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---
layout: default
title: Interactive Widgets
tagline: Jupyter widgets enable interactive data visualization in the Jupyter notebooks.
permalink: /widgets
---
<script
src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"
integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA="
crossorigin="anonymous">
</script>
<script src="https://unpkg.com/@jupyter-widgets/html-manager@*/dist/embed-amd.js" crossorigin="anonymous"></script>
{% include page-header.html
title=page.title
tagline=page.tagline
%}
<section>
<div class="section-white top-section-border">
<div class="container">
<img class="section-icon img-responsive" src="assets/homepage/widget.svg" alt="">
<h3 class="col-sm-12 section-header">Notebook Widgets</h3>
<p class="support-paragraph">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.</p>
</div>
</div>
</section>
<section>
<div class="container">
<div class="tabbable tabs-left">
<ul id="widgetTabs" class="nav nav-tabs" role="tablist">
<li class="nav-item">
<a href="#ipyleaflet" class="active" data-bs-toggle="tab">
<p>ipyleaflet</p>
<p>Geo-spatial analytics</p>
</a>
</li>
<li class="nav-item">
<a href="#bqplot" data-bs-toggle="tab">
<p>bqplot</p>
<p>2-D interactive data visualization</p>
</a>
</li>
<li class="nav-item">
<a href="#pythreejs" data-bs-toggle="tab">
<p>pythreejs</p>
<p>3-D data visualization</p>
</a>
</li>
<li class="nav-item">
<a href="#ipyvolume" data-bs-toggle="tab">
<p>ipyvolume</p>
<p>3-D plotting</p>
</a>
</li>
<li class="nav-item">
<a href="#nglview" data-bs-toggle="tab">
<p>nglview</p>
<p>3-D interactive molecular visualization</p>
</a>
</li>
<li class="nav-item">
<a href="#k3d" data-bs-toggle="tab" onclick="adjustK3D()">
<p>K3D-Jupyter</p>
<p>3-D data visualization</p>
</a>
</li>
<li class="nav-item">
<a href="#beakerx" data-bs-toggle="tab" onclick="adjustBeakerXTable()">
<p>BeakerX</p>
<p>tables, plotting, forms</p>
</a>
</li>
<li class="nav-item">
<a href="#jupyter-gmaps" data-bs-toggle="tab">
<p>jupyter-gmaps</p>
<p>Data visualization on Google Maps</p>
</a>
</li>
<li class="nav-item">
<a href="#cookiecutter" data-bs-toggle="tab">
<p>cookiecutter</p>
<p>Template widget project</p>
</a>
</li>
<li class="nav-item">
<a href="#perspective" data-bs-toggle="tab">
<p>perspective</p>
<p>Real-time Dataset Visualization</p>
</a>
</li>
</ul>
<div class="tab-content">
<div class="tab-pane active" id="ipyleaflet">
<div class="jupyter-widget-header">
<span class="gallery-title">ipyleaflet</span>
<span>
<a href="https://mybinder.org/v2/gh/jupyter-widgets/ipyleaflet/stable?filepath=examples">
<img class="img-scaling" src="assets/widgets/mybinder.svg" alt="Binder logo - ipyleaflet examples Binder">
</a>
<a href="https://github.com/jupyter-widgets/ipyleaflet">
<img class="img-scaling" src="assets/widgets/github.svg" alt="GitHub logo - ipyleaflet GitHub Repository">
</a>
</span>
</div>
<p>
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
data structure.
</p>
<h3>Example</h3>
{% highlight python %}{% include_relative assets/widgets/ipyleaflet-example.py %}{% endhighlight %}
<script type="application/vnd.jupyter.widget-state+json">
{% include_relative assets/widgets/ipyleaflet-example.json %}
</script>
<script type="application/vnd.jupyter.widget-view+json">
{
"model_id": "5e5cc11723794d639e8d7a3f0951fdea"
}
</script>
<h3>Installation</h3>
With conda:
{% highlight bash %}conda install -c conda-forge ipyleaflet{% endhighlight %}
With pip:
{% highlight bash %}pip install ipyleaflet{% endhighlight %}
If you are using the classic Jupyter Notebook < 5.3 you need to run this extra command:
{% highlight bash %}jupyter nbextension enable --py --sys-prefix ipyleaflet{% endhighlight %}
If you are using JupyterLab ≤ 2, you will need to install the JupyterLab extension:
{% highlight bash %}jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter-leaflet{% endhighlight %}
</div>
<div class="tab-pane" id="nglview">
<div class="jupyter-widget-header">
<span class="gallery-title">nglview</span>
<span>
<a href="https://mybinder.org/v2/gh/hainm/nglview-binder/master?urlpath=lab/tree/nglview/notebooks">
<img class="img-scaling" src="assets/widgets/mybinder.svg" alt="Binder logo - nglview example Binder">
</a>
<a href="https://github.com/nglviewer/nglview">
<img class="img-scaling" src="assets/widgets/github.svg" alt="GitHub logo - nglview Github repository">
</a>
</span>
</div>
<p>
A Jupyter widget to interactively view molecular structures and trajectories.
</p>
<h3>Example</h3>
{% highlight python %}{% include_relative assets/widgets/nglview-example.py %}{% endhighlight %}
<script type="application/vnd.jupyter.widget-state+json">
{% include_relative assets/widgets/nglview-example.json %}
</script>
<script type="application/vnd.jupyter.widget-view+json">
{
"model_id": "8a1512fb32fe47ee904e1ed8aa498e10"
}
</script>
<h3>Installation</h3>
With conda:
{% highlight bash %}conda install -c bioconda nglview{% endhighlight %}
With pip:
{% highlight bash %}pip install nglview
jupyter nbextension enable --py --sys-prefix nglview{% endhighlight %}
</div>
<div class="tab-pane" id="k3d">
<div class="jupyter-widget-header">
<span class="gallery-title">K3D-Jupyter</span>
<span>
<a href="https://github.com/K3D-tools/K3D-jupyter">
<img class="img-scaling" src="assets/widgets/github.svg" alt="GitHub logo - K3D Jupyter GitHub Repository">
</a>
</span>
</div>
<p>
<a href="https://github.com/K3D-tools/K3D-jupyter">K3D</a> lets you create 3D plots backed by WebGL with
high-level API (surfaces, isosurfaces, voxels, mesh, cloud points, vtk objects, volume renderer,
colormaps, etc). The primary aim of K3D-jupyter is to be easy for use as stand alone package like
matplotlib, but also to allow interoperation with existing libraries as VTK. The power of ipywidgets
makes it also a fast and performant visualisation tool for HPC computing e.g. fluid dynamics.
</p>
<p>
Showcase gallery: <a href="https://k3d-jupyter.org/gallery/index.html">https://k3d-jupyter.org/gallery/index.html</a>.
</p>
<h3>Example</h3>
{% highlight python %}{% include_relative assets/widgets/k3d-example.py %}{% endhighlight %}
<script type="application/vnd.jupyter.widget-state+json">
{% include_relative assets/widgets/k3d-example.json %}
</script>
<script type="application/vnd.jupyter.widget-view+json">
{
"model_id": "d3a24bac28644484b2aad2bdf34f31bf"
}
</script>
<script type="application/javascript">
function adjustK3D() {
setTimeout(function() {
window.dispatchEvent && window.dispatchEvent(new Event('resize'));
});
}
</script>
<h3>Installation</h3>
With pip:
{% highlight bash %}pip install k3d
jupyter nbextension enable --py --sys-prefix k3d{% endhighlight %}
If you are using JupyterLab, you will need to install the JupyterLab extension:
{% highlight bash %}jupyter labextension install @jupyter-widgets/jupyterlab-manager k3d{% endhighlight %}
</div>
<div class="tab-pane" id="bqplot">
<div class="jupyter-widget-header">
<span class="gallery-title">bqplot</span>
<span>
<a href="https://mybinder.org/v2/gh/bqplot/bqplot/stable?filepath=examples">
<img class="img-scaling" src="assets/widgets/mybinder.svg" alt="Binder logo - bqplot example Binder">
</a>
<a href="https://github.com/bqplot/bqplot">
<img class="img-scaling" src="assets/widgets/github.svg" alt="GitHub logo - bqplot GitHub Repository">
</a>
</span>
</div>
<p>
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.
</p>
<h3>Example</h3>
{% highlight python %}{% include_relative assets/widgets/bqplot-example.py %}{% endhighlight %}
<script type="application/vnd.jupyter.widget-state+json">
{% include_relative assets/widgets/bqplot-example.json %}
</script>
<script type="application/vnd.jupyter.widget-view+json">
{
"model_id": "10e4ea0790e94bcba5d41df5f3ce007c"
}
</script>
<h3>Installation</h3>
With conda:
{% highlight bash %}conda install -c conda-forge bqplot{% endhighlight %}
With pip:
{% highlight bash %}pip install bqplot{% endhighlight %}
If you are using the classic Jupyter Notebook < 5.3 you need to run this extra command:
{% highlight bash %}jupyter nbextension enable --py --sys-prefix bqplot{% endhighlight %}
If you are using JupyterLab ≤ 2, you will need to install the JupyterLab extension:
{% highlight bash %}jupyter labextension install @jupyter-widgets/jupyterlab-manager bqplot{% endhighlight %}
</div>
<div class="tab-pane" id="pythreejs">
<div class="jupyter-widget-header">
<span class="gallery-title">pythreejs</span>
<span>
<a href="https://mybinder.org/v2/gh/jupyter-widgets/pythreejs/HEAD?urlpath=lab%2Ftree%2Fexamples%2FExamples.ipynb">
<img class="img-scaling" src="assets/widgets/mybinder.svg" alt="Binder logo - pythreejs example Binder">
</a>
<a href="https://github.com/jupyter-widgets/pythreejs">
<img class="img-scaling" src="assets/widgets/github.svg" alt="GitHub logo - pythreejs GitHub Repository">
</a>
</span>
</div>
<p>
A 3-D visualization library enabling GPU-accelerated computer
graphics in Jupyter.
</p>
<h3>Example</h3>
{% highlight python %}{% include_relative assets/widgets/pythreejs-example.py %}{% endhighlight %}
<script type="application/vnd.jupyter.widget-state+json">
{% include_relative assets/widgets/pythreejs-example.json %}
</script>
<script type="application/vnd.jupyter.widget-view+json">
{
"model_id": "d6a0b86fa7434395ac0aca0659f35274"
}
</script>
<h3>Installation</h3>
With conda:
{% highlight bash %}conda install -c conda-forge pythreejs{% endhighlight %}
With pip:
{% highlight bash %}pip install pythreejs{% endhighlight %}
If you are using the classic Jupyter Notebook < 5.3 you need to run this extra command:
{% highlight bash %}jupyter nbextension enable --py --sys-prefix pythreejs{% endhighlight %}
If you are using JupyterLab ≤ 2, you will need to install the JupyterLab extension:
{% highlight bash %}jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter-threejs{% endhighlight %}
</div>
<div class="tab-pane" id="ipyvolume">
<div class="jupyter-widget-header">
<span class="gallery-title">ipyvolume</span>
<span>
<a href="https://mybinder.org/v2/gh/maartenbreddels/ipyvolume/master?filepath=notebooks/simple.ipynb">
<img class="img-scaling" src="assets/widgets/mybinder.svg" alt="Binder logo - ipyvolume example Binder">
</a>
<a href="https://github.com/maartenbreddels/ipyvolume">
<img class="img-scaling" src="assets/widgets/github.svg" alt="GitHub logo - ipyvolume GitHub Repository">
</a>
</span>
</div>
<p>
3-D plotting for Python in the Jupyter notebook based on IPython widgets using WebGL.
</p>
<h3>Example</h3>
{% highlight python %}{% include_relative assets/widgets/ipyvolume-example.py %}{% endhighlight %}
<script type="application/vnd.jupyter.widget-state+json">
{% include_relative assets/widgets/ipyvolume-example.json %}
</script>
<script type="application/vnd.jupyter.widget-view+json">
{
"model_id": "3a74fe1c2fc14b30a012b5754cd55af7"
}
</script>
<h3>Installation</h3>
With conda:
{% highlight bash %}conda install -c conda-forge ipyvolume{% endhighlight %}
With pip:
{% highlight bash %}pip install ipyvolume
jupyter nbextension enable --py --sys-prefix ipyvolume{% endhighlight %}
If you are using JupyterLab, you will need to install the JupyterLab extension:
{% highlight bash %}jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter-threejs ipyvolume{% endhighlight %}
</div>
<div class="tab-pane" id="beakerx">
<div class="jupyter-widget-header">
<span class="gallery-title">BeakerX</span>
<span>
<a href="https://github.com/twosigma/beakerx">
<img class="img-scaling" src="assets/widgets/github.svg" alt="GitHub logo - BeakerX GitHub Repository">
</a>
</span>
</div>
<p>
<a href="http://beakerx.com">BeakerX</a> includes widgets
for interactive tables, plots, forms, Apache Spark, and more.
The table widget automatically recognizes pandas dataframes
and allows you to search, sort, drag, filter, format,
select, graph, hide, pin, and export to CSV or
clipboard. This makes connecting to spreadsheets quick and
easy.
</p>
<p>
The table widget, shown below, is so fast because it's implemented with the PhosphorJS Data Grid,
part of Jupyter Lab's architecture.
</p>
<h3>Example</h3>
{% highlight python %}{% include_relative assets/widgets/beakerx-example.py %}{% endhighlight %}
<script type="application/vnd.jupyter.widget-state+json">
{% include_relative assets/widgets/beakerx-example.json %}
</script>
<script type="application/vnd.jupyter.widget-view+json">
{
"model_id": "d6371237-9b40-427e-9833-71cb25330855"
}
</script>
<script type="application/javascript">
function adjustBeakerXTable() {
setTimeout(function() {
window.dispatchEvent && window.dispatchEvent(new Event('resize'));
});
}
</script>
<h3>Installation</h3>
With conda:
{% highlight bash %}conda install -c conda-forge beakerx ipywidgets{% endhighlight %}
With pip:
{% highlight bash %}pip install beakerx
beakerx-install{% endhighlight %}
</div>
<div class="tab-pane" id="jupyter-gmaps">
<div class="jupyter-widget-header">
<span class="gallery-title">jupyter-gmaps</span>
<span>
<a href="https://github.com/pbugnion/gmaps">
<img class="img-scaling" src="assets/widgets/github.svg" alt="GitHub logo - jupyter-gmaps GitHub Repository">
</a>
</span>
</div>
<p>
<a href="https://jupyter-gmaps.readthedocs.io">Gmaps</a> lets you
embed interactive Google maps in Jupyter notebooks. Visualize
your data with heatmaps, GeoJSON, symbols and markers, or plot
directions, traffic, or cycle routes. Let users draw on the map
and capture the coordinates of the markers or polygons they are
placing to build interactive applications entirely in Python.
</p>
<h3>Example</h3>
{% highlight python %}{% include_relative assets/widgets/jupyter-gmaps-example.py %}{% endhighlight %}
<script type="application/vnd.jupyter.widget-state+json">
{% include_relative assets/widgets/jupyter-gmaps-example.json %}
</script>
<script type="application/vnd.jupyter.widget-view+json">
{
"model_id": "c3031eeeee3348ddb94c2f472822f829"
}
</script>
<h3>Installation</h3>
With conda:
{% highlight bash %}conda install -c conda-forge gmaps{% endhighlight %}
With pip:
{% highlight bash %}pip install gmaps
jupyter nbextension enable --py --sys-prefix gmaps{% endhighlight %}
If you are using JupyterLab, you will need to install the JupyterLab extension:
{% highlight bash %}jupyter labextension install @jupyter-widgets/jupyterlab-manager{% endhighlight %}
</div>
<div class="tab-pane" id="cookiecutter">
<div class="jupyter-widget-header">
<span class="gallery-title">widget cookiecutters</span>
<span>
<a href="https://github.com/jupyter-widgets/widget-cookiecutter">
<img class="img-scaling" src="assets/widgets/github.svg" alt="GitHub logo - widget-cookiecutter GitHub Repository">
</a>
<a href="https://github.com/jupyter-widgets/widget-ts-cookiecutter">
<img class="img-scaling" src="assets/widgets/github.svg" alt="GitHub logo - widget-ts-cookiecutter GitHub Repository">
</a>
</span>
</div>
<p>
The Jupyter widget framework is extensible and enables developers to create custom
widget libraries and bindings for visualization libraries of the JavaScript and TypeScript ecosystem.
</p>
<p>
The <code>cookiecutter</code> projects help widget authors get up to speed with the
packaging and distribution of Jupyter interactive widgets, in
<a href="https://github.com/jupyter-widgets/widget-cookiecutter">JavaScript</a> and
<a href="https://github.com/jupyter-widgets/widget-ts-cookiecutter">TypeScript</a>.
</p>
<p>
They produce 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.
</p>
</div>
<div class="tab-pane" id="perspective">
<div class="jupyter-widget-header">
<span class="gallery-title">perspective</span>
<span>
<a href="https://github.com/finos/perspective">
<img class="img-scaling" src="assets/widgets/perspective.gif" alt="GitHub logo - perspective GitHub Repository">
</a>
</span>
</div>
<p>
Perspective is an interactive visualization component for large, real-time datasets. Originally developed for J.P. Morgan's trading business, Perspective makes it simple to build real-time & user configurable analytics entirely in the browser, or in concert with Python and/or Jupyterlab.
</p>
<p>
<code>Perspective</code> can be used to create reports, dashboards, notebooks and applications, with static data or streaming updates via Apache Arrow..
</p>
</div>
</div>
</div>
</div>
</section>