Julia primer

This episodes provides a condensed overview of Julia’s main syntax and features.

Preparing for a workshop

To prepare for a workshop where this material is taught, please first install Julia as described in Setup, and then go through the overview below and experiment with it either in the Julia REPL, a Jupyter or Pluto notebook, or in VSCode (refer to Setup for installation and configuration instructions).

As an alternative to going through this page, you can also watch this video which covers “a 300 page book on Julia in one hour”.

If you are coming from MATLAB, R, Python, C/C++ or Common Lisp, you should also have a look at this page which lists the respective differences in Julia.

Running Julia

We can write Julia code in various ways:

  1. REPL (read-evaluate-print-loop). Start it by typing julia (or the full path of your Julia executable) on your command line. The REPL has four modes:

    • Julian mode - default mode where prompt starts with julia>. Here you enter Julia expressions and see output.

    • Type ? to go to Help mode where prompt starts with help?>. Julia will print help/documentation on anything you enter.

    • Type ; to go to Shell mode where prompt starts with shell>. You can type any shell commands as you would from terminal.

    • Type ] to go to Package mode where prompt starts with (@v1.5) pkg> (if you have Julia version 1.5). Here you can add packages with add, update packages with update etc. To see all options type ?.

    • To exit any non-Julian mode, hit Backspace key.

  2. Jupyter: Jupyter notebooks are familiar to many Python and R users.

  3. Pluto.jl: Pluto offers a similar notebook experience to Jupyter, but in contrast to Jupyter Pluto understands global references between cells, and reactively re-evaluates cells affected by a code change.

  4. Visual Studio Code (VSCode):

    • a full-fledged Integrated Development Environment which is very useful for larger codebases. Extensions are needed to activate Julia inside VSCode, see the official documentation for instructions.

  5. A text editor like nano, emacs, vim, etc., followed by running your code with julia filename.jl.

Basic syntax


Example syntax and its result/meaning


  • 2 + 3 * 1.1 Summing, multiplying

  • 2^3 Power

  • sqrt(9) Square root

  • 40 / 5 8.0 (Float)

  • 12 % 2 2 (remainder)

  • 10^19 Results in integer overflow!

  • 1e19 or big(10)^19 -> solves the problem

  • exp(pi*im) Exponentiation, imaginary nr.

  • sin(2*pi) Trigonometry


  • A = 3.14 Scalar, float

  • B = 10 Scalar, integer

  • C = "hello" String

  • D = true Boolean

  • typeof(A) Find type

  • supertype(Integer) Find supertypes

  • subtype(Integer) Find subtypes

  • Integer <: Real “Subtype of”, returns True

  • struct Immutable composite type

  • mutable struct Mutable composite type

Special values

  • Inf Infinity (e.g. 1 / 0)

  • Nan Not a number (e.g. 0 / 0)

  • nothing e.g. for variables w/o value

1D arrays

  • t = (1, 2, 3) Tuple (immutable)

  • t = (a=2, b=1+2) Named tuple, access: t.a

  • d = Dict("A"=>1, "B"=>2) Dictionary

  • a = [1, 2, 3, 4] 4-element Vector{Int64}

  • Vector{T}(undef, n) undef 1-D array length n

  • Float64[1,2] 2-element Vector{Float64}

  • [1:5;] 5-element Array{Int64,1}

  • [1:5] 1-element vector with a range

  • [range(0,stop=2π,length=5);] 5-element Vector{Float64}

  • collect(T, itr) array from iterable

  • rand(5) random 5-elem vector in [0,1)

  • rand(Int, 5) random vector with integers

  • ones(5) 5-elem vector with FP64 ones

  • zeros(5) 5-elem vector with FP64 zeros

  • [1,2,3].^2 Element-wise dot-operation

Indexing and slicing

  • a[1] first element

  • a[1:3] 3-element vector

  • a[3:end] end is last element

  • a[1:2:end] step size of 2

  • a[3:end] end is last element

  • splice!(a,2:3) Remove items at given indices

  • splice!(a,2:3, 5:7) Rm & add items at given inds

Multidimensional arrays

  • Array{T}(undef, dims) New undef array type T

  • mat = [1 2; 3 4] 2×2 Matrix{Int64}

  • zeros(4,4,4,4) Zero 4×4×4×4 Array{Float64,4}

  • rand(12,4) Random 12×4 Matrix{Float64}

Inspecting array properties

  • length(a)

  • first(a)

  • last(a)

  • minimum(a)

  • maximum(a)

  • argmin(a)

  • argmax(a)

  • size(a)

Manipulating arrays

  • push!(a, 10) Append in-place

  • insert!(a, 1, 42) Insert in given position

  • append!(a, [3, 5, 7]) Append another array

  • splice!(a, 3, -1]) Rm in given pos and replace


  • δ = 0.1 (type \delta <TAB>) Unicode names with LaTeX

  • println("A = $A") Print using interpolation

  • :something Symbol for a name or label

Loops and conditionals

for loops iterate over iterables, including types like Range, Array, Set and Dict.

for i in [1,2,3,4,5]
    println("i = $i")
for (k, v) in Dict("A" => 1, "B" => 2, "C" => 3)
    println("$k is $v")
for (i, j) in ([1, 2, 3], ("a", "b", "c"))
        println("$i $j")

Conditionals work like in other languages.

if x > 5
    println("x > 5")
elseif x < 5    # optional elseif
    println("x < 5")
else            # optional else
    println("x = 5")

The ternary operator exists in Julia:

a ? b : c

The meaning is [condition] ? [execute if true] : [execute if false].

While loops:

n = 0
while n < 10
    n += 1

Working with files

Obtain a file handle to start reading from file, and then close it:

f = open("myfile.txt")
# work with file...

The recommended way to work with files is to use a do-block. At the end of the do-block the file will be closed automatically:

open("myfile.txt") do f
    # read from file
    lines = readlines(f)

Writing to a file:

open("myfile.txt", "w") do f
    write(f, "another line")

Some useful functions to work with files:


What it does

  • pwd()

  • cd(path)

  • readdir(path)

  • mkdir(path)

  • abspath(path)

  • joinpath(p1, p2)

  • isdir(path)

  • splitdir(path)

  • homedir()

  • Show current directory

  • Change directory

  • Return list of current directory

  • Create directory

  • Add current dir to filename

  • Join two paths

  • Check if path is a directory

  • Split path into tuple of dirname and filename

  • Return home directory


A function is an object that maps a tuple of argument values to a return value.

Example of a regular, named function:

function f(x,y)
    x + y   # can also use "return" keyword

A more compact form:

f(x,y) = x + y

This function can be called by f(4,5).

The expression f refers to the function object, and can be passed around like any other value (functions in Julia are first-class objects):

g = f

Functions can be combined by composition:

f(x) = x^2
g(x) = sqrt(x)

f(g(3))   # returns 3.0

An alternative syntax is to use ∘ (typed by \circ<tab>)

(f  g)(3)   # returns 3.0

Most operators (+, -, * etc) are in fact functions, and can be used as such:

+(1, 2, 3)   # 6

# composition:
(sqrt  +)(3, 6)  # 3.0 (first summation, then square root)

Just like Vectors and Arrays can be operated on element-wise (vectorized) by dot-operators (e.g. [1, 2, 3].^2), functions can also be vectorized (broadcasting):

sin.([1.0, 2.0, 3.0])

Keyword arguments can be added after ;:

function greet_dog(; greeting = "Hi", dog_name = "Fido")  # note the ;
    println("$greeting $dog_name")

greet_dog(dog_name = "Coco", greeting = "Go fetch")   # "Go fetch Coco"

Optional arguments are given default value:

function date(y, m=1, d=1)
    month = lpad(m, 2, "0")  # lpad pads from the left
    day = lpad(d, 2, "0")

date(2021)   # "2021-01-01
date(2021, 2)   # "2021-02-01
date(2021, 2, 3)   # "2021-02-03

Argument types can be specified explicitly:

function f(x::Float64, y::Float64)
    return x*y

Return types can also be specified:

function g(x, y)::Int8
    return x * y

Additional methods can be added to functions simply by new definitions with different argument types:

function f(x::Int64, y::Int64)
    return x*y

To find out which method is being dispatched for a particular function call:

@which f(3, 4)

As functions in Julia are first-class objects, they can be passed as arguments to other functions. Anonymous functions are useful for such constructs:

map(x -> x^2 + 2x - 1, [1, 3, -1])  # passes each element of the vector to the anonymous function

Varargs functions can take an arbitrary number of arguments:

f(a,b,x...) = a + b + sum(x)

f(1,2,3)     # 6
f(1,2,3,4)   # 10

“Splatting” is when values contained in an iterable collection are split into individual arguments of a function call:

x = (3, 4, 5)

f(1,2,x...)    # 15

# also possible:
x = [1, 2, 3, 4, 5]

f(x...)    # 15

Julia functions can be piped (chained) together:

1:10 |> sum |> sqrt    # 7.416198487095663 (first summed, then square root)

Inbuilt functions ending with ! mutate their input variables, and this convention should be adhered to when writing own functions. Compare, for example:

A = [1 2; 3 4]
sum(A)   # gives 10
sum!([1 1], A)  # mutates A into 1x2 Matrix with elements 4, 6

Exception handling

Exceptions are thrown when an unexpected condition has occurred:

DomainError with -1.0:
sqrt will only return a complex result if called with a complex argument. Try sqrt(Complex(x)).

  [1] throw_complex_domainerror(::Symbol, ::Float64) at ./math.jl:33
  [2] sqrt at ./math.jl:573 [inlined]
  [3] sqrt(::Int64) at ./math.jl:599
  [4] top-level scope at In[130]:1
  [5] include_string(::Function, ::Module, ::String, ::String) at ./loading.jl:1091

Exceptions can be handled with a try/catch block:

catch e
    println("caught the error: $e")
caught the error: DomainError(-1.0, "sqrt will only return a complex result if called with a complex argument. Try sqrt(Complex(x)).")

Exceptions can be created explicitly with throw:

function negexp(x)
    if x>=0
        return exp(-x)
    throw(DomainError(x, "argument must be non-negative"))


The metaprogramming support in Julia allows code to be automatically transformed and generated. A full treatment of metaprogramming is outside the scope of this lesson but familiarity with macros is highly useful. Macros provide a mechanism to include generated code in the final body of a program. A simple macro can be created by:

macro sayhello(name)
        return :( println("Hello, ", $name) )

and called by:

@sayhello "world!"

Many useful macros are already predefined in base Julia or in various packages. For example:

# time an expression
@time sum(rand(1000,1000))
# which function method will be used for specified args
# print generated LLVM bitcode for given type
@code_llvm sin(2.0)


The scope of a variable is the region of code within which a variable is visible. Certain constructs introduce scope blocks:

  • Modules introduce a global scope that is separate from the global scopes of other modules.

  • There is no all-encompassing global scope.

  • Functions and macros define hard local scopes.

  • for, while and try blocks and structs define soft local scopes.

When x = 123 occurs in a local scope, the following rules apply:

  • Existing local: If x is already a local variable, then the existing local x is assigned.

  • Hard scope: If x is not already a local variable, a new local named x is created in the same scope.

  • Soft scope: If x is not already a local variable the behavior depends on whether the global variable x is defined:

    • if global x is undefined, a new local named x is created.

    • if global x is defined, the assignment is considered ambiguous.


x = 123 # global

function greet()
    x = "hello" # new local

greet()  # gives "hello"
println(x)  # gives 123

function greet2()
    global x = "hello"

println(x)  # gives "hello" (global x redefined)

Further details can be found at https://docs.julialang.org/en/v1/manual/variables-and-scoping/

Style conventions

  • Names of variables are in lower case.

  • Word separation can be indicated by underscores (_), but use of underscores is discouraged unless the name would be hard to read otherwise.

  • Names of Types and Modules begin with a capital letter and word separation is shown with upper camel case instead of underscores.

  • Names of functions and macros are in lower case, without underscores.

  • Functions that write to their arguments have names that end in !. These are sometimes called “mutating” or “in-place” functions because they are intended to produce changes in their arguments after the function is called, not just return a value.