peterdsteinberg / datashader

Turns even the largest data into images

Datashader

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:

1. Projection

   Each record is projected into zero or more bins, based on a specified glyph.

2. Aggregation

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

3. 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.

Installation

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

Datashader is not currently provided on pip/PyPI, to avoid broken or low-performance installations that come from not keeping track of C/C++binary dependencies such as LLVM (required by Numba).

Examples

There are lots of examples available in the examples directory, most of which are viewable as notebooks on Anaconda Cloud.

Learning more

Additional resources are linked from the [datashader documentation] (http://datashader.readthedocs.org), including API documentation and papers and talks about the approach.

Screenshots