Skip to main content

benfordslaw is a python library to test the frequency distribution of leading digits.

Project description


Python PyPI Version License BuyMeCoffee Github Forks GitHub Open Issues Project Status Downloads Downloads Open In Colab Sphinx DOI

  • benfordslaw is Python package to test if an empirical (observed) distribution differs significantly from a theoretical (expected, Benfords) distribution. The law states that in many naturally occurring collections of numbers, the leading significant digit is likely to be small. This method can be used if you want to test whether your set of numbers may be artificial (or manipulated). If a certain set of values follows Benford's Law then model's for the corresponding predicted values should also follow Benford's Law. Normal data (Unmanipulated) does trend with Benford's Law, whereas Manipulated or fraudulent data does not.

  • Assumptions of the data:

    1. The numbers need to be random and not assigned, with no imposed minimums or maximums.
    2. The numbers should cover several orders of magnitude
    3. Dataset should preferably cover at least 1000 samples. Though Benford's law has been shown to hold true for datasets containing as few as 50 numbers.

⭐️ Star this repo if you like it ⭐️

Install benfordslaw from PyPI

pip install benfordslaw

Import benfordslaw package

from benfordslaw import benfordslaw

Documentation pages

On the documentation pages you can find detailed information about the working of the benfordslaw with many examples.




Please cite in your publications if this is useful for your research (see citation).



  • All kinds of contributions are welcome!
  • If you wish to buy me a Coffee for this work, it is very appreciated :)


See LICENSE for details.

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

benfordslaw-1.2.2.tar.gz (10.6 kB view hashes)

Uploaded source

Built Distribution

benfordslaw-1.2.2-py3-none-any.whl (10.9 kB view hashes)

Uploaded py3

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