I have a function that operates over a 1D array, to return an array of a similar size. To use it in a 2D fashion I would have to do something like the following:

for row in range(np.size(arr, 0):

arr_out[row] = func(arr[row])

for col in range(np.size(arr, 1):

arr_out[:, col] = func(arr[:, col])

I would like to generalise this to N dimensions. Does anyone have any suggestions of how to achieve this? Presumably what I need to do is build an iterator, and then remove an axis:

# arr.shape=(2, 3, 4)

it = np.nditer(arr, flags=['multi_index'])

it.remove_axis(2)

while not it.finished:

arr_out[it.multi_index] = func(arr[it.multi_index])

it.iternext()

If I have an array with shape (2, 3, 4) this would allow me to iterate over the 6 1D arrays that are 4 elements long. However, how do I then construct the iterator for the preceding axes?