Andy R. Terrel and Christine Doig - Building Python Data Applications with Blaze and Bokeh
PyData Dallas 2015 "We use the Blaze and Bokeh libraries to interactively query and visualize large datasets through Python. Blaze provides a consistent query experience on data ranging from a small local CSV files to a large remote Impala or Spark clusters. It automates data migration and brings the power of other database systems into the hands of the armchair analyst. Bokeh is a Python interactive visualization library that targets modern web browsers for presentation. It provides elegant, concise construction of novel graphics in the style of D3.js, but also delivers this capability with high-performance interactivity over large or streaming datasets." View slides for this presentation: http://nbviewer.ipython.org/github/ContinuumIO/pydata-apps/tree/master/ 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps
PyData Dallas 2015 "We use the Blaze and Bokeh libraries to interactively query and visualize large datasets through Python. Blaze provides a consistent query experience on data ranging from a small local CSV files to a large remote Impala or Spark clusters. It automates data migration and brings the power of other database systems into the hands of the armchair analyst. Bokeh is a Python interactive visualization library that targets modern web browsers for presentation. It provides elegant, concise construction of novel graphics in the style of D3.js, but also delivers this capability with high-performance interactivity over large or streaming datasets." View slides for this presentation: http://nbviewer.ipython.org/github/ContinuumIO/pydata-apps/tree/master/ 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps