Update data_processor.py

data_processor.py

@@ -247,30 +247,30 @@ class DataProcessor:
         intervention_df = df[df[self.INTERVENTION_COLUMN].str.strip().str.lower() == 'yes']
         intervention_sessions_held = len(intervention_df)
         student_columns = [col for col in df.columns if col.startswith('Student Attendance')]
-
+    
         student_metrics = {}
         for col in student_columns:
             student_name = col.replace('Student Attendance [', '').replace(']', '').strip()
             student_data = intervention_df[[col]].copy()
             student_data[col] = student_data[col].fillna('Absent')
-
+    
             attendance_values = student_data[col].apply(lambda x: 1 if x in [
                 self.ENGAGED_STR,
                 self.PARTIALLY_ENGAGED_STR,
                 self.NOT_ENGAGED_STR
             ] else 0)
-
+    
             sessions_attended = attendance_values.sum()
             attendance_pct = (sessions_attended / intervention_sessions_held) * 100 if intervention_sessions_held > 0 else 0
             attendance_pct = round(attendance_pct)
-
+    
             engagement_counts = {
                 'Engaged': 0,
                 'Partially Engaged': 0,
                 'Not Engaged': 0,
                 'Absent': 0
             }
-
+    
             for x in student_data[col]:
                 if x == self.ENGAGED_STR:
                     engagement_counts['Engaged'] += 1
@@ -279,8 +279,8 @@ class DataProcessor:
                 elif x == self.NOT_ENGAGED_STR:
                     engagement_counts['Not Engaged'] += 1
                 else:
-                    engagement_counts['Absent'] += 1 # Count as Absent if not engaged
-
+                    engagement_counts['Absent'] += 1  # Count as Absent if not engaged
+    
             # Calculate percentages for engagement states
             total_sessions = sum(engagement_counts.values())
             
@@ -300,17 +300,25 @@ class DataProcessor:
             absent_pct = (engagement_counts['Absent'] / total_sessions * 100) if total_sessions > 0 else 0
             absent_pct = round(absent_pct)
     
+            # Determine if the student attended ≥ 90% of sessions
+            attended_90 = "Yes" if attendance_pct >= 90 else "No"
+    
+            # Determine if the student was engaged ≥ 80% of the time
+            engaged_80 = "Yes" if engaged_pct >= 80 else "No"
+    
             # Store metrics in the required order
             student_metrics[student_name] = {
                 'Attendance (%)': attendance_pct,
-                'Attendance #': sessions_attended,  # Raw number of sessions attended
+                'Attendance #': sessions_attended,
                 'Engagement (%)': engagement_pct,
                 'Engaged (%)': engaged_pct,
                 'Partially Engaged (%)': partially_engaged_pct,
                 'Not Engaged (%)': not_engaged_pct,
-                'Absent (%)': absent_pct
+                'Absent (%)': absent_pct,
+                'Attended ≥ 90%': attended_90,
+                'Engagement ≥ 80%': engaged_80
             }
-
+    
         # 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)