Refactor contest player metrics calculation in app.py

- Consolidated the logic for calculating contest player metrics by moving the initialization of player subsets and contest length calculations outside of the loop, improving code clarity and efficiency.
- This change enhances the maintainability of the code and ensures accurate calculations of player exposure percentages across different contest sizes.

app.py

@@ -226,17 +226,16 @@ with tab2:
             hide_index=True
         )
 
-        for col in player_columns:
-            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))
-            contest_len = len(st.session_state['Contest'])
-            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
+        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))
+        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
         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()
@@ -261,7 +260,7 @@ 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(len(each_frame_set[player_count_var]))
+                    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]}'})
@@ -279,7 +278,7 @@ 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(len(each_frame_set[stack_count_var]))
+                    st.write(each_len_set[player_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]}'})