NumPy multi-dimensional array shape intuition

Question

I am new to NumPy. I'm having trouble figuring out how to look at a multi-dimensional array and just "know" it's shape. For example:

# a 3D array (two stacked 2D arrays)
c = np.array( [[[  0,  1,  2],               
                [ 10, 12, 13]],
               [[100,101,102],
                [110,112,113]]] )
c.shape # (2, 2, 3)

To figure out the shape in my head I've been starting with the innermost entity (a 3 element array) then work outwards (there are 2 of the 3 element arrays) and there are 2 of those matrices, so (2, 2, 3).

Is that how you do it?

I usually start backwards, so if c.shape is (2, 2, 3) I know there are two elements in the first dimension, 2 in the next, and 3 in the last. I don't frequently find myself just looking at an array, the nested brackets are hard to judge and for large arrays (more than a few dozen elements) the data isn't all displayed on screen anyhow. — user2699
– user2699, Commented Jul 17, 2017 at 14:57
Thank you @user2699 - that makes total sense to me. The example I used came directly from the official NumPy tutorial so there really wasn't much context. — RobertJoseph
– RobertJoseph, Commented Jul 17, 2017 at 14:59
You're welcome. A good read is the broadcasting rules explaining how numpy sees array shape for different operations: docs.scipy.org/doc/numpy/user/basics.broadcasting.html — user2699
– user2699, Commented Jul 17, 2017 at 15:02
You showed us a nested list. To predict the resulting shape I have to count '[' and elements. print(c) provides more help with indentation and blank lines. — hpaulj
– hpaulj, Commented Jul 17, 2017 at 15:56

zfisher · Accepted Answer · 2017-07-17 14:56:37Z

1

Think of it as a cube or an excel spreadsheet. Worksheet 1 has 2 rows and 3 columns. Worksheet 2 also has 2 rows and 3 columns. Squash them together and you have a 2x3x2 3d matrix.

answered Jul 17, 2017 at 14:56

zfisher

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