Add a level to Numpy array

Question

I have a problem with a numpy array.

In particular, suppose to have a matrix

x = np.array([[1., 2., 3.], [4., 5., 6.]])

with shape (2,3), I want to convert the float numbers into list so to obtain the array [[[1.], [2.], [3.]], [[4.], [5.], [6.]]] with shape (2,3,1). I tried to convert each float number to a list (i.e., x[0][0] = [x[0][0]]) but it does not work.

Can anyone help me? Thanks

score 2 · Answer 1 · answered Aug 22 '20 at 07:46

2

What you want is adding another dimension to your numpy array. One way of doing it is using reshape:

x = x.reshape(2,3,1)

output:

[[[1.]
  [2.]
  [3.]]

 [[4.]
  [5.]
  [6.]]]

answered Aug 22 '20 at 07:46

Ehsan

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Valdi_Bo · Answer 2 · 2020-08-22T07:45:40.310

1

Actually, you want to add a dimension (not level).

To do it, run:

result = x[...,np.newaxis]

Its shape is just (2, 3, 1).

Or save the result back under x.

edited Aug 22 '20 at 07:45

answered Aug 22 '20 at 07:34

Valdi_Bo

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score 1 · Accepted Answer · answered Aug 22 '20 at 07:55

1

There is a function in Numpy to perform exactly what @Valdi_Bo mentions. You can use np.expand_dims and add a new dimension along axis 2, as follows:

x = np.expand_dims(x, axis=2)

Refer: np.expand_dims

answered Aug 22 '20 at 07:55

Nikhil Kumar

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score 0 · Answer 4 · answered Aug 22 '20 at 08:05

You are trying to add a new dimension to the numpy array. There are multiple ways of doing this as other answers mentioned np.expand_dims, np.new_axis, np.reshape etc. But I usually use the following as I find it the most readable, especially when you are working with vectorizing multiple tensors and complex operations involving broadcasting (check this Bounty question that I solved with this method).

x[:,:,None].shape

(2,3,1)

x[None,:,None,:,None].shape

(1,2,1,3,1)

score 0 · Answer 5 · answered Aug 22 '20 at 08:43

Well, maybe this is an overkill for the array you have, but definitely the most efficient solution is to use np.lib.stride_tricks.as_strided. This way no data is copied.

import numpy as np
x = np.array([[1., 2., 3.], [4., 5., 6.]])

newshape = x.shape[:-1] + (x.shape[-1], 1)
newstrides = x.strides + x.strides[-1:]
a = np.lib.stride_tricks.as_strided(x, shape=newshape, strides=newstrides)

results in:

array([[[1.],
        [2.],
        [3.]],

       [[4.],
        [5.],
        [6.]]])

>>> a.shape
(2, 3, 1)

Add a level to Numpy array

5 Answers5