Reactive Python for Data Science-0daytown

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.

Reactive Python for Data Science