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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 in for comprehensions

Pattern matching is useful in for comprehensions for extracting items from a collection that match a specific pattern. Let's build a collection of Name instances:

scala> val names = List(Name("Martin", "Odersky"), 
  Name("Derek", "Wyatt"))
names: List[Name] = List(Name(Martin,Odersky), Name(Derek,Wyatt))

We can use pattern matching to extract the internals of the class in a for-comprehension:

scala> for { Name(first, last) <- names } yield first
List[String] = List(Martin, Derek)

So far, nothing terribly ground-breaking. But what if we wanted to extract the surname of everyone whose first name is "Martin"?

scala> for { Name("Martin", last) <- names } yield last
List[String] = List(Odersky)

Writing Name("Martin", last) <- names extracts the elements of names that match the pattern. You might think that this is a contrived example, and it is, but the examples...

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