Update app.py

app.py

@@ -720,6 +720,10 @@ with tab2:
                   raw_baselines = dk_roo_raw
               elif slate_var1 == 'Paydirt (Secondary)':
                   raw_baselines = dk_roo_raw_2
+        del dk_roo_raw
+        del dk_roo_raw_2
+        del fd_roo_raw
+        del fd_roo_raw_2
         st.info("If you are uploading a portfolio, note that there is an adjustments to projections and deviation mapping to prevent 'Projection Bias' and create a fair simulation")
         insert_port1 = st.selectbox("Are you uploading a portfolio?", ('No', 'Yes'))
         if insert_port1 == 'Yes':
@@ -1030,9 +1034,12 @@ with tab2:
                 Sim_Winner_Frame['Projection'] = Sim_Winner_Frame['Projection'].astype(np.float16)
                 Sim_Winner_Frame['Fantasy'] = Sim_Winner_Frame['Fantasy'].astype(np.float16)
                 Sim_Winner_Frame['GPP_Proj'] = Sim_Winner_Frame['GPP_Proj'].astype(np.float16)
-                Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by='GPP_Proj', ascending=False)
+                st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by='GPP_Proj', ascending=False)
+                st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
                 
-                player_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,0:6].values, return_counts=True)),
+                del Sim_Winner_Frame
+                
+                player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Frame.iloc[:,0:6].values, return_counts=True)),
                                             columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
                 player_freq['Freq'] = player_freq['Freq'].astype(int)
                 player_freq['Position'] = player_freq['Player'].map(maps_dict['Pos_map'])
@@ -1046,7 +1053,7 @@ with tab2:
 
                 player_freq = player_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
                 
-                cpt_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,0:1].values, return_counts=True)),
+                cpt_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Frame.iloc[:,0:1].values, return_counts=True)),
                                             columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
                 cpt_freq['Freq'] = cpt_freq['Freq'].astype(int)
                 cpt_freq['Position'] = cpt_freq['Player'].map(maps_dict['Pos_map'])
@@ -1060,7 +1067,7 @@ with tab2:
 
                 cpt_freq = cpt_freq[['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge']]
                 
-                flex_freq = pd.DataFrame(np.column_stack(np.unique(Sim_Winner_Frame.iloc[:,[1, 2, 3, 4, 5]].values, return_counts=True)),
+                flex_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.Sim_Winner_Frame.iloc[:,[1, 2, 3, 4, 5]].values, return_counts=True)),
                                             columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
                 flex_freq['Freq'] = flex_freq['Freq'].astype(int)
                 flex_freq['Position'] = flex_freq['Player'].map(maps_dict['Pos_map'])
@@ -1091,7 +1098,7 @@ with tab2:
                 del Sim_size
                 
             with st.container():
-                    st.dataframe(Sim_Winner_Frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Own']).format(precision=2), use_container_width = True)
+                    st.dataframe(st.session_state.Sim_Winner_Frame.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Own']).format(precision=2), use_container_width = True)
             
             with st.container():
                     tab1, tab2, tab3 = st.tabs(['Overall Exposures', 'CPT Exposures', 'FLEX Exposures'])
@@ -1122,7 +1129,7 @@ with tab2:
 
             st.download_button(
                 label="Export Tables",
-                data=convert_df_to_csv(Sim_Winner_Frame),
+                data=convert_df_to_csv(st.session_state.Sim_Winner_Frame_export),
                 file_name='NFL_consim_export.csv',
                 mime='text/csv',
-            )
+            )