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)

Standard modules and paths

The code for Julia packages (also called libraries) is contained in a module whose name starts with an uppercase letter by convention, like this:

# see the code in Chapter 6\modules.jl
module Package1

export Type1, perc


# code
mutable struct Type1

perc(a::Type1) = * 0.01


This serves to separate all its definitions from those in other modules so that no name conflicts occur. Name conflicts are solved by qualifying the function by the module name. For example, the packages Winston and Gadfly both contain a function plot. If we needed these two versions in the same script, we would write it as follows:

import Winston
import Gadfly
Gadfly.plot(x=[1:10], y=rand(10))

All variables defined in the global scope are automatically added to the Main module. Thus, when...