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
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