Update salary and actual fantasy points calculations in app.py to apply a 1.5x multiplier for the first player in each row. This change enhances the accuracy of player evaluations in the data processing workflow.

app.py

@@ -211,8 +211,18 @@ with tab2:
                 ).most_common(1)[0][1] if any(map_dict['team_map'].get(player, '') for player in row[2:]) else '',
                 axis=1
             )
-            working_df['salary'] = working_df.apply(lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row), axis=1)
-            working_df['actual_fpts'] = working_df.apply(lambda row: sum(st.session_state['actual_dict'].get(player, 0) for player in row), axis=1)
+            # Modified salary calculation with 1.5x multiplier for first player
+            working_df['salary'] = working_df.apply(
+                lambda row: (map_dict['salary_map'].get(row[2], 0) * 1.5) + 
+                           sum(map_dict['salary_map'].get(player, 0) for player in row[3:]),
+                axis=1
+            )
+            # Modified actual_fpts calculation with 1.5x multiplier for first player
+            working_df['actual_fpts'] = working_df.apply(
+                lambda row: (st.session_state['actual_dict'].get(row[2], 0) * 1.5) + 
+                           sum(st.session_state['actual_dict'].get(player, 0) for player in row[3:]),
+                axis=1
+            )
             working_df['actual_own'] = working_df.apply(lambda row: sum(st.session_state['ownership_dict'].get(player, 0) for player in row), axis=1)
             working_df['sorted'] = working_df[player_columns].apply(
                 lambda row: ','.join(sorted(row.values)),