PyData London Bokeh Tutorial - Bryan Van de Ven
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This document provides information about Bokeh, an interactive visualization library for Python that allows creating browser-based visualizations. It describes Bokeh's capabilities like interactive glyphs that link visual elements to data, high-level expressions for interactivity, and outputting plots to the web. The document also outlines upcoming features and provides links to download materials, videos, and code repositories to learn more about Bokeh and data visualization.
PyData London Bokeh Tutorial - Bryan Van de Ven
- 1. Download Tutorial Materials: http://cdn.pydata.org/BokehTutorial.tgz ! http://cdn.pydata.org/BokehTutorial.zip (or ask for a USB stick)
- 2. Creating interactive browser visualizations with Bokeh Feb 21, 2014
- 3. 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
- 4. 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
- 5. Visualization Bokeh: Interactive, browser-based visualization for big data, driven from Python (and others!) http://bokeh.pydata.org !
- 6. 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
- 7. 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”)
- 8. 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 http://bokeh.pydata.org
- 9. 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. !
- 16. 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, ggplot.py, Seaborn • Language bindings — Scala underway, more later • Widget interactors and plugins
- 17. 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!)
- 18. More information and Contributing Public Github repos • • https://github.com/ContinuumIO/bokeh https://github.com/JosephCottam/AbstractRendering ! Videos • • Python & the Future of Data Analysis Bokeh Workshop ! Blogs • • • http://continuum.io/blog/index http://continuum.io/blog/painless_streaming_plots_w_bokeh http://continuum.io/blog/realtime-analytics-twitter