Book Image

PySpark Cookbook

By : Denny Lee, Tomasz Drabas
Book Image

PySpark Cookbook

By: Denny Lee, Tomasz Drabas

Overview of this book

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem. You’ll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You’ll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you’ll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You’ll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications.
Table of Contents (13 chapters)
Title Page
Packt Upsell

Extracting features from text

Often, data scientists need to deal with unstructured data such as free-flow text: companies receive feedback or recommendations (among other things) from customers that can be a gold mine for predicting a customer's next move or their sentiment toward a brand.

In this recipe, we will learn how to extract features from text.

Getting ready

To execute this recipe, you will need a working Spark environment.

No other prerequisites are required.

How to do it...

A general process that aims to extract data from text and convert it into something a machine learning model can use starts with the free-flow text. The first step is to take each sentence of the text and split it on the space character (most often). Next, all the stop words are removed. Finally, simply counting distinct words in the text or using a hashing trick takes us into the realm of numerical representations of free-flow text.

Here's how to achieve this with Spark's ML module:

some_text = spark.createDataFrame...