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Julia 1.0 Programming Cookbook

By : Kamiński, Szufel

3.3 (4)

Julia 1.0 Programming Cookbook

3.3 (4)

By: Kamiński, 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

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

Sections

Get in touch

Free Chapter

Installing and Setting Up Julia

Installing and Setting Up Julia

Introduction

Installing Julia from binaries

Julia IDEs

Julia support for text editors

Building Julia from sources on Linux

Running Julia inside the Cloud9 IDE in the AWS cloud

How to customize Julia on startup

Setting up Julia to use multiple cores

Useful options for interaction with Julia

Displaying computation results in Julia

Managing packages

Configuring Julia in Jupyter Notebook

Configuring Julia to work with JupyterLab

Configuring Julia with Jupyter Notebook in Terminal-only cloud environments

Data Structures and Algorithms

Data Structures and Algorithms

Introduction

Finding the index of a random minimum element in an array

Fast matrix multiplication

Implementing a custom pseudo-random number generator

Parsing Git logs with regular expressions

Non-standard ways to sort your data

Creating a function preimage - understanding how dictionaries and sets work

Working with UTF-8 strings

Data Engineering in Julia

Data Engineering in Julia

Introduction

Managing streams, and reading and writing files

Using IOBuffer to efficiently work with in-memory streams

Fetching data from the internet

Writing a simple RESTful service

Working with JSON data

Working with date and time

Using object serialization in Julia

Running Julia as a background process

Reading and writing Microsoft Excel files

Handling Feather data

Reading CSV and FWF files

Numerical Computing with Julia

Numerical Computing with Julia

Introduction

Traversing matrices efficiently

Executing loops efficiently with conditional statements

Generating full factorial designs

Approximating pi using partial series sums

Running Monte Carlo simulations

Analyzing a queuing system

Working with complex numbers

Writing a simple optimization routine

Estimating a linear regression

Understanding broadcasting in Julia

Improving code performance using @inbounds

Creating a matrix from a set of vectors as rows

Using array views to avoid memory allocation

Variables, Types, and Functions

Variables, Types, and Functions

Introduction

Understanding subtyping in Julia

Using multiple dispatch to handle branching behavior

Using functions as variables in Julia

Functional programming in Julia

Scope of variables in Julia

Handling exceptions in Julia

Working with NamedTuples

Metaprogramming and Advanced Typing

Metaprogramming and Advanced Typing

Introduction

Metaprogramming

Macros and generated functions

Defining your own types - linked list

Defining primitive types

Understanding the structure of Julia numeric types with introspection

Using static arrays

The efficiency of mutable versus immutable types

Ensuring type stability of your code

Handling Analytical Data

Handling Analytical Data

Introduction

Converting data between DataFrame and Matrix

Investigating the contents of a data frame

Reading CSV data from the internet

Working with categorical data

Handling missing data

Split-apply-combine in DataFrames

Converting a data frame between wide and narrow formats

Comparing data frames for identity

Transforming rows of DataFrame

Creating pivot tables by chaining transformations of data frames

Julia Workflow

Julia Workflow

Introduction

Julia development workflow with Revise.jl

Benchmarking code

Profiling Julia code

Setting up logging in your code

Calling Python from Julia

Calling R from Julia

Managing project dependencies

Data Science

Data Science

Introduction

Working with databases in Julia

Optimization using JuMP

Estimation using maximum likelihood

Complex plotting with Plots.jl

Building machine learning models with ScikitLearn.jl

Distributed Computing

Distributed Computing

Introduction

Multiprocessing in Julia

Sending parameters to remote Julia processes

Multithreading in Julia

Distributed computing with Julia

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Approximating pi using partial series sums

One of the powers of Julia is its flexibility in applying its type system. In this recipe, we explain how to write flexible code that can adjust to the required type, using the example of approximating π.

Getting ready

Approximation of π is a long-standing problem in mathematics. You can find many formulas for its calculation at http://mathworld.wolfram.com/PiFormulas.html.

One of the more interesting methods is the use of an infinite sum of terms
for , ranging from zero to infinity. The denominator in each summed fraction is a double factorial (see http://mathworld.wolfram.com/DoubleFactorial.html or https://en.wikipedia...

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