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Data Structures and Algorithms

In this chapter, we will cover the following recipes:

Finding the index of a random minimum element
Fast matrix multiplication
Implementing a custom pseudo-random number generator
Parsing Git logs with regular expressions
Non-standard ways to sort your data
Creating a function preimage - understanding how dictionaries and sets work
Working with UTF-8 strings

Key benefits

Address the core problems of programming in Julia with the most popular packages for common tasks

Tackle issues while working with Databases and Parallel data processing with Julia

Explore advanced features such as metaprogramming, functional programming, and user defined types

Description

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

Who is this book for?

The target audience of this book is data scientists or programmers that want to improve their skills in working with the Julia programming language. It is recommended that the user has a little experience with Julia or intermediate-level experience with other programming languages such as Python, R, or MATLAB.

What you will learn

Boost your code's performance using Julia's unique features

Organize data in to fundamental types of collections: arrays and dictionaries

Organize data science processes within Julia and solve related problems

Scale Julia computations with cloud computing

Write data to IO streams with Julia and handle web transfer

Define your own immutable and mutable types

Speed up the development process using metaprogramming

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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General Malfunction Jan 12, 2019

I have been developing large scale simulation models for many years now, mostly in Java. Recently I decided to check out Julia in this area. This book caught my attention, because it featured recipes directly from my field of interest such as Monte Carlo simulation or modeling queuing systems. The biggest benefit from reading it was understanding how to implement numerical computing routines efficiently in Julia (which is a pain in Java). Also the last chapter on distributed computing was very valuable, as I have found that with the functionalities shipped with the language I am was scale my simulations very easily. I think that the book is relatively well written, however, I would say that beginners might find it challenging, as it assumes that the reader has some basic knowledge of Julia and computing principles in general.

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Pawel Pralat Jan 12, 2019

I am a research scientist (professor of mathematics) with past experience in using C/C++ (for heavy number crunching) and Maple (for everything else).After studying the recipes in the book, I now see that Julia is a great language that combines easy code (such as Maple or Python) and speed of C. For other researchers I would especially recommend Chapter 4: “Numerical Computing with Julia”, where one can learn how to write high-performance Julia code. Also Chapter 1: “Installing and Setting Up Julia” saved me a lot of time, as usually starting using a new computational environment requires to learn a lot of small details, which are nicely laid out in the book. Julia is famous for its ease to scale out the computations for many CPU cores and nodes. The recipes in the Chapter 10, "Distributed Computing", turned to be great companion to the existing Julia documentation. To book presents Julia high performance computing from a very practical perspective and made it much easier to scale out scientific computations.

DrJimbo Dec 17, 2018

I was somewhat optimistic that this publication would be worthwhile insofar as one of the authors is a very frequent and helpful contributor to Julia support on StackOverflow. Regrettably this was not to be.For example, Chapter 1 provides a fairly thorough overview of Julia program installation along with various IDE's for Windows and Linux Ubuntu - iOS/mac systems are not even referenced! Now this is probably due to the authors residing in Poland where iOS/mac systems are less prevalent, but this publication is being promoted in the U.S. where iOS/mac systems are very prevalent especially in technical computing environments.Let's consider Chapter 2 - Data Structures and AlgorithmsConsider the first example - Finding the index of a random minimum element in an array. The authors launch right into the creation of functions which employ arrays without bothering to discuss the nature of function definitions or arrays. Moreover the second function in the section is relatively complex and involves Julia programming structures previously unmentioned, let alone discussed in detail until much later in the book. Later in Chapter #2 there is a far more complex set of functions to optimize matrix multiplication, perhaps valuable in numerical programming contexts but not necessarily a cookbook example of substantial urgency, especially so early in the volume.Chapter 5 - Variables, Types and Function. Put to the side for the moment that Chapters 2 through 4 have been making extensive use of these three aspects of Julia programming so this chapter arrives a bit late in the game. To begin the discussion of types Julia structures are used - would you like see where structures are thoroughly reviewed? Forget about looking at the Index in the back of the book - "Sudoku" is there, but no "struct". In short, you are left to understand the nature of structures while in the midst of a section on types and type hierarchy.While not altogether unworthy this volume betrays an all too frequent issue with Packt publications - no responsible editorial review. By way of comparison, O'Reilly technical publications, but particularly their "Cookbooks", are wonderfully organized and employ examples which are widely relevant and progress methodically from the simple to the more complex.Given the freely available Julia programing resources this volume is to my mind of tertiary value.

Marcello Vitaletti Dec 24, 2019

Many math expressions are unreadable in the Kindle edition

Julia 1.0 Programming Cookbook: Over 100 numerical and distributed computing recipes for your daily data science work?ow

What do you get with Print?

Julia 1.0 Programming Cookbook

Data Structures and Algorithms

Introduction

Finding the index of a random minimum element in an array

Getting ready

Fast matrix multiplication

Implementing a custom pseudo-random number generator

Getting ready

Parsing Git logs with regular expressions

Getting ready

Non-standard ways to sort your data

Getting ready

Creating a function preimage - understanding how dictionaries and sets work

Getting ready

Working with UTF-8 strings

Getting ready

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