Book Image

PySpark Cookbook

By : Denny Lee, Tomasz Drabas
Book Image

PySpark Cookbook

By: Denny Lee, Tomasz Drabas

Overview of this book

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem. You’ll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You’ll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you’ll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You’ll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications.
Table of Contents (13 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Visualizing interactions between features


Plotting the interactions between features can further your understanding of not only the distribution of your data, but also how the features relate to each other. In this recipe, we will show you how to create scatter plots from your data.

Getting ready

To execute this recipe, you need to have a working Spark environment. Also, we will be working off of the no_outliers DataFrame we created in the Handling outliers recipe, so we assume you have followed the steps to handle duplicates, missing observations, and outliers.

No other prerequisites are required.

How to do it...

Once again, we will select our data from the DataFrame and expose it locally:

scatter = (
    no_outliers
    .select('Displacement', 'Cylinders')
)

scatter.registerTempTable('scatter')

%%sql -o scatter_source -q
SELECT * FROM scatter

How it works...

First, we select the two features we want to learn more about to see how they interact with each other; in our case they are the displacement...