Book Image

Julia 1.0 Programming Complete Reference Guide

By : Ivo Balbaert, Adrian Salceanu
Book Image

Julia 1.0 Programming Complete Reference Guide

By: Ivo Balbaert, Adrian Salceanu

Overview of this book

Julia offers the high productivity and ease of use of Python and R with the lightning-fast speed of C++. There’s never been a better time to learn this language, thanks to its large-scale adoption across a wide range of domains, including fintech, biotech and artificial intelligence (AI). You will begin by learning how to set up a running Julia platform, before exploring its various built-in types. This Learning Path walks you through two important collection types: arrays and matrices. You’ll be taken through how type conversions and promotions work, and in further chapters you'll study how Julia interacts with operating systems and other languages. You’ll also learn about the use of macros, what makes Julia suitable for numerical and scientific computing, and how to run external programs. Once you have grasped the basics, this Learning Path goes on to how to analyze the Iris dataset using DataFrames. While building a web scraper and a web app, you’ll explore the use of functions, methods, and multiple dispatches. In the final chapters, you'll delve into machine learning, where you'll build a book recommender system. By the end of this Learning Path, you’ll be well versed with Julia and have the skills you need to leverage its high speed and efficiency for your applications. This Learning Path includes content from the following Packt products: • Julia 1.0 Programming - Second Edition by Ivo Balbaert • Julia Programming Projects by Adrian Salceanu
Table of Contents (18 chapters)

An example project – word frequency

A lot of the concepts and techniques that we have seen so far in this book come together in this little project. Its aim is to read a text file, remove all characters that are not used in words, and count the frequency of the words in the remaining text. This can be useful, for example, when counting the word density on a web page, the frequency of DNA sequences, or the number of hits on a website that came from various IP addresses. This can be done in some ten lines of code. For example, when words1.txt contains the sentence to be, or not to be, that is the question!, then this is the output of the program:

Word : frequency 
    
be : 2
is : 1
not : 1
or : 1
question : 1
that : 1
the : 1
to : 2

Here is the code with comments:

# code in chapter 5\word_frequency.jl: 
# 1- read in text file: 
str = read("words1.txt", String)...