85

This had me scratching my head for a while. I was unintentionally slicing an array with None and getting something other than an error (I expected an error). Instead, it returns an array with an extra dimension.

>>> import numpy
>>> a = numpy.arange(4).reshape(2,2)
>>> a
array([[0, 1],
       [2, 3]])
>>> a[None]
array([[[0, 1],
        [2, 3]]])

Is this behavior intentional or a side-effect? If intentional, is there some rationale for it?

2
  • 2
    Somewhat related, I've used slice(None) when I needed to pass something to slice but didn't want an extra dimension added. Commented Aug 7, 2018 at 20:03
  • 2
    a[None] is equivalent to a[None, :, :] or a[None, ...]. As with other indexing expressions, trailing slices are added as needed. Thus the None by itself, adds a new axis at the start. a[...,None] adds the new axis at the end. Commented Dec 11, 2018 at 0:45

1 Answer 1

85

Using None is equivalent to using numpy.newaxis, so yes, it's intentional. In fact, they're the same thing, but, of course, newaxis spells it out better.

The docs:

The newaxis object can be used in all slicing operations to create an axis of length one. newaxis is an alias for ‘None’, and ‘None’ can be used in place of this with the same result.

A related SO question.

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1 Comment

It's intentional but is it logical? I would expect array[None] == array because I read it as "there is no mask applied". It would also feel more explicit to use something like array.add_axis() or array = np.add_axis(array) than array = array[None].

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