Switch dimensions of 1D ndarray

Question

I have a ‘row’ vector cast as a numpy ndarray. I would simply like to make it a ‘column’ vector (I don’t care too much about the type as long as it is compatible with matplotlib). Here is an example of what I’m trying:

import numpy as np

a = np.ndarray(shape=(1,4), dtype=float, order='F')
print(a.shape)
a.T #I think this performs the transpose?
print(a.shape)

The output looks like this:

(1, 4)
(1, 4)

I was hoping to get:

(1, 4)
(4, 1)

Can someone point me in the right direction? I have seen that the transpose in numpy doesn’t do anything to a 1D array. But is this a 1D array?

Asked By: willpower2727

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Answer 1

Transposing an array does not happen in place. Writing a.T creates a view of the transpose of the array a, but this view is then lost immediately since no variable is assigned to it. a remains unchanged.

You need to write a = a.T to bind the name a to the transpose:

>>> a = a.T
>>> a.shape
(4, 1)

In your example a is indeed a 2D array. Transposing a 1D array (with shape (n,)) does not change that array at all.

Answered By: Alex Riley

Answer 2

You probably don’t want or need the singular dimension, unless you are trying to force a broadcasting operation.

Link

You can treat rank-1 arrays as either row or column vectors. dot(A,v)
treats v as a column vector, while dot(v,A) treats v as a row vector.
This can save you having to type a lot of transposes.

Answered By: YXD

Answer 3

you can alter the shape ‘in place’ which will be the same as a.T for (1,4) but see the comment by Mr E whether it’s needed. i.e.

...
print(a.shape)
a.shape = (4, 1)
print(a.shape)

Answered By: paddyg

Switch dimensions of 1D ndarray

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