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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

Multi-plot example – scatterplot matrix plots


In this section, we will learn how to have several plots in the same figure.

The key new method that allows multiple plots in the same figure is fig.subplot(nrows, ncols, plotIndex). This method, an overloaded version of the fig.subplot method we have been using up to now, both sets the number of rows and columns in the figure and returns a specific subplot. It takes three arguments:

  • nrows: The number of rows of subplots in the figure

  • ncols: The number of columns of subplots in the figure

  • plotIndex: The index of the plot to return

Users familiar with MATLAB or matplotlib will note that the .subplot method is identical to the eponymous methods in these frameworks. This might seem a little complex, so let's look at an example (you will find the code for this in BreezeDemo.scala):

import breeze.plot._

def subplotExample {
  val data = HWData.load
  val fig = new Figure("Subplot example")

  // upper subplot: plot index '0' refers to the first plot...
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