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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
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Tomasz Drabas
Denny Lee Denny Lee
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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

Inferring the schema using reflection

DataFrames have schema, RDDs don't. That is, unless RDDs are composed of Row(...) objects.

In this recipe, we will learn how to create DataFrames by inferring the schema using reflection.

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 first read our CSV sample data into an RDD and then create a DataFrame from it. Here's the code:

import pyspark.sql as sql

sample_data_rdd = sc.textFile('../Data/DataFrames_sample...
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