seaborn alternatives and similar packages
Based on the "Data Visualization" category.
Alternatively, view seaborn alternatives based on common mentions on social networks and blogs.
-
Apache Superset
Apache Superset (incubating) is a modern, enterprise-ready business intelligence web application -
redash
Connect to any data source, easily visualize, dashboard and share your data. -
folium
Manipulate your data in Python, then visualize it in a Leaflet map via folium. -
PyQtGraph
Interactive and realtime 2D/3D/Image plotting and science/engineering widgets. -
Flask JSONDash
Build javascript chart dashboards without any front-end code. Uses any json endpoint. JSON config only. Ready to go. -
SnakeViz
A browser based graphical viewer for the output of Python's cProfile module. -
GooPyCharts
A Google Charts API for Python, meant to be used as an alternative to matplotlib.
Scout APM - Leading-edge performance monitoring starting at $39/month
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest. Visit our partner's website for more details.
Do you think we are missing an alternative of seaborn or a related project?
Popular Comparisons
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).
Dependencies
Seaborn supports Python 3.6+ and no longer supports Python 2.
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
Testing seaborn requires installing additional packages listed in ci/utils.txt
.
To test the code, run make test
in the source directory. This will exercise both the unit tests and docstring examples (using pytest) and generate a coverate report.
The doctests require a network connection (unless all example datasets are cached), but the unit tests can be run offline with make unittests
.
Code style is enforced with flake8
using the settings in the [setup.cfg
](./setup.cfg) file. Run make lint
to check.
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.