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

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Table of Contents (18 chapters)
Title Page
Title Page
Copyright and Credits
Copyright and Credits
Dedication
Dedication
About Packt
About Packt
Contributors
Contributors
Preface
Preface
Free Chapter
1
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
2
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
3
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
4
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
5
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
6
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
7
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
8
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
9
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
10
Distributed Computing
Distributed Computing
Introduction
Multiprocessing in Julia
Sending parameters to remote Julia processes
Multithreading in Julia
Distributed computing with Julia
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think
Index
Index

Contributors

About the authors

Bogumił Kamiński (GitHub username: bkamins) is an associate professor and head of the Decision Support and Analysis Unit at the SGH Warsaw School of Economics, as well as adjunct professor at the data science laboratory, Ryerson University, Toronto. He is co-editor of the Central European Journal of Economic Modeling and Econometrics, and of the Multiple Criteria Decision Making journal. His scientific interests center on operational research and computational social science. He has authored over 50 research articles on simulation, optimization, and prediction methods. He also has 15+ years' experience in the deployment of large-scale advanced analytics solutions for industry and public administration.

 

 

 

 

 

 

 

Przemysław Szufel (GitHub username: pszufe, web: szufel.pl) is an assistant professor in the Decision Support and Analysis Unit at the SGH Warsaw School of Economics. His current research focuses on distributed systems and methods for the execution of large-scale simulations for numerical experiments and optimization. He is working on asynchronous algorithms for the parallel execution of large-scale computations in the cloud and distributed computational environments. He has authored, and co-authored, several open source tools for high-performance and numerical simulation.

About the reviewer

Jalem Raj Rohit is an IIT Jodhpur graduate with a keen interest in recommender systems, machine learning, and serverless and distributed systems. Raj currently works as a data scientist at GEP, prior to which he worked at GEP and Kayako. He contributes to open source projects in Python, Go, and Julia. He also speaks at tech conferences on serverless engineering and machine learning.

 

 

 

 

 

 

 

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