Julia Quick Syntax Reference - A Pocket Guide for Data Science Programming

Table of Contents

About the Author

About the Technical Reviewer

Acknowledgments

Introduction

Part I: Language Core

Chapter 1: Getting Started

1.1 Why Julia?

1.2 Installing Julia

1.3 Running Julia

1.4 Miscellaneous Syntax Elements

1.5 Packages

1.5.1 Using the Package Manager

1.5.2 Using Packages

1.6 Help System

Chapter 2: Data Types and Structures

2.1 Simple Types (Non-Containers)

2.1.1 Basic Mathematic Operations

2.1.2 Strings

Concatenation

2.2 Arrays (Lists)

2.2.1 Multidimensional and Nested Arrays

2.3 Tuples

2.4 Named Tuples

2.5 Dictionaries

2.6 Sets

2.7 Memory and Copy Issues

2.8 Various Notes on Data Types

2.8.1 Random Numbers

2.8.2 Missing, Nothing, and NaN

Chapter 3: Control Flow and  Functions

3.1 Code Block Structure and Variable Scope

3.2 Repeated Iteration: for and while Loops, List Comprehension, Maps

3.3 Conditional Statements: if Blocks, Ternary Operator

3.4 Functions

3.4.1 Arguments

3.4.2 Return Value

3.4.3 Multiple-Dispatch (aka Polymorphism)

3.4.4 Templates (Type Parameterization)

3.4.5 Functions as Objects

3.4.6 Call by Reference/Call by Value

3.4.7 Anonymous Functions (aka “Lambda” Functions)

3.4.8 Broadcasting Functions

3.5 Do Blocks

3.6 Exiting Julia

Chapter 4: Custom Types

4.1 Primitive Type Definition

4.2 Structure Definition

4.3 Object Initialization and Usage

4.4 Abstract Types and Inheritance

4.4.1 Implementation of the Object-Oriented Paradigm in Julia

4.5 Some Useful Functions Related to Types

Chapter 5: Input/Output

5.1 Reading (Input)

5.1.1 Reading from the Terminal

5.1.2 Reading from a File

Importing Data for a Matrix

Parsing Comma Separated Value (CSV) Files

5.1.3 Importing Data from Excel

5.1.4 Importing Data from JSON

5.1.5 Accessing Web Resources (HTTP)

5.2 Writing (Output)

5.2.1 Writing to the Terminal

5.2.2 Writing to a File

5.2.3 Exporting to CSV

5.2.4 Exporting Data to Excel

5.2.5 Exporting Data to JSON

5.3 Other Specialized IO

Chapter 6: Metaprogramming and Macros

6.1 Symbols

6.2 Expressions

6.2.1 Creating Expressions

Parse a String

Colon Prefix Operator

Quote Block

Use the Exp Constructor with a Tree

6.2.2 Evaluating Symbols and Expressions

6.3 Macros

6.3.1 Macro Definition

6.3.2 Macro Invocation

6.3.3 String Macros

Chapter 7: Interfacing Julia with Other Languages

7.1 Julia ? C

7.2 Julia ? C++

7.2.1 Interactive C++ Prompt

7.2.2 Embed C++ Code in a Julia Program

7.2.3 Load a C++ Library

7.3 Julia ? Python

7.3.1 Embed Python Code in a Julia Program

7.3.2 Use Python Libraries

7.3.3 PyJulia: Using Julia in Python

Installation

Usage

7.4 Julia ? R

7.4.1 Interactive R Prompt

7.4.2 Embed R Code in a Julia Program

7.4.3 Use R Libraries

7.4.4 JuliaCall: Using Julia in R

Installation

Usage

Chapter 8: Effectively Write Efficient Code

8.1 Performance

8.1.1 Benchmarking

8.1.2 Profiling

8.1.3 Type Stability

8.1.4 Other Tips to Improve Performance

Avoid Using Global Variables and Run Performance-Critical Code Within Functions

Annotate the Type of Data Structures

Annotate the Fields of Composite Types

Loop Matrix Elements by Column and Then by Row

8.2 Code Parallelization

8.2.1 Adding and Removing Processes

8.2.2 Running Heavy Computations on a List of Items

8.2.3 Aggregate Results

8.3 Debugging

8.3.1 Introspection Tools

8.3.2 Debugging Tools

8.4 Managing Runtime Errors (Exceptions)

Part II: Packages Ecosystem

Chapter 9: Working with Data

9.1 Using the DataFrames Package

9.1.1 Installing and Importing the Library

9.1.2 Creating a DataFrame or Loading Data

9.1.3 Getting Insights About the Data

9.1.4 Filtering Data (Selecting or Querying Data)

9.1.5 Editing Data

9.1.6 Editing Structure

Merging/Joining/Copying Datasets

9.1.7 Managing Missing Values

9.1.8 The Split-Apply-Combine Strategy

Aggregating

Computing the Cumulative Sum by Categories

9.1.9 Pivoting Data

Stacking Columns

Unstacking

The Pivot Function

Sorting

9.1.10 Dataframe Export

Exporting to CSV

Exporting to the OpenDocument Spreadsheet File

Exporting to a Matrix

Exporting to a Dict

Exporting to the hdf5 Format

9.2 Using IndexedTables

9.2.1 Creating an IndexedTable (NDSparse)

9.2.2 Row Filtering

9.2.3 Editing/Adding Values

9.3 Using the Pipe Operator

9.4 Plotting

9.4.1 Installation and Backends

9.4.2 The Plot Function

9.4.3 Plotting from DataFrames

9.4.4 Saving

Chapter 10: Mathematical Libraries

10.1 JuMP, an Optimization Framework

10.1.1 The Transport Problem: A Linear Problem

The Problem

Importing the Libraries

Defining the Sets

Defining the Parameters

Declaring the Model

Declaring the Model Variables

Declaring the Model Constraints

Declaring the Model Objective

Human-Readable Visualization of the Model (Optional)

Resolution of the Model

Visualization of the Results

10.1.2 Choosing Between Pizzas and Sandwiches, a Non-Linear Problem

The Problem

Importing the Libraries and Declaring the Model

Declaring the Model Variables, Constraints, and Objectives

Resolution of the Model and Visualization of the Results

10.2 SymPy, a CAS System

10.2.1 Loading the Library and Declaring Symbols

10.2.2 Creating and Manipulating Expressions

10.2.3 Solving a System of Equations

10.2.4 Retrieving Numerical Values

10.3 LsqFit, a Data Fit Library

10.3.1 Loading the Libraries and Defining the Model

10.3.2 Parameters

10.3.3 Fitting the Model

10.3.4 Retrieving the Parameters and  Comparing them with the Observations

Chapter 11: Utilities

11.1 Weave for Dynamic Documents

11.2 Zip Files

11.2.1 Writing a Zip Archive

11.2.2 Reading from a Zipped Archive

11.3 Interact and Mux: Expose Interacting Models on the Web

11.3.1 Importing the Libraries

11.3.2 Defining the Logic of the Model

11.3.3 Defining Controls and Layout

11.3.4 Providing Widgets to Web Users

Index