" WHERE\n",
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" opl.order_date >= '{order_date2}'\n",
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" AND DATE_FORMAT(opl.order_date, '%%Y-%%m') < '{after_date4}'\n",
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" AND DATE_FORMAT(sku.添加时间,'%%Y-%%m-%%d') < '{online_date}'\n",
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" AND opl.SKU IS NOT NULL and opl.SKU <>0\n",
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" AND opl.order_product_id NOT REGEXP '^[^_]*_[^_]*_[^_]*$'\n",
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" AND site_type REGEXP \"独立站\"\n",
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" AND ol.fund_status NOT REGEXP \"等待\"\n",
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" and sku.状态 REGEXP \"启用\"\n",
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" AND spu.状态 REGEXP \"正常\"\n",
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" AND 采购成本>0\n",
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" AND rn = 1\n",
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" and 产品品类 <> \"126 - Outdoor\"\n",
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" and 产品品类 <>\"57 - Rugs\"\n",
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" and 产品分类 <> \"151 - Peel & Stick Backsplash Tile\"\n",
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" and 产品分类 <> \"138 - Bathroom Sinks\"\n",
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" and 产品分类 <> \"184 - Toddler & Kids Chairs\"\n",
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" and opl.delivery_country regexp \"United States\"\n",
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" GROUP BY opl.SKU\n",
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" )\n",
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" \n",
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" \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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" SKU,\n",
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" 采购成本,\n",
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" 前第二月订单数+前一月订单数 AS 前两月订单数,\n",
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" 前一月订单数 AS 备货数,\n",
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" 后第一月产品数,\n",
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" 后第两月产品数,\n",
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" 后第三月产品数,\n",
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" 后第四月产品数,\n",
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" (后第一月产品数+后第两月产品数+后第三月产品数+后第四月产品数) AS 后四月总产品数\n",
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" FROM \n",
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" t1\n",
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" WHERE\n",
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" 前一月订单数 >=4 OR (前一月订单数>=2 AND (前第二月订单数+前一月订单数)>=5)\n",
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" ORDER BY 前一月订单数 DESC\n",
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" \"\"\"\n",
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" df = pd.read_sql(sql, enginal)"
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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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"# 取上诉SKU的体积\n",
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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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"sku_list = (\n",
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" df['SKU']\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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"# 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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" enginal = db.engine()\n",
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" quoted_spus = ','.join([f\"'{sku}'\" for sku in sku_list])\n",
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" sql = f\"\"\" \n",
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" WITH\n",
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" t1 AS (\n",
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" SELECT\n",
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" opl.order_id,\n",
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" SKU,\n",
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" ol.order_price_dollar AS 订单售价,\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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" 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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" left join ods.order_list ol ON opl.order_id = ol.order_id\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 opl.delivery_country regexp \"United States\"\n",
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" AND SKU in ({quoted_spus})\n",
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" AND SKU <> \"\" AND SKU IS NOT NULL AND SKU <> 0\n",
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" GROUP BY order_id\n",
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" ),\n",
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" t2 AS (\n",
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" SELECT \n",
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" t1.SKU,\n",
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" 订单售价,\n",
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" round(SUM(b.weight/1000 ),0)AS 重量,\n",
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" ROUND(SUM(b.length*b.width*b.hight/1000000),2) AS 体积,\n",
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" COUNT(package) AS 包裹数\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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" WHERE\n",
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" a.`包裹状态` = '客户签收'\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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" GROUP BY order_id\n",
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" )\n",
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" SELECT\n",
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" SKU,\n",
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" AVG(订单售价) AS 订单平均售价,\n",
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" AVG(重量) AS 平均重量,\n",
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" AVG(体积) AS 平均体积\n",
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" FROM t2\n",
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" GROUP BY SKU\n",
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" \n",
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"\n",
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" \"\"\"\n",
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" package_df = pd.read_sql(sql, enginal)\n",
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"\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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"all_df = pd.merge(df, package_df, on='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": "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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"# 处理每个条目的不同备货要求\n",
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"# 66 - Furniture\n",
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"fir_df = all_df[\n",
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" ((all_df['产品品类'] == '66 - Furniture') & (all_df['前两月订单数'] >5))|\n",
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" ((all_df['产品品类'] == '1 - Lighting' )& (all_df['前两月订单数'] >5))]\n",
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"\n",
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"fir_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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"len(fir_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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"fir_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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