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

Exploring the data

Jumping straight into modeling the data is a misstep almost every new data scientist makes; we get too eager to get to the reward stage, so we forget about the fact that most of the time is actually spent doing the boring stuff of cleaning up our data and getting familiar with it. In this recipe, we will explore the census dataset.

Getting ready

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

No other prerequisites are required.

How to do it...

First, we list all the columns we want to keep:

cols_to_keep = census.dtypes

cols_to_keep = (
    ] + [
        e[0] for e in cols_to_keep[:-1] 
        if e[1] == 'string'

Next, we select the numerical and categorical features as we will be exploring these separately:

census_subset =