Book Image

Julia 1.0 Programming. - Second Edition

By : Ivo Balbaert
Book Image

Julia 1.0 Programming. - Second Edition

By: Ivo Balbaert

Overview of this book

The release of Julia 1.0 is now ready to change the technical world by combining the high productivity and ease of use of Python and R with the lightning-fast speed of C++. Julia 1.0 programming gives you a head start in tackling your numerical and data problems. You will begin by learning how to set up a running Julia platform, before exploring its various built-in types. With the help of practical examples, this book walks you through two important collection types: arrays and matrices. In addition to this, you will be taken through how type conversions and promotions work. In the course of the book, you will be introduced to the homo-iconicity and metaprogramming concepts in Julia. You will understand how Julia provides different ways to interact with an operating system, as well as other languages, and then you'll discover what macros are. Once you have grasped the basics, you’ll study what makes Julia suitable for numerical and scientific computing, and learn about the features provided by Julia. By the end of this book, you will also have learned how to run external programs. This book covers all you need to know about Julia in order to leverage its high speed and efficiency for your applications.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

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 workspace with using DataFrames. Do the same for the DataArrays and RDatasets packages (which contain a collection...