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Reactive Python for Data Science

      • Welcome to the Course  (Free)

        Reactive programming is a radically effective approach that treats events as data, and data as events. With RxPy, learn how you can leverage RxPy's push-based iteration and chain-like operators to express business logic and concurrency.
      • 00:03:39

      • Thinking Reactively

        In this segment, we will introduce ReactiveX and learn to see the world from a reactive, rather than imperative, perspective.
      • 00:05:38

      • Hot and Cold Observables in RxPy

        The beauty of Observables is they treat events and data the same way. But let's clarify the subtle differences between cold and hot Observables and see an example of an Observable that pushes live Tweets.
      • 00:02:57

      • Filter and Take Using RxPy

        Oftentimes, we will want to suppress emissions that fail to meet critera, and we can do that with filter(). We will also cover take() which cuts off emissions at a certain number.
      • 00:02:55

      • Distinct Operators Using RxPy

        It is common in data analysis to remove duplicate data or events, and in this section, we will learn how to reactively leverage distinct() operators.
      • 00:04:18

      • Reduce and Scan Using RxPy

        In this section, we will learn how to aggregate emissions to create a single reduced emission, as well as emit a rolling aggregation for each emission.
      • 00:05:28

      • Lists and Dicts Using RxPy

        Sometimes we will want to reduce items into collections, and in this section, we will learn how to consolidate emissions into a List or Dict.
      • 00:03:08

      • Merging Observables in RxPy

        Data and events can come from multiple sources. In this segment, we will cover combining emissions from multiple Observables into a single Observable.
      • 00:07:07

      • Concatenating and Zipping in RxPy

        When we care about retaining the order of emissions, we can leverage concatentation instead of merging. We will also cover combining Observables by pairing their emissions using `zip()`.
      • 00:08:25

      • Using Group By in RxPy

        In data analysis, we often need to slice data on a given attribute. In this section, we will learn how to do this reactively and split an Observable into multiple Observables based on a given key.
      • 00:05:23

      • Multicasting

        Leverage the ConnectableObservable to broadcast emissions to multiple Subscribers simultaneously.
      • 00:05:36

      • Going Forward

        With your newfound knowledge in RxPy, learn how to grow your knowledge in reactive programming and continue to leverage it with your data science daily workflow.
      • 00:05:11

Reactive Python for Data Science

  • Publisher: O'Reilly Media
  • Released: January 2017
  • Run time: 2 hours 17 minutes

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.