Book Image

Practical Data Science Cookbook, Second Edition - Second Edition

By : Prabhanjan Narayanachar Tattar, Bhushan Purushottam Joshi, Sean Patrick Murphy, ABHIJIT DASGUPTA, Anthony Ojeda
Book Image

Practical Data Science Cookbook, Second Edition - Second Edition

By: Prabhanjan Narayanachar Tattar, Bhushan Purushottam Joshi, Sean Patrick Murphy, ABHIJIT DASGUPTA, Anthony Ojeda

Overview of this book

As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don’t. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python.
Table of Contents (17 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface

Benchmarking performance for some common tasks


R and its package ecosystem often provide several alternative ways of performing the same task. R also promotes users to create their own functions for particular tasks. When execution time is important, benchmarking performance is necessary to see which strategy works best. We will concentrate on speed in this recipe. The two tasks we will look at are loading the data into R and joining two data objects based on a common variable. All tests are done on a Windows 7 desktop running a 2.4 GHz Intel processor with 8 GB of RAM.

Getting ready

We will use the ann2012full and industry data objects for our performance experiments here, along with the 2012 annual employment data CSV file for data loading. Since you already have these, you are good to go. If you don't, you will need to install the two functions, rbenchmark and microbenchmark, using the install.packages() command.

How to do it...

The following steps will walk us through benchmarking two different...