Refactor stack data processing and session state management in app.py

- Removed the 'stack_frame' from session state upon submission to ensure a clean state for stack selections.
- Introduced new logic to calculate and display stack exposure data across various performance thresholds, enhancing data representation.
- Streamlined variable naming for clarity and improved the overall organization of stack-related data processing.

app.py

@@ -114,6 +114,7 @@ with tab2:
                     if submitted:
                         if 'player_frame' in st.session_state:
                             del st.session_state['player_frame']
+                            del st.session_state['stack_frame']
                         # Apply entry name filter if specific entries are selected
                         if entry_parse_var == 'Specific' and entry_names:
                             working_df = working_df[working_df['BaseName'].isin(entry_names)]
@@ -193,33 +194,49 @@ with tab2:
         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()
+        stack_counts = pd.Series(list(contest_players['stack'])).value_counts()
+        stack_1per_counts = pd.Series(list(players_1per['stack'])).value_counts()
+        stack_5per_counts = pd.Series(list(players_5per['stack'])).value_counts()
+        stack_10per_counts = pd.Series(list(players_10per['stack'])).value_counts()
+        stack_20per_counts = pd.Series(list(players_20per['stack'])).value_counts()
         each_set_name = ['Overall', ' Top 1%', ' Top 5%', 'Top 10%', 'Top 20%']
         each_frame_set = [contest_players, players_1per, players_5per, players_10per, players_20per]
         with st.container():
             tab1, tab2 = st.tabs(['Player Used Info', 'Stack Used Info'])
             with tab1:
-                count_var = 0
+                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'})
-                    set_frame['Percent'] = set_frame['Count'] / len(each_frame_set[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[count_var]}'})
+                    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')
-                    count_var += 1
+                    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:
-                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', 'Percent']]
-                st.dataframe(stack_frame)
+                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'})
+                    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]}'})
+                    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)
 
 
         # Initialize pagination in session state if not exists