Refactor column layout and update player data processing in app.py

- Adjusted the layout by reducing the number of columns from three to two for improved UI organization.
- Updated player data processing logic to reference 'actual_fpts' instead of 'actual', ensuring accurate calculations for player performance metrics.
- Enhanced overall data integrity by maintaining consistent column references across the application.

app.py

@@ -25,7 +25,7 @@ with tab1:
         st.session_state.clear()
     sport_select = st.selectbox("Select Sport", ['MLB', 'NBA', 'NFL'])
     # Add file uploaders to your app
-    col1, col2, col3 = st.columns(3)
+    col1, col2 = st.columns(2)
     
     with col1:
         st.subheader("Contest File")
@@ -188,10 +188,10 @@ with tab2:
         players_20per = set()
         for col in player_columns:
             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')
+            players_1per = working_df.nlargest(n=int(len(working_df) * 0.01), columns='actual_fpts')
+            players_5per = working_df.nlargest(n=int(len(working_df) * 0.05), columns='actual_fpts')
+            players_10per = working_df.nlargest(n=int(len(working_df) * 0.10), columns='actual_fpts')
+            players_20per = working_df.nlargest(n=int(len(working_df) * 0.20), columns='actual_fpts')
         player_counts = pd.Series(list(contest_players[player_columns].values.flatten())).value_counts()
         player_1per_counts = pd.Series(list(players_1per[player_columns].values.flatten())).value_counts()
         player_5per_counts = pd.Series(list(players_5per[player_columns].values.flatten())).value_counts()