How to add elements of a 1D NumPy array to NumPy subarrays with the same index?

Question

Let's say I have a NumPy array a with shape (3, 5, 7) and a 1D NumPy array l with shape (3, 1). I want to add the 0th element of l to the 0th subarray in a, the 1st element of l to the 1st subarray of a, and the 2nd element of l to the 2nd and last subarray of a.

For instance, if l = [[30 40 50]], then I want a[0] += 30, a[1] += 40, a[2] += 50.

I know that to achieve this, I can easily do the following:

for i in range(a.shape[0]):
    a[i] += l[i]

but I was wondering if there's a way to do this with pure NumPy, no for loops required. Trying np.add(a, l) invariably creates the error operands could not be broadcast together with shapes (3,5,7) (3,1) and trying the following:

def add_arrs(a, l):
    return a + l

a = np.apply_along_axis(add_arrs, 0, a, l)

gives me a final shape of (3, 3, 5, 7), which is not what I want. Thanks for your help!

EDIT: I figured it out:

a = np.apply_along_axis(np.add, 0, a, l.T)[0]

I'm not sure why l is nested. If it weren't the [0] wouldn't be necessary, or the .T — Chris
– Chris, Commented Apr 6, 2022 at 15:01

hpaulj · Accepted Answer · 2022-04-06 15:04:46Z

1

In [7]: a = np.arange(3*5*7).reshape(3,5,7)
In [8]: b = np.array([[30,40,50]])
In [9]: b.shape
Out[9]: (1, 3)

That leading 1 dimension doesn't help; you have to transpose anyways

In [10]: b = np.array([30,40,50])
In [11]: b.shape
Out[11]: (3,)

The broadcasting works fine if b is (3,1,1) shape:

In [12]: a += b[:,None,None]
In [13]: a
Out[13]: 
array([[[ 30,  31,  32,  33,  34,  35,  36],
        [ 37,  38,  39,  40,  41,  42,  43],
        [ 44,  45,  46,  47,  48,  49,  50],
        [ 51,  52,  53,  54,  55,  56,  57],
        [ 58,  59,  60,  61,  62,  63,  64]],

       [[ 75,  76,  77,  78,  79,  80,  81],
        [ 82,  83,  84,  85,  86,  87,  88],
        [ 89,  90,  91,  92,  93,  94,  95],
        [ 96,  97,  98,  99, 100, 101, 102],
        [103, 104, 105, 106, 107, 108, 109]],

       [[120, 121, 122, 123, 124, 125, 126],
        [127, 128, 129, 130, 131, 132, 133],
        [134, 135, 136, 137, 138, 139, 140],
        [141, 142, 143, 144, 145, 146, 147],
        [148, 149, 150, 151, 152, 153, 154]]])

answered Apr 6, 2022 at 15:04

hpaulj

233k14 gold badges260 silver badges392 bronze badges

Sign up to request clarification or add additional context in comments.

1 Comment

AJH Over a year ago

Thank you! Your solution is so much simpler than mine.

AJH · Accepted Answer · 2022-04-06 14:59:51Z

0

Figured it out, here's the answer:

a = np.apply_along_axis(np.add, 0, a, l.T)[0]

answered Apr 6, 2022 at 14:59

AJH

7991 gold badge5 silver badges16 bronze badges

Collectives™ on Stack Overflow

How to add elements of a 1D NumPy array to NumPy subarrays with the same index?

2 Answers 2

1 Comment

Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

2 Answers 2

1 Comment

Comments

Your Answer

Sign up or log in

Post as a guest

Related