From 2621e2b98ed3f6511dc2881d78a56466c50b23c8 Mon Sep 17 00:00:00 2001
From: panda <1415243231@qq.com>
Date: Wed, 31 Dec 2025 10:50:45 +0800
Subject: [PATCH] =?UTF-8?q?=E9=9D=9E=E6=A0=87=E3=80=81=E7=9C=81=E5=B8=82?=
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---
 .../non_standar_performance_to_BI.py          | 20 ++++++++++++++++++-
 .../province_city_person_relation_to_bi.py    |  4 +++-
 2 files changed, 22 insertions(+), 2 deletions(-)

diff --git a/back_ground_module/non_standar_performance_to_BI.py b/back_ground_module/non_standar_performance_to_BI.py
index a23e90c..157c1dd 100644
--- a/back_ground_module/non_standar_performance_to_BI.py
+++ b/back_ground_module/non_standar_performance_to_BI.py
@@ -155,7 +155,25 @@ class NonStandardPerformanceToBI:
             # 3. 去掉时区信息（变成 naive datetime），然后格式化为字符串
             df[col] = dt_beijing.dt.tz_localize(None).dt.strftime('%Y-%m-%d %H:%M:%S')
 
-        # 4.处理所有配置的列表字段
+        # 4.业绩动作等于拆单做复制
+
+        # 4.1. 定义条件
+        mask = df['业绩动作'] == '拆单'
+
+        # 4.2. 复制满足条件的行
+        new_rows = df[mask].copy()  # ⚠️ 一定要用 .copy() 避免 SettingWithCopyWarning
+
+        # 3. 修改新行中的某些列
+        new_rows['小六业绩金额'] = -new_rows['小六业绩金额']
+        new_rows['区域业绩金额'] = -new_rows['区域业绩金额']
+        new_rows['报备业绩归属小六'] = new_rows['原业绩归属人']
+        new_rows['报备业绩归属区域经理'] = new_rows['原业绩归属区域经理']
+        new_rows['报备业绩归属大区'] = new_rows['原业绩归属大区']
+
+        # 4. 合并回原 DataFrame
+        df = pd.concat([df, new_rows], ignore_index=True)
+
+        # 5.处理所有配置的列表字段
         if "新签阶段及提成比例" in df.columns:
             # 先处理订单登记表字段
             df["新签阶段及提成比例"] = df["新签阶段及提成比例"].apply(
diff --git a/back_ground_module/province_city_person_relation_to_bi.py b/back_ground_module/province_city_person_relation_to_bi.py
index 512c95a..5d36681 100644
--- a/back_ground_module/province_city_person_relation_to_bi.py
+++ b/back_ground_module/province_city_person_relation_to_bi.py
@@ -28,7 +28,6 @@ class ProvinceCityPersonRelationToBI:
         self.field_mapping = {
             "省": "_widget_1734677164861",
             "市": "_widget_1734677164862",
-            "区": "_widget_1734677164863",
             "运营顾问": "_widget_1734677164864",
             "区域经理": "_widget_1734677164865",
             "运营专家": "_widget_1734677164866",
@@ -60,6 +59,9 @@ class ProvinceCityPersonRelationToBI:
         for col in user_columns:
             df[col] = df[col].map(lambda x: x.get("name", "") if isinstance(x, dict) else "")
 
+        # 3.根据省市去重
+        df = df.drop_duplicates(subset=['省', '市'])
+
         return df
 
     def clear_table_data(self):