pdLSR: Pandas-aware least squares regression.
pdLSR by Michelle L. Gill
pdLSR is a library for performing least squares regression. It attempts to
seamlessly incorporate this task in a Pandas-focused workflow. Input data
are expected in dataframes, and multiple regressions can be performed using
functionality similar to Pandas
groupby. Results are returned as grouped
dataframes and include best-fit parameters, statistics, residuals, and more.
pdLSR has been tested on python 2.7, 3.4, and 3.5. It requires Numpy,
Pandas, multiprocess (https://github.com/uqfoundation/multiprocess), and
lmfit (https://github.com/lmfit/lmfit-py). All dependencies are installable
via pip or conda (see README.md).
A demonstration notebook is provided in the
demo directory or the demo
can be run via GitHub (see README.md).