Update utils.py

utils.py

@@ -92,7 +92,7 @@ def aggregate_data_optimized(df_a, df_b, uuid_col_name, ref_col_name, exclude_co
 
 
 
-def updateStreamAnalysisFast(client, new_data, stream_id, branch_name, geometryGroupPath=None, match_by_id="", return_original = False):
+def updateStreamAnalysisFast(client, new_data, stream_id, branch_name, geometryGroupPath=None, match_by_id="", return_original=False):
     if geometryGroupPath is None:
         geometryGroupPath = ["@Speckle", "Geometry"]
 
@@ -103,7 +103,6 @@ def updateStreamAnalysisFast(client, new_data, stream_id, branch_name, geometryG
     res = operations.receive(commit.referencedObject, transport)
     objects_raw = res[geometryGroupPath[0]][geometryGroupPath[1]]
 
-    # Assuming objects_raw is a list of dictionaries
     # Pre-create a mapping from IDs to objects for faster lookup
     id_to_object_map = {obj[match_by_id]: obj for obj in objects_raw} if match_by_id else {i: obj for i, obj in enumerate(objects_raw)}
 
@@ -111,11 +110,11 @@ def updateStreamAnalysisFast(client, new_data, stream_id, branch_name, geometryG
     if match_by_id:
         new_data.set_index(match_by_id, inplace=True)
 
-    # Update objects in a more efficient way
+    # Update objects in a more efficient way using .items()
     for local_id, updates in new_data.iterrows():
         target_object = id_to_object_map.get(str(local_id))
         if target_object:
-            for col_name, value in updates.iteritems():
+            for col_name, value in updates.items():
                 target_object[col_name] = value
 
     # Send updated objects back to Speckle
@@ -126,6 +125,7 @@ def updateStreamAnalysisFast(client, new_data, stream_id, branch_name, geometryG
         return objects_raw  # as back-up
 
 
+
         
 def getSpeckleStream(stream_id,
                      branch_name,