Add team stacking logic in app.py: implement a new feature to determine the most common team for players in the portfolio, enhancing team analysis capabilities and improving user insights into player distributions.

app.py

@@ -6,6 +6,7 @@ import time
 from rapidfuzz import process, fuzz
 import random
 import re
+from collections import Counter
 
 ## import global functions
 from global_func.clean_player_name import clean_player_name
@@ -230,6 +231,15 @@ with tab1:
             projections['player_names'] = projections['player_names'].map(lambda x: projections_match_dict.get(x, x))
             st.session_state['projections_df'] = projections
 
+            team_dict = dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['team']))
+            st.session_state['portfolio']['stack'] = st.session_state['portfolio'].apply(
+                lambda row: Counter(
+                    team_dict.get(player, '') for player in row[2:]
+                    if team_dict.get(player, '') != ''
+                ).most_common(1)[0][0] if any(team_dict.get(player, '') for player in row[2:]) else '',
+                axis=1
+            )
+
             working_frame = st.session_state['portfolio'].copy()
             try:
                 st.session_state['export_dict'] = dict(zip(st.session_state['csv_file']['Name'], st.session_state['csv_file']['Name + ID']))