Refactor player statistics calculation in app.py: update references from working_frame to display_frame for improved clarity and consistency in player data processing, and add logic to drop duplicate entries in the export base upon addition.

app.py

@@ -1086,6 +1086,7 @@ with tab2:
                 with merge_port:
                     if st.button("Add to export"):
                         st.session_state['export_base'] = pd.concat([st.session_state['export_base'], st.session_state['export_merge']])
+                        st.session_state['export_base'] = st.session_state['export_base'].drop_duplicates()
             else:
                 st.error("No portfolio to download")
             
@@ -1135,26 +1136,26 @@ with tab2:
             
         # Create player summary dataframe
         player_stats = []
-        player_columns = [col for col in st.session_state['working_frame'].columns if col not in excluded_cols]
+        player_columns = [col for col in display_frame.columns if col not in excluded_cols]
         
         if type_var == 'Showdown':
             for player in player_names:
                 # Create mask for lineups where this player is Captain (first column)
-                cpt_mask = st.session_state['working_frame'][player_columns[0]] == player
+                cpt_mask = display_frame[player_columns[0]] == player
                 
                 if cpt_mask.any():
                     player_stats.append({
                         'Player': f"{player} (CPT)",
                         'Lineup Count': cpt_mask.sum(),
-                        'Avg Median': st.session_state['working_frame'][cpt_mask]['median'].mean(),
-                        'Avg Own': st.session_state['working_frame'][cpt_mask]['Own'].mean(),
-                        'Avg Dupes': st.session_state['working_frame'][cpt_mask]['Dupes'].mean(),
-                        'Avg Finish %': st.session_state['working_frame'][cpt_mask]['Finish_percentile'].mean(),
-                        'Avg Lineup Edge': st.session_state['working_frame'][cpt_mask]['Lineup Edge'].mean(),
+                        'Avg Median': display_frame[cpt_mask]['median'].mean(),
+                        'Avg Own': display_frame[cpt_mask]['Own'].mean(),
+                        'Avg Dupes': display_frame[cpt_mask]['Dupes'].mean(),
+                        'Avg Finish %': display_frame[cpt_mask]['Finish_percentile'].mean(),
+                        'Avg Lineup Edge': display_frame[cpt_mask]['Lineup Edge'].mean(),
                     })
                 
                 # Create mask for lineups where this player is FLEX (other columns)
-                flex_mask = st.session_state['working_frame'][player_columns[1:]].apply(
+                flex_mask = display_frame[player_columns[1:]].apply(
                     lambda row: player in list(row), axis=1
                 )
                 
@@ -1162,32 +1163,32 @@ with tab2:
                     player_stats.append({
                         'Player': f"{player} (FLEX)",
                         'Lineup Count': flex_mask.sum(),
-                        'Avg Median': st.session_state['working_frame'][flex_mask]['median'].mean(),
-                        'Avg Own': st.session_state['working_frame'][flex_mask]['Own'].mean(),
-                        'Avg Dupes': st.session_state['working_frame'][flex_mask]['Dupes'].mean(),
-                        'Avg Finish %': st.session_state['working_frame'][flex_mask]['Finish_percentile'].mean(),
-                        'Avg Lineup Edge': st.session_state['working_frame'][flex_mask]['Lineup Edge'].mean(),
+                        'Avg Median': display_frame[flex_mask]['median'].mean(),
+                        'Avg Own': display_frame[flex_mask]['Own'].mean(),
+                        'Avg Dupes': display_frame[flex_mask]['Dupes'].mean(),
+                        'Avg Finish %': display_frame[flex_mask]['Finish_percentile'].mean(),
+                        'Avg Lineup Edge': display_frame[flex_mask]['Lineup Edge'].mean(),
                     })
         else:
             if sport_var == 'CS2':
                 # Handle Captain positions
                 for player in player_names:
                     # Create mask for lineups where this player is Captain (first column)
-                    cpt_mask = st.session_state['working_frame'][player_columns[0]] == player
+                    cpt_mask = display_frame[player_columns[0]] == player
                     
                     if cpt_mask.any():
                         player_stats.append({
                             'Player': f"{player} (CPT)",
                             'Lineup Count': cpt_mask.sum(),
-                            'Avg Median': st.session_state['working_frame'][cpt_mask]['median'].mean(),
-                            'Avg Own': st.session_state['working_frame'][cpt_mask]['Own'].mean(),
-                            'Avg Dupes': st.session_state['working_frame'][cpt_mask]['Dupes'].mean(),
-                            'Avg Finish %': st.session_state['working_frame'][cpt_mask]['Finish_percentile'].mean(),
-                            'Avg Lineup Edge': st.session_state['working_frame'][cpt_mask]['Lineup Edge'].mean(),
+                            'Avg Median': display_frame[cpt_mask]['median'].mean(),
+                            'Avg Own': display_frame[cpt_mask]['Own'].mean(),
+                            'Avg Dupes': display_frame[cpt_mask]['Dupes'].mean(),
+                            'Avg Finish %': display_frame[cpt_mask]['Finish_percentile'].mean(),
+                            'Avg Lineup Edge': display_frame[cpt_mask]['Lineup Edge'].mean(),
                         })
                     
                     # Create mask for lineups where this player is FLEX (other columns)
-                    flex_mask = st.session_state['working_frame'][player_columns[1:]].apply(
+                    flex_mask = display_frame[player_columns[1:]].apply(
                         lambda row: player in list(row), axis=1
                     )
                     
@@ -1195,16 +1196,16 @@ with tab2:
                         player_stats.append({
                             'Player': f"{player} (FLEX)",
                             'Lineup Count': flex_mask.sum(),
-                            'Avg Median': st.session_state['working_frame'][flex_mask]['median'].mean(),
-                            'Avg Own': st.session_state['working_frame'][flex_mask]['Own'].mean(),
-                            'Avg Dupes': st.session_state['working_frame'][flex_mask]['Dupes'].mean(),
-                            'Avg Finish %': st.session_state['working_frame'][flex_mask]['Finish_percentile'].mean(),
-                            'Avg Lineup Edge': st.session_state['working_frame'][flex_mask]['Lineup Edge'].mean(),
+                            'Avg Median': display_frame[flex_mask]['median'].mean(),
+                            'Avg Own': display_frame[flex_mask]['Own'].mean(),
+                            'Avg Dupes': display_frame[flex_mask]['Dupes'].mean(),
+                            'Avg Finish %': display_frame[flex_mask]['Finish_percentile'].mean(),
+                            'Avg Lineup Edge': display_frame[flex_mask]['Lineup Edge'].mean(),
                         })
             elif sport_var != 'CS2':
                 # Original Classic format processing
                 for player in player_names:
-                    player_mask = st.session_state['working_frame'][player_columns].apply(
+                    player_mask = display_frame[player_columns].apply(
                         lambda row: player in list(row), axis=1
                     )
                     
@@ -1212,11 +1213,11 @@ with tab2:
                         player_stats.append({
                             'Player': player,
                             'Lineup Count': player_mask.sum(),
-                            'Avg Median': st.session_state['working_frame'][player_mask]['median'].mean(),
-                            'Avg Own': st.session_state['working_frame'][player_mask]['Own'].mean(),
-                            'Avg Dupes': st.session_state['working_frame'][player_mask]['Dupes'].mean(),
-                            'Avg Finish %': st.session_state['working_frame'][player_mask]['Finish_percentile'].mean(),
-                            'Avg Lineup Edge': st.session_state['working_frame'][player_mask]['Lineup Edge'].mean(),
+                            'Avg Median': display_frame[player_mask]['median'].mean(),
+                            'Avg Own': display_frame[player_mask]['Own'].mean(),
+                            'Avg Dupes': display_frame[player_mask]['Dupes'].mean(),
+                            'Avg Finish %': display_frame[player_mask]['Finish_percentile'].mean(),
+                            'Avg Lineup Edge': display_frame[player_mask]['Lineup Edge'].mean(),
                         })
         
         player_summary = pd.DataFrame(player_stats)