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 missing observations

Missing observations are pretty much the second-most-common issue in datasets. These arise for many reasons, as we have already alluded to in the introduction. In this recipe, we will learn how to deal with them.

Getting ready

To execute this recipe, you need to have a working Spark environment. Also, we will be working off of the new_id DataFrame we created in the previous recipe, so we assume you have followed the steps to remove the duplicated records.

No other prerequisites are required.

How to do it...

Since our data has two dimensions (rows and columns), we need to check the percentage of data missing in each row and each column to make a determination of what to keep, what to drop, and what to (potentially) impute:

  1. To calculate how many missing observations there are in a row, use the following snippet:
           lambda row: (
               , sum([c == None for...