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

Handling outliers

Observations that differ greatly from the rest of the observations, that is, they are located in the long tail(s) of the data distribution, are outliers. In this recipe, we will learn how to locate and handle the outliers.

Getting ready

To execute this recipe, you need to have a working Spark environment. Also, we will be working off of the imputed DataFrame we created in the previous recipe, so we assume you have followed the steps to handle missing observations.

No other prerequisites are required.

How to do it...

Let's start with a popular definition of an outlier.

A point, 

, that meets the following criteria:

Is not considered an outlier; any point outside this range is. In the preceding equation, Q1 is the first quartile (25th percentile), Q3 is the third quartile, and IQR is the interquartile range and is defined as the difference between Q3 and Q1 : IQR= Q3-Q1

To flag the outliers, follow these steps:

  1. Let's calculate our ranges first:
features = ['Displacement', 'Cylinders...