Update app.py

app.py

@@ -313,6 +313,62 @@ def plot_intervention_statistics(intervention_stats):
 
     return fig
 
+# def compute_student_metrics(df):
+#     # Filter DataFrame for sessions where intervention happened
+#     intervention_df = df[df[INTERVENTION_COLUMN].str.strip().str.lower() == 'yes']
+#     intervention_sessions_held = len(intervention_df)
+
+#     # Get list of student columns
+#     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()
+#         # Get the attendance data for the student
+#         student_data = intervention_df[[col]].copy()
+
+#         # Treat blank entries as 'Absent'
+#         student_data[col] = student_data[col].fillna('Absent')
+
+#         # Assign attendance values
+#         attendance_values = student_data[col].apply(lambda x: 1 if x in [
+#             ENGAGED_STR,
+#             PARTIALLY_ENGAGED_STR,
+#             NOT_ENGAGED_STR
+#         ] else 0)
+
+#         # Number of Sessions Attended
+#         sessions_attended = attendance_values.sum()
+
+#         # Attendance (%)
+#         attendance_pct = (sessions_attended / intervention_sessions_held) * 100 if intervention_sessions_held > 0 else 0
+#         attendance_pct = round(attendance_pct)  # Round to whole number
+
+#         # For engagement calculation, include only sessions where attendance is not 'Absent'
+#         valid_engagement_indices = attendance_values[attendance_values == 1].index
+#         engagement_data = student_data.loc[valid_engagement_indices, col]
+
+#         # Assign engagement values
+#         engagement_values = engagement_data.apply(lambda x: 1 if x == ENGAGED_STR
+#                                                   else 0.5 if x == PARTIALLY_ENGAGED_STR else 0)
+
+#         # Sum of Engagement Values
+#         sum_engagement_values = engagement_values.sum()
+
+#         # Number of Sessions Attended for engagement (should be same as sessions_attended)
+#         number_sessions_attended = len(valid_engagement_indices)
+
+#         # Engagement (%)
+#         engagement_pct = (sum_engagement_values / number_sessions_attended) * 100 if number_sessions_attended > 0 else 0
+#         engagement_pct = round(engagement_pct) # Round to whole number
+
+#         # Store metrics
+#         student_metrics[student_name] = {
+#             'Attendance (%)': attendance_pct,
+#             'Engagement (%)': engagement_pct
+#         }
+
 def compute_student_metrics(df):
     # Filter DataFrame for sessions where intervention happened
     intervention_df = df[df[INTERVENTION_COLUMN].str.strip().str.lower() == 'yes']
@@ -345,30 +401,57 @@ def compute_student_metrics(df):
         attendance_pct = (sessions_attended / intervention_sessions_held) * 100 if intervention_sessions_held > 0 else 0
         attendance_pct = round(attendance_pct)  # Round to whole number
 
-        # For engagement calculation, include only sessions where attendance is not 'Absent'
-        valid_engagement_indices = attendance_values[attendance_values == 1].index
-        engagement_data = student_data.loc[valid_engagement_indices, col]
+        # Calculate the number of students in each engagement category
+        engagement_counts = {
+            'Engaged': 0,
+            'Partially Engaged': 0,
+            'Not Engaged': 0,
+            'Absent': 0
+        }
+
+        # Count the engagement states
+        for x in student_data[col]:
+            if x == ENGAGED_STR:
+                engagement_counts['Engaged'] += 1
+            elif x == PARTIALLY_ENGAGED_STR:
+                engagement_counts['Partially Engaged'] += 1
+            elif x == NOT_ENGAGED_STR:
+                engagement_counts['Not Engaged'] += 1
+            else:
+                engagement_counts['Absent'] += 1  # Count as Absent if not engaged
 
-        # Assign engagement values
-        engagement_values = engagement_data.apply(lambda x: 1 if x == ENGAGED_STR
-                                                  else 0.5 if x == PARTIALLY_ENGAGED_STR else 0)
+        # 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)
 
-        # Sum of Engagement Values
-        sum_engagement_values = engagement_values.sum()
+        engaged_pct = (engagement_counts['Engaged'] / total_sessions * 100) if total_sessions > 0 else 0
+        engaged_pct = round(engaged_pct)
 
-        # Number of Sessions Attended for engagement (should be same as sessions_attended)
-        number_sessions_attended = len(valid_engagement_indices)
+        partially_engaged_pct = (engagement_counts['Partially Engaged'] / total_sessions * 100) if total_sessions > 0 else 0
+        partially_engaged_pct = round(partially_engaged_pct)
 
-        # Engagement (%)
-        engagement_pct = (sum_engagement_values / number_sessions_attended) * 100 if number_sessions_attended > 0 else 0
-        engagement_pct = round(engagement_pct) # Round to whole number
+        not_engaged_pct = (engagement_counts['Not Engaged'] / total_sessions * 100) if total_sessions > 0 else 0
+        not_engaged_pct = round(not_engaged_pct)
 
-        # Store metrics
+        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,
-            '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 student_metrics
+
     # 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)