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
Contributors
Preface
Index

Transforming the data


Machine learning (ML) is a field of study that aims at using machines (computers) to understand world phenomena and predict their behavior. In order to build an ML model, all our data needs to be numeric. Since almost all of our features are categorical, we need to transform our features. In this recipe, we will learn how to use a hashing trick and dummy encoding.

Getting ready

To execute this recipe, you need to have a working Spark environment. You would have already gone through the Loading the data recipe where we loaded the census data into a DataFrame.

No other prerequisites are required.

How to do it...

We will be reducing the dimensionality of our dataset roughly by half, so first we need to extract the total number of distinct values in each column:

len_ftrs = []

for col in cols_cat:
    (
        len_ftrs
        .append(
            (col
             , census
                 .select(col)
                 .distinct()
                 .count()
            )
  ...