CODEWITHSUNDEEP
pythonarraysnumpyvectorizationnumpy-ndarray
EquipDev
EquipDev

Reputation: 5921

How to create or fill an numpy array with another array?

How to create an numpy array with shape [2, 2, 3], where the elements at axis 2 is another array, for example [1, 2, 3]?

So I would like to do something like this invalid code:

a = np.arange(1, 4)
b = np.full((3, 3), a)

Resulting in an array like:

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

Could of course make the loop for filling like, but thought there may be a shortcut:

for y in range(b.shape[0]):
    for x in range(b.shape[1]):
        b[y, x, :] = a

Upvotes: 5

Views: 6807

Answers (4)

kmario23
kmario23

Reputation: 61325

Also using np.concatenate or it's wrapper np.vstack

In [26]: a = np.arange(1,4)

In [27]: np.vstack([a[np.newaxis, :]]*4).reshape(2,2, 3)
Out[27]: 
array([[[1, 2, 3],
        [1, 2, 3]],

       [[1, 2, 3],
        [1, 2, 3]]])

In [28]: np.concatenate([a[np.newaxis, :]]*4, axis=0).reshape(2,2, 3)
Out[28]: 
array([[[1, 2, 3],
        [1, 2, 3]],

       [[1, 2, 3],
        [1, 2, 3]]])

Upvotes: 1

EquipDev
EquipDev

Reputation: 5921

Based on Divakar comment, an answer can also be:

import numpy as np
np.full([2, 2, 3], np.arange(1, 4))

Yet another possibility is:

import numpy as np
b = np.empty([2, 2, 3])
b[:] = np.arange(1, 4)

Upvotes: 2

paddyg
paddyg

Reputation: 2233

If your numpy version is >= 1.10 you can use broadcast_to

a = np.arange(1,4)
a.shape = (1,1,3)
b = np.broadcast_to(a,(2,2,3))

This produces a view rather than copying so will be quicker for large arrays. EDIT this looks to be the result you're asking for with your demo.

Upvotes: 2

jotasi
jotasi

Reputation: 5177

There are multiple ways to achieve this. One is to use np.full in np.full((2,2,3), a) as pointed out by Divakar in the comments. Alternatively, you can use np.tile for this, which allows you to construct an array by repeating an input array a given number of times. To construct your example you could do:

import numpy as np

np.tile(np.arange(1, 4), [2, 2, 1])

Upvotes: 3

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