Intersection of 2d and 1d Numpy array

If you feed in that sliced 2D array A[:,3:] to np.in1d, it would flatten it to a 1D array and compare with B for occurrences and thus create a 1D mask, which could be reshaped and used for boolean indexing into that sliced array to set the TRUE elements to zeros. A one-liner implementation would look something like this -

A[:,3:][np.in1d(A[:,3:],B).reshape(A.shape[0],-1)] = 0

Sample run -

In [37]: AOut[37]: array([[  1,   1,  10, 101, 102, 103,   0,   0],       [  2,   2,  10, 102, 108,   0,   0,   0],       [  3,   3,  11, 101, 102, 106, 107, 108]])In [38]: np.in1d(A[:,3:],B) # Flattened maskOut[38]: array([ True, False, False, False, False, False,  True, False, False,       False,  True, False,  True, False,  True], dtype=bool)In [39]: np.in1d(A[:,3:],B).reshape(A.shape[0],-1) # Reshaped maskOut[39]: array([[ True, False, False, False, False],       [False,  True, False, False, False],       [ True, False,  True, False,  True]], dtype=bool)In [40]: A[:,3:][np.in1d(A[:,3:],B).reshape(A.shape[0],-1)] = 0 # Final codeIn [41]: AOut[41]: array([[  1,   1,  10,   0, 102, 103,   0,   0],       [  2,   2,  10, 102,   0,   0,   0,   0],       [  3,   3,  11,   0, 102,   0, 107,   0]])

To make things simpler, you could create a view of the flattened A and use the 1D mask obtained from np.in1d to have a more elegant solution. For a solution that changes only the sliced A[:,3:], you can use .flat and then index like so -

A[:,3:].flat[np.in1d(A[:,3:],B)] = 0

For a case when you would like to set matching ones across entire A, you can use .ravel() -

A.ravel()[np.in1d(A,B)] = 0

I know .ravel() is a view and from the docs, it seems .flat doesn't create a copy either, so these should be cheap.

Here's a way to do this without using in1d(). You can use the regular Python in operator with a ravel-ed version of your array:

listed = [aa  in B for aa in A[:, 3:].ravel()]# mask for unaffected left columns of Amask1 = np.array([False]*A.shape[0]*3)mask1.shape = (A.shape[0], 3)# mask for affected right columns of Amask2 = np.array(listed)mask2.shape = (A.shape[0], A.shape[1]-3)# join masks together so you have a mask with same dimensions as Amask = np.hstack((mask1, mask2))result  = A.copy()result[mask] = 0

Or more succinctly:

listed = [aa  in B for aa in A[:, 3:].ravel()]listed_array = np.array(listed)listed.shape = (A.shape[0], A.shape[1]-3)A[:, 3:][listed_array] = 0

You're probably better off with in1d() but it's nice to know there are other options.