[FIXED] How to create an array with values along specified axis?

Issue

numpy.full() is a great function which allows us to generate an array of specific shape and values. For example,

>>>np.full((2,2),[1,2])
array([[1,2],
       [1,2]])

However, it does not have a built-in option to apply values along a specific axis. So, the following code would not work:

>>>np.full((2,2),[1,2],axis=0)
array([[1,1],
       [2,2]])

Hence, I am wondering how I can create a 10x48x271x397 multidimensional array with values [1,2,3,4,5,6,7,8,9,10] inserted along axis=0? In other words, an array with [1,2,3,4,5,6,7,8,9,10] repeated along the first dimensional axis. Is there a way to do this using numpy.full() or an alternative method?

#Does not work, no axis argument in np.full()
values=[1,2,3,4,5,6,7,8,9,10]
np.full((10, 48, 271, 397), values, axis=0)

Solution

Edit: adding ideas from Michael Szczesny

import numpy as np

shape = (10, 48, 271, 397)
root = np.arange(shape[0])

You can use np.full or np.broadcast_to (only get a view at creation time):

arr1 = np.broadcast_to(root, shape[::-1]).T
arr2 = np.full(shape[::-1], fill_value=root).T

%timeit np.broadcast_to(root, shape[::-1]).T
%timeit np.full(shape[::-1], fill_value=root).T

# 3.56 µs ± 18.2 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
# 75.6 ms ± 243 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

And instead of getting the shape backwards and the array backwards again, you can use singleton dimension, but it seems less generalizable:

root = root[:, None, None, None]

arr3 = np.broadcast_to(root, shape)
arr4 = np.full(shape, fill_value=root)

root = np.arange(shape[0])
%timeit root_ = root[:, None, None, None]; np.broadcast_to(root_, shape)
%timeit root_ = root[:, None, None, None]; np.full(shape, fill_value=root_)

# 3.61 µs ± 6.36 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
# 57.5 ms ± 114 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

Checks that everything is equal and actually what we want:

assert arr1.shape == shape

for i in range(shape[0]):
    sub = arr1[i]
    assert np.all(sub == i)

assert np.all(arr1 == arr2)
assert np.all(arr1 == arr3)
assert np.all(arr1 == arr4)

Answered By - paime