On Sat, Feb 13, 2016 at 8:57 PM, Antony Lee <antony....@berkeley.edu> wrote:
> 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). > IMO: I don't see a reason why this should be supported. There is np.arange after all for this usecase, and from_iter. range and the other guys are iterators, and in several cases we can use larange = list(range(...)) as a short cut to get python list.for python 2/3 compatibility. I think this might be partially a learning effect in the python 2 to 3 transition. After using almost only python 3 for maybe a year, I don't think it's difficult to remember the differences when writing code that is py 2.7 and py 3.x compatible. It's just **another** thing to watch out for if milliseconds matter in your application. Josef > > Antony > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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