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


We know that the notation [1, 2, 3] is used to create an array. In fact, this notation denotes a special type of array, called a (column) vector in Julia, as shown in the following screenshot:

To create this as a row vector (1 2 3), use the notation [1 2 3] with spaces instead of commas. This array is of type 1 x 3 Array{Int64,2}, so it has two dimensions. (The spaces used in [1, 2, 3] are for readability only, we could have written this as [1,2,3]).

A matrix is a two- or multidimensional array (in fact, a matrix is an alias for the two-dimensional case). We can write this as follows:

Array{Int64, 1} == Vector{Int64} #> true
Array{Int64, 2} == Matrix{Int64} #> true

As matrices are so prevalent in data science and numerical programming, Julia has an amazing range of functionalities for them.

To create a matrix, use space-separated values for the columns and semicolon-separated for the rows:

// code in Chapter 5\matrices.jl:
matrix = [1 2; 3 4] 
    2x2 Array{Int64,2}:
    1  2