attempted stretch goal. got runtime down to 0.004s from 0.006s

names/names.py

@@ -24,19 +24,25 @@
 # Append the items that exist in both lists to the duplicates list
 
 # initialize BST with a placeholder node
-bst = BSTNode('placeholder')
-for name in names_1:
-    bst.insert(name)
+# bst = BSTNode('placeholder')
+# for name in names_1:
+#     bst.insert(name)
 
-for name in names_2:
-    if bst.contains(name):
-        duplicates.append(name)
+# for name in names_2:
+#     if bst.contains(name):
+#         duplicates.append(name)
 
-end_time = time.time()
-print (f"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n")
-print (f"runtime: {end_time - start_time} seconds")
+# end_time = time.time()
+# print (f"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n")
+# print (f"runtime: {end_time - start_time} seconds")
 
 # ---------- Stretch Goal -----------
 # Python has built-in tools that allow for a very efficient approach to this problem
 # What's the best time you can accomplish?  Thare are no restrictions on techniques or data
 # structures, but you may not import any additional libraries that you did not write yourself.
+
+duplicates = set(names_1).intersection(names_2)
+
+end_time = time.time()
+print (f"{len(duplicates)} duplicates:\n\n{', '.join(duplicates)}\n\n")
+print (f"runtime: {end_time - start_time} seconds")