https://github.com/cgevans/seaborn
Statistical data visualization using matplotlib
Science Score: 23.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 3 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
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○Scientific vocabulary similarity
Low similarity (17.5%) to scientific vocabulary
Repository
Statistical data visualization using matplotlib
Basic Info
- Host: GitHub
- Owner: cgevans
- License: bsd-3-clause
- Language: Python
- Default Branch: master
- Homepage: http://stanford.edu/~mwaskom/software/seaborn/
- Size: 45.1 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
seaborn: statistical data visualization
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.
Dependencies
Seaborn supports Python 2.7 and 3.4+.
Installation requires numpy, scipy, pandas, and matplotlib. Some functions will optionally use statsmodels if it is installed.
Installation
The latest stable release (and older versions) can be installed from PyPI:
pip install seaborn
You may instead want to use the development version from Github:
pip install git+https://github.com/mwaskom/seaborn.git#egg=seaborn
Testing
To test seaborn, run make test in the source directory.
This will exercise both the unit tests and docstring examples (using pytest).
Development
Seaborn development takes place on Github: https://github.com/mwaskom/seaborn
Please submit any reproducible bugs you encounter to the issue tracker.
Owner
- Name: Constantine Evans
- Login: cgevans
- Kind: user
- Repositories: 13
- Profile: https://github.com/cgevans