saas1.6
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# -*- coding: utf-8 -*-
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import pandas as pd
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import datetime
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from config import Config
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from api import API
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import pymysql # 使用 pymysql 替代 mysql.connector
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from back_ground_module import CommonModule
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from tqdm import tqdm
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start_time = datetime.datetime.now()
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api_instance = API()
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common_module = CommonModule()
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class ImportPerformanceData:
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"""
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履约表数据支撑
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"""
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def __init__(self):
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self.staff_name_to_id = None
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self.staff_id_list = None
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self.performance_data_list = None
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self.field_mapping = {}
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self.fields()
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def load_all_data(self):
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"""加载所有数据"""
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payload = {"api_key": "675b900991ad2491c69389ca",
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"entry_id": "68637c9818bc333fc14c30ad", # 需要修改
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}
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performance_data = api_instance.entry_data_list(payload)
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self.performance_data_list = performance_data.get("data") # 履约表
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# 获取简道云员工id
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payload = {"api_key": "6694d3c4fcb69ca9a111a6c4",
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"entry_id": "6769204a1902c9341340a1bc",
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}
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staff_id = api_instance.entry_data_list(payload)
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self.staff_id_list = staff_id.get("data") # api请求格式，将数据封装在data字典里
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# 预处理员工姓名到ID的映射
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self.staff_name_to_id = {
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str(item["_widget_1734942794144"]): item["_widget_1734942794145"]
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for item in self.staff_id_list
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}
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def process_data(self, df):
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"""处理数据的主函数"""
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new_df = self.convert_to_utc(df)
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all_data = []
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# 预定义角色映射
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role_mapping = {
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'运营负责人': '运营负责人',
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'区域经理': '区域经理'
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}
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# 使用iterrows的替代方案itertuples更快，但需要确保列名是有效的Python标识符
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for row in tqdm(new_df.itertuples(index=False), total=len(new_df)):
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row_dict = row._asdict()
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# 成员字段替换
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for role, field in role_mapping.items():
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name = getattr(row, field, None)
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if name and str(name) in self.staff_name_to_id:
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row_dict[role] = self.staff_name_to_id[str(name)]
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else:
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row_dict[role] = None
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# 简道云字段替换
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data_dict = self.row_to_dict(row_dict, self.field_mapping)
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all_data.append(data_dict)
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return all_data
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def convert_to_utc(self, df):
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# 创建副本避免修改原DataFrame
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new_df = df.copy()
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time_columns = ['saas开户时间', '服务期起始时间', '下单支付成功时间', '操作时间',
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"下单支付成功日期", "服务期结束时间"]
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for col in tqdm(time_columns):
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if col in tqdm(new_df.columns): # 安全检查列是否存在
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try:
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# 1. 转换为datetime（自动推断格式，处理无效值为NaT）
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new_df[col] = pd.to_datetime(new_df[col], errors='coerce', utc=False)
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# 2. 时区转换（仅对有效日期操作）
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mask = new_df[col].notna() # 只处理非空值
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if mask.any(): # 如果有有效日期才转换
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# 本地化为北京时间，然后转换为UTC
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new_df.loc[mask, col + '_utc'] = (
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new_df.loc[mask, col]
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.dt.tz_localize('Asia/Shanghai', ambiguous='infer', nonexistent='shift_forward')
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.dt.tz_convert('UTC')
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.dt.strftime('%Y-%m-%dT%H:%M:%SZ')
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)
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else:
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new_df[col + '_utc'] = pd.NA # 全部为空时保持一致性
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except Exception as e:
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print(f"处理列 {col} 时出错: {str(e)}")
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new_df[col + '_utc'] = pd.NA # 出错时设为NA
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return new_df
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def main(self):
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task_start_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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self.load_all_data()
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# Step1:获取履约表数据
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df = common_module.get_perforamnce_details()
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print(df)
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print("数据获取完成")
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# Step2:清空现有数据
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id_list = [item["_id"] for item in self.performance_data_list]
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delete_payload = {
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"api_key": "675b900991ad2491c69389ca",
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"entry_id": "68637c9818bc333fc14c30ad",
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"data_ids": id_list
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}
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api_instance.entry_data_batch_delete(delete_payload)
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print("数据删除完成")
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# Step3:将数据写入简道云中
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all_data = self.process_data(df)
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# 分批处理，每批1000条
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batch_size = 1000
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for i in tqdm(range(0, len(all_data), batch_size)):
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batch = all_data[i:i + batch_size]
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payload = {
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"api_key": "675b900991ad2491c69389ca",
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"entry_id": "68637c9818bc333fc14c30ad",
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"data_list": batch
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}
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api_instance.entry_data_batch_create(payload)
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print("数据写入完成")
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common_module.send_task_status(task_start_time, "履约表数据支撑")
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@staticmethod
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def row_to_dict(row, field_mapping):
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"""将一行数据转换为指定格式的字典"""
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result = {}
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for col_name, widget_id in field_mapping.items():
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if col_name in row:
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value = row[col_name]
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# 处理Timestamp类型
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if pd.isna(value):
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clean_value = None
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elif isinstance(value, pd.Timestamp):
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clean_value = value.strftime('%Y-%m-%dT%H:%M:%SZ')
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else:
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clean_value = value
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result[widget_id] = {"value": clean_value}
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return result
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def fields(self):
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self.field_mapping = {
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'公司名称': '_widget_1751350424090', '门店名称': '_widget_1751350424083',
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'门店编码': '_widget_1751350424084',
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'运营负责人': '_widget_1751350424085', '区域经理': '_widget_1751350424086',
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'saas开户时间': '_widget_1751350424088', '服务期起始时间': '_widget_1751350424097',
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'下单支付成功时间': '_widget_1751350424101', '操作时间': '_widget_1751350424110',
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'下单支付成功日期': '_widget_1751350424115', '服务期结束时间': '_widget_1751350424098',
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'订单id': '_widget_1751350424075', 'f6订单编号': '_widget_1751350424076',
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'宜搭的实例id': '_widget_1751350424077', '商品id': '_widget_1751350424078',
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'商品名称': '_widget_1751350424079', '发布商品类型': '_widget_1751350424080',
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'发布商品类型描述': '_widget_1751350424081', '门店id': '_widget_1751350424082',
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'商户中心id': '_widget_1751350424087', '公司id': '_widget_1751350424089',
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'产生来源': '_widget_1751350424091', '产生来源描述': '_widget_1751350424092',
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'类型': '_widget_1751350424093', '类型描述': '_widget_1751350424094', '服务年份': '_widget_1751350424095',
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'订单服务期第几年': '_widget_1751350424096', '提成业务类型': '_widget_1751350424099',
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'提成类别': '_widget_1751350424100', '实付金额': '_widget_1751881109632',
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'系统成本价': '_widget_1751881109633', '版本费': '_widget_1751881109634',
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'服务费': '_widget_1751881109635', '介绍人员工ID': '_widget_1751350424106',
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'介绍业绩归属人员工ID': '_widget_1751350424107', '处理人ID employee_id': '_widget_1751350424108',
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'业绩归属人员工ID': '_widget_1751350424109', '处理人是否跟进,0: 未跟进，1: 已跟进': '_widget_1751350424111',
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'满意度评分': '_widget_1751350424112', '评价完成时间': '_widget_1751350424113',
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'介绍人用户类型': '_widget_1751350424114', '培训完成时间': '_widget_1751350424116',
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'订单所处阶段': '_widget_1751350424117', '日分区': '_widget_1751350424118',
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}
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if __name__ == '__main__':
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start = ImportPerformanceData()
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start.main()
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