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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

Joining DataFrames together

So far, we have only considered operations on a single DataFrame. Spark also offers SQL-like joins to combine DataFrames. Let's assume that we have another DataFrame mapping the patient id to a (systolic) blood pressure measurement. We will assume we have the data as a list of pairs mapping patient IDs to blood pressures:

scala> val bloodPressures = List((1 -> 110), (3 -> 100), (4 -> 125))
bloodPressures: List[(Int, Int)] = List((1,110), (3,100), (4,125))

scala> val bloodPressureRDD = sc.parallelize(bloodPressures)
res16: rdd.RDD[(Int, Int)] = ParallelCollectionRDD[74] at parallelize at <console>:24

We can construct a DataFrame from this RDD of tuples. However, unlike when constructing DataFrames from RDDs of case classes, Spark cannot infer column names. We must therefore pass these explicitly to .toDF:

scala> val bloodPressureDF = bloodPressureRDD.toDF(
  "patientId", "bloodPressure")
bloodPressureDF: DataFrame...
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