# What is up with raw boolean indices (like a[False])?

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## What is up with raw boolean indices (like a[False])?

 I've been trying to figure out this behavior. It doesn't seem to be documented at https://numpy.org/doc/stable/reference/arrays.indexing.html>>> a = np.empty((2, 3)) >>> a.shape (2, 5) >>> a[True].shape (1, 2, 5) >>> a[False].shape (0, 2, 5) It seems like indexing with a raw boolean (True or False) adds an axis with a dimension 1 or 0, resp. Except it only works once: >>> a[:,False] array([], shape=(2, 0, 3), dtype=float64) >>> a[:,False, False] array([], shape=(2, 0, 3), dtype=float64) >>> a[:,False,True].shape (2, 0, 3) >>> a[:,True,False].shape (2, 0, 3) The docs say "A single boolean index array is practically identical to x[obj.nonzero()]". I have a hard time seeing this as an extension of that, since indexing by `np.nonzero(False)` or `np.nonzero(True)` *replaces* the given axis.  >>> a[np.nonzero(True)].shape (1, 3) >>> a[np.nonzero(False)].shape (0, 3) I think at best this behavior should be documented. I'm trying to understand the motivation for it, or if it's even intentional. And in particular, why do multiple boolean indices not insert multiple axes? It would actually be useful to be able to generically add length 0 axes using an index, similar to how `newaxis` adds a length 1 axis. Aaron Meurer _______________________________________________ NumPy-Discussion mailing list [hidden email] https://mail.python.org/mailman/listinfo/numpy-discussion
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