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)

Using DataFrames

If you measure n variables (each of a different type) of a single object, then you get a table with n columns for each object row. If there are m observations, then we have m rows of data. For example, given the student grades as data, you might want to know compute the average grade for each socioeconomic group, where grade and socioeconomic group are both columns in the table, and there is one row per student.

DataFrame is the most natural representation to work with such a (m x n) table of data. They are similar to Pandas DataFrames in Python or data.frame in R. DataFrame is a more specialized tool than a normal array for working with tabular and statistical data, and it is defined in the DataFrames package, a popular Julia library for statistical work. Install it in your environment by typing in add DataFrames in the REPL. Then, import it into your current...