104 lines
3.3 KiB
Plaintext
104 lines
3.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"id": "initial_id",
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"metadata": {
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"collapsed": true,
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"ExecuteTime": {
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"end_time": "2025-12-04T09:43:46.485266Z",
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"start_time": "2025-12-04T09:43:15.261034Z"
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}
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},
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"source": [
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"from datetime import datetime, timezone, timedelta, date, UTC\n",
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"import holidays\n",
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"from config import Config\n",
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"import psycopg2\n",
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"import pandas as pd\n",
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"import pymysql\n",
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"from api import API\n",
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"from log_config import configure_task_logger, configure_error_task_logger\n",
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"import time\n",
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"\n",
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"def get_ngv_details(days_back=1):\n",
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" \"\"\"\n",
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" 从固定的数据库中获取前几天的NGV明细。\n",
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" 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",
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" 返回包含NGV明细的pandas DataFrame。\n",
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" \"\"\"\n",
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" try:\n",
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" # 获得连接\n",
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" conn = Config.CONN_INFO\n",
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" conn = psycopg2.connect(**conn)\n",
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" cursor = conn.cursor()\n",
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"\n",
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" # 获取指定天数前的日期\n",
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" now_time = datetime.now()\n",
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" target_time = now_time + timedelta(days=-days_back)\n",
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" target_date_id = int(target_time.strftime('%Y%m%d')) # 获取目标日期\n",
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"\n",
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" # sql语句查询\n",
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" sql = f\"\"\"\n",
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" SELECT * FROM \"public\".\"holo_ads_report_saas_profile_ngv_detail_d\" WHERE \"date_id\" = '{target_date_id}' ;\n",
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" \"\"\"\n",
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"\n",
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" # 执行语句并获取结果集\n",
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" cursor.execute(sql)\n",
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" rows = cursor.fetchall()\n",
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" all_fields = cursor.description\n",
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"\n",
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" # 执行结果转化为dataframe\n",
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" col = [i[0] for i in all_fields]\n",
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" data_NGV = pd.DataFrame(rows, columns=col)\n",
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"\n",
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" # 尝试自动解析日期时间字符串\n",
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" time_format = \"%Y-%m-%d %H:%M:%S\"\n",
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" if 'saas_create_time' in data_NGV.columns:\n",
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" data_NGV['saas_create_time'] = pd.to_datetime(data_NGV['saas_create_time'], format=time_format,\n",
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" errors='coerce')\n",
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" data_NGV['saas_create_time'] = data_NGV['saas_create_time'].dt.strftime('%Y-%m-%d')\n",
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"\n",
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" # 关闭游标和连接\n",
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" cursor.close()\n",
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" conn.close()\n",
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"\n",
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" return data_NGV\n",
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"\n",
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" except Exception as e:\n",
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" print(e)\n",
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" return None\n",
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"\n",
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"data_NGV_j = get_ngv_details(days_back=1)\n",
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"data_NGV_j1 = get_ngv_details(days_back=2)\n",
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"\n",
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"data_NGV_j.to_csv('data_NGV_j.csv', index=False)\n",
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"data_NGV_j1.to_csv('data_NGV_j1.csv', index=False)"
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],
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"outputs": [],
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"execution_count": 6
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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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": 2
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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": "ipython2",
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"version": "2.7.6"
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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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