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)

Types

Julia's type system is unique. Julia behaves as a dynamically typed language (such as Python, for instance) most of the time. This means that a variable bound to an integer at one point might later be bound to a string. For example, consider the following:

julia> x = 10 
10 
julia> x = "hello" 
"hello" 

However, one can, optionally, add type information to a variable. This causes the variable to only accept values that match that specific type. This is done through a type of annotation. For instance, declaring x::String implies that only strings can be bound to x; in general, it looks like var::TypeName. These are used the most often to qualify the arguments a function can take. The extra type information is useful for documenting the code, and often allows the JIT compiler to generate better-optimized native code. It also allows the development...