Add new stack exclusions and implement vectorized team calculation in app.py

- Expanded the list of excluded stacks to include 'P1' and 'P2' for improved data handling.
- Introduced a vectorized function to calculate the most common team from player data, enhancing performance and efficiency in team analysis.

app.py

@@ -199,7 +199,7 @@ with tab1:
 
 with tab2:
     excluded_cols = ['BaseName', 'EntryCount']
-    exclude_stacks = ['BaseName', 'EntryCount', 'SP', 'SP1', 'SP2']
+    exclude_stacks = ['BaseName', 'EntryCount', 'SP', 'SP1', 'SP2', 'P1', 'P2']
     if 'Contest' in st.session_state and 'display_contest_info' not in st.session_state:
         st.session_state['player_columns'] = [col for col in st.session_state['Contest'].columns if col not in excluded_cols]
         st.session_state['stack_columns'] = [col for col in st.session_state['Contest'].columns if col not in exclude_stacks]
@@ -292,6 +292,16 @@ with tab2:
             player_teams = working_df.iloc[:, 2:].apply(
                 lambda x: x.map(team_map).fillna('')
             )
+
+            # Vectorized stack and stack_size calculation
+            def get_most_common_team(teams):
+                if teams.empty or teams.isna().all():
+                    return '', 0
+                non_empty_teams = teams[teams != '']
+                if len(non_empty_teams) == 0:
+                    return '', 0
+                team_counts = non_empty_teams.value_counts()
+                return team_counts.index[0], team_counts.iloc[0]
             
             stack_results = player_teams.apply(get_most_common_team, axis=1)
             working_df['stack'] = [result[0] for result in stack_results]