Datashader is a graphics pipeline system for creating meaningful representations of large datasets quickly and flexibly. Datashader breaks the creation of images into a series of explicit steps that allow computations to be done on intermediate representations. This approach allows accurate and effective visualizations to be produced automatically, and also makes it simple for data scientists to focus on particular data and relationships of interest in a principled way. Using highly optimized rendering routines written in Python but compiled to machine code using Numba, datashader makes it practical to work with extremely large datasets even on standard hardware.

To make it concrete, here’s an example of what datashader code looks like:

>>> import datashader as ds
>>> import pandas as pd

>>> cvs = ds.Canvas(plot_width=400, plot_height=400)
>>> agg = cvs.points(df, 'x_col', 'y_col', ds.mean('z_col'))
>>> img = tf.shade(agg, cmap=['lightblue', 'darkblue'], how='log')


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.

And here are some sample outputs for data from the 2010 US census, each constructed using a similar set of code:

## FAQ¶

Q: When should I use datashader?

A: Datashader is designed for working with large datasets, for cases where it is most crucial to faithfully represent the distribution of your data. datashader can work easily with extremely large datasets, generating a fixed-size data structure (regardless of the original number of records) that gets transferred to your local browser for display. If you ever find yourself subsampling your data just so that you can plot it feasibly, or if you are forced for practical reasons to iterate over chunks of it rather than looking at all of it at once, then datashader can probably help you.

Q: When should I not use datashader?

A: If you have a very small number of data points (in the hundreds or thousands) or curves (in the tens or several tens, each with hundreds or thousands of points), then conventional plotting packages like Bokeh may be more suitable. With conventional browser-based packages, all of the data points are passed directly to the browser for display, allowing specific interaction with each curve or point, including display of metadata, linking to sources, etc. This approach offers the most flexibility per point or per curve, but rapidly runs into limitations on how much data can be processed by the browser, and how much can be displayed on screen and resolved by the human visual system. If you are not having such problems, i.e., your data is easily handled by your plotting infrastructure and you can easily see and work with all your data onscreen already, then you probably don’t need datashader.

Q: Is datashader part of bokeh?

A: datashader is an independent project, focusing on generating aggregate arrays and representations of them as images. Bokeh is a complementary project, focusing on building browser-based visualizations and dashboards. Bokeh (along with other plotting packages) can display images rendered by datashader, providing axes, interactive zooming and panning, selection, legends, hover information, and so on. Sample bokeh-based plotting code is provided with datashader, but viewers for maptlotlib are already under development, and similar code could be developed for any other plotting package that can display images. The library can also be used separately, without any external plotting packages, generating images that can be displayed directly or saved to disk, or generating aggregate arrays suitable for further analysis.

## Other resources¶

You can watch a short talk about datashader on YouTube: Datashader: Revealing the Structure of Genuinely Big Data. The video, Visualizing Billions of Points of Data, and its slides from a February 2016 one-hour talk introducing Datashader are also available, but do not cover more recent extensions to the library.

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