Add detailed player exposure metrics display in app.py

- Implemented comprehensive calculations for player exposure across various percentiles (Overall, Top 1%, Top 5%, Top 10%, Top 20%) based on user selection.
- Enhanced session state management to dynamically store and display exposure data, improving the clarity and usability of player performance metrics.
- Updated the dataframe presentation with background gradients for better visual representation of exposure percentages.

app.py

@@ -297,6 +297,65 @@ with tab2:
                         format(formatter='{:.2%}', subset=st.session_state['player_frame'].select_dtypes(include=['number']).columns),
                         hide_index=True)
             with tab2:
-                st.write('holding')
+                if entry_parse_var == 'All':
+                    overall_stacks = pd.Series(list(working_df['stack'])).value_counts()
+                    top_1per_stacks = pd.Series(list(working_df[working_df['percentile_finish'] <= 0.01]['stack'])).value_counts()
+                    top_5per_stacks = pd.Series(list(working_df[working_df['percentile_finish'] <= 0.05]['stack'])).value_counts()
+                    top_10per_stacks = pd.Series(list(working_df[working_df['percentile_finish'] <= 0.10]['stack'])).value_counts()
+                    top_20per_stacks = pd.Series(list(working_df[working_df['percentile_finish'] <= 0.20]['stack'])).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_stacks, top_1per_stacks, top_5per_stacks, top_10per_stacks, top_20per_stacks]
+                    each_len_set = [contest_len, len_1per, len_5per, len_10per, len_20per]
+                    stack_count_var = 0
+                    for each_set in each_frame_set:
+                        set_frame = each_set.to_frame().reset_index().rename(columns={'index': 'Stack', 'count': 'Count'})
+                        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]}'})
+                        if 'stack_frame' not in st.session_state:
+                            st.session_state['stack_frame'] = set_frame
+                        else:
+                            st.session_state['stack_frame'] = pd.merge(st.session_state['stack_frame'], set_frame, on='Stack', how='outer')
+                        player_count_var += 1
+                    st.dataframe(st.session_state['stack_frame'].
+                        sort_values(by='Exposure Overall', ascending=False).
+                        style.background_gradient(cmap='RdYlGn').
+                        format(formatter='{:.2%}', subset=st.session_state['stack_frame'].select_dtypes(include=['number']).columns),
+                        hide_index=True)
+                else:
+                    overall_stacks = pd.Series(list(working_df[working_df['BaseName'].isin(entry_names)]['stack'])).value_counts()
+                    top_1per_stacks = pd.Series(list(working_df[working_df['percentile_finish'] <= 0.01]['stack'])).value_counts()
+                    top_5per_stacks = pd.Series(list(working_df[working_df['percentile_finish'] <= 0.05]['stack'])).value_counts()
+                    top_10per_stacks = pd.Series(list(working_df[working_df['percentile_finish'] <= 0.10]['stack'])).value_counts()
+                    top_20per_stacks = pd.Series(list(working_df[working_df['percentile_finish'] <= 0.20]['stack'])).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_stacks, top_1per_stacks, top_5per_stacks, top_10per_stacks, top_20per_stacks]
+                    each_len_set = [contest_len, len_1per, len_5per, len_10per, len_20per]
+                    stack_count_var = 0
+                    for each_set in each_frame_set:
+                        set_frame = each_set.to_frame().reset_index().rename(columns={'index': 'Stack', 'count': 'Count'})
+                        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]}'})
+                        if 'stack_frame' not in st.session_state:
+                            st.session_state['stack_frame'] = set_frame
+                        else:
+                            st.session_state['stack_frame'] = pd.merge(st.session_state['stack_frame'], set_frame, on='Stack', how='outer')
+                        stack_count_var += 1
+                    st.dataframe(st.session_state['stack_frame'].
+                        sort_values(by='Exposure Overall', ascending=False).
+                        style.background_gradient(cmap='RdYlGn').
+                        format(formatter='{:.2%}', subset=st.session_state['stack_frame'].select_dtypes(include=['number']).columns),
+                        hide_index=True)
             with tab3:
                 st.write('holding')