Refactor player subset calculations in app.py

- Updated the logic for calculating player subsets based on percentile finishes, replacing head-based selection with conditional filtering for improved accuracy.
- Adjusted the calculation of lengths for each player subset to directly reference the newly defined subsets, enhancing clarity and reliability in metrics computation.

app.py

@@ -231,15 +231,15 @@ with tab2:
         )
 
         contest_players = working_df.copy()
-        players_1per = working_df.head(int(len(working_df) * 0.01))
-        players_5per = working_df.head(int(len(working_df) * 0.05))
-        players_10per = working_df.head(int(len(working_df) * 0.10))
-        players_20per = working_df.head(int(len(working_df) * 0.20))
+        players_1per = working_df[working_df['percentile_finish'] <= 0.01]
+        players_5per = working_df[working_df['percentile_finish'] <= 0.05]
+        players_10per = working_df[working_df['percentile_finish'] <= 0.10]
+        players_20per = working_df[working_df['percentile_finish'] <= 0.20]
         contest_len = len(contest_players)
-        len_1per = len(st.session_state['Contest']) * 0.01
-        len_5per = len(st.session_state['Contest']) * 0.05
-        len_10per = len(st.session_state['Contest']) * 0.10
-        len_20per = len(st.session_state['Contest']) * 0.20
+        len_1per = len(players_1per)
+        len_5per = len(players_5per)
+        len_10per = len(players_10per)
+        len_20per = len(players_20per)
         ######## Going to try a groupby based on finishing percentiles here next
         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()
@@ -265,7 +265,6 @@ with tab2:
                 player_count_var = 0
                 for each_set in [player_counts, player_1per_counts, player_5per_counts, player_10per_counts, player20_per_counts]:
                     set_frame = each_set.to_frame().reset_index().rename(columns={'index': 'Player', 'count': 'Count'})
-                    st.write(each_len_set[player_count_var])
                     set_frame['Percent'] = set_frame['Count'] / each_len_set[player_count_var]
                     set_frame = set_frame[['Player', 'Percent']]
                     set_frame = set_frame.rename(columns={'Percent': f'Exposure {each_set_name[player_count_var]}'})
@@ -283,7 +282,6 @@ with tab2:
                 stack_count_var = 0
                 for each_set in [stack_counts, stack_1per_counts, stack_5per_counts, stack_10per_counts, stack_20per_counts]:
                     set_frame = each_set.to_frame().reset_index().rename(columns={'index': 'Stack', 'count': 'Count'})
-                    st.write(each_len_set[stack_count_var])
                     set_frame['Percent'] = set_frame['Count'] / each_len_set[stack_count_var]
                     set_frame = set_frame[['Stack', 'Percent']]
                     set_frame = set_frame.rename(columns={'Percent': f'Exposure {each_set_name[stack_count_var]}'})