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PySpark Cookbook

You're reading from   PySpark Cookbook Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788835367
Length 330 pages
Edition 1st Edition
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Authors (2):
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Tomasz Drabas Tomasz Drabas
Author Profile Icon Tomasz Drabas
Tomasz Drabas
Denny Lee Denny Lee
Author Profile Icon Denny Lee
Denny Lee
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Toc

Table of Contents (9) Chapters Close

Preface 1. Installing and Configuring Spark FREE CHAPTER 2. Abstracting Data with RDDs 3. Abstracting Data with DataFrames 4. Preparing Data for Modeling 5. Machine Learning with MLlib 6. Machine Learning with the ML Module 7. Structured Streaming with PySpark 8. GraphFrames – Graph Theory with PySpark

Specifying the schema programmatically

In the previous recipe, we learned how to infer the schema of a DataFrame using reflection.

In this recipe, we will learn how to specify the schema programmatically.

Getting ready

To execute this recipe, you need to have a working Spark 2.3 environment. 

There are no other requirements.

How to do it...

In this example, we will learn how to specify the schema programmatically:

import pyspark.sql.types as typ

sch = typ.StructType([
typ.StructField('Id', typ.LongType(), False)
, typ.StructField('Model&apos...
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