Numpy Basics - How to Interpret [:,] in array access

Question

I have an nd-array A

A.shape

(2, 500, 3)

What's the difference between A[:] and A[:,2]

Coming from Python, the ',' in the array access is confusing me a lot.

the first just returns the original array, the latter will return you the Array with shape (2,3) so will skip the column or middle dimension, you could have just tried this — EdChum
– EdChum, Commented Sep 28, 2017 at 16:02
In case you're wondering how that maps to python, it calls A.__getitem__((slice(None), 2)) (note extra parens). — Eric
– Eric, Commented Sep 28, 2017 at 16:05
What's the process of mental interpretation when encountering : and , — Senthil Kumaran
– Senthil Kumaran, Commented Sep 28, 2017 at 16:05
@SenthilKumaran 1. select all elements from the first axis 2. select the third element from the second axis. 3. select all remaining elements from remaining axes (specific to numpy) — Aaron
– Aaron, Commented Sep 28, 2017 at 16:06
@Aaron: __slice__ is not a thing, and __getslice__ is deprecated/removed in all the versions of python that matter — Eric
– Eric, Commented Sep 29, 2017 at 4:36

lehiester · Accepted Answer · 2017-09-28 16:23:23Z

3

The commas separate the subscripts for each dimension. So, for example, if the matrix M is defined as

M = np.array([[1, 2, 3],
              [4, 5, 6],
              [7, 8, 9]])

then M[2, 1] would be 8 (third row, second column).

The subscript for each dimension can also be a slice, where : represents a full slice, like a slice in normal Python sequences. For example, M[:, 2] would select from every row the third column, which would be [3, 6, 9].

Any additional dimensions for which a subscript is not provided are implicitly full slices. In your example, A[:,2] is equivalent to A[:, 2, :]. If you consider the (2, 500, 3) shaped array to be two stacked matrices with 500 rows and 3 columns, then A[:, 2, :] would select from both matrices the third row (and every column of the third row), which should have a shape of (2, 3).

answered Sep 28, 2017 at 16:23

lehiester

9102 gold badges7 silver badges18 bronze badges

Sign up to request clarification or add additional context in comments.

2 Comments

lehiester Over a year ago

This certainly isn't a full explanation, but I had the same mental block when first starting to use numpy, and it was not immediately obvious where to find an answer. I certainly would have benefited from this post.

Senthil Kumaran Over a year ago

Thank you, @lehiester. This answer was helpful to me.

norok2 · Accepted Answer · 2017-09-28 17:15:55Z

1

When you have multidimensional NumPy arrays, the slicing operation [] can work if you provide tuple of slice() objects. If the number of tuples does not match your number of dimensions, this is equivalent to having a slice(None) (which abbreviates to :) in all the remaining dimensions. Note also that NumPy also accepts ... which means "fill the rest of the dimensions with :" - which is especially useful if you want to "fill" the initial dimensions.

So to recapitulate the following expression give identical results on your A array of A.ndim == 3:

A[:, 2]
A[:, 2, :]
A[:, 2, ...]


A[slice(None), 2]
A[slice(None), 2, slice(None)]
A[(slice(None), 2) + tuple(slice(None) for _ in range(A.ndim - 2))]

answered Sep 28, 2017 at 17:15

norok2

27.1k6 gold badges83 silver badges110 bronze badges

Collectives™ on Stack Overflow

Numpy Basics - How to Interpret [:,] in array access

2 Answers 2

2 Comments

Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

2 Answers 2

2 Comments

Comments

Your Answer

Sign up or log in

Post as a guest

Related