Add player comparison feature in app.py and create_player_comparison.py

- Introduced a new function, create_player_comparison, to facilitate player comparison based on exposure data.
- Updated app.py to include a form for selecting players to compare, enhancing user interaction and data analysis capabilities.
- Adjusted position selection logic for various sports, ensuring accurate filtering of player data based on selected positions.

app.py

@@ -63,6 +63,7 @@ from global_func.create_stack_exposures import create_stack_exposures
 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
 
 def is_valid_input(file):
     if isinstance(file, pd.DataFrame):
@@ -401,50 +402,79 @@ with tab2:
                     with col1:
                         pos_var = st.selectbox("Which position(s) would you like to view?",  ['All', 'Specific'], key='pos_var')
                     with col2:
-                        pos_select = st.multiselect("Select your position(s)", ['P', 'C', '1B', '2B', '3B', 'SS', 'OF'], key='pos_select')
+                        if sport_select == 'MLB':
+                            pos_select = st.multiselect("Select your position(s)", ['P', 'C', '1B', '2B', '3B', 'SS', 'OF'], key='pos_select')
+                        elif sport_select == 'NBA':
+                            pos_select = st.multiselect("Select your position(s)", ['PG', 'SG', 'SF', 'PF', 'C'], key='pos_select')
+                        elif sport_select == 'WNBA':
+                            pos_select = st.multiselect("Select your position(s)", ['PG', 'SG', 'SF', 'PF'], key='pos_select')
+                        elif sport_select == 'NFL':
+                            pos_select = st.multiselect("Select your position(s)", ['QB', 'RB', 'WR', 'TE', 'DST'], key='pos_select')
+                        elif sport_select == 'NHL':
+                            pos_select = st.multiselect("Select your position(s)", ['W', 'C', 'D', 'G'], key='pos_select')
+                        elif sport_select == 'MMA':
+                            pos_select = st.multiselect("Select your position(s)", ['All the same position', 'So', 'Yeah', 'Idk'], key='pos_select')
+                        elif sport_select == 'GOLF':
+                            pos_select = st.multiselect("Select your position(s)", ['All the same position', 'So', 'Yeah', 'Idk'], key='pos_select')
                     submitted = st.form_submit_button("Submit")
                     if submitted:
                         if pos_var == 'Specific':
                             pos_select = pos_select
                         else:
                             pos_select = None
-
-                if st.session_state['entry_parse_var'] == 'All':
-
-                    st.session_state['player_frame'] = create_player_exposures(st.session_state['display_contest_info'], st.session_state['player_columns'])
-                    hold_frame = st.session_state['player_frame'].copy()
-                    if sport_select == 'GOLF':
-                        hold_frame['Pos'] = 'G'
-                    else:
-                        hold_frame['Pos'] = hold_frame['Player'].map(st.session_state['map_dict']['pos_map'])
-                    st.session_state['player_frame'].insert(1, 'Pos', hold_frame['Pos'])
-                    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))
-                        st.session_state['player_frame'] = st.session_state['player_frame'][position_mask]
-                    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),
-                        hide_index=True)
+                
+                with st.form(key='player_exp_comp_form'):
+                    col1, col2 = st.columns(2)
+                    with col1:
+                        comp_player_var = st.selectbox("Would you like to compare with anyone?",  ['No', 'Yes'], key='comp_player_var')
+                    with col2:
+                        comp_player_select = st.multiselect("Select players to compare with:", st.session_state['display_contest_info']['BaseName'].sort_values().unique(), key='comp_player_select')
+                    submitted = st.form_submit_button("Submit")
+                    if submitted:
+                        if comp_player_var == 'No':
+                            comp_player_select = None
+                        else:
+                            comp_player_select = comp_player_select
+                if comp_player_var == 'Yes':
+                    st.session_state['player_frame'] = create_player_comparison(st.session_state['display_contest_info'], st.session_state['player_columns'], comp_player_select)
+                    st.dataframe(st.session_state['player_frame'].style.background_gradient(cmap='RdYlGn', axis=1).format(formatter='{:.2%}', subset=st.session_state['player_frame'].iloc[:, 2:].select_dtypes(include=['number']).columns), hide_index=True)
                 else:
+                    if st.session_state['entry_parse_var'] == 'All':
 
-                    st.session_state['player_frame'] = create_player_exposures(st.session_state['display_contest_info'], st.session_state['player_columns'], st.session_state['entry_names'])
-                    hold_frame = st.session_state['player_frame'].copy()
-                    if sport_select == 'GOLF':
-                        hold_frame['Pos'] = 'G'
+                        st.session_state['player_frame'] = create_player_exposures(st.session_state['display_contest_info'], st.session_state['player_columns'])
+                        hold_frame = st.session_state['player_frame'].copy()
+                        if sport_select == 'GOLF':
+                            hold_frame['Pos'] = 'G'
+                        else:
+                            hold_frame['Pos'] = hold_frame['Player'].map(st.session_state['map_dict']['pos_map'])
+                        st.session_state['player_frame'].insert(1, 'Pos', hold_frame['Pos'])
+                        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))
+                            st.session_state['player_frame'] = st.session_state['player_frame'][position_mask]
+                        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),
+                            hide_index=True)
                     else:
-                        hold_frame['Pos'] = hold_frame['Player'].map(st.session_state['map_dict']['pos_map'])
-                    st.session_state['player_frame'].insert(1, 'Pos', hold_frame['Pos'])
-                    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))
-                        st.session_state['player_frame'] = st.session_state['player_frame'][position_mask]
-                    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),
-                        hide_index=True)
+
+                        st.session_state['player_frame'] = create_player_exposures(st.session_state['display_contest_info'], st.session_state['player_columns'], st.session_state['entry_names'])
+                        hold_frame = st.session_state['player_frame'].copy()
+                        if sport_select == 'GOLF':
+                            hold_frame['Pos'] = 'G'
+                        else:
+                            hold_frame['Pos'] = hold_frame['Player'].map(st.session_state['map_dict']['pos_map'])
+                        st.session_state['player_frame'].insert(1, 'Pos', hold_frame['Pos'])
+                        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))
+                            st.session_state['player_frame'] = st.session_state['player_frame'][position_mask]
+                        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),
+                            hide_index=True)
             with tab2:
 
                 if st.session_state['entry_parse_var'] == 'All':

global_func/create_player_comparison.py

@@ -0,0 +1,22 @@
+import pandas as pd
+
+def create_player_comparison(df: pd.DataFrame, player_columns: list, entrants: list):
+    overall_players = pd.Series(list(df[player_columns].values.flatten())).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][player_columns].values.flatten())).value_counts()
+    
+        set_frame = overall_players.to_frame().reset_index().rename(columns={'index': 'Player', 'count': 'Count'})
+        set_frame['Percent'] = set_frame['Count'] / len(overall_players)
+        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