Transform numpy array to incorporate inside arrays

Question

I have a multidimensional numpy array of dtype object, which was filled with other arrays. As an example, here is a code reproducing that behavior:

arr = np.empty((3,4,2,1), dtype=object)
for i in range(arr.shape[0]):
    for j in range(arr.shape[1]):
        for k in range(arr.shape[2]):
            for l in range(arr.shape[3]):
                arr[i, j, k, l] = np.random.random(10)

Since all the inside arrays have the same size, I would like in this example to "incorporate" the last level into the array and make it an array of size (3,4,2,1,10). I cannot really change the above code, so what I am looking for is a clean way (few lines, possibly without for loops) to generate this new array once created.

Thank you.

Romain Simon · Accepted Answer · 2022-08-02 09:20:05Z

1

If I understood well your problem you could use random.random_sample() which should give the same result:

arr = np.random.random_sample((3, 4, 2, 1, 10))

After edit the solution is arr = np.array(arr.tolist())

edited Aug 2, 2022 at 9:20

answered Aug 2, 2022 at 9:04

Romain Simon

3551 silver badge8 bronze badges

Sign up to request clarification or add additional context in comments.

4 Comments

yvrob Over a year ago

Thank you for your answer. The code to create this loop is locked and I do not have access to it, I just have access to the created array. Therefore, I am looking for a way to modify the already existing array instead of creating a new one in a more clever way.

Romain Simon Over a year ago

Oh okay, I'll try to think of something else then!

Romain Simon Over a year ago

np.array(arr.tolist()) should be good !

yvrob Over a year ago

It works exactly how it should, thank you! Can you edit your answer so that I can flag it as the correct answer?

ArrowRise · Accepted Answer · 2022-08-02 09:14:10Z

Just by adding a new for loop :

arr = np.empty((3,4,2,1,10), dtype=object)
for i in range(arr.shape[0]):
    for j in range(arr.shape[1]):
        for k in range(arr.shape[2]):
            for l in range(arr.shape[3]):
                for m in range(arr.shape[4]):
                    arr[i, j, k, l, m] = np.random.randint(10)

However, you can one line this code with an optimized numpy function, every random function from numpy has a size parameter to build a array with random number with a particular shape :

arr = np.random.random((3,4,2,1,10))

EDIT :

You can flatten the array, replace every single number by a 1D array of length 10 and then reshape it to your desired shape :

import numpy as np
arr = np.empty((3,4,2,1), dtype=object)
for i in range(arr.shape[0]):
    for j in range(arr.shape[1]):
        for k in range(arr.shape[2]):
            for l in range(arr.shape[3]):
                arr[i, j, k, l] = np.random.randint(10)
   
flat_arr = arr.flatten()
for i in range(len(flat_arr)):
    flat_arr[i] = np.random.randint(0, high=10, size=(10))
res_arr = flat_arr.reshape((3,4,2,1))

Thank you for your answer. Unfortunately, I cannot change the loop. I can only modify it once created.

Collectives™ on Stack Overflow

Transform numpy array to incorporate inside arrays

2 Answers 2

4 Comments

2 Comments

Hot Network Questions

Collectives™ on Stack Overflow

2 Answers 2

4 Comments

2 Comments

Related