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
Dedication
About Packt
Contributors
Preface
Index

Handling missing data


In this recipe, you will find out how to create a correlation matrix from DataFrame of numbers whose entries can contain missing values.

Getting ready

Make sure you have the CSV.jland 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")

Also, download the following file and load it into a variable called df by using the following commands:

julia> download("https://openmv.net/file/class-grades.csv",
                "grades.csv")

julia> using CSV, DataFrames, Statistics

julia> df = CSV.read("grades.csv");

Note

In the GitHub repository for this recipe, you will find the commands.txt file, which contains the presented sequence of shell and Julia commands. An additional example related to this recipe can be found in the cor.jl file. The grades.csv file is also stored in the repository, in case you have problems with downloading it.

Now, continue working...