Compare (on Python3 -- for Python2, read "xrange" instead of "range"):
In [2]: %timeit np.array(range(1000000), np.int64) 10 loops, best of 3: 156 ms per loop In [3]: %timeit np.arange(1000000, dtype=np.int64) 1000 loops, best of 3: 853 µs per loop Note that while iterating over a range is not very fast, it is still much better than the array creation: In [4]: from collections import deque In [5]: %timeit deque(range(1000000), 1) 10 loops, best of 3: 25.5 ms per loop On one hand, special cases are awful. On the other hand, the range builtin is probably important enough to deserve a special case to make this construction faster. Or not? I initially opened this as https://github.com/numpy/numpy/issues/7233 but it was suggested there that this should be discussed on the ML first. (The real issue which prompted this suggestion: I was building sparse matrices using scipy.sparse.csc_matrix with some indices specified using range, and that construction step turned out to take a significant portion of the time because of the calls to np.array). Antony
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