Getting Started

Installation

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 lastest releases are avalailable on the pyviz channel conda install -c pyviz datashader and the latest pre-release versions are avalailable on the dev-labelled 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

Usage

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.

  • 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.

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.

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://github.com/pyviz/datashader.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 -e .