Add Showdown contest type handling in app.py for enhanced player data processing

- Implemented logic to calculate stack, stack size, salary, actual fantasy points, and ownership for 'Showdown' contest type in working_df.
- Enhanced data organization by introducing new columns and resetting the index for better clarity in player exposure data.
- Maintained existing functionality while improving the handling of player data specific to the Showdown format.

app.py

@@ -188,6 +188,34 @@ with tab2:
             working_df['percentile_finish'] = working_df['index'].rank(pct=True)
             working_df['finish'] = working_df['index']
             working_df = working_df.drop(['sorted', 'index'], axis=1)
+        
+        elif type_var == 'Showdown':
+            working_df['stack'] = working_df.apply(
+                lambda row: Counter(
+                    map_dict['team_map'].get(player, '') for player in row[2:]
+                    if map_dict['team_map'].get(player, '') != ''
+                ).most_common(1)[0][0] if any(map_dict['team_map'].get(player, '') for player in row[2:]) else '',
+                axis=1
+            )
+            working_df['stack_size'] = working_df.apply(
+                lambda row: Counter(
+                    map_dict['team_map'].get(player, '') for player in row[2:]
+                    if map_dict['team_map'].get(player, '') != ''
+                ).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)
+            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)),
+                axis=1
+            )
+            working_df['dupes'] = working_df.groupby('sorted').transform('size')
+            working_df = working_df.reset_index()
+            working_df['percentile_finish'] = working_df['index'].rank(pct=True)
+            working_df['finish'] = working_df['index']
+            working_df = working_df.drop(['sorted', 'index'], axis=1)
         st.session_state['field_player_frame'] = create_player_exposures(working_df, player_columns)
         st.session_state['field_stack_frame'] = create_stack_exposures(working_df)