Book Image

Julia 1.0 Programming. - Second Edition

By : Ivo Balbaert
Book Image

Julia 1.0 Programming. - Second Edition

By: Ivo Balbaert

Overview of this book

The release of Julia 1.0 is now ready to change the technical world by combining the high productivity and ease of use of Python and R with the lightning-fast speed of C++. Julia 1.0 programming gives you a head start in tackling your numerical and data problems. You will begin by learning how to set up a running Julia platform, before exploring its various built-in types. With the help of practical examples, this book walks you through two important collection types: arrays and matrices. In addition to this, you will be taken through how type conversions and promotions work. In the course of the book, you will be introduced to the homo-iconicity and metaprogramming concepts in Julia. You will understand how Julia provides different ways to interact with an operating system, as well as other languages, and then you'll discover what macros are. Once you have grasped the basics, you’ll study what makes Julia suitable for numerical and scientific computing, and learn about the features provided by Julia. By the end of this book, you will also have learned how to run external programs. This book covers all you need to know about Julia in order to leverage its high speed and efficiency for your applications.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Regular expressions


To search for and match patterns in text and other data, regular expressions are an indispensable tool for the data scientist. Julia adheres to the Perl syntax of regular expressions. For a complete reference, refer to http://www.regular-expressions.info/reference.html. Regular expressions are represented in Julia as a double (or triple) quoted string preceded by r, such as r"..." (optionally, followed by one or more of the i, s, m, or x flags), and they are of type Regex. The regexp.jl script shows some examples.

In the first example, we will match the email addresses (#> shows the result):

email_pattern = r".+@.+" 
input = "[email protected]" 
println(occursin(email_pattern, input)) #> true

The regular expression pattern + matches any (non-empty) group of characters. Thus, this pattern matches any string that contains @ somewhere in the middle.

In the second example, we will try to determine whether a credit card number is valid or not:

visa = r"^(?:4[0-9]{12}(?:[0...