Book Image

Clean Data

By : Megan Squire
Book Image

Clean Data

By: Megan Squire

Overview of this book

<p>Is much of your time spent doing tedious tasks such as cleaning dirty data, accounting for lost data, and preparing data to be used by others? If so, then having the right tools makes a critical difference, and will be a great investment as you grow your data science expertise.</p> <p>The book starts by highlighting the importance of data cleaning in data science, and will show you how to reap rewards from reforming your cleaning process. Next, you will cement your knowledge of the basic concepts that the rest of the book relies on: file formats, data types, and character encodings. You will also learn how to extract and clean data stored in RDBMS, web files, and PDF documents, through practical examples.</p> <p>At the end of the book, you will be given a chance to tackle a couple of real-world projects.</p>
Table of Contents (17 chapters)
Clean Data
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Summary


In this chapter, we learned some very practical tips for data cleaning using two easily accessible tools: text editors and spreadsheets. We outlined the available spreadsheet functions for splitting data, moving it around, finding and replacing parts, formatting it, and then putting it back together. Then, we learned how to get the most out of a simple text editor, including some of the built-in functions, and how to use find and replace and regular expressions most effectively.

In the next chapter, we will put together a variety of the techniques we have learned so far to perform some significant file conversions. Many of the techniques we will use will be based on what we have learned in the past two chapters about text editing, regular expressions, data types, and file formats, so get ready to solve some real-world data cleaning problems.