Julia cheat sheet

A scannable Julia reference: 14 short snippets across 7 topics, each linking back to the lesson it came from.

At a glance

Topic	What it covers
Multiple dispatch	Methods are selected on the types of all their arguments, which replaces type tags, if/else chains and class	lesson
Packages, environments and performance	Reproducible project environments with Pkg, and the handful of rules that separate fast Julia from slow Julia	lesson
Installing Julia and the REPL workflow	A Julia release is usually a minor version rather than a patch: packages frequently require 1.10 or newer, and the	lesson
Arrays and broadcasting in depth	Julia stores arrays in column-major order, matching Fortran and BLAS. It matters for cache behaviour: iterating down a	lesson
Strings, IO and working with files	Build strings with interpolation or join, never by concatenating in a loop. A String is immutable, so each	lesson
Modules and organising a Julia project	To add a method to a function owned by another module you must import it, not using it. That rule prevents a stray	lesson
Plotting with Plots.jl and Makie	Layouts accept tuples such as (2, 2) or a custom grid with mixed spans through @layout. Build each subplot as a value	lesson

Quick snippets

Multiple dispatch

One name, many methods

describe(x::Integer)       = "an integer: $x"
describe(x::AbstractFloat) = "a float: $x"
describe(x::AbstractString) = "text of length $(length(x))"
describe(x)                = "something else: $(typeof(x))"

describe(3)        # "an integer: 3"
describe(3.0)      # "a float: 3.0"
describe("abc")    # "text of length 3"
describe(:sym)     # "something else: Symbol"

methods(describe)  # the whole method table, with signatures

Full lesson: Multiple dispatch →

Packages, environments and performance

Project environments

using Pkg

Pkg.activate(".")                  # use the environment defined by Project.toml
Pkg.add("DataFrames")
Pkg.add(name = "CSV", version = "0.10")
Pkg.status()

Pkg.instantiate()                  # install exactly the versions in Manifest.toml
Pkg.resolve()

Project environments

Pkg.compat("DataFrames", "1.6")
Pkg.status(; outdated = true)

Full lesson: Packages, environments and performance →

Installing Julia and the REPL workflow

Install

# juliaup manages versions and keeps them up to date
# macOS / Linux
curl -fsSL https://install.julialang.org | sh

# Windows (PowerShell)
winget install julia -s msstore

juliaup add 1.10          # install a specific release
juliaup status
julia --version

The REPL modes

# in the REPL, press ] for pkg mode
#   (@v1.10) pkg> activate .
#   (myproject) pkg> add DataFrames CSV

# then from Julian mode
using DataFrames
?DataFrame            # the help prompt, also available as a function
varinfo()             # what is currently defined in Main

Scripts, include and environments

julia script.jl                 # run a file, then exit
julia --project=. script.jl     # run with the environment in the current folder
julia -e 'println(1 + 1)'       # evaluate one expression
julia --project=. -i script.jl  # run, then stay in the REPL

Full lesson: Installing Julia and the REPL workflow →

Arrays and broadcasting in depth

Building and inspecting arrays

zeros(3)                 # Vector{Float64}, length 3
ones(Int, 2, 3)          # 2x3 Matrix{Int}
fill(7, 4)
[1, 2, 3]                # Vector{Int}
[1 2 3]                  # 1x3 Matrix{Int}
[1 2; 3 4]               # 2x2 Matrix

A = reshape(1:12, 3, 4)  # column-major: columns fill first
ndims(A), size(A), length(A)
A[2, 3]                  # row 2, column 3
A[7]                     # linear index, column-major order
axes(A)                  # (Base.OneTo(3), Base.OneTo(4))

Views and copies

A = reshape(1.0:12.0, 3, 4)

col = @view A[:, 2]
col[1] = 99.0
A[1, 2]                  # 99.0 - the view wrote into the parent

copy(col)                # an independent array
B = A'                   # adjoint, not a transposed copy
size(B), typeof(B)

sum(A; dims = 1)         # reduce along dimension 1, keeping the dimension
dropdims(sum(A; dims = 1), dims = 1)

Full lesson: Arrays and broadcasting in depth →

Strings, IO and working with files

CSV and downloads

using CSV, DataFrames, Downloads

df = CSV.read("data.csv", DataFrame)
CSV.write("out.csv", df)

# types, missing values and delimiters are explicit
df2 = CSV.read("export.psv", DataFrame;
               delim = '|', missingstring = "NA", types = Dict(:amount => Float64))

url = "https://example.com/data.csv"
Downloads.download(url, "download.csv")

Full lesson: Strings, IO and working with files →

Modules and organising a Julia project

Projects and environments

mkdir MyPkg && cd MyPkg
julia --project=. -e 'using Pkg; Pkg.generate("MyPkg")'

# resulting layout
# MyPkg/
#   Project.toml        name, uuid, version, [deps], [compat]
#   Manifest.toml       exact resolved versions and hashes
#   src/MyPkg.jl        module MyPkg ... end
#   test/runtests.jl    @testset for the package

Projects and environments

using Pkg
Pkg.activate(".")
Pkg.add(["DataFrames", "CSV"])       # writes into Project.toml and Manifest.toml
Pkg.status()
Pkg.update()
Pkg.instantiate()                     # install exactly what Manifest.toml records

Pkg.test()                            # runs test/runtests.jl in a clean environment

Layout and testing

# src/MyPkg.jl
module MyPkg

include("types.jl")
include("operations.jl")

using .Types            # a submodule declared inside include files
using .Operations
export transform, Point

end

Full lesson: Modules and organising a Julia project →

Plotting with Plots.jl and Makie

Layout and series

p1 = scatter(rand(50), rand(50); title = "scatter", markerstrokewidth = 0)
p2 = histogram(randn(1000); bins = 30, title = "histogram", legend = false)
p3 = bar(["a", "b", "c"], [3, 7, 2]; title = "bar")

grid = plot(p1, p2, p3; layout = (2, 2), size = (900, 700))
savefig(grid, "grid.png")

# a 3D surface
xs = ys = range(-2, 2, length = 50)
p4 = surface(xs, ys, (x, y) -> exp(-(x^2 + y^2)))
savefig(p4, "surface.png")

When to use Makie

using CairoMakie        # static, no GPU needed
using GLMakie          # interactive window or browser

fig = Figure(size = (800, 500))
ax = Axis(fig[1, 1], xlabel = "angle (rad)", ylabel = "value", title = "sin")
lines!(ax, x, sin.(x); color = :steelblue, linewidth = 2)
scatter!(ax, x[1:20:end], sin.(x[1:20:end]); color = :tomato)

fig[2, 1] = Legend(fig, ax, "series")
save("makie.png", fig)

Full lesson: Plotting with Plots.jl and Makie →

FAQ

Is this Julia cheat sheet free to use?

Yes. No sign-up and no tracking: the page is static, every example is on the page itself, and you can print it or save it as a one-page reference.

Where do the examples come from?

Every snippet is taken from the 7 lessons of the Julia course on this site, and each section links back to the lesson it was pulled from.

How do I go deeper than a cheat sheet?

Open the full Julia course — it carries the worked explanations, the edge cases and the exercises behind every line here.

Python 3 NumPy pandas Matplotlib Jupyter Notebook Flask

Last refreshed 2026-09-27.