Skip to main content

Toolbox for testing if a probability distribution fits a power law

Project description

powerlaw is a toolbox using the statistical methods developed in Clauset et al. 2007 and Klaus et al. 2011 to determine if a probability distribution fits a power law. Academics, please cite as:

Jeff Alstott, Ed Bullmore, Dietmar Plenz. (2014). powerlaw: a Python package for analysis of heavy-tailed distributions. PLoS ONE 9(1): e85777

Also available at arXiv:1305.0215 []

Basic Usage

For the simplest, typical use cases, this tells you everything you need to know.:

import powerlaw
data = array([1.7, 3.2 ...]) #data can be list or Numpy array
results = powerlaw.Fit(data)
print results.power_law.alpha
print results.power_law.xmin
R, p = results.distribution_compare('power_law', 'lognormal')

For more explanation, understanding, and figures, see the working paper, which illustrates all of powerlaw’s features. For details of the math, see Clauset et al. 2007, which developed these methods.


Many thanks to Andreas Klaus, Mika Rubinov and Shan Yu for helpful discussions. Thanks also to Andreas Klaus, Aaron Clauset, Cosma Shalizi, and Adam Ginsburg for making their code available. Their implementations were a critical starting point for making powerlaw.

Project details

Download files

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

Source Distribution

powerlaw-1.2.tar.gz (21.6 kB view hashes)

Uploaded Source

Built Distribution

powerlaw-1.2.linux-x86_64.exe (85.2 kB view hashes)

Uploaded Source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page