Refactor export functionality in app.py: streamline portfolio download and merging processes, introduce export base for consolidated data management, and enhance display options for portfolio and export data.

app.py

@@ -985,7 +985,6 @@ with tab2:
                         st.session_state['portfolio'] = st.session_state['portfolio'].sort_values(by='median', ascending=False)
 
         with col2:
-            st.write('initiated')
             st.session_state['portfolio'] = predict_dupes(st.session_state['portfolio'], map_dict, site_var, type_var, Contest_Size, strength_var, sport_var)
             if 'trimming_dict_maxes' not in st.session_state:
                 st.session_state['trimming_dict_maxes'] = {
@@ -1096,53 +1095,73 @@ with tab2:
                                 st.write('Export portfolio updated!')
                 else:
                     st.session_state['export_file'] = st.session_state['portfolio'].copy()
+                    st.session_state['export_base'] = pd.DataFrame(columns=st.session_state['portfolio'].columns)
                     for col in st.session_state['export_file'].columns:
                                     if col not in excluded_cols:
                                         st.session_state['export_file'][col] = st.session_state['export_file'][col].map(st.session_state['export_dict'])
-            if 'export_file' in st.session_state:    
-                st.download_button(label="Download Portfolio", data=st.session_state['export_file'].to_csv(index=False), file_name="portfolio.csv", mime="text/csv")
+            if 'export_file' in st.session_state:
+                download_port, merge_port, blank_export_col = st.columns([1, 1, 8])
+                with download_port:
+                    st.download_button(label="Download Portfolio", data=st.session_state['export_file'].to_csv(index=False), file_name="portfolio.csv", mime="text/csv")
+                with merge_port:
+                    with st.button("Add to export"):
+                        st.session_state['export_base'] = pd.concat([st.session_state['export_base'], st.session_state['export_file']])
+                with blank_export_col:
+                    st.button("Clear export portfolio")
             else:
                 st.error("No portfolio to download")
             
-            # Add pagination controls below the dataframe
-            total_rows = len(st.session_state['portfolio'])
-            rows_per_page = 500
-            total_pages = (total_rows + rows_per_page - 1) // rows_per_page  # Ceiling division
+            display_frame_source = st.selectbox("Display:", options=['Portfolio', 'Export Base'], key='display_frame_source')
+            if display_frame_source == 'Portfolio':
+                # Add pagination controls below the dataframe
+                total_rows = len(st.session_state['portfolio'])
+                rows_per_page = 500
+                total_pages = (total_rows + rows_per_page - 1) // rows_per_page  # Ceiling division
 
-            # Initialize page number in session state if not exists
-            if 'current_page' not in st.session_state:
-                st.session_state.current_page = 1
+                # Initialize page number in session state if not exists
+                if 'current_page' not in st.session_state:
+                    st.session_state.current_page = 1
 
-            # Display current page range info and pagination control in a single line
-            st.write(
-                f"Showing rows {(st.session_state.current_page - 1) * rows_per_page + 1} "
-                f"to {min(st.session_state.current_page * rows_per_page, total_rows)} of {total_rows}"
-            )
-            
-            # Add page number input
-            st.session_state.current_page = st.number_input(
-                f"Page (1-{total_pages})", 
-                min_value=1, 
-                max_value=total_pages,
-                value=st.session_state.current_page
-            )
+                # Display current page range info and pagination control in a single line
+                st.write(
+                    f"Showing rows {(st.session_state.current_page - 1) * rows_per_page + 1} "
+                    f"to {min(st.session_state.current_page * rows_per_page, total_rows)} of {total_rows}"
+                )
+                
+                # Add page number input
+                st.session_state.current_page = st.number_input(
+                    f"Page (1-{total_pages})", 
+                    min_value=1, 
+                    max_value=total_pages,
+                    value=st.session_state.current_page
+                )
 
-            # Calculate start and end indices for current page
-            start_idx = (st.session_state.current_page - 1) * rows_per_page
-            end_idx = min(start_idx + rows_per_page, total_rows)
+                # Calculate start and end indices for current page
+                start_idx = (st.session_state.current_page - 1) * rows_per_page
+                end_idx = min(start_idx + rows_per_page, total_rows)
 
-            # Get the subset of data for the current page
-            current_page_data = st.session_state['portfolio'].iloc[start_idx:end_idx]
-            # Display the paginated dataframe first
-            st.dataframe(
-                current_page_data.style
-                .background_gradient(axis=0)
-                .background_gradient(cmap='RdYlGn')
-                .background_gradient(cmap='RdYlGn_r', subset=['Finish_percentile', 'Own', 'Dupes'])
-                .format(freq_format, precision=2), 
-                height=1000,
-                use_container_width=True
-            )
+                # Get the subset of data for the current page
+                current_page_data = st.session_state['portfolio'].iloc[start_idx:end_idx]
+                # Display the paginated dataframe first
+                st.dataframe(
+                    current_page_data.style
+                    .background_gradient(axis=0)
+                    .background_gradient(cmap='RdYlGn')
+                    .background_gradient(cmap='RdYlGn_r', subset=['Finish_percentile', 'Own', 'Dupes'])
+                    .format(freq_format, precision=2), 
+                    height=1000,
+                    use_container_width=True
+                )
+            elif display_frame_source == 'Export Base':
+                st.dataframe(
+                    st.session_state['export_base'].style
+                    .background_gradient(axis=0)
+                    .background_gradient(cmap='RdYlGn')
+                    .background_gradient(cmap='RdYlGn_r', subset=['Finish_percentile', 'Own', 'Dupes'])
+                    .format(freq_format, precision=2), 
+                    height=1000,
+                    use_container_width=True
+                )
             
         # Create player summary dataframe
         player_stats = []