Efficiently find row intersections of two 2-D numpy arrays

I am trying to figure out an efficient way of finding row intersections of two np.arrays.

Two arrays have the same shapes, and duplicate values in each row cannot happen.

For example:

import numpy as np a = np.array([[2,5,6], [8,2,3], [4,1,5], [1,7,9]]) b = np.array([[2,3,4], # one element(2) in common with a[0] -> 1 [7,4,3], # one element(3) in common with a[1] -> 1 [5,4,1], # three elements(5,4,1) in common with a[2] -> 3 [7,6,9]]) # two element(9,7) in common with a[3] -> 2 

My desired output is : np.array([1,1,3,2])

It is easy to do this with a loop:

def get_intersect1ds(a, b): result = np.empty(a.shape[0], dtype=np.int) for i in xrange(a.shape[0]): result[i] = (len(np.intersect1d(a[i], b[i]))) return result 

Result:

>>> get_intersect1ds(a, b) array([1, 1, 3, 2]) 

But is there a more efficient way to do it?