seaborn alternatives and similar packages
Based on the "Data Visualization" category.
Alternatively, view seaborn alternatives based on common mentions on social networks and blogs.
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Apache Superset
DISCONTINUED. Apache Superset is a Data Visualization and Data Exploration Platform [Moved to: https://github.com/apache/superset] -
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redash
Make Your Company Data Driven. Connect to any data source, easily visualize, dashboard and share your data. -
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#<Sawyer::Resource:0x00007fbd82367850>
Panel: The powerful data exploration & web app framework for Python -
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Flask JSONDash
:snake: :bar_chart: :chart_with_upwards_trend: Build complex dashboards without any front-end code. Use your own endpoints. JSON config only. Ready to go. -
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ipyvizzu
Build animated charts in Jupyter Notebook and similar environments with a simple Python syntax. -
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InfluxDB – Built for High-Performance Time Series Workloads
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They vary from L1 to L5 with "L5" being the highest.
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README
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.
To build the documentation locally, please refer to [doc/README.md](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](./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.
*Note that all licence references and agreements mentioned in the seaborn README section above
are relevant to that project's source code only.