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

Complex plotting with Plots.jl

In this recipe, we show how to use Julia to generate various plots. The standard approach for graph visualization in Julia is thePlots.jlpackage. The package supports several graphical backends for the actual generation of figures. The most mature backends includeGR.jlandPyPlot.jl. In this recipe, we show how to usePlots.jlwith theGR.jlbackend.

Getting ready

For this recipe, you need several packages for generating and manipulating the data, as well as for making the plot itself. All packages can simply be installed with the Julia package manager. In the Julia command line, simply press the] key and run the following commands:

(v1.0) pkg> add DataFrames
(v1.0) pkg> add Plots
(v1.0) pkg> add Distributions
(v1.0) pkg> add StatPlots
(v1.0) pkg> add CSV

This will install the packages and all their dependencies.


Here is the citation for the Iris dataset is as follows:

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