for Loops

Syntax#

for i in iter; …; end
while cond; …; end
break
continue
@parallel (op) for i in iter; …; end
@parallel for i in iter; …; end
@goto label
@label label

Remarks#

Whenever it makes code shorter and easier to read, consider using higher-order functions, such as map or filter, instead of loops.

Fizz Buzz

A common use case for a for loop is to iterate over a predefined range or collection, and do the same task for all its elements. For instance, here we combine a for loop with a conditional if-elseif-else statement:

for i in 1:100
    if i % 15 == 0
        println("FizzBuzz")
    elseif i % 3 == 0
        println("Fizz")
    elseif i % 5 == 0
        println("Buzz")
    else
        println(i)
    end
end

This is the classic Fizz Buzz interview question. The (truncated) output is:

1
2
Fizz
4
Buzz
Fizz
7
8

Find smallest prime factor

In some situations, one might want to return from a function before finishing an entire loop. The return statement can be used for this.

function primefactor(n)
    for i in 2:n
        if n % i == 0
            return i
        end
    end
    @assert false  # unreachable
end

Usage:

julia> primefactor(100)
2

julia> primefactor(97)
97

Loops can also be terminated early with the break statement, which terminates just the enclosing loop instead of the entire function.

Multidimensional iteration

In Julia, a for loop can contain a comma (,) to specify iterating over multiple dimensions. This acts similarly to nesting a loop within another, but can be more compact. For instance, the below function generates elements of the Cartesian product of two iterables:

function cartesian(xs, ys)
    for x in xs, y in ys
        produce(x, y)
    end
end

Usage:

julia> collect(@task cartesian(1:2, 1:4))
8-element Array{Tuple{Int64,Int64},1}:
 (1,1)
 (1,2)
 (1,3)
 (1,4)
 (2,1)
 (2,2)
 (2,3)
 (2,4)

However, indexing over arrays of any dimension should be done with eachindex, not with a multidimensional loop (if possible):

s = zero(eltype(A))
for ind in eachindex(A)
    s += A[ind]
end

Reduction and parallel loops

Julia provides macros to simplify distributing computation across multiple machines or workers. For instance, the following computes the sum of some number of squares, possibly in parallel.

function sumofsquares(A)
    @parallel (+) for i in A
        i ^ 2
    end
end

Usage:

julia> sumofsquares(1:10)
385

For more on this topic, see the example on @parallel within the Parallel Processesing topic.