add value to each element in array python

Question

I have an array like this

a= np.arange(4).reshape(2,2)
array([[0, 1],[2, 3]])

I want to add a value to each element in the array. I want my result return 4 array like

array([[1, 1],[2, 3]])
array([[0, 2],[2, 3]])
array([[0, 1],[3, 3]])
array([[0, 1],[2, 4]])

Answer 1

[a + i.reshape(2, 2) for i in np.identity(4)]

Answer 2

Assuming a as the input array into which values are to be added and val is the scalar value to be added, you can use an approach that works for any multi-dimensional array a using broadcasting and reshaping. Here's the implementation -

shp = a.shape # Get shape
# Get an array of 1-higher dimension than that of 'a' with vals placed at each
# "incrementing" index along the entire length(.size) of a and add to a 
out = a + val*np.identity(a.size).reshape(np.append(-1,shp))

Sample run -

In [437]: a
Out[437]: 
array([[[8, 1],
 [0, 5]],
 [[3, 2],
 [5, 1]]])
In [438]: val
Out[438]: 20
In [439]: out
Out[439]: 
array([[[[ 28., 1.],
 [ 0., 5.]],
 [[ 3., 2.],
 [ 5., 1.]]],
 [[[ 8., 21.],
 [ 0., 5.]],
 [[ 3., 2.],
 [ 5., 1.]]],
 [[[ 8., 1.],
 [ 20., 5.]],
 [[ 3., 2.],
 [ 5., 1.]]],
 [[[ 8., 1.],
 [ 0., 25.]],
 [[ 3., 2.],
 [ 5., 1.]]],
 [[[ 8., 1.],
 [ 0., 5.]],
 [[ 23., 2.],
 [ 5., 1.]]], ....

If you wish to create separate arrays from out, you can use an additional step: np.array_split(out,a.size). But for efficiency, I would advise using indexing to access all those submatrices like out[0] (for the first sub-matrix), out[1] (for the second sub-matrix) and so on.