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.
- Gain detailed awareness of the benefits of reactive programming in data science
- Discover how to solve problems “the reactive way” using push-based versus pull-based iteration
- Understand why reactive programming produces strong, simple, resilient code models
- Learn to leverage RxPy for concurrency when cluster computing hardware is unavailable
- Master the use of RxPy and create more robust Python code for all your data science tasks
Thomas Nield is a senior-level business analyst for Southwest Airlines where he's developed multiple reactive applications that generate revenue for the airline's entire network. A master programmer working in Java, Kotlin, ReactiveX, Python, and database design, Thomas writes a popular blog covering ReactiveX concepts, maintains RxJavaFX and RxKotlinFX, and is the author of the O'Reilly title Getting Started with SQL.