Skip to main content

pdLSR: Pandas-aware least squares regression.

Project description

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 (, and lmfit ( All dependencies are installable via pip or conda (see

A demonstration notebook is provided in the demo directory or the demo can be run via GitHub (see

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for pdLSR, version 0.3.6
Filename, size File type Python version Upload date Hashes
Filename, size pdLSR-0.3.6.tar.gz (336.2 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page