130 lines
4.4 KiB
Plaintext
130 lines
4.4 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"metadata": {},
|
|
"cell_type": "markdown",
|
|
"source": "",
|
|
"id": "4eeb08f90b26d53f"
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "initial_id",
|
|
"metadata": {
|
|
"collapsed": true,
|
|
"ExecuteTime": {
|
|
"end_time": "2025-08-20T08:27:40.142050Z",
|
|
"start_time": "2025-08-20T08:27:38.703087Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"from datetime import datetime, timezone, timedelta, date, UTC\n",
|
|
"import holidays\n",
|
|
"from config import Config\n",
|
|
"import psycopg2\n",
|
|
"import pandas as pd\n",
|
|
"import pymysql\n",
|
|
"from api import API\n",
|
|
"from log_config import configure_task_logger, configure_error_task_logger\n",
|
|
"\n",
|
|
"api_instance = API()\n",
|
|
"# 获取已经配置好的常规日志记录器\n",
|
|
"logger = configure_task_logger()\n",
|
|
"\n",
|
|
"# 获取已经配置好的错误任务日志记录器\n",
|
|
"error_task_logger = configure_error_task_logger()\n",
|
|
"\n",
|
|
"\n",
|
|
"def get_ngv_details():\n",
|
|
" \"\"\"\n",
|
|
" 从固定的数据库中获取前几天的NGV明细。\n",
|
|
" 参数 `days_back` 表示相对于今天的天数偏移量，默认为1（即前一天）。\n",
|
|
" 返回包含NGV明细的pandas DataFrame。\n",
|
|
" \"\"\"\n",
|
|
" try:\n",
|
|
" # 获得连接\n",
|
|
" conn = psycopg2.connect(**Config.CONN_INFO)\n",
|
|
" cursor = conn.cursor()\n",
|
|
"\n",
|
|
" # sql语句查询\n",
|
|
" sql = f\"\"\"\n",
|
|
" SELECT * FROM \"public\".\"saas_ngv_yesterday\";\n",
|
|
" \"\"\"\n",
|
|
"\n",
|
|
" # 执行语句并获取结果集\n",
|
|
" cursor.execute(sql)\n",
|
|
" rows = cursor.fetchall()\n",
|
|
" all_fields = cursor.description\n",
|
|
"\n",
|
|
" # 执行结果转化为dataframe\n",
|
|
" col = [i[0] for i in all_fields]\n",
|
|
" data_NGV = pd.DataFrame(rows, columns=col)\n",
|
|
"\n",
|
|
" # 尝试自动解析日期时间字符串\n",
|
|
" time_format = \"%Y-%m-%d %H:%M:%S\"\n",
|
|
" if 'saas_create_time' in data_NGV.columns:\n",
|
|
" data_NGV['saas_create_time'] = pd.to_datetime(data_NGV['saas_create_time'], format=time_format,\n",
|
|
" errors='coerce')\n",
|
|
" data_NGV['saas_create_time'] = data_NGV['saas_create_time'].dt.strftime('%Y-%m-%d')\n",
|
|
"\n",
|
|
" # 关闭游标和连接\n",
|
|
" cursor.close()\n",
|
|
" conn.close()\n",
|
|
"\n",
|
|
" return data_NGV\n",
|
|
"\n",
|
|
" except Exception as e:\n",
|
|
" print(f\"Error occurred: {e}\")\n",
|
|
" return None\n",
|
|
"\n",
|
|
"df = get_ngv_details()\n",
|
|
"df.to_csv(\"中石化ngv同步.csv\", index=False)"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Error occurred: relation \"public.saas_ngv_yesterday\" does not exist\n",
|
|
"LINE 2: SELECT * FROM \"public\".\"saas_ngv_yesterday\";\n",
|
|
" ^\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"ename": "AttributeError",
|
|
"evalue": "'NoneType' object has no attribute 'to_csv'",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
|
|
"\u001B[31mAttributeError\u001B[39m Traceback (most recent call last)",
|
|
"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[1]\u001B[39m\u001B[32m, line 63\u001B[39m\n\u001B[32m 60\u001B[39m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[32m 62\u001B[39m df = get_ngv_details()\n\u001B[32m---> \u001B[39m\u001B[32m63\u001B[39m df.to_csv(\u001B[33m\"\u001B[39m\u001B[33m中石化ngv同步.csv\u001B[39m\u001B[33m\"\u001B[39m, index=\u001B[38;5;28;01mFalse\u001B[39;00m)\n",
|
|
"\u001B[31mAttributeError\u001B[39m: 'NoneType' object has no attribute 'to_csv'"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 1
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 2
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython2",
|
|
"version": "2.7.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|