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.
To enable the ipympl backend, simply use the matplotlib Jupyter
magic:
%matplotlib ipympl
See the documentation at: https://matplotlib.org/ipympl/
You can easily try ipympl without installing anything, by simply accessing the example Notebook from Notebook.link or MyBinder:
See the example notebook for more!
conda install -c conda-forge ipympl
pip install ipympl
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
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 [email protected]
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 |
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
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.
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