I understand that kmeans2 is usually used for multidimensional vector space, but it is sometimes useful for 1D clustering, such as clustering the pixels of an image based solely on pixel intensities. And kmeans2 does theoretically support 1D, as stated in its function doc: def kmeans2(data, k, iter = 10, thresh = 1e-5, minit = 'random', missing = 'warn'): """ ...... data : ndarray Expect a rank 1 or 2 array. Rank 1 are assumed to describe one dimensional data, rank 2 multidimensional data, in which case one row is one observation. ........ But the truth is if data is 1D and if minit is 'random', there's an error message when calling vq(data, code) in _kmeans(), because apparently code is a rank-2 array. The cause is in _krandinit() where the return value, x, is rank-2 no matter input data is rank 1 or 2. I "fixed" it by replacing "return x" with "return x.squeeze()". I'm not sure if that's the right way. -- The magic of the microscope is not that it makes little creatures larger, but that it makes a large one smaller.