On Sun, Feb 14, 2016 at 10:36 PM, Charles R Harris < charlesr.har...@gmail.com> wrote:
> > > On Sun, Feb 14, 2016 at 7:36 AM, Ralf Gommers <ralf.gomm...@gmail.com> > wrote: > >> >> >> On Sun, Feb 14, 2016 at 9:21 AM, Antony Lee <antony....@berkeley.edu> >> wrote: >> >>> re: no reason why... >>> This has nothing to do with Python2/Python3 (I personally stopped using >>> Python2 at least 3 years ago.) Let me put it this way instead: if >>> Python3's "range" (or Python2's "xrange") was not a builtin type but a type >>> provided by numpy, I don't think it would be controversial at all to >>> provide an `__array__` special method to efficiently convert it to a >>> ndarray. It would be the same if `np.array` used a >>> `functools.singledispatch` dispatcher rather than an `__array__` special >>> method (which is obviously not possible for chronological reasons). >>> >>> re: iterable vs iterator: check for the presence of the __next__ special >>> method (or isinstance(x, Iterable) vs. isinstance(x, Iterator) and not >>> isinstance(x, Iterable)) >>> >> >> I think it's good to do something about this, but it's not clear what the >> exact proposal is. I could image one or both of: >> >> - special-case the range() object in array (and asarray/asanyarray?) >> such that array(range(N)) becomes as fast as arange(N). >> - special-case all iterators, such that array(range(N)) becomes as fast >> as deque(range(N)) >> > > I think the last wouldn't help much, as numpy would still need to > determine dimensions and type. I assume that is one of the reason sparse > itself doesn't do that. > Not orders of magnitude, but this shows that there's something to optimize for iterators: In [1]: %timeit np.array(range(100000)) 100 loops, best of 3: 14.9 ms per loop In [2]: %timeit np.array(list(range(100000))) 100 loops, best of 3: 9.68 ms per loop Ralf
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