Book Image

Julia Programming Projects

By : Adrian Salceanu
Book Image

Julia Programming Projects

By: Adrian Salceanu

Overview of this book

Julia is a new programming language that offers a unique combination of performance and productivity. Its powerful features, friendly syntax, and speed are attracting a growing number of adopters from Python, R, and Matlab, effectively raising the bar for modern general and scientific computing. After six years in the making, Julia has reached version 1.0. Now is the perfect time to learn it, due to its large-scale adoption across a wide range of domains, including fintech, biotech, education, and AI. Beginning with an introduction to the language, Julia Programming Projects goes on to illustrate how to analyze the Iris dataset using DataFrames. You will explore functions and the type system, methods, and multiple dispatch while building a web scraper and a web app. Next, you'll delve into machine learning, where you'll build a books recommender system. You will also see how to apply unsupervised machine learning to perform clustering on the San Francisco business database. After metaprogramming, the final chapters will discuss dates and time, time series analysis, visualization, and forecasting. We'll close with package development, documenting, testing and benchmarking. By the end of the book, you will have gained the practical knowledge to build real-world applications in Julia.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
About Packt

Finishing touches

Our gameplay evolves nicely. Only a few pieces left. Thinking about our game's UI, we'll want to show the game's progression, indicating the articles the player has navigated through. For this, we'll need the titles of the articles. If we could also include an image, that would make our game much prettier.

Fortunately, we are now using CSS selectors, so extracting the missing data should be a piece of cake. All we need to do is add the following to the Wikipedia module:

import Cascadia: matchFirst 
function extracttitle(elem) 
  matchFirst(Selector("#section_0"), elem) |> nodeText 
function extractimage(elem) 
  e = matchFirst(Selector(".content a.image img"), elem) 
  isa(e, Void) ? "" : e.attributes["src"] 

The extracttitle and extractimage functions will retrieve the corresponding content from our article pages. In both cases, since we only want to select a single element, the main page heading and the first image respectively, we use Cascadia.matchFirst...