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

Split-apply-combine in DataFrames

Split-apply-combine is a basic pattern in data analytics that allows you to obtain aggregated information about your dataset. In this recipe, we demonstrate how you can perform this task using the DataFrames.jl package.

Getting ready

Make sure you have theiris.csvfile in your working directory, which was downloaded in the Reading CSV data from the internet recipe. Open the Julia command line and install the DataFrames.jl and CSV.jlpackages if required, using the following commands:

julia> using Pkg

julia> Pkg.add("DataFrames")

julia> Pkg.add("CSV")


In the GitHub repository for this recipe, you will find the commands.txt file, which contains the presented sequence of shell and Julia commands. The iris.csv file contains the data that we will analyze.

Now, continue to execute the commands in the Julia command line.

The citation is as follows:

@misc{R.A. Fisher , author = "R.A. Fisher ", year = "2017", title = "{UCI} Machine Learning Repository", url...