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

Standardizing the data

Data standardization (or normalization) is important for a number of reasons:

  • Some algorithms converge faster on standardized (or normalized) data
  • If your input variables are on vastly different scales, the interpretability of coefficients might be hard or conclusions drawn might be wrong
  • For some models, the optimal solution might be wrong if you do not standardize

In this recipe, we will show you how to standardize the data so if your modeling project requires standardized data, you will know how to do it.

Getting ready

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

No other prerequisites are required.

How to do it...

MLlib offers a method to do most of this work for us. Even though the following code might be confusing at first, we will walk through it step by step:

standardizer = feat.StandardScaler(True, True)
sModel = row...