Enhance position filtering in create_position_export_dict to include 'QB' for NCAAF, and streamline sorting logic for Draftkings and other sites.

app.py

@@ -54,6 +54,8 @@ def create_position_export_dict(column_name, csv_file, site_var, type_var, sport
                 elif position_filter == 'FLEX' or position_filter == 'UTIL':
                     if sport_var == 'NFL':
                         filtered_df = csv_file['Position'].isin(['RB', 'WR', 'TE'])
+                    elif sport_var == 'NCAAF':
+                        filtered_df = csv_file['Position'].isin(['RB', 'WR', 'TE', 'QB'])
                     elif sport_var == 'SOC':
                         filtered_df = csv_file['Position'].str.contains(['D', 'M', 'F'], na=False, regex=False)
                     elif sport_var == 'NCAAF':
@@ -77,17 +79,12 @@ def create_position_export_dict(column_name, csv_file, site_var, type_var, sport
             filtered_df = csv_file
         
         # Create the export dictionary for this position
-        
-        try:
+        if site_var == 'Draftkings':
             filtered_df = filtered_df.sort_values(by='Salary', ascending=False).drop_duplicates(subset=['Name'])
             return dict(zip(filtered_df['Name'], filtered_df['Name + ID']))
-        except:
-            try:
-                filtered_df = filtered_df.sort_values(by='Salary', ascending=False).drop_duplicates(subset=['Nickname'])
-                return dict(zip(filtered_df['Nickname'], filtered_df['Id']))
-            except:
-                # Final fallback
-                return {}
+        else:
+            filtered_df = filtered_df.sort_values(by='Salary', ascending=False).drop_duplicates(subset=['Nickname'])
+            return dict(zip(filtered_df['Nickname'], filtered_df['Id']))
                 
     except Exception as e:
         st.error(f"Error creating position export dict for {column_name}: {str(e)}")