Refactor salary, median, and ownership calculations in app.py to handle GOLF sport variable separately, improving clarity and accuracy in data processing.

app.py

@@ -1581,26 +1581,31 @@ with tab2:
                                             axis=1
                                         )
                         elif type_var == 'Showdown':
-                            # Calculate salary (CPT uses cpt_salary_map, others use salary_map)
-                            parsed_frame['salary'] = parsed_frame.apply(
-                                lambda row: st.session_state['map_dict']['cpt_salary_map'].get(row.iloc[0], 0) + 
-                                        sum(st.session_state['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)
-                            parsed_frame['median'] = parsed_frame.apply(
-                                lambda row: st.session_state['map_dict']['cpt_proj_map'].get(row.iloc[0], 0) + 
-                                        sum(st.session_state['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)
-                            parsed_frame['Own'] = parsed_frame.apply(
-                                lambda row: st.session_state['map_dict']['cpt_own_map'].get(row.iloc[0], 0) + 
-                                        sum(st.session_state['map_dict']['own_map'].get(player, 0) for player in row.iloc[1:]),
-                                axis=1
-                            )
+                            if sport_var == 'GOLF':
+                                
+                                parsed_frame['salary'] = parsed_frame.apply(lambda row: sum(st.session_state['map_dict']['salary_map'].get(player, 0) for player in row), axis=1)
+                                parsed_frame['median'] = parsed_frame.apply(lambda row: sum(st.session_state['map_dict']['proj_map'].get(player, 0) for player in row), axis=1)
+                                parsed_frame['Own'] = parsed_frame.apply(lambda row: sum(st.session_state['map_dict']['own_map'].get(player, 0) for player in row), axis=1)
+                            else:
+                                parsed_frame['salary'] = parsed_frame.apply(
+                                    lambda row: st.session_state['map_dict']['cpt_salary_map'].get(row.iloc[0], 0) + 
+                                            sum(st.session_state['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)
+                                parsed_frame['median'] = parsed_frame.apply(
+                                    lambda row: st.session_state['map_dict']['cpt_proj_map'].get(row.iloc[0], 0) + 
+                                            sum(st.session_state['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)
+                                parsed_frame['Own'] = parsed_frame.apply(
+                                    lambda row: st.session_state['map_dict']['cpt_own_map'].get(row.iloc[0], 0) + 
+                                            sum(st.session_state['map_dict']['own_map'].get(player, 0) for player in row.iloc[1:]),
+                                    axis=1
+                                )
                         st.session_state['export_base'] = parsed_frame.reset_index(drop=True)
                         # st.session_state['export_base']['Own'] = st.session_state['export_base']['Own'].astype('float32')
                         st.session_state['export_base']['median'] = st.session_state['export_base']['median'].astype('float32')