[Numpy-discussion] Use of booleans in slices

[Sebastian Berg]

This is because slicing with a boolean has just no confusing meaning I
can think of [1].  NumPy even used to reject it, but there seems no
reason to add maintenance/code complexity (i.e. duplicate code from
Python already provides) to reject bools. There used to be a reason to
using `__index__()` in slices, because Python did not. But now Python
caught up.

- Sebastian

[1] I have never seen anyone index with a bool, and I have no
conception of what `arr[masked:not_masked]` would mean.
There is not a small step from `arr[True]` to `arr[True:]`.



On Thu, 2020-08-13 at 15:14 -0600, Aaron Meurer wrote:
> I noticed that np.bool_.__index__() gives a DeprecationWarning
> 
> > > > np.bool_(True).__index__()
> __main__:1: DeprecationWarning: In future, it will be an error for
> 'np.bool_' scalars to be interpreted as an index
> 1
> 
> This is good, because booleans don't actually act like integers in
> indexing contexts. However, raw Python bools also allow __index__()
> 
> > > > True.__index__()
> 1
> 
> A consequence of this is that NumPy slices allow booleans, as long as
> they are the Python type (if you use the NumPy bool_ type you get the
> deprecation warning).
> 
> > > > a = np.arange(10)
> > > > a[True:]
> array([1, 2, 3, 4, 5, 6, 7, 8, 9])
> 
> Should this behavior also be considered deprecated? Presumably
> deprecating bool.__index__() in Python is a no-go, but it could be
> deprecated in NumPy contexts (in the pure Python collections,
> booleans
> don't have a special indexing meaning anyway).
> 
> Interestingly, places that use a shape don't allow booleans (I guess
> they don't necessarily use __index__()?)
> 
> > > > np.empty((True,))
> Traceback (most recent call last):
>   File "<stdin>", line 1, in <module>
> TypeError: an integer is required
> 
> Aaron Meurer
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