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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

Appendix A. Pattern Matching and Extractors

Pattern matching is a powerful tool for control flow in Scala. It is often underused and under-estimated by people coming to Scala from imperative languages.

Let's start with a few examples of pattern matching before diving into the theory. We start by defining a tuple:

scala> val names = ("Pascal", "Bugnion")
names: (String, String) = (Pascal,Bugnion)

We can use pattern matching to extract the elements of this tuple and bind them to variables:

scala> val (firstName, lastName) = names
firstName: String = Pascal
lastName: String = Bugnion

We just extracted the two elements of the names tuple, binding them to the variables firstName and lastName. Notice how the left-hand side defines a pattern that the right-hand side must match: we are declaring that the variable names must be a two-element tuple. To make the pattern more specific, we could also have specified the expected types of the elements in the tuple...

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