This is an efficient implementation of Friedman's SuperSmoother [1] algorithm in pure Python. It makes use of numpy for fast numerical computation.
Installation is simple: To install the released version, type
$ pip install supersmoother
To install the bleeding-edge source, download the source code from http://github.com/jakevdp/supersmoother and type:
$ python setup.py install
The only package dependency is numpy; scipy is also required if you want to run the unit tests.
The package includes several example notebooks showing the code in action.
You can see these in the examples/ directory, or view them statically
on nbviewer
This code has full unit tests implemented in nose. With nose installed, you can run the test suite using
$ nosetests supersmoother
[1]: Friedman, J. H. (1984) A variable span scatterplot smoother. Laboratory for Computational Statistics, Stanford University Technical Report No. 5. (pdf)