I implemented kmeans1d, a Python library for performing k-means clustering on 1D data, based on the algorithm from Xiaolin (1991), as presented by Grønlund et al. (2017, Section 2.2).
Globally optimal k-means clustering is NP-hard for multi-dimensional data. Lloyd’s algorithm is a popular approach for finding a locally optimal solution. For 1-dimensional data, there are polynomial time algorithms.
kmeans1d contains an O(kn + n log n) dynamic programming algorithm for finding the globally optimal k clusters for n 1D data points. The code is written in C++—for faster execution than a pure Python implementation—and wrapped in Python.
The source code is available on GitHub:
The package is available on PyPI, the Python Package Index. It can be installed with pip.
$ pip3 install kmeans1d
The snippet below includes an example of how to use the library.
 Wu, Xiaolin. “Optimal Quantization by Matrix Searching.” Journal of Algorithms 12, no. 4 (December 1, 1991): 663
 Grønlund, Allan, Kasper Green Larsen, Alexander Mathiasen, Jesper Sindahl Nielsen, Stefan Schneider, and Mingzhou Song. “Fast Exact K-Means, k-Medians and Bregman Divergence Clustering in 1D.” ArXiv:1701.07204 [Cs], January 25, 2017. http://arxiv.org/abs/1701.07204.