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

Coding defensively


An error like the previous one, when part of a larger script, has the potential to completely alter a program's execution, leading to undesired and potentially costly results. In general, when something unexpected occurs during the execution of a program, it may leave the software in an erroneous state, making it impossible to return a correct value. In such cases, rather than pushing on and potentially propagating the problem throughout the whole execution stack, it's preferable to explicitly notify the calling code about the situation by throwing an Exception.

Many functions, both in Julia's core and within third-party packages, make good use of the error-throwing mechanism. It's good practice to check the docs for the functions you use and to see what kinds of errors they throw. An error is called an exception in programming lingo.

As in the case of getattr, the author of the Gumbo package warned us that attempting to read an attribute that was not defined would result...