numpy array of array adding up another array - numpy

Question

I am having the following array of array
a = np.array([[1,2,3],[4,5,6]])
b = np.array([[1,5,10])
and want to add up the value in b into a, like
np.array([[2,7,13],[5,10,16]])
what is the best approach with performance concern to achieve the goal?
Thanks

Answer 1

Broadcasting does that for you, so:
>>> a+b
just works:
array([[ 2, 7, 13],
[ 5, 10, 16]])
And it can also be done with
>>> a + np.tile(b,(2,1))
which gives the result
array([[ 2, 7, 13],
[ 5, 10, 16]])

Answer 2

Depending on size of inputs and time constraints, both methods might be of consideration
Method 1: Numpy Broadcasting
Operation on two arrays are possible if they are compatible
Operation generally done along with broadcasting
broadcasting in lay man terms could be called repeating elements along a specified axis
Conditions for broadcasting
Arrays need to be compatible
Compatibility is decided based on their shapes
shapes are compared from right to left.
from right to left while comparing, either they should be equal or one of them should be 1
smaller array is broadcasted(repeated) over bigger array
a.shape, b.shape
((2, 3), (1, 3))
From the rules they are compatible, so they can be added, b is smaller, so b is repeated long 1 dimension, so b can be treated as [[ 5, 10, 16], [ 5, 10, 16]]. But note numpy does not allocate new memory, it is just view.
a + b
array([[ 2, 7, 13],
[ 5, 10, 16]])
Method 2: Numba
Numba gives parallelism
It will convert to optimized machine code
Why this is because, sometimes numpy broadcasting is not good enough, ufuncs(np.add, np.matmul, etc) allocate temp memory during operations and it might be time consuming if already on memory limits
Easy parallelization
Using numba based on your requirement, you might not need temp memory allocation or various checks which numpy does, which can speed up code for huge inputs, for example. Why are np.hypot and np.subtract.outer very fast?
import numba as nb
#nb.njit(parallel=True)
def sum(a, b):
s = np.empty(a.shape, dtype=a.dtype)
# nb.prange gives numba hint to what to parallelize
for i in nb.prange(a.shape[0]):
s[i] = a[i] + b
return s
sum(a, b)