Book Image

Julia Cookbook

By : Raj R Jalem, Jalem Raj Rohit
Book Image

Julia Cookbook

By: Raj R Jalem, Jalem Raj Rohit

Overview of this book

Want to handle everything that Julia can throw at you and get the most of it every day? This practical guide to programming with Julia for performing numerical computation will make you more productive and able work with data more efficiently. The book starts with the main features of Julia to help you quickly refresh your knowledge of functions, modules, and arrays. We’ll also show you how to utilize the Julia language to identify, retrieve, and transform data sets so you can perform data analysis and data manipulation. Later on, you’ll see how to optimize data science programs with parallel computing and memory allocation. You’ll get familiar with the concepts of package development and networking to solve numerical problems using the Julia platform. This book includes recipes on identifying and classifying data science problems, data modelling, data analysis, data manipulation, meta-programming, multidimensional arrays, and parallel computing. By the end of the book, you will acquire the skills to work more effectively with your data.
Table of Contents (12 chapters)

Metaprogramming with DataFrames


In this section, you will learn about implementing the concept of metaprogramming to dataframes. Dataframes are data structures used for expressing data efficiently. So, using metaprogramming techniques helps speed up the process of dealing with data frames, by automated generation of repetitive tasks and easy syntax.

Getting ready

To get started with this section, you must install the DataArraysDataFrames, and DataFramesMeta packages of Julia. They can be installed using the Pkg.add() function. Check for successful installation by executing the following in the REPL:

using DataFrames
using DataArrays
using DataFramesMeta

How to do it...

Let's start with the @with macro. It is used to express the columns of DataFrames as symbols. Let's verify this and play with the macro. Before that you need to define a DataFrame. Here is how you do it:

df = DataFrame(a = [1,2,3], b = [4,5,6])
@with(df, :b + 1)

This would add +1 to every value in the y column of the data frame...