Skip to main content

Plot histograms in a scalable way and a beautiful style.

Project description

Plot histograms in a scalable way and a beautiful style.

Example Example

GitHub Project PyPI version Docs from latest Docs from main Code style: black

Major advantages over other plotting libraries: scalability, style and user-friendly way of managing variables and a stunning image gallery.

  1. First idea: default style is already presentation-ready and/or paper-ready (with no effort).

  2. Second idea: separate the histogram creation from its plotting. Then you can easily manage histogram objects (boost_histogram library) and plot large amount of variables and data really fast.

  3. Third idea: plot a lot of variable easily with a variable manager. Really easy to modify the plotting information and do multiple plots with same variable but different settings.

  4. Fourth idea: detailed and user-friendly documentation including a marvelous gallery of examples.

2D histogram with projections

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

plothist-1.0.1.tar.gz (4.0 MB view hashes)

Uploaded Source

Built Distribution

plothist-1.0.1-py3-none-any.whl (2.8 MB view hashes)

Uploaded Python 3

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