Add actual player data mapping in Contest Analysis tab of app.py

- Integrated 'actual_map' into the player frames by mapping player names to their corresponding actual data from the session state dictionary.
- Enhanced the player experience comparison by including actual performance information, improving the analysis capabilities within the Contest Analysis section.

app.py

@@ -374,6 +374,7 @@ if selected_tab == 'Data Load':
             'team_map': st.session_state['team_dict'],
             'salary_map': st.session_state['salary_dict'],
             'own_map': st.session_state['ownership_dict'],
+            'actual_map': st.session_state['actual_dict'],
             'own_percent_rank': dict(zip(st.session_state['ownership_df']['Player'], st.session_state['ownership_df']['Own'].rank(pct=True)))
         }
         
@@ -771,8 +772,10 @@ if selected_tab == 'Contest Analysis':
                     else:
                         hold_frame['Pos'] = hold_frame['Player'].map(st.session_state['map_dict']['pos_map'])
                     hold_frame['Salary'] = hold_frame['Player'].map(st.session_state['map_dict']['salary_map'])
+                    hold_frame['Actual'] = hold_frame['Player'].map(st.session_state['map_dict']['actual_map'])
                     player_exp_comp.insert(1, 'Pos', hold_frame['Pos'])
                     player_exp_comp.insert(2, 'Salary', hold_frame['Salary'])
+                    player_exp_comp.insert(3, 'Actual', hold_frame['Actual'])
                     player_exp_comp = player_exp_comp.dropna(subset=['Pos'])
                     if pos_select:
                         position_mask = player_exp_comp['Pos'].apply(lambda x: any(pos in x for pos in pos_select))
@@ -791,8 +794,10 @@ if selected_tab == 'Contest Analysis':
                         else:
                             hold_frame['Pos'] = hold_frame['Player'].map(st.session_state['map_dict']['pos_map'])
                         hold_frame['Salary'] = hold_frame['Player'].map(st.session_state['map_dict']['salary_map'])
+                        hold_frame['Actual'] = hold_frame['Player'].map(st.session_state['map_dict']['actual_map'])
                         st.session_state['player_frame'].insert(1, 'Pos', hold_frame['Pos'])
                         st.session_state['player_frame'].insert(2, 'Salary', hold_frame['Salary'])
+                        st.session_state['player_frame'].insert(3, 'Actual', hold_frame['Actual'])
                         st.session_state['player_frame'] = st.session_state['player_frame'].dropna(subset=['Pos'])
                         if pos_select:
                             position_mask = st.session_state['player_frame']['Pos'].apply(lambda x: any(pos in x for pos in pos_select))
@@ -800,7 +805,7 @@ if selected_tab == 'Contest Analysis':
                         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'].iloc[:, 2:].select_dtypes(include=['number']).columns),
+                            format(formatter='{:.2%}', subset=st.session_state['player_frame'].iloc[:, 4:].select_dtypes(include=['number']).columns),
                             hide_index=True)
                         st.download_button(label="Download Player Info", data=st.session_state['player_frame'].to_csv(index=False), file_name="player_info.csv", mime="text/csv", key='player_exp_comp_download')
                     else:
@@ -815,6 +820,8 @@ if selected_tab == 'Contest Analysis':
                         st.session_state['player_frame'].insert(1, 'Pos', hold_frame['Pos'])
                         hold_frame['Salary'] = hold_frame['Player'].map(st.session_state['map_dict']['salary_map'])
                         st.session_state['player_frame'].insert(2, 'Salary', hold_frame['Salary'])
+                        hold_frame['Actual'] = hold_frame['Player'].map(st.session_state['map_dict']['actual_map'])
+                        st.session_state['player_frame'].insert(3, 'Actual', hold_frame['Actual'])
                         st.session_state['player_frame'] = st.session_state['player_frame'].dropna(subset=['Pos'])
                         if pos_select:
                             position_mask = st.session_state['player_frame']['Pos'].apply(lambda x: any(pos in x for pos in pos_select))
@@ -822,7 +829,7 @@ if selected_tab == 'Contest Analysis':
                         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'].iloc[:, 2:].select_dtypes(include=['number']).columns),
+                            format(formatter='{:.2%}', subset=st.session_state['player_frame'].iloc[:, 4:].select_dtypes(include=['number']).columns),
                             hide_index=True)
                         st.download_button(label="Download Player Info", data=st.session_state['player_frame'].to_csv(index=False), file_name="player_info.csv", mime="text/csv", key='player_exp_comp_download')
             with tab2: