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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

Querying with the DataFrame API


As noted in the previous section, you can start off by using collect(), show(), or take() to view the data within your DataFrame (with the last two including the option to limit the number of returned rows).

Number of rows

To get the number of rows within your DataFrame, you can use the count() method:

swimmers.count()

This gives the following output:

Out[13]: 3

Running filter statements

To run a filter statement, you can use the filter clause; in the following code snippet, we are using the select clause to specify the columns to be returned as well:

# Get the id, age where age = 22
swimmers.select("id", "age").filter("age = 22").show()

# Another way to write the above query is below
swimmers.select(swimmers.id, swimmers.age).filter(swimmers.age == 22).show()

The output of this query is to choose only the id and age columns, where age = 22:

If we only want to get back the name of the swimmers who have an eye color that begins with the letter b, we can use a SQL-like...

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