Update data_processor.py

data_processor.py

@@ -369,6 +369,12 @@ class DataProcessor:
         for col in student_columns:
             student_name = col.replace('Student Attendance [', '').replace(']', '').strip()
             student_data = intervention_df[[col]].copy()
+
+            # Check if the student is still active (has non-empty cells in recent sessions)
+            is_active = student_data[col].notna().any()
+            if not is_active:
+                continue  # Skip inactive students
+            
             student_data[col] = student_data[col].fillna('Absent')
     
             # Classify each entry
@@ -452,17 +458,19 @@ class DataProcessor:
         return student_metrics_df
     
     def compute_average_metrics(self, student_metrics_df):
-        # Calculate the attendance and engagement average percentages across students
-        attendance_avg_stats = student_metrics_df['Attendance (%)'].mean()  # Average attendance percentage
-        # engagement_avg_stats = student_metrics_df['Engagement (%)'].mean()  # Average engagement percentage REMOVED REMOVED
-        engagement_avg_stats = student_metrics_df[f'{self.ENGAGED_STR} (%)'].mean()  # Average engagement percentage
+        # Filter out rows with NaN values (inactive students)
+        active_students_df = student_metrics_df.dropna()
+        
+        # Calculate the attendance and engagement average percentages across active students
+        attendance_avg_stats = active_students_df['Attendance (%)'].mean()
+        engagement_avg_stats = active_students_df[f'{self.ENGAGED_STR} (%)'].mean()
         
         # Round the averages to whole numbers
         attendance_avg_stats = round(attendance_avg_stats)
         engagement_avg_stats = round(engagement_avg_stats)
         
         return attendance_avg_stats, engagement_avg_stats
-    
+
     def evaluate_student(self, row, attendance_threshold=90, engagement_threshold=80):
         if row["Attended ≥ 90%"] == "No":
             return "Address Attendance"