I have a 1D numpy array of values, and a 2D numpy array of values
x = array([4,5,6,7],[8,9,10,11])
y = array([0,1,2,3])
I want to create tuples of each row of y with the row in x
End result being
array([(4,0),(5,1),(6,2),(7,3),(8,0),(9,1),(10,2),(11,3)])
Is there anyway to do this with numpy functions instead of for loops?
Assuming len(y) divides a size of x, you can merge a flatten view of x with an artificial padding with np.tile and then transpose result:
np.array([x.flatten(), np.tile(y, x.size//len(y))]).T
Note that x.flatten() is a synonym of x.ravel().
Let's try:
np.concatenate([list(zip(a,y)) for a in x ])
Output:
array([[ 4, 0],
[ 5, 1],
[ 6, 2],
[ 7, 3],
[ 8, 0],
[ 9, 1],
[10, 2],
[11, 3]])
Or pure python:
list(zip(x.ravel(), np.tile(y,len(x))) )
Output:
[(4, 0), (5, 1), (6, 2), (7, 3), (8, 0), (9, 1), (10, 2), (11, 3)]
np.repeat works in a different manner than OP expects.