Bryan is a developer of Bokeh and is a software engineer at Continuum Analytics. He received undergraduate degrees in Computer Science and Mathematics from UT Austin, and a Master's degree in physics from UCLA. He has worked at the Applied Research Labs, developing software for sonar feature detection and classification systems on US Naval submarine platforms. He also spent time at Enthought, where he worked on problems in financial risk modeling and fluid mixing simulation, and also contributed to the Chaco visualization library. He has also worked on an assortment of iOS projects as an independent consultant.
Bokeh is an interactive data visualization library for Python (and other languages!) that targets modern web browsers for presentation. It can create versatile, data-driven graphics, and connect the full power of the entire Python data-science stack to rich, interactive visualizations.
An introduction to basic plotting with Bokeh. You will create your first plots, learn about different data formats Bokeh understands, and make visual customizations for selections and mouse hovering.
Learn how to combine mutiple Bokeh plots into different kinds of layouts on a page, how to easily link different plots together in various ways, and how to add annotations such as legends and hover tooltips.
In addition to versatile data-driven glyphs, Bokeh comes with a variety of high-level statistical chart types built in, so that you can get quick exploratory charts with very little code.
Bokeh server applications let you connect all of the powerful Python libraries for analytics and data science, such as NumPy and Pandas, to rich interactive Bokeh visualizations. Learn about Bokeh's built-in widgets, how to add them to Bokeh documents alongside plots, and how to connect everything to real python code using the Bokeh server.
In this final chapter, you'll build a more sophisticated Bokeh data exploration application from the ground up, based on the famous Gapminder data set.