Skip to main content

Statistical data visualization

Project description



seaborn: statistical data visualization

PyPI Version License DOI Tests Code Coverage

Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.

Documentation

Online documentation is available at seaborn.pydata.org.

The docs include a tutorial, example gallery, API reference, and other useful information.

To build the documentation locally, please refer to doc/README.md.

There is also a FAQ page, currently hosted on GitHub.

Dependencies

Seaborn supports Python 3.7+ and no longer supports Python 2.

Installation requires numpy, pandas, and matplotlib. Some advanced statistical functionality requires scipy and/or statsmodels.

Installation

The latest stable release (and required dependencies) can be installed from PyPI:

pip install seaborn

It is also possible to include optional statistical dependencies (only relevant for v0.12+):

pip install seaborn[stats]

Seaborn can also be installed with conda:

conda install seaborn

Note that the main anaconda repository lags PyPI in adding new releases, but conda-forge (-c conda-forge) typically updates quickly.

Citing

A paper describing seaborn has been published in the Journal of Open Source Software. The paper provides an introduction to the key features of the library, and it can be used as a citation if seaborn proves integral to a scientific publication.

Testing

Testing seaborn requires installing additional dependencies; they can be installed with the dev extra (e.g., pip install .[dev]).

To test the code, run make test in the source directory. This will exercise the unit tests (using pytest) and generate a coverage report.

Code style is enforced with flake8 using the settings in the setup.cfg file. Run make lint to check. Alternately, you can use pre-commit to automatically run lint checks on any files you are committing: just run pre-commit install to set it up, and then commit as usual going forward.

Development

Seaborn development takes place on Github: https://github.com/mwaskom/seaborn

Please submit bugs that you encounter to the issue tracker with a reproducible example demonstrating the problem. Questions about usage are more at home on StackOverflow, where there is a seaborn tag.

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

seaborn-0.12.1.tar.gz (1.4 MB view details)

Uploaded Source

Built Distribution

seaborn-0.12.1-py3-none-any.whl (288.2 kB view details)

Uploaded Python 3

File details

Details for the file seaborn-0.12.1.tar.gz.

File metadata

  • Download URL: seaborn-0.12.1.tar.gz
  • Upload date:
  • Size: 1.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: python-requests/2.28.1

File hashes

Hashes for seaborn-0.12.1.tar.gz
Algorithm Hash digest
SHA256 bb1eb1d51d3097368c187c3ef089c0288ec1fe8aa1c69fb324c68aa1d02df4c1
MD5 6971acbfbb3a1991f273d1807e3be805
BLAKE2b-256 cfc6e605f937d0301c1d00a159d55c96ae9edc1adf2fabec6dbc5e43333e5943

See more details on using hashes here.

File details

Details for the file seaborn-0.12.1-py3-none-any.whl.

File metadata

  • Download URL: seaborn-0.12.1-py3-none-any.whl
  • Upload date:
  • Size: 288.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: python-requests/2.28.1

File hashes

Hashes for seaborn-0.12.1-py3-none-any.whl
Algorithm Hash digest
SHA256 a9eb39cba095fcb1e4c89a7fab1c57137d70a715a7f2eefcd41c9913c4d4ed65
MD5 9d39a1ee351f322b8c07550ef6902fdc
BLAKE2b-256 77187354cb68dd7906d5a3118e0ed3e30c37502f9e6253139ecfcf4fa33af210

See more details on using hashes here.

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