On Fri, Aug 18, 2017 at 2:45 PM, Michael Lamparski <diagonaldev...@gmail.com> wrote: > Greetings, all. I am troubled. > > The TL;DR is that `bool(array([])) is False` is misleading, dangerous, and > unnecessary. Let's begin with some examples: > >>>> bool(np.array(1)) > True >>>> bool(np.array(0)) > False >>>> bool(np.array([0, 1])) > ValueError: The truth value of an array with more than one element is > ambiguous. Use a.any() or a.all() >>>> bool(np.array([1])) > True >>>> bool(np.array([0])) > False >>>> bool(np.array([])) > False > > One of these things is not like the other. > > The first three results embody a design that is consistent with some of the > most fundamental design choices in numpy, such as the choice to have > comparison operators like `==` work elementwise. And it is the only such > design I can think of that is consistent in all edge cases. (see footnote 1) > > The next two examples (involving arrays of shape (1,)) are a straightforward > extension of the design to arrays that are isomorphic to scalars. I can't > say I recall ever finding a use for this feature... but it seems fairly > harmless. > > So how about that last example, with array([])? Well... it's /kind of/ like > how other python containers work, right? Falseness is emptiness (see > footnote 2)... Except that this is actually *a complete lie*, due to /all > of the other examples above/!
Yeah, numpy tries to follow Python conventions, except sometimes you run into these cases where it's trying to simultaneously follow two incompatible extensions and things get... problematic. > Here's what I would like to see: > >>>> bool(np.array([])) > ValueError: The truth value of a non-scalar array is ambiguous. Use a.any() > or a.all() > > Why do I care? Well, I myself wasted an hour barking up the wrong tree > while debugging some code when it turned out that I was mistakenly using > truthiness to identify empty arrays. It just so happened that the arrays > always contained 1 or 0 elements, so it /appeared/ to work except in the > rare case of array([0]) where things suddenly exploded. Yeah, we should probably deprecate and remove this (though it will take some time). > 2: np.array([[[[]]]]) is also False, which makes this an interesting sort of > n-dimensional emptiness test; but if that's really what you're looking for, > you can achieve this much more safely with `np.all(x.shape)` or > `bool(x.flat)` x.size is also useful for emptiness checking. -n -- Nathaniel J. Smith -- https://vorpus.org _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
