修复empty

api/api_v1/endpoints/query.py

@@ -54,6 +54,8 @@ async def query_sql(
     sql = data_in.sql
     sql = sql.replace('$game', game)
     df = await ckdb.query_dataframe(sql)
+    if df.empty:
+        return schemas.Msg(code=-9, msg='无数据', data=None)
     df_to_stream = DfToStream((df, 'result'))
     with df_to_stream as d:
         export = d.to_stream()
@@ -93,6 +95,8 @@ async def event_model_export(request: Request,
         sql = item['sql']
         event_name = item['event_name']
         df = await ckdb.query_dataframe(sql)
+        if df.empty:
+            continue
         if 'date' in df:
             df.sort_values('date', inplace=True)
             try:
@@ -288,7 +292,7 @@ async def retention_model(request: Request,
     sql = res['sql']
     df = await ckdb.query_dataframe(sql)
     if df.empty:
-        return schemas.Msg(code=0, msg='无数据', data=None)
+        return schemas.Msg(code=-9, msg='无数据', data=None)
 
     date_range = res['date_range']
     unit_num = res['unit_num']
@@ -361,6 +365,8 @@ async def retention_model_export(request: Request,
 
     sql = data['sql']
     df = await ckdb.query_dataframe(sql)
+    if df.empty:
+        return schemas.Msg(code=-9, msg='无数据', data=None)
     df_to_stream = DfToStream((df, '留存分析'))
     with df_to_stream as d:
         export = d.to_stream()
@@ -386,6 +392,8 @@ async def retention_model_del(
     title = await crud.event_mana.get_show_name(db, game, event_a)
     title = f'{title}用户数'
     df = await ckdb.query_dataframe(sql)
+    if df.empty:
+        return schemas.Msg(code=-9, msg='无数据', data=None)
     concat_data = []
     df = pd.concat([df, pd.DataFrame(concat_data, columns=df.columns)])
     df['date'] = df['date'].apply(lambda x: x.date())
@@ -473,6 +481,8 @@ async def funnel_model(
     groupby = res['groupby']
 
     df = await ckdb.query_dataframe(sql)
+    if df.empty:
+        return schemas.Msg(code=-9, msg='无数据', data=None)
     # 补齐level数据
     concat_data = []
     for key, tmp_df in df.groupby(['date'] + groupby):
@@ -616,6 +626,8 @@ async def retention_model_export(request: Request,
     mime = mimetypes.guess_type(file_name)[0]
     sql = res['sql']
     df = await ckdb.query_dataframe(sql)
+    if df.empty:
+        return schemas.Msg(code=-9, msg='无数据', data=None)
     interval_type = res['interval_type']
     analysis = res['analysis']
     groupby = res['groupby']
@@ -734,6 +746,8 @@ async def scatter_model(
     res = await analysis.scatter_model_sql()
     sql = res['sql']
     df = await ckdb.query_dataframe(sql)
+    if df.empty:
+        return schemas.Msg(code=-9, msg='无数据', data=None)
     df.fillna(0, inplace=True)
     df['values'] = df['values'].astype(int)
     interval_type = res['interval_type']
@@ -858,6 +872,8 @@ async def trace_model_sql(
     res = await analysis.trace_model_sql()
     sql = res['sql']
     df = await ckdb.query_dataframe(sql)
+    if df.empty:
+        return schemas.Msg(code=-9, msg='无数据', data=None)
     chain_dict = defaultdict(dict)
     nodes = {'流失'}
     for event_names, count in zip(df['event_chain'], df['values']):
@@ -923,6 +939,8 @@ async def user_property_model_export(
 
     sql = data['sql']
     df = await ckdb.query_dataframe(sql)
+    if df.empty:
+        return schemas.Msg(code=-9, msg='无数据', data=None)
     df_to_stream = DfToStream((df, '用户属性'))
     with df_to_stream as d:
         export = d.to_stream()
@@ -944,12 +962,11 @@ async def user_property_model(
     quota = res['quota']
     groupby = res['groupby']
     df = await ckdb.query_dataframe(sql)
-    if len(df) == 0:
-        return schemas.Msg(code=0, msg='查无数据', data=None)
+    if df.empty:
+        return schemas.Msg(code=-9, msg='无数据', data=None)
 
     # 没有分组
-    data = {'groupby': groupby}
-    title = []
+    data = {}
 
     if not groupby:
         data['总体'] = int(df['values'][0])
@@ -957,7 +974,6 @@ async def user_property_model(
 
     else:
         sum_s = df.groupby(groupby)['values'].sum()
-        data = dict()
         for key, val in sum_s.items():
             if isinstance(key, tuple):
                 key = ','.join([str(i) for i in key])

api/api_v1/endpoints/xquery.py

@@ -54,7 +54,7 @@ async def ltv_model_sql(
     ltv_n = res['ltv_n']
     df = await ckdb.query_dataframe(sql)
     if df.empty:
-        return schemas.Msg(code=-1, msg='查无数据')
+        return schemas.Msg(code=-9, msg='查无数据')
     df.fillna(0, inplace=True)
     for d in set(res['date_range']) - set(df['date']):
         df.loc[len(df)] = 0
@@ -116,6 +116,8 @@ async def ltv_model_export(request: Request,
 
     sql = data['sql']
     df = await ckdb.query_dataframe(sql)
+    if df.empty:
+        return schemas.Msg(code=-9, msg='查无数据')
     df_to_stream = DfToStream((df, 'ltv'))
     with df_to_stream as d:
         export = d.to_stream()