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

Transforming rows of DataFrame

Performing a transformation on a set of columns in a DataFrame object is one of the most common operations. In this recipe, we describe how you can perform complex transformations on rows in DataFrame.

Getting ready

In this recipe, we use the grades dataset, which we have already used in the Working with categorical data recipe.

Assume there are the following grading rules in this course:

  • IfFinalis missing or less than 50, then the grade isfail
  • IfFinalis greater than or equal to 50 but less than 75, and both Midterm and TakeHomeare missing or less than 50, then the grade isfail
  • In all other cases, the grade ispass

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 the grades.csv file into a data frame, using the following commands:

julia> using CSV, DataFrames...