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.
Table of Contents
Part 1: Introduction
- Welcome to the Course 00:03:39
Part 2: Why Reactive Programming?
- Why Reactive Programming? 00:02:12
Part 3: Thinking Reactively
- Thinking Reactively 00:05:38
- Part 4: The Observable
- Part 5: Operators
- Part 6: Combining Observables
- Part 7: Reading and Analyzing Data
- Part 8: Hot Observables
- Part 9: Concurrency
Part 10: Wrap-up
- Going Forward 00:05:11
- Title: Reactive Python for Data Science
- Release date: January 2017
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781491979006