[Numpy-discussion] What is up with raw boolean indices (like a[False])?
asmeurer at gmail.com
Wed Aug 19 20:07:05 EDT 2020
> > 3. If you have multiple advanced indexing you get annoying broadcasting
> > of all of these. That is *always* confusing for boolean indices.
> > 0-D should not be too special there...
OK, now that I am learning more about advanced indexing, this
statement is confusing to me. It seems that scalar boolean indices do
not broadcast. For example:
>>> np.arange(2)[False, np.array([True, False])]
>>> np.arange(2)[tuple(np.broadcast_arrays(False, np.array([True, False])))]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
IndexError: too many indices for array: array is 1-dimensional, but 2
And indeed, the docs even say, as you noted, "the nonzero equivalence
for Boolean arrays does not hold for zero dimensional boolean arrays,"
which I guess also applies to the broadcasting.
>From what I can tell, the logic is that all integer and boolean arrays
(and scalar ints) are broadcast together, *except* for boolean
scalars. Then the first boolean scalar is replaced with and(all
boolean scalars) and the rest are removed from the index. Then that
index adds a length 1 axis if it is True and 0 if it is False.
So they don't broadcast, but rather "fake broadcast". I still contend
that it would be much more useful, if True were a synonym for newaxis
and False worked like newaxis but instead added a length 0 axis.
Alternately, True and False scalars should behave exactly like all
other boolean arrays with no exceptions (i.e., work like np.nonzero(),
broadcast, etc.). This would be less useful, but more consistent.
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