Enhance portfolio calculations in app.py: implement conditional logic for salary, median, and ownership calculations based on portfolio type and sport, ensuring accurate data handling for both Classic and Showdown formats.

app.py

@@ -830,9 +830,37 @@ with tab2:
                 'cpt_proj_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['median'] * 1.5)),
                 'cpt_own_map':dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership']))
             }
-        
-        if type_var == 'Classic':
-            if sport_var == 'CS2':
+        if 'portfolio' not in st.session_state:
+            if type_var == 'Classic':
+                if sport_var == 'CS2':
+                    # Calculate salary (CPT uses cpt_salary_map, others use salary_map)
+                    st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply(
+                        lambda row: map_dict['cpt_salary_map'].get(row.iloc[0], 0) + 
+                                sum(map_dict['salary_map'].get(player, 0) for player in row.iloc[1:]),
+                        axis=1
+                    )
+                    
+                    # Calculate median (CPT uses cpt_proj_map, others use proj_map)
+                    st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply(
+                        lambda row: map_dict['cpt_proj_map'].get(row.iloc[0], 0) + 
+                                sum(map_dict['proj_map'].get(player, 0) for player in row.iloc[1:]),
+                        axis=1
+                    )
+                    
+                    # Calculate ownership (CPT uses cpt_own_map, others use own_map)
+                    st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply(
+                        lambda row: map_dict['cpt_own_map'].get(row.iloc[0], 0) + 
+                                sum(map_dict['own_map'].get(player, 0) for player in row.iloc[1:]),
+                        axis=1
+                    )
+
+                elif sport_var != 'CS2':
+                    st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row), axis=1)
+                    st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row), axis=1)
+                    st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['own_map'].get(player, 0) for player in row), axis=1)
+                    if stack_dict is not None:
+                        st.session_state['portfolio']['Stack'] = st.session_state['portfolio'].index.map(stack_dict)
+            elif type_var == 'Showdown':
                 # Calculate salary (CPT uses cpt_salary_map, others use salary_map)
                 st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply(
                     lambda row: map_dict['cpt_salary_map'].get(row.iloc[0], 0) + 
@@ -853,34 +881,6 @@ with tab2:
                             sum(map_dict['own_map'].get(player, 0) for player in row.iloc[1:]),
                     axis=1
                 )
-
-            elif sport_var != 'CS2':
-                st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row), axis=1)
-                st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row), axis=1)
-                st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply(lambda row: sum(map_dict['own_map'].get(player, 0) for player in row), axis=1)
-                if stack_dict is not None:
-                    st.session_state['portfolio']['Stack'] = st.session_state['portfolio'].index.map(stack_dict)
-        elif type_var == 'Showdown':
-            # Calculate salary (CPT uses cpt_salary_map, others use salary_map)
-            st.session_state['portfolio']['salary'] = st.session_state['portfolio'].apply(
-                lambda row: map_dict['cpt_salary_map'].get(row.iloc[0], 0) + 
-                          sum(map_dict['salary_map'].get(player, 0) for player in row.iloc[1:]),
-                axis=1
-            )
-            
-            # Calculate median (CPT uses cpt_proj_map, others use proj_map)
-            st.session_state['portfolio']['median'] = st.session_state['portfolio'].apply(
-                lambda row: map_dict['cpt_proj_map'].get(row.iloc[0], 0) + 
-                          sum(map_dict['proj_map'].get(player, 0) for player in row.iloc[1:]),
-                axis=1
-            )
-            
-            # Calculate ownership (CPT uses cpt_own_map, others use own_map)
-            st.session_state['portfolio']['Own'] = st.session_state['portfolio'].apply(
-                lambda row: map_dict['cpt_own_map'].get(row.iloc[0], 0) + 
-                          sum(map_dict['own_map'].get(player, 0) for player in row.iloc[1:]),
-                axis=1
-            )
         col1, col2 = st.columns([2, 8])
         with col1:
             if 'trimming_dict_maxes' not in st.session_state:
@@ -1095,7 +1095,8 @@ 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)
+                    if 'export_base' not in st.session_state:
+                        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'])