K-means++ in Pandas
This package should not be used in production. The implementation of k-means++ contained therein is much slower than that of scikit-learn. Use that instead.
The only reason why I wrote any of this is to teach myself Pandas.
If you have pip, then just do
pip install k-means-plus-plus
Clone the repository:
git clone https://github.com/jackmaney/k-means-plus-plus-pandas.git
Enter the newly-created folder containing the repo
And run the installation manually:
python setup.py install
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
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
Once you've constructed a
KMeansPlusPlus object, then just call the
cluster method, and everything else should happen automagically. Take a look at the
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 to take a stab at an optimal value for
Make this into a proper Python module that can be installed via pip.
Python 3 compatibility (probably via six).