Book Image

Julia 1.0 Programming Cookbook

By : Bogumił Kamiński, Przemysław Szufel
Book Image

Julia 1.0 Programming Cookbook

By: Bogumił Kamiński, Przemysław Szufel

Overview of this book

Julia, with its dynamic nature and high-performance, provides comparatively minimal time for the development of computational models with easy-to-maintain computational code. This book will be your solution-based guide as it will take you through different programming aspects with Julia. Starting with the new features of Julia 1.0, each recipe addresses a specific problem, providing a solution and explaining how it works. You will work with the powerful Julia tools and data structures along with the most popular Julia packages. You will learn to create vectors, handle variables, and work with functions. You will be introduced to various recipes for numerical computing, distributed computing, and achieving high performance. You will see how to optimize data science programs with parallel computing and memory allocation. We will look into more advanced concepts such as metaprogramming and functional programming. Finally, you will learn how to tackle issues while working with databases and data processing, and will learn about on data science problems, data modeling, data analysis, data manipulation, parallel processing, and cloud computing with Julia. By the end of the book, you will have acquired the skills to work more effectively with your data
Table of Contents (18 chapters)
Title Page
Copyright and Credits
About Packt

Using multiple dispatch to handle branching behavior

Let's assume you have and object of the DataFrame type with heterogeneous contents. In this recipe, we will discuss how you can efficiently work with such data using multiple dispatch.

Getting ready

The DataFrame type is designed to hold different types of columns. Therefore, you often have a challenge to dynamically decide which operation to perform on a column, depending on its type. In this recipe, we want to create a simplified version of the describe function, which will behave differently depending on which type of column passed in.

In order to follow this recipe you need to have the DataFrames.jl package installed. If it is missing then you can add it by executing the following commands using Pkg; Pkg.add("DataFrames") in the Julia command line.


In the GitHub repository for this recipe you will find the commands.txt file that contains the presented sequence of shell and Julia commands.

Now open your favorite terminal to execute commands...