Add player exposure metrics by percentile in app.py

- Implemented calculations for player exposure across various percentiles (Overall, Top 1%, Top 5%, Top 10%, Top 20%) to enhance performance analysis.
- Updated session state management to store and display player exposure data, improving the clarity and usability of player performance metrics in the application.

app.py

@@ -263,6 +263,36 @@ with tab2:
                         style.background_gradient(cmap='RdYlGn').
                         format(formatter='{:.2%}', subset=st.session_state['player_frame'].select_dtypes(include=['number']).columns),
                         hide_index=True)
+                else:
+                    overall_players = pd.Series(list(working_df[working_df['BaseName'].isin(entry_names)][player_columns].values.flatten())).value_counts()
+                    top_1per_players = pd.Series(list(working_df[working_df['percentile_finish'] <= 0.01][player_columns].values.flatten())).value_counts()
+                    top_5per_players = pd.Series(list(working_df[working_df['percentile_finish'] <= 0.05][player_columns].values.flatten())).value_counts()
+                    top_10per_players = pd.Series(list(working_df[working_df['percentile_finish'] <= 0.10][player_columns].values.flatten())).value_counts()
+                    top_20per_players = pd.Series(list(working_df[working_df['percentile_finish'] <= 0.20][player_columns].values.flatten())).value_counts()
+                    contest_len = len(working_df)
+                    len_1per = len(working_df[working_df['percentile_finish'] <= 0.01])
+                    len_5per = len(working_df[working_df['percentile_finish'] <= 0.05])
+                    len_10per = len(working_df[working_df['percentile_finish'] <= 0.10])
+                    len_20per = len(working_df[working_df['percentile_finish'] <= 0.20])
+                    each_set_name = ['Overall', ' Top 1%', ' Top 5%', 'Top 10%', 'Top 20%']
+                    each_frame_set = [overall_players, top_1per_players, top_5per_players, top_10per_players, top_20per_players]
+                    each_len_set = [contest_len, len_1per, len_5per, len_10per, len_20per]
+                    player_count_var = 0
+                    for each_set in each_frame_set:
+                        set_frame = each_set.to_frame().reset_index().rename(columns={'index': 'Player', 'count': 'Count'})
+                        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]}'})
+                        if 'player_frame' not in st.session_state:
+                            st.session_state['player_frame'] = set_frame
+                        else:
+                            st.session_state['player_frame'] = pd.merge(st.session_state['player_frame'], set_frame, on='Player', how='outer')
+                        player_count_var += 1
+                    st.dataframe(st.session_state['player_frame'].
+                        sort_values(by='Exposure Overall', ascending=False).
+                        style.background_gradient(cmap='RdYlGn').
+                        format(formatter='{:.2%}', subset=st.session_state['player_frame'].select_dtypes(include=['number']).columns),
+                        hide_index=True)
             with tab2:
                 st.write('holding')
             with tab3: