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
Dedication
About Packt
Contributors
Preface
Index

Accessing the internet from Julia


Now that you have a good understanding of how web pages are accessed on the internet through client-server interactions, let's see how we can do this with Julia.

The most common web clients are the web browsers—apps such as Chrome or Firefox. However, these are meant to be used by human users, rendering web pages with fancy styled UIs and sophisticated interactions. Web scraping can be done manually through a web browser, it's true, but the most efficient and scalable way is through a fully automated, software-driven process. Although web browsers can be automated (with something like Selenium from https://www.seleniumhq.org), it's a more difficult, error-prone, and resource-intensive task. For most use cases, the preferred approach is to use a dedicated HTTP client.

Making requests with the HTTP package

Pkg, Julia's built-in package manager, provides access to the excellent HTTP package. It exposes a powerful functionality for building web clients and servers...