PyData London Bokeh Tutorial - Bryan Van de Ven

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Information about PyData London Bokeh Tutorial - Bryan Van de Ven

Published on March 4, 2014

Author: PyData



Pydata London 2014 Bokeh Turorial given by Bryan Van de Ven.

Download Tutorial Materials: ! (or ask for a USB stick)

Creating interactive browser visualizations with Bokeh Feb 21, 2014

About Me • Employee at Continuum, Analytics • Open-source contributor (Bokeh, Chaco, NumPy) • Scientific, financial, engineering domains using Python, C, C++, etc. • Interactive Visualization of “Big Data” • Background in Physics, Mathematics

About Continuum • Founded in 2012 by Travis Oliphant and Peter Wang • Headquartered in Austin, TX • Products, consulting, training • “big data” analytics • scientific & high-performance computing • interactive visualization, dashboards, web apps • collaborative analysis

Visualization Bokeh: Interactive, browser-based visualization for big data, driven from Python (and others!) !

Bokeh Interactive web viz without Javascript Object-oriented JS runtime library for dynamic, novel, interactive web graphics ! Python interfaces to output static plots or drive live ones ! Interop with IPython Notebook

Bokeh • Language-based (instead of GUI) visualization system • • High-level expressions of data binding, statistical transforms, interactivity and linked data Easy to learn, but expressive depth for power users
 • Interactive • • Data space configuration as well as data selection Specified from high-level language constructs
 • Web as first class interface target
 • Support for large datasets via intelligent downsampling (“abstract rendering”)

Bokeh • Rich interactivity over large datasets • HTML5 Canvas (faster than SVG) • Handles realtime streaming and updating data • Novel & custom visualizations • Integration with Google Maps • No need to learn Javascript - easy interfaces from Python & other langs

Bokeh Interface Concepts • Plots are based on glyphs • All or almost all visual elements of a glyph can be attached to a vector of data. !

Coming soon • Abstract Rendering — dynamic downsampling and data shading for millions of points • Contraints based layout system • Interactive tool improvements and additional tools • Matplotlib compatibility — use Bokeh from pandas,, Seaborn • Language bindings — Scala underway, more later • Widget interactors and plugins

But don’t forget • Usability improvements • Discoverable parameters • Informative error messaging • Expanded live gallery • “Do the right thing” when it is possible • expose capability when it’s not Need feedback from users (you!)

More information and Contributing Public Github repos • • ! Videos • • Python & the Future of Data Analysis Bokeh Workshop ! Blogs • • •

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