Refactor player data calculations in app.py for improved clarity and efficiency

- Updated the logic for calculating player usage by creating copies of the working dataframe for contest players and using nlargest for percentage-based player selection.
- Enhanced the player counts calculation to flatten the player columns, ensuring accurate representation of player usage in the display.
- Streamlined the code for better readability and maintainability, contributing to overall application performance.

app.py

@@ -184,15 +184,15 @@ with tab2:
         players_10per = set()
         players_20per = set()
         for col in player_columns:
-            contest_players.update(working_df[col].unique())
-            players_1per.update(working_df.nlargest(n=int(len(working_df) * 0.01), columns='actual')[col].unique())
-            players_5per.update(working_df.nlargest(n=int(len(working_df) * 0.05), columns='actual')[col].unique())
-            players_10per.update(working_df.nlargest(n=int(len(working_df) * 0.10), columns='actual')[col].unique())
-            players_20per.update(working_df.nlargest(n=int(len(working_df) * 0.20), columns='actual')[col].unique())
+            contest_players = working_df.copy()
+            players_1per = working_df.nlargest(n=int(len(working_df) * 0.01), columns='actual')
+            players_5per = working_df.nlargest(n=int(len(working_df) * 0.05), columns='actual')
+            players_10per = working_df.nlargest(n=int(len(working_df) * 0.10), columns='actual')
+            players_20per = working_df.nlargest(n=int(len(working_df) * 0.20), columns='actual')
         with st.container():
             tab1, tab2 = st.tabs(['Player Used Info', 'Stack Used Info'])
             with tab1:
-                player_counts = pd.Series(list(contest_players)).value_counts()
+                player_counts = pd.Series(list(contest_players[player_columns].values.flatten())).value_counts()
                 st.write(player_counts)
                 player_frame = player_counts.to_frame().reset_index().rename(columns={'index': 'Player', 0: 'Count'})
                 player_frame['Percent'] = player_frame['Count'] / len(working_df)