bqplot is a 2-D visualization system for Jupyter, based on the constructs of the Grammar of Graphics.



In bqplot, every component of a plot is an interactive widget. This allows the
user to integrate visualizations with other Jupyter interactive widgets to
create integrated GUIs with a few simple lines of Python code.


  • provide a unified framework for 2-D visualizations with a pythonic API.
  • provide a sensible API for adding user interactions (panning, zooming, selection, etc)

Two APIs are provided

  • Users can build custom visualizations using the internal object model, which
    is inspired by the constructs of the Grammar of Graphics (figure, marks, axes,
    scales), and enrich their visualization with our Interaction Layer.
  • Or they can use the context-based API similar to Matplotlib's pyplot, which
    provides sensible default choices for most parameters.

Trying it online

To try out bqplot interactively in your web browser, just click on the binder



This package depends on the following packages:

  • ipywidgets (version >=7.0.0, <8.0)
  • traitlets (version >=4.3.0, <5.0)
  • traittypes (Version >=0.2.1, <0.3)
  • numpy
  • pandas


Using pip:

$ pip install bqplot

Using conda

$ conda install -c conda-forge bqplot

If you are using JupyterLab <=2:

$ jupyter labextension install @jupyter-widgets/jupyterlab-manager bqplot

For a development installation (requires JupyterLab (version >= 3) and yarn):

$ git clone
$ cd bqplot
$ pip install -e .
$ jupyter nbextension install --py --overwrite --symlink --sys-prefix bqplot
$ jupyter nbextension enable --py --sys-prefix bqplot

Note for developers: the --symlink argument on Linux or OS X allows one to
modify the JavaScript code in-place. This feature is not available
with Windows.

For the experimental JupyterLab extension, install the Python package, make sure the Jupyter widgets extension is installed, and install the bqplot extension:

$ pip install "ipywidgets>=7.6"
$ jupyter labextension develop . --overwrite

Whenever you make a change of the JavaScript code, you will need to rebuild:

cd js
yarn run build

Then refreshing the JupyterLab/Jupyter Notebook is enough to reload the changes.

Loading bqplot

# In a Jupyter notebook
import bqplot

That's it! You're ready to go!


Using the pyplot API


Using the bqplot internal object model



To get started with using bqplot, check out the full documentation

Install a previous bqplot version (Only for JupyterLab <= 2)

In order to install a previous bqplot version, you need to know which front-end version (JavaScript) matches with the back-end version (Python).

For example, in order to install bqplot 0.11.9, you need the labextension version 0.4.9.

$ pip install bqplot==0.11.9
$ jupyter labextension install [email protected]

Versions lookup table:

back-end (Python) front-end (JavaScript)
0.12.14 0.5.14
0.12.13 0.5.13
0.12.12 0.5.12
0.12.11 0.5.11
0.12.10 0.5.10
0.12.9 0.5.9
0.12.8 0.5.8
0.12.7 0.5.7
0.12.6 0.5.6
0.12.4 0.5.4
0.12.3 0.5.3
0.12.2 0.5.2
0.12.1 0.5.1
0.12.0 0.5.0
0.11.9 0.4.9
0.11.8 0.4.8
0.11.7 0.4.7
0.11.6 0.4.6
0.11.5 0.4.5
0.11.4 0.4.5
0.11.3 0.4.4
0.11.2 0.4.3
0.11.1 0.4.1
0.11.0 0.4.0