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Spark Cookbook

You're reading from  Spark Cookbook

Product type Book
Published in Jul 2015
Publisher
ISBN-13 9781783987061
Pages 226 pages
Edition 1st Edition
Languages
Author (1):
Rishi Yadav Rishi Yadav
Profile icon Rishi Yadav

Table of Contents (19) Chapters

Spark Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started with Apache Spark 2. Developing Applications with Spark 3. External Data Sources 4. Spark SQL 5. Spark Streaming 6. Getting Started with Machine Learning Using MLlib 7. Supervised Learning with MLlib – Regression 8. Supervised Learning with MLlib – Classification 9. Unsupervised Learning with MLlib 10. Recommender Systems 11. Graph Processing Using GraphX 12. Optimizations and Performance Tuning Index

Calculating summary statistics


Summary statistics is used to summarize observations to get a collective sense of the data. The summary includes the following:

  • Central tendency of data—mean, mode, median

  • Spread of data—variance, standard deviation

  • Boundary conditions—min, max

This recipe covers how to produce summary statistics.

How to do it…

  1. Start the Spark shell:

    $ spark-shell
    
  2. Import the matrix-related classes:

    scala> import org.apache.spark.mllib.linalg.{Vectors,Vector}
    scala> import org.apache.spark.mllib.stat.Statistics
    
  3. Create a personRDD as RDD of vectors:

    scala> val personRDD = sc.parallelize(List(Vectors.dense(150,60,25), Vectors.dense(300,80,40)))
    
  4. Compute the column summary statistics:

    scala> val summary = Statistics.colStats(personRDD)
    
  5. Print the mean of this summary:

    scala> print(summary.mean)
    
  6. Print the variance:

    scala> print(summary.variance)
    
  7. Print the non-zero values in each column:

    scala> print(summary.numNonzeros)
    
  8. Print the sample size:

    scala> print(summary...
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