Reactive Python for data science : push-based data analysis with RxPy - Nield, Thomas (Computer programmer) (O'Reilly, 2016)
"Reactive programming is shaping the future of how we model data. With reactive, not only can you concisely wrangle and analyze static data, you can effectively work with data as a real-time infinite feed. Reactive Extensions (Rx) first gained traction in 2009 and has been ported to over a dozen major languages and platforms. In this course, you'll learn to use RxPy, a lightweight Rx library, in Python data analysis workflows. It's designed for basic Python users who want to move beyond ad hoc data analysis and make their code geared toward a production environment, as well as for programmers familiar with Scala, Java 8, C#, Swift, and Kotlin who are interested in using the modern higher-order functional chain patterns from those languages."--Resource description page.