Remove row if exist duplicated value in Numpy

I'm trying to find an efficient way to remove rows of numpy array that contains duplicated elements. For example, the array below:

[[1,2,3], [1,2,2], [2,2,2]]

should keep [[1,2,3]] only.

I know pandas apply can work row-wise but that's too slow. What is the quicker alternative?

Thanks!

Using pandas nunique (not fast!):

out = a[pd.DataFrame(a).nunique(axis=1).eq(a.shape[1])]

Or with numpy's sort and diff to ensure all values are different in a row (quite efficient if the number of columns is reasonable):

out = a[(np.diff(np.sort(a, axis=1))!=0).all(axis=1)]

Or with broadcasting (memory expensive if lots of columns):

out = a[(a[:,:,None] == a[:,None]).sum(axis=(1,2))==a.shape[1]]

Output: array([[1, 2, 3]])

Comparison of approaches: