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

Table of Contents
About the Author
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 Modules and Packages
			1.5.1 Using the Package Manager
			1.5.2 Using Modules
			1.5.3 Using Packages
		1.6 Environments
		1.7 Help System
	Chapter 2: Data Types and Structures
		2.1 Simple Types (Non-Containers)
			2.1.1 Basic Mathematical Operations
		2.2 Strings
			2.2.1 Concatenation
		2.3 Arrays (Lists)
			2.3.1 Multidimensional and Nested Arrays
		2.4 Tuples
		2.5 Named Tuples
		2.6 Dictionaries
		2.7 Sets
		2.8 Dates and Times
			2.8.1 Creation of a Date or Time Object (“Input”)
			2.8.2 Extraction of Information from a Date/Time Object (“Output”)
			2.8.3 Periods and Date/Time Arithmetic
		2.9 Memory and Copy Issues
		2.10 Random Numbers
		2.11 Missing, Nothing, and NaN
		2.12 Various Notes on Data Types
			2.12.1 Variable References
	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
			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 (a.k.a. “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
		4.6 Definition of Common Julia Terms
		4.7 Exercise 1: The Schelling Segregation Model
			4.7.1 Instructions
			4.7.2 Skeleton
				4.7.2.1 STEP 1: Set up the environment…
				4.7.2.2 STEP 2: Define the Agent and Env classes…
				4.7.2.3 STEP 3: Define some utility functions…
				4.7.2.4 STEP 4: Define the main functions of the algorithm…
				4.7.2.5 STEP 5: Set the parameters of the specific simulation to run…
				4.7.2.6 STEP 6: Initialize the simulation with the given parameters…
				4.7.2.7 STEP 7: Run the model…
			4.7.3 Results
			4.7.4 Possible Variations
	Chapter 5: Input/Output
		5.1 File System Functions
		5.2 Reading (Input)
			5.2.1 Reading from a Terminal
			5.2.2 Reading from a File
				5.2.2.1 Read the Whole File in a Single Operation
				5.2.2.2 Read the File Line by Line
				5.2.2.3 Parsing Comma-Separated Value (CSV) Files
			5.2.3 Importing Data from Excel
			5.2.4 Importing Data from JSON
			5.2.5 Accessing Web Resources
		5.3 Writing (Output)
			5.3.1 Writing to the Terminal
			5.3.2 Writing to a File
			5.3.3 Exporting to CSV
			5.3.4 Exporting to Excel and OpenDocument Spreadsheet (ODS) Files
			5.3.5 Exporting to JSON
		5.4 Other Specialized IO
	Chapter 6: Metaprogramming and Macros
		6.1 Symbols
		6.2 Expressions
			6.2.1 Expressions Definition
				6.2.1.1 Parse a String
				6.2.1.2 Colon Prefix Operator
				6.2.1.3 Quote Block
				6.2.1.4 Use the Exp Constructor with a Tree
			6.2.2 Evaluate 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 A “Hello World” Example
			7.2.2 Passing Arguments and Retrieving Data
			7.2.3 Functions with STD Classes
		7.3 Julia ⇄ Python
			7.3.1 PythonCall Installation
			7.3.2 Evaluate Python Code in Julia
			7.3.3 Use Python Packages in Julia
			7.3.4 JuliaCall (Python Package) Installation
			7.3.5 Evaluate Julia Code in Python
			7.3.6 Use Julia Packages in Python
		7.4 Julia ⇄ R
			7.4.1 RCall.jl Installation
			7.4.2 Evaluate R Code in Julia
			7.4.3 Use R Packages in Julia
			7.4.4 JuliaCall (R Package) Installation
			7.4.5 Evaluate Julia Code in R
			7.4.6 Use Julia Packages in R
	Chapter 8: Efficiently 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
				8.1.4.1 Avoid Using Global Variables and Run Performance-Critical Code Within Functions
				8.1.4.2 Annotate the Type of Data Structures
				8.1.4.3 Annotate the Fields of Composite Types
				8.1.4.4 Loop Matrix Elements by Column and Then by Row
		8.2 Debugging
			8.2.1 Introspection Tools
			8.2.2 Debugging Tools
			8.2.3 Actions to Take Before Debugging (or While Execution Is Paused)
			8.2.4 Things to Inspect when Execution Is Paused
			8.2.5 Debugging Choices when Execution Is Paused
		8.3 Managing Runtime Errors (Exceptions)
	Chapter 9: Parallel Computing in Julia
		9.1 GPU Programming
			9.1.1 Benchmarking CPU vs. GPU
		9.2 Multithreading (on the CPU)
		9.3 Multiprocessing
			9.3.1 Adding and Removing Processes
			9.3.2 Running Heavy Computations on a List of Items
			9.3.3 Aggregate Results
Part II: Packages Ecosystem
	Chapter 10: Working with Data
		10.1 Using the DataFrames.jl Package
			10.1.1 Installing and Importing the Library
			10.1.2 Creating a DataFrame or Loading Data
			10.1.3 Gaining Insight into the Data
			10.1.4 Filtering Data (Selecting or Querying Data)
			10.1.5 Editing Data
				10.1.5.1 Sorting
			10.1.6 Editing the Structure
				10.1.6.1 Merging/Joining/Copying Datasets
			10.1.7 Working with Categorical Data
			10.1.8 Managing Missing Values
			10.1.9 Pivoting Data
				10.1.9.1 Stacking Columns
				10.1.9.2 Unstacking
			10.1.10 The Split-Apply-Combine Strategy
			10.1.11 Dataframe Export
				10.1.11.1 Exporting to CSV, Excel, or ODS
				10.1.11.2 Exporting to a Matrix
				10.1.11.3 Exporting to a Dict
				10.1.11.4 Exporting to HDF5 Format
		10.2 Using IndexedTables
			10.2.1 Creating an IndexedTable (NDSParse)
			10.2.2 Row Filtering
			10.2.3 Editing/Adding Values
		10.3 Using the Pipe Operator
		10.4 Plotting
			10.4.1 Installation and Backends
			10.4.2 The plot Function
			10.4.3 Plotting from Dataframes
			10.4.4 Plotting Densities and Distributions
			10.4.5 Combine Multiple Plots in a Single Figure
			10.4.6 Saving a Plot
	Chapter 11: Scientific Libraries
		11.1 JuMP, an Optimization Framework
			11.1.1 The Transport Problem: A Linear Problem
				11.1.1.1 The Problem
				11.1.1.2 Importing the Libraries
				11.1.1.3 Defining the Sets
				11.1.1.4 Defining the Parameters
				11.1.1.5 Declaring the Model
				11.1.1.6 Declaring the Model Variables
				11.1.1.7 Declaring the Model Constraints
				11.1.1.8 Declaring the Model Objective
				11.1.1.9 Human-Readable Visualization of the Model (Optional)
				11.1.1.10 Resolution of the Model
				11.1.1.11 Visualization of the Results
			11.1.2 Choosing Between Pizzas and Sandwiches: A Nonlinear Problem
				11.1.2.1 The Problem
				11.1.2.2 Importing the Libraries and Declaring the Model
				11.1.2.3 Declaring Model Variables, Constraints, and Objective
				11.1.2.4 Resolving the Model and Visualizing the Results
		11.2 SymPy, a CAS System
			11.2.1 Loading the Library and Declaring Symbols
			11.2.2 Creating and Manipulating Expressions
			11.2.3 Solving a System of Equations
			11.2.4 Retrieving Numerical Values
		11.3 LsqFit, a Data Fit Library
			11.3.1 Loading the Libraries and Defining the Model
			11.3.2 Parameters
			11.3.3 Fitting the Model
			11.3.4 Retrieving the Parameters and Comparing the Results with the Observations
		11.4 Working with Distributions
			11.4.1 Main Supported Distributions
			11.4.2 API
		11.5 EXERCISE 2: Fitting a Forest Growth Model
			11.5.1 Instructions
			11.5.2 Skeleton
				11.5.2.1 STEP 1: Set up the environment
				11.5.2.2 STEP 2: Load the packages
				11.5.2.3 STEP 3: Load the data
				11.5.2.4 STEP 4: Filter out unused information
				11.5.2.5 STEP 5: Compute the timber volumes per hectare
				11.5.2.6 STEP 6: Aggregate the trees data
				11.5.2.7 STEP 7: Join datasets
				11.5.2.8 STEP 8: Filter data
				11.5.2.9 STEP 9: Compute the age class
				11.5.2.10 STEP 10: Define the model to fit
				11.5.2.11 STEP 11: Set the initial values for the parameters to fit
				11.5.2.12 STEP 12: Fit the model
				11.5.2.13 STEP 13: Compute the errors
				11.5.2.14 STEP 14: Plot fitted model
				11.5.2.15 STEP 15: Add the observations to the plot
				11.5.2.16 STEP 16: Differentiate the model per tree species
			11.5.3 Results
			11.5.4 Possible Variations
	Chapter 12: AI with Julia
		12.1 Machine Learning Goals and Approaches
		12.2 The BetaML Toolkit
			12.2.1 API and Key Principles
				12.2.1.1 Managing Stochasticity
				12.2.1.2 Other Functions
		12.3 Data Preprocessing
			12.3.1 Encoding Categorical Data
			12.3.2 Scaling
			12.3.3 Missing Value Imputation
				12.3.3.1 SimpleImputer
				12.3.3.2 GaussianMixtureImputer
				12.3.3.3 RandomForestImputer
				12.3.3.4 GeneralImputer
			12.3.4 Dimensionality Reduction
			12.3.5 Data Partitioning
		12.4 Model Fitting. An Overview of the Main Algorithms
			12.4.1 Perceptron-Like Classifiers
			12.4.2 Tree-Based Models
			12.4.3 Neural Networks
				12.4.3.1 Deep Neural Network Structure
				12.4.3.2 Neural Network Training
				12.4.3.3 Neural Network Example
			12.4.4 Clustering
		12.5 Model Evaluation, Interpretation, and Hyperparameter Tuning
			12.5.1 Regression Models
			12.5.2 Classification Models
			12.5.3 Clustering Models
			12.5.4 Hyperparameters Evaluation
			12.5.5 K-fold Cross-Validation
			12.5.6 Autotune
			12.5.7 Model Interpretation and Feature Importance
		12.6 Specialized AI Libraries in Julia
		12.7 EXERCISE 3: Predict the Values of Houses in Boston
			12.7.1 Instructions
			12.7.2 Skeleton
				12.7.2.1 STEP 1: Set up the environment
				12.7.2.2 STEP 2: Load the packages
				12.7.2.3 STEP 3: Load the data
				12.7.2.4 STEP 4: Create the feature matrix
				12.7.2.5 STEP 5: Build the label vector
				12.7.2.6 STEP 6: Partition the data
				12.7.2.7 STEP 7: Define the neural network architecture
				12.7.2.8 STEP 8: Train the model
				12.7.2.9 STEP 9: Predict the labels
				12.7.2.10 STEP 10: Evaluate the model
				12.7.2.11 STEP 11: Plot the errors and the estimated values vs. the true ones
				12.7.2.12 STEP 12: Hyperparameters tuning
				12.7.2.13 STEP 13: Model interpretation
			12.7.3 Results
			12.7.4 Possible Variations
	Chapter 13: Utilities
		13.1 Weave.jl for Dynamic Documents
		13.2 ZipFile
			13.2.1 Writing a Zip Archive
			13.2.2 Reading from a Zip Archive
Index