Performance Tuning - applying a function to a list

>>> import timeit
>>> timeit.Timer('map(f, range(10))', 'f=lambda x: str(6+x)').timeit()
15.467374484745967
>>> timeit.Timer('[f(x) for x in range(10)]', 'f=lambda x: str(6+x)').timeit()
16.062227741235269
>>> timeit.Timer('for x in range(10): f(x)', 'f=lambda x: str(6+x)').timeit()
14.686095299821623

And so, we can see that map is still faster than list comprehensions and the for loop beats them both. If you don't need the return value of that function, don't create the list: friends don't let friends create unnecessary objects. On the other hand, if performance is critical and you need the return values, you should prefer map over a list comprehension.