From b2ffc0a8b4a4f5704d534cb0d82122f540a94312 Mon Sep 17 00:00:00 2001
From: wuaho <wuaho@legu.cc>
Date: Fri, 27 Aug 2021 16:23:20 +0800
Subject: [PATCH] 1

---
 api/api_v1/endpoints/query.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/api/api_v1/endpoints/query.py b/api/api_v1/endpoints/query.py
index c9bc587..d93943b 100644
--- a/api/api_v1/endpoints/query.py
+++ b/api/api_v1/endpoints/query.py
@@ -60,7 +60,7 @@ async def event_model_export(request: Request,
     """ 事件分析模型 数据导出"""
     await analysis.init()
     sqls = analysis.event_model_sql()
-    file_name =  quote(f'{sqls[0]["report_name"]}.xlsx')
+    file_name = quote(f'{sqls[0]["report_name"]}.xlsx')
     mime = mimetypes.guess_type(file_name)[0]
     excels = []
     for item in sqls:
@@ -117,6 +117,7 @@ async def event_model(
             'groups': [],
             'values': [],
             'sum': [],
+            'avg': [],
             'event_name': item['event_name'],
             'format': item['format']
         }
@@ -135,10 +136,11 @@ async def event_model(
             q['groups'].append(groupby)
             q['values'].append(df['values'].to_list())
             q['sum'].append(int(df['values'].sum()))
+            q['avg'].append(round(df['values'].mean(), 2))
             if groupby:
                 q['date_range'] = [f'{i}' for i in df.set_index(groupby).index]
             else:
-                q['date_range'] =['合计']
+                q['date_range'] = ['合计']
 
             res.append(q)
             continue
@@ -159,6 +161,7 @@ async def event_model(
                 df_group.sort_values('date', inplace=True)
                 q['values'].append(df_group['values'].to_list())
                 q['sum'].append(int(df_group['values'].sum()))
+                q['avg'].append(int(df_group['values'].mean()))
 
         else:
             #     无分组
@@ -168,11 +171,12 @@ async def event_model(
             df = pd.concat([df, pd.DataFrame(concat_data, columns=df.columns)])
             df.sort_values('date', inplace=True)
             if len(df) >= 2:
-                q['chain_ratio'] = division((df.iloc[-1, 1] - df.iloc[-2, 1]) * 100 , df.iloc[-2, 1], 2)
+                q['chain_ratio'] = division((df.iloc[-1, 1] - df.iloc[-2, 1]) * 100, df.iloc[-2, 1], 2)
             if len(df) >= 8:
                 q['wow'] = division((df.iloc[-1, 1] - df.iloc[-8, 1]) * 100 / df.iloc[-8, 1], 2) or 0
             q['values'].append(df['values'].to_list())
             q['sum'].append(int(df['values'].sum()))
+            q['avg'].append(round(df['values'].mean(), 2))
         if item['time_particle'] in ('P1D', 'P1W'):
             q['date_range'] = [d.strftime('%Y-%m-%d') for d in q['date_range']]
         elif item['time_particle'] in ('P1M',):