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

Creating pivot tables by chaining transformations of data frames

Often in data analysis, you have to perform multiple steps on transformations of your data frame. In this recipe, we show how you can conveniently perform those operations using the DataFramesMeta.jl package, with an example of preparing a pivot table, one of the most basic methods of summarizing data.

Getting ready

Make sure you have the iris.csv file 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, DataFramesMeta.jl, and CSV.jl packages if required, using the following commands:

julia> using Pkg

julia> Pkg.add("DataFrames")

julia> Pkg.add("DataFramesMeta")

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.

Before we begin, start...