Getting Started#


Datashader supports Python 3.7, 3.8, 3.9 and 3.10 on Linux, Windows, or Mac and can be installed with conda:

conda install datashader

or with pip:

pip install datashader

For the best performance, we recommend using conda so that you are sure to get numerical libraries optimized for your platform. The latest releases are available on the pyviz channel conda install -c pyviz datashader and the latest pre-release versions are available on the dev-labeled channel conda install -c pyviz/label/dev datashader.

Fetching Examples#

Once you’ve installed datashader as above you can fetch the examples:

datashader examples
cd datashader-examples

This will create a new directory called datashader-examples with all the data needed to run the examples.

To run all the examples you will need some extra dependencies. If you installed datashader within a conda environment, with that environment active run:

conda env update --file environment.yml

Otherwise create a new environment:

conda env create --name datashader --file environment.yml
conda activate datashader


Detailed Datashader documentation is contained in the User Guide, and the Topics pages show examples of what you can do with Datashader. But to get started quickly, check out the introductory guide sections in order; it should take around 1 hour in total.

  • 1. Introduction Simple self-contained example to show how Datashader works.

  • 2. Pipeline Detailed step-by-step explanation how Datashader turns your data into an image.

  • 3. Interactivity Embedding images into rich, interactive plots in a web browser.

This web page was generated from a Jupyter notebook and not all interactivity will work on this website. Right click to download and run locally for full Python-backed interactivity.

If you have any questions, please refer to FAQ and if that doesn’t help, feel free to post an issue on GitHub or a question on discourse.

Developer Instructions#

  1. Install Python 3 miniconda or anaconda, if you don’t already have it on your system.

  2. Clone the datashader git repository if you do not already have it:

    git clone git://
  3. Set up a new conda environment with all of the dependencies needed to run the examples:

    cd datashader
    conda env create --name datashader --file ./examples/environment.yml
    conda activate datashader
  4. Put the datashader directory into the Python path in this environment:

    pip install --no-deps -e .