So you have an array
1
2
3
60
70
80
100
220
230
250
For a better understanding:
Observe that your data points are actually one-dimensional if x just represents an index. You can cluster your points using Scipy's cluster.vq module, which implements the k-means algorithm.
>>> import numpy as np
>>> from scipy.cluster.vq import kmeans, vq
>>> y = np.array([1,2,3,60,70,80,100,220,230,250])
>>> codebook, _ = kmeans(y, 3) # three clusters
>>> cluster_indices, _ = vq(y, codebook)
>>> cluster_indices
array([1, 1, 1, 0, 0, 0, 0, 2, 2, 2])
The result means: the first three points form cluster 1 (an arbitrary label), the next four form cluster 0 and the last three form cluster 2. Grouping the original points according to the indices is left as an exercise for the reader.
For more clustering algorithms in Python, check out scikit-learn.