Developer Guide#

The Panel library is a complex project which provides a wide range of data interfaces and an extensible set of plotting backends, which means the development and testing process involves a wide set of libraries.



The Panel source code is stored in a Git source control repository. The first step to working on Panel is to install Git on to your system. There are different ways to do this depending on whether, you are using Windows, OSX, or Linux.

To install Git on any platform, refer to the Installing Git section of the Pro Git Book.


Developing Panel requires a wide range of packages that are not easily and quickly available using pip. To make this more manageable, core developers rely heavily on the conda package manager for the free Anaconda Python distribution. However, conda can also install non-Python package dependencies, which helps streamline Panel development greatly. It is strongly recommended that anyone developing Panel also use conda, and the remainder of the instructions will assume that conda is available.

To install Conda on any platform, see the Download conda section of the conda documentation_.

Cloning the Repository#

The source code for the Panel project is hosted on GitHub. To clone the source repository, issue the following command:

git clone

This will create a panel directory at your file system location. This panel directory is referred to as the source checkout for the remainder of this document.

Create a development environment#

Since Panel interfaces with a large range of different libraries the full test suite requires a wide range of dependencies. To make it easier to install and run different parts of the test suite across different platforms Panel uses a library called pyctdev to make things more consistent and general. To start with cd into the panel directory and set up conda using the following commands:

cd panel
conda install -c pyviz "pyctdev>0.5.0"

Once pyctdev is available and you are in the cloned panel repository you can set up an environment with:

doit env_create -c pyviz/label/dev -c conda-forge --name=panel_dev --python=3.9

Specify the desired Python version, currently Panel officially supports Python 3.7, 3.8, 3.9 and 3.10. Once the environment has been created you can activate it with:

conda activate panel_dev

Install Panel in editable mode#

To perform an editable install of Panel, including all the dependencies required to run the full unit test suite, run the following:

doit develop_install -c pyviz/label/dev -c conda-forge -c bokeh -o build -o tests -o recommended

The above command installs Panel’s dependencies using conda, then performs a pip editable install of Panel. If it fails, nodejs>=14.0.0 may be missing from your environment, fix it with conda install -c conda-forge nodejs then rerun above command.

If you also want to run the UI tests run the following:

pip install playwright pytest-playwright
playwright install chromium

Enable the Jupyter extension#

If you are running UI tests or intend to use the Panel Preview feature in Jupyter you must enable the server extension. To enable the classic notebook server extension:

jupyter serverextension enable --sys-prefix

For Jupyter Server:

jupyter server extension enable --sys-prefix

Setting up pre-commit#

Panel uses pre-commit to automatically apply linting to Panel code. If you intend to contribute to Panel we recommend you enable it with:

pre-commit install

This will ensure that every time you make a commit linting will automatically be applied.

Developing custom models#

Panel ships with a number of custom Bokeh models, which have both Python and Javascript components. When developing Panel these custom models have to be compiled. This happens automatically with SETUPTOOLS_ENABLE_FEATURES=legacy-editable pip install -e . or python develop, however when running actively developing you can rebuild the extension with panel build panel. The build command is just an alias for bokeh build; see the Bokeh developer guide for more information about developing bokeh models.

Just like any other Javascript (or Typescript) library Panel defines a package.json and package-lock.json files. When adding, updating or removing a dependency in the package.json file ensure you commit the changes to the package-lock.json after running npm install.

Bundling resources#

Panel bundles external resources required for custom models and templates into the panel/dist directory. The bundled resources have to be collected whenever they change, so rerun SETUPTOOLS_ENABLE_FEATURES=legacy-editable pip install -e . or python develop whenever you change one of the following:

  • A new model is added with a __javascript_raw__ declaration or an existing model is updated

  • A new template with a _resources declaration is added or an existing template is updated

  • A CSS file in one of template directories (panel/template/*/) is added or modified

Next Steps#

You will likely want to check out the testing guide. Meanwhile, if you have any problems with the steps here, please visit our Discourse.