{ "cells": [ { "metadata": {}, "cell_type": "markdown", "source": "## 获取数据", "id": "1af8678c57a7160" }, { "cell_type": "code", "id": "initial_id", "metadata": { "collapsed": true, "ExecuteTime": { "end_time": "2025-08-20T08:58:29.047944Z", "start_time": "2025-08-20T08:58:27.944621Z" } }, "source": [ "# -*- coding: utf-8 -*-\n", "import pandas as pd\n", "import datetime\n", "from config import Config\n", "from api import API\n", "import pymysql # 使用 pymysql 替代 mysql.connector\n", "from back_ground_module import CommonModule\n", "import os\n", "import mysql.connector\n", "import pandas as pd\n", "import json\n", "import numpy as np\n", "import mysql.connector\n", "from mysql.connector import Error\n", "from log_config import configure_task_logger, configure_error_task_logger\n", "\n", "logger = configure_task_logger()\n", "error_task_logger = configure_error_task_logger()\n", "api_instance = API()\n", "common_module = CommonModule()\n", "output_dir = \"output\" # 设置输出目录\n", "os.makedirs(output_dir, exist_ok=True)\n", "\n", "\n", "class NonStandardPerformanceToBI:\n", " def __init__(self):\n", " self.dealer_service_data = None\n", " self.field_mapping = {\n", " \"报备类型\": \"_widget_1753770875899\",\n", " \"协作内容\": \"_widget_1753770875915\",\n", " \"订单类型\": \"_widget_1753770875966\",\n", " \"情况说明\": \"_widget_1753770875944\",\n", " \"订单编号\": \"_widget_1753770875887\",\n", " \"实付金额\": \"_widget_1753770875889\",\n", " \"门店编码\": \"_widget_1753770875890\",\n", " \"门店名称\": \"_widget_1753770875888\",\n", " \"版本\": \"_widget_1753770875891\",\n", " \"年限\": \"_widget_1753948745953\",\n", " \"支付日期\": \"_widget_1753770875893\",\n", " \"开户/处理日期\": \"_widget_1753770875894\",\n", " \"小六业绩金额\": \"_widget_1753770875898\",\n", " \"区域业绩金额\": \"_widget_1753770875937\",\n", " \"报备业绩归属人\": \"_widget_1753770875901\",\n", " \"报备业绩归属区域经理\": \"_widget_1753770875903\",\n", " \"报备业绩归属大区\": \"_widget_1753866196486\",\n", " \"原业绩归属人\": \"_widget_1753856032683\",\n", " \"原业绩归属区域经理\": \"_widget_1753866196485\",\n", " \"小六业绩比例\": \"_widget_1753770875917\",\n", " \"区域业绩比例\": \"_widget_1753770875921\",\n", " \"运营专家\": \"_widget_1753770875902\",\n", " \"提成类型\": \"_widget_1753778922504\",\n", " \"SaaS新签提成比例\": \"_widget_1753770875949\",\n", " \"服务包提成比例\": \"_widget_1753778922567\",\n", " \"提成金额\": \"_widget_1753770875948\",\n", " \"新签提成比例-首年\": \"_widget_1753778922503\",\n", " \"新签提成比例-非首年\": \"_widget_1753778922548\",\n", " \"新签阶段及提成比例\": \"_widget_1753778656359\",\n", " \"新签阶段及提成比例.选择提成阶段\": \"_widget_1753778656359._widget_1753778656361\",\n", " \"新签阶段及提成比例.新签阶段\": \"_widget_1753778656359._widget_1753948745962\",\n", " \"新签阶段及提成比例.提成比例\": \"_widget_1753778656359._widget_1753778656362\",\n", " }\n", "\n", " # 定义需要特殊处理的列表字段及其内部字段映射\n", " self.list_fields_config = {\n", " \"新签阶段及提成比例\": {\n", " \"_widget_1753778656361\": \"选择提成阶段\",\n", " \"_widget_1753948745962\": \"新签阶段\",\n", " \"_widget_1753778656362\": \"提成比例\"\n", " },\n", " # 可以在这里添加其他列表字段的配置\n", " # \"另一个列表字段\": {\n", " # \"原始字段名1\": \"映射后字段名1\",\n", " # \"原始字段名2\": \"映射后字段名2\"\n", " # }\n", " }\n", "\n", " def load_all_data(self):\n", " # 获取非标业绩提报数据\n", " payload = {\"api_key\": \"66b9678280b37f8a276b1d01\",\n", " \"entry_id\": \"68886b7c0382a7249ae0b5d6\",\n", " }\n", " dealer_service = api_instance.entry_data_list(payload)\n", " self.dealer_service_data = dealer_service.get(\"data\") # api请求格式，将数据封装在data字典里\n", "\n", " def process_list_field(self, field_value, field_config):\n", " \"\"\"通用方法：处理列表类型的字段\"\"\"\n", " if not isinstance(field_value, (list, np.ndarray)):\n", " return field_value\n", "\n", " processed_list = []\n", " for item in field_value:\n", " if not isinstance(item, dict):\n", " processed_list.append(item)\n", " continue\n", "\n", " processed_item = {}\n", " for original_key, mapped_key in field_config.items():\n", " if original_key in item:\n", " # 处理包含id的字典字段\n", " if isinstance(item[original_key], dict) and \"id\" in item[original_key]:\n", " processed_item[mapped_key] = item[original_key][\"id\"]\n", " else:\n", " processed_item[mapped_key] = item[original_key]\n", " else:\n", " processed_item[mapped_key] = None\n", " processed_list.append(processed_item)\n", " return processed_list\n", "\n", " def data_process(self):\n", " df = pd.DataFrame(self.dealer_service_data)\n", " # 反转映射字典\n", " reverse_mapping = {v: k for k, v in self.field_mapping.items()}\n", " # 1.列明替换\n", " df.columns = [reverse_mapping.get(col, col) for col in df.columns]\n", "\n", " # 2.成员字段取值\n", " user_columns = [\"报备业绩归属人\", \"报备业绩归属区域经理\", \"原业绩归属人\", \"原业绩归属区域经理\", \"运营专家\"]\n", "\n", " for col in user_columns:\n", " df[col] = df[col].map(lambda x: x.get(\"name\", \"\") if isinstance(x, dict) else \"\")\n", "\n", " # 3.日期字段转为北京时间\n", " time_columns = [\"支付日期\", \"开户/处理日期\"]\n", "\n", " df[time_columns] = df[time_columns].apply(\n", " lambda col: pd.to_datetime(col, errors='coerce')\n", " .dt.tz_localize(None)\n", " .dt.strftime('%Y-%m-%d %H:%M:%S')\n", " )\n", "\n", " # 4.处理所有配置的列表字段\n", " if \"新签阶段及提成比例\" in df.columns:\n", " # 先处理订单登记表字段\n", " df[\"新签阶段及提成比例\"] = df[\"新签阶段及提成比例\"].apply(\n", " lambda x: self.process_list_field(x, self.list_fields_config[\"新签阶段及提成比例\"])\n", " if x is not None and (isinstance(x, (list, dict, np.ndarray)) or not pd.isna(x))\n", " else None\n", " )\n", "\n", " # 拆分行\n", " df_exploded = df.explode(\"新签阶段及提成比例\")\n", "\n", " # 将订单登记表中的字段提取到主表中\n", " order_fields = self.list_fields_config[\"新签阶段及提成比例\"].values()\n", " for field in order_fields:\n", " df_exploded[field] = df_exploded[\"新签阶段及提成比例\"].apply(\n", " lambda x: x.get(field) if isinstance(x, dict) else None\n", " )\n", "\n", " # 删除原始的订单登记表列\n", " df_exploded = df_exploded.drop(columns=[\"新签阶段及提成比例\"])\n", "\n", " # 重置索引\n", " df = df_exploded.reset_index(drop=True)\n", "\n", " return df\n", "\n", " def write_to_bi(self, df):\n", " # 数据库连接信息\n", " HS_DB_Config = {\n", " 'host': \"f6-public.rwlb.rds.aliyuncs.com\",\n", " 'user': \"rw_operation_data_relay\",\n", " 'password': \"m+q5Z4%IVuF9bf\",\n", " 'database': \"f6operation_data_relay\"\n", " }\n", " table_name = \"non_standard_performance_to_BI\" # 替换为你的实际表名\n", "\n", " # 建立数据库连接\n", " connection = mysql.connector.connect(\n", " host=HS_DB_Config[\"host\"],\n", " user=HS_DB_Config[\"user\"],\n", " password=HS_DB_Config[\"password\"],\n", " database=HS_DB_Config[\"database\"]\n", " )\n", " cursor = connection.cursor()\n", "\n", " try:\n", " # 查询表列名\n", " cursor.execute(f\"SHOW COLUMNS FROM {table_name}\")\n", " columns_info = cursor.fetchall()\n", " db_columns = [col[0] for col in columns_info] # 提取列名\n", " df = df.replace([None, np.nan, pd.NA, 'nan', 'NaN', 'NAN', ''], None)\n", " # 保留 DataFrame 中与数据库列名匹配的列\n", " filtered_df = df[df.columns.intersection(db_columns)]\n", "\n", " # 如果没有匹配的列，直接返回\n", " if filtered_df.empty:\n", " print(\"DataFrame 中没有与数据库表结构匹配的列。\")\n", " return\n", "\n", " # 筛选列之后，插入前处理 dict 类型\n", " filtered_df = filtered_df.copy()\n", " for col in filtered_df.columns:\n", " if filtered_df[col].apply(lambda x: isinstance(x, (dict, list)) if x is not None else False).any():\n", " filtered_df.loc[:, col] = filtered_df[col].apply(\n", " lambda x: json.dumps(x, ensure_ascii=False) if x is not None else x\n", " )\n", "\n", " # 构建插入语句\n", " placeholders = ', '.join(['%s'] * len(filtered_df.columns))\n", " # 使用反引号避免特殊列明\n", " columns = ', '.join([f\"`{col}`\" for col in filtered_df.columns])\n", " insert_sql = f\"INSERT INTO {table_name} ({columns}) VALUES ({placeholders})\"\n", "\n", " # 将 DataFrame 写入数据库\n", " for _, row in filtered_df.iterrows():\n", " cursor.execute(insert_sql, tuple(row))\n", "\n", " connection.commit()\n", " print(f\"成功写入 {len(filtered_df)} 条记录到 {table_name} 表中。\")\n", "\n", " except Exception as e:\n", " print(\"写入数据库时发生错误：\", e)\n", " connection.rollback()\n", " finally:\n", " cursor.close()\n", " connection.close()\n", "\n", " def clear_table_data(self):\n", " \"\"\"\n", " 清空指定 MySQL 表的数据。\n", " 参数已写死在函数内部，直接调用即可。\n", " \"\"\"\n", " # 数据库连接信息\n", " HS_DB_Config = {\n", " 'host': \"f6-public.rwlb.rds.aliyuncs.com\",\n", " 'user': \"rw_operation_data_relay\",\n", " 'password': \"m+q5Z4%IVuF9bf\",\n", " 'database': \"f6operation_data_relay\"\n", " }\n", " table_name = \"non_standard_performance_to_BI\" # 要清空的表名\n", "\n", " connection = None\n", " try:\n", " # 建立数据库连接\n", " connection = mysql.connector.connect(\n", " host=HS_DB_Config[\"host\"],\n", " user=HS_DB_Config[\"user\"],\n", " password=HS_DB_Config[\"password\"],\n", " database=HS_DB_Config[\"database\"]\n", " )\n", " if connection.is_connected():\n", " cursor = connection.cursor()\n", "\n", " # 使用TRUNCATE清空表数据\n", " cursor.execute(f\"TRUNCATE TABLE {table_name}\")\n", " connection.commit()\n", "\n", " print(f\"成功清空表 {table_name} 中的所有数据\")\n", "\n", " except Error as e:\n", " print(f\"清空表时发生错误: {e}\")\n", " if connection and connection.is_connected():\n", " connection.rollback()\n", " finally:\n", " if connection and connection.is_connected():\n", " cursor.close()\n", " connection.close()\n", " print(\"数据库连接已关闭\")\n", "\n", " def main(self):\n", " task_start_time = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n", "\n", " # step1: 获取数据\n", " self.load_all_data()\n", "\n", " # step2:数据处理\n", " df = self.data_process()\n", " # df.to_csv(os.path.join(output_dir, \"new_dealer_service_order_to_bi.csv\"))\n", "\n", " # step3:数据库删除\n", " self.clear_table_data()\n", "\n", " # step4:数据写入BI\n", " self.write_to_bi(df)\n", "\n", " common_module.send_task_status(task_start_time, \"非标业绩提报转BI\")\n", "\n", "\n", "if __name__ == '__main__':\n", " start = NonStandardPerformanceToBI()\n", " start.main()" ], "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001B[92m2025-08-20 16:58:28,211 - api.py - task_logger - INFO - 已获取 8 条数据\u001B[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "成功清空表 non_standard_performance_to_BI 中的所有数据\n", "数据库连接已关闭\n", "成功写入 8 条记录到 non_standard_performance_to_BI 表中。\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001B[92m2025-08-20 16:58:29,045 - common_module.py - task_logger - INFO - 任务状态发送成功: {'data': {'creator': {'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}, 'updater': {'name': 'F6汽车科技', 'username': '#admin', 'status': 1, 'type': 0}, 'deleter': None, 'createTime': '2025-08-20T08:58:26.821Z', 'updateTime': '2025-08-20T08:58:26.821Z', 'deleteTime': None, '_widget_1744873387500': '2025-08-20T00:00:00.000Z', '_widget_1743644977694': '非标业绩提报转BI', '_widget_1744873387501': '2025-08-20T08:58:28.000Z', '_widget_1744873387502': '2025-08-20T08:58:28.000Z', '_widget_1744873387504': '0', '_id': '68a58e326435007d9a859fa2', 'appId': '6694d3c4fcb69ca9a111a6c4', 'entryId': '67ede908eb9c22261016466e'}}\u001B[0m\n" ] } ], "execution_count": 2 }, { "metadata": { "ExecuteTime": { "end_time": "2025-08-14T06:21:29.349444Z", "start_time": "2025-08-14T06:21:28.784674Z" } }, "cell_type": "code", "source": [ "# -*- coding: utf-8 -*-\n", "import pandas as pd\n", "import datetime\n", "from config import Config\n", "from api import API\n", "import pymysql # 使用 pymysql 替代 mysql.connector\n", "from back_ground_module import CommonModule\n", "import os\n", "import mysql.connector\n", "import pandas as pd\n", "import json\n", "import numpy as np\n", "import mysql.connector\n", "from mysql.connector import Error\n", "\n", "start_time = datetime.datetime.now()\n", "api_instance = API()\n", "common_module = CommonModule()\n", "\n", "# 保存为CSV文件\n", "output_dir = \"output\" # 设置输出目录\n", "\n", "# 创建输出目录（如果不存在）\n", "os.makedirs(output_dir, exist_ok=True)\n", "\n", "\n", "class NewDealerServiceOrderToBI:\n", " def __init__(self):\n", " self.dealer_service_data = None\n", " self.field_mapping = {\n", " \"报备类型\": \"_widget_1753770875899\",\n", " \"协作内容\": \"_widget_1753770875915\",\n", " \"订单类型\": \"_widget_1753770875966\",\n", " \"情况说明\": \"_widget_1753770875944\",\n", " \"订单编号\": \"_widget_1753770875887\",\n", " \"实付金额\": \"_widget_1753770875889\",\n", " \"门店编码\": \"_widget_1753770875890\",\n", " \"门店名称\": \"_widget_1753770875888\",\n", " \"版本\": \"_widget_1753770875891\",\n", " \"年限\": \"_widget_1753948745953\",\n", " \"支付日期\": \"_widget_1753770875893\",\n", " \"开户/处理日期\": \"_widget_1753770875894\",\n", " \"小六业绩金额\": \"_widget_1753770875898\",\n", " \"区域业绩金额\": \"_widget_1753770875937\",\n", " \"报备业绩归属人\": \"_widget_1753770875901\",\n", " \"报备业绩归属区域经理\": \"_widget_1753770875903\",\n", " \"报备业绩归属大区\": \"_widget_1753866196486\",\n", " \"原业绩归属人\": \"_widget_1753856032683\",\n", " \"原业绩归属区域经理\": \"_widget_1753866196485\",\n", " \"小六业绩比例\": \"_widget_1753770875917\",\n", " \"区域业绩比例\": \"_widget_1753770875921\",\n", " \"运营专家\": \"_widget_1753770875902\",\n", " \"提成类型\": \"_widget_1753778922504\",\n", " \"SaaS新签提成比例\": \"_widget_1753770875949\",\n", " \"服务包提成比例\": \"_widget_1753778922567\",\n", " \"提成金额\": \"_widget_1753770875948\",\n", " \"新签提成比例-首年\": \"_widget_1753778922503\",\n", " \"新签提成比例-非首年\": \"_widget_1753778922548\",\n", " \"新签阶段及提成比例\": \"_widget_1753778656359\",\n", " \"新签阶段及提成比例.选择提成阶段\": \"_widget_1753778656359._widget_1753778656361\",\n", " \"新签阶段及提成比例.新签阶段\": \"_widget_1753778656359._widget_1753948745962\",\n", " \"新签阶段及提成比例.提成比例\": \"_widget_1753778656359._widget_1753778656362\",\n", " }\n", "\n", " # 定义需要特殊处理的列表字段及其内部字段映射\n", " self.list_fields_config = {\n", " \"新签阶段及提成比例\": {\n", " \"_widget_1753778656361\": \"选择提成阶段\",\n", " \"_widget_1753948745962\": \"新签阶段\",\n", " \"_widget_1753778656362\": \"提成比例\"\n", " },\n", " # 可以在这里添加其他列表字段的配置\n", " # \"另一个列表字段\": {\n", " # \"原始字段名1\": \"映射后字段名1\",\n", " # \"原始字段名2\": \"映射后字段名2\"\n", " # }\n", " }\n", "\n", " def load_all_data(self):\n", " # 获取非标业绩提报数据\n", " payload = {\"api_key\": \"66b9678280b37f8a276b1d01\",\n", " \"entry_id\": \"68886b7c0382a7249ae0b5d6\",\n", " }\n", " dealer_service = api_instance.entry_data_list(payload)\n", " self.dealer_service_data = dealer_service.get(\"data\") # api请求格式，将数据封装在data字典里\n", "\n", " def process_list_field(self, field_value, field_config):\n", " \"\"\"通用方法：处理列表类型的字段\"\"\"\n", " if not isinstance(field_value, list):\n", " return field_value\n", "\n", " processed_list = []\n", " for item in field_value:\n", " if not isinstance(item, dict):\n", " processed_list.append(item)\n", " continue\n", "\n", " processed_item = {}\n", " for original_key, mapped_key in field_config.items():\n", " if original_key in item:\n", " # 处理包含id的字典字段\n", " if isinstance(item[original_key], dict) and \"id\" in item[original_key]:\n", " processed_item[mapped_key] = item[original_key][\"id\"]\n", " else:\n", " processed_item[mapped_key] = item[original_key]\n", " else:\n", " processed_item[mapped_key] = None\n", " processed_list.append(processed_item)\n", " return processed_list\n", "\n", " def data_process(self):\n", " df = pd.DataFrame(self.dealer_service_data)\n", " # 反转映射字典\n", " reverse_mapping = {v: k for k, v in self.field_mapping.items()}\n", " # 1.列明替换\n", " df.columns = [reverse_mapping.get(col, col) for col in df.columns]\n", "\n", " # 2.成员字段取值\n", " user_columns = [\"报备业绩归属人\", \"报备业绩归属区域经理\", \"原业绩归属人\", \"原业绩归属区域经理\", \"运营专家\"]\n", "\n", " for col in user_columns:\n", " df[col] = df[col].map(lambda x: x.get(\"name\", \"\") if isinstance(x, dict) else \"\")\n", "\n", " # 3.日期字段转为北京时间\n", " time_columns = [\"支付日期\", \"开户/处理日期\"]\n", "\n", " df[time_columns] = df[time_columns].apply(\n", " lambda col: pd.to_datetime(col, errors='coerce')\n", " .dt.tz_localize(None)\n", " .dt.strftime('%Y-%m-%d %H:%M:%S')\n", " )\n", " df.to_csv(\"feibiao.csv\", index=False)\n", "\n", " # 4.处理所有配置的列表字段\n", " for field_name, field_config in self.list_fields_config.items():\n", " if field_name in df.columns:\n", " df[field_name] = df[field_name].apply(\n", " lambda x: (\n", " self.process_list_field(x, field_config)\n", " if (isinstance(x, np.ndarray) and x.size > 0) # 非空 NumPy 数组\n", " or (isinstance(x, list) and len(x) > 0) # 非空列表\n", " or (not isinstance(x, (np.ndarray, list)) and x is not None and not pd.isna(x)) # 其他非空值\n", " else None # 空数组、空列表、None、NaN 都返回 None\n", " )\n", " )\n", "\n", " return df\n", "\n", " def write_to_bi(self, df):\n", " # 数据库连接信息\n", " HS_DB_Config = {\n", " 'host': \"f6-public.rwlb.rds.aliyuncs.com\",\n", " 'user': \"rw_operation_data_relay\",\n", " 'password': \"m+q5Z4%IVuF9bf\",\n", " 'database': \"f6operation_data_relay\"\n", " }\n", " table_name = \"new_dealer_service_order_to_bi\" # 替换为你的实际表名\n", "\n", " # 建立数据库连接\n", " connection = mysql.connector.connect(\n", " host=HS_DB_Config[\"host\"],\n", " user=HS_DB_Config[\"user\"],\n", " password=HS_DB_Config[\"password\"],\n", " database=HS_DB_Config[\"database\"]\n", " )\n", " cursor = connection.cursor()\n", "\n", " try:\n", " # 查询表列名\n", " cursor.execute(f\"SHOW COLUMNS FROM {table_name}\")\n", " columns_info = cursor.fetchall()\n", " db_columns = [col[0] for col in columns_info] # 提取列名\n", " df = df.replace([None, np.nan, pd.NA, 'nan', 'NaN', 'NAN', ''], None)\n", " # 保留 DataFrame 中与数据库列名匹配的列\n", " filtered_df = df[df.columns.intersection(db_columns)]\n", "\n", " # 如果没有匹配的列，直接返回\n", " if filtered_df.empty:\n", " print(\"DataFrame 中没有与数据库表结构匹配的列。\")\n", " return\n", "\n", " # 筛选列之后，插入前处理 dict 类型\n", " filtered_df = filtered_df.copy()\n", " for col in filtered_df.columns:\n", " if filtered_df[col].apply(lambda x: isinstance(x, (dict, list)) if x is not None else False).any():\n", " filtered_df.loc[:, col] = filtered_df[col].apply(\n", " lambda x: json.dumps(x, ensure_ascii=False) if x is not None else x\n", " )\n", "\n", " # 构建插入语句\n", " placeholders = ', '.join(['%s'] * len(filtered_df.columns))\n", " # 使用反引号避免特殊列明\n", " columns = ', '.join([f\"`{col}`\" for col in filtered_df.columns])\n", " insert_sql = f\"INSERT INTO {table_name} ({columns}) VALUES ({placeholders})\"\n", "\n", " # 将 DataFrame 写入数据库\n", " for _, row in filtered_df.iterrows():\n", " cursor.execute(insert_sql, tuple(row))\n", "\n", " connection.commit()\n", " print(f\"成功写入 {len(filtered_df)} 条记录到 {table_name} 表中。\")\n", "\n", " except Exception as e:\n", " print(\"写入数据库时发生错误：\", e)\n", " connection.rollback()\n", " finally:\n", " cursor.close()\n", " connection.close()\n", "\n", " def clear_table_data(self):\n", " \"\"\"\n", " 清空指定 MySQL 表的数据。\n", " 参数已写死在函数内部，直接调用即可。\n", " \"\"\"\n", " # 数据库连接信息\n", " HS_DB_Config = {\n", " 'host': \"f6-public.rwlb.rds.aliyuncs.com\",\n", " 'user': \"rw_operation_data_relay\",\n", " 'password': \"m+q5Z4%IVuF9bf\",\n", " 'database': \"f6operation_data_relay\"\n", " }\n", " table_name = \"new_dealer_service_order_to_bi\" # 要清空的表名\n", "\n", " connection = None\n", " try:\n", " # 建立数据库连接\n", " connection = mysql.connector.connect(\n", " host=HS_DB_Config[\"host\"],\n", " user=HS_DB_Config[\"user\"],\n", " password=HS_DB_Config[\"password\"],\n", " database=HS_DB_Config[\"database\"]\n", " )\n", " if connection.is_connected():\n", " cursor = connection.cursor()\n", "\n", " # 使用TRUNCATE清空表数据\n", " cursor.execute(f\"TRUNCATE TABLE {table_name}\")\n", " connection.commit()\n", "\n", " print(f\"成功清空表 {table_name} 中的所有数据\")\n", "\n", " except Error as e:\n", " print(f\"清空表时发生错误: {e}\")\n", " if connection and connection.is_connected():\n", " connection.rollback()\n", " finally:\n", " if connection and connection.is_connected():\n", " cursor.close()\n", " connection.close()\n", " print(\"数据库连接已关闭\")\n", "\n", " def main(self):\n", " task_start_time = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n", "\n", " # step1: 获取数据\n", " self.load_all_data()\n", "\n", " # step2:数据处理\n", " df = self.data_process()\n", " print(df)\n", " df.to_csv(os.path.join(output_dir, \"new_dealer_service_order_to_bi.csv\"))\n", "\n", " # step3:数据库删除\n", " # self.clear_table_data()\n", "\n", " # step4:数据写入BI\n", " # self.write_to_bi(df)\n", "\n", " # common_module.send_task_status(task_start_time, \"非标业绩提报转BI\")\n", "\n", "\n", "if __name__ == '__main__':\n", " start = NewDealerServiceOrderToBI()\n", " start.main()" ], "id": "c63f1a4b217a1576", "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001B[92m2025-08-14 14:21:29,218 - api.py - task_logger - INFO - 已获取 2 条数据\u001B[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " creator \\\n", "0 {'name': 'F6汽车科技', 'username': '#admin', 'stat... \n", "1 {'name': 'F6汽车科技', 'username': '#admin', 'stat... \n", "\n", " updater deleter \\\n", "0 {'name': '张阳', 'username': '4210192048793363',... None \n", "1 {'name': '张阳', 'username': '4210192048793363',... None \n", "\n", " createTime updateTime deleteTime flowState \\\n", "0 2025-08-07T02:24:56.648Z 2025-08-07T02:25:35.002Z None 1 \n", "1 2025-08-07T02:27:43.775Z 2025-08-07T02:28:39.026Z None 1 \n", "\n", " 报备类型 协作内容 订单类型 ... 提成类型 SaaS新签提成比例 服务包提成比例 提成金额 新签提成比例-首年 \\\n", "0 新签超3年 SaaS新签 ... 0.00 None NaN NaN \n", "1 大数报表漏统计 服务包 ... 新签 0.25 None 555.0 0.25 \n", "\n", " 新签提成比例-非首年 新签阶段及提成比例 \\\n", "0 0 None \n", "1 0 [{'选择提成阶段': '688889a36f36b252847d811e', '新签阶段'... \n", "\n", " _id appId \\\n", "0 68940e78593f4b6b032ff3dd 66b9678280b37f8a276b1d01 \n", "1 68940f1f6226ebc6c7b57f65 66b9678280b37f8a276b1d01 \n", "\n", " entryId \n", "0 68886b7c0382a7249ae0b5d6 \n", "1 68886b7c0382a7249ae0b5d6 \n", "\n", "[2 rows x 39 columns]\n" ] } ], "execution_count": 11 } ], "metadata": { "kernelspec": { "display_name": "saas", "language": "python", "name": "saas" }, "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 }