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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

Product type Paperback

Published in Jun 2018

Publisher Packt

ISBN-13 9781788835367

Length 330 pages

Edition 1st Edition

Languages

Python

Tools

Apache Spark

Concepts

Big Data

Authors (2):

Tomasz Drabas

Denny Lee

View More author details

Table of Contents (9) Chapters

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

Creating DataFrames

A Spark DataFrame is an immutable collection of data distributed within a cluster. The data inside a DataFrame is organized into named columns that can be compared to tables in a relational database.

In this recipe, we will learn how to create Spark DataFrames.

Getting ready

To execute this recipe, you need to have a working Spark 2.3 environment. If you do not have one, you might want to go back to Chapter 1, Installing and Configuring Spark, and follow the recipes you find there.

All the code that you will need for this chapter can be found in the GitHub repository we set up for the book: http://bit.ly/2ArlBck; go to Chapter 3 and open the 3. Abstracting data with DataFrames.ipynb notebook.

There are no other requirements.

How to do it...

There are many ways to create a DataFrame, but the simplest way is to create an RDD and convert it into a DataFrame:

sample_data = sc.parallelize([
      (1, 'MacBook Pro', 2015, '15"', '16GB', '512GB SSD'
        , 13.75, 9.48, 0.61, 4...

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Authors (2)

Lee

Lee

Joanna Lee has more than 8 years of experience in game development. She has designed and programmed various video games. She first started working with Unreals game engine in 2005 and is very excited to be able to author a book about the newest Unreal Engine 4. She has also worked with many other engines as well as reviewed books and videos on Cry Engine 4.

See other products by Lee

Drabas

Drabas

Tomasz Drabas is a Data Scientist working for Microsoft and currently residing in the Seattle area. He has over 12 years' international experience in data analytics and data science in numerous fields: advanced technology, airlines, telecommunications, finance, and consulting. Tomasz started his career in 2003 with LOT Polish Airlines in Warsaw, Poland while finishing his Master's degree in strategy management. In 2007, he moved to Sydney to pursue a doctoral degree in operations research at the University of New South Wales, School of Aviation; his research crossed boundaries between discrete choice modeling and airline operations research. During his time in Sydney, he worked as a Data Analyst for Beyond Analysis Australia and as a Senior Data Analyst/Data Scientist for Vodafone Hutchison Australia among others. He has also published scientific papers, attended international conferences, and served as a reviewer for scientific journals. In 2015 he relocated to Seattle to begin his work for Microsoft. While there, he has worked on numerous projects involving solving problems in high-dimensional feature space.

See other products by Drabas

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