Datashader is a graphics pipeline system for creating meaningful representations of large amounts of data. It breaks the creation of images into 3 main steps:
Projection
Each record is projected into zero or more bins, based on a specified glyph.
Aggregation
Reductions are computed for each bin, compressing the potentially large dataset into a much smaller aggregate.
Transformation
These aggregates are then further processed to create 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 is available on most platforms using the conda package manager, from the bokeh channel:
conda install -c bokeh datashader Alternatively, you can manually install from the repository:
git clone https://github.com/bokeh/datashader.git cd datashader conda install -c bokeh --file requirements.txt python setup.py install There are lots of examples available in the examples directory, most of which are viewable as notebooks on Anaconda Cloud.
Additional resources are linked from the [datashader documentation] (http://datashader.readthedocs.org), including API documentation and papers and talks about the approach.
