Update data_processor.py

data_processor.py

@@ -117,21 +117,50 @@ class DataProcessor:
                 elif x == self.NOT_ENGAGED_STR:
                     engagement_counts['Not Engaged'] += 1
                 else:
-                    engagement_counts['Absent'] += 1
+                    engagement_counts['Absent'] += 1 # Count as Absent if not engaged
 
+            # Calculate percentages for engagement states
             total_sessions = sum(engagement_counts.values())
+            
+            # Engagement (%)
             engagement_pct = (engagement_counts['Engaged'] / total_sessions * 100) if total_sessions > 0 else 0
             engagement_pct = round(engagement_pct)
-
+    
+            engaged_pct = (engagement_counts['Engaged'] / total_sessions * 100) if total_sessions > 0 else 0
+            engaged_pct = round(engaged_pct)
+    
+            partially_engaged_pct = (engagement_counts['Partially Engaged'] / total_sessions * 100) if total_sessions > 0 else 0
+            partially_engaged_pct = round(partially_engaged_pct)
+    
+            not_engaged_pct = (engagement_counts['Not Engaged'] / total_sessions * 100) if total_sessions > 0 else 0
+            not_engaged_pct = round(not_engaged_pct)
+    
+            absent_pct = (engagement_counts['Absent'] / total_sessions * 100) if total_sessions > 0 else 0
+            absent_pct = round(absent_pct)
+    
+            # Store metrics in the required order
             student_metrics[student_name] = {
                 'Attendance (%)': attendance_pct,
-                'Attendance #': sessions_attended,
-                'Engagement (%)': engagement_pct
+                'Attendance #': sessions_attended,  # Raw number of sessions attended
+                'Engagement (%)': engagement_pct,
+                'Engaged (%)': engaged_pct,
+                'Partially Engaged (%)': partially_engaged_pct,
+                'Not Engaged (%)': not_engaged_pct,
+                'Absent (%)': absent_pct
             }
 
-        return pd.DataFrame.from_dict(student_metrics, orient='index').reset_index().rename(columns={'index': 'Student'})
-
-    def compute_average_metrics(self, student_metrics_df):
-        attendance_avg_stats = student_metrics_df['Attendance (%)'].mean()
-        engagement_avg_stats = student_metrics_df['Engagement (%)'].mean()
-        return round(attendance_avg_stats), round(engagement_avg_stats)
+        # Create a DataFrame from student_metrics
+        student_metrics_df = pd.DataFrame.from_dict(student_metrics, orient='index').reset_index()
+        student_metrics_df.rename(columns={'index': 'Student'}, inplace=True)
+        return student_metrics_df
+    
+    def compute_average_metrics(student_metrics_df):
+        # Calculate the attendance and engagement average percentages across students
+        attendance_avg_stats = student_metrics_df['Attendance (%)'].mean()  # Calculate the average attendance percentage
+        engagement_avg_stats = student_metrics_df['Engagement (%)'].mean()  # Calculate the average engagement percentage
+        
+        # Round the averages to make them whole numbers
+        attendance_avg_stats = round(attendance_avg_stats)
+        engagement_avg_stats = round(engagement_avg_stats)
+        
+        return attendance_avg_stats, engagement_avg_stats