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

Converting a data frame between wide and narrow formats

There are two typical approaches to storing data in a data frame:

  • The wide format: Each row of a data frame contains one observation, possibly consisting of several measurements

  • The long format(sometimes called the entity-attribute-valuemodel): Each row of a data frame contains one measurement, a single observation can span across several rows of a data frame

Both formats can be useful in statistical analysis; therefore, the DataFrames.jl package provides functionality allowing data frames to be converted from one format to another.

Getting ready

In this recipe, we use the Iris data set that we already used in the Reading CSV data from the internet recipe.

Make sure you have the CSV.jl and DataFrames.jl packages installed. If they are missing, add them using the following commands:

julia> using Pkg

julia> Pkg.add("DataFrames")

julia> Pkg.add("CSV")

Before we begin, start the Julia command line and load theiris.csvfile into adata...