Refactor combined frame creation in 'Manage Portfolio' to exclude unnecessary columns, enhancing data processing efficiency and consistency.

app.py

@@ -1619,7 +1619,7 @@ if selected_tab == 'Manage Portfolio':
                         num_modified_rows = len(st.session_state['working_frame'])
                         
                         # Concatenate the modified frame with the base frame
-                        combined_frame = pd.concat([st.session_state['working_frame'], st.session_state['base_frame']], ignore_index=True)
+                        combined_frame = pd.concat([st.session_state['working_frame'].drop(columns=['Dupes', 'Finish_percentile', 'Lineup Edge', 'Win%', 'Weighted Own', 'Geomean', 'Diversity']), st.session_state['base_frame'].drop(columns=['Dupes', 'Finish_percentile', 'Lineup Edge', 'Win%', 'Weighted Own', 'Geomean', 'Diversity'])], ignore_index=True)
                         
                         # Run predict_dupes on the combined frame
                         updated_combined_frame = predict_dupes(combined_frame, st.session_state['map_dict'], site_var, type_var, Contest_Size, strength_var, sport_var, salary_max)
@@ -1726,7 +1726,7 @@ if selected_tab == 'Manage Portfolio':
                         print(num_modified_rows)
                         
                         # Concatenate the modified frame with the base frame
-                        combined_frame = pd.concat([st.session_state['export_base'], st.session_state['base_frame']], ignore_index=True)
+                        combined_frame = pd.concat([st.session_state['export_base'].drop(columns=['Dupes', 'Finish_percentile', 'Lineup Edge', 'Win%', 'Weighted Own', 'Geomean', 'Diversity']), st.session_state['base_frame'].drop(columns=['Dupes', 'Finish_percentile', 'Lineup Edge', 'Win%', 'Weighted Own', 'Geomean', 'Diversity'])], ignore_index=True)
                         print(len(combined_frame))
                         
                         # Run predict_dupes on the combined frame