I agree, this behavior seems actively harmful. Let's fix it.
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/!
>
> 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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>
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