Refactor contest data handling in app.py

- Consolidated the logic for calculating metrics based on game type into the session state 'Contest' dataframe, improving code organization and readability.
- Updated the player selection and filtering process to utilize session state variables, enhancing the user experience and maintaining state across interactions.
- This change streamlines data processing and ensures consistent handling of player metrics for both 'Classic' and 'Showdown' game types.

app.py

@@ -108,88 +108,91 @@ with tab2:
             players_5per = st.session_state['Contest'].head(int(len(st.session_state['Contest']) * 0.05))
             players_10per = st.session_state['Contest'].head(int(len(st.session_state['Contest']) * 0.10))
             players_20per = st.session_state['Contest'].head(int(len(st.session_state['Contest']) * 0.20))
-        working_df = st.session_state['Contest'].copy()
-
-        with col1:
-            with st.expander("Info and filters"):
-                if st.button('Clear data', key='reset3'):
-                    st.session_state.clear()
-                with st.form(key='filter_form'):
-                    type_var = st.selectbox("Select Game Type", ['Classic', 'Showdown'])
-                    entry_parse_var = st.selectbox("Do you want to view a specific player(s) or a group of players?", ['All', 'Specific'])
-                    entry_names = st.multiselect("Select players", options=st.session_state['entry_list'], default=[])
-                    submitted = st.form_submit_button("Submit")
-                    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)]
+        
+        st.session_state['type_var'] = 'Classic'
 
         # Calculate metrics based on game type
-        if type_var == 'Classic':
-            working_df['stack'] = working_df.apply(
+        if st.session_state['type_var'] == 'Classic':
+            st.session_state['Contest']['stack'] = st.session_state['Contest'].apply(
                 lambda row: Counter(
                     map_dict['team_map'].get(player, '') for player in row[4:]
                     if map_dict['team_map'].get(player, '') != ''
                 ).most_common(1)[0][0] if any(map_dict['team_map'].get(player, '') for player in row[4:]) else '',
                 axis=1
             )
-            working_df['stack_size'] = working_df.apply(
+            st.session_state['Contest']['stack_size'] = st.session_state['Contest'].apply(
                 lambda row: Counter(
                     map_dict['team_map'].get(player, '') for player in row[4:]
                     if map_dict['team_map'].get(player, '') != ''
                 ).most_common(1)[0][1] if any(map_dict['team_map'].get(player, '') for player in row[4:]) else '',
                 axis=1
             )
-            working_df['salary'] = working_df.apply(lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row), axis=1)
-            working_df['median'] = working_df.apply(lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row), axis=1)
-            working_df['actual_fpts'] = working_df.apply(lambda row: sum(st.session_state['actual_dict'].get(player, 0) for player in row), axis=1)
-            working_df['Own'] = working_df.apply(lambda row: sum(map_dict['own_map'].get(player, 0) for player in row), axis=1)
-            working_df['actual_own'] = working_df.apply(lambda row: sum(st.session_state['ownership_dict'].get(player, 0) for player in row), axis=1)
-            working_df['sorted'] = working_df[player_columns].apply(
+            st.session_state['Contest']['salary'] = st.session_state['Contest'].apply(lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row), axis=1)
+            st.session_state['Contest']['median'] = st.session_state['Contest'].apply(lambda row: sum(map_dict['proj_map'].get(player, 0) for player in row), axis=1)
+            st.session_state['Contest']['actual_fpts'] = st.session_state['Contest'].apply(lambda row: sum(st.session_state['actual_dict'].get(player, 0) for player in row), axis=1)
+            st.session_state['Contest']['Own'] = st.session_state['Contest'].apply(lambda row: sum(map_dict['own_map'].get(player, 0) for player in row), axis=1)
+            st.session_state['Contest']['actual_own'] = st.session_state['Contest'].apply(lambda row: sum(st.session_state['ownership_dict'].get(player, 0) for player in row), axis=1)
+            st.session_state['Contest']['sorted'] = st.session_state['Contest'][player_columns].apply(
                 lambda row: ','.join(sorted(row.values)),
                 axis=1
             )
-            working_df['dupes'] = working_df.groupby('sorted').transform('size')
-            working_df = working_df.drop('sorted', axis=1)
-        elif type_var == 'Showdown':
-            working_df['stack'] = working_df.apply(
+            st.session_state['Contest']['dupes'] = st.session_state['Contest'].groupby('sorted').transform('size')
+            st.session_state['Contest'] = st.session_state['Contest'].drop('sorted', axis=1)
+        elif st.session_state['type_var'] == 'Showdown':
+            st.session_state['Contest']['stack'] = st.session_state['Contest'].apply(
                 lambda row: Counter(
                     map_dict['team_map'].get(player, '') for player in row
                     if map_dict['team_map'].get(player, '') != ''
                 ).most_common(1)[0][0] if any(map_dict['team_map'].get(player, '') for player in row) else '',
                 axis=1
             )
-            working_df['stack_size'] = working_df.apply(
+            st.session_state['Contest']['stack_size'] = st.session_state['Contest'].apply(
                 lambda row: Counter(
                     map_dict['team_map'].get(player, '') for player in row
                     if map_dict['team_map'].get(player, '') != ''
                 ).most_common(1)[0][1] if any(map_dict['team_map'].get(player, '') for player in row) else '',
                 axis=1
             )
-            working_df['salary'] = working_df.apply(
+            st.session_state['Contest']['salary'] = st.session_state['Contest'].apply(
                 lambda row: map_dict['cpt_salary_map'].get(row.iloc[0], 0) + 
                             sum(map_dict['salary_map'].get(player, 0) for player in row.iloc[1:]),
                 axis=1
             )
-            working_df['median'] = working_df.apply(
+            st.session_state['Contest']['median'] = st.session_state['Contest'].apply(
                 lambda row: map_dict['cpt_proj_map'].get(row.iloc[0], 0) + 
                             sum(map_dict['proj_map'].get(player, 0) for player in row.iloc[1:]),
                 axis=1
             )
-            working_df['Own'] = working_df.apply(
+            st.session_state['Contest']['Own'] = st.session_state['Contest'].apply(
                 lambda row: map_dict['cpt_own_map'].get(row.iloc[0], 0) + 
                             sum(map_dict['own_map'].get(player, 0) for player in row.iloc[1:]),
                 axis=1
             )
-            working_df['sorted'] = working_df[player_columns].apply(
+            st.session_state['Contest']['sorted'] = st.session_state['Contest'][player_columns].apply(
                 lambda row: row[0] + '|' + ','.join(sorted(row[1:].values)),
                 axis=1
             )
-            working_df['dupes'] = working_df.groupby('sorted').transform('size')
-            working_df = working_df.drop('sorted', axis=1)
+            st.session_state['Contest']['dupes'] = st.session_state['Contest'].groupby('sorted').transform('size')
+            st.session_state['Contest'] = st.session_state['Contest'].drop('sorted', axis=1)
+
+        working_df = st.session_state['Contest'].copy()
+
+        with col1:
+            with st.expander("Info and filters"):
+                if st.button('Clear data', key='reset3'):
+                    st.session_state.clear()
+                with st.form(key='filter_form'):
+                    st.session_state['type_var'] = st.selectbox("Select Game Type", ['Classic', 'Showdown'])
+                    entry_parse_var = st.selectbox("Do you want to view a specific player(s) or a group of players?", ['All', 'Specific'])
+                    entry_names = st.multiselect("Select players", options=st.session_state['entry_list'], default=[])
+                    submitted = st.form_submit_button("Submit")
+                    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)]
         
         # Initialize pagination in session state if not exists
         if 'current_page' not in st.session_state: