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