Data visualization toolchain based on aggregating into a grid
Project description
Datashader
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:
-
Projection
Each record is projected into zero or more bins of a nominal plotting grid shape, based on a specified glyph.
-
Aggregation
Reductions are computed for each bin, compressing the potentially large dataset into a much smaller aggregate array.
-
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.
Installation
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
or
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 https://github.com/pyviz/datashader.git
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.
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