Datashader Review

Datashader Review

is a platform for visualizing large datasets on the internet. It enables users to perform computations on intermediate representations and automatically generates an image in a single step. This approach allows accurate and effective visualizations to be produced automatically without trial and error. One straightforward way to make your code more efficient is to tune the configuration parameters of the code you are working with. 

Especially when you have measurements you can compare against, you could run a few quick tests to see where there is a big difference between the two values. For example, computing the mean and standard deviation for a set of points is a common test that determines how different two sets of data are. Comparing these two statistics can help you determine the existence (or not) of an effect. The Datashader library allows you to create high-performance, scalable applications, and web services. It takes advantage of the latest features of the C++ programming language and advances in parallel computing techniques to provide a highly optimized rendering pipeline while making it practical to work with very large datasets even on standard hardware. This is an example of what Datashader code resembles :

import data shader as ds, pandas as pd, colorcet

df  = pd.read_csv('census.csv')

cvs = ds.Canvas(plot_width=850, plot_height=500)

agg = cvs.points(df, 'longitude', 'latitude')

img =,, how='log')

What is Datalayer?

Datalayer is a machine learning platform that identifies patterns in large datasets by identifying trends and relationships. It can segment data into a variety of categories, including time, geo, and other dimensions. With Datashader, you can create an image of your data in a few simple steps, and you can compute the data. Datashader is a scalable and high-performance data processing framework that can be used for processing large datasets in real-time. It uses a modular architecture that can handle very large sets of data, while simultaneously running pipelines on multiple CPUs and distributed systems such as GPUs.

How does Datashader work?

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. Instead of building a complex, hard-to-maintain image library from scratch, Datashader allows you to create flexible pipelines for creating sophisticated images with little or no programming capability.

This code loads a data file into a Pandas data frame and then projects the field's longitude and latitude onto an 850x500 grid where the values are aggregated into counts. The resulting image shows binned observations in shades of black, white, and gray. The code reads a data frame into memory and then plots it as an image with custom-built matplotlib functions.

datashader pypi

This code reads in a data file and creates a Pandas data frame, then uses d3's projection method to create an image. The projection method is based on the idea that SciPy has a matrix of color scales, from light to dark, which represent values in the pixel values at any point.


Learn how to use Datashader to generate rich graphics from Python and machine learning models in the 2019 HoloViz SciPy tutorial. The three-hour training teaches you how to create stunning visuals using Datashader. The hands-on programming exercises in the course demonstrate many of the capabilities of HoloViz's open-source machine learning library. You can download Datashader with free software for 3D data visualization from the HoloViz website. It is also available as a free software package called Datashader on the GitHub project site. You can also explore the open-source tool, Datashader, with our interactive tutorial in the 2019 SciPy tutorial (3 hours!


The API is the definitive guide to each part of Datashader, a comprehensive suite of libraries for Adobe Illustrator. The information in the documentation is more conveniently available via the help() command, but the same information is available more conveniently via the Help menu in Illustrator.
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