Book Image

Julia 1.0 Programming Complete Reference Guide

By : Ivo Balbaert, Adrian Salceanu
Book Image

Julia 1.0 Programming Complete Reference Guide

By: Ivo Balbaert, Adrian Salceanu

Overview of this book

Julia offers the high productivity and ease of use of Python and R with the lightning-fast speed of C++. There’s never been a better time to learn this language, thanks to its large-scale adoption across a wide range of domains, including fintech, biotech and artificial intelligence (AI). You will begin by learning how to set up a running Julia platform, before exploring its various built-in types. This Learning Path walks you through two important collection types: arrays and matrices. You’ll be taken through how type conversions and promotions work, and in further chapters you'll study how Julia interacts with operating systems and other languages. You’ll also learn about the use of macros, what makes Julia suitable for numerical and scientific computing, and how to run external programs. Once you have grasped the basics, this Learning Path goes on to how to analyze the Iris dataset using DataFrames. While building a web scraper and a web app, you’ll explore the use of functions, methods, and multiple dispatches. In the final chapters, you'll delve into machine learning, where you'll build a book recommender system. By the end of this Learning Path, you’ll be well versed with Julia and have the skills you need to leverage its high speed and efficiency for your applications. This Learning Path includes content from the following Packt products: • Julia 1.0 Programming - Second Edition by Ivo Balbaert • Julia Programming Projects by Adrian Salceanu
Table of Contents (18 chapters)

Building our Wikipedia crawler - take two

Our code runs as expected, refactored and neatly packed into a module. However, there's one more thing I'd like us to refactor before moving on. I'm not especially fond of our extractlinks function.

First of all, it naively iterates over all the HTML elements. For example, say that we also want to extract the title of the page—every time we want to process something that's not a link, we'll have to iterate over the whole document again. That's going to be resource-hungry and slow to run.

Secondly, we're reinventing the wheel. In Chapter 12, Setting Up the Wiki Game, we said that CSS selectors are the lingua franca of DOM parsing. We'd benefit massively from using the concise syntax of CSS selectors with the underlying optimizations provided by specialized libraries.

Fortunately, we don&apos...