On Fri, Aug 18, 2017 at 7:34 PM, Eric Firing <efir...@hawaii.edu> wrote: > I don't agree. I think the consistency between bool([]) and bool(array([])) > is worth preserving. Nothing you have shown is inconsistent with "Falseness > is emptiness", which is quite fundamental in Python. The inconsistency is > in distinguishing between 1 element and more than one element. To be > consistent, bool(array([0])) and bool(array([0, 1])) should both be True. > Contrary to the ValueError message, there need be no ambiguity, any more > than there is an ambiguity in bool([1, 2]).
Yeah, this is a mess. But we're definitely not going to make bool(array([0])) be True. That would break tons of code that currently relies on the current behavior. And the current behavior does make sense, in every case except empty arrays: bool broadcasts over the array, and then, oh shoot, Python requires that bool's return value be a scalar, so if this results in anything besides an array of size 1, raise an error. OTOH you can't really write code that depends on using the current bool(array([])) semantics for emptiness checking, unless the only two cases you care about are "empty" and "non-empty with exactly one element and that element is truthy". So it's much less likely that changing that will break existing code, plus any code that does break was already likely broken in subtle ways. The consistency-with-Python argument cuts two ways: if an array is a container, then for consistency bool should do emptiness checking. If an array is a bunch of scalars with broadcasting, then for consistency bool should do truthiness checking on the individual elements and raise an error on any array with size != 1. So we can't just rely on consistency-with-Python to resolve the argument -- we need to pick one :-). Though internal consistency within numpy would argue for the latter option, because numpy almost always prefers the bag-of-scalars semantics over the container semantics, e.g. for + and *, like Eric Wieser mentioned. Though there are exceptions like iteration. ...Though actually, iteration and indexing by scalars tries to be consistent with Python in yet a third way. They pretend that an array is a unidimensional container holding a bunch of arrays: In [3]: np.array([[1]])[0] Out[3]: array([1]) In [4]: next(iter(np.array([[1]]))) Out[4]: array([1]) So according to this model, bool(np.array([])) should be False, but bool(np.array([[]])) should be True (note that with lists, bool([[]]) is True). But alas: In [5]: bool(np.array([])), bool(np.array([[]])) Out[5]: (False, False) -n -- Nathaniel J. Smith -- https://vorpus.org _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
