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{
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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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"id": "d40f14fe",
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"metadata": {},
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"outputs": [],
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"source": [
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"from login_for_cookie import Vc\n",
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"import requests\n",
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"import pandas as pd\n",
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"import re \n",
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"cookie = Vc()\n",
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"header = {\"cookie\":cookie}"
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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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"id": "fa7b4aa7",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 固定汇率\n",
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"def rate_to_rmb(curruency):\n",
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" if curruency == \"CNY\":\n",
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" rate = 1\n",
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" if curruency == \"USD\":\n",
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" rate = 7\n",
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" if curruency == \"EUR\":\n",
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" rate = 8 \n",
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" if curruency == \"GBP\":\n",
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" rate = 9\n",
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" if curruency == \"JPY\":\n",
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" rate = 0.05\n",
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" if curruency == \"AUD\":\n",
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" rate = 5\n",
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" if curruency == \"CAD\":\n",
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" rate = 5\n",
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" if curruency == \"HKD\":\n",
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" rate = 1\n",
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" return rate"
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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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"id": "99fd242f",
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"metadata": {},
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"outputs": [],
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"source": [
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"aurl = \"https://cp.maso.hk/index.php?main=biphp&act=package_fund&key=W6BOYJ7BH27YCGRFCA0LWBVKMU1KRU5Q&package=991517855\"\n",
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"burl = \"https://cp.maso.hk/index.php?main=biphp&key=W6BOYJ7BH27YCGRFCA0LWBVKMU1KRU5Q&act=expend_settle_detail&id=3277\"\n",
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"curl = \"https://cp.maso.hk/index.php?main=biphp&key=W6BOYJ7BH27YCGRFCA0LWBVKMU1KRU5Q&act=bol_settle_detail&id=2324\"\n",
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"durl = \"https://cp.maso.hk/index.php?main=biphp&key=W6BOYJ7BH27YCGRFCA0LWBVKMU1KRU5Q&act=express_settle_detail&id=7787\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "5e6e24f4",
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"metadata": {},
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"source": [
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"处理尾端结算数据"
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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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"id": "705c5336",
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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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"with MySQLconnect(\"workflow\") as db:\n",
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" engine = db.engine()\n",
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" sql_list= pd.read_sql(\"SELECT 物流结算号 FROM `flow_check_tail`\", engine)\n",
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" # 转成列表\n",
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" D_LIST = sql_list['物流结算号'].dropna().astype(int).tolist()\n",
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"express_df = pd.DataFrame()\n",
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"for d in D_LIST:\n",
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" url = \"https://cp.maso.hk/index.php?main=biphp&key=W6BOYJ7BH27YCGRFCA0LWBVKMU1KRU5Q&act=express_settle_detail&id=%s\"% d\n",
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" p_get = requests.get(url)\n",
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" data = p_get.json()\n",
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" temp_df = pd.DataFrame(data[\"data\"])\n",
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" temp_df[\"结算号\"]=d\n",
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" temp_df[\"track_number\"] = temp_df[\"track_number\"].astype(str) \n",
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" express_df = pd.concat([express_df,temp_df],axis=0)\n",
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"\n",
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"from utils.gtools import MySQLconnect\n",
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"import json\n",
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"express_df['账单运费'] = pd.to_numeric(express_df['bill_express_fee'].str.replace(r'[A-Z]+', '', regex=True),errors='coerce')\n",
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"express_df['币种'] = express_df['bill_express_fee'].str.replace(r'[0-9.]+', '', regex=True)\n",
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"express_df['rmb'] = express_df.apply(lambda row: round(row['账单运费']*rate_to_rmb(row['币种'])[0],2), axis=1)\n",
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"\n",
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"# 根据快递跟踪号找到包裹号\n",
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"missing_odr_df = express_df[express_df['odr_express_id'].isna() | (express_df['odr_express_id']=='--')]\n",
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"track_list = missing_odr_df['track_number'].dropna().unique().tolist()\n",
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"if track_list:\n",
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" with MySQLconnect('ods') as db:\n",
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" conn = db.connect()\n",
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" format_strings = ','.join(['%s'] * len(track_list))\n",
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" sql = f\"SELECT 快递跟踪号 AS track_number,包裹号 AS odr_express_id FROM `order_express` WHERE 快递跟踪号 IN ({format_strings})\"\n",
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" df_mapping = pd.read_sql(sql, conn, params=track_list)\n",
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" mapping_dict = dict(zip(df_mapping['track_number'], df_mapping['odr_express_id']))\n",
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" def fill_odr(row):\n",
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" if pd.isna(row['odr_express_id']) or row['odr_express_id'] == '--':\n",
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" return mapping_dict.get(row['track_number'], row['odr_express_id'])\n",
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" else:\n",
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" return row['odr_express_id']\n",
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" express_df['odr_express_id'] = express_df.apply(fill_odr, axis=1)\n",
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"\n",
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"workflow = MySQLconnect('workflow')\n",
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"def safe_json(x):\n",
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" if isinstance(x, (dict, list, set)):\n",
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" return json.dumps(x, ensure_ascii=False)\n",
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" return x\n",
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"express_df['bill_add_express_fee'] = express_df['bill_add_express_fee'].apply(safe_json)\n",
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"express_df.to_sql('flow_check_express_wxx', workflow.engine(), if_exists='replace')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "af1fa88d",
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"metadata": {},
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"source": [
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"处理头程结算数据"
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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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"id": "f30b25ad",
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"metadata": {},
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"outputs": [],
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"source": [
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"with MySQLconnect(\"workflow\") as db:\n",
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" engine = db.engine()\n",
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" sql_list= pd.read_sql(\"SELECT 物流结算号 FROM `flow_check_head`\", engine)\n",
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" # 转成列表\n",
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" C_LIST = sql_list['物流结算号'].dropna().astype(int).tolist()\n",
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"\n",
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"bol_df = pd.DataFrame()\n",
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"for c in C_LIST:\n",
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" url = \"https://cp.maso.hk/index.php?main=biphp&key=W6BOYJ7BH27YCGRFCA0LWBVKMU1KRU5Q&act=bol_settle_detail&id=%s\"% c\n",
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" p_get = requests.get(url)\n",
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" # print(p_get.text\n",
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" p_get_new=p_get.text.replace(\"\\t\", \"\")\n",
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" data = json.loads(p_get_new)\n",
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" temp_df = pd.DataFrame(data[\"data\"])\n",
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" temp_df[\"结算号\"]=c\n",
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" bol_df = pd.concat([bol_df,temp_df],axis=0)\n",
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"bol_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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"id": "9bb83abf",
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"metadata": {},
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"outputs": [],
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"source": [
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"#处理头程费用转rmb和体积单价\n",
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"bol_df['price'] = bol_df['price'].astype(float)\n",
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"bol_df['rmb'] = bol_df.apply(lambda row: round(row['price']*rate_to_rmb(row['currency'])[0],2), axis=1)\n",
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"bol_list = bol_df['bol_code'].dropna().unique().tolist()\n",
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"if bol_list:\n",
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" with MySQLconnect('ods') as db:\n",
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" conn = db.connect()\n",
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" format_strings = ','.join(['%s'] * len(bol_list))\n",
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" sql = f\"\"\"WITH t1 AS (\n",
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" SELECT\n",
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" `提单/柜号` AS bol_code,\n",
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" `提单ID` AS bol_id ,\n",
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" `体积cm3`,\n",
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" ROW_NUMBER() OVER(PARTITION BY `提单/柜号` ORDER BY `离港时间` DESC) AS row_index\n",
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" FROM\n",
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" bol_list \n",
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" WHERE\n",
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" `提单/柜号` IN ({format_strings})\n",
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" )\n",
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" SELECT `bol_code`,`bol_id`,`体积cm3` FROM t1 WHERE row_index = 1\n",
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" \"\"\"\n",
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" df_mapping = pd.read_sql(sql, conn, params=bol_list)\n",
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" # 合并\n",
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" bol_df = pd.merge(bol_df, df_mapping, on=['bol_code'], how='left')\n",
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"bol_df['体积cm3'] = bol_df['体积cm3'].astype(float)\n",
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"bol_df['体积单价'] = bol_df['rmb']/bol_df['体积cm3'] \n",
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"workflow = MySQLconnect('workflow')\n",
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"bol_df.to_sql('flow_check_head_wxx', workflow.engine(), if_exists='replace') "
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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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"id": "4a78e03f",
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"metadata": {},
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"outputs": [],
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"source": [
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"#找bol_id下的所有包裹和体积，得到单包裹的头程价格\n",
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"bol_id_list = bol_df['bol_id'].dropna().unique().tolist()\n",
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"with MySQLconnect('ods') as db:\n",
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" conn = db.connect()\n",
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" format_strings = ','.join(['%s'] * len(bol_id_list))\n",
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" sql = f\"\"\"SELECT\n",
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" `包裹号`,\n",
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" btp.`提单ID` AS bol_id,\n",
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" length,\n",
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" width,\n",
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" hight,\n",
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" weight,\n",
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" length * width * hight AS 体积 \n",
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" FROM\n",
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" bol_to_package btp\n",
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" LEFT JOIN package_vol_info pvi ON btp.包裹号 = pvi.package \n",
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" WHERE btp.`提单ID` IN ({format_strings})\n",
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" \"\"\"\n",
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" bol_package = pd.read_sql(sql, conn, params=bol_id_list)\n",
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"\n",
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"# 根据bol_df计算每个bol_id的单价（多行求和）,保存为一个map字典\n",
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"bol_id_price = bol_df.groupby('bol_id')['体积单价'].sum().to_dict()\n",
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"# bol_package新增一列，为体积*体积单价,体积单价根据bol_id从字典中获取\n",
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"bol_package['头程价格'] = bol_package['体积'] * bol_package['bol_id'].map(bol_id_price)\n",
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"bol_package = bol_package.drop_duplicates()\n",
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"workflow = MySQLconnect('workflow')\n",
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"bol_package.to_sql('flow_check_package_head_wxx', workflow.engine(), if_exists='replace') "
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]
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},
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{
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"cell_type": "markdown",
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"id": "431bcdff",
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"metadata": {},
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"source": [
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"处理额外费用数据"
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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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"id": "7184abdf",
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"metadata": {},
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"outputs": [],
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"source": [
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"with MySQLconnect(\"workflow\") as db:\n",
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" engine = db.engine()\n",
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" sql_list= pd.read_sql(\"SELECT 物流结算号 FROM `flow_check_expend`\", engine)\n",
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" # 转成列表\n",
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" B_LIST = sql_list['物流结算号'].dropna().astype(int).tolist()\n",
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"\n",
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"expend_df = pd.DataFrame()\n",
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"for b in B_LIST:\n",
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" url = \"https://cp.maso.hk/index.php?main=biphp&key=W6BOYJ7BH27YCGRFCA0LWBVKMU1KRU5Q&act=expend_settle_detail&id=%s \"% b\n",
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" p_get = requests.get(url)\n",
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" data = p_get.json()\n",
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" temp_df = pd.DataFrame(data[\"data\"])\n",
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" temp_df[\"结算号\"]=b\n",
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" expend_df = pd.concat([expend_df,temp_df],axis=0)\n",
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"\n",
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"expend_df['账单运费'] = pd.to_numeric(expend_df['pay_price'].str.replace(r'[A-Z]+', '', regex=True),errors='coerce')\n",
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"expend_df['币种'] = expend_df['pay_price'].str.replace(r'[0-9.]+', '', regex=True)\n",
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"expend_df['rmb'] = expend_df.apply(lambda row: round(row['账单运费']*rate_to_rmb(row['币种'])[0],2), axis=1)\n",
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"\n",
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"workflow = MySQLconnect('workflow')\n",
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"expend_df.to_sql('flow_check_expend_wxx', workflow.engine(), if_exists='replace') "
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]
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},
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{
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"cell_type": "markdown",
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"id": "361b5796",
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"metadata": {},
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"source": [
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"三种费用合并，以包裹为单位保存"
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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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"id": "c6d66242",
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"metadata": {},
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"outputs": [],
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"source": [
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"with MySQLconnect('workflow') as db:\n",
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" conn = db.connect()\n",
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" format_strings = ','.join(['%s'] * len(bol_id_list))\n",
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" sql = \"\"\"SELECT DISTINCT\n",
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" tail.odr_express_id,\n",
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" head.`头程价格`,\n",
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" tail.rmb AS 尾程费用,\n",
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" expend.rmb AS 额外费用\n",
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" FROM\n",
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" flow_check_express_wxx tail \n",
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" LEFT JOIN flow_check_package_head_wxx head ON tail.odr_express_id = head.`包裹号`\n",
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" LEFT JOIN flow_check_expend_wxx expend ON tail.odr_express_id = expend.odr_express_id\n",
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" \"\"\"\n",
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" package_fee = pd.read_sql(sql, conn)\n",
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" package_fee.to_sql('flow_check_package_fee_wxx', workflow.engine(), if_exists='replace') \n"
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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": 5
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}
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@ -1,148 +0,0 @@
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{
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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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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_2152\\2535791683.py:19: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
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" order_df = pd.read_sql(query, db.con)\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" DATE_FORMAT(order_date,'%Y-%m-%d') order_id \\\n",
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"0 2024-08-01 240801000602903 \n",
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"1 2024-08-01 240801000802198 \n",
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"2 2024-08-01 240801001202641 \n",
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"3 2024-08-01 240801001402908 \n",
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"4 2024-08-01 240801001414785 \n",
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"... ... ... \n",
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"143421 2025-01-20 250120235520923 \n",
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"143422 2025-01-20 250120235602837 \n",
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"143423 2025-01-20 250120235616266 \n",
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"143424 2025-01-20 250120235627823 \n",
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"143425 2025-01-20 250120235802366 \n",
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"\n",
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" order_price_dollar order_freight_price_dollar order_cate \n",
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"0 201.66 64.30 家具 \n",
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"1 420.65 53.60 家具 \n",
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"2 51.93 13.66 家具 \n",
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"3 2099.55 112.20 家具 \n",
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"4 154.47 39.78 灯具 \n",
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"... ... ... ... \n",
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"143421 496.54 50.00 家具 \n",
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"143422 121.50 17.02 灯具 \n",
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"143423 281.34 9.66 家具 \n",
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"143424 312.36 73.47 家具 \n",
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"143425 117.39 17.21 灯具 \n",
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||||
"\n",
|
||||
"[143426 rows x 5 columns]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from utils.gtools import MySQLconnect\n",
|
||||
"import pandas as pd\n",
|
||||
"# 先找出去年八月之后的订单号，订单时间，订单费用，订单所属条目，订单物流成本\n",
|
||||
"with MySQLconnect('ods') as db:\n",
|
||||
" query = \"\"\"SELECT\n",
|
||||
" DATE_FORMAT( order_date, '%Y-%m-%d' ),\n",
|
||||
" order_id,\n",
|
||||
" order_price_dollar,\n",
|
||||
" order_cate,\n",
|
||||
" SUM(pfi.package_fund + pfi.head_way_express_fee + pfi.other_expend + pfi.indemnity)/7 AS `物流成本` \n",
|
||||
" FROM\n",
|
||||
" ods.order_list ol\n",
|
||||
" LEFT JOIN order_express oe ON ol.order_id = oe.`单号`\n",
|
||||
" LEFT JOIN package_fee_info pfi ON oe.包裹号 = pfi.package \n",
|
||||
" WHERE\n",
|
||||
" order_date >= '2024-08-01' \n",
|
||||
" AND order_date <= '2025-01-21' \n",
|
||||
" AND fund_status NOT REGEXP '等待|退款|失败|冻结' \n",
|
||||
" AND site_name = 'Litfad' \n",
|
||||
" GROUP BY\n",
|
||||
" order_id\n",
|
||||
" \"\"\"\n",
|
||||
" order_df = pd.read_sql(query, db.con)\n",
|
||||
" print(order_df)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 取采购价\n",
|
||||
"order_id = order_df['order_id'].tolist()\n",
|
||||
"order_ids = ','.join(f\"'{i}'\" for i in order_id)\n",
|
||||
"with MySQLconnect('ods') as db:\n",
|
||||
" query = f\"\"\"\n",
|
||||
"with t1 AS (SELECT LEFT\n",
|
||||
" ( ol.out_detials_outlink_id, 15 ) AS order_id,\n",
|
||||
" SUM( out_detials_qty * price )/ 7 AS instock_cost,\n",
|
||||
"\t\t\t\tNULL AS buy_cost\n",
|
||||
" FROM\n",
|
||||
" ods.outstock_list ol\n",
|
||||
" JOIN ods.instock_list il ON ol.store_in_id = il.id \n",
|
||||
" WHERE\n",
|
||||
" LEFT ( ol.out_detials_outlink_id, 15 ) IN ({order_ids}) \n",
|
||||
" GROUP BY\n",
|
||||
" LEFT ( ol.out_detials_outlink_id, 15 )\n",
|
||||
"UNION ALL\n",
|
||||
" SELECT\n",
|
||||
" LEFT ( order_product_id, 15 ) as order_id, \n",
|
||||
"\t\t\t\tNULL as instock_cost,\n",
|
||||
"\t\t\t\tSUM(buy_num * actual_price)/ 7 AS buy_cost\n",
|
||||
" FROM\n",
|
||||
" `warehouse_purchasing`\n",
|
||||
" WHERE\n",
|
||||
" LEFT ( order_product_id, 15 ) IN ({order_ids}) \n",
|
||||
" AND buy_audit = \"采购完成\"\n",
|
||||
" group by LEFT ( order_product_id, 15 )\n",
|
||||
"\t\t)\n",
|
||||
"\t\t\n",
|
||||
"\tSELECT\n",
|
||||
"\torder_id,\n",
|
||||
"\tSUM(CASE \n",
|
||||
"\tWHEN instock_cost is null THEN\n",
|
||||
"\t\tbuy_cost\n",
|
||||
"\tELSE\n",
|
||||
"\t\tinstock_cost END) AS pur_cost\n",
|
||||
"\tFROM\n",
|
||||
"\tt1 \n",
|
||||
"\tGROUP BY order_id\n",
|
||||
"\t\n",
|
||||
"\"\"\"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
BIN
data/~$售价尾端价格.xlsx (Stored with Git LFS)
BIN
data/~$售价尾端价格.xlsx (Stored with Git LFS)
Binary file not shown.
Binary file not shown.
BIN
data/澳洲三大渠道.xlsx (Stored with Git LFS)
BIN
data/澳洲三大渠道.xlsx (Stored with Git LFS)
Binary file not shown.
BIN
data/美国卡派-AM.xlsx (Stored with Git LFS)
BIN
data/美国卡派-AM.xlsx (Stored with Git LFS)
Binary file not shown.
BIN
data/美国快递.xlsx (Stored with Git LFS)
BIN
data/美国快递.xlsx (Stored with Git LFS)
Binary file not shown.
BIN
data/英国卡派.xlsx (Stored with Git LFS)
BIN
data/英国卡派.xlsx (Stored with Git LFS)
Binary file not shown.
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
BIN
product_property_data.xlsx (Stored with Git LFS)
BIN
product_property_data.xlsx (Stored with Git LFS)
Binary file not shown.
|
|
@ -0,0 +1,85 @@
|
|||
|
||||
import pandas
|
||||
|
||||
# 日本空运
|
||||
def jp_air_order_price(packages):
|
||||
# 先判断包裹数
|
||||
if len(packages) > 30:
|
||||
return 0,"包裹数超过30"
|
||||
air_order_price = 0
|
||||
air_order_type = '空运'
|
||||
for package in packages:
|
||||
m = package.density
|
||||
v = package.get_volume_weight(6)
|
||||
if package.weight <= 2000:
|
||||
order_price = ((min(max(m, 37), 337) * 0.038 + 9.482 - 1.08) / 6 + 1.38 / 6 - 1.06) * v / 1000 / 0.359 + 4.03
|
||||
elif package.weight <= 5000:
|
||||
order_price = ((min(max(m, 37), 337) * 0.038 + 9.482 - 1.08) / 6 + 1.38 / 6 - 1.06) * v / 1000 / 0.359 + 4.57
|
||||
elif package.weight <= 10000:
|
||||
order_price = ((min(max(m, 37), 337) * 0.038 + 9.482 - 1.08) / 6 + 1.38 / 6 - 1.06) * v / 1000 / 0.359 + 5.83
|
||||
elif package.weight <= 20000:
|
||||
order_price = ((min(max(m, 37), 337) * 0.038 + 9.482 - 1.08) / 6 + 1.38 / 6 - 1.06) * v / 1000 / 0.359 + 8.7
|
||||
elif package.weight <= 30000:
|
||||
order_price = ((min(max(m, 37),337) * 0.038 + 9.482 - 1.08) / 6 + 1.38 / 6 - 1.06) * v / 1000 / 0.359 + 11.48
|
||||
else:
|
||||
return 999999,"不可发"
|
||||
air_order_price += order_price
|
||||
air_order_price = round(air_order_price, 2)
|
||||
return air_order_price,air_order_type
|
||||
|
||||
def jp_order_yamato(packages,t):
|
||||
# 先判断包裹数
|
||||
if len(packages) > 30:
|
||||
return 0,"包裹数超过30"
|
||||
order_type = 'yamato'
|
||||
order_fee = 0
|
||||
# 判断不可发
|
||||
|
||||
for package in packages:
|
||||
v = package.get_volume_weight(6)
|
||||
billing_weight = max(package.weight,v)
|
||||
if package.weight > 30000 or v > 49400:
|
||||
return 999999,"不可发"
|
||||
if billing_weight <= 2000:
|
||||
jp_base_fee = 700 if v<=1400 else 920
|
||||
order_fee1 = jp_base_fee /0.359/146 -7.82
|
||||
elif billing_weight <= 5000:
|
||||
jp_base_fee = 920 if v<=3200 else 1162
|
||||
order_fee1 = jp_base_fee /0.359/146 -6.64
|
||||
elif billing_weight <= 10000:
|
||||
jp_base_fee = 1162 if v<=6200 else 1382
|
||||
order_fee1 = jp_base_fee /0.359/146 -6.91
|
||||
elif billing_weight <=15000:
|
||||
jp_base_fee = 1382 if v<=10700 else 1624
|
||||
order_fee1 = jp_base_fee /0.359/146 -7.84
|
||||
elif billing_weight <=20000:
|
||||
jp_base_fee = 1624 if v<=16900 else 1844
|
||||
order_fee1 = jp_base_fee /0.359/146 -8.83
|
||||
elif billing_weight <=25000:
|
||||
jp_base_fee = 1844
|
||||
order_fee1 = jp_base_fee /0.359/146 -10.99
|
||||
elif billing_weight <=30000:
|
||||
if package.weight >25000 or v >25300:
|
||||
jp_base_fee = 2548
|
||||
elif v <=25300:
|
||||
jp_base_fee =1844
|
||||
order_fee1 = jp_base_fee /0.359/146 -12.87
|
||||
elif billing_weight <=36000:
|
||||
jp_base_fee = 2548
|
||||
order_fee1 = jp_base_fee /0.359/146 -14.57
|
||||
elif billing_weight <=49400:
|
||||
jp_base_fee =2988
|
||||
order_fee1 = jp_base_fee /0.359/146 -17.62
|
||||
order_fee += (order_fee1 -0.4*v/1000 +t*0.05)/0.97
|
||||
return round(order_fee,2),order_type
|
||||
|
||||
def min_price(packages):
|
||||
air_order_fee,air_type = jp_air_order_price(packages)
|
||||
yamato_order_fee,yamato_type = jp_order_yamato(packages)
|
||||
if air_order_fee <= yamato_order_fee:
|
||||
order_fee =air_order_fee
|
||||
order_type = air_type
|
||||
else:
|
||||
order_fee = yamato_order_fee
|
||||
order_type = yamato_type
|
||||
return order_fee,order_type
|
||||
|
|
@ -0,0 +1,604 @@
|
|||
{
|
||||
"90 - Coffee Tables": [
|
||||
"210000540",
|
||||
"210000571",
|
||||
"210000572",
|
||||
"210000600",
|
||||
"210000625",
|
||||
"210000663",
|
||||
"210000858",
|
||||
"210000946",
|
||||
"210000967",
|
||||
"210000999",
|
||||
"210001035",
|
||||
"210001041",
|
||||
"210001059",
|
||||
"210001084",
|
||||
"210001165",
|
||||
"210001191",
|
||||
"210001219",
|
||||
"210001223",
|
||||
"210001247",
|
||||
"210001274",
|
||||
"210001291",
|
||||
"210001377",
|
||||
"210001425",
|
||||
"210001450",
|
||||
"210001512",
|
||||
"210001535",
|
||||
"210001643",
|
||||
"210001653",
|
||||
"210001661",
|
||||
"210001673",
|
||||
"210001674",
|
||||
"21249142",
|
||||
"21249588",
|
||||
"21252084",
|
||||
"21332340",
|
||||
"21332467",
|
||||
"21332469",
|
||||
"21332728",
|
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"21333212",
|
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"21333497",
|
||||
"21333523",
|
||||
"21333567",
|
||||
"21333588",
|
||||
"21333590",
|
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|
||||
"21333596",
|
||||
"21333599",
|
||||
"21333602",
|
||||
"21333634",
|
||||
"21333638",
|
||||
"21524553",
|
||||
"21535749",
|
||||
"21538593",
|
||||
"21538748",
|
||||
"21539009",
|
||||
"21539772",
|
||||
"21555357",
|
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"21589210",
|
||||
"21593737",
|
||||
"21595949",
|
||||
"21614455",
|
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"21614613",
|
||||
"21614659",
|
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"21617425",
|
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"21617473",
|
||||
"21619743",
|
||||
"21619753",
|
||||
"21628450",
|
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"21632720",
|
||||
"21633273",
|
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"21633292",
|
||||
"21633312",
|
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"21633358",
|
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"21633525",
|
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"21633535",
|
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"21633814",
|
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"21633815",
|
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"21634501",
|
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"21635275",
|
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"21635455",
|
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"21636588",
|
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"21636722",
|
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"21636821",
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"21686185",
|
||||
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|
||||
"21690675",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"21702866",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"21705852",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"21708877",
|
||||
"21708949",
|
||||
"21709030",
|
||||
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|
||||
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|
||||
"21709377",
|
||||
"21709469",
|
||||
"21709495",
|
||||
"21709497",
|
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|
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}
|
||||
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260021
test.ipynb
260021
test.ipynb
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10525
一票一件订单.ipynb
10525
一票一件订单.ipynb
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49
产品上限优化.ipynb
49
产品上限优化.ipynb
|
|
@ -2,7 +2,7 @@
|
|||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -29,7 +29,7 @@
|
|||
"\t\tLEFT JOIN dws.order_product_list opl ON t2.SKU = opl.SKU \n",
|
||||
"\tWHERE\n",
|
||||
"\t\tt1.添加时间 BETWEEN '2023-01-01' \n",
|
||||
"\t\tAND '2025-06-16 23:59:59' \n",
|
||||
"\t\tAND '2024-12-31 23:59:59' \n",
|
||||
"\t\tAND 产品分类 = '{categories}'\n",
|
||||
"\t\tAND t2.SKU IS NOT NULL \n",
|
||||
"\t),\n",
|
||||
|
|
@ -73,7 +73,9 @@
|
|||
"source": [
|
||||
"import json\n",
|
||||
"import re\n",
|
||||
"from utils import Package,Package_group\n",
|
||||
"\n",
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"from sell.sell_price import call_sell_and_order_price\n",
|
||||
"def extract_number(value):\n",
|
||||
" # 提取字符串中的第一个数字\n",
|
||||
|
|
@ -91,9 +93,9 @@
|
|||
" package['高'] = extract_number(package['高'])\n",
|
||||
" package['重量'] = extract_number(package['重量'])\n",
|
||||
" size =sorted([package['长'],package['宽'],package['高']])\n",
|
||||
" fst_size = size[0]\n",
|
||||
" fst_size = size[2]\n",
|
||||
" snd_size = size[1]\n",
|
||||
" thd_size = size[2]\n",
|
||||
" thd_size = size[0]\n",
|
||||
" max_length=max(max_length,fst_size)\n",
|
||||
" max_girth=max(max_girth,fst_size+(snd_size+thd_size)*2)\n",
|
||||
" all_weight+=package['重量']/1000\n",
|
||||
|
|
@ -107,8 +109,43 @@
|
|||
" df.loc[index,'最大围长'] = max_girth\n",
|
||||
" df.loc[index,'总重量'] = all_weight\n",
|
||||
" df.loc[index,'总抛重'] = all_vol_weight\n",
|
||||
" print(index)\n",
|
||||
"\n",
|
||||
"# 按照那个分组，按照总抛重，每5总抛重为一组，最长边取大，最短边取小，最大实重取大，最小实重取小，网站售价求和，物流分摊费求和，订单物流费求和，尾端类型不要，"
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 按照那个分组，按照总抛重，每5总抛重为一组，最长边取大，最短边取小，最大实重取大，最小实重取小，网站售价求和，物流分摊费求和，订单物流费求和，尾端类型不要，\n",
|
||||
"cost_bins = list(range(0, 4000, 10)) +[28700]\n",
|
||||
"df['成本价分组'] = pd.cut(df['成本价'], bins=cost_bins, right=True, labels=cost_bins[1:])\n",
|
||||
"\n",
|
||||
"# 2. 总抛重分组（按5为一组，0-5 为一组，5.01-10 为一组，等）\n",
|
||||
"df['总抛重分组'] = (np.ceil(df['总抛重'] / 5) * 5).astype(int)\n",
|
||||
"df = df.dropna(subset=['成本价分组'])\n",
|
||||
"# 3. 分组聚合\n",
|
||||
"agg_df = df.groupby(['成本价分组', '总抛重分组'], observed=True).agg({\n",
|
||||
" '最长边': ['max', 'min'], # 每组最大 每组最小\n",
|
||||
" '最大围长': 'max',\n",
|
||||
" '总重量': ['max', 'min','sum'], # 分别取最大/最小实重\n",
|
||||
" '网站售价': 'sum',\n",
|
||||
" '物流分摊费': 'sum',\n",
|
||||
" '订单物流费': 'sum',\n",
|
||||
" 'SKU': 'count'\n",
|
||||
"}).reset_index()\n",
|
||||
"\n",
|
||||
"# 4. 重命名列\n",
|
||||
"agg_df.columns = [\n",
|
||||
" '成本价分组', '总抛重分组',\n",
|
||||
" '最长边max', '最长边min', '最大围长',\n",
|
||||
" '总重量max', '总重量min','总重量',\n",
|
||||
" '网站售价', '物流分摊费', '订单物流费','SKU种类'\n",
|
||||
"]\n",
|
||||
"agg_df.to_clipboard(index=False)"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
|
|||
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|
|
@ -0,0 +1,188 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "a13175db",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"BASE_SQL = \"\"\"\n",
|
||||
"WITH \n",
|
||||
"odr_list AS ( \n",
|
||||
" SELECT \n",
|
||||
" dwd.order_list.order_id,\n",
|
||||
" dwd.order_list.site_name,\n",
|
||||
" dwd.order_list.order_date,\n",
|
||||
" dwd.order_list.delivery_country,\n",
|
||||
" dwd.order_list.postcode,\n",
|
||||
" dwd.order_list.currency,\n",
|
||||
" dwd.order_list.order_price,\n",
|
||||
" dwd.order_list.order_freight_price,\n",
|
||||
" dwd.order_list.order_price_dollar,\n",
|
||||
" dwd.order_list.order_freight_price_dollar,\n",
|
||||
" dwd.order_list.insurance,\n",
|
||||
" dwd.order_list.discount_code,\n",
|
||||
" dwd.order_list.order_cate,\n",
|
||||
" dwd.order_list.fund_status,\n",
|
||||
" dwd.order_list.convey \n",
|
||||
" FROM \n",
|
||||
" dwd.order_list \n",
|
||||
" WHERE \n",
|
||||
" site_name REGEXP \"^litfad|beau|kwowking|lakiq\" \n",
|
||||
" AND fund_status NOT REGEXP \"等待\" \n",
|
||||
" AND DATE_FORMAT(order_date,\"%Y-%m-01\") >= \"2025-06-01\"\n",
|
||||
"),\n",
|
||||
"\n",
|
||||
"refund AS (\n",
|
||||
" SELECT\n",
|
||||
" ol.order_id,\n",
|
||||
" SUM(`当次退款USD金额`) AS \"总退款金额\",\n",
|
||||
" sum(`当次退款申请比例`) AS \"当次退款申请比例\",\n",
|
||||
" `原订单退款比例`,\n",
|
||||
" MAX(`申请时间`) AS \"最后一次申请时间\"\n",
|
||||
" FROM\n",
|
||||
" dwd.`cpmaso_order_refund` cof\n",
|
||||
" LEFT JOIN dwd.order_list ol ON ol.order_id = cof.order_id \n",
|
||||
" WHERE\n",
|
||||
" 状态 = \"退款成功\" \n",
|
||||
" AND 网站 REGEXP \"litfad|lakiq|kwoking|beau\" \n",
|
||||
" AND ol.order_date >= \"2025-01-01\" \n",
|
||||
" AND EXISTS (\n",
|
||||
" SELECT 1 FROM odr_list WHERE odr_list.order_id = cof.order_id\n",
|
||||
" )\n",
|
||||
" GROUP BY\n",
|
||||
" ol.order_id\n",
|
||||
"),\n",
|
||||
"-- WITH\n",
|
||||
"sub_order AS (\n",
|
||||
"SELECT DISTINCT\n",
|
||||
" opl.order_id,\n",
|
||||
"\n",
|
||||
" CASE \n",
|
||||
" WHEN (LENGTH(opl.order_product_id) - LENGTH(REPLACE(opl.order_product_id, '_', ''))) = 1 THEN opl.order_product_id\n",
|
||||
" ELSE SUBSTRING_INDEX(opl.order_product_id, '_', 2)\n",
|
||||
" END AS 子订单号,\n",
|
||||
"\n",
|
||||
" CASE \n",
|
||||
" WHEN (LENGTH(opl.order_product_id) - LENGTH(REPLACE(opl.order_product_id, '_', ''))) = 2 THEN opl.order_product_id\n",
|
||||
" ELSE NULL\n",
|
||||
" END AS 子子订单号,\n",
|
||||
"\n",
|
||||
" lor.redo_type AS 补发类型\n",
|
||||
"-- wp.actual_price * wp.buy_num AS 采购金额,\n",
|
||||
"-- buy_audit\n",
|
||||
"\n",
|
||||
"FROM dwd.order_product_list opl \n",
|
||||
"LEFT JOIN dws.log_order_reissue_detail lord \n",
|
||||
" ON lord.order_product_id = opl.order_product_id\n",
|
||||
"LEFT JOIN dwd.log_order_reissue lor \n",
|
||||
" ON lord.order_product_id = lor.order_product_id\n",
|
||||
"WHERE DATE_FORMAT(opl.order_date, \"%Y-%m-01\") >= \"2025-06-01\"\n",
|
||||
"\n",
|
||||
"),\n",
|
||||
"sub_order2 AS (\n",
|
||||
"SELECT\n",
|
||||
"order_id,\n",
|
||||
"COUNT(CASE WHEN 子子订单号 IS NULL THEN 1 ELSE NULL END) AS 子订单数,\n",
|
||||
"COUNT(CASE WHEN 子子订单号 IS NOT NULL THEN 1 ELSE NULL END) AS 子子订单数,\n",
|
||||
"COUNT(CASE WHEN 子子订单号 IS NOT NULL AND 补发类型 REGEXP \"配件补发|整件补发\" THEN 1 ELSE NULL END) AS 补发订单数,\n",
|
||||
"COUNT(CASE WHEN 子子订单号 IS NOT NULL AND 补发类型 REGEXP \"换款\" THEN 1 ELSE NULL END) AS 换款订单数,\n",
|
||||
"COUNT(CASE WHEN 子子订单号 IS NOT NULL AND 补发类型 NOT REGEXP \"换款|配件补发|整件补发\" AND 补发类型 IS NOT NULL THEN 1 ELSE NULL END) AS 其他补发数,\n",
|
||||
"COUNT(CASE WHEN 子子订单号 IS NOT NULL AND 补发类型 IS NULL THEN 1 ELSE NULL END) AS 拆分订单数\n",
|
||||
"FROM\n",
|
||||
"sub_order\n",
|
||||
"GROUP BY order_id\n",
|
||||
"),\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"is_shipping AS (\n",
|
||||
"SELECT\n",
|
||||
" opl.order_id,\n",
|
||||
" count(distinct CASE WHEN oe.`关联提单号`<>\"--\" AND oe.`关联提单号`<>\"未关联\" AND oe.`包裹状态` not in ( \"已作废\",\"已删除\") THEN oe.`包裹号` ELSE null END) AS 已发货包裹数,\n",
|
||||
" COUNT(DISTINCT CASE WHEN oe.`包裹状态` not in ( \"已作废\",\"已删除\",\"--\") THEN oe.`包裹号` ELSE null END)AS 所有包裹数,\n",
|
||||
" MIN(oe.`客户签收时间`) AS 客户最早签收时间\n",
|
||||
" FROM order_express oe \n",
|
||||
" left join dwd.order_product_list opl on oe.`单号` = opl.order_id \n",
|
||||
" WHERE DATE_FORMAT(opl.order_date,\"%Y-%m-%d\") >'2025-01-01'\n",
|
||||
" GROUP BY opl.order_id\n",
|
||||
" \n",
|
||||
")\n",
|
||||
"\n",
|
||||
"SELECT\n",
|
||||
" ol.*,\n",
|
||||
" rf.总退款金额,\n",
|
||||
" 当次退款申请比例,\n",
|
||||
" 原订单退款比例,\n",
|
||||
" 最后一次申请时间,\n",
|
||||
" so.子订单数,\n",
|
||||
" so.子子订单数,\n",
|
||||
" so.补发订单数,\n",
|
||||
" so.换款订单数,\n",
|
||||
" so.其他补发数,\n",
|
||||
" so.拆分订单数,\n",
|
||||
" CASE when sp.已发货包裹数 =sp.所有包裹数 then \"订单已发货\"\n",
|
||||
" WHEN sp.已发货包裹数 < sp.所有包裹数 THEN \"部分发货\"\n",
|
||||
" WHEN sp.已发货包裹数 = 0 then\"未发货\" ELSE \"无包裹\" END AS \"订单发货状态\" ,\n",
|
||||
" sp.客户最早签收时间\n",
|
||||
"FROM\n",
|
||||
" odr_list ol\n",
|
||||
" LEFT JOIN refund rf ON ol.order_id = rf.order_id\n",
|
||||
" LEFT JOIN sub_order2 so ON ol.order_id = so.order_id\n",
|
||||
" LEFT JOIN is_shipping sp ON ol.order_id = sp.order_id\"\"\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "6b6089e2",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"#然后提取这些包裹号的提单ID\n",
|
||||
"#分析这些提单ID的总价和平均价格\n",
|
||||
"from utils.gtools import MySQLconnect\n",
|
||||
"import pandas as pd\n",
|
||||
"with MySQLconnect('ods') as db:\n",
|
||||
" engine = db.engine()\n",
|
||||
" order_df = pd.read_sql(BASE_SQL,engine)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "b9140af6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#提取这些订单号的包裹号\n",
|
||||
"order_id = order_df['order_id'].tolist()\n",
|
||||
"order_ids = ','.join(f\"'{i}'\" for i in order_id)\n",
|
||||
"with MySQLconnect('ods') as db:\n",
|
||||
" conn = db.connect()\n",
|
||||
" cursor = conn.cursor()\n",
|
||||
" sql = f\"SELECT * FROM order_info WHERE order_id IN ({order_ids})\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "5e4096b2",
|
||||
"metadata": {},
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "base",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python",
|
||||
"version": "3.11.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
3274
自动化跟单.ipynb
3274
自动化跟单.ipynb
File diff suppressed because it is too large
Load Diff
781
自动化跟单11.ipynb
781
自动化跟单11.ipynb
|
|
@ -375,7 +375,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2025-05-16T15:38:46.563968Z",
|
||||
|
|
@ -398,7 +398,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -412,7 +412,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -431,7 +431,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2025-05-16T15:38:58.960899Z",
|
||||
|
|
@ -446,7 +446,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2025-05-16T15:39:01.808655Z",
|
||||
|
|
@ -454,772 +454,7 @@
|
|||
},
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>cpmaso_task_id</th>\n",
|
||||
" <th>buy_id</th>\n",
|
||||
" <th>trade_id</th>\n",
|
||||
" <th>trade_platform_order_num</th>\n",
|
||||
" <th>pay_date</th>\n",
|
||||
" <th>maintain_date</th>\n",
|
||||
" <th>last_task_id</th>\n",
|
||||
" <th>follow_type</th>\n",
|
||||
" <th>max_logis_date</th>\n",
|
||||
" <th>agreed_delivery_time</th>\n",
|
||||
" <th>trans_id</th>\n",
|
||||
" <th>payment_datetime</th>\n",
|
||||
" <th>platform</th>\n",
|
||||
" <th>task_status</th>\n",
|
||||
" <th>need_refund</th>\n",
|
||||
" <th>has_image</th>\n",
|
||||
" <th>clearly_specify_the_delivery_time</th>\n",
|
||||
" <th>one_more_time</th>\n",
|
||||
" <th>create_date</th>\n",
|
||||
" <th>upload_cpmaso</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>343636</td>\n",
|
||||
" <td>2896029</td>\n",
|
||||
" <td>2896029</td>\n",
|
||||
" <td>2896029</td>\n",
|
||||
" <td>2025-05-20</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2025-05-21</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>1863624</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>淘天</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2025-05-21</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>343650</td>\n",
|
||||
" <td>2896044</td>\n",
|
||||
" <td>2896044</td>\n",
|
||||
" <td>2896044</td>\n",
|
||||
" <td>2025-05-20</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2025-05-27</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>1863645</td>\n",
|
||||
" <td>7</td>\n",
|
||||
" <td>淘天</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2025-05-21</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>345441</td>\n",
|
||||
" <td>2896633</td>\n",
|
||||
" <td>2896633</td>\n",
|
||||
" <td>2896633</td>\n",
|
||||
" <td>2025-05-21</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2025-05-22</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>1864349</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>淘天</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2025-05-22</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>345460</td>\n",
|
||||
" <td>2896653</td>\n",
|
||||
" <td>896653</td>\n",
|
||||
" <td>896653</td>\n",
|
||||
" <td>2025-05-21</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2025-05-22</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>1864373</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>淘天</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2025-05-22</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>345510</td>\n",
|
||||
" <td>2896706</td>\n",
|
||||
" <td>2896706</td>\n",
|
||||
" <td>2896706</td>\n",
|
||||
" <td>2025-05-21</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2025-05-23</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>1864440</td>\n",
|
||||
" <td>2</td>\n",
|
||||
" <td>淘天</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2025-05-22</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>5</th>\n",
|
||||
" <td>347335</td>\n",
|
||||
" <td>2897332</td>\n",
|
||||
" <td>2897332</td>\n",
|
||||
" <td>2897332</td>\n",
|
||||
" <td>2025-05-22</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2025-05-25</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>1865297</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" <td>淘天</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2025-05-23</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>6</th>\n",
|
||||
" <td>347339</td>\n",
|
||||
" <td>2897336</td>\n",
|
||||
" <td>2897336</td>\n",
|
||||
" <td>2897336</td>\n",
|
||||
" <td>2025-05-22</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2025-05-27</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>1865301</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>淘天</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2025-05-23</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>7</th>\n",
|
||||
" <td>347345</td>\n",
|
||||
" <td>2897342</td>\n",
|
||||
" <td>2897342</td>\n",
|
||||
" <td>2897342</td>\n",
|
||||
" <td>2025-05-22</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2025-05-25</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>1865307</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" <td>淘天</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2025-05-23</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>8</th>\n",
|
||||
" <td>347348</td>\n",
|
||||
" <td>2897345</td>\n",
|
||||
" <td>2897345</td>\n",
|
||||
" <td>2897345</td>\n",
|
||||
" <td>2025-05-22</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2025-05-24</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>1865308</td>\n",
|
||||
" <td>2</td>\n",
|
||||
" <td>淘天</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2025-05-23</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>9</th>\n",
|
||||
" <td>347415</td>\n",
|
||||
" <td>2897416</td>\n",
|
||||
" <td>2897416</td>\n",
|
||||
" <td>2897416</td>\n",
|
||||
" <td>2025-05-22</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2025-05-27</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>1865406</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>淘天</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2025-05-23</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>10</th>\n",
|
||||
" <td>349155</td>\n",
|
||||
" <td>2898014</td>\n",
|
||||
" <td>2578499365301538788</td>\n",
|
||||
" <td>1747991280236</td>\n",
|
||||
" <td>2025-05-23</td>\n",
|
||||
" <td>7</td>\n",
|
||||
" <td>532</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2025-05-30</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>1866518</td>\n",
|
||||
" <td>7</td>\n",
|
||||
" <td>淘天</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2025-05-24</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>11</th>\n",
|
||||
" <td>349191</td>\n",
|
||||
" <td>2898060</td>\n",
|
||||
" <td>2577712694448538788</td>\n",
|
||||
" <td>501566-186639528980603464</td>\n",
|
||||
" <td>2025-05-23</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" <td>533</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2025-05-28</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>1866395</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>淘天</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2025-05-24</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>12</th>\n",
|
||||
" <td>349219</td>\n",
|
||||
" <td>2898090</td>\n",
|
||||
" <td>2898090</td>\n",
|
||||
" <td>2898090</td>\n",
|
||||
" <td>2025-05-23</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2025-05-30</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>1866272</td>\n",
|
||||
" <td>7</td>\n",
|
||||
" <td>淘天</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2025-05-24</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>13</th>\n",
|
||||
" <td>349350</td>\n",
|
||||
" <td>2898239</td>\n",
|
||||
" <td>2898239</td>\n",
|
||||
" <td>2898239</td>\n",
|
||||
" <td>2025-05-23</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2025-05-28</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>1866500</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>淘天</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2025-05-24</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>14</th>\n",
|
||||
" <td>352340</td>\n",
|
||||
" <td>2899178</td>\n",
|
||||
" <td>2579933964195538788</td>\n",
|
||||
" <td>501566-186750228991783425</td>\n",
|
||||
" <td>2025-05-25</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>537</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2025-05-30</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>1867502</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>淘天</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2025-05-26</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>15</th>\n",
|
||||
" <td>352363</td>\n",
|
||||
" <td>2899201</td>\n",
|
||||
" <td>4354718580547688148</td>\n",
|
||||
" <td>1748139827823</td>\n",
|
||||
" <td>2025-05-25</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>540</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2025-05-30</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>1867773</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" <td>淘天</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2025-05-26</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>16</th>\n",
|
||||
" <td>352392</td>\n",
|
||||
" <td>2899231</td>\n",
|
||||
" <td>2580629125407538788</td>\n",
|
||||
" <td>501566-186754728992314910</td>\n",
|
||||
" <td>2025-05-25</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>542</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2025-05-30</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>1867547</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>淘天</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2025-05-26</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>17</th>\n",
|
||||
" <td>352494</td>\n",
|
||||
" <td>2899334</td>\n",
|
||||
" <td>2579651619722538788</td>\n",
|
||||
" <td>501566-186769728993341037</td>\n",
|
||||
" <td>2025-05-25</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>553</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2025-05-30</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>1867697</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>淘天</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2025-05-26</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>18</th>\n",
|
||||
" <td>352504</td>\n",
|
||||
" <td>2899345</td>\n",
|
||||
" <td>2580008664490538788</td>\n",
|
||||
" <td>501566-186771428993459718</td>\n",
|
||||
" <td>2025-05-25</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>555</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2025-05-30</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>1867714</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>淘天</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2025-05-26</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>19</th>\n",
|
||||
" <td>352526</td>\n",
|
||||
" <td>2899368</td>\n",
|
||||
" <td>2579710119285538788</td>\n",
|
||||
" <td>501566-186775328993689331</td>\n",
|
||||
" <td>2025-05-25</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>558</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2025-05-30</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>1867753</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>淘天</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2025-05-26</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>20</th>\n",
|
||||
" <td>352631</td>\n",
|
||||
" <td>2899481</td>\n",
|
||||
" <td>2579924751782538788</td>\n",
|
||||
" <td>501566-186789228994817432</td>\n",
|
||||
" <td>2025-05-25</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>580</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2025-05-30</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>1867892</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>淘天</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2025-05-26</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>21</th>\n",
|
||||
" <td>352649</td>\n",
|
||||
" <td>2899501</td>\n",
|
||||
" <td>2899501</td>\n",
|
||||
" <td>2899501</td>\n",
|
||||
" <td>2025-05-25</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2025-05-28</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>1867913</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" <td>淘天</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2025-05-26</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>22</th>\n",
|
||||
" <td>352698</td>\n",
|
||||
" <td>2899552</td>\n",
|
||||
" <td>4355089779147463948</td>\n",
|
||||
" <td>1748152477497</td>\n",
|
||||
" <td>2025-05-25</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>594</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2025-05-30</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>1867983</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>淘天</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2025-05-26</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>23</th>\n",
|
||||
" <td>352735</td>\n",
|
||||
" <td>2899590</td>\n",
|
||||
" <td>2580755954053538788</td>\n",
|
||||
" <td>501566-186804028995900068</td>\n",
|
||||
" <td>2025-05-25</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>601</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2025-05-30</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>1868040</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>淘天</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2025-05-26</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>24</th>\n",
|
||||
" <td>352741</td>\n",
|
||||
" <td>2899596</td>\n",
|
||||
" <td>2580765530729538788</td>\n",
|
||||
" <td>501566-186804828995968241</td>\n",
|
||||
" <td>2025-05-25</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>603</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>2025-05-30</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" <td>1868048</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>淘天</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>2025-05-26</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" cpmaso_task_id buy_id trade_id trade_platform_order_num \\\n",
|
||||
"0 343636 2896029 2896029 2896029 \n",
|
||||
"1 343650 2896044 2896044 2896044 \n",
|
||||
"2 345441 2896633 2896633 2896633 \n",
|
||||
"3 345460 2896653 896653 896653 \n",
|
||||
"4 345510 2896706 2896706 2896706 \n",
|
||||
"5 347335 2897332 2897332 2897332 \n",
|
||||
"6 347339 2897336 2897336 2897336 \n",
|
||||
"7 347345 2897342 2897342 2897342 \n",
|
||||
"8 347348 2897345 2897345 2897345 \n",
|
||||
"9 347415 2897416 2897416 2897416 \n",
|
||||
"10 349155 2898014 2578499365301538788 1747991280236 \n",
|
||||
"11 349191 2898060 2577712694448538788 501566-186639528980603464 \n",
|
||||
"12 349219 2898090 2898090 2898090 \n",
|
||||
"13 349350 2898239 2898239 2898239 \n",
|
||||
"14 352340 2899178 2579933964195538788 501566-186750228991783425 \n",
|
||||
"15 352363 2899201 4354718580547688148 1748139827823 \n",
|
||||
"16 352392 2899231 2580629125407538788 501566-186754728992314910 \n",
|
||||
"17 352494 2899334 2579651619722538788 501566-186769728993341037 \n",
|
||||
"18 352504 2899345 2580008664490538788 501566-186771428993459718 \n",
|
||||
"19 352526 2899368 2579710119285538788 501566-186775328993689331 \n",
|
||||
"20 352631 2899481 2579924751782538788 501566-186789228994817432 \n",
|
||||
"21 352649 2899501 2899501 2899501 \n",
|
||||
"22 352698 2899552 4355089779147463948 1748152477497 \n",
|
||||
"23 352735 2899590 2580755954053538788 501566-186804028995900068 \n",
|
||||
"24 352741 2899596 2580765530729538788 501566-186804828995968241 \n",
|
||||
"\n",
|
||||
" pay_date maintain_date last_task_id follow_type max_logis_date \\\n",
|
||||
"0 2025-05-20 0 0 1 2025-05-21 \n",
|
||||
"1 2025-05-20 0 0 1 2025-05-27 \n",
|
||||
"2 2025-05-21 0 0 1 2025-05-22 \n",
|
||||
"3 2025-05-21 0 0 1 2025-05-22 \n",
|
||||
"4 2025-05-21 0 0 1 2025-05-23 \n",
|
||||
"5 2025-05-22 0 0 1 2025-05-25 \n",
|
||||
"6 2025-05-22 0 0 1 2025-05-27 \n",
|
||||
"7 2025-05-22 0 0 1 2025-05-25 \n",
|
||||
"8 2025-05-22 0 0 1 2025-05-24 \n",
|
||||
"9 2025-05-22 0 0 1 2025-05-27 \n",
|
||||
"10 2025-05-23 7 532 1 2025-05-30 \n",
|
||||
"11 2025-05-23 3 533 1 2025-05-28 \n",
|
||||
"12 2025-05-23 0 0 1 2025-05-30 \n",
|
||||
"13 2025-05-23 0 0 1 2025-05-28 \n",
|
||||
"14 2025-05-25 0 537 1 2025-05-30 \n",
|
||||
"15 2025-05-25 5 540 1 2025-05-30 \n",
|
||||
"16 2025-05-25 0 542 1 2025-05-30 \n",
|
||||
"17 2025-05-25 0 553 1 2025-05-30 \n",
|
||||
"18 2025-05-25 0 555 1 2025-05-30 \n",
|
||||
"19 2025-05-25 0 558 1 2025-05-30 \n",
|
||||
"20 2025-05-25 0 580 1 2025-05-30 \n",
|
||||
"21 2025-05-25 0 0 1 2025-05-28 \n",
|
||||
"22 2025-05-25 5 594 1 2025-05-30 \n",
|
||||
"23 2025-05-25 0 601 1 2025-05-30 \n",
|
||||
"24 2025-05-25 5 603 1 2025-05-30 \n",
|
||||
"\n",
|
||||
" agreed_delivery_time trans_id payment_datetime platform task_status \\\n",
|
||||
"0 None 1863624 1 淘天 0 \n",
|
||||
"1 None 1863645 7 淘天 0 \n",
|
||||
"2 None 1864349 1 淘天 0 \n",
|
||||
"3 None 1864373 1 淘天 0 \n",
|
||||
"4 None 1864440 2 淘天 0 \n",
|
||||
"5 None 1865297 3 淘天 0 \n",
|
||||
"6 None 1865301 5 淘天 0 \n",
|
||||
"7 None 1865307 3 淘天 0 \n",
|
||||
"8 None 1865308 2 淘天 0 \n",
|
||||
"9 None 1865406 5 淘天 0 \n",
|
||||
"10 None 1866518 7 淘天 0 \n",
|
||||
"11 None 1866395 5 淘天 0 \n",
|
||||
"12 None 1866272 7 淘天 0 \n",
|
||||
"13 None 1866500 5 淘天 0 \n",
|
||||
"14 None 1867502 5 淘天 0 \n",
|
||||
"15 None 1867773 3 淘天 0 \n",
|
||||
"16 None 1867547 5 淘天 0 \n",
|
||||
"17 None 1867697 5 淘天 0 \n",
|
||||
"18 None 1867714 5 淘天 0 \n",
|
||||
"19 None 1867753 5 淘天 0 \n",
|
||||
"20 None 1867892 5 淘天 0 \n",
|
||||
"21 None 1867913 3 淘天 0 \n",
|
||||
"22 None 1867983 5 淘天 0 \n",
|
||||
"23 None 1868040 5 淘天 0 \n",
|
||||
"24 None 1868048 5 淘天 0 \n",
|
||||
"\n",
|
||||
" need_refund has_image clearly_specify_the_delivery_time one_more_time \\\n",
|
||||
"0 0 0 0 0 \n",
|
||||
"1 0 0 0 0 \n",
|
||||
"2 0 0 0 0 \n",
|
||||
"3 0 0 0 0 \n",
|
||||
"4 0 0 0 0 \n",
|
||||
"5 0 0 0 0 \n",
|
||||
"6 0 0 0 0 \n",
|
||||
"7 0 0 0 0 \n",
|
||||
"8 0 0 0 0 \n",
|
||||
"9 0 0 0 0 \n",
|
||||
"10 0 0 0 0 \n",
|
||||
"11 0 0 0 0 \n",
|
||||
"12 0 0 0 0 \n",
|
||||
"13 0 0 0 0 \n",
|
||||
"14 0 0 0 0 \n",
|
||||
"15 0 0 0 0 \n",
|
||||
"16 0 0 0 0 \n",
|
||||
"17 0 0 0 0 \n",
|
||||
"18 0 0 0 0 \n",
|
||||
"19 0 0 0 0 \n",
|
||||
"20 0 0 0 0 \n",
|
||||
"21 0 0 0 0 \n",
|
||||
"22 0 0 0 0 \n",
|
||||
"23 0 0 0 0 \n",
|
||||
"24 0 0 0 0 \n",
|
||||
"\n",
|
||||
" create_date upload_cpmaso \n",
|
||||
"0 2025-05-21 0 \n",
|
||||
"1 2025-05-21 0 \n",
|
||||
"2 2025-05-22 0 \n",
|
||||
"3 2025-05-22 0 \n",
|
||||
"4 2025-05-22 0 \n",
|
||||
"5 2025-05-23 0 \n",
|
||||
"6 2025-05-23 0 \n",
|
||||
"7 2025-05-23 0 \n",
|
||||
"8 2025-05-23 0 \n",
|
||||
"9 2025-05-23 0 \n",
|
||||
"10 2025-05-24 0 \n",
|
||||
"11 2025-05-24 0 \n",
|
||||
"12 2025-05-24 0 \n",
|
||||
"13 2025-05-24 0 \n",
|
||||
"14 2025-05-26 0 \n",
|
||||
"15 2025-05-26 0 \n",
|
||||
"16 2025-05-26 0 \n",
|
||||
"17 2025-05-26 0 \n",
|
||||
"18 2025-05-26 0 \n",
|
||||
"19 2025-05-26 0 \n",
|
||||
"20 2025-05-26 0 \n",
|
||||
"21 2025-05-26 0 \n",
|
||||
"22 2025-05-26 0 \n",
|
||||
"23 2025-05-26 0 \n",
|
||||
"24 2025-05-26 0 "
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.read_sql(sql,mdm.engine())\n",
|
||||
"df"
|
||||
|
|
@ -1227,7 +462,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2025-05-15T15:26:13.035452Z",
|
||||
|
|
@ -1251,7 +486,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2025-05-16T15:45:35.889562Z",
|
||||
|
|
|
|||
145
账单预测.ipynb
145
账单预测.ipynb
|
|
@ -4,75 +4,7 @@
|
|||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_18728\\4048319598.py:101: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
|
||||
" df=pd.read_sql(sql,db.con)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" 包裹测量时间 order_id SKU 订单时间 \\\n",
|
||||
"0 2025-03-01 14:06:35 250225025414549 22696 2025-February-25th \n",
|
||||
"1 2025-04-02 15:44:20 250330050647870 220934 2025-March-30th \n",
|
||||
"2 2025-03-03 16:31:27 250225062602893 221468 2025-February-25th \n",
|
||||
"3 2025-03-15 10:37:19 250311002220317 222890 2025-March-11th \n",
|
||||
"4 2025-04-03 18:16:25 250331190637251 224072 2025-March-31st \n",
|
||||
"... ... ... ... ... \n",
|
||||
"13489 2025-05-04 16:39:49 250429213610207 2207148642 2025-April-29th \n",
|
||||
"13490 2025-05-04 13:03:06 250501025810935 2207161446 2025-May-1st \n",
|
||||
"13491 2025-05-05 17:41:38 250430063602142 2207176415 2025-April-30th \n",
|
||||
"13492 2025-05-02 13:33:34 250430035236822 2207192083 2025-April-30th \n",
|
||||
"13493 2025-05-08 20:15:50 250430104809255 2207202010 2025-April-30th \n",
|
||||
"\n",
|
||||
" 包裹号 快递公司 运输方式 目的国 postcode ERP采购价 \\\n",
|
||||
"0 991344640 海MS-FEDEX 海运 United States 55414 50.0 \n",
|
||||
"1 991399418 海MS-FEDEX 海运 United States 37014 45.0 \n",
|
||||
"2 991348761 海MS-FEDEX02 海运 United States 11803 65.0 \n",
|
||||
"3 991369401 海MS-FEDEX02 海运 United States 54568-9248 35.0 \n",
|
||||
"4 991401728 海MS-FEDEX 海运 United States 34112 180.0 \n",
|
||||
"... ... ... ... ... ... ... \n",
|
||||
"13489 991447512 海MS-FEDEX-SAIR-H 海运 United States 32168 NaN \n",
|
||||
"13490 991446594 海MS-FEDEX-SAIR-H 海运 United States 92647 NaN \n",
|
||||
"13491 991449178 海MS-FEDEX-SAIR-H 海运 United States 90019 518.0 \n",
|
||||
"13492 991444756 海MS-FEDEX-SAIR-H 海运 United States 78572 228.0 \n",
|
||||
"13493 991454248 海MS-FEDEX-SAIR-H 海运 United States 89129 NaN \n",
|
||||
"\n",
|
||||
" ERP包裹数据 \\\n",
|
||||
"0 {\"包裹1\": {\"宽\": \"21.0\", \"长\": \"21.0\", \"高\": \"21.0\"... \n",
|
||||
"1 {\"包裹1\": {\"宽\": \"41.0\", \"长\": \"41.0\", \"高\": \"21.0\"... \n",
|
||||
"2 {\"包裹1\": {\"宽\": \"32.0\", \"长\": \"33.0\", \"高\": \"28.0\"... \n",
|
||||
"3 {\"包裹1\": {\"宽\": \"28.0\", \"长\": \"28.0\", \"高\": \"13.0\"... \n",
|
||||
"4 {\"包裹1\": {\"宽\": \"25.0\", \"长\": \"75.0\", \"高\": \"28.0\"... \n",
|
||||
"... ... \n",
|
||||
"13489 {\"包裹1\": {\"宽\": \"70.0\", \"长\": \"190.0\", \"高\": \"20.0... \n",
|
||||
"13490 {\"包裹1\": {\"宽\": \"48.0\", \"长\": \"54.0\", \"高\": \"14.0\"... \n",
|
||||
"13491 {\"包裹1\": {\"宽\": \"30.0\", \"长\": \"193.0\", \"高\": \"13.0... \n",
|
||||
"13492 {\"包裹1\": {\"宽\": \"51.0\", \"长\": \"53.0\", \"高\": \"33.0\"... \n",
|
||||
"13493 {\"包裹1\": {\"宽\": \"70.0\", \"长\": \"130.0\", \"高\": \"45.0... \n",
|
||||
"\n",
|
||||
" 实际包裹数据 \n",
|
||||
"0 {\"991344640\": {\"长\": 45.00, \"宽\": 45.00, \"高\": 17... \n",
|
||||
"1 {\"991399418\": {\"长\": 41.00, \"宽\": 41.00, \"高\": 21... \n",
|
||||
"2 {\"991348761\": {\"长\": 31.00, \"宽\": 31.00, \"高\": 24... \n",
|
||||
"3 {\"991369401\": {\"长\": 28.00, \"宽\": 28.00, \"高\": 12... \n",
|
||||
"4 {\"991401728\": {\"长\": 75.00, \"宽\": 25.00, \"高\": 16... \n",
|
||||
"... ... \n",
|
||||
"13489 {\"991447512\": {\"长\": 208.00, \"宽\": 54.00, \"高\": 1... \n",
|
||||
"13490 {\"991446594\": {\"长\": 53.00, \"宽\": 48.00, \"高\": 20... \n",
|
||||
"13491 {\"991449178\": {\"长\": 193.00, \"宽\": 30.00, \"高\": 1... \n",
|
||||
"13492 {\"991444756\": {\"长\": 53.00, \"宽\": 51.00, \"高\": 33... \n",
|
||||
"13493 {\"991454248\": {\"长\": 141.00, \"宽\": 76.00, \"高\": 2... \n",
|
||||
"\n",
|
||||
"[13494 rows x 12 columns]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"from utils.gtools import MySQLconnect\n",
|
||||
|
|
@ -388,18 +320,29 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"len(df)"
|
||||
"# 取需要获取实际采购价的表\n",
|
||||
"import pandas as pd\n",
|
||||
"df = pd.read_excel(r\"C:\\Users\\Admin\\Desktop\\售价数据类\\数据统计\\物流类\\2025-06测算物流.xlsx\",engine='openpyxl')\n",
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"已完成 50000 个 order_id 的查询\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from utils.gtools import MySQLconnect\n",
|
||||
"\n",
|
||||
"ods = MySQLconnect(\"ods\")\n",
|
||||
"ods = MySQLconnect(\"dwd\")\n",
|
||||
"engine = ods.engine()\n",
|
||||
"cursor = ods.connect().cursor()\n",
|
||||
"\n",
|
||||
|
|
@ -471,14 +414,7 @@
|
|||
"# purchase_order_df2[\"order_id\"] = purchase_order_df2[\"order_id\"].astype(str)\n",
|
||||
"# purchase_order_df = pd.merge(purchase_order_df1, purchase_order_df2, on='order_id', how='left')\n",
|
||||
"\n",
|
||||
"# 转换数据类型，确保匹配\n",
|
||||
"df[\"order_id\"] = df[\"order_id\"].astype(str)\n",
|
||||
"\n",
|
||||
"# 进行合并\n",
|
||||
"order_id_df_cal = pd.merge(df, purchase_order_df1, on='order_id', how='left')\n",
|
||||
"# order_id_df_cal.drop_duplicates(subset=[\"order_id\"], inplace=True)\n",
|
||||
"# 复制到剪贴板\n",
|
||||
"order_id_df_cal.to_clipboard(index=False)\n"
|
||||
"purchase_order_df1.to_clipboard(index=False) # 复制到剪贴板\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -487,7 +423,14 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(len(df),len(purchase_order_df1),len(order_id_df_cal))"
|
||||
"# 转换数据类型，确保匹配\n",
|
||||
"df[\"order_id\"] = df[\"order_id\"].astype(str)\n",
|
||||
"\n",
|
||||
"# 进行合并\n",
|
||||
"order_id_df_cal = pd.merge(df, purchase_order_df1, on='order_id', how='left')\n",
|
||||
"# order_id_df_cal.drop_duplicates(subset=[\"order_id\"], inplace=True)\n",
|
||||
"# 复制到剪贴板\n",
|
||||
"order_id_df_cal.to_clipboard(index=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -534,9 +477,21 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "ImportError",
|
||||
"evalue": "cannot import name 'call_sell_and_order_price' from 'sell.sell_price' (d:\\test\\logistics\\sell\\sell_price.py)",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[1;32mIn[4], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# 用系统售价模型和实际体积计算实际应该有的售价和订单价\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msell\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msell_price\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m call_sell_and_order_price\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mre\u001b[39;00m\n\u001b[0;32m 4\u001b[0m order_id_df_cal[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m预测售价\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
|
||||
"\u001b[1;31mImportError\u001b[0m: cannot import name 'call_sell_and_order_price' from 'sell.sell_price' (d:\\test\\logistics\\sell\\sell_price.py)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# 用系统售价模型和实际体积计算实际应该有的售价和订单价\n",
|
||||
"from sell.sell_price import call_sell_and_order_price\n",
|
||||
|
|
@ -1257,27 +1212,7 @@
|
|||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_8364\\2603640831.py:4: FutureWarning: The provided callable <built-in function max> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"max\" instead.\n",
|
||||
" all_df['SPU最大涨幅']=all_df.groupby('SPU')['售价涨跌幅'].transform(max)\n",
|
||||
"C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_8364\\2603640831.py:5: FutureWarning: The provided callable <built-in function min> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"min\" instead.\n",
|
||||
" all_df['SPU最小涨幅']=all_df.groupby('SPU')['售价涨跌幅'].transform(min)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"10{\"stdout\":\"[{\\\"variableName\\\": \\\"ID_TO_MEANING\\\", \\\"type\\\": \\\"dictionary\\\", \\\"supportedEngines\\\": [\\\"pandas\\\"], \\\"isLocalVariable\\\": true, \\\"rawType\\\": \\\"builtins.dict\\\"}, {\\\"variableName\\\": \\\"NULL\\\", \\\"type\\\": \\\"unknown\\\", \\\"supportedEngines\\\": [\\\"pandas\\\"], \\\"isLocalVariable\\\": true, \\\"rawType\\\": \\\"_pydevd_bundle.pydevd_constants.Null\\\"}]\\n\",\"stderr\":\"\",\"mime\":[]}\n",
|
||||
"10{\"stdout\":\"[{\\\"variableName\\\": \\\"ID_TO_MEANING\\\", \\\"type\\\": \\\"dictionary\\\", \\\"supportedEngines\\\": [\\\"pandas\\\"], \\\"isLocalVariable\\\": true, \\\"rawType\\\": \\\"builtins.dict\\\"}, {\\\"variableName\\\": \\\"NULL\\\", \\\"type\\\": \\\"unknown\\\", \\\"supportedEngines\\\": [\\\"pandas\\\"], \\\"isLocalVariable\\\": true, \\\"rawType\\\": \\\"_pydevd_bundle.pydevd_constants.Null\\\"}]\\n\",\"stderr\":\"\",\"mime\":[]}\n",
|
||||
"10{\"stdout\":\"[{\\\"variableName\\\": \\\"ID_TO_MEANING\\\", \\\"type\\\": \\\"dictionary\\\", \\\"supportedEngines\\\": [\\\"pandas\\\"], \\\"isLocalVariable\\\": true, \\\"rawType\\\": \\\"builtins.dict\\\"}, {\\\"variableName\\\": \\\"NULL\\\", \\\"type\\\": \\\"unknown\\\", \\\"supportedEngines\\\": [\\\"pandas\\\"], \\\"isLocalVariable\\\": true, \\\"rawType\\\": \\\"_pydevd_bundle.pydevd_constants.Null\\\"}]\\n\",\"stderr\":\"\",\"mime\":[]}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"all_df = pd.read_excel('单包裹SKU售价分析1.xlsx',sheet_name=\"Sheet1\")\n",
|
||||
|
|
|
|||
Loading…
Reference in New Issue