Update pages/1_Store Demand Forecasting.py

pages/1_Store Demand Forecasting.py

@@ -177,7 +177,7 @@ if option=='TFT':
            
     #--------------------------------------------------------------------------------------------------------------
         # tabs
-        tab1,tab2,tab3=st.tabs(['📈Forecast Plot','🗃Forecast Table','🗃Actual Table'])
+        tab1,tab2,tab3=st.tabs(['📈Forecast Plot','🗃Forecast Table','🗃Actual Table']) #
     #------------------------------------------------Tab-1-----------------------------------------------------------
         tab1.markdown("""
                         <div style='text-align: left; margin-top:-10px;margin-bottom:-10px;'>
@@ -397,7 +397,7 @@ elif option=='Prophet':
             st.dataframe(pd.DataFrame({"KPI":['RMSE','MAE'],"Prophet":[rmse,mae]}).set_index('KPI'),width=300)
        
         #---------------------------------------Tabs-----------------------------------------------------------------------
-        tab1,tab2,tab3=st.tabs(['📈Forecast Plot','🗃Forecast Table','🗃Actual Table'])
+        tab1,tab2,tab3=st.tabs(['📈Forecast Plot','🗃Forecast Table','🗃Actual Table']) # '🗃Actual Table'
         #-------------------------------------------Tab-1=Forecast plot---------------------------------------------------
         tab1.markdown("""
         <div style='text-align: left; margin-top:-10px;margin-bottom:-10px;'>
@@ -477,17 +477,18 @@ elif option=='Prophet':
                             )
 
     #------------------------------------------Tab-3--------------------------------------------------
-        train_a=fb_train_data.loc[fb_train_data[item]==1][['ds','y']]
-        train_a['store']=1
-        train_a['item']=item
-        train_a.rename({'y':'sales'})
-        test_a=fb_test_data.loc[fb_test_data[item]==1][['ds','y']]
-        test_a['store']=1
-        test_a['item']=item
-        test_a.rename({'y':'sales',"ds":'date'})
+        train_a=fb_train_data.loc[fb_train_data[item]==1][['ds','sales']]
+        # train_a['store']=1
+        # train_a['item']=item
+        test_a=fb_test_data.loc[fb_test_data[item]==1][['ds','sales']]
+        # test_a['store']=1
+        # test_a['item']=item.split('_')[-1]
         actual_final_data=pd.concat([train_a,test_a])
+        actual_final_data['store']=1
+        actual_final_data['item']=item.split('_')[-1]
         actual_final_data['ds']=actual_final_data['ds'].dt.date
-        tab3.dataframe(actual_final_data,width=500)
+        actual_final_data.rename({"ds":'date'},inplace=True)
+        tab3.dataframe(actual_final_data[['date','store','item','sales']],width=500)