Is iterating over some_dict.items() as efficient as iterating over a list of the same items in CPython?
3 Answers
It depends on which version of Python you're using. In Python 2, some_dict.items() creates a new list, which takes up some additional time and uses up additional memory. On the other hand, once the list is created, it's a list, and so should have identical performance characteristics after the overhead of list creation is complete.
In Python 3, some_dict.items() creates a view object instead of a list, and I anticipate that creating and iterating over items() would be faster than in Python 2, since nothing has to be copied. But I also anticipate that iterating over an already-created view would be a bit slower than iterating over an already-created list, because dictionary data is stored somewhat sparsely, and I believe there's no good way for python to avoid iterating over every bin in the dictionary -- even the empty ones.
In Python 2, some timings confirm my intuitions:
>>> some_dict = dict(zip(xrange(1000), reversed(xrange(1000)))) >>> some_list = zip(xrange(1000), xrange(1000)) >>> %timeit for t in some_list: t 10000 loops, best of 3: 25.6 us per loop >>> %timeit for t in some_dict.items(): t 10000 loops, best of 3: 57.3 us per loop Iterating over the items is roughly twice as slow. Using iteritems is a tad bit faster...
>>> %timeit for t in some_dict.iteritems(): t 10000 loops, best of 3: 41.3 us per loop But iterating over the list itself is basically the same as iterating over any other list:
>>> some_dict_list = some_dict.items() >>> %timeit for t in some_dict_list: t 10000 loops, best of 3: 26.1 us per loop Python 3 can create and iterate over items faster than Python 2 can (compare to 57.3 us above):
>>> some_dict = dict(zip(range(1000), reversed(range(1000)))) >>> %timeit for t in some_dict.items(): t 10000 loops, best of 3: 33.4 us per loop But the time to create a view is negligable; it is actually slower to iterate over than a list.
>>> some_list = list(zip(range(1000), reversed(range(1000)))) >>> some_dict_view = some_dict.items() >>> %timeit for t in some_list: t 10000 loops, best of 3: 18.6 us per loop >>> %timeit for t in some_dict_view: t 10000 loops, best of 3: 33.3 us per loop This means that in Python 3, if you want to iterate many times over the items in a dictionary, and performance is critical, you can get a 30% speedup by caching the view as a list.
>>> some_list = list(some_dict_view) >>> %timeit for t in some_list: t 100000 loops, best of 3: 18.6 us per loop A little benchmark shows me that iterating a list is definately faster.
def iterlist(list_): i = 0 for _ in list_: i += 1 return i def iterdict(dict_): i = 0 for _ in dict_.iteritems(): i += 1 return i def noiterdict(dict_): i = 0 for _ in dict_.items(): i += 1 return i list_ = range(1000000) dict_ = dict(zip(range(1000000), range(1000000))) Tested with IPython on Python 2.7 (Kubuntu):
%timeit iterlist(list_) 10 loops, best of 3: 28.5 ms per loop %timeit iterdict(dict_) 10 loops, best of 3: 39.7 ms per loop %timeit noiterdict(dict_) 10 loops, best of 3: 86.1 ms per loop Although iterating through some_list is 2x speedup than some_dict.items(), but iterating through some_list by index is almost as same as iterating through some_dict by key.
K = 1000000 some_dict = dict(zip(xrange(K), reversed(xrange(K)))) some_list = zip(xrange(K), xrange(K)) %timeit for t in some_list: t 10 loops, best of 3: 55.7 ms per loop %timeit for i in xrange(len(some_list)):some_list[i] 10 loops, best of 3: 94 ms per loop %timeit for key in some_dict: some_dict[key] 10 loops, best of 3: 115 ms per loop %timeit for i,t in enumerate(some_list): t 10 loops, best of 3: 103 ms per loop 
