Book Image

Learning Alteryx

By : Renato Baruti
Book Image

Learning Alteryx

By: Renato Baruti

Overview of this book

Alteryx, as a leading data blending and advanced data analytics platform, has taken self-service data analytics to the next level. Companies worldwide often find themselves struggling to prepare and blend massive datasets that are time-consuming for analysts. Alteryx solves these problems with a repeatable workflow designed to quickly clean, prepare, blend, and join your data in a seamless manner. This book will set you on a self-service data analytics journey that will help you create efficient workflows using Alteryx, without any coding involved. It will empower you and your organization to take well-informed decisions with the help of deeper business insights from the data.Starting with the fundamentals of using Alteryx such as data preparation and blending, you will delve into the more advanced concepts such as performing predictive analytics. You will also learn how to use Alteryx’s features to share the insights gained with the relevant decision makers. To ensure consistency, we will be using data from the Healthcare domain throughout this book. The knowledge you gain from this book will guide you to solve real-life problems related to Business Intelligence confidently. Whether you are a novice with Alteryx or an experienced data analyst keen to explore Alteryx’s self-service analytics features, this book will be the perfect companion for you.
Table of Contents (17 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback

Regular expressions

Regular expressions extract useful pieces of information from strings using matching patterns. They provide robust matching, parsing, or replacement of string data. The Match output is a Boolean value of true and false. True if the string pattern matches the regular expression and false if it doesn't. The Parse output returns the groups specified in the regular expression. The Replace output can replace groups specified in the replacement text window. The RegEx tool contains these powerful extraction pieces to obtain important information from strings. Let's go through an example of how versatile regular expressions can be. We will continue using the U.S. Chronic Disease Indicators workflow from the previous section, along with the resources located in the data folder.

Regular Expressions Example #1: Parse the QuestionID field to identify if a match is found containing one digit after the underscore _ punctuation.

Step 1: Select the RegEx tool from the Parse tool palette...