Book Image

Learning PySpark

By : Tomasz Drabas, Denny Lee
Book Image

Learning PySpark

By: Tomasz Drabas, Denny Lee

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. This book will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark. You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications.
Table of Contents (20 chapters)
Learning PySpark
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Index

Data operations


We have already presented some of the most common methods you will use with DataShapes (for example, .peek()), and ways to filter the data based on the column value. Blaze has implemented many methods that make working with any data extremely easy.

In this section, we will review a host of other commonly used ways of working with data and methods associated with them. For those of you coming from pandas and/or SQL, we will provide a respective syntax where equivalents exist.

Accessing columns

There are two ways of accessing columns: you can get a single column at a time by accessing them as if they were a DataShape attribute:

traffic.Year.head(2)

The preceding script produces the following output:

You can also use indexing that allows the selection of more than one column at a time:

(traffic[['Location', 'Year', 'Accident', 'Fatal', 'Alcohol']]
    .head(2))

This generates the following output:

The preceding syntax would be the same for pandas DataFrames. For those of you unfamiliar...