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

The efficiency of mutable versus immutable types

Julia allows the user to define mutable and immutable types. In this recipe, we will show how their performance compares in a process of simulating a two-dimensional random walk.

Getting ready

Consider a process starting from a point,

, and updated following the rule:

, where


 are sequences of independent random variables taking values 


with probability


Our objective is to generate two values:

  • The maximum distance reached from the origin point during the simulation measured as
  • A vector containing the path of the random walk



We will model this random walk process using a Monte Carlo simulation.


In the GitHub repository for this recipe, you will find the commands.txt file that contains the presented sequence of shell and Julia commands. Additionally, in the walk.jl and work.jl files you can find the definitions of the types and related methods used in this recipe.

Now open your favorite terminal to execute the commands.

How to do it...