Getting Set Up#

The hvPlot 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 hvPlot source code is stored in a Git source control repository. The first step to working on hvPlot 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 hvPlot 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 hvPlot development greatly. It is strongly recommended that anyone developing hvPlot 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 hvPlot project is hosted on GitHub. To clone the source repository, issue the following command:

git clone

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

Installing Dependencies#

hvPlot requires many additional packages for development and testing.

Conda Environments#

Create an empty conda environment with the name that you prefer, here we’ve chosen hvplot_dev. Activate and configure its channels to only use pyviz/label/dev and conda-forge. The former is used to install the development versions of the other HoloViz packages, such as HoloViews or Panel.

conda install mamba -c conda-forge
conda create -n hvplot_dev
conda activate hvplot_dev
conda config --env --append channels pyviz/label/dev --append channels conda-forge
conda config --env --remove channels defaults

Since hvPlot 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 hvPlot uses a library called pyctdev to make things more consistent and general. Specify also the desired Python version you want to base your environment on.

You will need to pick a Python version. The best practice is to choose the minimum version currently supported by hvPlot on the main development branch. If you cannot get the minimum version installed, then try with a more recent version of Python.

mamba install python=3.x pyctdev

Finally to install the dependencies required to run the full unit test suite and all the examples:

doit develop_install -o tests -o examples --conda-mode mamba

Add -o doc if you want to install the dependencies required to build the website.

Setting up pre-commit#

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

pre-commit install

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


You can list the available doit commands with doit list.

$ doit list
build_docs             build docs
develop_install        python develop install, with specified optional groups of dependencies (installed by conda only).
ecosystem_setup        Common conda setup (must be run in base env).
env_capture            Report all information required to recreate current conda environment
env_create             Create named environment if it doesn't already exist
env_dependency_graph   Write out dependency graph of named environment.
env_export             Generate a pinned environment.yaml from specified env, filtering
miniconda_download     Download Miniconda3-latest
miniconda_install      Install Miniconda3-latest to location if not already present
package_build          Build and then test conda.recipe/ (or specified alternative).
package_test           Test existing package
package_upload         Upload package built from conda.recipe/ (or specified alternative).
pip_on_conda           Experimental: provide pip build env via conda
test_all               Run all tests
test_examples          Test that default examples run
test_flakes            Flake check python and notebooks
test_unit              Run unit tests with coverage

You can learn more about using doit on the DoIt web site.

Next Steps#

If you have any problems with the steps here, please contact the developers.