ipympl

Leveraging the Jupyter interactive widgets framework, ipympl enables the interactive features of matplotlib in the Jupyter notebook and in JupyterLab.

Besides, the figure canvas element is a proper Jupyter interactive widget which can be positioned in interactive widget layouts.

Usage

To enable the ipympl backend, simply use the matplotlib Jupyter magic:

%matplotlib widget

Example

See the example notebook for more!

Installation

With conda:

conda install -c conda-forge ipympl

With pip:

pip install ipympl

Use in JupyterLab

If you want to use ipympl in JupyterLab, we recommend using JupyterLab >= 3.

Install an old JupyterLab extension

If you are using JupyterLab 1 or 2, you will need to install the right jupyter-matplotlib version, according to the ipympl and jupyterlab versions you installed. For example, if you installed ipympl 0.5.1, you need to install jupyter-matplotlib 0.7.0, and this version is only compatible with JupyterLab 1.

conda install -c conda-forge ipympl==0.5.1
jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter-matplotlib@0.7.0

Versions lookup table:

ipympl	jupyter-matplotlib	JupyterLab	Matplotlib
0.5.8	0.7.4	1 or 2	3.3.1
0.5.7	0.7.3	1 or 2	3.2.*
...	...	...
0.5.3	0.7.2	1 or 2
0.5.2	0.7.1	1
0.5.1	0.7.0	1
0.5.0	0.6.0	1
0.4.0	0.5.0	1
0.3.3	0.4.2	1
0.3.2	0.4.1	1
0.3.1	0.4.0	0 or 1

For a development installation (requires nodejs):

git clone https://github.com/matplotlib/ipympl.git
cd ipympl
pip install -e .

# If using classic Jupyter Notebook
jupyter nbextension install --py --symlink --sys-prefix ipympl
jupyter nbextension enable --py --sys-prefix ipympl

# If using JupyterLab
jupyter labextension install @jupyter-widgets/jupyterlab-manager --no-build
jupyter labextension install ./js

How to see your changes

Javascript:

To continuously monitor the project for changes and automatically trigger a rebuild, start Jupyter in watch mode:

jupyter lab --watch

After a change wait for the build to finish and then refresh your browser and the changes should take effect.

Python:

If you make a change to the python code then you will need to restart the notebook kernel to have it take effect.