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 ipympl

Documentation

See the documentation at: https://matplotlib.org/ipympl/

Try it now

You can easily try ipympl without installing anything, by simply accessing the example Notebook from Notebook.link or MyBinder:

• Notebook.link
• MyBinder link

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.

If you use JupyterLab 2, you still need to install the labextension manually:

conda install -c conda-forge nodejs
jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter-matplotlib

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
0.10.0	0.12.0	>=2,<5	>=3.5.0
0.9.5-8	0.11.5-8	>=2,<5	>=3.5.0
0.9.3-4	0.11.3-4	>=2,<5	3.4.0>=
0.9.0-2	0.11.0-2	>=2,<5	3.4.0>= <3.7
0.8.8	0.10.x	>=2,<5	3.3.1>= <3.7
0.8.0-7	0.10.x	>=2,<5	3.3.1>=, <3.6
0.7.0	0.9.0	>=2,<5	3.3.1>=
0.6.x	0.8.x	>=2,<5	3.3.1>=, <3.4
0.5.8	0.7.4	>=1,<3	3.3.1>=, <3.4
0.5.7	0.7.3	>=1,<3	3.2.*
...	...	...
0.5.3	0.7.2	>=1,<3
0.5.2	0.7.1	>=1,<2
0.5.1	0.7.0	>=1,<2
0.5.0	0.6.0	>=1,<2
0.4.0	0.5.0	>=1,<2
0.3.3	0.4.2	>=1,<2
0.3.2	0.4.1	>=1,<2
0.3.1	0.4.0	>=0<2

For a development installation

We recommend using pixi for development as it handles both Python and Node.js dependencies (matplotlib has compiled dependencies).

# Install dependencies and set up environment
pixi install

# Install the Python package in editable mode
pixi run pip install -e .

# Install JavaScript dependencies and build
pixi run jlpm install
pixi run jlpm build

# Set up JupyterLab extension in development mode
pixi run jupyter labextension develop --overwrite .

# Start development workflow (in separate terminals)
pixi run npm run watch    # Terminal 1: Auto-rebuild on changes
pixi run jupyter lab      # Terminal 2: Run JupyterLab

Alternative: Using conda/mamba

mamba env create --file dev-environment.yml
conda activate ipympl-dev

pip install -e .
jlpm install
jlpm build
jupyter labextension develop --overwrite .

# Start development workflow (in separate terminals)
npm run watch    # Terminal 1: Auto-rebuild on changes
jupyter lab      # Terminal 2: Run JupyterLab

How to see your changes

TypeScript/JavaScript: After a change, the watch command will automatically rebuild. Wait for the build to finish, then refresh your browser and the changes should take effect.

Python: If you make a change to the Python code, restart the notebook kernel to have it take effect.

Classic Jupyter Notebook

If you need to develop for classic Jupyter Notebook (not JupyterLab), also run:

# With pixi:
pixi run jupyter nbextension install --py --symlink --sys-prefix --overwrite ipympl
pixi run jupyter nbextension enable --py --sys-prefix ipympl

# Or with conda/mamba:
jupyter nbextension install --py --symlink --sys-prefix --overwrite ipympl
jupyter nbextension enable --py --sys-prefix ipympl