Sign In Start Free Trial

Book Overview & Buying
Table Of Contents

Getting started with Julia Programming Language

By : Ivo Balbaert

4.1 (17)

Getting started with Julia Programming Language

4.1 (17)

By: Ivo Balbaert

Overview of this book

This book is for you if you are a data scientist or working on any technical or scientific computation projects. The book assumes you have a basic working knowledge of high-level dynamic languages such as MATLAB, R, Python, or Ruby.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

The Rationale for Julia

The Rationale for Julia

The scope of Julia

Julia's place among the other programming languages

A comparison with other languages for the data scientist

Useful links

Summary

1. Installing the Julia Platform

1. Installing the Julia Platform

Installing Julia

Working with Julia's shell

Startup options and Julia scripts

Packages

Installing and working with Julia Studio

Installing and working with IJulia

Installing Sublime-IJulia

Installing Juno

Other editors and IDEs

How Julia works

Summary

2. Variables, Types, and Operations

2. Variables, Types, and Operations

Variables, naming conventions, and comments

Types

Integers

Floating point numbers

Elementary mathematical functions and operations

Rational and complex numbers

Characters

Strings

Regular expressions

Ranges and arrays

Dates and times

Scope and constants

Summary

3. Functions

3. Functions

Defining functions

Optional and keyword arguments

Anonymous functions

First-class functions and closures

Recursive functions

Map, filter, and list comprehensions

Generic functions and multiple dispatch

Summary

4. Control Flow

4. Control Flow

Conditional evaluation

Repeated evaluation

Exception handling

Scope revisited

Tasks

Summary

5. Collection Types

5. Collection Types

Matrices

Tuples

Dictionaries

Sets

Example project – word frequency

Summary

6. More on Types, Methods, and Modules

6. More on Types, Methods, and Modules

Type annotations and conversions

The type hierarchy – subtypes and supertypes

User-defined and composite types

Types and collections – inner constructors

Type unions

Parametric types and methods

Standard modules and paths

Summary

7. Metaprogramming in Julia

7. Metaprogramming in Julia

Expressions and symbols

Eval and interpolation

Defining macros

Built-in macros

Reflection capabilities

Summary

8. I/O, Networking, and Parallel Computing

8. I/O, Networking, and Parallel Computing

Basic input and output

Working with files

Using DataFrames

Working with TCP sockets and servers

Interacting with databases

Parallel operations and computing

Summary

9. Running External Programs

9. Running External Programs

Running shell commands

Calling C and FORTRAN

Calling Python

Performance tips

Summary

10. The Standard Library and Packages

10. The Standard Library and Packages

Digging deeper into the standard library

Julia's package manager

Publishing a package

Graphics in Julia

Using Gadfly on data

Summary

A. List of Macros and Packages

Macros

List of packages

Index

Index

First-class functions and closures

In this section, we will demonstrate the power and flexibility of functions (example code can be found in chapter 3\first_class.jl). First, functions have their own type: typing typeof(mult) in the REPL returns Function. Functions can also be assigned to a variable by their name:

julia> m = mult 
julia> m(6, 6) #> 36.

This is useful when working with anonymous functions, such as c = x -> x + 2, or as follows:

julia> plustwo = function (x)
                     x + 2
                 end
(anonymous function)
julia> plustwo(3)
5

Operators are just functions written with their arguments in an infix form, for example, x + y is equivalent to +(x, y). In fact, the first form is parsed to the second form when it is evaluated. We can confirm it in the REPL: +(3,4) returns 7 and typeof(+) returns Function.

A function can take a function (or multiple functions) as its argument that calculates the numerical derivative of a function f, as defined in...

CONTINUE READING

83

Tech Concepts

36

Programming languages

73

Tech Tools

Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

50+ new titles added per month and exclusive early access to books as they are being written.

Getting started with Julia Programming Language

Search

Your notes and bookmarks