Interactive Widgets

Jupyter widgets enable interactive data visualization in the Jupyter notebooks.

icon to represent multiple notebooks

Notebook Widgets

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.

ipyleaflet Binder GitHub

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.

Example

from ipyleaflet import Map

Map(center=[34.6252978589571, -77.34580993652344], zoom=10)

Installation

With conda: 
conda install -c conda-forge ipyleaflet
With pip: 
pip install ipyleaflet
jupyter nbextension enable --py --sys-prefix ipyleaflet
nglview

A Jupyter widget to interactively view molecular structures and trajectories.

Example

import pytraj as pt
import nglview as nv

traj = pt.load('sim.nc', top='sim.prmtop')
traj.strip(":TIP3")
view = nv.show_pytraj(traj)
view.clear()
view.add_cartoon('protein', color_scheme='residueindex')
view.add_ball_and_stick('not protein', opacity=0.5)
view

Installation

With conda: 
conda install -c bioconda nglview
With pip: 
pip install nglview
jupyter nbextension enable --py --sys-prefix nglview
bqplot Binder GitHub

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.

Example

import numpy as np
import bqplot.pyplot as plt

size = 100

plt.figure(title='Scatter plot with colors')
plt.scatter(np.random.randn(size), np.random.randn(size), color=np.random.randn(size))
plt.show()

Installation

With conda: 
conda install -c conda-forge bqplot
With pip: 
pip install bqplot
jupyter nbextension enable --py --sys-prefix bqplot
pythreejs GitHub

A 3-D visualization library enabling GPU-accelerated computer graphics in Jupyter.

Example

from pythreejs import *

f = """
function f(origu,origv) {
    // scale u and v to the ranges I want: [0, 2*pi]
    var u = 2*Math.PI*origu;
    var v = 2*Math.PI*origv;

    var x = Math.sin(u);
    var y = Math.cos(v);
    var z = Math.cos(u+v);

    return new THREE.Vector3(x,y,z);
}
"""

surf_g = ParametricGeometry(func=f, slices=16, stacks=16)
surf = Mesh(geometry=surf_g, material=MeshLambertMaterial(color='green', side='FrontSide'))
surf2 = Mesh(geometry=surf_g, material=MeshLambertMaterial(color='yellow', side='BackSide'))
c = PerspectiveCamera(position=[5, 5, 3], up=[0, 0, 1],
                      children=[DirectionalLight(color='white',
                                                 position=[3, 5, 1],
                                                 intensity=0.6)])
scene = Scene(children=[surf, surf2, c, AmbientLight(intensity=0.5)])
Renderer(camera=c, scene=scene, controls=[OrbitControls(controlling=c)], width=400, height=400)

Installation

With conda: 
conda install -c conda-forge pythreejs
With pip: 
pip install pythreejs
jupyter nbextension enable --py --sys-prefix pythreejs
ipyvolume Binder GitHub

3-D plotting for Python in the Jupyter notebook based on IPython widgets using WebGL.

Example

import ipyvolume.pylab as p3
import numpy as np

fig = p3.figure()
q = p3.quiver(*stream.data[:,0:50,:200], color="red", size=7)
p3.style.use("dark") # looks better
p3.animation_control(q, interval=200)
p3.show()

Installation

With conda: 
conda install -c conda-forge ipyvolume
With pip: 
pip install ipyvolume
jupyter nbextension enable --py --sys-prefix ipyvolume
BeakerX GitHub

BeakerX 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.

The table widget, shown below, is so fast because it's implemented with the PhosphorJS Data Grid, part of Jupyter Lab's architecture.

Example

import pandas as pd
from beakerx import *
pd.read_csv("UScity.csv")

Installation

With conda: 
conda install -c conda-forge beakerx ipywidgets
With pip: 
pip install beakerx
beakerx-install
jupyter-gmaps GitHub

Gmaps 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.

Example

import gmaps
import gmaps.datasets
gmaps.configure(api_key="AI...")  # Your Google API key

locations = gmaps.datasets.load_dataset("taxi_rides")

fig = gmaps.figure()

# locations could be an array, a dataframe or just a Python iterable
fig.add_layer(gmaps.heatmap_layer(locations))

fig

Installation

With conda: 
conda install -c conda-forge gmaps
With pip: 
pip install gmaps
jupyter nbextension enable --py --sys-prefix gmaps
widget cookiecutter GitHub

The Jupyter widget framework is extensible and enables developers to create custom widget libraries and bindings for visualization libraries of the JavaScript ecosystem.

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.