just an FYI. pandas implemented a RangeIndex in upcoming 0.18.0, mainly for memory savings, see here <http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#range-index>, similar to how python range/xrange work.
though there are substantial perf benefits, mainly with set operations, see here <https://github.com/pydata/pandas/blob/master/pandas/indexes/range.py#L274> though didn't officially benchmark thes. Jeff On Mon, Feb 15, 2016 at 11:13 AM, Antony Lee <antony....@berkeley.edu> wrote: > Indeed: > > In [1]: class C: > def __getitem__(self, i): > if i < 10: return i > else: raise IndexError > def __len__(self): > return 10 > ...: > > In [2]: np.array(C()) > Out[2]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) > > > (omitting __len__ results in the creation of an object array, consistently > with the fact that the sequence protocol requires __len__). > Meanwhile, I found a new way to segfault numpy :-) > > In [3]: class C: > def __getitem__(self, i): > if i < 10: return i > else: raise IndexError > def __len__(self): > return 42 > ...: > > In [4]: np.array(C()) > Fatal Python error: Segmentation fault > > > 2016-02-15 0:10 GMT-08:00 Nathaniel Smith <n...@pobox.com>: > >> On Sun, Feb 14, 2016 at 11:41 PM, Antony Lee <antony....@berkeley.edu> >> wrote: >> > I wonder whether numpy is using the "old" iteration protocol (repeatedly >> > calling x[i] for increasing i until StopIteration is reached?) A quick >> > timing shows that it is indeed slower. >> >> Yeah, I'm pretty sure that np.array doesn't know anything about >> "iterable", just about "sequence" (calling x[i] for 0 <= i < >> i.__len__()). >> >> (See Sequence vs Iterable: >> https://docs.python.org/3/library/collections.abc.html) >> >> Personally I'd like it if we could eventually make it so np.array >> specifically looks for lists and only lists, because the way it has so >> many different fallbacks right now creates all confusion between which >> objects are elements. Compare: >> >> In [5]: np.array([(1, 2), (3, 4)]).shape >> Out[5]: (2, 2) >> >> In [6]: np.array([(1, 2), (3, 4)], dtype="i4,i4").shape >> Out[6]: (2,) >> >> -n >> >> -- >> Nathaniel J. Smith -- https://vorpus.org >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> https://mail.scipy.org/mailman/listinfo/numpy-discussion >> > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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