Reshape an array using Numpy

Question

I have an array with the next distribution

array -> shape(878,)

one element inside array

shape(313,313,3)

What I have to do to get the next result?

shape(878,313,313,3)

thanks

So elements of the array are arrays? Can you provide minimal reproducible example? — anishtain4
– anishtain4, Commented Jun 21, 2018 at 19:41
If your array elements are 3D arrays of the same size, then it is automatically a 4D array. — DYZ
– DYZ, Commented Jun 21, 2018 at 19:42
What's the dtype? Object? Then you have an array of arrays. If so, then this other recent question is relevant: stackoverflow.com/questions/50971123/… — hpaulj
– hpaulj, Commented Jun 21, 2018 at 19:43
Is the dtype of your array object? There are a few ways you could have constructed a 1-D array of length 878 containing arrays, but typically they'd involve a mistake of sizing. — DSM
– DSM, Commented Jun 21, 2018 at 19:44

David · Accepted Answer · 2018-06-21 19:51:57Z

1

Assuming that datatypes are not an issue, you should be able to stack them using

np.stack(yourArray)

answered Jun 21, 2018 at 19:51

David

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Anurag Reddy · Accepted Answer · 2018-06-21 19:58:24Z

0

Is it an array or a dictionary. But even if you want to change the size , you could directly use np.reshape() for this operation but if you want ot change it in some different way, here is a way:

x = np.zeros((array.size,313,313,3)) # you could replace the numbers with shape of elements

for i in range(array.size):

    x[i] = a[i]

edited Jun 21, 2018 at 19:58

answered Jun 21, 2018 at 19:55

Anurag Reddy

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6 Comments

David Over a year ago

This will be less efficient than letting numpy handle the work for you. Check my answer.

Jay Calamari Over a year ago

Why do x = np.array([]) and then reassign to x with x = np.zeros((array.size,313,313,3))? :p

David Over a year ago

Another thing, setting x and then resetting it is a waste.

David Over a year ago

Beat me to it @JayCalamari

Anurag Reddy Over a year ago

Yeah, I know that that's why I gave it as an alternate solution if he wanted to see how that actually happens or yeah np.vstack() might directly work for him. But thanks for noticing.

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