Refactor player counts display logic in app.py for enhanced data aggregation

- Consolidated player counts for various percentage thresholds into a single dataframe for improved clarity and efficiency in presentation.
- Streamlined the logic for displaying player and stack counts, ensuring consistent data representation across the application.
- Enhanced user interaction by maintaining player data in session state, allowing for better data management and visibility.

app.py

@@ -189,18 +189,25 @@ with tab2:
             players_5per = working_df.nlargest(n=int(len(working_df) * 0.05), columns='actual')
             players_10per = working_df.nlargest(n=int(len(working_df) * 0.10), columns='actual')
             players_20per = working_df.nlargest(n=int(len(working_df) * 0.20), columns='actual')
+        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()
+        player_10per_counts = pd.Series(list(players_10per[player_columns].values.flatten())).value_counts()
+        player20_per_counts = pd.Series(list(players_20per[player_columns].values.flatten())).value_counts()
         with st.container():
             tab1, tab2 = st.tabs(['Player Used Info', 'Stack Used Info'])
             with tab1:
-                player_counts = pd.Series(list(contest_players[player_columns].values.flatten())).value_counts()
-                st.write(player_counts)
-                player_frame = player_counts.to_frame().reset_index().rename(columns={'index': 'Player', 'count': 'Count'})
-                player_frame['Percent'] = player_frame['Count'] / len(working_df)
-                player_frame = player_frame[['Player', 'Count', 'Percent']]
-                st.dataframe(player_frame)
+                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'})
+                    set_frame['Percent'] = set_frame['Count'] / len(working_df)
+                    set_frame = set_frame[['Player', 'Count', 'Percent']]
+                    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')
+                st.dataframe(st.session_state['player_frame'])
             with tab2:
-                stack_counts = pd.Series(list(working_df['stack'].unique())).value_counts()
-                st.write(stack_counts)
+                stack_counts = pd.Series(list(working_df['stack'])).value_counts()
                 stack_frame = stack_counts.to_frame().reset_index().rename(columns={'index': 'Stack', 'count': 'Count'})
                 stack_frame['Percent'] = stack_frame['Count'] / len(working_df)
                 stack_frame = stack_frame[['Stack', 'Count', 'Percent']]