Skip to main content

Data visualization toolchain based on aggregating into a grid

Project description


Travis build Status Windows build status Task Status

Datashader is a data rasterization pipeline for automating the process of creating meaningful representations of large amounts of data. Datashader breaks the creation of images of data into 3 main steps:

  1. Projection

    Each record is projected into zero or more bins of a nominal plotting grid shape, based on a specified glyph.

  2. Aggregation

    Reductions are computed for each bin, compressing the potentially large dataset into a much smaller aggregate array.

  3. Transformation

    These aggregates are then further processed, eventually creating an image.

Using this very general pipeline, many interesting data visualizations can be created in a performant and scalable way. Datashader contains tools for easily creating these pipelines in a composable manner, using only a few lines of code. Datashader can be used on its own, but it is also designed to work as a pre-processing stage in a plotting library, allowing that library to work with much larger datasets than it would otherwise.


The best way to get started with Datashader is install it together with our extensive set of examples, following the instructions in the examples README.

If all you need is datashader itself, without any of the files used in the examples, you can install it via conda or pip:

conda install datashader


pip install datashader

For the best performance, we recommend using conda so that you are sure to get numerical libraries optimized for your platform.

If you want the latest unreleased changes (e.g. to edit the source code yourself), first install datashader as above, but then clone the source code and tell Python to use the clone instead:

conda remove --force datashader
git clone
cd datashader
pip install -e .

To run the test suite, first conda install pytest or pip install pytest, then run py.test datashader in your datashader source directory.

Learning more

After working through the examples, you can find additional resources linked from the datashader documentation, including API documentation and papers and talks about the approach.


USA census

NYC races

NYC taxi

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

datashader-0.6.9.tar.gz (22.1 MB view hashes)

Uploaded Source

Built Distribution

datashader-0.6.9-py2.py3-none-any.whl (11.3 MB view hashes)

Uploaded Python 2 Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page