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Learning PySpark

You're reading from   Learning PySpark Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0

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Product type Paperback
Published in Feb 2017
Publisher Packt
ISBN-13 9781786463708
Length 274 pages
Edition 1st Edition
Languages
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Authors (2):
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Denny Lee Denny Lee
Author Profile Icon Denny Lee
Denny Lee
Tomasz Drabas Tomasz Drabas
Author Profile Icon Tomasz Drabas
Tomasz Drabas
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Toc

Table of Contents (13) Chapters Close

Preface 1. Understanding Spark FREE CHAPTER 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:

Accessing columns

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:

Accessing columns

The preceding syntax would be the same...

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