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Learning PySpark

You're reading from  Learning PySpark

Product type Book
Published in Feb 2017
Publisher Packt
ISBN-13 9781786463708
Pages 274 pages
Edition 1st Edition
Languages
Authors (2):
Tomasz Drabas Tomasz Drabas
Profile icon Tomasz Drabas
Denny Lee Denny Lee
Profile icon Denny Lee
View More author details

Table of Contents (20) Chapters

Learning PySpark
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Understanding Spark 2. Resilient Distributed Datasets 3. DataFrames 4. Prepare Data for Modeling 5. Introducing MLlib 6. Introducing the ML Package 7. GraphFrames 8. TensorFrames 9. Polyglot Persistence with Blaze 10. Structured Streaming 11. Packaging Spark Applications 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...

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