Book Image

Julia 1.0 Programming Complete Reference Guide

By : Ivo Balbaert, Adrian Salceanu
Book Image

Julia 1.0 Programming Complete Reference Guide

By: Ivo Balbaert, Adrian Salceanu

Overview of this book

Julia offers the high productivity and ease of use of Python and R with the lightning-fast speed of C++. There’s never been a better time to learn this language, thanks to its large-scale adoption across a wide range of domains, including fintech, biotech and artificial intelligence (AI). You will begin by learning how to set up a running Julia platform, before exploring its various built-in types. This Learning Path walks you through two important collection types: arrays and matrices. You’ll be taken through how type conversions and promotions work, and in further chapters you'll study how Julia interacts with operating systems and other languages. You’ll also learn about the use of macros, what makes Julia suitable for numerical and scientific computing, and how to run external programs. Once you have grasped the basics, this Learning Path goes on to how to analyze the Iris dataset using DataFrames. While building a web scraper and a web app, you’ll explore the use of functions, methods, and multiple dispatches. In the final chapters, you'll delve into machine learning, where you'll build a book recommender system. By the end of this Learning Path, you’ll be well versed with Julia and have the skills you need to leverage its high speed and efficiency for your applications. This Learning Path includes content from the following Packt products: • Julia 1.0 Programming - Second Edition by Ivo Balbaert • Julia Programming Projects by Adrian Salceanu
Table of Contents (18 chapters)

Metaprogramming in Julia

Everything in Julia is an expression that returns a value when executed. Every piece of the program code is internally represented as an ordinary Julia data structure, also called an expression. In this chapter, we will see how, by working on expressions, a Julia program can transform and even generate new code. This is a very powerful characteristic, also called homoiconicity. It inherits this property from Lisp, where code and data are just lists, and where it is commonly referred to with the phrase: Code is data and data is code.

In homoiconic languages, code can be expressed in terms of the language syntax. This is the case for the Lisp-like family of languages: Lisp, Scheme and, more recently, Clojure, which use s-expressions. Julia is homoiconic, as are others such as Prolog, IO, Rebol, and Red. As such, these are able to generate code during ...