Julia for Data Analysis Version 7

Julia for Data Analysis MEAP V07
Copyright
welcome
brief contents
Chapter 1: Introduction
	1.1 What is Julia and why is it useful?
	1.2 Key features of Julia from data scientist’s perspective
		1.2.1 Julia is fast because it is a compiled language
		1.2.2 Julia provides a full support for interactive workflows
		1.2.3 Julia programs are highly reusable and easy to compose together
		1.2.4 Julia has an in-built state-of-the-art package manager
		1.2.5 It is easy to integrate existing code with Julia
	1.3 The usage scenarios of tools presented in the book
	1.4 Julia’s drawbacks
	1.5 What data analysis skills will you learn?
	1.6 How can Julia be used for data analysis?
	1.7 Summary
Part 1: Essential Julia skills
	Chapter 2: Getting started with Julia
		2.1 Representing values
		2.2 Defining variables
		2.3 Most important control flow constructs
			2.3.1 Performing  computations depending on Boolean condition
			2.3.2 Loops
			2.3.3 Compound expressions
			2.3.4 A first approach to calculating the winsorized mean
		2.4 Defining functions
		2.5 Variable scoping rules
		2.6 Summary
	Chapter 3: Julia's support for scaling projects
		3.1 Understanding Julia's type system
		3.2 Multiple dispatch in Julia
		3.3 Working with packages and modules
		3.4 Using macros
		3.5 Summary
	Chapter 4: Working with collections in Julia
		4.1 Working with arrays
			4.1.1 Getting the data into a matrix
			4.1.2 Computing basic statistics of the data stored in a matrix
			4.1.3 Indexing into arrays
			4.1.4 Performance considerations of copying vs. making a view
			4.1.5 Calculation of correlations between variables
			4.1.6 Fitting a linear regression
			4.1.7 Plotting the Anscombe's quartet data
		4.2 Mapping key-value pairs with dictionaries
		4.3 Putting structure to your data using named tuples
			4.3.1 Defining named tuples and accessing their contents
			4.3.2 Analyzing Anscombe's quartet data stored in a named tuple
			4.3.3 Understanding composite types and mutability of values in Julia
		4.4 Summary
	Chapter 5: Advanced topics on handling collections
		5.1 Vectorizing your code using broadcasting
			5.1.1 Syntax and meaning of broadcasting in Julia
			5.1.2 Expansion of length-1 dimensions in broadcasting
			5.1.3 Protection of collections from being broadcasted over
			5.1.4 Analyzing Anscombe's quartet data using broadcasting
		5.2 Defining methods with parametric types
			5.2.1 Most collection types in Julia are parametric
			5.2.2 Rules for subtyping of parametric types
			5.2.3 Using subtyping rules to define the covariance function
		5.3 Integration with Python
		5.4 Summary
	Chapter 6: Working with strings
		6.1 Getting and inspecting the data
		6.2 Splitting strings
		6.3 Working with strings using regular expressions
		6.4 Extracting a subset from a string with indexing
		6.5 Analyzing genres frequency in movies.dat
		6.6 Introducing symbols
			6.6.1 Creating symbols
			6.6.2 Using symbols
		6.7 Using fixed-width string types to improve performance
		6.8 Compressing vectors of strings with PooledArrays.jl
			6.8.1 Creating a file containing flower names
			6.8.2 Reading in the data to a vector and compressing it
			6.8.3 Internal design of PooledArray
		6.9 Choosing an appropriate storage for collections of strings
		6.10 Summary
	Chapter 7: Handling time series data and missing values
		7.1 Understanding the NBP Web API
			7.1.1 Getting the data via a web browser
			7.1.2 Getting the data using Julia
			7.1.3 Handling cases when NBP Web API query fails
		7.2 Missing data in Julia
			7.2.1 Definition of the missing value
			7.2.2 Working with missing values
		7.3 Getting the time series data from NBP Web API
			7.3.1 Working with dates
			7.3.2 Fetching data from NBP Web API for a range of dates
		7.4 Analyzing the data fetched from NBP Web API
			7.4.1 Computing summary statistics
			7.4.2 Finding in which days of week we have the most missing values
			7.4.3 Plotting the PLN/USD exchange rate
		7.5 Summary
Part 2: Toolbox for Data Analysis
	Chapter 8: First steps with data frames
		8.1 Fetching, unpacking, and inspecting the data
		8.2 Loading the data to a data frame
		8.3 Getting a column out of a data frame
			8.3.1 Data frame's storage model
			8.3.2 Treating a data frame column as a property
			8.3.3 Getting a column using data frame indexing
			8.3.4 Visualizing data stored in columns of a data frame
		8.4 Reading and writing data frames using different formats
		8.5 Summary
	Chapter 9: Getting data from a data frame
		9.1 Advanced data frame indexing
			9.1.1 Getting a reduced puzzles data frame
			9.1.2 Overview of allowed column selectors
			9.1.3 Overview of allowed row subsetting values
			9.1.4 Making views of data frame objects
		9.2 Analyzing the relationship between puzzle difficulty and popularity
			9.2.1 Calculation of mean puzzle popularity by its rating
			9.2.2 Fitting LOESS regression
		9.3 Summary
Appendix A: First steps with Julia
	A.1 Installing and setting up Julia
	A.2 Getting help in and on Julia
	A.3 Managing packages in Julia
	A.4 Overview of standard ways to work with Julia
Appendix C: Julia packages for data science
	C.1 Plotting ecosystems in Julia
	C.2 Scaling computing with Julia
	C.3 Support for databases and data storage formats
	C.4 Data science methods
	C.5 Summary