Refactor player data handling in app.py

- Moved player selection logic to a new loop for improved clarity and organization, allowing for easier adjustments to player subsets based on contest size.
- Removed redundant code by consolidating player selection into a single section, enhancing maintainability and readability of the code.
- This change streamlines the data processing workflow, ensuring efficient calculations of player exposure percentages.

app.py

@@ -102,6 +102,12 @@ with tab2:
             'cpt_own_map': dict(zip(st.session_state['projections_df']['player_names'], st.session_state['projections_df']['captain ownership']))
         }
         # Create a copy of the dataframe for calculations
+        for col in player_columns:
+            contest_players = st.session_state['Contest'].copy()
+            players_1per = st.session_state['Contest'].head(int(len(st.session_state['Contest']) * 0.01))
+            players_5per = st.session_state['Contest'].head(int(len(st.session_state['Contest']) * 0.05))
+            players_10per = st.session_state['Contest'].head(int(len(st.session_state['Contest']) * 0.10))
+            players_20per = st.session_state['Contest'].head(int(len(st.session_state['Contest']) * 0.20))
         working_df = st.session_state['Contest'].copy()
 
         with col1:
@@ -226,12 +232,6 @@ 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))
         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()
@@ -255,7 +255,6 @@ 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(len(each_frame_set[player_count_var]))
                     set_frame['Percent'] = set_frame['Count'] / len(each_frame_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]}'})
@@ -273,7 +272,6 @@ 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(len(each_frame_set[stack_count_var]))
                     set_frame['Percent'] = set_frame['Count'] / len(each_frame_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]}'})