Datashader supports Python 2.7, 3.5, 3.6 and 3.7 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.
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
This section aims to get you using Datashader productively as quickly as possible. Detailed documentation is contained in the User Guide. To see examples of what can be done with Datashader, see Topics.
We recommend you proceed through the following in order; it should take around 1 hour in total.
If you have any questions, please refer to FAQ and if that doesn’t help, feel free to post an issue on GitHub, question on stackoverflow, or discuss on Gitter.
Clone the datashader git repository if you do not already have it:
git clone git://github.com/pyviz/datashader.git
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
Put the datashader directory into the Python path in this environment:
pip install -e .