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


An array is a data structure (and the corresponding type) that represents an ordered collection of elements. More specifically, in Julia, an array is a collection of objects stored in a multi-dimensional grid.

Arrays can have any number of dimensions and are defined by their type and number of dimensions—Array{Type, Dimensions}.

A one-dimensional array, also called a vector, can be easily defined using the array literal notation, the square brackets [...]:

julia> [1, 2, 3]  
3-element Array{Int64,1}: 

You can also constrain the type of the elements:

julia> Float32[1, 2, 3, 4] 
4-element Array{Float32,1}: 

A two D array (also called a matrix) can be initialized using the same array literal notation, but this time without the commas:

julia> [1 2 3 4] 
1×4 Array{Int64,2}: 
 1  2  3  4 

We can add more rows using semicolons:

julia> [1 2 3; 4 5 6; 7 8 9] 
3×3 Array{Int64,2}: 
 1  2  3 
 4  5  6 
 7  8  9 

Julia comes with a multitude of functions...