Skip to main content

K-Means++ Clustering for Pandas DataFrames

Project description

K-means++ in Pandas
===================

An implementation of the [k-means++ clustering algorithm](http://en.wikipedia.org/wiki/K-means%2B%2B) using [Pandas](http://pandas.pydata.org/).

Prerequisites
-------------

* Python 2.7 or lower; this is not Python 3 compatible (yet).
* [Pandas](http://pandas.pydata.org/) (obviously).
* [NumPy](http://numpy.org)

Usage
-----

Here are the constructor arguments:

* `data_frame`: A Pandas data frame representing the data that you wish to cluster. Rows represent observations, and columns represent variables.

* `k`: The number of clusters that you want.

* `columns=None`: A list of column names upon which you wish to cluster your data. If this argument isn't provided, then all of the columns are selected. **Note:** Columns upon which you want to cluster must be numeric and have no `numpy.nan` values.

* `max_iterations=None`: The maximum number of times that you wish to iterate k-means. If no value is provided, then the iterations continue until stability is reached (ie the cluster assignments don't change between one iteration and the next).

* `appended_column_name=None`: If this value is set with a string, then a column will be appended to your data with the given name that contains the cluster assignments (which are integers from 0 to `k-1`). If this argument is not set, then you still have access to the clusters via the `clusters` attribute.

Once you've constructed a `KMeansPlusPlus` object, then just call the `cluster` method, and everything else should happen automagically. Take a look at the `examples` folder.

TODO:
----

* Add on features that take iterations of k-means++ clusters and compares them via, eg, concordance matrices, Jaccard indices, etc.

* Given a data frame, implement the so-called [Elbow Method](http://en.wikipedia.org/wiki/Determining_the_number_of_clusters_in_a_data_set#The_Elbow_Method) to take a stab at an optimal value for `k`.

* Make this into a proper Python module that can be installed via pip.

* Python 3 compatibility (probably via six).

Project details


Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page