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

Handling Feather data

Feather is a file format that provides binary columnar serialization of data. Feather files use the Apache Arrow data format for data frame storage. This format is supported for data exchange across many platforms, including Python and GNU R.


Getting ready

In this recipe, you will learn how to useFeather.jlto store and retrieve aDataFrame object. The Feather.jlandDataFrames.jlpackages can be installed with the Julia package manager. Additionally, the packages RCall.jl and PyCall .jl will be used to illustrate our examples in R and Python (see the Calling R from Julia and Calling Python from Julia recipes for more details on how those packages work). In the Julia command line, press]and run the following commands:

(v1.0) pkg> add DataFrames
(v1.0) pkg> add Feather
(v1.0) pkg> add RCall
(v1.0) pkg> add PyCall
(v1.0) pkg> add Conda

This will install the requiredpackages and all their dependencies.


In the GitHub repository for this recipe, you will find the...