2 - Data types

Scalar types

In Julia, variable names can include a subset of Unicode symbols, allowing a variable to be represented, for example, by a Greek letter. In most Julia development environments (including the console), to type the Greek letter you can use a LaTeX-like syntax, typing \ and then the LaTeX name for the symbol, e.g. \alpha for α. Using LaTeX syntax, you can also add subscripts, superscripts and decorators.

The main types of scalar are Int64, Float64, Char (e.g. x = 'a'), String¹ (e.g. x="abc") and Bool.


Julia supports most typical string operations, for example: split(s) (default on whitespaces), join([s1,s2], ""), replace(s, "toSearch" => "toReplace") and strip(s) (remove leading and trailing whitespaces) Attention to use the single quote for chars and double quotes for strings. c


There are several ways to concatenate strings:

  • Concatenation operator: *;

  • Function string(str1,str2,str3);

  • Combine string variables in a bigger one using the dollar symbol: a = "$str1 is a string and $(myobject.int1) is an integer" ("interpolation")

Note: the first method doesn't automatically cast integer and floats to strings.

Arrays (lists)

Arrays are N-dimensional mutable containers, parametrized with the type of the object contained and the number of dimensions, e.g. Array{Float64,3} (we'll see parametrized types in details later in chapter "Custom Structures"). In this section, we deal with 1-dimensional arrays, in the next one we consider 2 or more dimensional arrays.

There are several ways to create an array:

  • Empty (zero-elements) arrays: a = []. Alternative ways:

    • a = T[], e.g. a = Int64[];

    • using explictitly the contructor a = Array{T,1}();

    • using the Vector alias: c = Vector{T}();

  • 5-elements zeros array: a=zeros(5) (or a=zeros(Int64,5)) (same with ones())

  • Column vector (Vector container, alias for 1-dimensions array) : a = [1;2;3] or a=[1,2,3]

  • Row vector (Matrix container, alias for 2-dimensions array, see next section "Multidimensional and nested arrays"): a = [1 2 3]

Arrays can be heterogeneous (but in this case the array will be of Any type and in general much slower): x = [10, "foo", false].

If you need to store a limited set of types in the array, you can use the Union keyword to still have an efficient implementation, e.g. a = Union{Int64,String,Bool}[10, "Foo", false].

a = Int64[] is just a shorthand for a = Array{Int64,1}() (e.g. a = Any[1,1.5,2.5] is equivalent to a = Array{Any,1}([1,1.5,2.5])). Attention that a = Array{Int64,1} (without the round brackets) doesn't create an Array at all, but just assign the "DataType" Array{Int64,1} to a. You can also declare an array of size n (with garbage content) with a=Array{T,1}(undef,n).

Square brackets are used to access the elements of an array (e.g. a[1]). The slice syntax [from:step:to] is generally supported and in several contexts will return a (fast) iterator rather than a list (you can use the keyword end, but not begin). To then transform the iterator in a list use collect(myiterator). You can initialise an array with a mix of values and ranges with either y=[2015; 2025:2030; 2100] (note the semicolon!) or y=vcat(2015, 2025:2030, 2100).

The following methods are useful while working with arrays:

  • Push an element to the end of a: push!(a,b) (as a single element even if it is an Array. Equivalent to python append)

  • To append all the elements of b to a: append!(a,b) (if b is a scalar obviously push! and append! are the same. Note that a string is treated as a list!. Equivalent to Python extend or +=)

  • Concatenation of arrays (new array): a = [1,2,3]; b = [4,5]; c = vcat(1,a,b)

  • Remove an element from the end: pop!(a)

  • Remove an element at the beginning (left): popfirst!(a)

  • Remove an element at an arbitrary position: deleteat!(a, pos)

  • Add an element (b) at the beginning (left): pushfirst!(a,b) (no, appendfirst! doesn't exists!)

  • Sorting: sort!(a) or sort(a) (depending on whether we want to modify or not the original array)

  • Reversing an array: a[end:-1:1]

  • Checking for existence: in(1, a)

  • Get the length: length(a)

  • Get the maximum value: maximum(a) or max(a...) (max returns the maximum value between the given arguments)

  • Get the minimum value: minimum(a) or min(a...) (min returns the minimum value between the given arguments)

  • Empty an array: empty!(a) (only column vector, not row vector)

  • Transform row vectors in column vectors: b = vec(a)

  • Random-shuffling the elements: shuffle(a) (or shuffle!(a). From Julia 1.0 this require using Random before)

  • Check if an array is empty: isempty(a)

  • Find the index of a value in an array: findall(x -> x == value, myarray). This is a bit tricky. The first argument is an anonymous function that returns a boolean value for each value of myarray, and then find() returns the index position(s).

  • Delete a given item from a list: deleteat!(myarray, findall(x -> x == myunwanteditem, myarray))

Multidimensional and nested arrays

In Julia, an array can have 1 dimension (a column, also known as Vector), 2 dimensions (that is, a Matrix) or more. Then each element of the Vector or Matrix can be a scalar, a vector or an other Matrix. The main difference between a Matrix and an array of arrays is that in the former the number of elements on each column (row) must be the same and rules of linear algebra applies.

There are two ways to create a Matrix:

  • a = [[1,2,3] [4,5,6]] [[elements of the first column] [elements of the second column] ...] (note that this is valid only if wrote in a single line. Use hcat(col1, col2) to write matrix by each column)

  • a = [1 4; 2 5; 3 6] [elements of the first row; elements of the second row; ...] (here you can also use vcat(row1, row2) to concatenate several rows)

Note this difference:

  • a = [[1,2,3],[4,5,6]] creates a 1-dimensional array with 2-elements (each of those is again a vector);

  • a = [[1,2,3] [4,5,6]] creates a 2-dimensional array (a matrix with 2 columns) with three elements (scalars).

When outputed or printed, the first dimension of an Array is always interpreted as the rows and the second one (if exists) as the columns. So, a unidimensional array is interpreted as a column vector. Arrays are stored in memory contiguously by the first dimension, so making a loop over a matrix by column and then by row is significantly faster than doing it by row and by column, as the inner loop would operate on contiguous pieces of memory.

Empty matrices can be constructed as:

m = Array{Float64}(undef, 0, 0)

for an (0,0)-size 2-D Matrix of type Float64 and more in general:

m = Array{T}(undef, a, b, ...,z)

for an (a,b,...,z)-size multidimensional Matrix (whose content, of type T,is garbage)

A 2x3 matrix can be constructed in one of the following ways:

  • a = [[1,2] [3,4] [5,6]]

  • a = zeros(2,3) or a = ones(2,3) (the zeros and ones are strored as Float64)

  • a = fill("abc",2,3) (content is "abc")

Nested arrays can be accessed with double square brackets, e.g. a[2][3]. Elements of bidimensional arrays can be accessed instead with the a[row,col] syntax, where again the slice syntax can be used, for example, given a is a 3x3 Matrix, a[1:2,:] would return a 2x3 Matrix with all the column elements of the first and second row.

Boolean selection is implemented using a boolean array/matrix for the selection:

a = [[1,2,3] [4,5,6]]
mask = [[true,true,false] [false,true,false]]

a[mask] returns an 1-D array with 1, 2 and 5. Note that boolean selection results always in a flatted array, even if delete a whole row or a whole column of the original data. It is up to the programmer to then reshape the data accordingly.

Note: for row vectors, both a[2] or a[1,2] returns the second element.

n-D arrays support several methods:

  • size(a) returns a tuple with the sizes of the n dimensions

  • ndims(a) returns the number of dimensions of the array (e.g. 2 for a Matrix)

  • Arrays can be changed dimension with either reshape(a, nElementsDim1, nElementsDim2) or dropdims(a, dims=(dimToDrop1,dimToDrop2)) (where the dim(s) to drop must all have a single element for all the other dimensions, e.r. be of size1) the transpose ' operator. These operations perform a shadow copy, returning just a different "view" of the underlying data (so modifying the original matrix modifies also the reshaped/transposed matrix). You can use collect(reshape/dropdims/transpose) to force a deepcopy.

At the opposite, using the slice operator (e.g. a[:,1:4]) performs by default a copy of the data, while if you prefer instead a shadow copy you need to use view/@view/@views (e.g. @view a[:,1:4]).

Attention that transpose(a)/a' is a linear-algebra operation and works only when the content of a is numerical. For general a (e.g. strings) use instead permutedims(a).

Also, put attention to this difference:

  • a = [1,2,3] creates a 1,2,3 column vector

  • b = collect([1 2 3]') traspose a 1,2,3 row vector to a 1,2,3 column

While mathematically they are the same concept, in Julia they are two different objects. The first one is a uni-dimensional array, the second one is a two- dimensional array where it happens that the second dimension has size 1, i.e. there is only one column. This possible confusion doesn't arise with row vectors, as all row vectors in Julia have 2 dimensions.

AbstractVector{T} is just an alias to AbstractArray{T,1}, as AbstractMatrix{T} is just an alias to AbstractArray{T,2}.

Multidimensional Arrays can arise for example from using list comprehension: a = [3x + 2y + z for x in 1:2, y in 2:3, z in 1:2]

For further operations on arrays and matrices have a look at the QuantEcon tutorial.


Use tuples to have a list of immutable elements: a = (1,2,3) or even without parenthesis a = 1,2,3

Tuples can be easily unpacked to multiple variable: var1, var2 = (x,y) (this is useful, for example, to collect the values of functions returning multiple values)

Useful tricks:

  • Convert a tuple in a vector: a=(1,2,3); v = [a...] or v = [i[1] for i in a] or v=collect(a)

  • Convert an array in tuple: a = (v...,)


NamedTuples are collections of items whose position in the collection (index) can be identified not only by the position but also by name.

  • Define a NamedTuple: aNamedTuple = (a=1, b=2)

  • Access them with the dot notation: aNamedTuple.a (index notation can be used, too: aNamedTuple[:a]).

  • Get a tuple of the keys: keys(aNamedTuple)

  • Get a tuple of the values: values(aNamedTuple)

  • Get an Array of the values: collect(aNamedTuple)

  • Get a iterable of the pairs (k,v): pairs(aNamedTuple). Useful for looping: for (k,v) in pairs(aNamedTuple) [...] end

As "normal" tuples, NamedTuples can hold any values, but cannot be modified (i.e. are "immutable").

Before Julia 1.0 Named Tuples were implemented in a separate package (NamedTuple.jl). The idea is that, like for the Missing type, the separate package provides additional functionality to the core NamedTuple type, but there is still a bit of confusion over it and, at time of writing, the additional package still provide its own implementation (and many other external packages require it), resulting in crossed incompatibilies.


Dictionaries store mappings from keys to values, and they have an apparently random sorting.

You can create an empty (zero-elements) dictionary with mydict = Dict(), or initialize a dictionary with values: mydict = Dict('a'=>1, 'b'=>2, 'c'=>3)

There are some useful methods to work with dictionaries:

  • Add pairs to the dictionary: mydict[akey] = avalue

  • Add pairs using maps (i.e. from vector of keys and vector of values to dictionary): map((i,j) -> mydict[i]=j, [1,2,3], [10,20,30])

  • Look up values: mydict['a'] (it raises an error if looked-up value doesn't exist)

  • Look up value with a default value for non-existing key: get(mydict,'a',0)

  • Get all keys: keys(mydict) (the result is an iterator, not an Array. Use collect() to transform it.)

  • Get all values: values(mydict) (result is again an iterator)

  • Check if a key exists: haskey(mydict, 'a')

  • Check if a given key/value pair exists (that is, if the key exists and has that specific value): in(('a' => 1), mydict)

You can iterate through both the key and the values of a dictionary at the same time:

for (k,v) in mydict
   println("$k is $v")

While named tuples and dictionaries can look similar, there are some important difference between them:

  • NamedTuples are immutable while Dictionaries are mutable

  • Dictionaries are type unstable if different type of values are stored, while NamedTuples remain type-stable:

    • d = Dict(:k1=>"v1", :k2=>2) # Dict{Symbol,Any}

    • nt = (k1="v1", k2=2,) # NamedTuple{(:k1, :k2),Tuple{String,Int64}}

  • The syntax is a bit less verbose and readable with NamedTuples: nt.k1 vs d[:k1]

Overall, NamedTuple are generally more efficient and should be thought more as anonymous struct (see the "Custom structure" section) than Dictionaries.


Use sets to represent collections of unordered, unique values.

Some methods:

  • Empty (zero-elements) set: a = Set()

  • Initialize a set with values: a = Set([1,2,2,3,4])

  • Set intersection, union, and difference: intersect(set1,set2), union(set1,set2), setdiff(set1,set2)

Memory and copy issues

In order to avoid unnecessarily copying large amounts of data, Julia by default copies only the memory address of large objects, unless the programmer explicitly request a so-called "deep" copy. In detail:

Equal sign (a=b)

  • "simple" types (e.g. Float64, Int64, but also String) are deep copied

  • containers of simple types (or other containers) are shadow copied (their internal is only referenced, not copied)


  • simple types are deep copied

  • containers of simple types are deep copied

  • containers of containers: the content is shadow copied (the content of the content is only referenced, not copied)


  • everything is deep copied recursively

You can check if two objects have the same values with == and if two objects are actually the same with === (in the sense that immutable objects are checked at the bit level and mutable objects are checked for their memory address):

  • given a = [1, 2]; b = [1, 2];, a == b and a === a are true, but a === b is false;

  • given a = (1, 2); b = (1, 2);, all a == b, a === a anda === bare true.

Various notes on Data types

While boolean values true and false are evaluated to 1 and 0 respectively, the opposite is not true. So, if 0 [...] end brings a non-boolean (Int64) used in boolean context TypeError.

Attention to the keyword const. When applied to a variable (e.g. const x = 5) doesn't mean that the variable can't change value (as in C), but simply that it can not change type. Only global variables can be declared constant.

To convert ("cast") between types, use convertedObj = convert(T,x). Still, when conversion is not possible, e.g. trying to convert a 6.4 Float64 in a Int64 value, an error, will be risen (InexactError in this case).

To convert strings (representing numbers) to integers or floats use myInt = parse(Int,"2017").

For the opposite (to convert integers or floats to strings), use myString = string(123).

You can "broadcast" a function to work over an Array (instead of a scalar) using the dot (.) operator. For example, to broadcast parse to work over an array use:myNewList = parse.(Float64,["1.1","1.2"]) (see also Broadcast in the "Functions" Section)

Variable names have to start with a letter, as if they start with a number there is ambiguity if the initial number is a multiplier or not, e.g. in the expression 6ax the variable ax is multiplied by 6, and it is equal to 6 * ax (and note that 6 ax would result in a compile error). Conversely, ax6 would be a variable named ax6 and not ax * 6.

You can import data from a file to a matrix using readdlm() (in standard library package DelimitedFiles). You can skip rows and/or columns using the slice operator and then convert to the desidered type, e.g.

myData = convert(Array{Float64,2},readdlm(myinputfile,'\t')[2:end,4:end]); # skip the first 1 row and the first 3 columns

Random numbers

  • Random float in [0,1]: rand()

  • Random integer in [a,b]: rand(a:b)

  • Random float in [a,b] with "precision" to the second digit : rand(a:0.01:b)

    This last can be executed faster and more elegantly using the Distribution package:

    using Pkg; Pkg.add("Distributions")
    import Distributions: Uniform

You can obtain an Array or a Matrix of random numbers simply specifying the requested size to rand(), e.g. rand(2,3)or rand(Uniform(a,b),2,3) for a 2x3 Matrix.

Missing, nothing and NaN

Julia supports different concepts of missingness:

  • nothing (type Nothing): is the value returned by code blocks and functions which do not return anything. It is a single instance of the singleton type Nothing, and the closer to C style NULL (sometimes it is referred as to the "software engineer’s null"). Most operations with nothing values will result in a run-type error. In some contexts it is printed as #NULL;

  • missing (type Missing): represents a missing value in a statistical sense: there should be a value but we don't know which is (so it is sometimes referred to as the "data scientist’s null"). Most operations with missing values will result in missing propagate (silently). Containers can handle missing values efficiently when declared of type Union{T,Missing}. The Missing.jl package provides additional methods to handle missing elements;

  • NaN (type Float64): represents when an operation results in a Not-a-Number value (e.g. 0/0). It is similar to missing in the fact that it propagates silently. Similarly, Julia also offers Inf (e.g. 1/0) and -Inf (e.g. -1/0).

¹: Technically a String is an array in Julia (try to append a String to an array!), but for most uses it can be thought as a scalar type.

While an updated, expanded and revised version of this chapter is available in "Chapter 2 - Data Types and Structures" of Antonello Lobianco (2019), "Julia Quick Syntax Reference", Apress, this tutorial remains in active development.

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