81 lines
3.1 KiB
Python
81 lines
3.1 KiB
Python
import time
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from tqdm import tqdm
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from api import API
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import pandas as pd
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import json
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import requests
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# 需要注意 人员多选，以列表形式传递
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# 筛选进行中的实例
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class Main:
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api_instance = API()
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# 获取token
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TOKEN = api_instance.generateToken()
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# 表单id
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FORMID2 = "FORM-PE866MD1MJMU0WGLYRFLYEN5YN9L1I55Z7ZUK22" # [流程]续约服务流程
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FORMID = "FORM-L89662816B04LXH893M4K50Q7MIZ1SVQI08ALU2" # 读取新签节点化服务待办
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FORMID1 = "FORM-33666CB1XDU37AU57RKPK990C79S2YMOEEC8LS" # [表单]异常服务跟进待办2023
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# 读取excel表格
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df = pd.read_excel(r"C:\Users\hp_z66\Desktop\钉钉文件\(流程)续约服务流程_20260129144824.xlsx",
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sheet_name='Sheet1', dtype='string', header=1) # 此处将表头设置为了第二行
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df.fillna('', inplace=True)
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@staticmethod
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def str_to_list(value):
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# 如果是字符串类型，并且包含逗号
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if isinstance(value, str) and ',' in value:
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# 分割并去除前后空格
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return [item.strip() for item in value.split(',')]
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else:
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# 否则返回单元素列表或原始值
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return value.strip() if isinstance(value, str) else value
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# print(df)
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# 执行自动化脚本
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column_names_list = df.columns.tolist()
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i = 1
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for column_name in tqdm(column_names_list[1:]):
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# 获取表头名字，此处需要新建一行，将表头换为宜搭的关键字
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for df_len in tqdm(range(len(df))):
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# 遍历表格内容，两次遍历定位数据具体位置
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if df.iloc[df_len, i] != "":
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df.iloc[df_len, i] = str(df.iloc[df_len, i])
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# 数据不为空时传递data_new
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data_new = {
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column_name: str_to_list(df.iloc[df_len, i])
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}
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print(data_new)
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# 指定主键
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formInstanceId = df.loc[df_len, '实例ID']
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# 引入重试机制
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retry_count = 0
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success = False
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while retry_count < 15 and not success:
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try:
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res = api_instance.update_from(token=TOKEN, formInstanceId=formInstanceId, data_new=data_new)
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# 假设成功返回的状态码是200
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if res.status_code == 200:
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print(column_name, df_len, df.loc[df_len, '实例ID'], data_new, res.json())
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success = True
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else:
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print(f"Error: {res.status_code}, Retrying...")
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retry_count += 1
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time.sleep(2)
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except Exception as e:
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print(f"Exception occurred: {e}, Retrying...")
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retry_count += 1
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time.sleep(2)
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if not success:
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print(f"Failed after 15 retries for formInstanceId: {formInstanceId}")
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time.sleep(0.4)
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i += 1
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