powerlaw
Toolbox for testing if a probability distribution fits a power law
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has no known vulnerabilities, low install friction, and a permissive license. It is the standard tool for rigorous power-law fitting in Python and is well-suited for academic research, data analysis, and any work requiring careful statistical comparison of heavy-tailed distributions. Install it if you need to test whether data actually follows a power law rather than relying on visual inspection or naive fitting.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction: pure Python wheel with five standard scientific dependencies (scipy, numpy, matplotlib, mpmath, tqdm) that most data science environments already have.
- Actively maintained with recent commits and CI across Python 3.8–3.12.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use, modify, and distribute this package freely with minimal restrictions, making it suitable for academic, commercial, and private projects.
last release 2026-01-02 (224 days) · last repo commit 2026-04-09 · 640 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 118,899 downloads/mo, #12,100 on PyPI
Alternatives
Verify before relying
import powerlaw
import numpy as np
data = np.array([1.7, 3.2, 5.4, 2.1, 1.5, 2.8])
fit = powerlaw.Fit(data)
print(fit.power_law.alpha)
R, p = fit.distribution_compare('power_law', 'lognormal')- Whether mpmath's numerical precision requirement for negative numbers in scipy's gamma functions remains a practical limitation in current scipy versions.
- Performance characteristics and scalability limits on very large datasets.
What it is and what it does
powerlaw is a statistical toolbox for testing whether empirical data follows a power-law distribution and comparing it against competing heavy-tailed models like lognormals. It implements the rigorous methods from Clauset et al. 2007 and Klaus et al. 2011, which address the common problem of incorrectly identifying power laws in real-world data. The package fits power-law parameters (alpha and xmin), performs goodness-of-fit tests, and conducts likelihood-ratio comparisons between distributions.
The library is designed for researchers and data analysts who need to determine whether their data exhibits true power-law behavior or whether simpler distributions (like lognormal) provide equally good or better explanations. It integrates with matplotlib for visualization and uses numpy and scipy for numerical computation. The package includes a lognormal_positive variant for cases where theoretical constraints require the underlying distribution parameters to remain positive.
Use it for
- Analyze network degree distributions or other empirical data to test whether they follow power laws.
- Compare competing heavy-tailed distribution hypotheses using likelihood-ratio tests to determine the best fit.
- Visualize fitted power-law distributions and raw data together using matplotlib integration.
- Validate whether observed tail behavior in scientific datasets (e.g., biological, physical) matches power-law predictions.
- Fit power-law exponents (alpha) and minimum values (xmin) for downstream modeling or theoretical comparison.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has no known vulnerabilities, low install friction, and a permissive license. It is the standard tool for rigorous power-law fitting in Python and is well-suited for academic research, data analysis, and any work requiring careful statistical comparison of heavy-tailed distributions. Install it if you need to test whether data actually follows a power law rather than relying on visual inspection or naive fitting.
Install
powerlaw on PyPI
Before you install
Low friction: pure Python wheel with five standard scientific dependencies (scipy, numpy, matplotlib, mpmath, tqdm) that most data science environments already have. Actively maintained with recent commits and CI across Python 3.8–3.12.
License in practice
MIT license is permissive; you can use, modify, and distribute this package freely with minimal restrictions, making it suitable for academic, commercial, and private projects.
Quickstart
import powerlaw
import numpy as np
data = np.array([1.7, 3.2, 5.4, 2.1, 1.5, 2.8])
fit = powerlaw.Fit(data)
print(fit.power_law.alpha)
R, p = fit.distribution_compare('power_law', 'lognormal')Verify before relying
- Whether mpmath's numerical precision requirement for negative numbers in scipy's gamma functions remains a practical limitation in current scipy versions.
- Performance characteristics and scalability limits on very large datasets.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesscipynumpymatplotlibmpmathtqdm |
| Maintenance | Actively maintained 224 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 118,899 / month, #12,100 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: Mathematics |
Evidence: powerlaw-2.0.0-py3-none-any.whl
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