How do I change values in my subarrays into the avarage of those values?

Question

I have an array:

> a

array([[1, 2, 3], [2, 3, 4], [3, 4, 5]])

I want to change those values to the avarage of those values, so the output will be:

> a

array([[2, 2, 2], [3, 3, 3], [4, 4, 4]])

Is there a fast way to do this without using a for loop?

Answer 1

If you are able to use numpy module the broadcasting functions are useful for this (Though they do this by highly optimized loops as well).

a.mean(axis=1) gets the row mean and np.broadcast_to broadcasts the result to the desired shape. .T just returns transpose of the result

import numpy as np
a = np.array([[1, 2, 3], [2, 3, 4], [3, 4, 5]])
np.broadcast_to(a.mean(axis=1), a.shape).T

Output:

array([[2., 2., 2.],
       [3., 3., 3.],
       [4., 4., 4.]])

Answer 2

"... Is there a fast way to do this without using a for loop? ..."

You can use a list comprehension.
Note, I'm using the int function here, since your provided data was non-fractional.

a = [[1, 2, 3], [2, 3, 4], [3, 4, 5]]
a = [([int(sum(x) / len(x))] * 3) for x in a]

Output

[[2, 2, 2], [3, 3, 3], [4, 4, 4]]

Answer 3

Another possible solution:

n = a.shape[1]
np.repeat(a.mean(axis=1), n).reshape(-1, n)

Output:

array([[2., 2., 2.],
       [3., 3., 3.],
       [4., 4., 4.]])