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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
Languages
Concepts
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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 2. Abstracting Data with RDDs FREE CHAPTER 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

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In order to build a machine learning model, we need data. Thus, before we start, we need to read some data. In this recipe, and throughout this chapter, we will be using the 1994 census income data. 

Getting ready

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

The dataset was sourced from http://archive.ics.uci.edu/ml/datasets/Census+Income.

Note

The dataset is located in the data folder in the GitHub repository for the book.

All the code that you will need in this chapter can be found in the GitHub repository we set up for the book: http://bit.ly/2ArlBck; go to Chapter05 and open the 5. Machine Learning with MLlib.ipynb notebook. 

No other prerequisites are required.

How to do it...

We will read the data into a DataFrame so it is easier for us to work with. Later on, we will convert it into an RDD of labeled points....

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