Most efficient way to apply operation on each element of Numpy array

Question

Assume I have a function which takes a numpy array of shape (m, k) and I want to apply that function on each element of numpy array of shape (n, m, k). Naive approach is to iterate through the given numpy array and append the transformed element to an empty numpy array of shape (0, m, k)

result = np.empty(shape=(0, m, k))
for element in elements:
    result = np.append(result, [some_operation(element)], axis=0)

What's the efficient way to apply some, let's say, "operation" on numpy array of shape (n, m, k)? I guess there is a more "numpy" approach.

Many thanks.

Many numpy functions and methods can work with 3d array, but without knowing what's in your function we can't suggest a way of doing that. So the most general way is to iterate on that first dimension, and collect the results. If some_operation is complex it enough the iteration mechanism doesn't matter much; it's calling some_operation n` times that will take up most time. But np.append is the least efficient; list append is better, or assignment to result[element, :,:]. — hpaulj
– hpaulj, Commented Apr 6, 2021 at 20:25
list(map(f, array)) is marginally faster than the list.append loop, particularly for very large arrays — tofd
– tofd, Commented Apr 6, 2021 at 22:05

Souha Gaaloul · Accepted Answer · 2021-04-07 09:19:25Z

1

I think map is the most appropriate function for this

answered Apr 7, 2021 at 9:19

Souha Gaaloul

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1 Comment

Mikayel Smbatyan Over a year ago

Thanks that was very helpful!

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