453 lines
26 KiB
Plaintext
453 lines
26 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"id": "initial_id",
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"metadata": {
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"collapsed": true,
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"ExecuteTime": {
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"end_time": "2025-04-21T03:11:22.784774Z",
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"start_time": "2025-04-21T03:11:10.187047Z"
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}
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},
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"source": [
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"from datetime import date, timedelta, datetime\n",
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"import holidays\n",
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"from config import Config\n",
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"import pandas as pd\n",
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"import pymysql # 使用 pymysql 替代 mysql.connector\n",
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"from log_config import configure_task_logger, configure_error_task_logger\n",
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"from api import API\n",
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"from back_ground_module import CommonModule\n",
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"common_module = CommonModule()\n",
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"api_instance = API()\n",
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"global last_day_end_customer_service, is_customer_service_data_id, customer_service_data_id\n",
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"\n",
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"\n",
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"class JCBAbnormalRevisit:\n",
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" def __init__(self):\n",
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" # 使用 pymysql 连接数据库\n",
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" self.daily_revisit_list = None\n",
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" self.abnormal_list = None\n",
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" self.field_mapping = {}\n",
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" self.staff_id_list = None\n",
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" self.customer_service_list = None\n",
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"\n",
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" def load_all_data(self):\n",
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" # 获取接车宝异常待办\n",
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" payload = {\"api_key\": \"6717470a0b3975ef583c6df1\",\n",
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" \"entry_id\": \"67c156ba635191b64af8a110\",\n",
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" }\n",
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" abnormal_service = api_instance.entry_data_list(payload)\n",
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" self.abnormal_list = abnormal_service.get(\"data\") # api请求格式，将数据封装在data字典里\n",
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"\n",
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" # 获取接车宝日常回访单\n",
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" payload = {\"api_key\": \"6717470a0b3975ef583c6df1\",\n",
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" \"entry_id\": \"67d2369f244cf21d615aa87f\",\n",
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" }\n",
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" daily_revisit = api_instance.entry_data_list(payload)\n",
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" self.daily_revisit_list = daily_revisit.get(\"data\") # api请求格式，将数据封装在data字典里\n",
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"\n",
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" def load_cus_data(self):\n",
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" # 获取接车宝客服表单\n",
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" payload = {\"api_key\": \"6717470a0b3975ef583c6df1\",\n",
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" \"entry_id\": \"67b6f2462f9ac03b783d409a\",\n",
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" }\n",
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" customer_service = api_instance.entry_data_list(payload)\n",
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" customer_service_list = customer_service.get(\"data\") # api请求格式，将数据封装在data字典里\n",
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" return customer_service_list\n",
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"\n",
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" def today_customer_service_list(self):\n",
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" # 获取今日接车宝派发客服顺序\n",
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" today_customer_service_list = []\n",
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" all_customer_service_list = []\n",
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" today_customer_service_start_list = []\n",
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" for row_items in self.load_cus_data():\n",
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" # print(row_items)\n",
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" customer_service_name_id = row_items.get(\"_widget_1740042824214\", {}).get(\"username\", {})\n",
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" customer_service_name = row_items.get(\"_widget_1740042824214\", {}).get(\"name\", {})\n",
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" customer_service_state = row_items.get(\"_widget_1740117343937\", {})\n",
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" is_last_day_end = row_items.get(\"_widget_1740042824216\", {})\n",
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" customer_service_data_id = row_items.get(\"_id\", {})\n",
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" print(customer_service_name, customer_service_name_id, customer_service_state, is_last_day_end)\n",
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" all_customer_service_list.append(\n",
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" [customer_service_name, customer_service_name_id, customer_service_state, is_last_day_end,\n",
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" customer_service_data_id])\n",
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" if is_last_day_end == \"是\": # 判断是否是下次开始位置\n",
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" last_day_end_customer_service = customer_service_name_id\n",
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" is_customer_service_data_id = row_items.get(\"_id\", {})\n",
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"\n",
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" split_index = None\n",
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" for index, row in enumerate(all_customer_service_list):\n",
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" print(row[3])\n",
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" if row[3] == \"是\":\n",
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" split_index = index\n",
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" print(f\"找到索引 {index}\")\n",
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" break\n",
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"\n",
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" if split_index is not None:\n",
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" # 根据索引切割列表\n",
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" first_part = all_customer_service_list[split_index:] # 索引位置及之后的行\n",
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" second_part = all_customer_service_list[:split_index] # 索引位置之前的行\n",
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" # 调换两个子列表的位置并重新组合\n",
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" today_customer_service_start_list = first_part + second_part\n",
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" else:\n",
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" # 如果没有找到“是”，保持原列表不变\n",
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" today_customer_service_start_list = all_customer_service_list\n",
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" pass\n",
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"\n",
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" for index, row in enumerate(today_customer_service_start_list):\n",
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" if row[2] == \"开\":\n",
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" today_customer_service_list.append(row[1])\n",
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"\n",
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" return today_customer_service_list, is_customer_service_data_id, all_customer_service_list\n",
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"\n",
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" def send_request(self, df):\n",
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" today_customer_service_list, is_customer_service_data_id, all_customer_service_list = self.today_customer_service_list()\n",
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" # 初始化派发索引\n",
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" next_dispatcher_index = 0\n",
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"\n",
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" # 显式循环分配跟进人\n",
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" follow_up_persons = []\n",
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" for _ in range(len(df)):\n",
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" follow_up_person = today_customer_service_list[next_dispatcher_index]\n",
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" follow_up_persons.append(follow_up_person)\n",
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" next_dispatcher_index = (next_dispatcher_index + 1) % len(today_customer_service_list)\n",
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"\n",
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" # 添加跟进人到 DataFrame\n",
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" df[\"跟进人\"] = follow_up_persons\n",
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"\n",
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" # 获取下一个派发人\n",
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" next_dispatcher = today_customer_service_list[next_dispatcher_index]\n",
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"\n",
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" new_sign_abnormal_data = [self.row_to_dict(row, self.field_mapping) for index, row in\n",
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" df.iterrows()]\n",
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"\n",
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" data = {'api_key': Config.EFFICIENT_CAR_PICKUP_APP_ID, 'entry_id': \"67d2369f244cf21d615aa87f\",\n",
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" \"data_list\": new_sign_abnormal_data} # 派发数据\n",
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"\n",
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" api_instance.entry_data_batch_create(data)\n",
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"\n",
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" data1 = {\"api_key\": Config.EFFICIENT_CAR_PICKUP_APP_ID,\n",
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" \"entry_id\": Config.EFFICIENT_CAR_PICKUP_CUSTOMER_SERVICE_ID,\n",
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" \"data_id\": is_customer_service_data_id,\n",
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" \"data\":\n",
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" {\"_widget_1740042824216\": {\"value\": \"\"}, }\n",
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" } # 原来的是\"_widget_1740042824216\": {\"value\": \"是\"},修改昨日截至人员\n",
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" next_customer_service_data_id = None\n",
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" for index, row in enumerate(all_customer_service_list):\n",
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" print(row[3])\n",
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" if row[1] == next_dispatcher:\n",
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" next_customer_service_data_id = row[4]\n",
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" break\n",
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"\n",
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" data2 = {\"api_key\": Config.EFFICIENT_CAR_PICKUP_APP_ID,\n",
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" \"entry_id\": Config.EFFICIENT_CAR_PICKUP_CUSTOMER_SERVICE_ID,\n",
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" \"data_id\": next_customer_service_data_id,\n",
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" \"data\":\n",
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" {\"_widget_1740042824216\": {\"value\": \"是\"}, }}# 明日派发起点人员\n",
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"\n",
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" api_instance.entry_data_update(data1)\n",
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" api_instance.entry_data_update(data2)\n",
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"\n",
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" def main(self):\n",
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" self.load_all_data()\n",
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" task_start_time =datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n",
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" print(task_start_time)\n",
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" print(type(task_start_time))\n",
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" data_JCB = common_module.get_jcb_details()\n",
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"\n",
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" # 保存为CSV文件\n",
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" output_dir = \"output\" # 设置输出目录\n",
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"\n",
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" # 创建输出目录（如果不存在）\n",
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" import os\n",
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" os.makedirs(output_dir, exist_ok=True)\n",
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"\n",
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" # data_JCB.to_csv(os.path.join(output_dir, 'JCB_all_data.csv'), index=False)\n",
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" self.fields()\n",
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"\n",
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" # 异常待办回访 近1个月开单为0客户\n",
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" # 当前日期\n",
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" current_date = datetime.now()\n",
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" current_date = current_date + timedelta(days=1)\n",
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" current_date_str = current_date.strftime(\"%Y-%m-%d\")\n",
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" # current_date = datetime.now()\n",
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" thirty_days_ago = current_date - timedelta(days=30)\n",
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" thirty_days_ago = thirty_days_ago.date()\n",
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" abnormal_data = []\n",
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" JDY_abnormal_data = []\n",
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" JDY_revisit_data = []\n",
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" # df = pd.read_csv(os.path.join(output_dir, \"JCB_异常待办.csv\")) # 读取异常待办表\n",
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" # print(df)\n",
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" for index, row in data_JCB.iterrows():\n",
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" new_row = row.copy()\n",
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" new_row['开户日'] = datetime.strptime(new_row['开户日'], \"%Y-%m-%d\").date()\n",
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" if new_row['开户日'] < thirty_days_ago and row['近30天开单天数'] == 0 and row['客户状态'] == \"留存\":\n",
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" # print(row['账号'], row['开户日'], row['近30天开单天数'], row[\"客户状态\"])\n",
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" row[\"日期\"] = datetime.strptime(row['开户日'], \"%Y-%m-%d\").date()\n",
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" row['日期'] = row[\"日期\"].strftime(\"%Y-%m-%d\")\n",
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" abnormal_data.append(row)\n",
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" # 推送给客服\n",
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" abnormal_data = pd.DataFrame(abnormal_data)\n",
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" abnormal_data[\"表单类型\"] = \"异常待办\"\n",
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" abnormal_data[\"派发日期\"] = current_date_str\n",
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" abnormal_data.to_excel(os.path.join(output_dir, 'JCB_异常待办.xlsx'), index=False) # 派发B(所有异常待办)\n",
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"\n",
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" for abnormal_items in self.abnormal_list:\n",
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" last_send_date = abnormal_items.get(\"_widget_1740723898405\", {}) # 派发日期\n",
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" last_30_days_orders = abnormal_items.get(\"_widget_1740723898401\", {}) # 近30天开单数\n",
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" phone = abnormal_items.get(\"_widget_1740723898391\", {}) # 手机号\n",
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" account = abnormal_items.get(\"_widget_1740723898390\", {}) # 账号\n",
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" data_id = abnormal_items.get(\"_id\", {}) # 数据id\n",
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" JDY_abnormal_data.append([data_id, account, phone, last_send_date, last_30_days_orders])\n",
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"\n",
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" JDY_abnormal_data = pd.DataFrame(JDY_abnormal_data,\n",
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" columns=[\"数据id\", \"账号\", \"联系手机号\", \"派发日期\",\n",
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" \"近30天开单天数\"]) # 派发A（简道云上异常待办）\n",
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" # JDY_abnormal_data.columns = [\"数据id\", \"账号\", \"联系手机号\", \"派发日期\", \"近30天开单天数\"]\n",
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" JDY_abnormal_data.to_excel(os.path.join(output_dir, 'JCB_云端异常待办.xlsx'), index=False) # 派发A\n",
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"\n",
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" # 将 '联系手机号' 列转换为字符串类型\n",
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" JDY_abnormal_data['联系手机号'] = JDY_abnormal_data['联系手机号'].astype(str).str.replace('.0', '')\n",
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" abnormal_data['联系手机号'] = abnormal_data['联系手机号'].astype(str)\n",
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" JDY_abnormal_data.to_excel(os.path.join(output_dir, 'JCB_云端异常待办.xlsx'), index=False) # 派发A\n",
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" abnormal_data.to_excel(os.path.join(output_dir, 'JCB_今日异常待办.xlsx'), index=False) # 派发B\n",
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"\n",
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" today = datetime.now().weekday()\n",
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" \n",
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" # 随机抽40条派发\n",
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" df_40 = pd.DataFrame()\n",
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" if 0 <= today <= 4:\n",
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" # if 1>2:\n",
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" # 假设 JDY_abnormal_data 和 abnormal_data 都有重复列 '重复列'\n",
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" df3 = pd.merge(JDY_abnormal_data, abnormal_data, on=[\"联系手机号\", \"账号\"], how='inner',\n",
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" suffixes=('', '_y'))\n",
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" # 删除以 _y 结尾的列（即来自右侧 DataFrame 的重复列）\n",
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" df3 = df3.loc[:, ~df3.columns.str.endswith('_y')]\n",
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" df3['派发日期'] = pd.to_datetime(df3['派发日期']).dt.strftime(\"%Y-%m-%d\")\n",
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" df3.to_excel(os.path.join(output_dir, 'JCB_异常待办情况1.xlsx'),\n",
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" index=False, ) # B存在，A存在 ，今日派发与历史派发都存在，派发并删历史\n",
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"\n",
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" df_40 = df3[df3.index < 40]\n",
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" df_40.to_excel(os.path.join(output_dir, 'JCB_异常待办情况2.xlsx'), index=False, )\n",
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"\n",
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" for index, row in df_40.iterrows(): # 删除已推送的数据\n",
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" delete_data = {\"api_key\": Config.EFFICIENT_CAR_PICKUP_APP_ID,\n",
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" \"entry_id\": Config.EFFICIENT_CAR_PICKUP_CUSTOMER_HISTORY_ID,\n",
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" \"data_id\": row[\"数据id\"]}\n",
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" # print(delete_data)\n",
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" api_instance.entry_data_delete(delete_data)\n",
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"\n",
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" # B不存在A存在 今日派发不存在，历史存在，删历史\n",
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" # 使用 outer 合并，并添加指示器列 _merge\n",
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" df_merged = pd.merge(JDY_abnormal_data, abnormal_data, on=[\"联系手机号\", \"账号\"], how='outer', indicator=True,\n",
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" suffixes=('', '_y')) # outer保留所有数据，indicator标注来源\n",
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" # 筛选出只存在于 JDY_abnormal_data 中的行\n",
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" df_a_not_in_b = df_merged[df_merged['_merge'] == 'left_only']\n",
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" # 删除以 _y 结尾的列（即来自右侧 DataFrame 的重复列）\n",
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" df_a_not_in_b = df_a_not_in_b.loc[:, ~df_a_not_in_b.columns.str.endswith('_y')]\n",
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" df_a_not_in_b['派发日期'] = pd.to_datetime(df_a_not_in_b['派发日期']).dt.strftime(\"%Y-%m-%d\")\n",
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" # 保存到 Excel 文件\n",
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" df_a_not_in_b.to_excel(os.path.join(output_dir, 'JCB_异常待办情况_A存在B不存在.xlsx'), index=False)\n",
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" for index, row in df_a_not_in_b.iterrows(): # 删除已推送的数据\n",
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" delete_data = {\"api_key\": Config.EFFICIENT_CAR_PICKUP_APP_ID,\n",
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" \"entry_id\": Config.EFFICIENT_CAR_PICKUP_CUSTOMER_HISTORY_ID,\n",
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" \"data_id\": row[\"数据id\"]}\n",
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" # print(delete_data)\n",
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" api_instance.entry_data_delete(delete_data)\n",
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"\n",
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" # B存在A不存在 今日派发存在，历史不存在，为新增异常，直接派发\n",
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" df_merged = pd.merge(JDY_abnormal_data, abnormal_data, on=[\"联系手机号\", \"账号\"], how='outer', indicator=True,\n",
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" suffixes=('_x', '')) # outer保留所有数据，indicator标注来源\n",
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" df_merged.to_excel(os.path.join(output_dir, 'JCB_异常待办情况_B存在A不存在_134434.xlsx'), index=False)\n",
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" # 筛选出只存在于 JDY_abnormal_data 中的行\n",
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" df_b_not_in_a = df_merged[df_merged['_merge'] == 'right_only']\n",
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" df_b_not_in_a.to_excel(os.path.join(output_dir, 'JCB_异常待办情况_B存在A不存在_111.xlsx'), index=False)\n",
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" # 删除以 _y 结尾的列（即来自右侧 DataFrame 的重复列）\n",
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" df_b_not_in_a = df_b_not_in_a.loc[:, ~df_b_not_in_a.columns.str.endswith('_x')]\n",
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" df_b_not_in_a.to_excel(os.path.join(output_dir, 'JCB_异常待办情况_B存在A不存在_122.xlsx'), index=False)\n",
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" df_b_not_in_a['派发日期'] = pd.to_datetime(df_b_not_in_a['派发日期']).dt.strftime(\"%Y-%m-%d\")\n",
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" # 保存到 Excel 文件\n",
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" df_b_not_in_a.to_excel(os.path.join(output_dir, 'JCB_异常待办情况_B存在A不存在.xlsx'), index=False)\n",
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"\n",
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" # 合并两个当日派发的df\n",
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" df_abnormal_data = pd.concat([df_40, df_b_not_in_a], ignore_index=True)\n",
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" df_abnormal_data.to_excel(os.path.join(output_dir, 'JCB_异常待办情况_合并当日派发.xlsx'), index=False)\n",
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"\n",
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" for abnormal_items in self.daily_revisit_list: # 遍历云端已经派发的数据\n",
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" account = abnormal_items.get(\"_widget_1739258942667\", {}) # 账号\n",
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" sub_date = abnormal_items.get(\"createTime\", {}) # 提交时间\n",
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" update_date = abnormal_items.get(\"updateTime\", {}) # 更新时间\n",
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" entry_style = abnormal_items.get(\"_widget_1739951204545\", {}) # 表单类型\n",
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" entry_type = abnormal_items.get(\"flowState\", {}) # 表单状态 0流转中 1流转完成 2 手动结束\n",
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"\n",
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" data_id = abnormal_items.get(\"_id\", {}) # 数据id\n",
|
|
" JDY_revisit_data.append([data_id, account, sub_date, update_date, entry_style, entry_type])\n",
|
|
"\n",
|
|
" JDY_revisit_data = pd.DataFrame(JDY_revisit_data)\n",
|
|
" JDY_revisit_data.columns = [\"数据id\", \"账号\", \"提交时间\", \"更新时间\", \"表单类型\", \"表单状态\"]\n",
|
|
" JDY_revisit_data.to_excel(os.path.join(output_dir, 'JCB_日常回访_原始数据.xlsx'), index=False)\n",
|
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"\n",
|
|
" filtered_data = JDY_revisit_data[JDY_revisit_data['表单类型'] == '异常待办'] # 过滤表单类型\n",
|
|
" # filtered_data = filtered_data[filtered_data['表单状态'] == 1] # 过滤表单状态\n",
|
|
" # filtered_data.to_excel(os.path.join(output_dir, 'JCB_日常回访_过滤数据.xlsx'), index=False)\n",
|
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"\n",
|
|
" filtered_data['提交时间'] = pd.to_datetime(filtered_data['提交时间']).dt.strftime(\"%Y-%m-%d\")\n",
|
|
" latest_update_time = filtered_data.groupby('账号')['提交时间'].max().reset_index()\n",
|
|
" latest_update_time.rename(columns={'提交时间': '最新提交时间'}, inplace=True)\n",
|
|
"\n",
|
|
"\n",
|
|
" filtered_data_with_latest = pd.merge(\n",
|
|
" filtered_data,\n",
|
|
" latest_update_time,\n",
|
|
" left_on=['账号', '提交时间'],\n",
|
|
" right_on=['账号', '最新提交时间']\n",
|
|
" )\n",
|
|
"\n",
|
|
" # 过滤出每个账号中提交时间为最新的记录\n",
|
|
" latest_JDY_abnormal_data = filtered_data_with_latest[\n",
|
|
" filtered_data_with_latest['提交时间'] == filtered_data_with_latest['最新提交时间']\n",
|
|
" ]\n",
|
|
" latest_JDY_abnormal_data.to_excel(os.path.join(output_dir, 'JCB_日常回访_最新数据_1.xlsx'), index=False)\n",
|
|
"\n",
|
|
"\n",
|
|
" latest_JDY_abnormal_data['提交时间'] = pd.to_datetime(latest_JDY_abnormal_data['提交时间']).dt.strftime(\"%Y-%m-%d\")\n",
|
|
"\n",
|
|
" thirty_days_ago = (current_date - timedelta(days=30)).strftime(\"%Y-%m-%d\")\n",
|
|
"\n",
|
|
" final_JDY_abnormal_data = latest_JDY_abnormal_data[latest_JDY_abnormal_data['提交时间'] > thirty_days_ago] # 筛选出提交时间为近30天的数据\n",
|
|
"\n",
|
|
" final_JDY_abnormal_data.to_excel(os.path.join(output_dir, 'JCB_日常回访_最新数据.xlsx'), index=False)\n",
|
|
"\n",
|
|
" df_abnormal_data = df_abnormal_data[~df_abnormal_data['账号'].isin(final_JDY_abnormal_data['账号'])]\n",
|
|
" # empty_num = df_abnormal_data['手机号'].isnull().sum()\n",
|
|
" df_abnormal_data = df_abnormal_data[df_abnormal_data[\"联系手机号\"] != \"None\"]\n",
|
|
" df_abnormal_data.to_excel(os.path.join(output_dir, 'JCB_异常待办情况_派发数据.xlsx'), index=False)\n",
|
|
"\n",
|
|
" self.send_request(df_abnormal_data)\n",
|
|
" common_module.send_task_status(task_start_time, \"测试\")\n",
|
|
" \n",
|
|
"\n",
|
|
" # df_abnormal_data = [self.row_to_dict(row, self.field_mapping) for index, row in\n",
|
|
" # df_abnormal_data.iterrows()]\n",
|
|
" # \n",
|
|
" # data = {'api_key': Config.EFFICIENT_CAR_PICKUP_APP_ID, 'entry_id':\"67d2369f244cf21d615aa87f\",\n",
|
|
" # \"data_list\": df_abnormal_data}\n",
|
|
" # \n",
|
|
" # \n",
|
|
" # result = api_instance.entry_data_batch_create(data)\n",
|
|
"\n",
|
|
" @staticmethod\n",
|
|
" def row_to_dict(row, field_mapping):\n",
|
|
" \"\"\"将一行数据转换为指定格式的字典\"\"\"\n",
|
|
" result = {}\n",
|
|
" # print(field_mapping)\n",
|
|
" for col_name, widget_id in field_mapping.items():\n",
|
|
" # print(col_name, widget_id)\n",
|
|
" if col_name in row:\n",
|
|
" value = row[col_name]\n",
|
|
" clean_value = None if pd.isna(value) else value\n",
|
|
" result[widget_id] = {\"value\": clean_value}\n",
|
|
" return result\n",
|
|
"\n",
|
|
" def fields(self):\n",
|
|
" self.field_mapping = {\"日期\": \"_widget_1739252804406\", \"产品名称\": \"_widget_1739252804397\",\n",
|
|
" \"账号\": \"_widget_1739258942667\", \"联系手机号\": \"_widget_1739252804407\",\n",
|
|
" \"使用时长\": \"_widget_1739252804409\", \"开户日\": \"_widget_1739252804396\",\n",
|
|
" \"到期日\": \"_widget_1739252804408\", \"续约日\": \"_widget_1739252804410\",\n",
|
|
" \"客户状态\": \"_widget_1739252804400\", \"近一周开单量\": \"_widget_1739252804413\",\n",
|
|
" \"近一周是否活跃\": \"_widget_1739252804414\",\n",
|
|
" \"G状态：近30天开单大于等于10天\": \"_widget_1739252804415\",\n",
|
|
" \"当月开单天数\": \"_widget_1739252804416\", \"近30天开单天数\": \"_widget_1739252804417\",\n",
|
|
" \"当月G天数\": \"_widget_1739252804418\", \"日分区\": \"_widget_1739252804419\",\n",
|
|
" \"表单类型\": \"_widget_1739951204545\", \"派发日期\": \"_widget_1740036367181\",\n",
|
|
" \"跟进人\": \"_widget_1740043340255\",\n",
|
|
" }\n",
|
|
"\n",
|
|
"\n",
|
|
"if __name__ == \"__main__\":\n",
|
|
" start = JCBAbnormalRevisit()\n",
|
|
" start.main()\n",
|
|
" # if result is not None:\n",
|
|
" # print(result.head()) # 打印前几行数据\n"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"已获取 100 条数据\n",
|
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"已获取 200 条数据\n",
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"已获取 300 条数据\n",
|
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"已获取 400 条数据\n",
|
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"已获取 500 条数据\n",
|
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"已获取 600 条数据\n",
|
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"已获取 700 条数据\n",
|
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"已获取 800 条数据\n",
|
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"已获取 900 条数据\n",
|
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"已获取 1000 条数据\n",
|
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"已获取 1100 条数据\n",
|
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"已获取 1133 条数据\n",
|
|
"已获取 100 条数据\n",
|
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"已获取 200 条数据\n",
|
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"已获取 300 条数据\n",
|
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"已获取 400 条数据\n",
|
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"已获取 500 条数据\n",
|
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"已获取 600 条数据\n",
|
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"已获取 700 条数据\n",
|
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"已获取 800 条数据\n",
|
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"已获取 900 条数据\n",
|
|
"已获取 1000 条数据\n",
|
|
"已获取 1088 条数据\n",
|
|
"2025-04-21 11:11:18\n",
|
|
"<class 'str'>\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"C:\\Users\\Administrator.DESKTOP-7IC2USJ\\AppData\\Local\\Temp\\ipykernel_11188\\624376566.py:285: SettingWithCopyWarning: \n",
|
|
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
|
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
|
"\n",
|
|
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
|
" filtered_data['提交时间'] = pd.to_datetime(filtered_data['提交时间']).dt.strftime(\"%Y-%m-%d\")\n",
|
|
"2025-04-21 11:11:22,775 - 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': '2025-04-21T03:11:22.379Z', 'updateTime': '2025-04-21T03:11:22.379Z', 'deleteTime': None, '_widget_1744873387500': '2025-04-21T00:00:00.000Z', '_widget_1743644977694': '测试', '_widget_1744873387501': '2025-04-21T11:11:18.000Z', '_widget_1744873387502': '2025-04-21T11:11:22.000Z', '_widget_1744873387504': '4', '_id': '6805b75ac1e7d8a6d2b1863d', 'appId': '6694d3c4fcb69ca9a111a6c4', 'entryId': '67ede908eb9c22261016466e'}}\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"返回结果: {'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': '2025-04-21T03:11:22.379Z', 'updateTime': '2025-04-21T03:11:22.379Z', 'deleteTime': None, '_widget_1744873387500': '2025-04-21T00:00:00.000Z', '_widget_1743644977694': '测试', '_widget_1744873387501': '2025-04-21T11:11:18.000Z', '_widget_1744873387502': '2025-04-21T11:11:22.000Z', '_widget_1744873387504': '4', '_id': '6805b75ac1e7d8a6d2b1863d', 'appId': '6694d3c4fcb69ca9a111a6c4', 'entryId': '67ede908eb9c22261016466e'}}\n"
|
|
]
|
|
}
|
|
],
|
|
"execution_count": 13
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"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
|
|
}
|