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异常回访修改客服获取路径、客户资料更新重构、api修改

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z66
2025-10-24 17:46:11 +08:00
parent f212bd6a12
commit 8bdef477d9
8 changed files with 3297 additions and 82452 deletions
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.idea/csv-editor.xml
Generated
+35
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@@ -38,6 +38,41 @@
</Attribute>
</value>
</entry>
<entry key="\test\output\重复org_code记录.csv">
<value>
<Attribute>
<option name="separator" value="," />
</Attribute>
</value>
</entry>
<entry key="\test\test\output\1_员工ID映射表.csv">
<value>
<Attribute>
<option name="separator" value="," />
</Attribute>
</value>
</entry>
<entry key="\test\test\output\4_查缺补漏_缺失门店详情.csv">
<value>
<Attribute>
<option name="separator" value="," />
</Attribute>
</value>
</entry>
<entry key="\test\test\output\5_人员字段匹配统计.csv">
<value>
<Attribute>
<option name="separator" value="," />
</Attribute>
</value>
</entry>
<entry key="\test\test\output\6_待创建数据列表.csv">
<value>
<Attribute>
<option name="separator" value="," />
</Attribute>
</value>
</entry>
</map>
</option>
</component>
api.py
+9 -5
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@@ -382,7 +382,7 @@ class API:
}
# 获取data_list长度
total_length = len(data['data_ids'])
logger.info(f"多数据写入行数: {total_length}")
logger.info(f"多数据修改行数: {total_length}")
# 计算需要发送的次数
num_chunks = (total_length + chunk_size - 1) // chunk_size # //整除向下取证，需要加上chunk_size - 1保证不会有缺失数据
@@ -391,12 +391,16 @@ class API:
for i in range(num_chunks):
start_index = i * chunk_size
end_index = min(start_index + chunk_size, total_length)
payload = json.dumps({
payload = {
"app_id": data['api_key'], # 应用ID
"entry_id": data['entry_id'], # 表单ID
"data_list": data['data_ids'][start_index:end_index],
"data_ids": data['data_ids'][start_index:end_index],
"data": data['data']
}, cls=NpEncoder)
}
if "transaction_id" in data:
payload["transaction_id"] = data["transaction_id"]
payload = json.dumps(payload, cls=NpEncoder)
retries = 0
while retries <= max_retries:
try:
@@ -416,7 +420,7 @@ class API:
retries += 1
time.sleep(10) # 在重试之间稍作停顿
if retries > max_retries:
error_task_logger.error(f"任务 {data['data_id']} 连续{max_retries}次请求失败，放弃此次请求。")
error_task_logger.error(f"任务 {data['data_ids']} 连续{max_retries}次请求失败，放弃此次请求。")
continue
return data_get_list
back_ground_module/Exception_Task.py
+37 -5
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@@ -225,7 +225,6 @@ class NewExceptionTask:
task_start_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
try:
global png_url, key, upload_key, province_name, city_name, area_name
self.load_all_data()
self.data_yichang_S = common_module.get_yichang_details(days_back=1).astype(str) # 获取data_NGV 并转为str
@@ -245,6 +244,10 @@ class NewExceptionTask:
for index_num, row in data_yichang.iterrows(): # 对过滤后的每一条进行派发
try:
# 每次循环前清空省市区变量
province_name = None
city_name = None
area_name = None
is_pass = False
for exception_service in self.exception_service_todo :
@@ -287,14 +290,33 @@ class NewExceptionTask:
reason = None
create_exception =None
create_date = None
# 优先从 data_yichang_S 获取省市区信息
province_name = row.get('province_name')
city_name = row.get('city_name')
area_name = row.get('area_name') if 'area_name' in row else row.get('district_name')
# 检查省市区是否完整（省市区是一体的，任意一个缺失就需要从NGV获取）
use_ngv_location = False
if (not province_name or province_name in ['', 'None', 'NA'] or
not city_name or city_name in ['', 'None', 'NA'] or
not area_name or area_name in ['', 'None', 'NA']):
use_ngv_location = True
logger.info(f"门店 {row['org_code']} 的省市区信息不完整，将从NGV_data_list获取")
# 获取关联数据
for NGV_Data in self.NGV_data_list:
# NGV_Data = NGV_Data.get("data")
if row["org_code"] == NGV_Data.get("_widget_1734062123071"): # 门店编码
NGV_data_id = NGV_Data.get("_id")
province_name = NGV_Data.get("_widget_1734062123090")
city_name = NGV_Data.get("_widget_1734062123092")
area_name = NGV_Data.get("_widget_1734062123094")
# 如果需要从 NGV_data_list 获取省市区信息
if use_ngv_location:
province_name = NGV_Data.get("_widget_1734062123090")
city_name = NGV_Data.get("_widget_1734062123092")
area_name = NGV_Data.get("_widget_1734062123094")
logger.info(f"【从NGV获取省市区】门店 {row['org_code']}: {province_name}, {city_name}, {area_name}")
# 门店原因
reason = NGV_Data.get("_widget_1758617393828")
logger.info(f"获取关联数据成功:{NGV_data_id}, {province_name}, {city_name}, {area_name}")
@@ -302,6 +324,7 @@ class NewExceptionTask:
create_exception = NGV_Data.get("_widget_1758769279995")
# 获取上线日期（文本）
create_date = NGV_Data.get("_widget_1734062123176")
break # 找到匹配的数据后退出循环
# 判断门店原因
# if reason in ["门店倒闭", "门店转让", "加盟其他连锁","切换竞品","虚拟门店","重新开户","已退款","二套系统"]:
@@ -353,7 +376,16 @@ class NewExceptionTask:
logger.warning(f"未找到关联数据，请检查门店编码: {row['org_code']}")
# 根据省市区派发给异常回访客服
customer_service = self.assign_customer_service(province_name, city_name, area_name, self.index)
# 检查省市区是否都有值，如果有任何一个为空，则客服为空
if (not province_name or province_name in ['', 'None', 'NA'] or
not city_name or city_name in ['', 'None', 'NA'] or
not area_name or area_name in ['', 'None', 'NA']):
customer_service = None
logger.warning(f"【省市区信息缺失】门店 {row['org_code']} 省市区信息不完整，异常回访客服设置为空")
logger.warning(f"省: {province_name}, 市: {city_name}, 区: {area_name}")
else:
customer_service = self.assign_customer_service(province_name, city_name, area_name, self.index)
logger.info(f"【派发客服】门店 {row['org_code']} 派发给客服: {customer_service}")
payload_dict.update({
"_widget_1748241895829": {"value": row["health_warning_info"]}, # 活跃健康状态变化
back_ground_module/logs/task.log
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back_ground_module/update_all_NGV_data_daily.py
+1014 -369
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test/logs/error_task.log
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test/logs/task.log
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test/ngv更新.ipynb
-899
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@@ -1,899 +0,0 @@
{
"cells": [
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-08-05T01:17:26.665971Z",
"start_time": "2025-08-05T01:17:14.509013Z"
}
},
"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",
"from back_ground_module import CommonModule\n",
"from log_config import configure_task_logger, configure_error_task_logger\n",
"import concurrent.futures\n",
"\n",
"# 获取已经配置好的常规日志记录器\n",
"logger = configure_task_logger()\n",
"\n",
"# 获取已经配置好的错误任务日志记录器\n",
"error_task_logger = configure_error_task_logger()\n",
"\n",
"start_time = datetime.datetime.now()\n",
"api_instance = API()\n",
"common_module = CommonModule()\n",
"\n",
"\n",
"class UpdateAllNGVDataDaily:\n",
" \"\"\"NGV数据每日更新\"\"\"\n",
"\n",
" def __init__(self):\n",
" self.field_mapping = {}\n",
" self.fields()\n",
"\n",
" def main(self):\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",
" import os\n",
" os.makedirs(output_dir, exist_ok=True)\n",
"\n",
" task_start_time = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n",
" # 获取NGV数据\n",
" payload = {\"api_key\": \"675b900991ad2491c69389ca\", \"entry_id\": \"675bb02bd2d53c2034c665e4\"}\n",
" NGV_data_list = api_instance.entry_data_list(payload).get(\"data\", [])\n",
" jdy_NGV_data = pd.DataFrame(NGV_data_list)\n",
"\n",
" payload = {\"api_key\": \"6694d3c4fcb69ca9a111a6c4\",\n",
" \"entry_id\": \"6769204a1902c9341340a1bc\",\n",
" }\n",
" staff_id = api_instance.entry_data_list(payload)\n",
" staff_id_list = staff_id.get(\"data\") # api请求格式，将数据封装在data字典里\n",
"\n",
" # for i in range(1,2):\n",
" data_NGV_j = common_module.get_ngv_details(days_back=1)\n",
" data_NGV_j.to_csv(os.path.join(output_dir, f\"data_NGV_j.csv\"), index=False)\n",
" data_NGV_j1 = common_module.get_ngv_details(days_back=2)\n",
"\n",
" # 对 data_NGV 进行进一步的过滤，只保留 org_type 为 \"一般\" 的记录\n",
" data_NGV_j = data_NGV_j[data_NGV_j['org_type'] == '一般']\n",
" data_NGV_j1 = data_NGV_j1[data_NGV_j1['org_type'] == '一般']\n",
"\n",
" # 去除不需要的列\n",
" columns_to_remove = {'date_id', 'date_fmt', 'pt', 'etl_time'}\n",
"\n",
" # 获取所有列名并计算要保留的列\n",
" columns_to_keep_df1 = list(set(data_NGV_j.columns) - columns_to_remove)\n",
" columns_to_keep_df2 = list(set(data_NGV_j1.columns) - columns_to_remove)\n",
"\n",
" # 过滤DataFrame以去除指定列\n",
" df1_filtered = data_NGV_j[columns_to_keep_df1]\n",
" df2_filtered = data_NGV_j1[columns_to_keep_df2]\n",
"\n",
" # 设置唯一标识列作为索引\n",
" df1_set_index = df1_filtered.set_index('id_own_org')\n",
" df2_set_index = df2_filtered.set_index('id_own_org')\n",
" # df1_set_index.to_csv(os.path.join(output_dir, f\"df1_set_index.csv\"), index=False)\n",
" # df2_set_index.to_csv(os.path.join(output_dir, f\"df2_set_index.csv\"), index=False)\n",
"\n",
" df1_set_index = df1_set_index.astype(str).replace(['nan', 'None'], '', ).fillna(\"\")\n",
" df2_set_index = df2_set_index.astype(str).replace(['nan', 'None'], '', ).fillna(\"\")\n",
"\n",
" # 找到两个DataFrame共有的索引\n",
" common_index = df1_set_index.index.intersection(df2_set_index.index)\n",
"\n",
" # 使用共同的索引来重新索引两个DataFrame\n",
" df1_common = df1_set_index.reindex(common_index).fillna('')\n",
" df2_common = df2_set_index.reindex(common_index).fillna('')\n",
"\n",
" # 确保两个DataFrame有相同的列顺序\n",
" common_columns = df1_common.columns.intersection(df2_common.columns)\n",
" df1_common = df1_common[common_columns]\n",
" df2_common = df2_common[common_columns]\n",
"\n",
" # 比较两个DataFrame的内容\n",
" comparison_column = 'match_status'\n",
"\n",
" # 创建一个布尔Series，指示每一行是否完全相同\n",
" matches = (df1_common == df2_common).all(axis=1)\n",
"\n",
" # 添加新列到第一个DataFrame，标记是否匹配\n",
" df1_common[comparison_column] = matches.map({True: '一致', False: '不一致'})\n",
" df1_common.to_csv(os.path.join(output_dir, f\"df1_common.csv\"))\n",
"\n",
" # 如果需要也可以添加到第二个DataFrame（这里假设只需要处理df1_common）\n",
" # df2_common[comparison_column] = matches.map({True: '一致', False: '不一致'})\n",
"\n",
" # 提取只在一个DataFrame中存在的索引对应的行\n",
" df1_only_index = df1_set_index.index.difference(df2_set_index.index)\n",
" df2_only_index = df2_set_index.index.difference(df1_set_index.index)\n",
"\n",
" df1_only_rows = df1_set_index.loc[df1_only_index].copy()\n",
" df2_only_rows = df2_set_index.loc[df2_only_index].copy()\n",
"\n",
" # 保存匹配结果\n",
" # df1_common.to_csv(os.path.join(output_dir, 'matched_results.csv'), index_label='id_own_org')\n",
"\n",
" # 保存仅在df1中的行\n",
" # df1_only_rows.to_csv(os.path.join(output_dir, 'df1_only_rows.csv'), index_label='id_own_org')\n",
"\n",
" # 保存仅在df2中的行\n",
" # df2_only_rows.to_csv(os.path.join(output_dir, 'df2_only_rows.csv'), index_label='id_own_org')\n",
" # data_NGV_j.to_csv(os.path.join(output_dir, 'data_NGV_j.csv'), index_label='id_own_org')\n",
" # data_NGV_j1.to_csv(os.path.join(output_dir, 'data_NGV_j1.csv'), index_label='id_own_org')\n",
" # jdy_NGV_data.to_csv(os.path.join(output_dir, 'jdy_NGV_data.csv'), index_label='id_own_org')\n",
"\n",
" # print(f\"\\nCSV文件已保存到目录: {output_dir}\")\n",
"\n",
" temp_jdy_NGV_data = jdy_NGV_data.copy()\n",
"\n",
" # temp_jdy_NGV_data.to_csv(os.path.join(output_dir, 'jdy_NGV_data.csv'), index=False)\n",
" temp_jdy_NGV_data.reset_index(inplace=True) # 如果 '门店id' 是索引，则先将其转换为普通列\n",
" # temp_jdy_NGV_data.to_csv(os.path.join(output_dir, 'jdy_NGV_data1.csv'), index=False)\n",
" if '_widget_1734062123069' not in temp_jdy_NGV_data.columns:\n",
" print(\"列 '门店id' 不存在\")\n",
" temp_jdy_NGV_data.rename(columns={'_widget_1734062123069': 'id_own_org'}, inplace=True)\n",
" temp_jdy_NGV_data.set_index('id_own_org', inplace=True)\n",
"\n",
" # 如果简道云存在，NGV不存在则标记NGV已删除\n",
" # 找出在 temp_jdy_NGV_data 中存在，但在 df1_common 中不存在的索引\n",
" ids_in_jdy_not_in_df1 = temp_jdy_NGV_data.index[~temp_jdy_NGV_data.index.isin(df1_common.index)]\n",
" # 提取这些行，形成新的 DataFrame\n",
" only_in_temp_jdy = temp_jdy_NGV_data.loc[ids_in_jdy_not_in_df1]\n",
" # 对数据源已经去掉的门店进行标记\n",
" for index, only_row in only_in_temp_jdy.iterrows():\n",
" result = {}\n",
" if '_id' in only_in_temp_jdy.columns:\n",
" _id_value = str(only_row['_id']) if not pd.isna(only_row['_id']) else None\n",
" result[\"_id\"] = _id_value\n",
"\n",
" if result[\"_id\"]:\n",
" data = {\n",
" 'api_key': Config.SaaS_Tasks_APP_ID,\n",
" 'entry_id': Config.NGV_TASKS_ENTRY_ID,\n",
" \"data_id\": result[\"_id\"],\n",
" \"data\": {\"_widget_1754285499851\": {\"value\": \"已删除\"}}\n",
" }\n",
"\n",
" api_instance.entry_data_update(data=data, max_retries=20)\n",
"\n",
" df1_common = df1_common.join(temp_jdy_NGV_data[\"_id\"], how='left')\n",
" # df1_common.to_csv(os.path.join(output_dir, 'matched_results_with_data_id.csv'))\n",
" df1_common = df1_common[df1_common['match_status'] == '不一致']\n",
" # df1_common.to_csv(os.path.join(output_dir, 'matched_results_with_data_id1.csv'))\n",
"\n",
" # 日期字段转换为日期格式\n",
" time_columns = ['saas_create_time', 'expiry_time', 'install_create_time', \"last_end_date\",\n",
" \"renew_date\"]\n",
" new_filtered_df = df1_common.copy() # 复制df，以调整时间\n",
" for col in time_columns:\n",
" # 1. 转换为datetime类型（带错误处理）\n",
" # 使用.loc安全赋值\n",
" new_filtered_df[col] = pd.to_datetime(df1_common[col], errors='coerce', utc=False)\n",
"\n",
" # 2. 优化后的时区转换（高效向量化操作）\n",
" df1_common[col + '_date'] = (\n",
" new_filtered_df[col]\n",
" # 本地化为北京时间（东八区）\n",
" .dt.tz_localize('Asia/Shanghai', ambiguous='infer', nonexistent='NaT')\n",
" # 转换为UTC时区\n",
" .dt.tz_convert('UTC')\n",
" # 格式化为ISO8601字符串\n",
" .dt.strftime('%Y-%m-%dT%H:%M:%SZ')\n",
" )\n",
"\n",
" # 人员字段转换为人员字段\n",
" staff_columns = ['area_manager', 'service_impl_principal', \"service_salesmen\", \"technician\"]\n",
" # 将员工列表转为DataFrame\n",
" # 三重循环临时方案（确保可写入）\n",
" for col in staff_columns:\n",
" staff_ids = []\n",
" for _, row in df1_common.iterrows():\n",
" matched = False\n",
" for staff in staff_id_list:\n",
" if str(staff['_widget_1734942794144']) == str(row[col]):\n",
" staff_ids.append(staff['_widget_1734942794145'])\n",
" matched = True\n",
" break\n",
" if not matched:\n",
" staff_ids.append(None)\n",
" df1_common[col + \"_staff_id\"] = staff_ids\n",
"\n",
" # 并发请求\n",
" futures = []\n",
" all_data = []\n",
"\n",
" for idx, row in df1_common.iterrows():\n",
" result = {}\n",
" data_dict = {}\n",
"\n",
" # 根据 field_mapping 进行字段替换\n",
" for col_name, widget_id in self.field_mapping.items():\n",
" if col_name in df1_common.columns:\n",
" value = row[col_name]\n",
" clean_value = None if pd.isna(value) else value\n",
" data_dict[widget_id] = {\"value\": clean_value}\n",
"\n",
" # 单独处理 _id 列，并将其转换为字符串\n",
" if '_id' in df1_common.columns:\n",
" _id_value = str(row['_id']) if not pd.isna(row['_id']) else None\n",
" result[\"_id\"] = _id_value\n",
"\n",
" # 组装最终结果\n",
" if result[\"_id\"]:\n",
" data = {\n",
" 'api_key': Config.SaaS_Tasks_APP_ID,\n",
" 'entry_id': Config.NGV_TASKS_ENTRY_ID,\n",
" \"data_id\": result[\"_id\"],\n",
" \"data\": data_dict\n",
" }\n",
"\n",
" api_instance.entry_data_update(data=data, max_retries=20)\n",
" else:\n",
" # continue\n",
" data1 = {'api_key': Config.SaaS_Tasks_APP_ID, 'entry_id': Config.NGV_TASKS_ENTRY_ID,\n",
" \"data\": data_dict}\n",
" api_instance.data_batch_create(data=data1, max_retries=20)\n",
"\n",
" # all_data.append(data_dict)\n",
"\n",
" # 收集所有结果\n",
" for future in concurrent.futures.as_completed(futures):\n",
" try:\n",
" result = future.result()\n",
" print(\"请求结果:\", result)\n",
" except Exception as exc:\n",
" print(f\"请求发生异常: {exc}\")\n",
"\n",
" end_time = datetime.datetime.now()\n",
" # df11 = pd.DataFrame(all_data)\n",
" # df11.to_csv(f\"all_data.csv\")\n",
" time_diff = end_time - start_time\n",
"\n",
" # 打印天数、秒数和微秒数\n",
" print(f\"执行时间: {time_diff.days} 天, {time_diff.seconds} 秒, {time_diff.microseconds} 微秒\")\n",
" common_module.send_task_status(task_start_time, \"NGV更新数据\")\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",
" for col_name, widget_id in field_mapping.items():\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",
"\n",
" return result\n",
"\n",
" def fields(self):\n",
" self.field_mapping = dict(date_id='_widget_1734062123065', date_fmt='_widget_1734062123066',\n",
" id_own_group='_widget_1734062123067', group_name='_widget_1734062123068',\n",
" id_own_org='_widget_1734062123069', org_name='_widget_1734062123070',\n",
" org_code='_widget_1734062123071', group_grade='_widget_1734062123072',\n",
" org_type='_widget_1734062123073', org_status='_widget_1734062123074',\n",
" saas_version='_widget_1734062123075', is_wechat='_widget_1734062123076',\n",
" is_mini_app='_widget_1734062123077', is_wx_shop='_widget_1734062123078',\n",
" is_camera_service='_widget_1734062123079',\n",
" is_maintenance_service='_widget_1734062123080',\n",
" saas_create_time='_widget_1734062123081', expiry_time='_widget_1734062123082',\n",
" saas_use_days='_widget_1734062123083', saas_use_year='_widget_1734062123084',\n",
" is_main_org='_widget_1734062123085', license_code='_widget_1734062123086',\n",
" license_name='_widget_1734062123087', org_crm_id='_widget_1734062123088',\n",
" province_id='_widget_1734062123089', province_name='_widget_1734062123090',\n",
" city_id='_widget_1734062123091', city_name='_widget_1734062123092',\n",
" area_id='_widget_1734062123093', area_name='_widget_1734062123094',\n",
" region_name='_widget_1734062123095', region_short_name='_widget_1734062123096',\n",
" branch_name='_widget_1734062123097', carzone_store_id='_widget_1734062123098',\n",
" carzone_store_name='_widget_1734062123099',\n",
" customer_carzone_id='_widget_1734062123100', salesmen='_widget_1734062123101',\n",
" area_manager='_widget_1734062123102', service_salesmen='_widget_1734062123103',\n",
" impl_principal='_widget_1734062123104',\n",
" service_impl_principal='_widget_1734062123105',\n",
" active_user_count='_widget_1734062123106', active_user_type='_widget_1734062123107',\n",
" limit_user_count='_widget_1734062123108', limit_user_type='_widget_1734062123109',\n",
" is_n='_widget_1734062123110', is_g='_widget_1734062123111',\n",
" is_v='_widget_1734062123112', is_visited='_widget_1734062123113',\n",
" is_active='_widget_1734062123114', active_status_fmt='_widget_1734062123115',\n",
" bill_count_last_30_day='_widget_1734062123116',\n",
" bill_day_count_last_30_day='_widget_1734062123117',\n",
" bill_day_count_this_month='_widget_1734062123118',\n",
" bill_count_last_7_day='_widget_1734062123119',\n",
" bill_day_count_last_7_day='_widget_1734062123120', pv_count='_widget_1734062123121',\n",
" uv_count='_widget_1734062123122', bill_count_1d='_widget_1734062123123',\n",
" bill_count_2d='_widget_1734062123124', bill_count_3d='_widget_1734062123125',\n",
" bill_count_4d='_widget_1734062123126', bill_count_5d='_widget_1734062123127',\n",
" bill_count_6d='_widget_1734062123128', bill_count_7d='_widget_1734062123129',\n",
" bill_count_8d='_widget_1734062123130', bill_count_9d='_widget_1734062123131',\n",
" bill_count_10d='_widget_1734062123132', bill_count_11d='_widget_1734062123133',\n",
" bill_count_12d='_widget_1734062123134', bill_count_13d='_widget_1734062123135',\n",
" bill_count_14d='_widget_1734062123136', bill_count_15d='_widget_1734062123137',\n",
" bill_count_16d='_widget_1734062123138', bill_count_17d='_widget_1734062123139',\n",
" bill_count_18d='_widget_1734062123140', bill_count_19d='_widget_1734062123141',\n",
" bill_count_20d='_widget_1734062123142', bill_count_21d='_widget_1734062123143',\n",
" bill_count_22d='_widget_1734062123144', bill_count_23d='_widget_1734062123145',\n",
" bill_count_24d='_widget_1734062123146', bill_count_25d='_widget_1734062123147',\n",
" bill_count_26d='_widget_1734062123148', bill_count_27d='_widget_1734062123149',\n",
" bill_count_28d='_widget_1734062123150', bill_count_29d='_widget_1734062123151',\n",
" bill_count_30d='_widget_1734062123152', bill_count_31d='_widget_1734062123153',\n",
" etl_time='_widget_1734062123154',\n",
" maintain_bill_count_last_30_day='_widget_1734062123155',\n",
" washing_bill_count_last_30_day='_widget_1734062123156',\n",
" maintain_bill_day_count_last_30_day='_widget_1734062123157',\n",
" washing_bill_day_count_last_30_day='_widget_1734062123158',\n",
" retail_bill_count_last_30_day='_widget_1734062123159',\n",
" retail_bill_day_count_last_30_day='_widget_1734062123160',\n",
" purchase_bill_count_last_30_day='_widget_1734062123161',\n",
" purchase_bill_day_count_last_30_day='_widget_1734062123162',\n",
" card_bill_count_last_30_day='_widget_1734062123163',\n",
" card_bill_day_count_last_30_day='_widget_1734062123164',\n",
" gd_sales_bill_count_last_30_day='_widget_1734062123165',\n",
" gd_sales_bill_day_count_last_30_day='_widget_1734062123166',\n",
" g_change_flag='_widget_1734062123167', saas_package='_widget_1734062123168',\n",
" manage_model='_widget_1734062123169', contacts='_widget_1734062123170',\n",
" contact_number='_widget_1734062123171', contact_mobile='_widget_1734062123172',\n",
" g_month_count='_widget_1734062123173', g_month_percentage='_widget_1734062123174',\n",
" is_install_service='_widget_1734062123175',\n",
" install_create_time='_widget_1734062123176', last_end_date='_widget_1734062123177',\n",
" renew_date='_widget_1734062123178', is_chain_owner='_widget_1734062123179',\n",
" group_org_count='_widget_1734062123180',\n",
" recent_bill_warning_days='_widget_1734062123181',\n",
" g_change_flag_d='_widget_1734062123182', g_lost_warning_days='_widget_1734062123183',\n",
" saas_edition_fmt='_widget_1734062123184', g_flag_1m='_widget_1734062123185',\n",
" g_flag_2m='_widget_1734062123186', g_flag_3m='_widget_1734062123187',\n",
" g_flag_4m='_widget_1734062123188', g_flag_5m='_widget_1734062123189',\n",
" g_flag_6m='_widget_1734062123190', g_flag_day_count='_widget_1734062123191',\n",
" add_org_flag='_widget_1734062123192', pt='_widget_1734062123193',\n",
" org_size='_widget_1734062123194', qualification_type_fmt='_widget_1734062123195',\n",
" business_scope_fmt='_widget_1734062123196', store_type_fmt='_widget_1734062123197',\n",
" area='_widget_1734062123198', station_number='_widget_1734062123199',\n",
" header_type_fmt='_widget_1734062123200', org_stage='_widget_1734062123201',\n",
" g_count_this_month='_widget_1734062123202',\n",
" saas_customer_type='_widget_1734062123203', technician='_widget_1734062123204',\n",
" tmall_maintain_service_status_desc='_widget_1734062123205',\n",
" date_fmt_date='_widget_1749000071375',\n",
" area_manager_staff_id='_widget_1748496855779',\n",
" service_impl_principal_staff_id=\"_widget_1748496855780\",\n",
" service_salesmen_staff_id=\"_widget_1748496855778\",\n",
" technician_staff_id=\"_widget_1751877712235\",\n",
" saas_create_time_date=\"_widget_1749000071377\",\n",
" expiry_time_date=\"_widget_1749000071382\",\n",
" install_create_time_date=\"_widget_1749000071384\",\n",
" last_end_date_date=\"_widget_1749000071389\", renew_date_date=\"_widget_1749000071391\")\n",
"\n",
"\n",
"if __name__ == '__main__':\n",
" start = UpdateAllNGVDataDaily()\n",
" start.main()\n"
],
"id": "1cf6e084873cf5b8",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"已获取 100 条数据\n",
"已获取 200 条数据\n",
"已获取 300 条数据\n",
"已获取 400 条数据\n",
"已获取 500 条数据\n",
"已获取 600 条数据\n",
"已获取 700 条数据\n",
"已获取 800 条数据\n",
"已获取 900 条数据\n",
"已获取 1000 条数据\n",
"已获取 1100 条数据\n",
"已获取 1200 条数据\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[1;31mKeyboardInterrupt\u001B[0m Traceback (most recent call last)",
"Cell \u001B[1;32mIn[2], line 362\u001B[0m\n\u001B[0;32m 360\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;18m__name__\u001B[39m \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124m__main__\u001B[39m\u001B[38;5;124m'\u001B[39m:\n\u001B[0;32m 361\u001B[0m start \u001B[38;5;241m=\u001B[39m UpdateAllNGVDataDaily()\n\u001B[1;32m--> 362\u001B[0m start\u001B[38;5;241m.\u001B[39mmain()\n",
"Cell \u001B[1;32mIn[2], line 39\u001B[0m, in \u001B[0;36mUpdateAllNGVDataDaily.main\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 37\u001B[0m \u001B[38;5;66;03m# 获取NGV数据\u001B[39;00m\n\u001B[0;32m 38\u001B[0m payload \u001B[38;5;241m=\u001B[39m {\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mapi_key\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m675b900991ad2491c69389ca\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mentry_id\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m675bb02bd2d53c2034c665e4\u001B[39m\u001B[38;5;124m\"\u001B[39m}\n\u001B[1;32m---> 39\u001B[0m NGV_data_list \u001B[38;5;241m=\u001B[39m api_instance\u001B[38;5;241m.\u001B[39mentry_data_list(payload)\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdata\u001B[39m\u001B[38;5;124m\"\u001B[39m, [])\n\u001B[0;32m 40\u001B[0m jdy_NGV_data \u001B[38;5;241m=\u001B[39m pd\u001B[38;5;241m.\u001B[39mDataFrame(NGV_data_list)\n\u001B[0;32m 42\u001B[0m payload \u001B[38;5;241m=\u001B[39m {\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mapi_key\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m6694d3c4fcb69ca9a111a6c4\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m 43\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mentry_id\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m6769204a1902c9341340a1bc\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m 44\u001B[0m }\n",
"File \u001B[1;32mD:\\Idea Project\\SaaS_V1.5\\api.py:106\u001B[0m, in \u001B[0;36mAPI.entry_data_list\u001B[1;34m(self, data, replace, max_retries)\u001B[0m\n\u001B[0;32m 104\u001B[0m \u001B[38;5;28;01mwhile\u001B[39;00m retries \u001B[38;5;241m<\u001B[39m\u001B[38;5;241m=\u001B[39m max_retries:\n\u001B[0;32m 105\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 106\u001B[0m res \u001B[38;5;241m=\u001B[39m requests\u001B[38;5;241m.\u001B[39mpost(url\u001B[38;5;241m=\u001B[39murl, data\u001B[38;5;241m=\u001B[39mpayload, headers\u001B[38;5;241m=\u001B[39mheaders, timeout\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m10\u001B[39m)\n\u001B[0;32m 107\u001B[0m res\u001B[38;5;241m.\u001B[39mraise_for_status() \u001B[38;5;66;03m# 检查HTTP响应状态码，如果不等于200会抛出异常\u001B[39;00m\n\u001B[0;32m 108\u001B[0m data_get \u001B[38;5;241m=\u001B[39m res\u001B[38;5;241m.\u001B[39mjson()\n",
"File \u001B[1;32mD:\\ProgramTools\\Anaconda\\Lib\\site-packages\\requests\\api.py:115\u001B[0m, in \u001B[0;36mpost\u001B[1;34m(url, data, json, **kwargs)\u001B[0m\n\u001B[0;32m 103\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mpost\u001B[39m(url, data\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, json\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 104\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124mr\u001B[39m\u001B[38;5;124;03m\"\"\"Sends a POST request.\u001B[39;00m\n\u001B[0;32m 105\u001B[0m \n\u001B[0;32m 106\u001B[0m \u001B[38;5;124;03m :param url: URL for the new :class:`Request` object.\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 112\u001B[0m \u001B[38;5;124;03m :rtype: requests.Response\u001B[39;00m\n\u001B[0;32m 113\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m--> 115\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m request(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpost\u001B[39m\u001B[38;5;124m\"\u001B[39m, url, data\u001B[38;5;241m=\u001B[39mdata, json\u001B[38;5;241m=\u001B[39mjson, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n",
"File \u001B[1;32mD:\\ProgramTools\\Anaconda\\Lib\\site-packages\\requests\\api.py:59\u001B[0m, in \u001B[0;36mrequest\u001B[1;34m(method, url, **kwargs)\u001B[0m\n\u001B[0;32m 55\u001B[0m \u001B[38;5;66;03m# By using the 'with' statement we are sure the session is closed, thus we\u001B[39;00m\n\u001B[0;32m 56\u001B[0m \u001B[38;5;66;03m# avoid leaving sockets open which can trigger a ResourceWarning in some\u001B[39;00m\n\u001B[0;32m 57\u001B[0m \u001B[38;5;66;03m# cases, and look like a memory leak in others.\u001B[39;00m\n\u001B[0;32m 58\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m sessions\u001B[38;5;241m.\u001B[39mSession() \u001B[38;5;28;01mas\u001B[39;00m session:\n\u001B[1;32m---> 59\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m session\u001B[38;5;241m.\u001B[39mrequest(method\u001B[38;5;241m=\u001B[39mmethod, url\u001B[38;5;241m=\u001B[39murl, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n",
"File \u001B[1;32mD:\\ProgramTools\\Anaconda\\Lib\\site-packages\\requests\\sessions.py:589\u001B[0m, in \u001B[0;36mSession.request\u001B[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001B[0m\n\u001B[0;32m 584\u001B[0m send_kwargs \u001B[38;5;241m=\u001B[39m {\n\u001B[0;32m 585\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtimeout\u001B[39m\u001B[38;5;124m\"\u001B[39m: timeout,\n\u001B[0;32m 586\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mallow_redirects\u001B[39m\u001B[38;5;124m\"\u001B[39m: allow_redirects,\n\u001B[0;32m 587\u001B[0m }\n\u001B[0;32m 588\u001B[0m send_kwargs\u001B[38;5;241m.\u001B[39mupdate(settings)\n\u001B[1;32m--> 589\u001B[0m resp \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msend(prep, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39msend_kwargs)\n\u001B[0;32m 591\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m resp\n",
"File \u001B[1;32mD:\\ProgramTools\\Anaconda\\Lib\\site-packages\\requests\\sessions.py:703\u001B[0m, in \u001B[0;36mSession.send\u001B[1;34m(self, request, **kwargs)\u001B[0m\n\u001B[0;32m 700\u001B[0m start \u001B[38;5;241m=\u001B[39m preferred_clock()\n\u001B[0;32m 702\u001B[0m \u001B[38;5;66;03m# Send the request\u001B[39;00m\n\u001B[1;32m--> 703\u001B[0m r \u001B[38;5;241m=\u001B[39m adapter\u001B[38;5;241m.\u001B[39msend(request, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m 705\u001B[0m \u001B[38;5;66;03m# Total elapsed time of the request (approximately)\u001B[39;00m\n\u001B[0;32m 706\u001B[0m elapsed \u001B[38;5;241m=\u001B[39m preferred_clock() \u001B[38;5;241m-\u001B[39m start\n",
"File \u001B[1;32mD:\\ProgramTools\\Anaconda\\Lib\\site-packages\\requests\\adapters.py:589\u001B[0m, in \u001B[0;36mHTTPAdapter.send\u001B[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001B[0m\n\u001B[0;32m 586\u001B[0m timeout \u001B[38;5;241m=\u001B[39m TimeoutSauce(connect\u001B[38;5;241m=\u001B[39mtimeout, read\u001B[38;5;241m=\u001B[39mtimeout)\n\u001B[0;32m 588\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 589\u001B[0m resp \u001B[38;5;241m=\u001B[39m conn\u001B[38;5;241m.\u001B[39murlopen(\n\u001B[0;32m 590\u001B[0m method\u001B[38;5;241m=\u001B[39mrequest\u001B[38;5;241m.\u001B[39mmethod,\n\u001B[0;32m 591\u001B[0m url\u001B[38;5;241m=\u001B[39murl,\n\u001B[0;32m 592\u001B[0m body\u001B[38;5;241m=\u001B[39mrequest\u001B[38;5;241m.\u001B[39mbody,\n\u001B[0;32m 593\u001B[0m headers\u001B[38;5;241m=\u001B[39mrequest\u001B[38;5;241m.\u001B[39mheaders,\n\u001B[0;32m 594\u001B[0m redirect\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m,\n\u001B[0;32m 595\u001B[0m assert_same_host\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m,\n\u001B[0;32m 596\u001B[0m preload_content\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m,\n\u001B[0;32m 597\u001B[0m decode_content\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m,\n\u001B[0;32m 598\u001B[0m retries\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmax_retries,\n\u001B[0;32m 599\u001B[0m timeout\u001B[38;5;241m=\u001B[39mtimeout,\n\u001B[0;32m 600\u001B[0m chunked\u001B[38;5;241m=\u001B[39mchunked,\n\u001B[0;32m 601\u001B[0m )\n\u001B[0;32m 603\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m (ProtocolError, \u001B[38;5;167;01mOSError\u001B[39;00m) \u001B[38;5;28;01mas\u001B[39;00m err:\n\u001B[0;32m 604\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mConnectionError\u001B[39;00m(err, request\u001B[38;5;241m=\u001B[39mrequest)\n",
"File \u001B[1;32mD:\\ProgramTools\\Anaconda\\Lib\\site-packages\\urllib3\\connectionpool.py:789\u001B[0m, in \u001B[0;36mHTTPConnectionPool.urlopen\u001B[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001B[0m\n\u001B[0;32m 786\u001B[0m response_conn \u001B[38;5;241m=\u001B[39m conn \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m release_conn \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m 788\u001B[0m \u001B[38;5;66;03m# Make the request on the HTTPConnection object\u001B[39;00m\n\u001B[1;32m--> 789\u001B[0m response \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_make_request(\n\u001B[0;32m 790\u001B[0m conn,\n\u001B[0;32m 791\u001B[0m method,\n\u001B[0;32m 792\u001B[0m url,\n\u001B[0;32m 793\u001B[0m timeout\u001B[38;5;241m=\u001B[39mtimeout_obj,\n\u001B[0;32m 794\u001B[0m body\u001B[38;5;241m=\u001B[39mbody,\n\u001B[0;32m 795\u001B[0m headers\u001B[38;5;241m=\u001B[39mheaders,\n\u001B[0;32m 796\u001B[0m chunked\u001B[38;5;241m=\u001B[39mchunked,\n\u001B[0;32m 797\u001B[0m retries\u001B[38;5;241m=\u001B[39mretries,\n\u001B[0;32m 798\u001B[0m response_conn\u001B[38;5;241m=\u001B[39mresponse_conn,\n\u001B[0;32m 799\u001B[0m preload_content\u001B[38;5;241m=\u001B[39mpreload_content,\n\u001B[0;32m 800\u001B[0m decode_content\u001B[38;5;241m=\u001B[39mdecode_content,\n\u001B[0;32m 801\u001B[0m \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mresponse_kw,\n\u001B[0;32m 802\u001B[0m )\n\u001B[0;32m 804\u001B[0m \u001B[38;5;66;03m# Everything went great!\u001B[39;00m\n\u001B[0;32m 805\u001B[0m clean_exit \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mTrue\u001B[39;00m\n",
"File \u001B[1;32mD:\\ProgramTools\\Anaconda\\Lib\\site-packages\\urllib3\\connectionpool.py:536\u001B[0m, in \u001B[0;36mHTTPConnectionPool._make_request\u001B[1;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001B[0m\n\u001B[0;32m 534\u001B[0m \u001B[38;5;66;03m# Receive the response from the server\u001B[39;00m\n\u001B[0;32m 535\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 536\u001B[0m response \u001B[38;5;241m=\u001B[39m conn\u001B[38;5;241m.\u001B[39mgetresponse()\n\u001B[0;32m 537\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m (BaseSSLError, \u001B[38;5;167;01mOSError\u001B[39;00m) \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m 538\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_raise_timeout(err\u001B[38;5;241m=\u001B[39me, url\u001B[38;5;241m=\u001B[39murl, timeout_value\u001B[38;5;241m=\u001B[39mread_timeout)\n",
"File \u001B[1;32mD:\\ProgramTools\\Anaconda\\Lib\\site-packages\\urllib3\\connection.py:464\u001B[0m, in \u001B[0;36mHTTPConnection.getresponse\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 461\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mresponse\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m HTTPResponse\n\u001B[0;32m 463\u001B[0m \u001B[38;5;66;03m# Get the response from http.client.HTTPConnection\u001B[39;00m\n\u001B[1;32m--> 464\u001B[0m httplib_response \u001B[38;5;241m=\u001B[39m \u001B[38;5;28msuper\u001B[39m()\u001B[38;5;241m.\u001B[39mgetresponse()\n\u001B[0;32m 466\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m 467\u001B[0m assert_header_parsing(httplib_response\u001B[38;5;241m.\u001B[39mmsg)\n",
"File \u001B[1;32mD:\\ProgramTools\\Anaconda\\Lib\\http\\client.py:1428\u001B[0m, in \u001B[0;36mHTTPConnection.getresponse\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 1426\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m 1427\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m-> 1428\u001B[0m response\u001B[38;5;241m.\u001B[39mbegin()\n\u001B[0;32m 1429\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mConnectionError\u001B[39;00m:\n\u001B[0;32m 1430\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mclose()\n",
"File \u001B[1;32mD:\\ProgramTools\\Anaconda\\Lib\\http\\client.py:331\u001B[0m, in \u001B[0;36mHTTPResponse.begin\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 329\u001B[0m \u001B[38;5;66;03m# read until we get a non-100 response\u001B[39;00m\n\u001B[0;32m 330\u001B[0m \u001B[38;5;28;01mwhile\u001B[39;00m \u001B[38;5;28;01mTrue\u001B[39;00m:\n\u001B[1;32m--> 331\u001B[0m version, status, reason \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_read_status()\n\u001B[0;32m 332\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m status \u001B[38;5;241m!=\u001B[39m CONTINUE:\n\u001B[0;32m 333\u001B[0m \u001B[38;5;28;01mbreak\u001B[39;00m\n",
"File \u001B[1;32mD:\\ProgramTools\\Anaconda\\Lib\\http\\client.py:292\u001B[0m, in \u001B[0;36mHTTPResponse._read_status\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 291\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_read_status\u001B[39m(\u001B[38;5;28mself\u001B[39m):\n\u001B[1;32m--> 292\u001B[0m line \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mstr\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfp\u001B[38;5;241m.\u001B[39mreadline(_MAXLINE \u001B[38;5;241m+\u001B[39m \u001B[38;5;241m1\u001B[39m), \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124miso-8859-1\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m 293\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(line) \u001B[38;5;241m>\u001B[39m _MAXLINE:\n\u001B[0;32m 294\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m LineTooLong(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mstatus line\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n",
"File \u001B[1;32mD:\\ProgramTools\\Anaconda\\Lib\\socket.py:707\u001B[0m, in \u001B[0;36mSocketIO.readinto\u001B[1;34m(self, b)\u001B[0m\n\u001B[0;32m 705\u001B[0m \u001B[38;5;28;01mwhile\u001B[39;00m \u001B[38;5;28;01mTrue\u001B[39;00m:\n\u001B[0;32m 706\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 707\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_sock\u001B[38;5;241m.\u001B[39mrecv_into(b)\n\u001B[0;32m 708\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m timeout:\n\u001B[0;32m 709\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_timeout_occurred \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mTrue\u001B[39;00m\n",
"File \u001B[1;32mD:\\ProgramTools\\Anaconda\\Lib\\ssl.py:1252\u001B[0m, in \u001B[0;36mSSLSocket.recv_into\u001B[1;34m(self, buffer, nbytes, flags)\u001B[0m\n\u001B[0;32m 1248\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m flags \u001B[38;5;241m!=\u001B[39m \u001B[38;5;241m0\u001B[39m:\n\u001B[0;32m 1249\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[0;32m 1250\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnon-zero flags not allowed in calls to recv_into() on \u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;241m%\u001B[39m\n\u001B[0;32m 1251\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__class__\u001B[39m)\n\u001B[1;32m-> 1252\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mread(nbytes, buffer)\n\u001B[0;32m 1253\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 1254\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28msuper\u001B[39m()\u001B[38;5;241m.\u001B[39mrecv_into(buffer, nbytes, flags)\n",
"File \u001B[1;32mD:\\ProgramTools\\Anaconda\\Lib\\ssl.py:1104\u001B[0m, in \u001B[0;36mSSLSocket.read\u001B[1;34m(self, len, buffer)\u001B[0m\n\u001B[0;32m 1102\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m 1103\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m buffer \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m-> 1104\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_sslobj\u001B[38;5;241m.\u001B[39mread(\u001B[38;5;28mlen\u001B[39m, buffer)\n\u001B[0;32m 1105\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m 1106\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_sslobj\u001B[38;5;241m.\u001B[39mread(\u001B[38;5;28mlen\u001B[39m)\n",
"\u001B[1;31mKeyboardInterrupt\u001B[0m: "
]
}
],
"execution_count": 2
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-10-15T07:03:16.566548Z",
"start_time": "2025-10-15T07:02:49.835394Z"
}
},
"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",
"from back_ground_module import CommonModule\n",
"from log_config import configure_task_logger, configure_error_task_logger\n",
"import concurrent.futures\n",
"\n",
"# 获取已经配置好的常规日志记录器\n",
"logger = configure_task_logger()\n",
"\n",
"# 获取已经配置好的错误任务日志记录器\n",
"error_task_logger = configure_error_task_logger()\n",
"\n",
"start_time = datetime.datetime.now()\n",
"api_instance = API()\n",
"common_module = CommonModule()\n",
"\n",
"\n",
"class UpdateAllNGVDataDaily:\n",
" \"\"\"NGV数据每日更新\"\"\"\n",
"\n",
" def __init__(self):\n",
" self.field_mapping = {}\n",
" # self.fields()\n",
"\n",
" def main(self):\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",
" import os\n",
" os.makedirs(output_dir, exist_ok=True)\n",
"\n",
" task_start_time = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n",
" # 获取NGV数据\n",
" payload = {\"api_key\": \"675b900991ad2491c69389ca\", \"entry_id\": \"675bb02bd2d53c2034c665e4\"}\n",
" # NGV_data_list = api_instance.entry_data_list(payload).get(\"data\", [])\n",
" # jdy_NGV_data = pd.DataFrame(NGV_data_list)\n",
"\n",
" payload = {\"api_key\": \"6694d3c4fcb69ca9a111a6c4\",\n",
" \"entry_id\": \"6769204a1902c9341340a1bc\",\n",
" }\n",
" staff_id = api_instance.entry_data_list(payload)\n",
" staff_id_list = staff_id.get(\"data\") # api请求格式，将数据封装在data字典里\n",
"\n",
" # for i in range(1,2):\n",
" data_NGV_j = common_module.get_ngv_details(days_back=1)\n",
" data_NGV_j.to_csv(os.path.join(output_dir, f\"data_NGV_j.csv\"), index=False)\n",
" data_NGV_j1 = common_module.get_ngv_details(days_back=2)\n",
"\n",
" # 对 data_NGV 进行进一步的过滤，只保留 org_type 为 \"一般\" 的记录\n",
" data_NGV_j = data_NGV_j[data_NGV_j['org_type'] == '一般']\n",
" data_NGV_j1 = data_NGV_j1[data_NGV_j1['org_type'] == '一般']\n",
"\n",
" # 去除不需要的列\n",
" columns_to_remove = {'date_id', 'date_fmt', 'pt', 'etl_time'}\n",
"\n",
" # 获取所有列名并计算要保留的列\n",
" columns_to_keep_df1 = list(set(data_NGV_j.columns) - columns_to_remove)\n",
" columns_to_keep_df2 = list(set(data_NGV_j1.columns) - columns_to_remove)\n",
"\n",
" # 过滤DataFrame以去除指定列\n",
" df1_filtered = data_NGV_j[columns_to_keep_df1]\n",
" df2_filtered = data_NGV_j1[columns_to_keep_df2]\n",
"\n",
"\n",
"if __name__ == '__main__':\n",
" start = UpdateAllNGVDataDaily()\n",
" start.main()\n"
],
"id": "82d58cced4a6e02",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"已获取 100 条数据\n",
"已获取 145 条数据\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001B[92m2025-10-15 15:02:50,242 - api.py - task_logger - INFO - 获取了145条数据\u001B[0m\n"
]
}
],
"execution_count": 4
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-10-23T09:43:17.547186Z",
"start_time": "2025-10-23T09:42:48.434237Z"
}
},
"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",
"from back_ground_module import CommonModule\n",
"from log_config import configure_task_logger, configure_error_task_logger\n",
"\n",
"logger = configure_task_logger()\n",
"\n",
"# 获取已经配置好的错误任务日志记录器\n",
"error_task_logger = configure_error_task_logger()\n",
"start_time = datetime.datetime.now()\n",
"api_instance = API()\n",
"common_module = CommonModule()\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",
"import os\n",
"os.makedirs(output_dir, exist_ok=True)\n",
"\n",
"class UpdateNGVData:\n",
" \"\"\"NGV数据每日新增\"\"\"\n",
"\n",
" def __init__(self):\n",
" self.staff_id_list = None\n",
" self.field_mapping = {}\n",
" self.fields()\n",
"\n",
" def load_all_data(self):\n",
" # 获取简道云员工id\n",
" payload = {\"api_key\": \"6694d3c4fcb69ca9a111a6c4\",\n",
" \"entry_id\": \"6769204a1902c9341340a1bc\",\n",
" }\n",
" staff_id = api_instance.entry_data_list(payload)\n",
" self.staff_id_list = staff_id.get(\"data\") # api请求格式，将数据封装在data字典里\n",
"\n",
" @staticmethod\n",
" def get_staff_id(row_item, name):\n",
" \"\"\"辅助函数，用于获取员工ID\"\"\"\n",
" if str(row_item[\"_widget_1734942794144\"]) == str(name): # 检查姓名是否匹配\n",
" return row_item[\"_widget_1734942794145\"] # 返回员工ID\n",
" return None\n",
"\n",
" def main(self):\n",
" task_start_time = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n",
" try:\n",
" self.load_all_data()\n",
" logger.info(f\"数据加载完成\")\n",
"\n",
" data_NGV_j = common_module.get_ngv_details(days_back=1)\n",
" data_NGV_j.to_csv(os.path.join(output_dir, f\"data_NGV_j.csv\"), index=False)\n",
" data_NGV_j1 = common_module.get_ngv_details(days_back=2)\n",
"\n",
" # 找出在 data_NGV_j 中存在但在 data_NGV_j1 中不存在的 data_id\n",
" unique_data_ids = data_NGV_j[~data_NGV_j['org_code'].isin(data_NGV_j1['org_code'])]\n",
"\n",
" # 创建一个新的 DataFrame 保存这些唯一的 data_id 及其对应的数据\n",
" new_df = data_NGV_j[data_NGV_j['org_code'].isin(unique_data_ids['org_code'])]\n",
"\n",
" # 对 new_df 进行进一步的过滤，只保留 org_type 为 \"一般\" 的记录\n",
" data_NGV_j = data_NGV_j[data_NGV_j['org_type'] == '一般']\n",
" data_NGV_j1 = data_NGV_j1[data_NGV_j1['org_type'] == '一般']\n",
" filtered_df = new_df[new_df['org_type'] == '一般']\n",
"\n",
" # 日期字段转换为日期格式\n",
" time_columns = ['date_fmt', 'saas_create_time', 'expiry_time', 'install_create_time', \"last_end_date\",\n",
" \"renew_date\"]\n",
" new_filtered_df = filtered_df.copy() # 复制df，以调整时间\n",
" for col in time_columns:\n",
" # 1. 转换为datetime类型（带错误处理）\n",
" # 使用.loc安全赋值\n",
" new_filtered_df[col] = pd.to_datetime(filtered_df[col], errors='coerce', utc=False)\n",
"\n",
" # 2. 优化后的时区转换（高效向量化操作）\n",
" filtered_df[col + '_date'] = (\n",
" new_filtered_df[col]\n",
" # 本地化为北京时间（东八区）\n",
" .dt.tz_localize('Asia/Shanghai', ambiguous='infer', nonexistent='NaT')\n",
" # 转换为UTC时区\n",
" .dt.tz_convert('UTC')\n",
" # 格式化为ISO8601字符串\n",
" .dt.strftime('%Y-%m-%dT%H:%M:%SZ')\n",
" )\n",
" logger.info(f\"时间转换完成\")\n",
"\n",
" # 人员字段转换为人员字段\n",
" staff_columns = ['area_manager', 'service_impl_principal', \"service_salesmen\", \"technician\"]\n",
" # 将员工列表转为DataFrame\n",
" # 三重循环临时方案（确保可写入）\n",
" for col in staff_columns:\n",
" staff_ids = []\n",
" for _, row in filtered_df.iterrows():\n",
" matched = False\n",
" for staff in self.staff_id_list:\n",
" if str(staff['_widget_1734942794144']) == str(row[col]):\n",
" staff_ids.append(staff['_widget_1734942794145'])\n",
" matched = True\n",
" break\n",
" if not matched:\n",
" staff_ids.append(None)\n",
" filtered_df[col + \"_staff_id\"] = staff_ids\n",
" logger.info(f\"人员转换完成\")\n",
"\n",
" # filtered_df.to_csv(r\"D:\\Idea Project\\SaaS_V1.3\\back_ground_module\\output\\NGV.csv\")\n",
"\n",
" # 生成包含所有行转换后的字典列表\n",
" # all_data = [self.row_to_dict(row, self.field_mapping) for index, row in data_NGV_j1.iterrows()] # 前两天的全部数据\n",
" # all_data = [self.row_to_dict(row, self.field_mapping) for index, row in data_NGV_j.iterrows()] # 前一天的全部数据\n",
" all_data = [self.row_to_dict(row, self.field_mapping) for index, row in filtered_df.iterrows()] # 增量数据\n",
" # filtered_df.to_csv(output_dir + \"\\\\filtered_df.csv\", index=False)\n",
"\n",
"\n",
" #\n",
" # data = {'api_key': Config.SaaS_Tasks_APP_ID, 'entry_id': Config.NGV_TASKS_ENTRY_ID, \"data_list\": all_data}\n",
" #\n",
" # result = api_instance.entry_data_batch_create(data)\n",
" # logger.info(f\"数据已推送:{result}\")\n",
" # result_str = str(result)\n",
" # print(result_str[:500])\n",
"\n",
" # 保存到Excel文件\n",
" # output_path = r'D:\\Idea Project\\F6+宜搭+其它(1)\\new\\文件输出\\ngv明细1.xlsx'\n",
" # filtered_df.to_excel(output_path, index=False)\n",
" # data_NGV_j1.to_excel( r'D:\\Idea Project\\F6+宜搭+其它(1)\\new\\文件输出\\ngv明细j1.xlsx', index=False)\n",
" # data_NGV_j.to_excel( r'D:\\Idea Project\\F6+宜搭+其它(1)\\new\\文件输出\\ngv明细j.xlsx', index=False)\n",
" # new_df.to_excel(r'D:\\Idea Project\\F6+宜搭+其它(1)\\new\\文件输出\\ngv明细ndf.xlsx', index=False)\n",
"\n",
" # common_module.send_task_status(task_start_time, \"NGV新增数据\")\n",
" # logger.info(f\"任务完成。\")\n",
" except Exception as e:\n",
" error_task_logger.error(f\"任务执行时发生异常: {e}\")\n",
" common_module.send_task_error(task_start_time, \"NGV新增数据\", str(e))\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",
" for col_name, widget_id in field_mapping.items():\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 = dict(date_id='_widget_1734062123065', date_fmt='_widget_1734062123066',\n",
" id_own_group='_widget_1734062123067', group_name='_widget_1734062123068',\n",
" id_own_org='_widget_1734062123069', org_name='_widget_1734062123070',\n",
" org_code='_widget_1734062123071', group_grade='_widget_1734062123072',\n",
" org_type='_widget_1734062123073', org_status='_widget_1734062123074',\n",
" saas_version='_widget_1734062123075', is_wechat='_widget_1734062123076',\n",
" is_mini_app='_widget_1734062123077', is_wx_shop='_widget_1734062123078',\n",
" is_camera_service='_widget_1734062123079',\n",
" is_maintenance_service='_widget_1734062123080',\n",
" saas_create_time='_widget_1734062123081', expiry_time='_widget_1734062123082',\n",
" saas_use_days='_widget_1734062123083', saas_use_year='_widget_1734062123084',\n",
" is_main_org='_widget_1734062123085', license_code='_widget_1734062123086',\n",
" license_name='_widget_1734062123087', org_crm_id='_widget_1734062123088',\n",
" province_id='_widget_1734062123089', province_name='_widget_1734062123090',\n",
" city_id='_widget_1734062123091', city_name='_widget_1734062123092',\n",
" area_id='_widget_1734062123093', area_name='_widget_1734062123094',\n",
" region_name='_widget_1734062123095', region_short_name='_widget_1734062123096',\n",
" branch_name='_widget_1734062123097', carzone_store_id='_widget_1734062123098',\n",
" carzone_store_name='_widget_1734062123099',\n",
" customer_carzone_id='_widget_1734062123100', salesmen='_widget_1734062123101',\n",
" area_manager='_widget_1734062123102', service_salesmen='_widget_1734062123103',\n",
" impl_principal='_widget_1734062123104',\n",
" service_impl_principal='_widget_1734062123105',\n",
" active_user_count='_widget_1734062123106', active_user_type='_widget_1734062123107',\n",
" limit_user_count='_widget_1734062123108', limit_user_type='_widget_1734062123109',\n",
" is_n='_widget_1734062123110', is_g='_widget_1734062123111',\n",
" is_v='_widget_1734062123112', is_visited='_widget_1734062123113',\n",
" is_active='_widget_1734062123114', active_status_fmt='_widget_1734062123115',\n",
" bill_count_last_30_day='_widget_1734062123116',\n",
" bill_day_count_last_30_day='_widget_1734062123117',\n",
" bill_day_count_this_month='_widget_1734062123118',\n",
" bill_count_last_7_day='_widget_1734062123119',\n",
" bill_day_count_last_7_day='_widget_1734062123120', pv_count='_widget_1734062123121',\n",
" uv_count='_widget_1734062123122', bill_count_1d='_widget_1734062123123',\n",
" bill_count_2d='_widget_1734062123124', bill_count_3d='_widget_1734062123125',\n",
" bill_count_4d='_widget_1734062123126', bill_count_5d='_widget_1734062123127',\n",
" bill_count_6d='_widget_1734062123128', bill_count_7d='_widget_1734062123129',\n",
" bill_count_8d='_widget_1734062123130', bill_count_9d='_widget_1734062123131',\n",
" bill_count_10d='_widget_1734062123132', bill_count_11d='_widget_1734062123133',\n",
" bill_count_12d='_widget_1734062123134', bill_count_13d='_widget_1734062123135',\n",
" bill_count_14d='_widget_1734062123136', bill_count_15d='_widget_1734062123137',\n",
" bill_count_16d='_widget_1734062123138', bill_count_17d='_widget_1734062123139',\n",
" bill_count_18d='_widget_1734062123140', bill_count_19d='_widget_1734062123141',\n",
" bill_count_20d='_widget_1734062123142', bill_count_21d='_widget_1734062123143',\n",
" bill_count_22d='_widget_1734062123144', bill_count_23d='_widget_1734062123145',\n",
" bill_count_24d='_widget_1734062123146', bill_count_25d='_widget_1734062123147',\n",
" bill_count_26d='_widget_1734062123148', bill_count_27d='_widget_1734062123149',\n",
" bill_count_28d='_widget_1734062123150', bill_count_29d='_widget_1734062123151',\n",
" bill_count_30d='_widget_1734062123152', bill_count_31d='_widget_1734062123153',\n",
" etl_time='_widget_1734062123154',\n",
" maintain_bill_count_last_30_day='_widget_1734062123155',\n",
" washing_bill_count_last_30_day='_widget_1734062123156',\n",
" maintain_bill_day_count_last_30_day='_widget_1734062123157',\n",
" washing_bill_day_count_last_30_day='_widget_1734062123158',\n",
" retail_bill_count_last_30_day='_widget_1734062123159',\n",
" retail_bill_day_count_last_30_day='_widget_1734062123160',\n",
" purchase_bill_count_last_30_day='_widget_1734062123161',\n",
" purchase_bill_day_count_last_30_day='_widget_1734062123162',\n",
" card_bill_count_last_30_day='_widget_1734062123163',\n",
" card_bill_day_count_last_30_day='_widget_1734062123164',\n",
" gd_sales_bill_count_last_30_day='_widget_1734062123165',\n",
" gd_sales_bill_day_count_last_30_day='_widget_1734062123166',\n",
" g_change_flag='_widget_1734062123167', saas_package='_widget_1734062123168',\n",
" manage_model='_widget_1734062123169', contacts='_widget_1734062123170',\n",
" contact_number='_widget_1734062123171', contact_mobile='_widget_1734062123172',\n",
" g_month_count='_widget_1734062123173', g_month_percentage='_widget_1734062123174',\n",
" is_install_service='_widget_1734062123175',\n",
" install_create_time='_widget_1734062123176', last_end_date='_widget_1734062123177',\n",
" renew_date='_widget_1734062123178', is_chain_owner='_widget_1734062123179',\n",
" group_org_count='_widget_1734062123180',\n",
" recent_bill_warning_days='_widget_1734062123181',\n",
" g_change_flag_d='_widget_1734062123182', g_lost_warning_days='_widget_1734062123183',\n",
" saas_edition_fmt='_widget_1734062123184', g_flag_1m='_widget_1734062123185',\n",
" g_flag_2m='_widget_1734062123186', g_flag_3m='_widget_1734062123187',\n",
" g_flag_4m='_widget_1734062123188', g_flag_5m='_widget_1734062123189',\n",
" g_flag_6m='_widget_1734062123190', g_flag_day_count='_widget_1734062123191',\n",
" add_org_flag='_widget_1734062123192', pt='_widget_1734062123193',\n",
" org_size='_widget_1734062123194', qualification_type_fmt='_widget_1734062123195',\n",
" business_scope_fmt='_widget_1734062123196', store_type_fmt='_widget_1734062123197',\n",
" area='_widget_1734062123198', station_number='_widget_1734062123199',\n",
" header_type_fmt='_widget_1734062123200', org_stage='_widget_1734062123201',\n",
" g_count_this_month='_widget_1734062123202',\n",
" saas_customer_type='_widget_1734062123203', technician='_widget_1734062123204',\n",
" tmall_maintain_service_status_desc='_widget_1734062123205',\n",
" date_fmt_date='_widget_1749000071375',\n",
" area_manager_staff_id='_widget_1748496855779',\n",
" service_impl_principal_staff_id=\"_widget_1748496855780\",\n",
" service_salesmen_staff_id=\"_widget_1748496855778\",\n",
" technician_staff_id=\"_widget_1751877712235\",\n",
" saas_create_time_date=\"_widget_1749000071377\",\n",
" expiry_time_date=\"_widget_1749000071382\",\n",
" install_create_time_date=\"_widget_1749000071384\",\n",
" last_end_date_date=\"_widget_1749000071389\", renew_date_date=\"_widget_1749000071391\")\n",
"\n",
"\n",
"if __name__ == '__main__':\n",
" start = UpdateNGVData()\n",
" start.main()\n"
],
"id": "6ce49b7686e91712",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dfc264b77bee3205aaf2099572a46c57\n",
"已获取 100 条数据\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001B[92m2025-10-23 17:42:50,287 - api.py - task_logger - INFO - 获取了146条数据\u001B[0m\n",
"\u001B[92m2025-10-23 17:42:50,288 - 3381905813.py - task_logger - INFO - 数据加载完成\u001B[0m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"已获取 146 条数据\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\zy187\\AppData\\Local\\Temp\\ipykernel_24808\\3381905813.py:77: 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_df[col + '_date'] = (\n",
"C:\\Users\\zy187\\AppData\\Local\\Temp\\ipykernel_24808\\3381905813.py:77: 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_df[col + '_date'] = (\n",
"C:\\Users\\zy187\\AppData\\Local\\Temp\\ipykernel_24808\\3381905813.py:77: 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_df[col + '_date'] = (\n",
"C:\\Users\\zy187\\AppData\\Local\\Temp\\ipykernel_24808\\3381905813.py:77: 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_df[col + '_date'] = (\n",
"C:\\Users\\zy187\\AppData\\Local\\Temp\\ipykernel_24808\\3381905813.py:77: 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_df[col + '_date'] = (\n",
"C:\\Users\\zy187\\AppData\\Local\\Temp\\ipykernel_24808\\3381905813.py:77: 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_df[col + '_date'] = (\n",
"\u001B[92m2025-10-23 17:43:17,224 - 3381905813.py - task_logger - INFO - 时间转换完成\u001B[0m\n",
"C:\\Users\\zy187\\AppData\\Local\\Temp\\ipykernel_24808\\3381905813.py:103: 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_df[col + \"_staff_id\"] = staff_ids\n",
"C:\\Users\\zy187\\AppData\\Local\\Temp\\ipykernel_24808\\3381905813.py:103: 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_df[col + \"_staff_id\"] = staff_ids\n",
"C:\\Users\\zy187\\AppData\\Local\\Temp\\ipykernel_24808\\3381905813.py:103: 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_df[col + \"_staff_id\"] = staff_ids\n",
"C:\\Users\\zy187\\AppData\\Local\\Temp\\ipykernel_24808\\3381905813.py:103: 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_df[col + \"_staff_id\"] = staff_ids\n",
"\u001B[92m2025-10-23 17:43:17,245 - 3381905813.py - task_logger - INFO - 人员转换完成\u001B[0m\n"
]
}
],
"execution_count": 1
}
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