Bogumił Kamiński 3bc2ed1cb7

2023-03-03 19:42:48 +01:00

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Julia for Data Analysis

This repository contains source codes for the “Julia for Data Analysis” book that has been written by Bogumił Kamiński and has been published by Manning Publications Co.

Additional teaching materials
Setting up your environment
- General instructions
- Note for Linux users
Organization of the code
Running the example codes
Accompanying materials
Data used in the book
Citation
Errata

Additional teaching materials

in the /exercises folder for each book chapter you can find 10 additional exercises with solutions (they are meant for self study and are not discussed in the book)
in the /lectures folder for each book chapter you can find a Jupyter Notebook file with code from this chapter (note that the code is slightly adjusted in comparison to code contained in .jl files in the root folder to accommodate it for running in Jupyter Notebook).

Setting up your environment

General instructions

In order to prepare the Julia environment before working with the materials presented in the book please perform the following setup steps:

download and install Julia; all the codes were tested under Julia 1.7 (under never versions of Julia the code will work, but you might get warning messages when loading packages due to the fact that their versions are pinned in this repository);
make sure you can start Julia by running julia command in your terminal;
download this repository to a local folder on your computer;
start Julia in a folder containing the downloaded material using the command julia --project; the folder must contain the Project.toml and Manifest.toml files prepared for this book that allow Julia to automatically set up the project environment that will allow you to work with material presented in this book (a more detailed explanation what these files do and why they are required is given in appendix A to the book);
press ], write instantiate and press Enter (this process will ensure that Julia properly configures the working environment for working with the codes from the book); in some cases running the resolve command also might be required;
press Backspace, write exit() and press Enter; now you should exit Julia and everything is set up to work with the materials presented in the book.

Additional instructions how to manage your Julia installation are given in appendix A to the book. In particular I explain there how to perform a correct configuration of your environment when doing:

integration with Python using the PyCall.jl package;
integration with R using the RCall.jl package;
installation of Plots.jl (which by default uses the GR Framework that requires installation of extra dependencies on operating system level under Linux).

In particular, if you use Visual Studio Code with Julia extension then open the folder with the materials contained in this repository (you can open it in Folder/Open Folder… menu option). Then if you run Start Julia REPL command (e.g. under Windows its keyboard shortcut is Alt-J Alt-O) a proper project environment will be automatically activated (the Julia extension will use the Project.toml and Manifest.toml files that are present in this folder).

Note for Linux users

Installation of Julia under Linux requires that you choose the folder to which you extract the precompiled binaries you have downloaded. Next, assuming that you extracted Julia in, for example, the /opt folder, the simplest way to make sure that your system can find julia executable is to add it to your system PATH environment variable. A standard way to do it is to edit your ~/.bashrc (or ~/.bash_profile) file and add there the:

export PATH="$PATH:/opt/julia-1.7.2/bin"

line (assuming you have downloaded Julia 1.7.2 and extracted it to /opt folder).

Organization of the code

The codes for each chapter are stored in files named chXX.jl, where XX is chapter number. The exceptions are

chapter 14, where additionally a separate ch14_server.jl is present along with ch14.jl (the reason is that in this chapter we create a web service and the ch14_server.jl contains the server-side code that should be run in a separate Julia process);
appendix A, where the file name used is appA.txt because it also contains other instructions than only Julia code (in particular package manager mode instructions).

Solutions to the exercises that are presented in appendix B in the book are stored in appB.jl file. These solutions assume that they are executed in the same Julia session as the codes from the chapter where the question was posted (so that appropriate variables and functions are defined and appropriate packages are loaded).

Running the example codes

To work with codes from some given chapter:

it is recommended to use a machine with at least 8GB of RAM when working with the examples in this book (some examples require more RAM, which is clearly indicated in the book);
start a fresh Julia session using the julia --project command in a folder containing the downloaded material (or alternatively use Visual Studio Code to activate the appropriate project environment automatically);
execute the commands sequentially as they appear in the file; the codes were prepared in a way that you do not need to restart Julia when working with material from a single chapter, unless it is explicitly written in the instructions to restart Julia (some of the codes require this); when you move to a new chapter start a new Julia session;
before each code there is a comment allowing you to locate the relevant part of the book where it is used; if in the code there is a blank line between consecutive code sections this means that in the book these codes are separated by the text of the book explaining what the code does;

Accompanying materials

There are the following videos that feature material related to this book: * Analysis of Lichess puzzles database (a shortened version of material covered in chapters 8 and 9); also covered in this blogpost; * Analysis of GitHub developer graph (a shortened version of material covered in chapter 12)

Data used in the book

For your convenience I additionally stored data files that we use in this book. They are respectively:

movies.dat (for chapter 6, shared on GitHub repository https://github.com/sidooms/MovieTweetings under MIT license)
puzzles.csv.bz2 (for chapter 8, available puzzles at https://database.lichess.org/. The data is distributed under Creative Commons CC0 license);
git_web_ml.zip (for chapter 12, available on Stanford Large Network Dataset Collection website https://snap.stanford.edu/data/github-social.html under GPL-3.0 License)
owensboro.zip (for chapter 13, available at The Stanford Open Policing Project under the Open Data Commons Attribution License)

Citation

Plain text (Chicago style):

Kamiński, Bogumił. 2023. Julia for Data Analysis. Manning.

BibTeX:

@book{Kaminski2023,
  title     = "Julia for Data Analysis",
  author    = "Kamiński, Bogumił",
  year      = 2023,
  publisher = "Manning",
  address   = "Shelter Island, NY"
}

Errata

Chapter 1, section 1.2.1, page 7

I show the following example of code execution:

julia> function sum_n(n)
           s = 0
           for i in 1:n
               s += i
           end
           return s
       end
sum_n (generic function with 1 method)

julia> @time sum_n(1_000_000_000)
  0.000001 seconds
500000000500000000

This timing is very fast (and the reason is explained in the book). The issue is that this is the situation under Julia 1.7.

Under Julia 1.8 and Julia 1.9 running the same code takes longer (tested under Julia 1.9-beta4):

julia> @time sum_n(1_000_000_000)
  2.265569 seconds
500000000500000000

The reason for this inconsistency is a bug in the @time macro introduced in Julia 1.8 release. The sum_n(1_000_000_000) call (without @time) is executed fast. Here is a simplified benchmark (run under Julia 1.9-beta4):

julia> let
           start = time_ns()
           v = sum_n(1_000_000_000)
           stop=time_ns()
           v, Int(stop - start)
       end
(500000000500000000, 1000)

Unfortunately there is an issue with the @time macro used in global scope, that needs to be resolved in Base Julia. See this issue.

Chapter 2, section 2.3.1, page 30

I compare the following expressions:

x > 0 && println(x)

and

if x > 0
    println(x)
end

where x = -7.

I write there that Julia interprets them both in the same way. It is true in terms of the fact that in both cases the println function is not called (and this is the focus point of the example). However, there is a difference in the value of these expressions. The first expression evaluates to false, while the second evaluates to nothing.

Here is how you can check it:

julia> x = -7
-7

julia> show(x > 0 && println(x))
false
julia> show(if x > 0
           println(x)
       end)
nothing

Chapter 3, section 3.2.3, page 58

middle of page 58: y[end - the + 1] = y[end -- k] should be y[end - i + 1] = y[end - k]

Chapter 3, section 3.2.3, page 59

top of page 59: sort(v::AbstractVector; kwthe.) should be sort(v::AbstractVector; kws...)

Chapter 6, section 6.4.1, page 132

middle of Listing 6.4: codeunits("?") should be codeunits("ε")

Chapter 8, section 8.1.2, page 189

middle of page 189: zsdf format should be zstd format

Chapter 8, section 8.2.1, page 191

bottom of page 191: misssingstring should be missingstring

Chapter 10, section 10.2.2, page 255

bottom of page 255: ? Error: Error adding value to column :b. should be ┌ Error: Error adding value to column :b.

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