Python: Implementing a k-means algorithm with sklearn

The below is an example of how sklearn in Python can be used to develop a k-means clustering algorithm. The purpose of k-means clustering is to be able to partition observations in a dataset into a specific number of clusters in order to aid in analysis of the data. From this perspective, it has particular value from a data visualisation perspective. The particular example used here is that of stock returns. Specifically, the k-means scatter plot will illustrate the clustering of specific stock returns according to their dividend yield.