Book Image

Julia 1.0 Programming Cookbook

By : Bogumił Kamiński, Przemysław Szufel
Book Image

Julia 1.0 Programming Cookbook

By: Bogumił Kamiński, Przemysław Szufel

Overview of this book

Julia, with its dynamic nature and high-performance, provides comparatively minimal time for the development of computational models with easy-to-maintain computational code. This book will be your solution-based guide as it will take you through different programming aspects with Julia. Starting with the new features of Julia 1.0, each recipe addresses a specific problem, providing a solution and explaining how it works. You will work with the powerful Julia tools and data structures along with the most popular Julia packages. You will learn to create vectors, handle variables, and work with functions. You will be introduced to various recipes for numerical computing, distributed computing, and achieving high performance. You will see how to optimize data science programs with parallel computing and memory allocation. We will look into more advanced concepts such as metaprogramming and functional programming. Finally, you will learn how to tackle issues while working with databases and data processing, and will learn about on data science problems, data modeling, data analysis, data manipulation, parallel processing, and cloud computing with Julia. By the end of the book, you will have acquired the skills to work more effectively with your data
Table of Contents (18 chapters)
Title Page
Copyright and Credits
About Packt


A key condition for successfully working with any programming language is the careful configuration of the development environment. Julia, being an open source language, offers integration with several popular tools. This means that developers have a number of alternatives at hand when setting up the complete Julia toolbox.

The first decision to be made is the choice of Julia distribution. Available options include binary and source code forms. For non-typical hardware configurations, or when one wishes to use all the latest compiler features, Julia source code can be downloaded. Another decision concerns which compiler to use in order to build Julia (GCC or Intel) and whether to link Julia against Intel's mathematical libraries.

The second decision lies in the choice of IDE. Julia can be integrated with various editors, with Atom plus the Juno plugin being the most popular choice. Yet another option is to use a browser-based Julia IDE—Jupyter Notebook, JupyterLab, or Cloud9.

In this chapter, we discuss all the preceding options and show how to set up the complete Julia programmer's environment, along with the most important technical tips and recommendations.