diff --git a/api/api_v1/endpoints/query.py b/api/api_v1/endpoints/query.py
index 5525531..b5fb180 100644
--- a/api/api_v1/endpoints/query.py
+++ b/api/api_v1/endpoints/query.py
@@ -136,11 +136,11 @@ async def event_model(
             #     df_group.reset_index(drop=True, inplace=True)
             q['groups'].append(groupby)
             q['values'].append(df['values'].to_list())
-            q['sum'].append(int(df['values'].sum()))
-            q['avg'].append(int(df['values'].mean()))
+            q['sum'].append(float(df['values'].sum()))
+            q['avg'].append(float(df['values'].mean()))
             for last_value in df['values'].values[::-1]:
                 if last_value > 0:
-                    q['last_value'] = int(last_value)
+                    q['last_value'] = float(last_value)
                     break
             if groupby:
                 q['date_range'] = [f'{i}' for i in df.set_index(groupby).index]
@@ -165,11 +165,11 @@ async def event_model(
                 df_group = pd.concat([df_group, pd.DataFrame(concat_data, columns=df_group.columns)])
                 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()))
+                q['sum'].append(float(df_group['values'].sum()))
+                q['avg'].append(float(df_group['values'].mean()))
                 for last_value in df['values'].values[::-1]:
                     if last_value > 0:
-                        q['last_value'] = int(last_value)
+                        q['last_value'] = float(last_value)
                         break
 
         else:
@@ -186,10 +186,10 @@ async def event_model(
             q['values'].append(df['values'].to_list())
             for last_value in df['values'].values[::-1]:
                 if last_value > 0:
-                    q['last_value'] = int(last_value)
+                    q['last_value'] = float(last_value)
                     break
-            q['sum'].append(int(df['values'].sum()))
-            q['avg'].append(round(df['values'].mean(), 2))
+            q['sum'].append(float(df['values'].sum()))
+            q['avg'].append(float(df['values'].mean()))
         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',):
@@ -239,6 +239,7 @@ async def retention_model(request: Request,
     days = [i for i in range(unit_num + 1)]
     summary_values = {}
     today = datetime.datetime.today().date()
+
     for date, value in df.T.items():
         tmp = summary_values.setdefault(date.strftime('%Y-%m-%d'), dict())
         tmp['d0'] = int(value.cnt0)