Add stack comparison feature in app.py and create_stack_comparison.py

- Introduced a new function, create_stack_comparison, to facilitate the comparison of stack exposures based on user selections.
- Updated app.py to include a form for selecting stacks to compare, enhancing user interaction and data analysis capabilities.
- Improved player exposure comparison by adding position filtering logic and ensuring accurate data representation in the displayed DataFrame.

app.py

@@ -64,6 +64,7 @@ from global_func.create_stack_size_exposures import create_stack_size_exposures
 from global_func.create_general_exposures import create_general_exposures
 from global_func.grab_contest_data import grab_contest_data
 from global_func.create_player_comparison import create_player_comparison
+from global_func.create_stack_comparison import create_stack_comparison
 
 def is_valid_input(file):
     if isinstance(file, pd.DataFrame):
@@ -438,6 +439,16 @@ with tab2:
                                 comp_player_select = comp_player_select
                 if comp_player_var == 'Yes':
                     player_exp_comp = create_player_comparison(st.session_state['display_contest_info'], st.session_state['player_columns'], comp_player_select)
+                    hold_frame = player_exp_comp.copy()
+                    if sport_select == 'GOLF':
+                        hold_frame['Pos'] = 'G'
+                    else:
+                        hold_frame['Pos'] = hold_frame['Player'].map(st.session_state['map_dict']['pos_map'])
+                    player_exp_comp.insert(1, 'Pos', hold_frame['Pos'])
+                    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))
+                        player_exp_comp = player_exp_comp[position_mask]
                     st.dataframe(player_exp_comp.style.background_gradient(cmap='RdYlGn', axis=0).format(formatter='{:.2%}', subset=player_exp_comp.select_dtypes(include=['number']).columns), hide_index=True)
                 else:
                     if st.session_state['entry_parse_var'] == 'All':
@@ -476,21 +487,36 @@ with tab2:
                             format(formatter='{:.2%}', subset=st.session_state['player_frame'].iloc[:, 2:].select_dtypes(include=['number']).columns),
                             hide_index=True)
             with tab2:
-
-                if st.session_state['entry_parse_var'] == 'All':
-                    st.session_state['stack_frame'] = create_stack_exposures(st.session_state['display_contest_info'])
-                    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'].iloc[:, 1:].select_dtypes(include=['number']).columns),
-                        hide_index=True)
+                with st.form(key='stack_exp_comp_form'):
+                    col1, col2 = st.columns(2)
+                    with col1:
+                        comp_stack_var = st.selectbox("Would you like to compare with anyone?",  ['No', 'Yes'], key='comp_stack_var')
+                    with col2:
+                        comp_stack_select = st.multiselect("Select stacks to compare with:", st.session_state['display_contest_info']['BaseName'].sort_values().unique(), key='comp_stack_select')
+                    submitted = st.form_submit_button("Submit")
+                    if submitted:
+                        if comp_stack_var == 'No':
+                            comp_stack_select = None
+                        else:
+                            comp_stack_select = comp_stack_select
+                if comp_stack_var == 'Yes':
+                    stack_exp_comp = create_stack_comparison(st.session_state['display_contest_info'], comp_stack_select)
+                    st.dataframe(stack_exp_comp.style.background_gradient(cmap='RdYlGn', axis=0).format(formatter='{:.2%}', subset=stack_exp_comp.select_dtypes(include=['number']).columns), hide_index=True)
                 else:
-                    st.session_state['stack_frame'] = create_stack_exposures(st.session_state['display_contest_info'], st.session_state['entry_names'])
-                    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'].iloc[:, 1:].select_dtypes(include=['number']).columns),
-                        hide_index=True)
+                    if st.session_state['entry_parse_var'] == 'All':
+                        st.session_state['stack_frame'] = create_stack_exposures(st.session_state['display_contest_info'])
+                        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'].iloc[:, 1:].select_dtypes(include=['number']).columns),
+                            hide_index=True)
+                    else:
+                        st.session_state['stack_frame'] = create_stack_exposures(st.session_state['display_contest_info'], st.session_state['entry_names'])
+                        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'].iloc[:, 1:].select_dtypes(include=['number']).columns),
+                            hide_index=True)
             with tab3:
                 
                 if st.session_state['entry_parse_var'] == 'All':

global_func/create_player_comparison.py

@@ -19,4 +19,4 @@ def create_player_comparison(df: pd.DataFrame, player_columns: list, entrants: l
         set_frame = set_frame.rename(columns={'Percent': f'Exposure {each_user}'})
         player_frame = pd.merge(player_frame, set_frame, on='Player', how='outer')
     
-    return player_frame
+    return player_frame.sort_values(by='Exposure Overall', ascending=False)

global_func/create_stack_comparison.py

@@ -0,0 +1,22 @@
+import pandas as pd
+
+def create_stack_comparison(df: pd.DataFrame, entrants: list):
+    overall_players = pd.Series(list(df['stack'])).value_counts()
+    contest_len = len(df)
+
+    set_frame = overall_players.to_frame().reset_index().rename(columns={'index': 'Player', 'count': 'Count'})
+    set_frame['Percent'] = set_frame['Count'] / contest_len
+    set_frame = set_frame[['Player', 'Percent']]
+    set_frame = set_frame.rename(columns={'Percent': f'Exposure Overall'})
+    player_frame = set_frame
+    
+    for each_user in entrants:
+        overall_players = pd.Series(list(df[df['BaseName'] == each_user]['stack'])).value_counts()
+    
+        set_frame = overall_players.to_frame().reset_index().rename(columns={'index': 'Player', 'count': 'Count'})
+        set_frame['Percent'] = set_frame['Count'] / len(df[df['BaseName'] == each_user])
+        set_frame = set_frame[['Player', 'Percent']]
+        set_frame = set_frame.rename(columns={'Percent': f'Exposure {each_user}'})
+        player_frame = pd.merge(player_frame, set_frame, on='Player', how='outer')
+    
+    return player_frame.sort_values(by='Exposure Overall', ascending=False)