Using array-initialization
to achieve that interweaving task -
def interweave(a, b):
N = a.shape[1]
M = a.shape[0] + b.shape[0]
out_dtype = np.result_type(a.dtype, b.dtype)
out = np.empty((M,N),dtype=out_dtype)
out[::2] = a
out[1::2] = b
return out
Sample run -
In [274]: A
Out[274]:
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
In [275]: B
Out[275]:
array([[ 31, 42, 53],
[ 11, 17, 29],
[100, 59, 32]])
In [276]: interweave(A, B)
Out[276]:
array([[ 1, 2, 3],
[ 31, 42, 53],
[ 4, 5, 6],
[ 11, 17, 29],
[ 7, 8, 9],
[100, 59, 32]])
If A
and B
are of same shapes, we can also stack and reshape -
In [283]: np.hstack((A,B)).reshape(-1,A.shape[1])
Out[283]:
array([[ 1, 2, 3],
[ 31, 42, 53],
[ 4, 5, 6],
[ 11, 17, 29],
[ 7, 8, 9],
[100, 59, 32]])
Or np.stack((A,B),axis=1).reshape(-1,A.shape[1])
.