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


The dictionary, called Dict, is one of Julia's most powerful and versatile data structures. It's an associative collection—it associates keys with values. You can think of a Dict as a look-up table implementation—given a single piece of information, the key, it will return the corresponding value.

Constructing dictionaries

Creating an empty instance of a Dict is as simple as the following:

julia> d = Dict() 
Dict{Any,Any} with 0 entries

The information between the curly brackets, {Any,Any}, represents the types of keys and values of the Dict. Thus, the concrete type of a Dict itself is defined by the type of its keys and values. The compiler will do its best to infer the type of the collection from the types of its parts. In this case, since the dictionary was empty, no information could be inferred, so Julia defaulted to Any and Any.

An {Any,Any} type of Dict allows us to add any kind of data, indiscriminately. We can use the setindex! method to add a new key-value pair to the...