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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