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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
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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

Operations on columns


In the previous section, you learned about the different invokers and how they mapped to SQL statements. We brushed over the methods supported by columns themselves, however: we can compare for equality using ===, but what other operations are supported by Slick columns?

Most of the SQL functions are supported. For instance, to get the total donations to candidates whose name starts with "O", we could run the following:

scala> db.withSession { implicit session =>
  Tables.transactions.filter { 
    _.candidate.startsWith("O") 
  }.take(5).list 
}
List[Tables.Transactions#TableElementType] = List(Transaction(Some(1594098)...

Similarly, to count donations that happened between January 1, 2011 and February 1, 2011, we can use the .between method on the date column:

scala> val dateParser = new SimpleDateFormat("dd-MM-yyyy")
dateParser: java.text.SimpleDateFormat = SimpleDateFormat

scala> val startDate = new java.sql.Date(dateParser.parse("01-01-2011").getTime...
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