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K-means in python: Determine what data is associated with each centroid

I use scipy.cluster.vq.kmeansk-mean to perform clustering, but I wondered if there is a way to determine which centroid each of your data points relates to (putativly) associated with.

Clearly, you can do it manually, but as far as I can tell, the kmeans function does not return this?

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There scipy.cluster.vqis a function kmeans2that also returns labels.

In [8]: X = scipy.randn(100, 2)

In [9]: centroids, labels = kmeans2(X, 3)

In [10]: labels
Out[10]: 
array([2, 1, 2, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 2, 2, 1, 2, 1, 2, 1, 2, 0,
       1, 0, 2, 0, 1, 2, 0, 1, 0, 1, 1, 2, 2, 2, 2, 1, 2, 1, 1, 1, 2, 0, 0,
       2, 2, 0, 1, 0, 0, 0, 2, 2, 2, 0, 0, 1, 2, 1, 0, 0, 0, 2, 1, 1, 1, 1,
       1, 0, 0, 1, 0, 1, 2, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 2, 0, 2, 2, 0,
       1, 1, 0, 1, 0, 0, 0, 2])

Otherwise, if you must use kmeans, you can also use vqto get tags:

In [17]: from scipy.cluster.vq import kmeans, vq

In [18]: codebook, distortion = kmeans(X, 3)

In [21]: code, dist = vq(X, codebook)

In [22]: code
Out[22]: 
array([1, 0, 1, 0, 2, 2, 2, 0, 1, 1, 0, 2, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1,
       2, 2, 1, 2, 0, 1, 1, 0, 2, 2, 0, 1, 0, 1, 0, 2, 1, 2, 0, 2, 1, 1, 1,
       0, 1, 2, 0, 1, 2, 2, 1, 1, 1, 2, 2, 0, 0, 2, 2, 2, 2, 1, 0, 2, 2, 2,
       0, 1, 1, 2, 1, 0, 0, 0, 0, 1, 2, 1, 2, 0, 2, 0, 2, 2, 1, 1, 1, 1, 1,
       2, 0, 2, 0, 2, 1, 1, 1])

Documentation: scipy.cluster.vq

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