液化气更改存储文件为xlsx
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workpc
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81bf9a262a
commit
11592714ea
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@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 24,
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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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@ -33,15 +33,13 @@
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"source": [
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"import requests\n",
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"import json\n",
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"import xlrd\n",
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"import xlwt\n",
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"\n",
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"from datetime import datetime,timedelta\n",
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"import time\n",
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"\n",
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"# 变量定义\n",
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"login_url = \"http://10.200.32.39/jingbo-api/api/server/login\"\n",
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"search_url = \"http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryByItemNos\"\n",
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"# search_url = \"http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos\" \n",
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"queryDataListItemNos_url = \"http://10.200.32.39/jingbo-api//api/warehouse/dwDataItem/queryDataListItemNos\"\n",
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"queryDataListItemNos_url = \"http://10.200.32.39/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos\"\n",
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"\n",
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"\n",
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"login_push_url = \"http://10.200.32.39/jingbo-api/api/server/login\"\n",
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@ -157,15 +155,6 @@
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" \"funcOperation\": \"查询\"\n",
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" }\n",
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" \n",
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"# search_data = {\n",
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"# \"funcModule\": \"数据项\",\n",
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"# \"funcOperation\": \"查询\",\n",
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"# \"data\": {\n",
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"# \"dateStart\": date,\n",
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"# \"dateEnd\": date,\n",
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"# \"dataItemNoList\":dataItemNoList # 数据项编码,代表 brent最低价和最高价\n",
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"# }\n",
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"# }\n",
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" \n",
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" headers = {\"Authorization\": token}\n",
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" search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))\n",
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@ -189,46 +178,6 @@
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" return search_value\n",
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"\n",
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"\n",
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"\n",
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"# xls文件处理\n",
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"\n",
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"def write_xls(data):\n",
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" # 创建一个Workbook对象\n",
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" workbook = xlwt.Workbook()\n",
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"\n",
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" # 创建一个Sheet对象，可指定名称\n",
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" sheet = workbook.load('Sheet1')\n",
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"\n",
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" # 写入数据行\n",
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" for row_index, row_data in enumerate(data):\n",
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" for col_index, cell_data in enumerate(row_data):\n",
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" sheet.write(row_index, col_index, cell_data)\n",
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"\n",
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" # 保存Workbook到文件\n",
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" workbook.save(get_cur_time()[0] + '.xls')\n",
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"\n",
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"\n",
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"def get_cur_time(date = ''):\n",
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" if date == '':\n",
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" now = datetime.now()\n",
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" else:\n",
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" now = date\n",
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" year = now.year\n",
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" month = now.month\n",
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" day = now.day\n",
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"\n",
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" if month < 10:\n",
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" month = \"0\" + str(month)\n",
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" if day < 10:\n",
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" day = \"0\" + str(day)\n",
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" cur_time = str(year) + str(month) + str(day)\n",
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" cur_time2 = str(year) + \"-\" + str(month) + \"-\" + str(day)\n",
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"# cur_time = '20231011'\n",
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"# cur_time2 = '2023-10-11'\n",
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" return cur_time, cur_time2\n",
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"\n",
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"\n",
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"\n",
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"def get_head_push_auth():\n",
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" login_res = requests.post(url=login_push_url, json=login_push_data, timeout=(3, 5))\n",
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" text = json.loads(login_res.text)\n",
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@ -498,100 +447,8 @@
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" with open(Pkl_Filename, 'wb') as file: \n",
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" pickle.dump(grid_search_XGB, file)\n",
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"\n",
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"\n",
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"def updateYesterdayExcelData(date='', token=None):\n",
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" # 使用pandas读取Excel文件\n",
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" df = pd.read_excel(read_file_path_name, engine='openpyxl')\n",
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"\n",
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" # 获取第二行的数据作为列名\n",
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" one_cols = df.iloc[0,:].tolist()\n",
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"\n",
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" # 获取当前日期的前一天\n",
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" if date == '':\n",
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" previous_date = (datetime.now() - timedelta(days=1)).strftime('%Y-%m-%d')\n",
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" else:\n",
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" # 字符串转日期\n",
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" previous_date = (datetime.strptime(date, \"%Y-%m-%d\")-timedelta(days=1)).strftime('%Y-%m-%d')\n",
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" \n",
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"\n",
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" cur_time, cur_time2 = getNow(previous_date)\n",
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" search_data = {\n",
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" \"data\": {\n",
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" \"date\": cur_time,\n",
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" \"dataItemNoList\": one_cols[1:]\n",
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" },\n",
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" \"funcModule\": \"数据项\",\n",
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" \"funcOperation\": \"查询\"\n",
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" }\n",
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" headers = {\"Authorization\": token}\n",
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" search_res = requests.post(url=search_url, headers=headers, json=search_data, timeout=(3, 5))\n",
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" search_value = json.loads(search_res.text)[\"data\"]\n",
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" if search_value:\n",
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" datas = search_value\n",
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" else:\n",
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" datas = None\n",
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"\n",
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" append_rows = [cur_time2]\n",
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" dataItemNo_dataValue = {}\n",
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" for data_value in datas:\n",
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" if \"dataValue\" not in data_value:\n",
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" print(data_value)\n",
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" dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
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" else:\n",
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" dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
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" for value in one_cols[1:]:\n",
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" if value in dataItemNo_dataValue:\n",
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" append_rows.append(dataItemNo_dataValue[value])\n",
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" else:\n",
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" append_rows.append(\"\")\n",
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"\n",
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" print('更新数据前')\n",
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" print(df.tail(1))\n",
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" # 检查日期是否已存在于数据中\n",
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" if previous_date not in df['Date'].values:\n",
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" # 将新的数据添加到DataFrame中\n",
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" new_row = pd.DataFrame([append_rows], columns=df.columns.tolist())\n",
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" df = pd.concat([df, new_row], ignore_index=True)\n",
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" else:\n",
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" # 更新现有数据\n",
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" print('日期存在，即将更新')\n",
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" print('新数据',append_rows[1:])\n",
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" df.loc[df['Date'] == previous_date, df.columns.tolist()[1:]] = append_rows[1:]\n",
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"\n",
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" print('更新数据后')\n",
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" print(df.tail(1))\n",
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" # 使用pandas保存Excel文件\n",
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" df.to_excel(\"液化气数据.xlsx\", index=False, engine='openpyxl')\n",
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"\n",
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"\n",
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"def read_xls_data_bak():\n",
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" global one_cols, two_cols\n",
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" # 打开 XLS 文件\n",
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" workbook = xlrd.open_workbook(read_file_path_name)\n",
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"\n",
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" # 获取所有表格名称\n",
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" # sheet_names = workbook.sheet_names()\n",
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"\n",
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" # 选择第一个表格\n",
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" sheet = workbook.sheet_by_index(0)\n",
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"\n",
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" # 获取行数和列数\n",
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" num_rows = sheet.nrows\n",
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" # num_cols = sheet.ncols\n",
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"\n",
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" # 遍历每一行，获取单元格数据\n",
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" # for i in range(num_rows):\n",
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" # row_data = sheet.row_values(i)\n",
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" # one_cols.append(row_data)\n",
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" # two_cols.append(row_data[1])\n",
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"\n",
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" row_data = sheet.row_values(1)\n",
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" one_cols = row_data\n",
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"\n",
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" # 关闭 XLS 文件\n",
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" # workbook.close()\n",
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"\n",
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"def read_xls_data():\n",
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" \"\"\"获取特征项ID\"\"\"\n",
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" global one_cols, two_cols\n",
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" # 使用pandas读取Excel文件\n",
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" df = pd.read_excel(read_file_path_name, header=None) # 不自动识别列名\n",
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" one_cols = df.iloc[1].tolist()[1:]\n",
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" print(f'获取到的数据项ID{one_cols}')\n",
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"\n",
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"\n",
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"def start(date=''):\n",
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" \"\"\"获取当日数据\"\"\"\n",
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" read_xls_data()\n",
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"\n",
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" token = get_head_auth()\n",
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" if not token:\n",
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" return\n",
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" token_push = get_head_push_auth()\n",
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" if not token_push:\n",
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" return\n",
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" \n",
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" cur_time,cur_time2 = getNow(date)\n",
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" datas = get_data_value(token, one_cols[1:],cur_time)\n",
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"# if not datas:\n",
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"# return\n",
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"\n",
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" # data_list = [two_cols, one_cols]\n",
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" append_rows = [cur_time2]\n",
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" dataItemNo_dataValue = {}\n",
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" for data_value in datas:\n",
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" if \"dataValue\" not in data_value:\n",
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" dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
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" else:\n",
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" dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
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" \n",
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" for value in one_cols[1:]:\n",
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" if value in dataItemNo_dataValue:\n",
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" append_rows.append(dataItemNo_dataValue[value])\n",
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" else:\n",
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" append_rows.append(\"\")\n",
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" save_xlsx(append_rows)\n",
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" \n",
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" \n",
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" # 获取当月的数据写入到指定文件\n",
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" # queryDataListItemNos(token=token)\n",
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" \n",
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" # optimize_Model()\n",
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" # upload_data_to_system(token_push,cur_time)\n",
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" \n",
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" \n",
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" \n",
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" # data_list.append(three_cols)\n",
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" # write_xls(data_list)\n",
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"\n",
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"def getNow(date='',offset=0):\n",
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" \n",
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" if date == '':\n",
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" now = datetime.now() - timedelta(days=offset)\n",
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" else:\n",
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" if isinstance(date, datetime):\n",
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" now = date\n",
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" else:\n",
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" try:\n",
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" # 先尝试常见日期格式解析\n",
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" now = datetime.strptime(str(date), \"%Y-%m-%d\")\n",
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" except ValueError:\n",
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" # 失败后尝试无分隔符格式\n",
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" now = datetime.strptime(str(date), \"%Y%m%d\")\n",
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" print(now,type(now))\n",
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" now = now - timedelta(days=offset)\n",
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" year = now.year\n",
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" month = now.month\n",
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" day = now.day\n",
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"\n",
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" if month < 10:\n",
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" month = \"0\" + str(month)\n",
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" if day < 10:\n",
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" day = \"0\" + str(day)\n",
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" cur_time = str(year) + str(month) + str(day)\n",
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" cur_time2 = str(year) + \"-\" + str(month) + \"-\" + str(day)\n",
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" return cur_time,cur_time2\n",
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" \n",
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"def start_1(date=''):\n",
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" read_xls_data()\n",
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" token = get_head_auth()\n",
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" if not token:\n",
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" print(f\"获取{cur_time}数据\")\n",
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" datas = get_data_value(token, one_cols,date=cur_time)\n",
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" if not datas:\n",
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" return\n",
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" \n",
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" cur_time,cur_time2 = getNow(date,offset=1)\n",
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" print(f\"补充{cur_time}数据\")\n",
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" datas = get_data_value(token, one_cols[1:],date=cur_time)\n",
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"# if not datas:\n",
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"# return\n",
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"\n",
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" # data_list = [two_cols, one_cols]\n",
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" append_rows = [cur_time]\n",
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" append_rows = [cur_time2]\n",
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" dataItemNo_dataValue = {}\n",
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" for data_value in datas:\n",
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" if \"dataValue\" not in data_value:\n",
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@ -699,70 +487,94 @@
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" print('添加的行：',append_rows)\n",
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" save_xls_2(append_rows)\n",
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"\n",
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"\n",
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"def getNow(date='', offset=0):\n",
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" \"\"\"生成指定日期的两种格式字符串\n",
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" Args:\n",
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" date: 支持多种输入类型：\n",
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" - datetime对象\n",
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" - 字符串格式（支持'%Y-%m-%d'和'%Y%m%d'）\n",
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" - 空字符串表示当前日期\n",
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" offset: 日期偏移天数\n",
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" Returns:\n",
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" tuple: (紧凑日期字符串, 标准日期字符串)\n",
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" \"\"\"\n",
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" # 日期解析逻辑\n",
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" if isinstance(date, datetime):\n",
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" now = date\n",
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" else:\n",
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" now = datetime.now()\n",
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" if date:\n",
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" # 尝试多种日期格式解析\n",
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" for fmt in ('%Y-%m-%d', '%Y%m%d', '%Y/%m/%d'):\n",
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" try:\n",
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" now = datetime.strptime(str(date), fmt)\n",
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" break\n",
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" except ValueError:\n",
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" continue\n",
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" else:\n",
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" raise ValueError(f\"无法解析的日期格式: {date}\")\n",
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"\n",
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" # 应用日期偏移\n",
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" now = now - timedelta(days=offset)\n",
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" \n",
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" # data_list.append(three_cols)\n",
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" # write_xls(data_list)\n",
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" # 统一格式化输出\n",
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" date_str = now.strftime(\"%Y-%m-%d\")\n",
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" compact_date = date_str.replace(\"-\", \"\")\n",
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" return compact_date, date_str\n",
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"\n",
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"def start_1(date=''):\n",
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" \"\"\"补充昨日数据\"\"\"\n",
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" read_xls_data()\n",
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" token = get_head_auth()\n",
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" if not token:\n",
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" return\n",
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" \n",
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"def save_xls_1(append_rows):\n",
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" cur_time,cur_time2 = getNow(date,offset=1)\n",
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" print(f\"补充{cur_time}数据\")\n",
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" datas = get_data_value(token, one_cols,date=cur_time)\n",
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" if not datas:\n",
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" print(f\"{cur_time}没有数据\")\n",
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" return\n",
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"\n",
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" # 打开xls文件\n",
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" workbook = xlrd.open_workbook('液化气数据.xlsx')\n",
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" append_rows = [cur_time2]\n",
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" dataItemNo_dataValue = {}\n",
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" for data_value in datas:\n",
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" if \"dataValue\" not in data_value:\n",
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" print(data_value)\n",
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" dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
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" else:\n",
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" dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
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" \n",
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" for value in one_cols:\n",
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" if value in dataItemNo_dataValue:\n",
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" append_rows.append(dataItemNo_dataValue[value])\n",
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" else:\n",
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" append_rows.append(\"\")\n",
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" print('添加的行：',append_rows)\n",
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" save_xls_2(append_rows)\n",
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"\n",
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" # 获取所有sheet的个数\n",
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" sheet_count = len(workbook.sheet_names())\n",
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"\n",
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" # 获取所有sheet的名称\n",
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" sheet_names = workbook.sheet_names()\n",
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"\n",
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" new_workbook = xlwt.Workbook()\n",
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" for i in range(sheet_count):\n",
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" # 获取当前sheet\n",
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" sheet = workbook.sheet_by_index(i)\n",
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"\n",
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" # 获取sheet的行数和列数\n",
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" row_count = sheet.nrows - 1\n",
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" col_count = sheet.ncols\n",
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" # 获取原有数据\n",
|
||||
" data = []\n",
|
||||
" for row in range(row_count):\n",
|
||||
" row_data = []\n",
|
||||
" for col in range(col_count):\n",
|
||||
" row_data.append(sheet.cell_value(row, col))\n",
|
||||
" data.append(row_data)\n",
|
||||
" # 创建xlwt的Workbook对象\n",
|
||||
" # 创建sheet\n",
|
||||
" new_sheet = new_workbook.add_sheet(sheet_names[i])\n",
|
||||
"\n",
|
||||
" # 将原有的数据写入新的sheet\n",
|
||||
" for row in range(row_count):\n",
|
||||
" for col in range(col_count):\n",
|
||||
" new_sheet.write(row, col, data[row][col])\n",
|
||||
"\n",
|
||||
" if i == 0:\n",
|
||||
" # 在新的sheet中添加数据\n",
|
||||
" for col in range(col_count):\n",
|
||||
" new_sheet.write(row_count, col, append_rows[col])\n",
|
||||
"\n",
|
||||
" # 保存新的xls文件\n",
|
||||
" new_workbook.save(\"液化气数据.xlsx\") \n",
|
||||
"\n",
|
||||
"def save_xls_2(append_rows):\n",
|
||||
" \"\"\"保存或更新数据到Excel文件\n",
|
||||
" 参数:\n",
|
||||
" append_rows (list): 需要追加/更新的数据行，格式为[日期, 数据项1, 数据项2,...]\n",
|
||||
" \"\"\"\n",
|
||||
" try:\n",
|
||||
" # 读取现有数据（假设第一行为列名）\n",
|
||||
" df = pd.read_excel('液化气数据.xlsx', sheet_name=0)\n",
|
||||
" print(df.columns)\n",
|
||||
" append_rows = pd.DataFrame(append_rows, index=df.columns)\n",
|
||||
" # 转换append_rows为DataFrame\n",
|
||||
" append_rows = pd.DataFrame([append_rows],columns=df.columns)\n",
|
||||
" # 创建新数据行\n",
|
||||
" new_date = append_rows['Date'].values\n",
|
||||
" new_date = append_rows['Date'].values[0]\n",
|
||||
" \n",
|
||||
" dates = df['Date'].to_list()\n",
|
||||
" \n",
|
||||
" # 判断日期是否存在\n",
|
||||
" if new_date in dates:\n",
|
||||
" # 找到日期所在行的索引\n",
|
||||
" date_mask = df['Date'] == new_date\n",
|
||||
" # 存在则更新数据\n",
|
||||
" df.loc[date_mask] = append_rows\n",
|
||||
" df.loc[date_mask] = append_rows.values\n",
|
||||
" print(f\"更新 {new_date} 数据\")\n",
|
||||
" else:\n",
|
||||
" # 不存在则追加数据\n",
|
||||
@ -779,8 +591,7 @@
|
||||
" pd.DataFrame([append_rows]).to_excel('液化气数据.xlsx', index=False, engine='openpyxl')\n",
|
||||
" except Exception as e:\n",
|
||||
" print(f\"保存数据时发生错误: {str(e)}\")\n",
|
||||
" \n",
|
||||
" \n",
|
||||
"\n",
|
||||
"def check_data(dataItemNo):\n",
|
||||
" token = get_head_auth()\n",
|
||||
" if not token:\n",
|
||||
@ -790,89 +601,6 @@
|
||||
" if not datas:\n",
|
||||
" return\n",
|
||||
"\n",
|
||||
"def save_xlsx(append_rows):\n",
|
||||
" from openpyxl import load_workbook\n",
|
||||
"\n",
|
||||
" # 加载现有工作簿\n",
|
||||
" workbook = load_workbook('液化气数据.xlsx')\n",
|
||||
" \n",
|
||||
" # 创建新工作簿并复制所有工作表结构\n",
|
||||
" new_workbook = load_workbook('液化气数据.xlsx')\n",
|
||||
" \n",
|
||||
" # 遍历所有工作表\n",
|
||||
" for sheetname in workbook.sheetnames:\n",
|
||||
" sheet = workbook[sheetname]\n",
|
||||
" new_sheet = new_workbook[sheetname]\n",
|
||||
" \n",
|
||||
" # 获取原始数据行数\n",
|
||||
" row_count = sheet.max_row\n",
|
||||
" \n",
|
||||
" # 在第一个工作表中追加数据\n",
|
||||
" if sheetname == workbook.sheetnames[0]:\n",
|
||||
" # 追加新行数据\n",
|
||||
" new_row = (row_count + 1, )\n",
|
||||
" for col_idx, value in enumerate(append_rows, start=1):\n",
|
||||
" new_sheet.cell(row=row_count+1, column=col_idx, value=value)\n",
|
||||
"\n",
|
||||
" # 保存修改后的工作簿\n",
|
||||
" new_workbook.save(\"液化气数据.xlsx\")\n",
|
||||
"\n",
|
||||
"def save_xls(append_rows):\n",
|
||||
"\n",
|
||||
" # 修改文件打开方式\n",
|
||||
" try:\n",
|
||||
" # 尝试用xlrd打开旧版xls文件\n",
|
||||
" workbook = xlrd.open_workbook('液化气数据.xlsx')\n",
|
||||
" except:\n",
|
||||
" # 使用openpyxl打开新版xlsx文件\n",
|
||||
" from openpyxl import load_workbook\n",
|
||||
" workbook = load_workbook('液化气数据.xlsx')\n",
|
||||
"\n",
|
||||
" # 获取所有sheet的个数\n",
|
||||
" sheet_count = len(workbook.sheet_names())\n",
|
||||
"\n",
|
||||
" # 获取所有sheet的名称\n",
|
||||
" sheet_names = workbook.sheet_names()\n",
|
||||
"\n",
|
||||
" new_workbook = xlwt.Workbook()\n",
|
||||
" for i in range(sheet_count):\n",
|
||||
" # 获取当前sheet\n",
|
||||
" sheet = workbook.sheet_by_index(i)\n",
|
||||
"\n",
|
||||
" # 获取sheet的行数和列数\n",
|
||||
" row_count = sheet.nrows\n",
|
||||
" col_count = sheet.ncols\n",
|
||||
" # 获取原有数据\n",
|
||||
" data = []\n",
|
||||
" for row in range(row_count):\n",
|
||||
" row_data = []\n",
|
||||
" for col in range(col_count):\n",
|
||||
" row_data.append(sheet.cell_value(row, col))\n",
|
||||
" data.append(row_data)\n",
|
||||
" # 创建xlwt的Workbook对象\n",
|
||||
" # 创建sheet\n",
|
||||
" new_sheet = new_workbook.add_sheet(sheet_names[i])\n",
|
||||
"\n",
|
||||
" # 将原有的数据写入新的sheet\n",
|
||||
" for row in range(row_count):\n",
|
||||
" for col in range(col_count):\n",
|
||||
" new_sheet.write(row, col, data[row][col])\n",
|
||||
"\n",
|
||||
" if i == 0:\n",
|
||||
" # 在新的sheet中添加数据\n",
|
||||
" for col in range(col_count):\n",
|
||||
" new_sheet.write(row_count, col, append_rows[col])\n",
|
||||
"\n",
|
||||
" # 保存新的xls文件\n",
|
||||
" new_workbook.save(\"液化气数据.xlsx\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def get_queryDataListItemNos_value(token, url, dataItemNoList, dateStart, dateEnd):\n",
|
||||
"\n",
|
||||
" search_data = {\n",
|
||||
@ -927,86 +655,15 @@
|
||||
" # 保存修改后的xlsx文件\n",
|
||||
" new_workbook.save(\"液化气数据.xlsx\")\n",
|
||||
"\n",
|
||||
"# def save_queryDataListItemNos_xls(data_df,dataItemNoList):\n",
|
||||
"\n",
|
||||
"# current_year_month = datetime.now().strftime('%Y-%m')\n",
|
||||
"# grouped = data_df.groupby(\"dataDate\")\n",
|
||||
"\n",
|
||||
"# # 打开xls文件\n",
|
||||
"# workbook = xlrd.open_workbook('液化气数据.xlsx')\n",
|
||||
"\n",
|
||||
"# # 获取所有sheet的个数\n",
|
||||
"# sheet_count = len(workbook.sheet_names())\n",
|
||||
"\n",
|
||||
"# # 获取所有sheet的名称\n",
|
||||
"# sheet_names = workbook.sheet_names()\n",
|
||||
"\n",
|
||||
"# new_workbook = xlwt.Workbook()\n",
|
||||
"# for i in range(sheet_count):\n",
|
||||
"# # 获取当前sheet\n",
|
||||
"# sheet = workbook.sheet_by_index(i)\n",
|
||||
"\n",
|
||||
"# # 获取sheet的行数和列数\n",
|
||||
"# row_count = sheet.nrows\n",
|
||||
"# col_count = sheet.ncols\n",
|
||||
"# # 获取原有数据\n",
|
||||
"# data = []\n",
|
||||
"# for row in range(row_count):\n",
|
||||
"# row_data = []\n",
|
||||
"# for col in range(col_count):\n",
|
||||
"# row_data.append(sheet.cell_value(row, col))\n",
|
||||
"# data.append(row_data)\n",
|
||||
"\n",
|
||||
"# # 创建xlwt的Workbook对象\n",
|
||||
"# # 创建sheet\n",
|
||||
"# new_sheet = new_workbook.add_sheet(sheet_names[i])\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# current_year_month_row = 0\n",
|
||||
"# # 将原有的数据写入新的sheet\n",
|
||||
"# for row in range(row_count):\n",
|
||||
"# for col in range(col_count):\n",
|
||||
"# col0 = data[row][0]\n",
|
||||
"# # print(\"col0\",col0[:7])\n",
|
||||
"# if col0[:7] == current_year_month:\n",
|
||||
"# current_year_month_row += 1\n",
|
||||
"# break\n",
|
||||
"# new_sheet.write(row, col, data[row][col])\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# # print(\"current_year_month_row\",current_year_month_row)\n",
|
||||
"# if i == 0:\n",
|
||||
"# rowFlag = 0\n",
|
||||
"# # 查看每组数据\n",
|
||||
"# for date, group in grouped:\n",
|
||||
"# new_sheet.write(row_count + rowFlag - current_year_month_row, 0, date)\n",
|
||||
"# for j in range(len(dataItemNoList)):\n",
|
||||
"# dataItemNo = dataItemNoList[j]\n",
|
||||
"\n",
|
||||
"# # for dataItemNo in dataItemNoList:\n",
|
||||
"# if group[group[\"dataItemNo\"] == dataItemNo][\"dataValue\"].values:\n",
|
||||
"\n",
|
||||
"# new_sheet.write(row_count + rowFlag - current_year_month_row, j + 1, group[group[\"dataItemNo\"] == dataItemNo][\"dataValue\"].values[0])\n",
|
||||
"\n",
|
||||
"# rowFlag += 1\n",
|
||||
"\n",
|
||||
"# # 保存新的xls文件\n",
|
||||
"# new_workbook.save(\"液化气数据.xlsx\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def queryDataListItemNos(date=None,token=None):\n",
|
||||
" df = pd.read_excel('液化气数据.xlsx')\n",
|
||||
" dataItemNoList = df.iloc[0].tolist()[1:]\n",
|
||||
" \n",
|
||||
" if token is None:\n",
|
||||
" token = get_head_auth()\n",
|
||||
"\n",
|
||||
" if not token:\n",
|
||||
" print('token获取失败')\n",
|
||||
" return\n",
|
||||
"\n",
|
||||
" # 获取当前日期\n",
|
||||
" if date is None:\n",
|
||||
" current_date = datetime.now()\n",
|
||||
@ -1014,62 +671,18 @@
|
||||
" current_date = date\n",
|
||||
" # 获取当月1日\n",
|
||||
" first_day_of_month = current_date.replace(day=1)\n",
|
||||
"\n",
|
||||
" # 格式化为 YYYYMMDD 格式\n",
|
||||
" dateEnd = current_date.strftime('%Y%m%d')\n",
|
||||
" dateStart = first_day_of_month.strftime('%Y%m%d')\n",
|
||||
"\n",
|
||||
" search_value = get_queryDataListItemNos_value(token, queryDataListItemNos_url, dataItemNoList, dateStart, dateEnd)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" data_df = pd.DataFrame(search_value)\n",
|
||||
"\n",
|
||||
" data_df[\"dataDate\"] = pd.to_datetime(data_df[\"dataDate\"])\n",
|
||||
" data_df[\"dataDate\"] = data_df[\"dataDate\"].dt.strftime('%Y-%m-%d')\n",
|
||||
" print(data_df)\n",
|
||||
" save_queryDataListItemNos_xls(data_df,dataItemNoList)\n",
|
||||
" print('当月数据更新完成')\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def start_3(date,token,token_push):\n",
|
||||
" '''预测上传数据'''\n",
|
||||
" read_xls_data()\n",
|
||||
"\n",
|
||||
" # token = get_head_auth()\n",
|
||||
" # if not token:\n",
|
||||
" # return\n",
|
||||
" # token_push = get_head_push_auth()\n",
|
||||
" # if not token_push:\n",
|
||||
" # return\n",
|
||||
"\n",
|
||||
" datas = get_data_value(token, one_cols[1:],date)\n",
|
||||
" if not datas:\n",
|
||||
" return\n",
|
||||
"\n",
|
||||
" # data_list = [two_cols, one_cols]\n",
|
||||
" append_rows = [get_cur_time(date)[1]]\n",
|
||||
" dataItemNo_dataValue = {}\n",
|
||||
" for data_value in datas:\n",
|
||||
" if \"dataValue\" not in data_value:\n",
|
||||
" print(data_value)\n",
|
||||
" dataItemNo_dataValue[data_value[\"dataItemNo\"]] = \"\"\n",
|
||||
" else:\n",
|
||||
" dataItemNo_dataValue[data_value[\"dataItemNo\"]] = data_value[\"dataValue\"]\n",
|
||||
" \n",
|
||||
" for value in one_cols[1:]:\n",
|
||||
" if value in dataItemNo_dataValue:\n",
|
||||
" append_rows.append(dataItemNo_dataValue[value])\n",
|
||||
" else:\n",
|
||||
" append_rows.append(\"\")\n",
|
||||
" save_xlsx(append_rows)\n",
|
||||
"\n",
|
||||
" # 获取当月的数据写入到指定文件\n",
|
||||
" # optimize_Model()\n",
|
||||
" # upload_data_to_system(token_push,date)\n",
|
||||
" # data_list.append(three_cols)\n",
|
||||
" # write_xls(data_list)\n",
|
||||
"\n",
|
||||
"def main(start_date=None,token=None,token_push=None):\n",
|
||||
" if start_date is None:\n",
|
||||
" start_date = datetime.now()\n",
|
||||
@ -1081,21 +694,18 @@
|
||||
" print(date)\n",
|
||||
" # 更新当月数据\n",
|
||||
" queryDataListItemNos(start_date,token)\n",
|
||||
" # 更新当日数据\n",
|
||||
" # start(date)\n",
|
||||
" # 训练模型\n",
|
||||
" optimize_Model()\n",
|
||||
" # 预测&上传预测结果\n",
|
||||
" upload_data_to_system(token_push,start_date)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# if __name__ == \"__main__\":\n",
|
||||
"\n",
|
||||
"# # 获取当月的数据写入到指定文件\n",
|
||||
"# queryDataListItemNos()\n"
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
@ -1144,7 +754,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -1152,23 +762,102 @@
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"运行中ing...\n",
|
||||
"获取到的数据项ID['YHQMXBB|C01100008|STRIKE_PRICE', 'C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|AMOUNT', 'C01100008|YEDAY_AMOUNT', '100028046|LISTING_PRICE', 'ICE_CL0_LAST_YEDAY_PRICE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']\n",
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0NDE0NDIwMCwianRpIjoiMTVhZTZjMDg4NWRmNDM0YWFiMDdkNDE4YzM3NDMwZjAifQ._GK6U4SGiCed-P8EBXIuSAU3QgV_8Zx4DNviupKYkeQ\n",
|
||||
"补充20250406数据\n",
|
||||
"数据项查询参数search_data：\n",
|
||||
"{'data': {'date': '20250406', 'dataItemNoList': ['C01100008|CORTED_VALUE', 'C01100008|AUCTION_MAX_PRICE', 'C01100008|AMOUNT', 'C01100008|YEDAY_AMOUNT', '100028046|LISTING_PRICE', 'ICE_CL0_LAST_YEDAY_PRICE', '91370200163576944B|C01100008|STRIKE_PRICE', '9137078672073757X8|C01100008|STRIKE_PRICE', '91370500674526498A|C01100008|STRIKE_PRICE', '91370305773165341A|C01100008|STRIKE_PRICE', '91370521164880008P|C01100008|STRIKE_PRICE', '91370321164425136B|C01100008|STRIKE_PRICE', 'SD|GC|ZDW|LIST_PRICE', '370500|ISOBUTANE|LIST_PRICE', 'SD|YT|SG|LIST_PRICE', '91110000710926094P|C01100008|SUPPLY_MERE', '91110000710932515R|C01100008|SUPPLY_MERE', '91370500674526498A|C01100008|SUPPLY_MERE', '91370321164425136B|C01100008|SUPPLY_MERE', 'C01100008|OTHER|SUPPLY_MERE', 'SD|WJH|DEMANDS', 'C01100008|SUY_DED_DAP', 'C01100008|EFFECTIVE_STOCK', '912102117169477344|C01100008|STRIKE_PRICE', '91110304102767480H|C01100008|STRIKE_PRICE', '91130193670310403L|C01100008|STRIKE_PRICE', 'HD|LPG|IMPORT_PRICE', 'SD|WJH|SALES_PRICE']}, 'funcModule': '数据项', 'funcOperation': '查询'}\n",
|
||||
"数据项查询结果search_res：\n",
|
||||
"{\"confirmFlg\":false,\"data\":[{\"dataDate\":\"20250406\",\"dataItemNo\":\"100028046|LISTING_PRICE\",\"dataValue\":8114.00000000},{\"dataDate\":\"20250406\",\"dataItemNo\":\"91110000710926094P|C01100008|SUPPLY_MERE\",\"dataValue\":1300.00000000},{\"dataDate\":\"20250406\",\"dataItemNo\":\"91110000710932515R|C01100008|SUPPLY_MERE\"},{\"dataDate\":\"20250406\",\"dataItemNo\":\"91110304102767480H|C01100008|STRIKE_PRICE\",\"dataValue\":5050.00000000},{\"dataDate\":\"20250406\",\"dataItemNo\":\"91130193670310403L|C01100008|STRIKE_PRICE\",\"dataValue\":4950.00000000},{\"dataDate\":\"20250406\",\"dataItemNo\":\"912102117169477344|C01100008|STRIKE_PRICE\",\"dataValue\":4360.00000000},{\"dataDate\":\"20250406\",\"dataItemNo\":\"91370200163576944B|C01100008|STRIKE_PRICE\",\"dataValue\":5050.00000000},{\"dataDate\":\"20250406\",\"dataItemNo\":\"91370321164425136B|C01100008|STRIKE_PRICE\",\"dataValue\":5150.00000000},{\"dataDate\":\"20250406\",\"dataItemNo\":\"91370321164425136B|C01100008|SUPPLY_MERE\",\"dataValue\":400.00000000},{\"dataDate\":\"20250406\",\"dataItemNo\":\"91370500674526498A|C01100008|STRIKE_PRICE\",\"dataValue\":5108.00000000},{\"dataDate\":\"20250406\",\"dataItemNo\":\"91370500674526498A|C01100008|SUPPLY_MERE\",\"dataValue\":175.00000000},{\"dataDate\":\"20250406\",\"dataItemNo\":\"91370521164880008P|C01100008|STRIKE_PRICE\",\"dataValue\":5105.00000000},{\"dataDate\":\"20250406\",\"dataItemNo\":\"C01100008|AMOUNT\",\"dataValue\":199.18000000},{\"dataDate\":\"20250406\",\"dataItemNo\":\"C01100008|AUCTION_MAX_PRICE\",\"dataValue\":5180.00000000},{\"dataDate\":\"20250406\",\"dataItemNo\":\"C01100008|CORTED_VALUE\",\"dataValue\":5180.00000000},{\"dataDate\":\"20250406\",\"dataItemNo\":\"C01100008|EFFECTIVE_STOCK\",\"dataValue\":1481.48640000},{\"dataDate\":\"20250406\",\"dataItemNo\":\"C01100008|OTHER|SUPPLY_MERE\",\"dataValue\":5000.00000000},{\"dataDate\":\"20250406\",\"dataItemNo\":\"C01100008|SUY_DED_DAP\",\"dataValue\":-50.00000000},{\"dataDate\":\"20250406\",\"dataItemNo\":\"HD|LPG|IMPORT_PRICE\",\"dataValue\":5600.00000000},{\"dataDate\":\"20250406\",\"dataItemNo\":\"SD|WJH|DEMANDS\",\"dataValue\":8500.00000000},{\"dataDate\":\"20250406\",\"dataItemNo\":\"SD|WJH|SALES_PRICE\",\"dataValue\":6100.00000000}],\"status\":true}\n",
|
||||
"数据项查询结果: [{'dataDate': '20250406', 'dataItemNo': '100028046|LISTING_PRICE', 'dataValue': 8114.0}, {'dataDate': '20250406', 'dataItemNo': '91110000710926094P|C01100008|SUPPLY_MERE', 'dataValue': 1300.0}, {'dataDate': '20250406', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}, {'dataDate': '20250406', 'dataItemNo': '91110304102767480H|C01100008|STRIKE_PRICE', 'dataValue': 5050.0}, {'dataDate': '20250406', 'dataItemNo': '91130193670310403L|C01100008|STRIKE_PRICE', 'dataValue': 4950.0}, {'dataDate': '20250406', 'dataItemNo': '912102117169477344|C01100008|STRIKE_PRICE', 'dataValue': 4360.0}, {'dataDate': '20250406', 'dataItemNo': '91370200163576944B|C01100008|STRIKE_PRICE', 'dataValue': 5050.0}, {'dataDate': '20250406', 'dataItemNo': '91370321164425136B|C01100008|STRIKE_PRICE', 'dataValue': 5150.0}, {'dataDate': '20250406', 'dataItemNo': '91370321164425136B|C01100008|SUPPLY_MERE', 'dataValue': 400.0}, {'dataDate': '20250406', 'dataItemNo': '91370500674526498A|C01100008|STRIKE_PRICE', 'dataValue': 5108.0}, {'dataDate': '20250406', 'dataItemNo': '91370500674526498A|C01100008|SUPPLY_MERE', 'dataValue': 175.0}, {'dataDate': '20250406', 'dataItemNo': '91370521164880008P|C01100008|STRIKE_PRICE', 'dataValue': 5105.0}, {'dataDate': '20250406', 'dataItemNo': 'C01100008|AMOUNT', 'dataValue': 199.18}, {'dataDate': '20250406', 'dataItemNo': 'C01100008|AUCTION_MAX_PRICE', 'dataValue': 5180.0}, {'dataDate': '20250406', 'dataItemNo': 'C01100008|CORTED_VALUE', 'dataValue': 5180.0}, {'dataDate': '20250406', 'dataItemNo': 'C01100008|EFFECTIVE_STOCK', 'dataValue': 1481.4864}, {'dataDate': '20250406', 'dataItemNo': 'C01100008|OTHER|SUPPLY_MERE', 'dataValue': 5000.0}, {'dataDate': '20250406', 'dataItemNo': 'C01100008|SUY_DED_DAP', 'dataValue': -50.0}, {'dataDate': '20250406', 'dataItemNo': 'HD|LPG|IMPORT_PRICE', 'dataValue': 5600.0}, {'dataDate': '20250406', 'dataItemNo': 'SD|WJH|DEMANDS', 'dataValue': 8500.0}, {'dataDate': '20250406', 'dataItemNo': 'SD|WJH|SALES_PRICE', 'dataValue': 6100.0}]\n",
|
||||
"{'dataDate': '20250406', 'dataItemNo': '91110000710932515R|C01100008|SUPPLY_MERE'}\n",
|
||||
"添加的行： ['20250406', '', 5180.0, 5180.0, 199.18, '', 8114.0, '', 5050.0, '', 5108.0, '', 5105.0, 5150.0, '', '', '', 1300.0, '', 175.0, 400.0, 5000.0, 8500.0, -50.0, 1481.4864, 4360.0, 5050.0, 4950.0, 5600.0, 6100.0]\n",
|
||||
"Index(['Date', 'Price', '修正价', '竞拍最高价', '液化石油气|发货量', '昨日布伦特价格', '昨日92#汽油价格',\n",
|
||||
" '计划出货量', '青岛石化', '中化工-昌邑', '海科瑞林', '鑫泰石化|液化石油气|成交价', '垦利价格', '汇丰价格',\n",
|
||||
" '正丁烷', '异丁烷价格', '顺酐', '中石化供应量', '中化工供应量', '海科供应量', '汇丰供应量', '京博和其他供应量',\n",
|
||||
" '烷基化需求量', '昨日烷基化价差', '我司库存', '东北-大连石化', '华北-燕山石化', '华北-石家庄炼化',\n",
|
||||
" '昨日原料气价格', '烷基化油销售价格'],\n",
|
||||
" dtype='object')\n",
|
||||
"保存数据时发生错误: 'Date'\n"
|
||||
"获取的token: eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfZGV2IiwidGgiOiI4YTQ1NzdkYmQ5MTk2NzU3NThkNTc5OTlhMWU4OTFmZSIsImx0IjoiYXBpIiwiaXNzIjoiIiwidG0iOiJQQyIsImV4cCI6MTc0NDE5ODg0NywianRpIjoiZmJlMmI4MzA5NzFmNDBhMzhiZTA5YTZjMDEyZjU4YmQifQ.rGLp0UBfeu5JmoYXbGSgCpkrO2QnlAx8hFbbbDDXC8I\n",
|
||||
"20250409\n",
|
||||
" dataDate dataItemNo dataValue\n",
|
||||
"0 2025-04-01 100028046|LISTING_PRICE 8208.0\n",
|
||||
"1 2025-04-02 100028046|LISTING_PRICE 8244.0\n",
|
||||
"2 2025-04-03 100028046|LISTING_PRICE 8244.0\n",
|
||||
"3 2025-04-04 100028046|LISTING_PRICE 8165.0\n",
|
||||
"4 2025-04-05 100028046|LISTING_PRICE 8114.0\n",
|
||||
".. ... ... ...\n",
|
||||
"183 2025-04-07 YHQMXBB|C01100008|STRIKE_PRICE 5180.0\n",
|
||||
"184 2025-04-02 YHQMXBB|C01100008|STRIKE_PRICE 5310.0\n",
|
||||
"185 2025-04-01 YHQMXBB|C01100008|STRIKE_PRICE 5260.0\n",
|
||||
"186 2025-04-04 YHQMXBB|C01100008|STRIKE_PRICE 5230.0\n",
|
||||
"187 2025-04-05 YHQMXBB|C01100008|STRIKE_PRICE 5180.0\n",
|
||||
"\n",
|
||||
"[188 rows x 3 columns]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_9964\\3261286938.py:614: DeprecationWarning:\n",
|
||||
"\n",
|
||||
"The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"当月数据更新完成\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_9964\\3261286938.py:255: UserWarning:\n",
|
||||
"\n",
|
||||
"The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
|
||||
"\n",
|
||||
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_9964\\3261286938.py:257: UserWarning:\n",
|
||||
"\n",
|
||||
"The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Using matplotlib backend: QtAgg\n",
|
||||
"%pylab is deprecated, use %matplotlib inline and import the required libraries.\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n",
|
||||
"Fitting 3 folds for each of 180 candidates, totalling 540 fits\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"d:\\ProgramData\\anaconda3\\Lib\\site-packages\\IPython\\core\\magics\\pylab.py:162: UserWarning:\n",
|
||||
"\n",
|
||||
"pylab import has clobbered these variables: ['plot', 'random', '__version__', 'datetime']\n",
|
||||
"`%matplotlib` prevents importing * from pylab and numpy\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Date\n",
|
||||
"2025-04-09 5179.792969\n",
|
||||
"Name: 日度预测价格, dtype: float32\n",
|
||||
"预测值： 5179.79\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_9964\\3261286938.py:203: UserWarning:\n",
|
||||
"\n",
|
||||
"The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
|
||||
"\n",
|
||||
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_9964\\3261286938.py:205: UserWarning:\n",
|
||||
"\n",
|
||||
"The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
|
||||
"\n",
|
||||
"C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_9964\\3261286938.py:237: FutureWarning:\n",
|
||||
"\n",
|
||||
"Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@ -1185,13 +874,12 @@
|
||||
" # if current_time == \"12:00:00\":\n",
|
||||
" # print(\"执行定时任务\")\n",
|
||||
" # main()\n",
|
||||
" # elif current_time == \"17:45:00\":\n",
|
||||
" # elif current_time == \"20:00:00\":\n",
|
||||
" # start_1()\n",
|
||||
" # time.sleep(1)\n",
|
||||
" # except:\n",
|
||||
" # print(f\"{current_time}执行失败\")\n",
|
||||
"\n",
|
||||
" start_1()\n",
|
||||
" # 检测数据准确性, 需要检测放开\n",
|
||||
" # check_data(\"100028098|LISTING_PRICE\")\n",
|
||||
" # check_data(\"9137070016544622XB|DAY_Yield\")\n"
|
||||
@ -1199,7 +887,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@ -1231,7 +919,7 @@
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"display_name": "base",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
|
||||
Binary file not shown.
Loading…
Reference in New Issue
Block a user