But I would have expected these to raise ValueErrors recommending any() and all():
bool(np.array([1])) True bool(np.array([0])) False
While I can't confess to know the *actual* reason why single-element arrays evaluate the way they do, this is how I understand it: One thing that single-element arrays have going for them is that, for arrays like this, `x.any() == x.all()`. Hence, in these cases, there is no ambiguity. In this same light, we can see yet another argument against bool(np.array([])), because guess what: This one IS ambiguous!
np.array([]).any() False np.array([]).all() True
On Fri, Aug 18, 2017 at 6:37 PM, Paul Hobson <pmhobson@gmail.com> wrote:
Maybe I'm missing something.
This seems fine to me:
bool(np.array([])) False
But I would have expected these to raise ValueErrors recommending any() and all():
bool(np.array([1])) True bool(np.array([0])) False
On Fri, Aug 18, 2017 at 3:00 PM, Stephan Hoyer <shoyer@gmail.com> wrote:
I agree, this behavior seems actively harmful. Let's fix it.
On Fri, Aug 18, 2017 at 2:45 PM, Michael Lamparski < diagonaldevice@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/!
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.
I posit that there is no usage of the fact that `bool(array([])) is False` in any real-world code which is not accompanied by a horrible bug writhing in hiding just beneath the surface. For this reason, I wish to see this behavior *abolished*.
Thank you. -Michael
Footnotes: 1: Every now and then, I wish that `ndarray.__{bool,nonzero}__` would just implicitly do `all()`, which would make `if a == b:` work like it does for virtually every other reasonably-designed type in existence. But then I recall that, if this were done, then the behavior of `if a != b:` would stand out like a sore thumb instead. Truly, punting on 'any/all' was the right choice.
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)`
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