On 2017/08/18 11:45 AM, Michael Lamparski 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/!
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]).
Eric
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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