Julia

Julia is a programming language designed for fast performance while also aiming for ease of coding, with ambitious goals to replace languages such as Fortran, MATLAB, Python, and R. As the mentioned languages suggest, Julia is gaining traction in the scientific computing domain, and the “Fresh Shrimp Sushi” community is also aiming to popularize Julia in Korea.

Marker	Classification
	Features derived from Python
	Features derived from MATLAB
	Features derived from R
	Features unique to Julia

The term “features unique to Julia” means that these features differentiate Julia from MATLAB, Python, and R, though they might be common in other programming languages. If you are already familiar with these languages, the notion of “learning Julia” might seem odd, as many functions and expressions are similar across these languages, making Julia intuitively understandable. Refer to the post on Equivalent Codes in Julia, MATLAB, Python, R.

For content on machine learning with Julia, see the Machine Learning category.

Tutorials

Installation and Packages

• Julia Programming Language
  • Types and Annotations in Julia
• How to Install the Latest Version of Julia on Windows
  • Changing the Number of Threads for Parallel Computing in Julia on Windows
  • Using Julia in Windows cmd, PowerShell
• How to Install the Latest Version of Julia on Linux
  • Changing the Number of Threads for Parallel Computing in Julia on Linux
• Installing and Using Packages with Pkg.jl
  • Commands Available in Package Management Mode ]?
  • Solving ‘No such file or directory for \General\Registry.toml’ During Package Installation
  • Checking Package Versions with status name
  • Installing Specific Versions of Packages with add foo@1.0
  • Importing Packages with a List @eval using $(package)
• Using Functions Defined in Other Files with include("file.jl")
• Clearing the Console with Ctrl + L, print("\033c")

I/O

• Reading and Writing *.mat Files with MAT.jl
  • Saving Data like *.mat Files with JLD2.jl
• Loading *.npy Files with PyCall.jl
• Reading *.csv Files with CSV.read()
  • Reading Only Columns from *.csv Files
  • Loading DataFrames without String7, String15 using stringtype = String
• Exporting 2D Arrays to *.csv Files with CSV.write()
  • Fixing Broken Characters When Exporting CSVs with CSV.write(bom = true)
• Reading *.shp Files with Shapefile.Table()
• Selecting Files with a Dialog Box like file.choose() with open_dialog()
• Importing Built-in Datasets from R with RDatasets.jl
• How to use clipboard clipboard(), clipboard(x)

Basic Syntax

• Random Sampling with rand()
  • Fixing the Random Seed with Random.seed!()
• Essential Julia Syntax: Broadcasting f.(x)
  • Broadcasting with Multivariable Functions
• Powerful Convenience in Julia, Macros @
  • Parallel Processing with @threads
  • Checking the File Location of Executing Code with @__DIR__
  • Handling Exceptions with error(), @error
• Using Greek Letters and Subscripts in Variable Names \alpha → α
• Elegant Use of Loops with while, for, zip()
  • Referencing Index and Value Simultaneously in Loops with enumerate()
• Symbols :foo
• Exclamation Mark Convention foo!()
• Comprehensions

Advanced Syntax

• The Difference Between == and ===
• Lambda Expressions foo -> bar
• Using the Pipe Operator |>
• Ternary Operator ? :
  • Short Form of Conditional Statements <condition> && <statement>
• Printing Without Data Truncation show(stdout, "text/plain", foo)
• Meta-Programming with Meta.parse(), eval()
• Executing External Programs with run()
  • Including Command Line Arguments with ARGS
  • Referencing Environment Variables with Base.ENV
• Using Progress Bars with ProgressMeter.jl, ProgressBars.jl
• Sending Emails via Naver with SMTPClient.jl

Splat Operator ...

• The Splat Operator ...
• Defining Variadic Functions with function f(x...)
• Converting Between 2D Arrays and Matrices with [eachrow(M)...], hcat(A...)
• Passing Optional Arguments with Splat Operator in Julia foo(; args...)

Data Structures

• Array Slicing and Indexing x[1:end], x[[1,3,4]]
  • Referencing Specific Locations in Arrays with Functions getindex(), first(), last()
• Checking Properties of Structures with propertynames()
  • Referencing Structure Properties with Functions getproperty()
  • Checking Element Types Inside Containers with eltype()
• Dictionaries and Pairs Dict(), x => y
  • Creating Dictionaries from Arrays Dict(Key .=> Value)
• Set Data Types and Operators Set
• Differences Between Vectors and Tuples
• Named Tuples (; x, y)
• Changing Base Data Types with @changeprecision
• Converting Strings to Numbers with parse(type, str)
• Removing Duplicates in Collections with unique(), allunique()

Arrays

• Creating Empty Arrays
  • Checking if an Array is Empty with isempty()
• Defining an Array by Specifying Its Type
• Various Ways to Create Vectors
  • Creating Arrays Filled with Specific Values with fill()
  • Flattening Arrays with vec()
• Defining Higher Dimensional Vectors Directly
  • Dimensional Differences Between Numpy, Pytorch Arrays and Julia
  • Functions Related to 2D Array Operations
    • Operating Component-Wise on Two Vectors of Different Sizes
    • Scalar Multiplication of Matrices by Rows and Columns d .* X, X .* d'
    • Normalizing Columns of a Matrix X ./ norm.(eachcol(X))'
  • Creating n-Dimensional Grids
• Calculating Arrays Column-wise
• Shifting Arrays with circshift()
  • Using Circular Arrays with circshift()
  • Calculating Differences in Arrays with diff()
• Multi-Dimensional Indexing CatesianIndex
  • Finding Indexes with Functions findall(), findfirst(), findnext(), argmax()
  • Checking if Array Elements Belong to a List with in Ref()
• Function to access the element of a singleton only
• Sorting permutation function and its applications sortperm
• Categorical Arrays CategoricalArrays.jl
• Named Arrays NamedArrays.jl

Strings

• Concatenating Strings with *, string(), join(), ^
  • Slicing Parts of Unicode Strings
• Conveniently Printing Variable Values with Interpolation $string
• Handling Strings like Python with replace(, "a" => "A"), join(), split()
  • Using Python’s zfill() with lpad()
• Finding Specific Patterns in Strings with findfirst(), findnext(), findlast()
  • Checking for the Presence of Specific Strings with contains(Ref, Query)
• Styling Text Output with Built-in Function printstyled()
  • Styling Text Output with Package Crayons.jl

DataFrame DataFrames.jl

• Creating Empty DataFrames

  • Inserting New Rows with vcat(), push!()
  • Inserting New Columns with df[!, :"column_name"]

• Converting Between DataFrames and 2D Arrays with Array(df), DataFrame(array, :auto)

• Creating DataFrames with Variable Names as Column Names DataFrame(; x, y)

  • Sorting DataFrames Based on Columns with sort(df,:colname)
  • Excluding Specific Columns from DataFrames with Not(:colname)
  • Renaming Columns in DataFrames with rename!(, :x => :y)

• Summarizing DataFrames with describe()

  • Checking the Size of DataFrames with nrow(), ncol()

• Changing Specific Values in DataFrames with replace!(df, A=>B)

  • Deleting Duplicate Rows with unique(df,:colname)
  • Replacing NaN with 0 in DataFrames
  • Removing Missing Values from DataFrames with dropmissing()

• Dividing and Calculating DataFrames by Groups with groupby(), combine()

• Viewing DataFrames Neatly in VSCode with Main.VSCodeServer.vscodedisplay(df)

Images Images.jl

• Rotating Image Arrays with imrotate()
• Changing Image Sizes with imresize()
• Loading Images and Saving as Matrices with convert(Array{Float64},)
• How to flip an image upside down, mirror it rotr90(img')

Visualization

• Properties List for Plots
• Setting the Size and Resolution of Figures with size=(w,h), dpi=
• Creating GIFs with @animate
• Creating Plot Recipes with RecipesBase.jl
• Changing Default Settings of Figures with default()
• Printing Simple Figures in the Console with UnicodePlots.jl
• 3D 描画のカメラ角度の調整方法 camera=(方位角, 高度)

Rendering

• Adding Line Segments to Figures with plot!(xs, ys)
  • Adding Vertical and Horizontal Lines to Figures with vline!(), hline!()
  • Drawing Arrows with plot!([x1, x2], [y1, y2], arrow:true)
• Making Figure Backgrounds Transparent with background_color = :transparent
• Adjusting the Aspect Ratio of Figures with ratio = :equal
• Filling Areas in Curves, Between Curves, and Inside Closed Curves with fill=()
• Drawing Vector Fields with quiver(, quiver=)

Text

• Adding Text to Figures with annotate!()
• Using $\TeX$ in Figures with LaTexStrings.jl, L"$f(x)$"
• Including Korean Text in Figures with default(fontfamily = ""), Plots.plotly()
• Adjusting the Position of Legends in Figures with legend = :topbottom
  • Hiding Labels for Specific Data in Figures with label = ""

Style

• Sharing Axes with Differently Scaled Data with twinx(), twiny()
• Decorating the Grid Background of Figures
• Changing the Style of Axes in Figures with framestyle
  • Removing Axes, Ticks, etc., for a Clean Output with framestyle=:none
  • Erasing Only the Values of Axes with formatter = (_...) -> ""
• List of Marker and Line Styles plot(shape=, linesytle=)
  • Adding Color to Scatter Plot Markers with scatter(marker_z=), scatter(group=)
• Drawing Regression Lines in Scatter Plots with scatter(smooth = true)

Color

• Using Colors
• Using Palettes
• Using Color Gradients
• Packages for Color Handling Colors.jl
  • Using RGB Codes with RGB(1, 0, 0)
  • Using HEX Codes with "#000000"
• Specifying Colors for Graph Elements
  • Specifying Graph Colors for Each Subplot
• Specifying Colors for Axes, Axis Titles, Ticks, Tick Labels
• Specifying Background Colors

Layout

• Assigning Layouts and Drawing Subplots with layout = (n,m)
• Adding a Main Title to Subplots with plot_title = ""

Heatmaps

• Specifying Color Range for Heatmaps with clim = (,)
• Saving Arrays as Heatmap Images
• Overlaying Plots on Heatmaps

StatsPlots.jl

• Drawing Dendrograms
• Omitting DataFrame Names with Macro @df
• Drawing Box Plots with boxplot(data, labels="label")

Mathematics

Packages related to graphs (networks) like Graphs.jl can be found in the Graph Theory category.

Computation

• Random Sampling with Weights sample(, Weights())
  • Sampling from a Given Distribution rand(distribution, )
• Rounding to a Specific Number of Decimal Places round(digits=)
• Inverting Bit Arrays with .!(foo)
• Calculating Frequencies with freqtable()
• Using Date and Time Related Functions with Dates
  • Calculating the Difference Between Two Times in Hours, Minutes, Seconds with Dates.canonicalize()
• Using Composite Functions with ∘
• Using Infinity ∞
• Checking for Approximate Equality ≈
• Using Complex Numbers im
• Calculating the Mean Excluding Zeros and Missing Values mean(!iszero,)
• The Difference Between Inf and NaN When Dividing by Zero
• Comparing NaN in Julia
• 🔒 Distributed Computing with @distributed

Libraries

• Linear Algebra Module LinearAlgebra
• Polynomial Functions Package Polynomial.jl
• Number Theory Package Primes.jl
• Derivatives Package Calculus.jl
  • Automatic Differentiation Package Zygote.jl
• Calculating Distance Matrices with Distances.jl
• Symbolic Operations with SymEngine.jl
• Performing Regression Analysis with GLM.jl
• Fourier Transform Package FFTW.jl
• Using Infinite Arrays with InfiniteArrays.jl
• Numerical Interpolation with Interpolations.jl
• Using Finite Differences with FiniteDifferences.jl
• Clustering Package Clustering.jl
  • $K-$Means Clustering kmeans()
  • Hierarchical Clustering hclust()
• Using Decision Trees with DecisionTree.jl

Code Optimization Techniques

• Comparing Speed of Equality Operators == for Characters and Integers
• Optimizing Distance Matrix Calculation with pairwise()
  • Quickly Calculating Distances with k-d Trees NearstNeighbors.jl
• Converting to Integer Type by Truncating Decimal Points with trunc()
• Quickly Referencing Subarrays with @views
• Short Circuiting with &&, ||

Looking for Something Else?

Although not published as posts due to being considered not substantial enough, the following list might contain what you’re looking for.

• Textbooks
  • First Semester in Numerical Analysis with Julia: Basics of numerical analysis and their implementation in Julia. PDF available for download.
  • Julia Data Science: An online book published in November 2021, available in English, Portuguese, and Chinese. Focuses mainly on DataFrames.jl and the visualization package Makie.jl. PDF download available.
  • Algorithm for Optimization: Covers various methods for solving optimization problems and their implementation in Julia. Original work and chapter-wise PDFs available for download. A Korean translation titled Practical Optimization Algorithms (Understanding Optimization Algorithms with Julia) is available in Korea.
• Packages
  • JuliaDB.jl: A data processing library known for its speed and memory management.
  • Metatheory.jl: A library for metaprogramming and symbolic computation.
  • Cairo.jl: A package for powerful image creation capabilities.
• Miscellaneous
  • Profiler: Offers profiling similar to MATLAB to show performance metrics.
  • The Fast Track to Julia: A cheat sheet summarizing Julia syntax.
  • Julia?: Appears to be a personal blog sharing advanced tips for using Julia.