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 (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).

Project details


Release history Release notifications

This version
History Node

0.3.6

History Node

0.3.5

History Node

0.3.3

History Node

0.3.2

History Node

0.3.1

History Node

0.3

History Node

0.2.7

History Node

0.2.6

History Node

0.2.5

Download files

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

Filename, size & hash SHA256 hash help File type Python version Upload date
pdLSR-0.3.6.tar.gz (336.2 kB) Copy SHA256 hash SHA256 Source None Jul 24, 2016

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging CloudAMQP CloudAMQP RabbitMQ AWS AWS Cloud computing Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page