by Thomas Nield | Released January 2017 | ISBN: 9781491978993

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 scienceDiscover how to solve problems “the reactive way” using push-based versus pull-based iterationUnderstand why reactive programming produces strong, simple, resilient code modelsLearn to leverage RxPy for concurrency when cluster computing hardware is unavailableMaster the use of RxPy and create more robust Python code for all your data science tasksThomas 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. Show and hide more Publisher resources Download Example Code

1. Part 1: Introduction
   • Welcome to the Course 00:03:39
2. Part 2: Why Reactive Programming?
   • Why Reactive Programming? 00:02:12
3. Part 3: Thinking Reactively
   • Thinking Reactively 00:05:38
4. Part 4: The Observable
   • Using the Observable in RxPy 00:07:14
   • RxPy Operators and Other Sources 00:06:14
   • Creating Observables and Intervals with RxPy 00:07:10
   • Hot and Cold Observables in RxPy 00:02:57
5. Part 5: Operators
   • Filter and Take Using RxPy 00:02:55
   • Distinct Operators Using RxPy 00:04:18
   • Reduce and Scan Using RxPy 00:05:28
   • Lists and Dicts Using RxPy 00:03:09
6. Part 6: Combining Observables
   • Merging Observables in RxPy 00:07:07
   • Concatenating and Zipping in RxPy 00:08:25
   • Using Group By in RxPy 00:05:24
7. Part 7: Reading and Analyzing Data
   • Reading Text Files and URL's with RxPy 00:07:20
   • Querying SQLAlchemy with RxPy 00:05:38
   • PROJECT- Scheduling a Reactive Word Counter with RxPy 00:11:36
8. Part 8: Hot Observables
   • Multicasting 00:05:36
   • PROJECT- Creating a Twitter Observable 00:08:54
9. Part 9: Concurrency
   • Understanding Concurrency 00:05:27
   • Using `subscribe_on()` with RxPy 00:05:08
   • Using `observe_on()` with RxPy 00:03:41
   • Achieving Parallelization with RxPy 00:04:31
   • Killing and Redirecting Work with `switch_map()` 00:02:43
10. Part 10: Wrap-up
    • Going Forward 00:05:11
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