Access each element of a multidimensional array without knowing the individual dimensions

Question

I have a multidimensional array but I won't know the number of dimensions or the size of each dimension. How can I generalize the code such that I can access each element of the array individually?

import numpy as np
import random

myRand = np.random.rand(5, 6, 7)

#print (myRand.shape[0])
# This works great if I already know that myRand has 3 dimensions. What if I don't know that?
mySum = 0.0
for i in range(myRand.shape[0]):
    for j in range(myRand.shape[1]):
        for k in range(myRand.shape[2]):
#           Do something with myRand[i,j,k]

David · Accepted Answer · 2020-01-06 14:27:20Z

1

You could use itertools to do so.

the following piece of code will generate the indices which you will be able to access the array while obtaining them:

import numpy as np
import itertools

v1 = np.random.randint(5, size=2)
v2 = np.random.randint(5, size=(2, 4))
v3 = np.random.randint(5, size=(2, 3, 2))

# v1
args1 = [list(range(e)) for e in list(v1.shape)]
print(v1)
for combination in itertools.product(*args1):
    print(v1[combination])

# v2
args2 = [list(range(e)) for e in list(v2.shape)]
print(v2)
for combination in itertools.product(*args2):
    print(v2[combination])

# v3
args3 = [list(range(e)) for e in list(v3.shape)]
print(v3)
for combination in itertools.product(*args3):
    print(v3[combination])

Tested it on a simple arrays with different sizes and it works fine.

answered Jan 6, 2020 at 14:27

David

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Comments

sshashank124 · Accepted Answer · 2020-01-06 14:24:09Z

1

If you do not need to retain the indices within each of the dimensions for calculation, you can just use numpy.nditer

>>> a = np.arange(8).reshape((2, 2, 2))
>>> a
array([[[0, 1],
        [2, 3]],

       [[4, 5],
        [6, 7]]])

>>> for i in np.nditer(a):
...     print(i)
...
0
1
2
3
4
5
6
7

You shouldn't really need to iterate over the array using a for-loop like this. There is usually a better way to perform whatever computation you are doing using numpy methods

answered Jan 6, 2020 at 14:24

sshashank124

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