Book Image

Using OpenRefine

Book Image

Using OpenRefine

Overview of this book

Data today is like gold - but how can you manage your most valuable assets? Managing large datasets used to be a task for specialists, but the game has changed - data analysis is an open playing field. Messy data is now in your hands! With OpenRefine the task is a little easier, as it provides you with the necessary tools for cleaning and presenting even the most complex data. Once it's clean, that's when you can start finding value. Using OpenRefine takes you on a practical and actionable through this popular data transformation tool. Packed with cookbook style recipes that will help you properly get to grips with data, this book is an accessible tutorial for anyone that wants to maximize the value of their data. This book will teach you all the necessary skills to handle any large dataset and to turn it into high-quality data for the Web. After you learn how to analyze data and spot issues, we'll see how we can solve them to obtain a clean dataset. Messy and inconsistent data is recovered through advanced techniques such as automated clustering. We'll then show extract links from keyword and full-text fields using reconciliation and named-entity extraction. Using OpenRefine is more than a manual: it's a guide stuffed with tips and tricks to get the best out of your data.
Table of Contents (13 chapters)
Using OpenRefine
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Recipe 6 – removing matching rows


In this recipe, you will learn how to suppress problematic rows that have been previously singled out through the use of facets and filters.

Detecting duplicates or flagging redundant rows is fine, but it is only part of the job. At some point, you will want to cross the mark between data profiling (or analysis) and data cleaning. In practice, this means that rows that have been identified as inappropriate during the diagnosis phase (and probably flagged as such) will need to be removed from the dataset, since they are detrimental to its quality.

To remove rows, be sure to have a facet or filter in place first, otherwise you will remove all rows in the dataset. Let's start from the clean project again (import it for a second time or toggle the Undo / Redo tab and select 0. Create project to cancel all modifications) and see what we can do to clean up this dataset. Also, check that OpenRefine shows your data as rows, not records.

We will first remove the rows...