Refactor portfolio calculation logic in app.py: streamline salary, median, and ownership computations for Classic and Showdown types, ensuring accurate data handling and improved clarity in the code structure.

app.py

@@ -830,37 +830,8 @@ 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 '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':
+        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) + 
@@ -881,6 +852,34 @@ 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: