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Scala for Data Science

You're reading from   Scala for Data Science Leverage the power of Scala with different tools to build scalable, robust data science applications

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Product type Paperback
Published in Jan 2016
Publisher
ISBN-13 9781785281372
Length 416 pages
Edition 1st Edition
Languages
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Author (1):
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Pascal Bugnion Pascal Bugnion
Author Profile Icon Pascal Bugnion
Pascal Bugnion
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Table of Contents (17) Chapters Close

Preface 1. Scala and Data Science FREE CHAPTER 2. Manipulating Data with Breeze 3. Plotting with breeze-viz 4. Parallel Collections and Futures 5. Scala and SQL through JDBC 6. Slick – A Functional Interface for SQL 7. Web APIs 8. Scala and MongoDB 9. Concurrency with Akka 10. Distributed Batch Processing with Spark 11. Spark SQL and DataFrames 12. Distributed Machine Learning with MLlib 13. Web APIs with Play 14. Visualization with D3 and the Play Framework A. Pattern Matching and Extractors Index

Pattern matching internals

If you define a case class, as we saw with Name, you get pattern matching against the constructor for free. You should be using case classes to represent your data as much as possible, thus reducing the need to implement your own pattern matching. It is nevertheless useful to understand how pattern matching works.

When you create a case class, Scala automatically builds a companion object:

scala> case class Name(first: String, last: String)
defined class Name

scala> Name.<tab>
apply   asInstanceOf   curried   isInstanceOf   toString   tupled   unapply

The method used (internally) for pattern matching is unapply. This method takes, as argument, an object and returns Option[T], where T is a tuple of the values of the case class.

scala> val name = Name("Martin", "Odersky")
name: Name = Name(Martin,Odersky)

scala> Name.unapply(name)
Option[(String, String)] = Some((Martin,Odersky))

The unapply method is an extractor. It plays the...

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