Update app.py to include 'FLEX5' in player selection for various slate types. This change enhances the data structure for player lineups, ensuring compatibility with expanded player options in the application.

app.py

@@ -296,19 +296,19 @@ def init_FD_lineups(type_var,slate_var):
                 cursor = collection.find().limit(10000)
 
                 raw_display = pd.DataFrame(list(cursor))
-                raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+                raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
             elif slate_var == 'Secondary':
                 collection = db['FD_MLB_SD2_seed_frame']
                 cursor = collection.find().limit(10000)
 
                 raw_display = pd.DataFrame(list(cursor))
-                raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+                raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
             elif slate_var == 'Auxiliary':
                 collection = db['FD_MLB_SD3_seed_frame']
                 cursor = collection.find().limit(10000)
             
                 raw_display = pd.DataFrame(list(cursor))
-                raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+                raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
     
         FD_seed = raw_display.to_numpy()
 
@@ -676,7 +676,7 @@ with tab3:
         if slate_type_var3 == 'Regular':
             map_columns = ['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL']
         elif slate_type_var3 == 'Showdown':
-            map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4']
+            map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
         for col_idx in map_columns:
             export_file[col_idx] = export_file[col_idx].map(fd_id_map)
             
@@ -776,7 +776,7 @@ with tab3:
                     if slate_type_var3 == 'Regular':
                         player_columns = st.session_state.data_export_display.iloc[:, :9]
                     elif slate_type_var3 == 'Showdown':
-                        player_columns = st.session_state.data_export_display.iloc[:, :5]
+                        player_columns = st.session_state.data_export_display.iloc[:, :6]
                 
                 # Flatten the DataFrame and count unique values
                 value_counts = player_columns.values.flatten().tolist()
@@ -818,7 +818,7 @@ with tab3:
                     if slate_type_var3 == 'Regular':
                         player_columns = st.session_state.working_seed[:, :9]
                     elif slate_type_var3 == 'Showdown':
-                        player_columns = st.session_state.working_seed[:, :6]
+                        player_columns = st.session_state.working_seed[:, :7]
                 
                 # Flatten the DataFrame and count unique values
                 value_counts = player_columns.flatten().tolist()