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     "end_time": "2025-12-10T08:15:04.273374Z",
     "start_time": "2025-12-10T08:14:32.229923Z"
    }
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   "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",
    "import time\n",
    "\n",
    "def get_ngv_details(days_back=1):\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 = Config.CONN_INFO\n",
    "        conn = psycopg2.connect(**conn)\n",
    "        cursor = conn.cursor()\n",
    "\n",
    "        # 获取指定天数前的日期\n",
    "        now_time = datetime.now()\n",
    "        target_time = now_time + timedelta(days=-days_back)\n",
    "        target_date_id = int(target_time.strftime('%Y%m%d'))  # 获取目标日期\n",
    "\n",
    "        # sql语句查询\n",
    "        sql = f\"\"\"\n",
    "        SELECT * FROM \"public\".\"holo_ads_report_saas_profile_ngv_detail_d\" WHERE \"date_id\" = '{target_date_id}' ;\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(e)\n",
    "        return None\n",
    "\n",
    "data_NGV_j = get_ngv_details(days_back=1)\n",
    "data_NGV_j1 = get_ngv_details(days_back=2)\n",
    "\n",
    "# 步骤1：将文本转为数字（无法转换的会变成 NaN）\n",
    "data_NGV_j['g_month_percentage'] = (pd.to_numeric(data_NGV_j['g_month_percentage'], errors='coerce')\n",
    "                    .round(3)\n",
    "                    .apply(lambda x: f\"{x:.3f}\" if pd.notna(x) else ''))\n",
    "\n",
    "data_NGV_j.to_csv('data_NGV_j.csv', index=False)\n",
    "data_NGV_j1.to_csv('data_NGV_j1.csv', index=False)"
   ],
   "outputs": [],
   "execution_count": 4
  }
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