Instead of using in, you can use np.in1d to check which values in the first column of ar are also in another_ar and then use the boolean index returned to fetch the rows of ar:
>>> ar[np.in1d(ar[:,0], another_ar)]array([[ 1, 2], [ 6, -15]])This is likely to be much faster than using any kind of for loop and testing membership with in.
