This article is inspired by a tweet from Peter Baumgartner. In the tweet he mentioned the
Fisher-Jenks algorithm and showed a simple example of ranking data into natural breaks using the algorithm.
Since I had never heard about it before, I did some research.
After learning more about it, I realized that it is very complimentary to my previous article on Binning
Data and it is intuitive and easy to use in standard pandas analysis. It is definitely an
approach I would have used in the past if I had known it existed.
I suspect many people are like me and have never heard of the concept of natural breaks before
but have probably done something similar on their own data. I hope this article will expose
this simple and useful approach to others so that they can add it to their python toolbox.
The rest of this article will discuss what the Jenks optimization method (or Fisher-Jenks algorithm)
is and how it can be used as a simple tool to cluster data using “natural breaks”.
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