This code reads a data file into a Pandas dataframe df , and then projects the fields x_col and y_col onto the x and y dimensions of 400x400 grid, aggregating it by the mean value of the z_col of each datapoint. The results are rendered into an image where the minimum count will be plotted in lightblue , the maximum in darkblue , and ranging logarithmically in between.

With code like the above, you can plot 300 million points of data (one per person in the USA) from the 2010 census without any parameter tuning:

Or you can plot attractors with 10 million points each, using random colormaps:

See the topics page for these and many other examples.

Please follow the instructions on Getting Started if you want to reproduce the specific examples on this website, or follow the instructions at HoloViz.org if you want to try out Datashader together with related plotting tools.

Other resources¶

You can see Datashader in action in the 2019 HoloViz SciPy tutorial (3 hours!), listen to the Open Source Directions episode from July 2019, or see how it is used in many of the projects at examples.pyviz.org.

Some of the original ideas for Datashader were developed under the name Abstract Rendering, which is described in a 2014 SPIE VDA paper.

The source code for datashader is maintained on Github, and is documented using the API link on this page.

We recommend the Getting Started Guide to learn the basic concepts and start using Datashader as quickly as possible.

The User Guide covers specific topics in more detail.

The API is the definitive guide to each part of Datashader, but the same information is available more conveniently via the help() command as needed when using each component.

Please feel free to report issues or contribute code. You are also welcome to chat with the developers on gitter.