diff --git a/build/generate_term_versions.py b/build/generate_term_versions.py
index cd25d96..9877c92 100644
--- a/build/generate_term_versions.py
+++ b/build/generate_term_versions.py
@@ -55,7 +55,8 @@ for term_list_index in range(len(term_lists)):
         accumulated_frame = versions_df.copy()
     else:
         # append subsequent term lists data to the DataFrame
-        accumulated_frame = accumulated_frame.append(versions_df.copy(), sort=True)
+        #accumulated_frame = accumulated_frame.append(versions_df.copy(), sort=True)
+        accumulated_frame = pd.concat([accumulated_frame, versions_df], sort=True)
         
 # Special procedure for obsolete terms
 # Retrieve versions metadata
@@ -84,7 +85,9 @@ for row_index,row in versions_df.iterrows():
 # Add the curren term IRI list to the DataFrame as the term_iri column
 versions_df['term_iri'] = term_iri_list
 # Add the obsolete terms DataFrame to the accumulated DataFrame
-accumulated_frame = accumulated_frame.append(versions_df.copy(), sort=True)
+#accumulated_frame = accumulated_frame.append(versions_df.copy(), sort=True)
+accumulated_frame = pd.concat([accumulated_frame, versions_df], sort=True)
+
 
 accumulated_frame.reset_index(drop=True, inplace=True) # reset the row indices to consecutive starting with zero
 accumulated_frame.fillna('', inplace=True) # replace all missing values with empty strings
@@ -166,7 +169,8 @@ for qrg_index,qrg_row in qrg_df.iterrows():
     for row_index,row in normative_doc_df.iterrows():
         if (qrg_row['recommended_term_iri'] == row['term_iri']) and (row['status'] == 'recommended'):
             found = True
-            built_rows_df = built_rows_df.append(row)
+            #built_rows_df = built_rows_df.append(row)
+            built_rows_df.loc[len(built_rows_df.index)] = row
             remaining_rows_df.drop(row['iri'], axis=0, inplace=True)
             break
     if not found:
@@ -178,7 +182,8 @@ sorted_output = remaining_rows_df.iloc[remaining_rows_df.iri.str.lower().argsort
 
 # Concatenate ordered terms and remaining versions
 #normative_doc_df = built_rows_df.append(remaining_rows_df)
-normative_doc_df = built_rows_df.append(sorted_output)
+#normative_doc_df = built_rows_df.append(sorted_output)
+normative_doc_df = pd.concat([built_rows_df, sorted_output])
 
 # Save the normative document DataFrame as a CSV
 normative_doc_df.to_csv('../vocabulary/term_versions.csv', index = False)