Julia is a new programming language that was developed at MIT in the Applied Computing Group under the supervision of Prof. Alan Edelman. Its development started in 2009, and it was first presented publicly in February 2012. It is still a fairly young language when you look at the current Version number (0.3), but its foundation is stable; the core language has had no backwards incompatible changes since Version 0.1. It is based on clear and solid principles, and its popularity is steadily increasing in the technical, data scientist, and high-performance computing arenas. In the section The Rationale for Julia, we present an overview of the principles on which Julia is based and compare them to other languages.
Chapter 1, Installing the Julia Platform, guides you with the installation of all the necessary components required for a Julia environment. It teaches you how to work with Julia's console (the REPL) and discusses some of the more elaborate development editors you can use.
Chapter 2, Variables, Types, and Operations, discusses the elementary built-in types in Julia, and the operations that can be performed on them, so that you are prepared to start writing the code with them.
Chapter 3, Functions, explains why functions are the basic building blocks of Julia, and how to effectively use them.
Chapter 4, Control Flow, shows Julia's elegant control constructs, how to perform error handling, and how to use coroutines (called Tasks in Julia) to structure the execution of your code.
Chapter 5, Collection Types, explores the different types that group individual values, such as arrays and matrices, tuples, dictionaries, and sets.
Chapter 6, More on Types, Methods, and Modules, digs deeper into the type concept and explains how this is used in multiple dispatch to get C-like performance. Modules, a higher code organizing concept, are discussed as well.
Chapter 7, Metaprogramming in Julia, touches on the deeper layers of Julia, such as expressions and reflection capabilities, and demonstrates the power of macros.
Chapter 8, I/O, Networking, and Parallel Computing, shows how to work with data in files and databases using DataFrames. We can explore the networking capabilities, and shows how to set up a parallel computing environment with Julia.
Chapter 9, Running External Programs, looks at how Julia interacts with the command line and other languages and also discusses performance tips.
Chapter 10, The Standard Library and Packages, digs deeper into the standard library and demonstrates the important packages for visualization of data.
Appendix, List of Macros and Packages, provides you with handy reference lists of the macros and packages used in this book.
To run the code examples in the book, you will need the Julia platform for your computer, which can be downloaded from http://julialang.org/downloads/. To work more comfortably with Julia scripts, a development environment such as IJulia, Sublime Text, or LightTable is advisable. Chapter 1, Installing the Julia Platform, contains detailed instructions to set up your Julia environment.
This book is intended for the data scientist and for all those who work in technical and scientific computation projects. It will get you up and running quickly with Julia to start simplifying your projects applications. The book assumes that you already have some basic working knowledge of high-level dynamic languages such as MATLAB, R, Python, or Ruby.
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "As an example, we use the data-file winequality.csv that contains 1599 sample measurements, 12 data columns."
A block of code is set as follows:
using DataFrames fname = "winequality.csv" data = readtable(fname, separator = ';')
When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
using DataFrames
fname = "winequality.csv"
data = readtable(fname, separator = ';')
Any command-line input or output is written as follows:
julia main.jl
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Navigate to Configuration | System Administration | ODBC Data Sources."
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