Jupyter widgets enable interactive data visualization in the Jupyter notebooks.
Notebooks come alive when interactive widgets are used. Users can visualize and control changes in the data. Learning becomes an immersive, plus fun, experience. Researchers can easily see how changing inputs to a model impacts the results.
A library for creating simple interactive maps with panning and
zooming, ipyleaflet supports annotations such as polygons,
markers, and more generally any geojson-encoded geographical
A 2-D interactive data visualization library implementing the
constructs of the grammar of graphics, bqplot provides a simple
API for creating custom user interactions.
A 3-D visualization library enabling GPU-accelerated computer
graphics in Jupyter.
The Jupyter widget framework is extensible and enables developers to create custom
The cookiecutter project helps widget authors get up to speed with the
packaging and distribution of Jupyter interactive widgets.
It produces a base project for a Jupyter interactive widget library following the current best practices.
An implementation for a placeholder "Hello World" widget is provided. Following these practices will
help make your custom widgets work in static web pages (like the examples of this page) and be compatible
with future versions of Jupyter.